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Evolution of Human Brain Atlases in Terms of Content, Applications, Functionality, and Availability

Evolution of Human Brain Atlases in Terms of Content, Applications, Functionality, and Availability Human brain atlases have been evolving tremendously, propelled recently by brain big projects, and driven by sophisticated imaging techniques, advanced brain mapping methods, vast data, analytical strategies, and powerful computing. We overview here this evolution in four categories: content, applications, functionality, and availability, in contrast to other works limited mostly to content. Four atlas generations are distinguished: early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms, and 5 avenues in electronic atlases spanning the last two generations. Content-wise, new electronic atlases are categorized into eight groups considering their scope, parcellation, modality, plurality, scale, ethnicity, abnormality, and a mixture of them. Atlas content developments in these groups are heading in 23 various directions. Application-wise, we overview atlases in neuroeducation, research, and clinics, including stereotactic and functional neurosur- gery, neuroradiology, neurology, and stroke. Functionality-wise, tools and functionalities are addressed for atlas creation, navigation, individualization, enabling operations, and application-specific. Availability is discussed in media and platforms, ranging from mobile solutions to leading-edge supercomputers, with three accessibility levels. The major application-wise shift has been from research to clinical practice, particularly in stereotactic and functional neurosurgery, although clinical applications are still lagging behind the atlas content progress. Atlas functionality also has been relatively neglected until recently, as the management of brain data explosion requires powerful tools. We suggest that the future human brain atlas-related research and development activities shall be founded on and benefit from a standard framework containing the core virtual brain model cum the brain atlas platform general architecture. . . . . Keywords Human brain atlas Brain atlas evolution Brain atlas generations Brain atlas review Brain atlas platforms Introduction Human Brain Project to create a research infrastructure to decode the human brain, reconstruct the brain’s multiscale We witness in recent years a tremendous explosion of human organization, and create brain-inspired information technolo- brain atlas projects with various goals, scopes, and sizes, as gy (Amunts et al. 2016); The Human Connectome Project to addressed, for instance, in (Amunts et al. 2014;Frackowiak map structural and functional connections in the brain in order and Markram 2015;Nowinski 2017a;Hessetal. 2018). This to study the relationship between brain circuits and behavior explosion is propelled by brain-related big and well-funded (Van Essen et al. 2013); The Allen Brain Atlas to map gene initiatives and projects, including The BRAIN Initiative (Brain expression (Sunkin et al. 2013); The Big Brain to obtain ultra- Research through Advancing Innovate Neurotechnologies) high resolution neuroimages (Amunts et al. 2013); The Blue (BRAIN Working Group 2014) to develop technology to cat- Brain Project to simulate neocortical micro-circuitry alyze neuroscience discovery (Jorgenson et al. 2015); The (Markram et al. 2015); The CONNECT project to combine macro- and micro-structure (Assaf et al. 2013); the Brainnetome project to understand the brain and its disorders, develop methods of brain network analysis at different scales, * Wieslaw L. Nowinski w.nowinski@uksw.edu.pl; https://www.WieslawNowinski.com and create the brainnetome atlas (Jiang 2013); the Chinese Color Nest Project to study human connectomics across the John Paul II Center for Virtual Anatomy and Surgical Simulation, life span (Zuo et al. 2017); and the Japanese Brain/MINDS University of Cardinal Stefan Wyszynski, Woycickiego 1/3, Block (Brain Mapping by Integrating Neurotechnologies for 12, room 1220, 01-938 Warsaw, Poland 2 Neuroinform (2021) 19:1–22 Disease Studies) project to further understand the human brain specific cerebral regions, structure versus function, single data and neuropsychiatric disorders through ‘‘translatable’’ bio- acquisition modality versus multi-modal data, single brain markers (Sadato et al. 2019). Therefore, with the new acqui- specimen and individual features versus a population of spec- sition techniques introduced and big data acquired, sophisti- imens and/or aspects, in health versus diseased, static print cated applications and tools developed, and novel concepts versus dynamic digital, single atlas versus multi-atlases, slow proposed, this explosion dynamically changes over time the versus fast dynamic, and mono scale versus multi-scale, concept, role, and understanding of a human brain atlas. among others. Consequently, it is believed that the use of big digital science The initial development of cerebral cortical maps was car- to neuroscience will create new avenues for the development ried out predominantly in a single direction, meaning studying of a modern human cerebral cartography (Frackowiak and the cortical parcellation. Several early maps of the parcellated Markram 2015). Two major forces driving this brain atlas cerebral cortex were created in the first three decades of the development these are human curiosity along with scientific- 20th century by Campbell (1905), Brodmann (1909), Vogt based interest empowered by the developments in brain map- and Vogt (1919), Flechsig (1920), and Von Economo and ping technology and computing, and clinical needs urged by Koskinas (1925). These first, postmortem, hand-drawn corti- the growing cost of brain disorders and society aging. cal maps were produced for a single modality, The purpose of this state-of-the-art review is to attempt cytoarchitectonics (Brodmann 1909; Von Economo and capturing the evolution of human brain atlases as well as to Koskinas 1925) or myeloarchitectonics (Vogt and Vogt demonstrate the immense breadth of the ongoing work and its 1919;Flechsig 1920), and they varied in terms of the number tremendous potential. We track this process of evolution over of the parcellated cortical areas. Namely, in the neocortex time, identify its numerous directions and categorize them, try Campbell (1905) identified 14 areas, Brodmann (1909)44 to distinguish brain atlas generations, and capture the present areas, Von Economo and Koskinas (1925) 54 areas, and state. Typically this evolution is considered in terms of atlas Vogt and Vogt (1919) 185 areas. This process of cortical content, particularly, in research applications. However, the parcellation pioneered by Brodmann and the other early brain usefulness of human brain atlases depends not only on the mappers a century ago continues until the present time being atlas content but also on the functionality enabling and extended (1) from schematic two-dimensional (2D) single supporting various atlas-based applications as well as atlas brain-derived surface drawings to multi-modal, population- availability. Therefore, our goal here is to take a wider per- based probabilistic three-dimensional (3D) maps (Glasser spective and address the human brain atlas development in et al. 2016) facilitating to study intersubject variability; and terms of four major categories: (1) atlas content; (2) atlas ap- (2) from pure visual inspection of the examined material to the plications in various areas; (3) functionality enabling and fa- application of robust, objective and observer-independent cor- cilitating atlas use; and (4) availability in terms of access, tical parcellation rules based on quantitative criteria and sta- media, and platforms enabling the atlas delivery to its user. tistical measures (Amunts and Zilles 2015), additionally en- The rest of the paper is organized as follows. The brain hanced by employing in vivo mapping with high-field mag- atlas evolution is reviewed in Sect. 2 in terms of atlas content netic resonance imaging (Geyer et al. 2011). (categorized into 8 main groups from 23 directions for the new The need in neurosurgery to localize cerebral structures in electronic brain atlases taking into account diverse criteria), the pre-tomographic imaging era caused the creation of ste- applications, functionality, and availability. In Sect. 3 four reotactic brain atlases (a review of print and electronic stereo- generations of brain atlases are distinguished and the process tactic atlases of the human brain is presented by Alho et al., of atlas evolution is captured diagrammatically, followed in (2011)). These initially print atlases represented a big step Sect. 4 by the discussion along with some suggested future forward in atlas development both in terms of atlas content directions. and concept. In the 1950th stereotactic brain atlases were pro- duced by Speigel and Wycis (1952), Talairach et al. (1957), and Schaltenbrand and Bailey (1959). This development was Evolution of Human Brain Atlases continued by Andrew and Watkins (1969), Van Buren and Borke (1972), Schaltenbrand and Wahren (1977), Afshar We track below the evolution of human brain atlases in terms et al. (1978), and Talairach and Tournoux (1988, 1993). of content, applications, functionality, and availability. The major content-wise progress was made in four main di- rections: (1) from a few maps capturing the state-of-the-art about Evolution of Brain Atlas Content the brain to brain atlases applicable clinically; (2) from cerebral cortical maps to atlases of the whole brain (or its specific parts, The brain atlas content is the richest and most dynamic cate- including subcortical structures, cerebellum, and brainstem); (3) gory whose development proceeds along multiple divisions, from a single specimen to multiple specimens with marked ana- tomic variability, although without any probabilistic maps yet including postmortem versus in vivo data, whole brain versus Neuroinform (2021) 19:1–22 3 (e.g., Schaltenbrand and Wahren (1977) used 111 brain speci- atlas for deep brain stimulation surgery by employing smoothing mens to create their atlas); and (4) integrating structure with to reduce artifacts inherent in the print version. Similarly, digital function (neuroelectrophysiologic stimulation) like in the versions of the Schaltenbrand and Wahren atlas were built and Schaltenbrand and Wahren atlas. incorporated into atlas-aided software systems for stereotactic Besides stereotactic, some other print atlases were pub- and functional neurosurgery by Sramka et al. (1997)and St- lished for neuroradiology, neurosurgery, neuroscience, and Jean et al. (1998), who developed a deformable volumetric ver- medical education and training, among others, a brain atlas sion of the atlas. for computed tomography (Takayoshi and Hirano 1978), an We created a multi-brain atlas database with about 1000 atlas of the hippocampus (Duvernoy 1988), an atlas of brain structures and 400 sulcal patterns embedded into a neuroim- function (Orrison 1995), an atlas of the brain stem and cere- aging system (Nowinski et al. 1997a) based on the content of bellum with surface anatomy and vascularization (Duvernoy four complementary classic Thieme brain atlases: 1995), an atlas of morphology and functional neuroanatomy Schaltenbrand and Wahren, Talairach and Tournoux (1988), (Scarabino et al. 2006), an atlas of the brain stem and cerebel- Talairach and Tournoux (1993), and Ono et al. (1990) .The lum with 9.4T images of 40–60 micron resolution (Naidich original atlases were highly processed, manually edited, en- et al. 2009), and the Netter’s atlas of neuroscience (Felten et al, hanced, fully segmented and labeled, extended including into 2015). In particular, some stereo brain atlases are created as a 3D, and mutually spatially co-registered. Various content rep- 3D depth perception is essential in neurosurgical routine. resentations were created, including color-coded, contour Bassett (1952) produced a stereoscopic atlas of human anato- (closed for structures and open for sulcal patterns), and polyg- my with stereo cadaveric images of the central nervous sys- onal. They facilitate atlas use and navigation and, particularly, tem, head, and neck. Poletti (1985) built a stereo atlas of op- the unique (color-coded or contour) representation enables erative microneurosurgery with stereo photographs taken in- automated labeling. A high-quality content along with a pro- traoperatively. Kraus and Bailey (1994) created a stereo atlas posed method of atlas use (Nowinski 1998) caused the inte- of microsurgical neuroanatomy with successive surgical steps gration of this multi-brain atlas database into a majority of recorded photographically; moreover, a binocular viewer is surgical workstations for clinical use (Nowinski 2009). attached to the atlas to perceive depth. In addition, new dedicated neurosurgical atlases have been A natural step forward in brain atlasing was the development developed in the second decade of this century and they are of computerized electronic brain atlases aiming to overcome lim- discussed below. itations of their print counterparts, such as static content, image Neuroeducation has driven the extension of the brain atlas plate sparseness, lack or limited functionality, cumbersome use, content from 2D to 3D. The three-dimensional effect has been lack of interactivity, and difficulty in the mapping of the atlas achieved by various techniques ranging from a simple form of content into an individual brain scan. These efforts have been virtual reality (VR) through QuickTime VR technology headed at least in five directions: (1) direct digitization of the (Kling-Petersen and Rydmark 1997) to visualization of truly existing print atlases (Kall et al. 1985); (2) creation of bi-media 3D representations by employing volume rendering of volu- atlases with both print and digital content (Zhang et al. 2003;Mai metric data (Hoehne et al. 1992) and surface rendering of et al. 2004;Morel 2007); (3) 3D extension of the existing print geometric (polygonal) models (Nowinski et al. 2011b). The atlases (Yoshida 1987; St-Jean et al. 1998); (4) creation of im- latter approach provides fast rendering of geometric models proved atlases derived from the print content by postprocessing, created with subpixel resolution, e.g., as small as 1/10th of the enhancements, and extensions (Nowinski et al. 1997a; pixel size (Nowinski et al. 2012a). An overview of methods Sudhyadhom et al. 2012); and (5) development of new electronic for 3D visualization of neuroanatomical image data and re- atlases (such as early ones, e.g., by Bohm et al. (1983)and Greitz construction of neuronal structures in brain atlases is presented et al. (1991) constructed from digitized crysection photographs, by Maye et al. (2006). and many more created recently, as reviewed below). Note that Atlas-assisted neuroeducation, training, and simulation the first two directions require no change in the original atlas have greatly benefitted from the Visible Human Project content. (VHP) comprising the most complete volumetric data of hu- To our best knowledge the first computer program with dig- man anatomy, including cryosection photographs, computed itized (and scalable) stereotactic atlases was developed by tomography and magnetic resonance images of American Bertrand et al. (1974). A digital version of the Schaltenbrand male and female specimens (Spitzer et al. 1996). The VHP and Wahren atlas resident in a computer was created by Kall provides excellent source material for the creation of brain et al. (1985). Several groups developed electronic versions of atlases and maps, for instance by Drury and Van Essen the Schaltenbrand and Bailey atlas, namely, Yoshida (1987)built (1997) and Juanes et al. (2012). The VHP additionally sparked a 3D atlas by interpolating print plates, a 3D volumetric model of subcortical structures was produced by Kazarnovskaya et al. Note that we were the only research group that received rights from the (1991), and Sudhyadhom et al. (2012) created a deformable 3D publisher to use these atlases. 4 Neuroinform (2021) 19:1–22 other projects, including Chinese VHP and Korean VHP, architecture (Amunts et al. 2010), and/or multiplicity of resulting in the construction of new atlases (Zhang et al. them (Van Essen 2013; Glasser et al. 2016), among 2003;Li etal. 2014) along with suitable tools for sectional others. and surface anatomy navigation as well as virtual dissection and endoscopy simulation (Chung and Park 2007). 3. Modality Tremendous advancements in imaging, brain mapping, and a. From postmortem to in vivo data (Lehmann et al. 1991; computing propelled the development of new human electron- Nowinski et al. 2015a; Dickieetal. 2017; Oishi et al. ic brain atlases. Various criteria can be employed to identify 2019); and systemize multiple directions in the content evolution of b. Integrating postmortem – in vivo data (Nowinski et al. new atlases, including parcellation, modality, plurality, quali- 1997b; 2002b;Yelnik etal. 2007; Cho et al. 2008; ty, ab/normality, lifespan, extendibility, ethnicity, spatial and Amunts et al. 2014); temporal scales, integration, transformation, techniques of cre- c. Increased teslage, from 1.5T (Tesla) (Hoehne 2001)to ation, and combination of them. We determine 23 directions 3T (Nowinski et al. 2009b;Rohlfing et al. 2010)to7T and categorize them into eight (seven main and one com- (Cho et al. 2008;Nowinskiet al. 2015a; Saygin et al. bined) groups of brain atlas content development. Then, by 2017;Hucketal. 2019; Liu et al. 2020)to9.4T taking into account this categorization, a brain atlas instant can (Yushkevich et al. 2009); be considered as an element in a seven-dimensional brain atlas d. From image to non-image data, transforming into brain space. These groups along with their component directions are atlases non-image data, such as stimulating electrode as follows: geometry (Nowinski et al. 2003) and neurologic param- eters (Nowinski et al. 2014a). 1. Scope (content extent) a. From cerebral parts (e.g., the basal ganglia (Yelnik et al. 4. Plurality 2007), thalamus and basal ganglia (Morel 2007), thala- a. Specimen-related: from a single specimen to population mus (Krauth et al. 2010), and deep brain structures atlases for cerebral parts (such as the cerebellar nuclei (Lemaire et al. 2019)) to the whole brain (Kikinis et al. (Dimitrova et al. 2006), insula (Faillenot et al. 2017), 1996; Hoehne 2001; Tzourio-Mazoyer et al, 2002; cortical structures (Shattuck et al. 2008), and cerebral Nowinski et al. 2011b); arteries (Dunås et al. 2017) to the whole human brain b. From structural neuroanatomy (Rohlfing et al. 2010; (Mazziotta et al. 1995; 2001;Thompson etal. 2000); Mandal et al. 2012; Nowinski and Chua 2014) to vascu- b. Variant-related: from a single variant to a collection of lar neuroanatomy (Nowinski et al. 2009b; 2011a; Huck variants, for instance, the cerebrovascular variants et al. 2019) to connectional neuroanatomy (Mori et al. (Nowinski et al. 2009a); 2005;Nowinski et al. 2012b; Van Essen 2013;Van c. Modality-related: from uni-modal to multi-modal atlases Essen et al. 2013; Baker et al. 2018;Briggsetal. 2018) with the use of multi-modal complementary data (e.g., to gene expression (Sunkin et al. 2013) including gene Johnson and Becker 1999;Toga et al. 2006;Nowinski expression in brain development (Kanton et al. 2019); et al. 2010; Hawrylycz et al. 2012;Ding etal. 2016); c. From brain to head (Tiede et al. 1996;Chen et al. 2018), d. Channel-related: e.g., with anatomy, diffusion, and tis- and to head and neck (Nowinski 2017b); sue channels (Rohlfing et al. 2010); d. From structure to function, including functional atlases e. Atlas-related: from a single atlas to arrays of fully (Minoshima et al. 1994;Zhao et al. 2017; Haegelen et al. parcellated atlases or mega multi-atlases (Wu et al. 2018; Varoquaux et al. 2018;Lehman etal. 2020), inte- 2016). grated anatomic-functional atlases (Nowinski 2004; Nowinski et al. 2010), and functional connectivity 5. Scale atlases (Craddock et al. 2012; James et al. 2016). a. Spatial scale, from macro- to meso- to micro- to nano- scales along with integrating atlas data across multiple 2. Parcellation spatial scales (Assaf et al. 2013;Ding et al. 2016; Ecker Use of diverse, often multiple parcellation criteria, et al. 2017); from classic cytoarchitecture, myeloarchitecture and b. Temporal scale covering atlases from development gross anatomy to fMRI, chemoarchitecture (Yelnik (Kanton et al. 2019) to lifespan including age-matched et al. 2007), vascular territories (Nowinski et al. 2006), atlases to accommodate age-dependent anatomical anatomic connectivity (Mori et al. 2005), functional con- changes ranging from pediatric to geriatric populations nectivity (Arsiwalla et al. 2015), anatomic-functional (Wu et al. 2016; Zuo et al. 2017; Zhang et al. 2018; connectivity (Fan et al. 2016), (multi)receptor Oishi et al. 2019); Neuroinform (2021) 19:1–22 5 c. Integrating spatio-temporal scales (Sunkin et al. 2013; 4D (four-dimensional) probabilistic atlas of the developing brain Bozek et al. 2018). (Kuklisova-Murgasova, et al. 2011). The baby brain atlases de- veloped for specimens younger than 12 months old (for the fetus, 6. Ethnicity neonate, and infant) are reviewed by Oishi et al. (2019). Ethnic-specific atlases, for instance, for Chinese Besides probabilistic structural atlases also a variety of (Zhang et al. 2003), Korean (Cho et al. 2008), and probabilistic connectional atlases (Meola et al. 2016;Figley Caucasian (Nowinski 2017b) specimens. et al. 2017; Yeh et al. 2018; Chenot et al. 2019), functional maps and atlases (Nowinski et al. 2003;Nowinski 2009; 7. Abnormality Breshears et al. 2015), and vascular atlases (Dunås et al. From normal to disease-specific atlases for various 2017; Bernier et al. 2018;Mouches andForkert 2019)have brain disorders, for instance, Alzheimer’sdisease been created. (Thompson et al. 2001), dementia (Mega et al. 2005), Developments in mapping the microscopical organization of and stroke (Nowinski et al. 2014a; de Haan and Karnath the brain along with the progress in nanoscience (Alivisatos et al. 2017). 2013) enable the construction of brain maps and atlases across spatial scales extending from macro to meso to micro to nano. 8. Multiple (combined) groups Examples include the BigBrain with 20-micrometer resolution a. Population multi-modal atlases (Iglesias et al. 2018); (Amunts et al. 2013), a comprehensive cellular-resolution (of b. Population functional maps and atlases (Nowinski et al. 1 µm/pixel) brain atlas linking macroscopic anatomical and mi- 2003; Nowinski 2009; Breshears et al. 2015); croscopic cytoarchitectural parcellations (Ding et al. 2016), the c. Population spatio-temporal atlases, for instance, of Brain Activity Map as the functional connectome to elucidate brain development (Kuklisova-Murgasova, et al. 2011); emergent levels of neural circuit function (Alivisatos et al. d. Population ethnic atlases and templates, for instance, 2012), a temporal cell atlas of gene expression in brain develop- Chinese brain atlas (Tang et al. 2010), Indian brain tem- ment (Kanton et al. 2019), a genomics brain atlas (Sunkin et al. plate (Bhalerao et al. 2018) and atlas (Sivaswamy et al. 2013), a proteomic brain atlas (McKetney et al. 2019), an atlas of 2019), Korean brain template (Lee et al. 2005), and serotonin (Beliveau et al. 2017), and an atlas of brain tran- French brain template (Lalys et al. 2010). scriptome (Hawrylycz et al. 2012). In particular, identifying the different brain cell types to determine their roles in health and We witness recently an enormous development of disease is of great importance and it is established as one of the population-based brain atlases both in health and disease. six goals of the BRAIN Initiative (BRAIN Working Group Population-based structural atlases have been built for the whole 2014). Toward achieving this goal a whole-brain cell atlas is brain (Liang et al. 2015;Wuetal. 2016) and its specific regions, under development by Ecker et al. (2017) that integrates molec- such as the cortical areas (Shattuck et al. 2008; Glasser et al. ular, anatomical, and physiological annotations of neuronal cell 2016), cerebellum (Diedrichsen et al. 2009), brainstem (Meola types for a comprehensive characterization of cell types, their et al. 2016), subcortical nuclei (Pauli et al. 2018), thalamic nuclei distributions, and patterns of connectivity. (Iglesias et al. 2018;Najdenovskaetal. 2018), insula (Faillenot et al. 2017), some gyri including the parietal lobe gyri (Wild et al. Evolution of Brain Atlas Applications 2017) and the inferior frontal gyrus (Hammers et al. 2007), and venous cerebrovasculature (Huck et al. 2019). The rationale of creating the early cortical maps, the result of More advanced atlases have been developed in terms of pop- human curiosity, was to represent the knowledge of new discov- ulation (Liang et al. 2015), specimen age range span eries about the human brain. The brain knowledge capturing, (Wu et al. 2016; Zhang et al. 2018), and age appropriateness aggregation, and representation by means of human brain atlases (Fonov et al. 2011). For instance, the atlas of Chinese adults has been the first application of the atlases, and this central role contains a large number of 2020 specimens whose age spans remains until the present. Research has been the dominant appli- from 20 to 75 years at a 5-year interval (Liang et al. 2015). The cation of human brain atlases (Roland and Zilles 1994)employed longitudinal atlas for normative brain development and aging as tools for analysis of brain structure and function (Hess et al. spans the age range of 1–83 years, while the quantitative suscep- 2018), means to integrate neuroscience research data from tibility mapping used for its creation may facilitate the estimation healthy and diseased brains to increase data sharing and re- of age-related iron changes in deep gray matter nuclei and myelin using (Bjerke et al. 2018), and a potential tool suitable for image changes in white matter (Zhang et al. 2018). A mega multi-atlas structurization through atlas-based image parcellation to utilize a (Wu et al. 2016) constitutes an inventory of 90 brain atlases with vast amount of imaging information available in medical record the specimens ranging from 4 to 82 years of age. Several age- systems, such as the PACS (picture archiving and communica- dependent brain atlases have been built also for children (Ou tion system) (Mori et al. 2013). Moreover, disease-specific et al. 2017; Bozek et al. 2018) and fetuses, such as a dynamic atlases, such as (Thompson et al. 2001;Megaetal. 2005;de 6 Neuroinform (2021) 19:1–22 Haan and Karnath 2017) facilitate quantification of brain struc- and Rydmark 1997), and The Electronic Clinical Brain Atlas tural deficits in epilepsy, depression, schizophrenia, Alzheimer’s (Nowinski et al. 1997b). disease, bipolar disorders, autism and others disorders as These initial efforts were followed by the development of discussed by Toga and Thompson (2005). more advanced atlases in terms of content and functionality, Human brain atlases are also useful beyond research in such as Voxel-man (Hoehne 2001), The Cerefy Atlas of Brain medical education and clinical applications. Stereotactic and Anatomy (Nowinski et al. 2002b), Primal’s Interactive Head functional neurosurgery was the first, major clinical applica- &Neck (Berkovitz et al. 2003), The Cerefy Clinical Brain tion of brain atlases. We observe that every two decades mark Atlas (Nowinski and Thirunavuukarasuu 2004), The Cerefy major progress in this field. The first print atlases were created Atlas of Cerebral Vasculature (Nowinski et al. 2009b), The in the 1950th, the first digitized brain atlas was developed in Human Brain in 1492 Pieces (Nowinski et al. 2011b), The the 1970th (Bertrand et al. 1974), and the acceptance of our Human Brain in 1969 Pieces: Structure, Vasculature, electronic brain atlases to clinical practice by the community Tracts,Cranial Nerves,Systems,HeadMuscles,and Glands (and 13 surgical companies) started in the 1990th (Nowinski (Nowinski and Chua 2014), The Human Brain, Head and 2009). Initially, a digital atlas was used off-line for referencing Neck in 2953 Pieces (Nowinski et al. 2015a), and the while being placed beside the displayed patient-specific scan. Human Anatomy Atlas (Visible Body n.d.). In addition, indi- In this way, for instance, The Electronic Clinical Brain Atlas vidualized atlases that parcellate and annotate brain scans are (Nowinski et al. 1997b) had been employed next to a surgical useful for the creation of teaching files of brain anatomy and workstation to plan neurosurgery before our brain atlas data- function (Oishi et al. 2019). base was directly integrated with surgical workstations, such The brain atlases also play a role in training and simulation, as the StealthStation (Nowinski 2009). In the second decade e.g., in neurosurgery (Serra et al. 1997) and radiotherapy of this century several novel, neurosurgery-dedicated atlases (Roniotis et al. 2012). have been developed for electrode placement in deep brain Human brain mapping in research and clinical practice is stimulation (Sadikot et al. 2011; Dergachyova et al. 2018; another major area of brain atlas employment. Digital brain Haegelen et al. 2018; Nowacki et al. 2018). atlases are exploited here to provide the underlying neuroanato- In the pre-tomographic era, stereotactic brain atlases were my and to automatically label activation loci in functional images useful to localize deep stereotactic targets. The introduction of with cortical areas and stereotactic coordinates. Application ex- diagnostic imaging has not eliminated brain atlases but rather amples include the BrainMap (Lancaster et al. 2000)and the changed their role and function (Nowinski 2009). Namely Brain Atlas for Functional Imaging (Nowinski et al. 2000b). firstly, a high atlas parcellation, typically greater than that of Both these tools employ digital Brodmann’s areas (derived from a scan, allows the individualized atlas to facilitate targeting. the Talairach and Tournoux (1988) brain atlas) that are hidden in Secondly, extensive atlas features in combination with its ease the BrainMap while explicitly available and displayed to the user of use and precision facilitate neurosurgery planning and pro- in the Brain Atlas for Functional Imaging. Brodmann’sareas, vide intraoperative support, like those available in The Cerefy despite being one century old and originally limited to two views Clinical Brain Atlas: Extended Edition with Surgery Planning on the visible part of the cortical surface only, are still today and Intraoperative Support (Nowinski et al. 2005a). Thirdly, applicable references in human brain mapping to register func- several new dedicated brain atlases have been created that are tional activations to the underlying anatomy (Amunts and Zilles derived from various modalities including histology 2015). ). Because of well-known limitations of the Talairach and (Chakravarty et al. 2006), electrophysiology (Finnis et al. Tournoux atlas (see, e.g., (Nowinski and Thirunavuukarasuu, 2003; Nowinski et al. 2003), and multi-modalities (Yelnik 2009)) in order to improve labeling of functional foci a dedicated et al. 2007;Nowinskietal. 2010;Haegelen et al. 2018). AAL (Automated Anatomical Labeling) atlas was developed Other examples of atlas use in neurosurgery include a dig- from a T1-weighted scan with 45 anatomical volumes of interest ital brain atlas for surgical planning (Kikinis et al. 1996), an in each hemisphere (Tzourio-Mazoyer et al, 2002). Internet portal for stereotactic and functional neurosurgery Nuclear medicine, such as SPECT (single-photon emission shifting the paradigm in atlas building from manufacturer- computed tomography) and PET (positron emission tomogra- centric (dependent) to neurosurgical community-centric phy), produces images of relatively poor spatial resolution, (Nowinski et al. 2002a), and a practical 3D atlas for a preop- which makes it difficult to relate the functional information erative white matter-specific planning of subcortical trajecto- contained there to the corresponding underlying neuroanato- ries (Jennings et al. 2018). my. In order to enhance the accuracy and consistency of the Several neuroeducational atlases were created in the anatomic interpretation of PET functional brain images, 1990th, the Decade of the Brain, including BrainStorm (Dev Minoshima et al. (1994) constructed a PET stereotactic brain et al. 1992), Digital Anatomist (Sundsten et al. 1994), atlas from a high-resolution [18F]FDG (fluorodeoxyglucose) A.D.A.M. (A.D.A.M 1996), Microvascular Atlas of the Head images of a normal volunteer. In addition, to assist in the and Neck (Bayer 1996 ), The BRAIN project (Kling-Petersen interpretation of SPECT scans of the brain, a 3D Neuroinform (2021) 19:1–22 7 neuroanatomical atlas was created from an MRI scan of a indices (along with some accompanying textual description), normal, healthy volunteer by Lehmann et al. (1991). were placed in a stereotactic coordinate system enabling lo- Human brain atlases have potential in stroke manage- calization and referencing. Moreover, certain atlases were ment for prediction, diagnosis, and treatment equipped with transparent overlays with structure annotations (Nowinski 2020). The atlases of anatomy and blood over the brain plates to facilitate structure delineation and supply territories support decision making in thrombol- identification. Conceptually, this simple functionality already ysis and provide a quantitative assessment of the infarct signaled the necessity of equipping brain atlases with suitable and penumbra (Nowinski et al. 2006). These two atlases tools enabling their clinical applications. This necessity in the also facilitate rapid and automatic detection, localiza- pre-digital era was clearly expressed by a popular practice tion, and classification of ischemic and hemorrhagic le- performed in the operating room of generating resized sions in the emergency room (Nowinski 2020). The (individualized) brain atlas plates by means of an overhead probabilistic stroke atlas, created by the integration of projector and drawing a planned stereotactic trajectory directly brain scans with textual neurologic parameters of previ- on the displayed projection. ously managed stroke patients, enables prediction of The need of generating individualized atlases in neurosur- stroke outcomes (Nowinski et al. 2014a). gery was met in the first computer program with digitized We have also developed atlas-based applications in several stereotactic atlases developed by Bertrand et al. (1974)that other areas, including neuroradiology, neurology, psycholo- provided 1D (one-dimensional) atlas scaling along the inter- gy, psychiatry, and proposed new solutions in some niche commissural distance. This solution was followed by a 3D applications, such as atlas-guided do-it-yourself neurosurgery piece-wise linear (Nowinski et al. 2000a) and non-linear suitable for patients (Nowinski 2009) and an atlas-enhanced (Ganser et al. 2004) brain atlas warping. The requirements operating room for the future (Benabid and Nowinski 2003). from stereotactic and functional neurosurgery have been an In neurology, the 3D Atlas of Neurologic Disorders initial major driving force behind the brain atlas development, (Nowinski et al. 2014b) facilitates the understanding of neu- both in terms of content (as discussed above) and functional- rologic deficits resulting from brain damage. The atlas bridges ity. Specific atlas-related tools have been proposed for pre- neuroanatomy, neuroradiology, and neurology (Nowinski and operative planning, intra-operative support, and postoperative Chua 2013a). It serves as an educational means for neurology assessment (Nowinski 2001a). students and residents as well as a reference for neurologists. Pre-operatively, the atlas facilitates the target and trajectory This atlas is also a potentially useful tool for psychologists, planning to avoid some critical structures (such as the optic and particularly neuropsychologists, to communicate with tract), and provides the list of trajectory-intersected structures. patients. In order to increase both the quality of planning and the sur- The Cerefy Neuroradiology Atlas (Nowinski and Belov geon’s confidence, multiple complementary atlases are 2003) available over the Internet contains a fully segmented employed (Nowinski et al. 2000a; 2010). In general, the atlas and labeled anatomic brain atlas. It provides functions for a facilitates the planning of the access corridor to any target rapid atlas-to-scan registration, interactive structure labeling structure by determining all the structures encountered along and annotating, and mensuration. To our best knowledge, this the selected corridor and those neighboring it, allowing the is the first online, publicly available atlas-based application for neurosurgeon to assess various potential corridors in the pro- neuroradiology. In general, brain atlases have a still unexploit- cess of decision making. Intra-operatively, the atlas provides ed potential in neuroradiology. For instance, in (Nowinski the actual structure where the tip of the electrode is located, the 2016) nine various scenarios of atlas use in neuroradiology list of structures already intersected by the electrode, distances were discussed based on the earlier developed working proto- to critical structures, and the surrounding anatomic and vas- types ranging from image interpretation to reporting to dealing cular context (Nowinski et al. 2010). Additionally, the proba- with data explosion and to communication (for both doctor-to- bilistic functional atlas makes the targeting more accurate by doctor and doctor-to-patient). determining the best location within the whole target structure In psychiatry, we employed a brain atlas to automatically (Nowinski et al. 2003; 2005b). Post-operatively, the atlas fa- generate anatomic volumes of interest for subsequent analysis cilitates to analyze the correctness of placement of a stimulat- in a population of schizophrenic patients and controls to study ing electrode or a permanent lesion. the passivity phenomenon (Sim et al. 2009). The first (to our best knowledge) collaborative use and construction of a brain atlas over the Internet by the neurosur- Development of Atlas Functionality gical community was offered in the portal for stereotactic and functional neurosurgery supporting a probabilistic functional The early bare brain maps and atlases had no or a very limited atlas (Nowinski et al. 2002a). This atlas is calculated from supporting functionality. Therefore, certain early print stereo- neuroelectrophysiologic and neuroimaging patient-specific data acquired during functional neurosurgical procedures tactic brain atlases, besides providing standard anatomical 8 Neuroinform (2021) 19:1–22 (Nowinski et al. 2003). The portal supports the atlas with the and Ou et al. (2014). Image registration is also employed for functionality enabling data uploading to the central database multi-modal atlas construction and atlas-guided segmentation or downloading locally in order to combine them with the of brain images. Automatic segmentation of brain images, in neurosurgeon’s own data, followed by the calculation of the particular, is of great importance and it can be performed individualized probabilistic functional atlas and surgery plan- through atlas-to-scan registration, as the individualized atlas ning. The atlas is displayed graphically in 2D, 3D, and as a segments and labels the underlying neuroanatomy. The ma- probability distribution histogram along with the data tree (in- jority of methods are for the segmentation of structural neuro- cluding patients, electrodes, contacts, and coordinates) in a anatomy, though some approaches are developed to provide text format. atlas-based processing of connectional neuroanatomy (Labra The development of atlas functionality has also been driven et al. 2017) and cerebrovascular anatomy (Passat et al. 2006; by the needs of the human brain mapping community, mainly Dunås et al. 2016). As multi-atlases are more powerful than for the integration of structural and functional, and, generally, single atlases, numerous approaches have been developed for multi-modal images in the same stereotactic space as well as multi-atlas based brain segmentation (Aljabar et al. 2009; for atlas-assisted automatic labeling of activation loci in func- Artaechevarria et al. 2009; Lötjönen et al. 2010; Wu et al. tional images with cortical areas and stereotactic coordinates. 2016; Zaffino et al. 2018;Liet al. 2019). The SPM anatomy toolbox (Eickhoff et al. 2005)isan exam- In neuroeducation a typical atlas-related functionality in- ple of an image integration tool that enables the combination cludes labeling, searching, and atlas display and manipulation. of probabilistic cytoarchitectonic maps and results of func- Beyond typical operations, some atlases provide more sophis- tional imaging studies. Another data integration tool is the ticated operations, such as advanced labeling with vessel di- Neuroinformatics Platform within the Human Brain Project ameters (Nowinski et al. 2011b) and pathology description (Bjerke et al. 2018). Examples of labeling tools are the (Nowinski et al. 2014b) as well as quantification, such as BrainMap (Lancaster et al. 2000) and the Brain Atlas for geometric measurements (Nowinski et al. 2015a). Functional Imaging (Nowinski et al. 2000b). The BrainMap The atlas also enables automatic testing suitable for both assigns a label of the cortical area closest to the examined self-testing and classroom testing. A testing module for atlas- activation locus. This process is blind to the user as the brain enabled evaluation of brain knowledge in neuroeducation was atlas employed is hidden from display. In the Brain Atlas for designed and incorporated into The Cerefy Atlas of Brain Functional Imaging the parcellated, labeled, and color-coded Anatomy (Nowinskietal. 2002b), and the corresponding meth- cortical areas are explicitly available and displayed to the user, od presented in (Nowinski et al. 2009c). The module allows the who has full control over the process of activation loci label- instructor to set the testing parameters first, such as the scope of ing and is able to edit their positions if needed. This atlas the tested knowledge, scoring points, and the number of at- processes functional images through a locus-driven analysis tempts. The items (structures) in the index are consecutively (Nowinski and Thirunavuukarasuu 2003). The activation loci numbered forming a list. A random generator selects randomly in functional images are extracted automatically by items from the list while avoiding repetition. There are two thresholding with the option of interactive editing. Then, the types of queries “Where is?” and “What is?” to test location atlas of anatomy extended with Brodmann’s areas is and naming of cerebral structures, respectively. When the name employed for labeling of the activation loci with the names of the selected item is highlighted in the index, the student is of cerebral structures, Brodmann’s areas, and stereotactic co- tested against “Where is?” aiming to point to the selected struc- ordinates. The activation loci are marked on the images with ture in the atlas (image or model). When the selected structure is the superimposed atlas, and the list of all labeled loci along highlighted in the atlas, the student is tested against “What is?” with their values on the anatomic and functional images is aiming to indicate the name of this structure in the index. After provided to the user. all the structures have randomly been selected, the module pro- Neuroinformatics tools and repositories also have been de- vides the total score and the time spent to perform the test. Note veloped to store various and heterogeneous results of analy- that for the same scope of a tested brain knowledge the queries, ses. For instance, NeuroVault.org stores these results in a form which are randomly generated, are different each time avoiding of statistical maps, parcellations, and atlases (Gorgolewski this way the situation that the student copies someone else’s et al. 2016); and BALSA is a database of the brain analysis answers or memorizes them. library of spatial maps and atlases (Van Essen et al. 2017). Some other examples of application-specific functionality Construction of population atlases as well as atlas-assisted in our atlas-based solutions include brain scan interpretation in neuroimage processing and analysis in any application re- neuroradiology (Nowinski and Belov 2003), segmentation quires atlas-to-scan (or scan-to-atlas) registration. Pioneering and labeling of pathological neuroimages (Nowinski and work on atlas-to-scan elastic registration was done by Bajcsy Belov 2005), automatic generation of atlas-derived regions et al. (1983) and Gee et al. (1993). Brain image registration and volumes of interest (VOI/ROI) for fast comparison of algorithms are evaluated, for instance, by Klein et al. (2009) the left and right cerebral hemispheres to detect pathology Neuroinform (2021) 19:1–22 9 (Nowinski 2020) and for statistical analysis in populations dramatic needs to handle big data including their storage, vi- (Sim et al. 2009), aggregation of image and clinical brain data sualization, processing, analysis, and (most importantly) inter- (Nowinskietal. 2014a), dealing with data explosion pretation. Moreover, technology advancement will enhance (Nowinski 2016), radiology reporting (Nowinski 2016), and the brain atlas functionality development to create new atlases, brain knowledge communication (for both doctor-to-doctor such as a holographic brain atlas (Petersen et al. 2019)(note and doctor-to-patient) (Nowinski 2016). that much earlier we proposed to use holography in an atlas- The abovementioned operations and tools are incorporated enhanced operating room for the future (Benabid and into the atlasing software platforms, mostly to enable and en- Nowinski 2003)). hance the atlas use. However, there exist numerous stand-alone tools suitable for atlas creation and use that are not incorporated Evolution of Atlas Availability directly into the created brain atlas platforms, such as FreeSurfer, a suite of tools for a cortical surface generation The human brain atlases reviewed above are available to the and quantification of functional, connectional and structural community in various ways and this availability can be consid- properties of the human brain (Fischl 2012) extended recently ered in terms of what is available and how it can be accessed. with the probabilistic atlas of the thalamic nuclei (Iglesias et al. The atlases are available on two major media, print and 2018); SPM for a neuroanatomical variability assessment electronic, resulting in three categories: print atlases, electron- (Ashburner 2009); FSL, a comprehensive library of analysis ic atlases on various platforms, and transitional atlases from tools for functional, structural, and diffusion MRI brain imaging the print to the electronic medium. data (Jenkinson et al. 2012); the Medical Imaging Interaction The early maps and atlases were available in a print form. Toolkit (MITK) integrating two other powerful toolkits, the The transition from the print to electronic medium has been Visualization Toolkit (VTK) and the Insight Toolkit (ITK) done via two channels by (1) creating both print and electronic (Wolf et al. 2005); and the Vascular Editor to create and edit (bi-media) atlases, and (2) derivation of early electronic vascular and, generally, tubular-like such as cranial nerve net- atlases from print materials by direct atlas plate digitization works (Marchenko et al. 2010). Our experience shows that the with no content change or with content extension by tools directly integrated with the atlas have proved their value postprocessing and enhancement. Tremendous developments allowing any new atlas modules to be created and edited within in computing enable almost an unlimited growth of electronic the already existing neural context (Nowinski et al. 2012a; b). brain atlases on numerous platforms ranging from mobile so- Recently, a new generation of methods, tools, reposi- lutions to leading-edge supercomputers. tories, and neurotechnologies is planned or already under Electronic brain atlas platforms can generally be clas- development. For instance, intensive technology develop- sified along multiple divisions: stand-alone versus web- ment and validation is outlined under the BRAIN Initiative based; stationary versus mobile; low-cost versus high- (BRAIN Working Group 2014; Jorgenson et al. 2015). end workstations; with standard interaction and display Numerous atlas-related tools are being developed within versus VR-enhanced, augmented reality (AR)-enhanced brain big projects. For instance, a common automated and holographic display; standard computer versus super- preprocessing framework has been developed within the computer; and single computer versus computer clusters, Human Connectome Project to bring multiple magnetic networks, and cloud computing. resonance imaging modalities together across a large co- In neuroscience research, it is usually required to pro- hort of subjects (Glasser et al. 2013). The vide a brain atlas within a web-based solution. In general, Neuroinformatics Platform within the Human Brain brain atlases can run on various platforms. For instance, we Project develops tools to facilitate data acquisition and have developed electronic brain atlases available and run- annotation, assignment of the anatomical location to data, ning on several platforms, including stand-alone plug-in and assembly of and access to spatially indexed informa- library (Nowinski 2009); workstation (Nowinski 2009); tion (Bjerke et al. 2018). Other examples of such tools notebook and desktop (for Windows and MAC) developed within the Allen Brain Atlas, BigBrain,and (Nowinskietal. 2005a; 2011b; Nowinski and Chua FSL atlas, among others, are given in (Amunts and 2014); Internet-based (Nowinski et al. 2002a); mobile Zilles 2015). The Scalable Brain Atlas is a collection of (iPhone (Nowinski et al. 2009c), iPad (Nowinski and web services that provides unified access to a large col- Chua 2013b), Android (Nowinski et al. 2014c); and VR- lection of public brain atlasing resources for the human enabled (Serra et al. 1997). It is worth mentioning that the and non-human species (Bakker et al. 2015). latter pioneering 3D brain atlas, employing a VR environ- Therefore, despite relatively slow progress in the develop- ment with a 3D natural interaction and stereoscopic dis- ment of the brain atlas enabling functionality so far in com- play, was completed as early as in 1997. Brain big projects, parison to that of the atlas content, the recent brain big projects however, such as The Human Brain Project, require leading-edge supercomputers (Amunts et al. 2016). will strongly drive this functionality development due to 10 Neuroinform (2021) 19:1–22 From a user’s standpoint, we distinguish three levels of Generations of Human Brain Atlases accessibility: non-accessible, private (with limited or unlimit- ed access), and public (with registered or unregistered access; Observing the evolution of human brain maps and atlases, free or payable). four atlas generations can be distinguished, namely: (1) early The non-accessible level means that the atlas is pub- cortical maps, (2) print stereotactic atlases, (3) early digital lished by its creators and available only to them, and the atlases, and (4) advanced brain atlas platforms. From a time- community is aware of the atlas but has no access to view frame standpoint, approximately the first atlas generation was it completely nor use it. This is probably the most com- developed in the first half of the 20th century (with the major mon situation. Private access indicates accessibility of the maps published in the first three decades), the second genera- atlas to a certain group of users, such as members of a tion in the second half of the 20th century (with the majority of consortium. Public access implies that any user may have atlases published in its first four decades), the third generation access to the atlas after meeting certain condition(s), such in the Decade of the Brain (and a handful of atlases a little as registration and/or payment. For instance, the print earlier), and the fourth generation in the 21st century, the atlases are public, payable with unregistered access. century of the brain and mind. Most educational electronic brain atlases are public and The above review, generations of atlases, and their present payable, such as Voxel-man (Hoehne 2001), The Human state are summarized in a form of the human brain atlas evo- Brain in 1492 Pieces (Nowinskietal. 2011b), Focus lution diagram in Fig. 1. Digital Anatomy Atlas. Neuroanatomy running on iPhone and iPad (Focus Medica), and Human Anatomy Atlas (Visible Body n.d.). Our latest and most advanced atlas The Human Brain, Head and Neck in 2953 Pieces Discussion (Nowinskietal. 2015a) is public, free of charge with registration required by its publisher at http://www. The human brain atlases have been evolved tremendously, thieme.com/nowinski/. especially in recent decades, in multiple directions, as cap- Although restricted access may constrict in some cases tured diagrammatically in Fig. 1. This evolution has been atlas availability, we may guess that overall this availability driven by sophisticated imaging techniques, advanced brain substantially grows over time, as the numbers of both atlas mapping methods, vast resources of brain data accumulated at creators and their users have been rising tremendously. This an unprecedented rate, analytical strategies, and powerful guess is corroborated by Table 1 that provides the numbers of computing. The effects of this explosive growth span from a publications over time cited on PubMed under the term “hu- few hand-drawn maps to multi-atlases, from print editions to man brain atlas” indicating the growth over 470 times from 1 web-based repositories, from 2D to nD, from determinist to publication in the year 1950 to 474 publication in the year probabilistic, from unimodal to multi-modal, from a cortical 2018. Between years 2010–2018 this growth was almost 12 organization to an all-level brain organization from genes to fold. The overall number of citations is 4350. the whole brain, from normal to pathologic, from gross to The same term “human brain atlas” searched on Google nanoscales, and various combinations of these above, among Scholar gives “about 762,000 results”. others. Several papers and reports have addressed the future trends that can be expected in the human brain atlas evolution, mainly in terms of an atlas content (Toga et al. 2006; Evans et al. 2012;Amunts etal. 2014; BRAIN Working Group 2014). Table 1 Number of The brain atlases have been employed in a wide spectrum Year Number Year Number citations under the term of applications and their usefulness depends not only on the “human brain atlas” on atlas content, but also on functionality and availability. Hence 1950 1 2011 174 PubMed versus years of publications (as of 18 this review has been conducted from these four perspectives: 1960 1 2012 213 May 2020) content, applications, functionality, and availability, in con- 1970 3 2013 241 trast to other works limited mostly to atlas content. 1980 5 2014 297 Content-wise, the human brain atlases have evolved from a 1985 18 2015 312 few hand-drawn maps to an atlas as a collection of maps and 1990 16 2016 337 images; to multi-atlases; to repositories of multi-modal brain 1995 22 2017 362 images in health and disease; to heterogeneous databases; to 2000 42 2018 474 composable, manipulable and explorable 3D and, generally, 2005 121 2019 389 nD cerebral models; to platforms for brain knowledge aggre- 2010 185 2020 132 gation and integration; to brain atlas data at macro, meso, Neuroinform (2021) 19:1–22 11 Fig. 1 A human brain atlas evolution diagram with four generations and four categories: content (only for the new electronic atlases), applications, functionality, and availability, each subsequently divided into sub-categories micro, nano, and hybrid scales with the resolution ranging avenues. The recent and most prominent avenue is the crea- from the whole brain to synapses; and to large databases with tion of new electronic brain atlases. We give numerous exam- massive amounts of data aiming to discover knowledge being ples of vast activities in the area of atlas content development developed within multi-center and/or multi-national projects heading in at least 23 various (though non-exhaustive) direc- and initiatives. tions, which are categorized in eight groups taking into ac- In an over century-long process of the human brain map count scope (content extent), parcellation, modality, plurality, and atlas creation, we have distinguished four generations: (1) scale, ethnicity, abnormality, and a mixture of them. early cortical maps (created in the first half of the 20th centu- Application-wise, brain atlases are employed in education, ry), (2) print stereotactic atlases (published in the second half research, and clinical practice. The role and usefulness of the of the 20th century), (3) early digital atlases (produced pre- brain atlases have been expanding both within the research dominantly in the Decade of the Brain), and (4) advanced area and beyond it. The main atlas application area is research brain atlas platforms (being developed in this century). We and brain knowledge gathering ranging from knowledge cap- also noticed that every two decades mark major progress in turing to knowledge aggregation to knowledge discovery. the human brain atlas evolution. The first stereotactic brain Other areas of atlas applications include human brain mapping atlases were created in the 1950th, the first digitized brain atlas (spanning research and clinical practice), stereotactic and was developed in the 1970th, the introduction of electronic functional neurosurgery, neuroeducation, and specific areas, brain atlases to clinical practice began in the 1990th followed such as neuroradiology, neurology, psychology, stroke, and by an explosion in brain atlas development propelled by the psychiatry. brain big projects that started in the 2010th . The major application-wise shift has been from research to The development of electronic brain atlases spans the last clinical practice, particularly in stereotactic and functional neu- two generations and in this area we have distinguished five rosurgery to treat patients. A brain atlas importance and 12 Neuroinform (2021) 19:1–22 potential in clinical applications have been raised and addressed Efficient and user-friendly tools are, in particular, required, by a few authors (Mori et al. 2013; BRAIN Working Group in education. We have attempted to develop new education 2014). In fact, the development of brain atlas-based clinical tools going beyond those available in standard educational applications for prediction, diagnosis, and treatment has been atlases, such as Voxel-man (Hoehne 2001)or Interactive a major focus of our work. Neurosurgery planning and assess- Head & Neck (Berkovitz et al. 2003). These tools enable novel ment (Nowinski 1998; 2001; 2009;Nowinskietal. 2010)was educational use of the atlas, such as self-testing and classroom our first clinical application with anatomic, functional, and vas- assessment (Nowinski et al. 2009c) available on notebooks cular atlases created. Our solutions in stereotactic and functional and mobile devices, interdisciplinary education across neurosurgery have been licensed to 13 surgical companies and neuroanatomy-neuroradiology-neurology (Nowinski et Chua integrated with surgical workstations of the leading companies, 2013a), advanced education for residents and clinicians with a namely, Medtronic, Brainlab, and Elekta (Nowinski 2009). In user’s “de/composable” content and context (Nowinski et al. addition, we have developed working prototypes in other fields 2014b; 2015a), and patients’ education and instruction for atlas-assisted brain pathology detection (Nowinski 2020), (Nowinski 2016). quantification of cerebrallesions (Nowinskietal. 2006), seg- From a usage standpoint, atlas tools can be classified into mentation and labeling of pathological neuroimages with tu- two broad categories: general and specific. General tools sup- mors causing a mass effect in brain cancer (Nowinski and port typical atlas-enabled operations, such as segmentation, Belov 2005), stroke management (Nowinski et al. 2006; labeling, manipulation, quantification, and querying. Nowinski 2020), and stroke outcome prediction (Nowinski Specific operations are those customized to a certain field et al. 2014a). A vast, still unexploited, potential of brain atlas and/or particular use, such as automatic testing, generation in neuroradiology has been addressed in (Nowinski 2016)de- of teaching materials, ROIs/VOIs generation and analysis, scribing nine applications for which working prototypes (proofs targeting, safety analysis, postoperative assessment, locus- of concept) we developed earlier and presented at clinical meet- driven analysis, decision making support, prediction of occur- ings. However, despite several examples of brain atlas use in rence and outcomes, scan interpretation, knowledge commu- clinical applications (as products or working prototypes), these nication, and a combination of them. From an integration applications are still lagging behind the progress in the devel- standpoint, atlas-related tools can be stand-alone or directly opment of the atlas content. One of the main obstacles in intro- integrated with the atlas platform. ducing the brain atlas solutions to clinical practice is their val- Availability-wise, the major developmental step was obvi- idation, which is tedious, time-consuming, and costly; particu- ously from print to digital atlases. Enormous progress in com- larly, clinical validation is beyond a reach of a research lab puting enables almost unlimited development of digital brain because of its high cost. atlases to run on numerous platforms ranging from mobile In contrast to the content-wise atlas development being solutions to notebooks to interactive web-based visualization widely carried out by numerous groups as well as by national platforms to VR/AR systems to high-end workstations to and multi-national consortia, the development of atlas func- computer clusters, networks, and leading-edge tionality has been relatively neglected, until recently as the supercomputers. problem of managing data explosion requires powerful, suit- The atlas availability substantially grows over time with the able, and dedicated tools. numbers of both atlas creators and their users tremendously The early atlases evolved from bare, hand-drawn maps to raising. If approximated by the growth of the human brain print stereotactic atlases with images scalable using an over- atlas publications on PubMed, the atlas availability growth head projector to electronic deformable atlas platforms to VR- from the year 1950 to the year 2018 would be over 470 times. and AR-enhanced atlases to atlas-engines meaning the atlases This work has several limitations. We have tried our best to serving as tools by themselves that support brain data man- make this state-of-the-art review in the human brain atlas evo- agement, neuroimage processing and analysis, decision mak- lution as complete as possible. However, the overall number ing, and knowledge discovery. of publications about this subject on PubMed is vast of 4350 From an application standpoint, the tools providing an (and about 762,000 references on Google Scholar) and rapidly atlas with its supporting functionality belong to three cat- growing, which makes a fairly complete state-of-the-art re- egories: (1) educational tools to explore the atlas, test view quite difficult (if possible at all). In some areas, such as knowledge, and prepare teaching materials (that can be clinical applications, any relevant research publications may grouped as student-oriented, educator-oriented, self-test- simply not exist, and the names of atlas creators and devel- ing, and a mixture of them); (2) research tools enabling opers may not be disclosed by the providers to the atlas users brain investigation and knowledge discovery; and (3) clin- (as, for instance, is in the case of our brain atlases licensed to ical tools to allow the clinicians to better prevent, diag- surgical companies). nose, treat, and cure brain diseases. Neuroinform (2021) 19:1–22 13 The brain atlas content evolution is divided at two levels The two most widely used coordinate systems in the neu- into 8 groups at the first and 23 directions at the second level. roscience community are the Talairach system (Talairach and We believe that the categorization of the atlas content devel- Tournoux 1988) and the Montreal Neurological Institute opment into these 8 groups covers the whole landscape, (MNI) system, and any coordinates of the latter can be con- though it is not unique and other criteria might be applied. verted to the Talairach space (Chau and McIntosh 2005). The This categorization is neither distinctive and some groups Talairach coordinate system has become the standard refer- may overlap, for instance, the increasing scale may result in ence for reporting the brain locations in scientific publications, the increasing scope. The overall 23 directions in the atlas though its definition is not unique. The Talairach system is content development are not exhaustive and could be finer, based on the anterior (AC) and posterior (PC) commissure line especially in the last (combined) group. Likewise, the ethnic- and its center is located at the AC point landmark. However, ity and abnormality groups could be subdivided into direc- the AC and PC point landmarks can be defined at least in four tions, each for a specific ethnicity or disease, respectively. different ways resulting in a substantial discrepancy among The approximation of the atlas availability growth through the coordinates depending on a selected landmark definition the number of publications about human brain atlas may be (Nowinski 2001b). Typically the centers of the AC and PC underestimated even by a few orders. Usually a publication structures are taken as the point landmarks, while the original- about a free atlas attracts a plethora of its users, and even the ly defined point landmarks by Talairach are beyond the AC number of citations may not be representative. For instance, and PC structures (consequently, for instance, the AC is miss- our free brain atlas (Nowinski 2017b) has a download-to- ing on the coronal plane passing through the center of the citation ratio of 160. coordinate system (and in my print version of his atlas, prof. This review is restricted to the human brain atlases. Several Talairach “corrected” that by manually drawing it)). authors have overviewed non-human brain atlases for various A core reference high-quality cerebral model is miss- species, including primates (marmoset, mouse lemur, squirrel ing in neuroinformatics. An example of such a long- monkey, macaque, and chimpanzee by Thiebaut de Schotten lasting reference model for the cerebral cortex are et al. (2018)), rodents and marsupials (rat, mouse, and opos- Brodmann’s areas (Brodmann 1909). Brodmann’sareas, sum by Bakker et al. (2015)), and other animals by Hess et al. though being one century old and based on a single (2018). It is also worth mentioning that several human spinal brain specimen, are most widely used and remain until cord atlases have been constructed, for instance, by Taso et al. today applicable references in human brain mapping to (2013) and Lévy et al. (2015). correlate functional activations to the underlying neuro- Finally, this review reflects a personal perspective and anatomy (Amunts and Zilles 2015), despite the creation three-decade-long experience of the author in the field with of more advanced and accurate cortical maps (Glasser et al. 2016 35 diverse human brain atlases created, where 15 of them have ). For a certain period the Talairach and been released for the global use by Thieme Medical Tournoux (1988) atlas has played a similar role for Publishers. the whole brain, despite its well-known limitations in- Making an overview of a field also encourages an attempt cluding spatial consistency as quantified by Nowinski to predict future developments in brain atlasing. On one hand, and Thirunavuukarasuu (2009). Another example is the the future brain research directions are well determined in the Schaltenbrand and Wahren (1977) atlas that for a few brain big projects, such as the BRAIN Initiative that sets six decades until the present remains the reference in ste- grand goals (BRAIN Working Group 2014). These efforts reotactic and functional neurosurgery. will result in the acquisition of more and more massive The construction of the core virtual brain model of the amounts of data and the creation of more advanced and com- highest possible quality is a complicated, tedious, and time- plex brain atlases with an ever-growing scope, population, and consuming process, which requires sophisticated, dedicated, spatial and temporal resolutions, additionally empowered by and precise tools and, of course, the state-of-the-art data, be- more advanced tools. On the other hand these efforts keep sides meticulous attention to details. Therefore, such a virtual increasing a sort of atlas landscape inhomogeneity as well as brain model shall be built incrementally. We have attempted difficulty in the atlas standardization and the integration and to create this kind of virtual brain model from multi-modal, interpretation of various outcomes. Moreover, as the majority multi-sequence scans of a living specimen (in a process which of efforts is devoted to brain atlas-related research, we can took almost 15 years until funding lasted), see Fig. 2.This expect a growing imbalance and chasms among research, clin- model has been designed as modular (Nowinski 2017b)with ical, and educational applications of human brain atlases. its consecutive modules being developed and validated (in- There are at least three central components related to atlas cluding cortical areas and subcortical structures (Nowinski standardization, namely, an atlas coordinate system, a core et al. 2012a), white matter tracts (Nowinski et al. 2012b), reference cerebral model, and a brain atlas platform intracranial vasculature (Nowinski et al. 2011a), cranial architecture. nerves and nuclei (2012c), head muscle and glands (2013c), 14 Neuroinform (2021) 19:1–22 Neuroinform (2021) 19:1–22 15 Fig. 2 The virtual decomposable brain model extended to the head and health and disease to mega multi-atlases across the neck with about 3,000 fully segmented, labeled, and color-coded 3D lifespan to atlas platforms at macro, meso, micro, and components. Shown here: the right central nervous system with the cere- nanoscales, as diagrammatically summarized in Fig. 1. brum (parcellated into gyri and sulci), cerebellum, brainstem, and cervical This atlas evolution review differentiates from other spine; deep gray nuclei; cerebral ventricles; white matter (deep and pos- terior fossa); white matter tracts; right visual system; auditory system; works, usually focusing on the atlas content mostly in intracranial arteries; intracranial veins; dural sinuses; cranial nerves with research applications, as we take here a wider perspective nuclei (partly exposed on the left side); right head (masticatory) muscles; and analyze this evolution in four categories: content, ap- right glands; upper skull with the frontal bone removed; cervical spine plications, functionality, and availability. Four generations (3rd and 4th cervical vertebrae); extracranial arteries; and extracranial veins of human brain atlases are distinguished, namely, early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms. The develop- extracranial vasculature (2015b), skull (2015c), and systems, ment of electronic brain atlases spans the last two gener- while releasing subsequent five versions (termed The Human ations and in this area we identify five avenues, the recent Brain in 1492/1969/2953 Pieces) for public use (Nowinski and most prominent is the creation of new electronic brain et al. 2011b; 2015a; Nowinski et Chua 2014). Our effort, atlases. The brain atlas content evolution in this avenue is though uncompleted, has demonstrated the feasibility of this categorized in eight groups taking into account scope, approach. parcellation, modality, plurality, scale, ethnicity, abnor- mality, and a mixture of them, in which, in turn, the atlas Although the advantages of population atlases are enor- developments are heading in 23 various directions. mous and obvious, we believe that these atlases shall be con- We suggest that the future human brain atlas-related re- structed around a very detailed, accurate, fully segmented, search and development activities shall be founded on and completely labeled, validated, and deterministic core model benefitted from a standard framework containing the core vir- of a virtual brain created with the highest quality possible tual brain model cum the brain atlas platform general and accepted as the reference standard (similarly as architecture. Brodmann created his long-lasting standard for the cortical areas from a single specimen). Moreover, the use of a single Acknowledgements The author’s work on brain atlas development was funded by ASTAR, Singapore. specimen enables its continuous rescanning to create new modules with advances in imaging technology (for instance, Compliance with Ethical Standards to build our virtual brain model, the same specimen was rescanned for over 10 years on various 1.5T, 3T, and 7T as Conflict of Interest None. well as CT and US (ultrasonography) scanners). Creating a population brain atlas even with a high number of specimens Open Access This article is licensed under a Creative Commons but without ensuring the highest quality and thorough valida- Attribution 4.0 International License, which permits use, sharing, adap- tation, distribution and reproduction in any medium or format, as long as tion just increases the abovementioned inhomogeneity of the you give appropriate credit to the original author(s) and the source, pro- field with a difficulty to cross-relate various atlases. vide a link to the Creative Commons licence, and indicate if changes were The final factor that might potentially counterbalance this made. The images or other third party material in this article are included atlas inhomogeneity trend is the establishing a standardized, in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's general architecture of the human brain atlas platform Creative Commons licence and your intended use is not permitted by supporting equally research, clinical, and educational applica- statutory regulation or exceeds the permitted use, you will need to obtain tions and enabling the clinicians to grow the initial core brain permission directly from the copyright holder. To view a copy of this model with their own new data. We believe that the future licence, visit http://creativecommons.org/licenses/by/4.0/. human brain atlas-related research and development activities shall be founded on and benefitted from such a standard framework containing the core virtual brain model cum the brain atlas platform general architecture. References A.D.A.M. (1996). A.D.A.M Animated Dissection of Anatomy for Summary Medicine.User’s Guide, A.D.A.M.. Afshar, E., Watkins, E. S., & Yap, J. C. (1978). 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Evolution of Human Brain Atlases in Terms of Content, Applications, Functionality, and Availability

Neuroinformatics , Volume 19 (1) – Jul 29, 2020

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10.1007/s12021-020-09481-9
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Abstract

Human brain atlases have been evolving tremendously, propelled recently by brain big projects, and driven by sophisticated imaging techniques, advanced brain mapping methods, vast data, analytical strategies, and powerful computing. We overview here this evolution in four categories: content, applications, functionality, and availability, in contrast to other works limited mostly to content. Four atlas generations are distinguished: early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms, and 5 avenues in electronic atlases spanning the last two generations. Content-wise, new electronic atlases are categorized into eight groups considering their scope, parcellation, modality, plurality, scale, ethnicity, abnormality, and a mixture of them. Atlas content developments in these groups are heading in 23 various directions. Application-wise, we overview atlases in neuroeducation, research, and clinics, including stereotactic and functional neurosur- gery, neuroradiology, neurology, and stroke. Functionality-wise, tools and functionalities are addressed for atlas creation, navigation, individualization, enabling operations, and application-specific. Availability is discussed in media and platforms, ranging from mobile solutions to leading-edge supercomputers, with three accessibility levels. The major application-wise shift has been from research to clinical practice, particularly in stereotactic and functional neurosurgery, although clinical applications are still lagging behind the atlas content progress. Atlas functionality also has been relatively neglected until recently, as the management of brain data explosion requires powerful tools. We suggest that the future human brain atlas-related research and development activities shall be founded on and benefit from a standard framework containing the core virtual brain model cum the brain atlas platform general architecture. . . . . Keywords Human brain atlas Brain atlas evolution Brain atlas generations Brain atlas review Brain atlas platforms Introduction Human Brain Project to create a research infrastructure to decode the human brain, reconstruct the brain’s multiscale We witness in recent years a tremendous explosion of human organization, and create brain-inspired information technolo- brain atlas projects with various goals, scopes, and sizes, as gy (Amunts et al. 2016); The Human Connectome Project to addressed, for instance, in (Amunts et al. 2014;Frackowiak map structural and functional connections in the brain in order and Markram 2015;Nowinski 2017a;Hessetal. 2018). This to study the relationship between brain circuits and behavior explosion is propelled by brain-related big and well-funded (Van Essen et al. 2013); The Allen Brain Atlas to map gene initiatives and projects, including The BRAIN Initiative (Brain expression (Sunkin et al. 2013); The Big Brain to obtain ultra- Research through Advancing Innovate Neurotechnologies) high resolution neuroimages (Amunts et al. 2013); The Blue (BRAIN Working Group 2014) to develop technology to cat- Brain Project to simulate neocortical micro-circuitry alyze neuroscience discovery (Jorgenson et al. 2015); The (Markram et al. 2015); The CONNECT project to combine macro- and micro-structure (Assaf et al. 2013); the Brainnetome project to understand the brain and its disorders, develop methods of brain network analysis at different scales, * Wieslaw L. Nowinski w.nowinski@uksw.edu.pl; https://www.WieslawNowinski.com and create the brainnetome atlas (Jiang 2013); the Chinese Color Nest Project to study human connectomics across the John Paul II Center for Virtual Anatomy and Surgical Simulation, life span (Zuo et al. 2017); and the Japanese Brain/MINDS University of Cardinal Stefan Wyszynski, Woycickiego 1/3, Block (Brain Mapping by Integrating Neurotechnologies for 12, room 1220, 01-938 Warsaw, Poland 2 Neuroinform (2021) 19:1–22 Disease Studies) project to further understand the human brain specific cerebral regions, structure versus function, single data and neuropsychiatric disorders through ‘‘translatable’’ bio- acquisition modality versus multi-modal data, single brain markers (Sadato et al. 2019). Therefore, with the new acqui- specimen and individual features versus a population of spec- sition techniques introduced and big data acquired, sophisti- imens and/or aspects, in health versus diseased, static print cated applications and tools developed, and novel concepts versus dynamic digital, single atlas versus multi-atlases, slow proposed, this explosion dynamically changes over time the versus fast dynamic, and mono scale versus multi-scale, concept, role, and understanding of a human brain atlas. among others. Consequently, it is believed that the use of big digital science The initial development of cerebral cortical maps was car- to neuroscience will create new avenues for the development ried out predominantly in a single direction, meaning studying of a modern human cerebral cartography (Frackowiak and the cortical parcellation. Several early maps of the parcellated Markram 2015). Two major forces driving this brain atlas cerebral cortex were created in the first three decades of the development these are human curiosity along with scientific- 20th century by Campbell (1905), Brodmann (1909), Vogt based interest empowered by the developments in brain map- and Vogt (1919), Flechsig (1920), and Von Economo and ping technology and computing, and clinical needs urged by Koskinas (1925). These first, postmortem, hand-drawn corti- the growing cost of brain disorders and society aging. cal maps were produced for a single modality, The purpose of this state-of-the-art review is to attempt cytoarchitectonics (Brodmann 1909; Von Economo and capturing the evolution of human brain atlases as well as to Koskinas 1925) or myeloarchitectonics (Vogt and Vogt demonstrate the immense breadth of the ongoing work and its 1919;Flechsig 1920), and they varied in terms of the number tremendous potential. We track this process of evolution over of the parcellated cortical areas. Namely, in the neocortex time, identify its numerous directions and categorize them, try Campbell (1905) identified 14 areas, Brodmann (1909)44 to distinguish brain atlas generations, and capture the present areas, Von Economo and Koskinas (1925) 54 areas, and state. Typically this evolution is considered in terms of atlas Vogt and Vogt (1919) 185 areas. This process of cortical content, particularly, in research applications. However, the parcellation pioneered by Brodmann and the other early brain usefulness of human brain atlases depends not only on the mappers a century ago continues until the present time being atlas content but also on the functionality enabling and extended (1) from schematic two-dimensional (2D) single supporting various atlas-based applications as well as atlas brain-derived surface drawings to multi-modal, population- availability. Therefore, our goal here is to take a wider per- based probabilistic three-dimensional (3D) maps (Glasser spective and address the human brain atlas development in et al. 2016) facilitating to study intersubject variability; and terms of four major categories: (1) atlas content; (2) atlas ap- (2) from pure visual inspection of the examined material to the plications in various areas; (3) functionality enabling and fa- application of robust, objective and observer-independent cor- cilitating atlas use; and (4) availability in terms of access, tical parcellation rules based on quantitative criteria and sta- media, and platforms enabling the atlas delivery to its user. tistical measures (Amunts and Zilles 2015), additionally en- The rest of the paper is organized as follows. The brain hanced by employing in vivo mapping with high-field mag- atlas evolution is reviewed in Sect. 2 in terms of atlas content netic resonance imaging (Geyer et al. 2011). (categorized into 8 main groups from 23 directions for the new The need in neurosurgery to localize cerebral structures in electronic brain atlases taking into account diverse criteria), the pre-tomographic imaging era caused the creation of ste- applications, functionality, and availability. In Sect. 3 four reotactic brain atlases (a review of print and electronic stereo- generations of brain atlases are distinguished and the process tactic atlases of the human brain is presented by Alho et al., of atlas evolution is captured diagrammatically, followed in (2011)). These initially print atlases represented a big step Sect. 4 by the discussion along with some suggested future forward in atlas development both in terms of atlas content directions. and concept. In the 1950th stereotactic brain atlases were pro- duced by Speigel and Wycis (1952), Talairach et al. (1957), and Schaltenbrand and Bailey (1959). This development was Evolution of Human Brain Atlases continued by Andrew and Watkins (1969), Van Buren and Borke (1972), Schaltenbrand and Wahren (1977), Afshar We track below the evolution of human brain atlases in terms et al. (1978), and Talairach and Tournoux (1988, 1993). of content, applications, functionality, and availability. The major content-wise progress was made in four main di- rections: (1) from a few maps capturing the state-of-the-art about Evolution of Brain Atlas Content the brain to brain atlases applicable clinically; (2) from cerebral cortical maps to atlases of the whole brain (or its specific parts, The brain atlas content is the richest and most dynamic cate- including subcortical structures, cerebellum, and brainstem); (3) gory whose development proceeds along multiple divisions, from a single specimen to multiple specimens with marked ana- tomic variability, although without any probabilistic maps yet including postmortem versus in vivo data, whole brain versus Neuroinform (2021) 19:1–22 3 (e.g., Schaltenbrand and Wahren (1977) used 111 brain speci- atlas for deep brain stimulation surgery by employing smoothing mens to create their atlas); and (4) integrating structure with to reduce artifacts inherent in the print version. Similarly, digital function (neuroelectrophysiologic stimulation) like in the versions of the Schaltenbrand and Wahren atlas were built and Schaltenbrand and Wahren atlas. incorporated into atlas-aided software systems for stereotactic Besides stereotactic, some other print atlases were pub- and functional neurosurgery by Sramka et al. (1997)and St- lished for neuroradiology, neurosurgery, neuroscience, and Jean et al. (1998), who developed a deformable volumetric ver- medical education and training, among others, a brain atlas sion of the atlas. for computed tomography (Takayoshi and Hirano 1978), an We created a multi-brain atlas database with about 1000 atlas of the hippocampus (Duvernoy 1988), an atlas of brain structures and 400 sulcal patterns embedded into a neuroim- function (Orrison 1995), an atlas of the brain stem and cere- aging system (Nowinski et al. 1997a) based on the content of bellum with surface anatomy and vascularization (Duvernoy four complementary classic Thieme brain atlases: 1995), an atlas of morphology and functional neuroanatomy Schaltenbrand and Wahren, Talairach and Tournoux (1988), (Scarabino et al. 2006), an atlas of the brain stem and cerebel- Talairach and Tournoux (1993), and Ono et al. (1990) .The lum with 9.4T images of 40–60 micron resolution (Naidich original atlases were highly processed, manually edited, en- et al. 2009), and the Netter’s atlas of neuroscience (Felten et al, hanced, fully segmented and labeled, extended including into 2015). In particular, some stereo brain atlases are created as a 3D, and mutually spatially co-registered. Various content rep- 3D depth perception is essential in neurosurgical routine. resentations were created, including color-coded, contour Bassett (1952) produced a stereoscopic atlas of human anato- (closed for structures and open for sulcal patterns), and polyg- my with stereo cadaveric images of the central nervous sys- onal. They facilitate atlas use and navigation and, particularly, tem, head, and neck. Poletti (1985) built a stereo atlas of op- the unique (color-coded or contour) representation enables erative microneurosurgery with stereo photographs taken in- automated labeling. A high-quality content along with a pro- traoperatively. Kraus and Bailey (1994) created a stereo atlas posed method of atlas use (Nowinski 1998) caused the inte- of microsurgical neuroanatomy with successive surgical steps gration of this multi-brain atlas database into a majority of recorded photographically; moreover, a binocular viewer is surgical workstations for clinical use (Nowinski 2009). attached to the atlas to perceive depth. In addition, new dedicated neurosurgical atlases have been A natural step forward in brain atlasing was the development developed in the second decade of this century and they are of computerized electronic brain atlases aiming to overcome lim- discussed below. itations of their print counterparts, such as static content, image Neuroeducation has driven the extension of the brain atlas plate sparseness, lack or limited functionality, cumbersome use, content from 2D to 3D. The three-dimensional effect has been lack of interactivity, and difficulty in the mapping of the atlas achieved by various techniques ranging from a simple form of content into an individual brain scan. These efforts have been virtual reality (VR) through QuickTime VR technology headed at least in five directions: (1) direct digitization of the (Kling-Petersen and Rydmark 1997) to visualization of truly existing print atlases (Kall et al. 1985); (2) creation of bi-media 3D representations by employing volume rendering of volu- atlases with both print and digital content (Zhang et al. 2003;Mai metric data (Hoehne et al. 1992) and surface rendering of et al. 2004;Morel 2007); (3) 3D extension of the existing print geometric (polygonal) models (Nowinski et al. 2011b). The atlases (Yoshida 1987; St-Jean et al. 1998); (4) creation of im- latter approach provides fast rendering of geometric models proved atlases derived from the print content by postprocessing, created with subpixel resolution, e.g., as small as 1/10th of the enhancements, and extensions (Nowinski et al. 1997a; pixel size (Nowinski et al. 2012a). An overview of methods Sudhyadhom et al. 2012); and (5) development of new electronic for 3D visualization of neuroanatomical image data and re- atlases (such as early ones, e.g., by Bohm et al. (1983)and Greitz construction of neuronal structures in brain atlases is presented et al. (1991) constructed from digitized crysection photographs, by Maye et al. (2006). and many more created recently, as reviewed below). Note that Atlas-assisted neuroeducation, training, and simulation the first two directions require no change in the original atlas have greatly benefitted from the Visible Human Project content. (VHP) comprising the most complete volumetric data of hu- To our best knowledge the first computer program with dig- man anatomy, including cryosection photographs, computed itized (and scalable) stereotactic atlases was developed by tomography and magnetic resonance images of American Bertrand et al. (1974). A digital version of the Schaltenbrand male and female specimens (Spitzer et al. 1996). The VHP and Wahren atlas resident in a computer was created by Kall provides excellent source material for the creation of brain et al. (1985). Several groups developed electronic versions of atlases and maps, for instance by Drury and Van Essen the Schaltenbrand and Bailey atlas, namely, Yoshida (1987)built (1997) and Juanes et al. (2012). The VHP additionally sparked a 3D atlas by interpolating print plates, a 3D volumetric model of subcortical structures was produced by Kazarnovskaya et al. Note that we were the only research group that received rights from the (1991), and Sudhyadhom et al. (2012) created a deformable 3D publisher to use these atlases. 4 Neuroinform (2021) 19:1–22 other projects, including Chinese VHP and Korean VHP, architecture (Amunts et al. 2010), and/or multiplicity of resulting in the construction of new atlases (Zhang et al. them (Van Essen 2013; Glasser et al. 2016), among 2003;Li etal. 2014) along with suitable tools for sectional others. and surface anatomy navigation as well as virtual dissection and endoscopy simulation (Chung and Park 2007). 3. Modality Tremendous advancements in imaging, brain mapping, and a. From postmortem to in vivo data (Lehmann et al. 1991; computing propelled the development of new human electron- Nowinski et al. 2015a; Dickieetal. 2017; Oishi et al. ic brain atlases. Various criteria can be employed to identify 2019); and systemize multiple directions in the content evolution of b. Integrating postmortem – in vivo data (Nowinski et al. new atlases, including parcellation, modality, plurality, quali- 1997b; 2002b;Yelnik etal. 2007; Cho et al. 2008; ty, ab/normality, lifespan, extendibility, ethnicity, spatial and Amunts et al. 2014); temporal scales, integration, transformation, techniques of cre- c. Increased teslage, from 1.5T (Tesla) (Hoehne 2001)to ation, and combination of them. We determine 23 directions 3T (Nowinski et al. 2009b;Rohlfing et al. 2010)to7T and categorize them into eight (seven main and one com- (Cho et al. 2008;Nowinskiet al. 2015a; Saygin et al. bined) groups of brain atlas content development. Then, by 2017;Hucketal. 2019; Liu et al. 2020)to9.4T taking into account this categorization, a brain atlas instant can (Yushkevich et al. 2009); be considered as an element in a seven-dimensional brain atlas d. From image to non-image data, transforming into brain space. These groups along with their component directions are atlases non-image data, such as stimulating electrode as follows: geometry (Nowinski et al. 2003) and neurologic param- eters (Nowinski et al. 2014a). 1. Scope (content extent) a. From cerebral parts (e.g., the basal ganglia (Yelnik et al. 4. Plurality 2007), thalamus and basal ganglia (Morel 2007), thala- a. Specimen-related: from a single specimen to population mus (Krauth et al. 2010), and deep brain structures atlases for cerebral parts (such as the cerebellar nuclei (Lemaire et al. 2019)) to the whole brain (Kikinis et al. (Dimitrova et al. 2006), insula (Faillenot et al. 2017), 1996; Hoehne 2001; Tzourio-Mazoyer et al, 2002; cortical structures (Shattuck et al. 2008), and cerebral Nowinski et al. 2011b); arteries (Dunås et al. 2017) to the whole human brain b. From structural neuroanatomy (Rohlfing et al. 2010; (Mazziotta et al. 1995; 2001;Thompson etal. 2000); Mandal et al. 2012; Nowinski and Chua 2014) to vascu- b. Variant-related: from a single variant to a collection of lar neuroanatomy (Nowinski et al. 2009b; 2011a; Huck variants, for instance, the cerebrovascular variants et al. 2019) to connectional neuroanatomy (Mori et al. (Nowinski et al. 2009a); 2005;Nowinski et al. 2012b; Van Essen 2013;Van c. Modality-related: from uni-modal to multi-modal atlases Essen et al. 2013; Baker et al. 2018;Briggsetal. 2018) with the use of multi-modal complementary data (e.g., to gene expression (Sunkin et al. 2013) including gene Johnson and Becker 1999;Toga et al. 2006;Nowinski expression in brain development (Kanton et al. 2019); et al. 2010; Hawrylycz et al. 2012;Ding etal. 2016); c. From brain to head (Tiede et al. 1996;Chen et al. 2018), d. Channel-related: e.g., with anatomy, diffusion, and tis- and to head and neck (Nowinski 2017b); sue channels (Rohlfing et al. 2010); d. From structure to function, including functional atlases e. Atlas-related: from a single atlas to arrays of fully (Minoshima et al. 1994;Zhao et al. 2017; Haegelen et al. parcellated atlases or mega multi-atlases (Wu et al. 2018; Varoquaux et al. 2018;Lehman etal. 2020), inte- 2016). grated anatomic-functional atlases (Nowinski 2004; Nowinski et al. 2010), and functional connectivity 5. Scale atlases (Craddock et al. 2012; James et al. 2016). a. Spatial scale, from macro- to meso- to micro- to nano- scales along with integrating atlas data across multiple 2. Parcellation spatial scales (Assaf et al. 2013;Ding et al. 2016; Ecker Use of diverse, often multiple parcellation criteria, et al. 2017); from classic cytoarchitecture, myeloarchitecture and b. Temporal scale covering atlases from development gross anatomy to fMRI, chemoarchitecture (Yelnik (Kanton et al. 2019) to lifespan including age-matched et al. 2007), vascular territories (Nowinski et al. 2006), atlases to accommodate age-dependent anatomical anatomic connectivity (Mori et al. 2005), functional con- changes ranging from pediatric to geriatric populations nectivity (Arsiwalla et al. 2015), anatomic-functional (Wu et al. 2016; Zuo et al. 2017; Zhang et al. 2018; connectivity (Fan et al. 2016), (multi)receptor Oishi et al. 2019); Neuroinform (2021) 19:1–22 5 c. Integrating spatio-temporal scales (Sunkin et al. 2013; 4D (four-dimensional) probabilistic atlas of the developing brain Bozek et al. 2018). (Kuklisova-Murgasova, et al. 2011). The baby brain atlases de- veloped for specimens younger than 12 months old (for the fetus, 6. Ethnicity neonate, and infant) are reviewed by Oishi et al. (2019). Ethnic-specific atlases, for instance, for Chinese Besides probabilistic structural atlases also a variety of (Zhang et al. 2003), Korean (Cho et al. 2008), and probabilistic connectional atlases (Meola et al. 2016;Figley Caucasian (Nowinski 2017b) specimens. et al. 2017; Yeh et al. 2018; Chenot et al. 2019), functional maps and atlases (Nowinski et al. 2003;Nowinski 2009; 7. Abnormality Breshears et al. 2015), and vascular atlases (Dunås et al. From normal to disease-specific atlases for various 2017; Bernier et al. 2018;Mouches andForkert 2019)have brain disorders, for instance, Alzheimer’sdisease been created. (Thompson et al. 2001), dementia (Mega et al. 2005), Developments in mapping the microscopical organization of and stroke (Nowinski et al. 2014a; de Haan and Karnath the brain along with the progress in nanoscience (Alivisatos et al. 2017). 2013) enable the construction of brain maps and atlases across spatial scales extending from macro to meso to micro to nano. 8. Multiple (combined) groups Examples include the BigBrain with 20-micrometer resolution a. Population multi-modal atlases (Iglesias et al. 2018); (Amunts et al. 2013), a comprehensive cellular-resolution (of b. Population functional maps and atlases (Nowinski et al. 1 µm/pixel) brain atlas linking macroscopic anatomical and mi- 2003; Nowinski 2009; Breshears et al. 2015); croscopic cytoarchitectural parcellations (Ding et al. 2016), the c. Population spatio-temporal atlases, for instance, of Brain Activity Map as the functional connectome to elucidate brain development (Kuklisova-Murgasova, et al. 2011); emergent levels of neural circuit function (Alivisatos et al. d. Population ethnic atlases and templates, for instance, 2012), a temporal cell atlas of gene expression in brain develop- Chinese brain atlas (Tang et al. 2010), Indian brain tem- ment (Kanton et al. 2019), a genomics brain atlas (Sunkin et al. plate (Bhalerao et al. 2018) and atlas (Sivaswamy et al. 2013), a proteomic brain atlas (McKetney et al. 2019), an atlas of 2019), Korean brain template (Lee et al. 2005), and serotonin (Beliveau et al. 2017), and an atlas of brain tran- French brain template (Lalys et al. 2010). scriptome (Hawrylycz et al. 2012). In particular, identifying the different brain cell types to determine their roles in health and We witness recently an enormous development of disease is of great importance and it is established as one of the population-based brain atlases both in health and disease. six goals of the BRAIN Initiative (BRAIN Working Group Population-based structural atlases have been built for the whole 2014). Toward achieving this goal a whole-brain cell atlas is brain (Liang et al. 2015;Wuetal. 2016) and its specific regions, under development by Ecker et al. (2017) that integrates molec- such as the cortical areas (Shattuck et al. 2008; Glasser et al. ular, anatomical, and physiological annotations of neuronal cell 2016), cerebellum (Diedrichsen et al. 2009), brainstem (Meola types for a comprehensive characterization of cell types, their et al. 2016), subcortical nuclei (Pauli et al. 2018), thalamic nuclei distributions, and patterns of connectivity. (Iglesias et al. 2018;Najdenovskaetal. 2018), insula (Faillenot et al. 2017), some gyri including the parietal lobe gyri (Wild et al. Evolution of Brain Atlas Applications 2017) and the inferior frontal gyrus (Hammers et al. 2007), and venous cerebrovasculature (Huck et al. 2019). The rationale of creating the early cortical maps, the result of More advanced atlases have been developed in terms of pop- human curiosity, was to represent the knowledge of new discov- ulation (Liang et al. 2015), specimen age range span eries about the human brain. The brain knowledge capturing, (Wu et al. 2016; Zhang et al. 2018), and age appropriateness aggregation, and representation by means of human brain atlases (Fonov et al. 2011). For instance, the atlas of Chinese adults has been the first application of the atlases, and this central role contains a large number of 2020 specimens whose age spans remains until the present. Research has been the dominant appli- from 20 to 75 years at a 5-year interval (Liang et al. 2015). The cation of human brain atlases (Roland and Zilles 1994)employed longitudinal atlas for normative brain development and aging as tools for analysis of brain structure and function (Hess et al. spans the age range of 1–83 years, while the quantitative suscep- 2018), means to integrate neuroscience research data from tibility mapping used for its creation may facilitate the estimation healthy and diseased brains to increase data sharing and re- of age-related iron changes in deep gray matter nuclei and myelin using (Bjerke et al. 2018), and a potential tool suitable for image changes in white matter (Zhang et al. 2018). A mega multi-atlas structurization through atlas-based image parcellation to utilize a (Wu et al. 2016) constitutes an inventory of 90 brain atlases with vast amount of imaging information available in medical record the specimens ranging from 4 to 82 years of age. Several age- systems, such as the PACS (picture archiving and communica- dependent brain atlases have been built also for children (Ou tion system) (Mori et al. 2013). Moreover, disease-specific et al. 2017; Bozek et al. 2018) and fetuses, such as a dynamic atlases, such as (Thompson et al. 2001;Megaetal. 2005;de 6 Neuroinform (2021) 19:1–22 Haan and Karnath 2017) facilitate quantification of brain struc- and Rydmark 1997), and The Electronic Clinical Brain Atlas tural deficits in epilepsy, depression, schizophrenia, Alzheimer’s (Nowinski et al. 1997b). disease, bipolar disorders, autism and others disorders as These initial efforts were followed by the development of discussed by Toga and Thompson (2005). more advanced atlases in terms of content and functionality, Human brain atlases are also useful beyond research in such as Voxel-man (Hoehne 2001), The Cerefy Atlas of Brain medical education and clinical applications. Stereotactic and Anatomy (Nowinski et al. 2002b), Primal’s Interactive Head functional neurosurgery was the first, major clinical applica- &Neck (Berkovitz et al. 2003), The Cerefy Clinical Brain tion of brain atlases. We observe that every two decades mark Atlas (Nowinski and Thirunavuukarasuu 2004), The Cerefy major progress in this field. The first print atlases were created Atlas of Cerebral Vasculature (Nowinski et al. 2009b), The in the 1950th, the first digitized brain atlas was developed in Human Brain in 1492 Pieces (Nowinski et al. 2011b), The the 1970th (Bertrand et al. 1974), and the acceptance of our Human Brain in 1969 Pieces: Structure, Vasculature, electronic brain atlases to clinical practice by the community Tracts,Cranial Nerves,Systems,HeadMuscles,and Glands (and 13 surgical companies) started in the 1990th (Nowinski (Nowinski and Chua 2014), The Human Brain, Head and 2009). Initially, a digital atlas was used off-line for referencing Neck in 2953 Pieces (Nowinski et al. 2015a), and the while being placed beside the displayed patient-specific scan. Human Anatomy Atlas (Visible Body n.d.). In addition, indi- In this way, for instance, The Electronic Clinical Brain Atlas vidualized atlases that parcellate and annotate brain scans are (Nowinski et al. 1997b) had been employed next to a surgical useful for the creation of teaching files of brain anatomy and workstation to plan neurosurgery before our brain atlas data- function (Oishi et al. 2019). base was directly integrated with surgical workstations, such The brain atlases also play a role in training and simulation, as the StealthStation (Nowinski 2009). In the second decade e.g., in neurosurgery (Serra et al. 1997) and radiotherapy of this century several novel, neurosurgery-dedicated atlases (Roniotis et al. 2012). have been developed for electrode placement in deep brain Human brain mapping in research and clinical practice is stimulation (Sadikot et al. 2011; Dergachyova et al. 2018; another major area of brain atlas employment. Digital brain Haegelen et al. 2018; Nowacki et al. 2018). atlases are exploited here to provide the underlying neuroanato- In the pre-tomographic era, stereotactic brain atlases were my and to automatically label activation loci in functional images useful to localize deep stereotactic targets. The introduction of with cortical areas and stereotactic coordinates. Application ex- diagnostic imaging has not eliminated brain atlases but rather amples include the BrainMap (Lancaster et al. 2000)and the changed their role and function (Nowinski 2009). Namely Brain Atlas for Functional Imaging (Nowinski et al. 2000b). firstly, a high atlas parcellation, typically greater than that of Both these tools employ digital Brodmann’s areas (derived from a scan, allows the individualized atlas to facilitate targeting. the Talairach and Tournoux (1988) brain atlas) that are hidden in Secondly, extensive atlas features in combination with its ease the BrainMap while explicitly available and displayed to the user of use and precision facilitate neurosurgery planning and pro- in the Brain Atlas for Functional Imaging. Brodmann’sareas, vide intraoperative support, like those available in The Cerefy despite being one century old and originally limited to two views Clinical Brain Atlas: Extended Edition with Surgery Planning on the visible part of the cortical surface only, are still today and Intraoperative Support (Nowinski et al. 2005a). Thirdly, applicable references in human brain mapping to register func- several new dedicated brain atlases have been created that are tional activations to the underlying anatomy (Amunts and Zilles derived from various modalities including histology 2015). ). Because of well-known limitations of the Talairach and (Chakravarty et al. 2006), electrophysiology (Finnis et al. Tournoux atlas (see, e.g., (Nowinski and Thirunavuukarasuu, 2003; Nowinski et al. 2003), and multi-modalities (Yelnik 2009)) in order to improve labeling of functional foci a dedicated et al. 2007;Nowinskietal. 2010;Haegelen et al. 2018). AAL (Automated Anatomical Labeling) atlas was developed Other examples of atlas use in neurosurgery include a dig- from a T1-weighted scan with 45 anatomical volumes of interest ital brain atlas for surgical planning (Kikinis et al. 1996), an in each hemisphere (Tzourio-Mazoyer et al, 2002). Internet portal for stereotactic and functional neurosurgery Nuclear medicine, such as SPECT (single-photon emission shifting the paradigm in atlas building from manufacturer- computed tomography) and PET (positron emission tomogra- centric (dependent) to neurosurgical community-centric phy), produces images of relatively poor spatial resolution, (Nowinski et al. 2002a), and a practical 3D atlas for a preop- which makes it difficult to relate the functional information erative white matter-specific planning of subcortical trajecto- contained there to the corresponding underlying neuroanato- ries (Jennings et al. 2018). my. In order to enhance the accuracy and consistency of the Several neuroeducational atlases were created in the anatomic interpretation of PET functional brain images, 1990th, the Decade of the Brain, including BrainStorm (Dev Minoshima et al. (1994) constructed a PET stereotactic brain et al. 1992), Digital Anatomist (Sundsten et al. 1994), atlas from a high-resolution [18F]FDG (fluorodeoxyglucose) A.D.A.M. (A.D.A.M 1996), Microvascular Atlas of the Head images of a normal volunteer. In addition, to assist in the and Neck (Bayer 1996 ), The BRAIN project (Kling-Petersen interpretation of SPECT scans of the brain, a 3D Neuroinform (2021) 19:1–22 7 neuroanatomical atlas was created from an MRI scan of a indices (along with some accompanying textual description), normal, healthy volunteer by Lehmann et al. (1991). were placed in a stereotactic coordinate system enabling lo- Human brain atlases have potential in stroke manage- calization and referencing. Moreover, certain atlases were ment for prediction, diagnosis, and treatment equipped with transparent overlays with structure annotations (Nowinski 2020). The atlases of anatomy and blood over the brain plates to facilitate structure delineation and supply territories support decision making in thrombol- identification. Conceptually, this simple functionality already ysis and provide a quantitative assessment of the infarct signaled the necessity of equipping brain atlases with suitable and penumbra (Nowinski et al. 2006). These two atlases tools enabling their clinical applications. This necessity in the also facilitate rapid and automatic detection, localiza- pre-digital era was clearly expressed by a popular practice tion, and classification of ischemic and hemorrhagic le- performed in the operating room of generating resized sions in the emergency room (Nowinski 2020). The (individualized) brain atlas plates by means of an overhead probabilistic stroke atlas, created by the integration of projector and drawing a planned stereotactic trajectory directly brain scans with textual neurologic parameters of previ- on the displayed projection. ously managed stroke patients, enables prediction of The need of generating individualized atlases in neurosur- stroke outcomes (Nowinski et al. 2014a). gery was met in the first computer program with digitized We have also developed atlas-based applications in several stereotactic atlases developed by Bertrand et al. (1974)that other areas, including neuroradiology, neurology, psycholo- provided 1D (one-dimensional) atlas scaling along the inter- gy, psychiatry, and proposed new solutions in some niche commissural distance. This solution was followed by a 3D applications, such as atlas-guided do-it-yourself neurosurgery piece-wise linear (Nowinski et al. 2000a) and non-linear suitable for patients (Nowinski 2009) and an atlas-enhanced (Ganser et al. 2004) brain atlas warping. The requirements operating room for the future (Benabid and Nowinski 2003). from stereotactic and functional neurosurgery have been an In neurology, the 3D Atlas of Neurologic Disorders initial major driving force behind the brain atlas development, (Nowinski et al. 2014b) facilitates the understanding of neu- both in terms of content (as discussed above) and functional- rologic deficits resulting from brain damage. The atlas bridges ity. Specific atlas-related tools have been proposed for pre- neuroanatomy, neuroradiology, and neurology (Nowinski and operative planning, intra-operative support, and postoperative Chua 2013a). It serves as an educational means for neurology assessment (Nowinski 2001a). students and residents as well as a reference for neurologists. Pre-operatively, the atlas facilitates the target and trajectory This atlas is also a potentially useful tool for psychologists, planning to avoid some critical structures (such as the optic and particularly neuropsychologists, to communicate with tract), and provides the list of trajectory-intersected structures. patients. In order to increase both the quality of planning and the sur- The Cerefy Neuroradiology Atlas (Nowinski and Belov geon’s confidence, multiple complementary atlases are 2003) available over the Internet contains a fully segmented employed (Nowinski et al. 2000a; 2010). In general, the atlas and labeled anatomic brain atlas. It provides functions for a facilitates the planning of the access corridor to any target rapid atlas-to-scan registration, interactive structure labeling structure by determining all the structures encountered along and annotating, and mensuration. To our best knowledge, this the selected corridor and those neighboring it, allowing the is the first online, publicly available atlas-based application for neurosurgeon to assess various potential corridors in the pro- neuroradiology. In general, brain atlases have a still unexploit- cess of decision making. Intra-operatively, the atlas provides ed potential in neuroradiology. For instance, in (Nowinski the actual structure where the tip of the electrode is located, the 2016) nine various scenarios of atlas use in neuroradiology list of structures already intersected by the electrode, distances were discussed based on the earlier developed working proto- to critical structures, and the surrounding anatomic and vas- types ranging from image interpretation to reporting to dealing cular context (Nowinski et al. 2010). Additionally, the proba- with data explosion and to communication (for both doctor-to- bilistic functional atlas makes the targeting more accurate by doctor and doctor-to-patient). determining the best location within the whole target structure In psychiatry, we employed a brain atlas to automatically (Nowinski et al. 2003; 2005b). Post-operatively, the atlas fa- generate anatomic volumes of interest for subsequent analysis cilitates to analyze the correctness of placement of a stimulat- in a population of schizophrenic patients and controls to study ing electrode or a permanent lesion. the passivity phenomenon (Sim et al. 2009). The first (to our best knowledge) collaborative use and construction of a brain atlas over the Internet by the neurosur- Development of Atlas Functionality gical community was offered in the portal for stereotactic and functional neurosurgery supporting a probabilistic functional The early bare brain maps and atlases had no or a very limited atlas (Nowinski et al. 2002a). This atlas is calculated from supporting functionality. Therefore, certain early print stereo- neuroelectrophysiologic and neuroimaging patient-specific data acquired during functional neurosurgical procedures tactic brain atlases, besides providing standard anatomical 8 Neuroinform (2021) 19:1–22 (Nowinski et al. 2003). The portal supports the atlas with the and Ou et al. (2014). Image registration is also employed for functionality enabling data uploading to the central database multi-modal atlas construction and atlas-guided segmentation or downloading locally in order to combine them with the of brain images. Automatic segmentation of brain images, in neurosurgeon’s own data, followed by the calculation of the particular, is of great importance and it can be performed individualized probabilistic functional atlas and surgery plan- through atlas-to-scan registration, as the individualized atlas ning. The atlas is displayed graphically in 2D, 3D, and as a segments and labels the underlying neuroanatomy. The ma- probability distribution histogram along with the data tree (in- jority of methods are for the segmentation of structural neuro- cluding patients, electrodes, contacts, and coordinates) in a anatomy, though some approaches are developed to provide text format. atlas-based processing of connectional neuroanatomy (Labra The development of atlas functionality has also been driven et al. 2017) and cerebrovascular anatomy (Passat et al. 2006; by the needs of the human brain mapping community, mainly Dunås et al. 2016). As multi-atlases are more powerful than for the integration of structural and functional, and, generally, single atlases, numerous approaches have been developed for multi-modal images in the same stereotactic space as well as multi-atlas based brain segmentation (Aljabar et al. 2009; for atlas-assisted automatic labeling of activation loci in func- Artaechevarria et al. 2009; Lötjönen et al. 2010; Wu et al. tional images with cortical areas and stereotactic coordinates. 2016; Zaffino et al. 2018;Liet al. 2019). The SPM anatomy toolbox (Eickhoff et al. 2005)isan exam- In neuroeducation a typical atlas-related functionality in- ple of an image integration tool that enables the combination cludes labeling, searching, and atlas display and manipulation. of probabilistic cytoarchitectonic maps and results of func- Beyond typical operations, some atlases provide more sophis- tional imaging studies. Another data integration tool is the ticated operations, such as advanced labeling with vessel di- Neuroinformatics Platform within the Human Brain Project ameters (Nowinski et al. 2011b) and pathology description (Bjerke et al. 2018). Examples of labeling tools are the (Nowinski et al. 2014b) as well as quantification, such as BrainMap (Lancaster et al. 2000) and the Brain Atlas for geometric measurements (Nowinski et al. 2015a). Functional Imaging (Nowinski et al. 2000b). The BrainMap The atlas also enables automatic testing suitable for both assigns a label of the cortical area closest to the examined self-testing and classroom testing. A testing module for atlas- activation locus. This process is blind to the user as the brain enabled evaluation of brain knowledge in neuroeducation was atlas employed is hidden from display. In the Brain Atlas for designed and incorporated into The Cerefy Atlas of Brain Functional Imaging the parcellated, labeled, and color-coded Anatomy (Nowinskietal. 2002b), and the corresponding meth- cortical areas are explicitly available and displayed to the user, od presented in (Nowinski et al. 2009c). The module allows the who has full control over the process of activation loci label- instructor to set the testing parameters first, such as the scope of ing and is able to edit their positions if needed. This atlas the tested knowledge, scoring points, and the number of at- processes functional images through a locus-driven analysis tempts. The items (structures) in the index are consecutively (Nowinski and Thirunavuukarasuu 2003). The activation loci numbered forming a list. A random generator selects randomly in functional images are extracted automatically by items from the list while avoiding repetition. There are two thresholding with the option of interactive editing. Then, the types of queries “Where is?” and “What is?” to test location atlas of anatomy extended with Brodmann’s areas is and naming of cerebral structures, respectively. When the name employed for labeling of the activation loci with the names of the selected item is highlighted in the index, the student is of cerebral structures, Brodmann’s areas, and stereotactic co- tested against “Where is?” aiming to point to the selected struc- ordinates. The activation loci are marked on the images with ture in the atlas (image or model). When the selected structure is the superimposed atlas, and the list of all labeled loci along highlighted in the atlas, the student is tested against “What is?” with their values on the anatomic and functional images is aiming to indicate the name of this structure in the index. After provided to the user. all the structures have randomly been selected, the module pro- Neuroinformatics tools and repositories also have been de- vides the total score and the time spent to perform the test. Note veloped to store various and heterogeneous results of analy- that for the same scope of a tested brain knowledge the queries, ses. For instance, NeuroVault.org stores these results in a form which are randomly generated, are different each time avoiding of statistical maps, parcellations, and atlases (Gorgolewski this way the situation that the student copies someone else’s et al. 2016); and BALSA is a database of the brain analysis answers or memorizes them. library of spatial maps and atlases (Van Essen et al. 2017). Some other examples of application-specific functionality Construction of population atlases as well as atlas-assisted in our atlas-based solutions include brain scan interpretation in neuroimage processing and analysis in any application re- neuroradiology (Nowinski and Belov 2003), segmentation quires atlas-to-scan (or scan-to-atlas) registration. Pioneering and labeling of pathological neuroimages (Nowinski and work on atlas-to-scan elastic registration was done by Bajcsy Belov 2005), automatic generation of atlas-derived regions et al. (1983) and Gee et al. (1993). Brain image registration and volumes of interest (VOI/ROI) for fast comparison of algorithms are evaluated, for instance, by Klein et al. (2009) the left and right cerebral hemispheres to detect pathology Neuroinform (2021) 19:1–22 9 (Nowinski 2020) and for statistical analysis in populations dramatic needs to handle big data including their storage, vi- (Sim et al. 2009), aggregation of image and clinical brain data sualization, processing, analysis, and (most importantly) inter- (Nowinskietal. 2014a), dealing with data explosion pretation. Moreover, technology advancement will enhance (Nowinski 2016), radiology reporting (Nowinski 2016), and the brain atlas functionality development to create new atlases, brain knowledge communication (for both doctor-to-doctor such as a holographic brain atlas (Petersen et al. 2019)(note and doctor-to-patient) (Nowinski 2016). that much earlier we proposed to use holography in an atlas- The abovementioned operations and tools are incorporated enhanced operating room for the future (Benabid and into the atlasing software platforms, mostly to enable and en- Nowinski 2003)). hance the atlas use. However, there exist numerous stand-alone tools suitable for atlas creation and use that are not incorporated Evolution of Atlas Availability directly into the created brain atlas platforms, such as FreeSurfer, a suite of tools for a cortical surface generation The human brain atlases reviewed above are available to the and quantification of functional, connectional and structural community in various ways and this availability can be consid- properties of the human brain (Fischl 2012) extended recently ered in terms of what is available and how it can be accessed. with the probabilistic atlas of the thalamic nuclei (Iglesias et al. The atlases are available on two major media, print and 2018); SPM for a neuroanatomical variability assessment electronic, resulting in three categories: print atlases, electron- (Ashburner 2009); FSL, a comprehensive library of analysis ic atlases on various platforms, and transitional atlases from tools for functional, structural, and diffusion MRI brain imaging the print to the electronic medium. data (Jenkinson et al. 2012); the Medical Imaging Interaction The early maps and atlases were available in a print form. Toolkit (MITK) integrating two other powerful toolkits, the The transition from the print to electronic medium has been Visualization Toolkit (VTK) and the Insight Toolkit (ITK) done via two channels by (1) creating both print and electronic (Wolf et al. 2005); and the Vascular Editor to create and edit (bi-media) atlases, and (2) derivation of early electronic vascular and, generally, tubular-like such as cranial nerve net- atlases from print materials by direct atlas plate digitization works (Marchenko et al. 2010). Our experience shows that the with no content change or with content extension by tools directly integrated with the atlas have proved their value postprocessing and enhancement. Tremendous developments allowing any new atlas modules to be created and edited within in computing enable almost an unlimited growth of electronic the already existing neural context (Nowinski et al. 2012a; b). brain atlases on numerous platforms ranging from mobile so- Recently, a new generation of methods, tools, reposi- lutions to leading-edge supercomputers. tories, and neurotechnologies is planned or already under Electronic brain atlas platforms can generally be clas- development. For instance, intensive technology develop- sified along multiple divisions: stand-alone versus web- ment and validation is outlined under the BRAIN Initiative based; stationary versus mobile; low-cost versus high- (BRAIN Working Group 2014; Jorgenson et al. 2015). end workstations; with standard interaction and display Numerous atlas-related tools are being developed within versus VR-enhanced, augmented reality (AR)-enhanced brain big projects. For instance, a common automated and holographic display; standard computer versus super- preprocessing framework has been developed within the computer; and single computer versus computer clusters, Human Connectome Project to bring multiple magnetic networks, and cloud computing. resonance imaging modalities together across a large co- In neuroscience research, it is usually required to pro- hort of subjects (Glasser et al. 2013). The vide a brain atlas within a web-based solution. In general, Neuroinformatics Platform within the Human Brain brain atlases can run on various platforms. For instance, we Project develops tools to facilitate data acquisition and have developed electronic brain atlases available and run- annotation, assignment of the anatomical location to data, ning on several platforms, including stand-alone plug-in and assembly of and access to spatially indexed informa- library (Nowinski 2009); workstation (Nowinski 2009); tion (Bjerke et al. 2018). Other examples of such tools notebook and desktop (for Windows and MAC) developed within the Allen Brain Atlas, BigBrain,and (Nowinskietal. 2005a; 2011b; Nowinski and Chua FSL atlas, among others, are given in (Amunts and 2014); Internet-based (Nowinski et al. 2002a); mobile Zilles 2015). The Scalable Brain Atlas is a collection of (iPhone (Nowinski et al. 2009c), iPad (Nowinski and web services that provides unified access to a large col- Chua 2013b), Android (Nowinski et al. 2014c); and VR- lection of public brain atlasing resources for the human enabled (Serra et al. 1997). It is worth mentioning that the and non-human species (Bakker et al. 2015). latter pioneering 3D brain atlas, employing a VR environ- Therefore, despite relatively slow progress in the develop- ment with a 3D natural interaction and stereoscopic dis- ment of the brain atlas enabling functionality so far in com- play, was completed as early as in 1997. Brain big projects, parison to that of the atlas content, the recent brain big projects however, such as The Human Brain Project, require leading-edge supercomputers (Amunts et al. 2016). will strongly drive this functionality development due to 10 Neuroinform (2021) 19:1–22 From a user’s standpoint, we distinguish three levels of Generations of Human Brain Atlases accessibility: non-accessible, private (with limited or unlimit- ed access), and public (with registered or unregistered access; Observing the evolution of human brain maps and atlases, free or payable). four atlas generations can be distinguished, namely: (1) early The non-accessible level means that the atlas is pub- cortical maps, (2) print stereotactic atlases, (3) early digital lished by its creators and available only to them, and the atlases, and (4) advanced brain atlas platforms. From a time- community is aware of the atlas but has no access to view frame standpoint, approximately the first atlas generation was it completely nor use it. This is probably the most com- developed in the first half of the 20th century (with the major mon situation. Private access indicates accessibility of the maps published in the first three decades), the second genera- atlas to a certain group of users, such as members of a tion in the second half of the 20th century (with the majority of consortium. Public access implies that any user may have atlases published in its first four decades), the third generation access to the atlas after meeting certain condition(s), such in the Decade of the Brain (and a handful of atlases a little as registration and/or payment. For instance, the print earlier), and the fourth generation in the 21st century, the atlases are public, payable with unregistered access. century of the brain and mind. Most educational electronic brain atlases are public and The above review, generations of atlases, and their present payable, such as Voxel-man (Hoehne 2001), The Human state are summarized in a form of the human brain atlas evo- Brain in 1492 Pieces (Nowinskietal. 2011b), Focus lution diagram in Fig. 1. Digital Anatomy Atlas. Neuroanatomy running on iPhone and iPad (Focus Medica), and Human Anatomy Atlas (Visible Body n.d.). Our latest and most advanced atlas The Human Brain, Head and Neck in 2953 Pieces Discussion (Nowinskietal. 2015a) is public, free of charge with registration required by its publisher at http://www. The human brain atlases have been evolved tremendously, thieme.com/nowinski/. especially in recent decades, in multiple directions, as cap- Although restricted access may constrict in some cases tured diagrammatically in Fig. 1. This evolution has been atlas availability, we may guess that overall this availability driven by sophisticated imaging techniques, advanced brain substantially grows over time, as the numbers of both atlas mapping methods, vast resources of brain data accumulated at creators and their users have been rising tremendously. This an unprecedented rate, analytical strategies, and powerful guess is corroborated by Table 1 that provides the numbers of computing. The effects of this explosive growth span from a publications over time cited on PubMed under the term “hu- few hand-drawn maps to multi-atlases, from print editions to man brain atlas” indicating the growth over 470 times from 1 web-based repositories, from 2D to nD, from determinist to publication in the year 1950 to 474 publication in the year probabilistic, from unimodal to multi-modal, from a cortical 2018. Between years 2010–2018 this growth was almost 12 organization to an all-level brain organization from genes to fold. The overall number of citations is 4350. the whole brain, from normal to pathologic, from gross to The same term “human brain atlas” searched on Google nanoscales, and various combinations of these above, among Scholar gives “about 762,000 results”. others. Several papers and reports have addressed the future trends that can be expected in the human brain atlas evolution, mainly in terms of an atlas content (Toga et al. 2006; Evans et al. 2012;Amunts etal. 2014; BRAIN Working Group 2014). Table 1 Number of The brain atlases have been employed in a wide spectrum Year Number Year Number citations under the term of applications and their usefulness depends not only on the “human brain atlas” on atlas content, but also on functionality and availability. Hence 1950 1 2011 174 PubMed versus years of publications (as of 18 this review has been conducted from these four perspectives: 1960 1 2012 213 May 2020) content, applications, functionality, and availability, in con- 1970 3 2013 241 trast to other works limited mostly to atlas content. 1980 5 2014 297 Content-wise, the human brain atlases have evolved from a 1985 18 2015 312 few hand-drawn maps to an atlas as a collection of maps and 1990 16 2016 337 images; to multi-atlases; to repositories of multi-modal brain 1995 22 2017 362 images in health and disease; to heterogeneous databases; to 2000 42 2018 474 composable, manipulable and explorable 3D and, generally, 2005 121 2019 389 nD cerebral models; to platforms for brain knowledge aggre- 2010 185 2020 132 gation and integration; to brain atlas data at macro, meso, Neuroinform (2021) 19:1–22 11 Fig. 1 A human brain atlas evolution diagram with four generations and four categories: content (only for the new electronic atlases), applications, functionality, and availability, each subsequently divided into sub-categories micro, nano, and hybrid scales with the resolution ranging avenues. The recent and most prominent avenue is the crea- from the whole brain to synapses; and to large databases with tion of new electronic brain atlases. We give numerous exam- massive amounts of data aiming to discover knowledge being ples of vast activities in the area of atlas content development developed within multi-center and/or multi-national projects heading in at least 23 various (though non-exhaustive) direc- and initiatives. tions, which are categorized in eight groups taking into ac- In an over century-long process of the human brain map count scope (content extent), parcellation, modality, plurality, and atlas creation, we have distinguished four generations: (1) scale, ethnicity, abnormality, and a mixture of them. early cortical maps (created in the first half of the 20th centu- Application-wise, brain atlases are employed in education, ry), (2) print stereotactic atlases (published in the second half research, and clinical practice. The role and usefulness of the of the 20th century), (3) early digital atlases (produced pre- brain atlases have been expanding both within the research dominantly in the Decade of the Brain), and (4) advanced area and beyond it. The main atlas application area is research brain atlas platforms (being developed in this century). We and brain knowledge gathering ranging from knowledge cap- also noticed that every two decades mark major progress in turing to knowledge aggregation to knowledge discovery. the human brain atlas evolution. The first stereotactic brain Other areas of atlas applications include human brain mapping atlases were created in the 1950th, the first digitized brain atlas (spanning research and clinical practice), stereotactic and was developed in the 1970th, the introduction of electronic functional neurosurgery, neuroeducation, and specific areas, brain atlases to clinical practice began in the 1990th followed such as neuroradiology, neurology, psychology, stroke, and by an explosion in brain atlas development propelled by the psychiatry. brain big projects that started in the 2010th . The major application-wise shift has been from research to The development of electronic brain atlases spans the last clinical practice, particularly in stereotactic and functional neu- two generations and in this area we have distinguished five rosurgery to treat patients. A brain atlas importance and 12 Neuroinform (2021) 19:1–22 potential in clinical applications have been raised and addressed Efficient and user-friendly tools are, in particular, required, by a few authors (Mori et al. 2013; BRAIN Working Group in education. We have attempted to develop new education 2014). In fact, the development of brain atlas-based clinical tools going beyond those available in standard educational applications for prediction, diagnosis, and treatment has been atlases, such as Voxel-man (Hoehne 2001)or Interactive a major focus of our work. Neurosurgery planning and assess- Head & Neck (Berkovitz et al. 2003). These tools enable novel ment (Nowinski 1998; 2001; 2009;Nowinskietal. 2010)was educational use of the atlas, such as self-testing and classroom our first clinical application with anatomic, functional, and vas- assessment (Nowinski et al. 2009c) available on notebooks cular atlases created. Our solutions in stereotactic and functional and mobile devices, interdisciplinary education across neurosurgery have been licensed to 13 surgical companies and neuroanatomy-neuroradiology-neurology (Nowinski et Chua integrated with surgical workstations of the leading companies, 2013a), advanced education for residents and clinicians with a namely, Medtronic, Brainlab, and Elekta (Nowinski 2009). In user’s “de/composable” content and context (Nowinski et al. addition, we have developed working prototypes in other fields 2014b; 2015a), and patients’ education and instruction for atlas-assisted brain pathology detection (Nowinski 2020), (Nowinski 2016). quantification of cerebrallesions (Nowinskietal. 2006), seg- From a usage standpoint, atlas tools can be classified into mentation and labeling of pathological neuroimages with tu- two broad categories: general and specific. General tools sup- mors causing a mass effect in brain cancer (Nowinski and port typical atlas-enabled operations, such as segmentation, Belov 2005), stroke management (Nowinski et al. 2006; labeling, manipulation, quantification, and querying. Nowinski 2020), and stroke outcome prediction (Nowinski Specific operations are those customized to a certain field et al. 2014a). A vast, still unexploited, potential of brain atlas and/or particular use, such as automatic testing, generation in neuroradiology has been addressed in (Nowinski 2016)de- of teaching materials, ROIs/VOIs generation and analysis, scribing nine applications for which working prototypes (proofs targeting, safety analysis, postoperative assessment, locus- of concept) we developed earlier and presented at clinical meet- driven analysis, decision making support, prediction of occur- ings. However, despite several examples of brain atlas use in rence and outcomes, scan interpretation, knowledge commu- clinical applications (as products or working prototypes), these nication, and a combination of them. From an integration applications are still lagging behind the progress in the devel- standpoint, atlas-related tools can be stand-alone or directly opment of the atlas content. One of the main obstacles in intro- integrated with the atlas platform. ducing the brain atlas solutions to clinical practice is their val- Availability-wise, the major developmental step was obvi- idation, which is tedious, time-consuming, and costly; particu- ously from print to digital atlases. Enormous progress in com- larly, clinical validation is beyond a reach of a research lab puting enables almost unlimited development of digital brain because of its high cost. atlases to run on numerous platforms ranging from mobile In contrast to the content-wise atlas development being solutions to notebooks to interactive web-based visualization widely carried out by numerous groups as well as by national platforms to VR/AR systems to high-end workstations to and multi-national consortia, the development of atlas func- computer clusters, networks, and leading-edge tionality has been relatively neglected, until recently as the supercomputers. problem of managing data explosion requires powerful, suit- The atlas availability substantially grows over time with the able, and dedicated tools. numbers of both atlas creators and their users tremendously The early atlases evolved from bare, hand-drawn maps to raising. If approximated by the growth of the human brain print stereotactic atlases with images scalable using an over- atlas publications on PubMed, the atlas availability growth head projector to electronic deformable atlas platforms to VR- from the year 1950 to the year 2018 would be over 470 times. and AR-enhanced atlases to atlas-engines meaning the atlases This work has several limitations. We have tried our best to serving as tools by themselves that support brain data man- make this state-of-the-art review in the human brain atlas evo- agement, neuroimage processing and analysis, decision mak- lution as complete as possible. However, the overall number ing, and knowledge discovery. of publications about this subject on PubMed is vast of 4350 From an application standpoint, the tools providing an (and about 762,000 references on Google Scholar) and rapidly atlas with its supporting functionality belong to three cat- growing, which makes a fairly complete state-of-the-art re- egories: (1) educational tools to explore the atlas, test view quite difficult (if possible at all). In some areas, such as knowledge, and prepare teaching materials (that can be clinical applications, any relevant research publications may grouped as student-oriented, educator-oriented, self-test- simply not exist, and the names of atlas creators and devel- ing, and a mixture of them); (2) research tools enabling opers may not be disclosed by the providers to the atlas users brain investigation and knowledge discovery; and (3) clin- (as, for instance, is in the case of our brain atlases licensed to ical tools to allow the clinicians to better prevent, diag- surgical companies). nose, treat, and cure brain diseases. Neuroinform (2021) 19:1–22 13 The brain atlas content evolution is divided at two levels The two most widely used coordinate systems in the neu- into 8 groups at the first and 23 directions at the second level. roscience community are the Talairach system (Talairach and We believe that the categorization of the atlas content devel- Tournoux 1988) and the Montreal Neurological Institute opment into these 8 groups covers the whole landscape, (MNI) system, and any coordinates of the latter can be con- though it is not unique and other criteria might be applied. verted to the Talairach space (Chau and McIntosh 2005). The This categorization is neither distinctive and some groups Talairach coordinate system has become the standard refer- may overlap, for instance, the increasing scale may result in ence for reporting the brain locations in scientific publications, the increasing scope. The overall 23 directions in the atlas though its definition is not unique. The Talairach system is content development are not exhaustive and could be finer, based on the anterior (AC) and posterior (PC) commissure line especially in the last (combined) group. Likewise, the ethnic- and its center is located at the AC point landmark. However, ity and abnormality groups could be subdivided into direc- the AC and PC point landmarks can be defined at least in four tions, each for a specific ethnicity or disease, respectively. different ways resulting in a substantial discrepancy among The approximation of the atlas availability growth through the coordinates depending on a selected landmark definition the number of publications about human brain atlas may be (Nowinski 2001b). Typically the centers of the AC and PC underestimated even by a few orders. Usually a publication structures are taken as the point landmarks, while the original- about a free atlas attracts a plethora of its users, and even the ly defined point landmarks by Talairach are beyond the AC number of citations may not be representative. For instance, and PC structures (consequently, for instance, the AC is miss- our free brain atlas (Nowinski 2017b) has a download-to- ing on the coronal plane passing through the center of the citation ratio of 160. coordinate system (and in my print version of his atlas, prof. This review is restricted to the human brain atlases. Several Talairach “corrected” that by manually drawing it)). authors have overviewed non-human brain atlases for various A core reference high-quality cerebral model is miss- species, including primates (marmoset, mouse lemur, squirrel ing in neuroinformatics. An example of such a long- monkey, macaque, and chimpanzee by Thiebaut de Schotten lasting reference model for the cerebral cortex are et al. (2018)), rodents and marsupials (rat, mouse, and opos- Brodmann’s areas (Brodmann 1909). Brodmann’sareas, sum by Bakker et al. (2015)), and other animals by Hess et al. though being one century old and based on a single (2018). It is also worth mentioning that several human spinal brain specimen, are most widely used and remain until cord atlases have been constructed, for instance, by Taso et al. today applicable references in human brain mapping to (2013) and Lévy et al. (2015). correlate functional activations to the underlying neuro- Finally, this review reflects a personal perspective and anatomy (Amunts and Zilles 2015), despite the creation three-decade-long experience of the author in the field with of more advanced and accurate cortical maps (Glasser et al. 2016 35 diverse human brain atlases created, where 15 of them have ). For a certain period the Talairach and been released for the global use by Thieme Medical Tournoux (1988) atlas has played a similar role for Publishers. the whole brain, despite its well-known limitations in- Making an overview of a field also encourages an attempt cluding spatial consistency as quantified by Nowinski to predict future developments in brain atlasing. On one hand, and Thirunavuukarasuu (2009). Another example is the the future brain research directions are well determined in the Schaltenbrand and Wahren (1977) atlas that for a few brain big projects, such as the BRAIN Initiative that sets six decades until the present remains the reference in ste- grand goals (BRAIN Working Group 2014). These efforts reotactic and functional neurosurgery. will result in the acquisition of more and more massive The construction of the core virtual brain model of the amounts of data and the creation of more advanced and com- highest possible quality is a complicated, tedious, and time- plex brain atlases with an ever-growing scope, population, and consuming process, which requires sophisticated, dedicated, spatial and temporal resolutions, additionally empowered by and precise tools and, of course, the state-of-the-art data, be- more advanced tools. On the other hand these efforts keep sides meticulous attention to details. Therefore, such a virtual increasing a sort of atlas landscape inhomogeneity as well as brain model shall be built incrementally. We have attempted difficulty in the atlas standardization and the integration and to create this kind of virtual brain model from multi-modal, interpretation of various outcomes. Moreover, as the majority multi-sequence scans of a living specimen (in a process which of efforts is devoted to brain atlas-related research, we can took almost 15 years until funding lasted), see Fig. 2.This expect a growing imbalance and chasms among research, clin- model has been designed as modular (Nowinski 2017b)with ical, and educational applications of human brain atlases. its consecutive modules being developed and validated (in- There are at least three central components related to atlas cluding cortical areas and subcortical structures (Nowinski standardization, namely, an atlas coordinate system, a core et al. 2012a), white matter tracts (Nowinski et al. 2012b), reference cerebral model, and a brain atlas platform intracranial vasculature (Nowinski et al. 2011a), cranial architecture. nerves and nuclei (2012c), head muscle and glands (2013c), 14 Neuroinform (2021) 19:1–22 Neuroinform (2021) 19:1–22 15 Fig. 2 The virtual decomposable brain model extended to the head and health and disease to mega multi-atlases across the neck with about 3,000 fully segmented, labeled, and color-coded 3D lifespan to atlas platforms at macro, meso, micro, and components. Shown here: the right central nervous system with the cere- nanoscales, as diagrammatically summarized in Fig. 1. brum (parcellated into gyri and sulci), cerebellum, brainstem, and cervical This atlas evolution review differentiates from other spine; deep gray nuclei; cerebral ventricles; white matter (deep and pos- terior fossa); white matter tracts; right visual system; auditory system; works, usually focusing on the atlas content mostly in intracranial arteries; intracranial veins; dural sinuses; cranial nerves with research applications, as we take here a wider perspective nuclei (partly exposed on the left side); right head (masticatory) muscles; and analyze this evolution in four categories: content, ap- right glands; upper skull with the frontal bone removed; cervical spine plications, functionality, and availability. Four generations (3rd and 4th cervical vertebrae); extracranial arteries; and extracranial veins of human brain atlases are distinguished, namely, early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms. The develop- extracranial vasculature (2015b), skull (2015c), and systems, ment of electronic brain atlases spans the last two gener- while releasing subsequent five versions (termed The Human ations and in this area we identify five avenues, the recent Brain in 1492/1969/2953 Pieces) for public use (Nowinski and most prominent is the creation of new electronic brain et al. 2011b; 2015a; Nowinski et Chua 2014). Our effort, atlases. The brain atlas content evolution in this avenue is though uncompleted, has demonstrated the feasibility of this categorized in eight groups taking into account scope, approach. parcellation, modality, plurality, scale, ethnicity, abnor- mality, and a mixture of them, in which, in turn, the atlas Although the advantages of population atlases are enor- developments are heading in 23 various directions. mous and obvious, we believe that these atlases shall be con- We suggest that the future human brain atlas-related re- structed around a very detailed, accurate, fully segmented, search and development activities shall be founded on and completely labeled, validated, and deterministic core model benefitted from a standard framework containing the core vir- of a virtual brain created with the highest quality possible tual brain model cum the brain atlas platform general and accepted as the reference standard (similarly as architecture. Brodmann created his long-lasting standard for the cortical areas from a single specimen). Moreover, the use of a single Acknowledgements The author’s work on brain atlas development was funded by ASTAR, Singapore. specimen enables its continuous rescanning to create new modules with advances in imaging technology (for instance, Compliance with Ethical Standards to build our virtual brain model, the same specimen was rescanned for over 10 years on various 1.5T, 3T, and 7T as Conflict of Interest None. well as CT and US (ultrasonography) scanners). Creating a population brain atlas even with a high number of specimens Open Access This article is licensed under a Creative Commons but without ensuring the highest quality and thorough valida- Attribution 4.0 International License, which permits use, sharing, adap- tation, distribution and reproduction in any medium or format, as long as tion just increases the abovementioned inhomogeneity of the you give appropriate credit to the original author(s) and the source, pro- field with a difficulty to cross-relate various atlases. vide a link to the Creative Commons licence, and indicate if changes were The final factor that might potentially counterbalance this made. The images or other third party material in this article are included atlas inhomogeneity trend is the establishing a standardized, in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's general architecture of the human brain atlas platform Creative Commons licence and your intended use is not permitted by supporting equally research, clinical, and educational applica- statutory regulation or exceeds the permitted use, you will need to obtain tions and enabling the clinicians to grow the initial core brain permission directly from the copyright holder. To view a copy of this model with their own new data. We believe that the future licence, visit http://creativecommons.org/licenses/by/4.0/. human brain atlas-related research and development activities shall be founded on and benefitted from such a standard framework containing the core virtual brain model cum the brain atlas platform general architecture. References A.D.A.M. (1996). A.D.A.M Animated Dissection of Anatomy for Summary Medicine.User’s Guide, A.D.A.M.. Afshar, E., Watkins, E. S., & Yap, J. C. (1978). 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