We aimed to develop an adult human brain atlas with many of the features of modern digital atlases in model organisms (Lein et al., 2007 ; Saleem and Logothetis, 2012 ; Papp et al., 2014 ). First, the atlas requires whole‐brain coverage with neuroimaging (MRI, DWI) and histology using multiple stains in the same brain, allowing brain parcellation based on convergent evidence from cyto‐ and chemoarchitecture, to reflect functional properties of corresponding brain regions more accurately (Ding et al., 2009 ; Amunts et al., 2010 ; Caspers et al., 2013a, 2013b ; Pascual et al., 2015 ). Second, we aimed for true cellular resolution (1 µm/pixel) on histological images to link microscopic features with the macroscopic scales more common in neuroimaging studies. Most critically, we performed comprehensive structural annotation at a very detailed level, based on a hierarchical structural ontology and using multiple forms of neocortical annotations to link gross anatomical (gyral, sulcal) and histology‐based parcellation schemes modified from Brodmann. Finally, these data are combined in an interactive, publicly accessible online application with direct linkage to other large‐scale human brain gene expression databases ( http://human.brain-map.org ; Hawrylycz et al., 2012 ).

A tremendous amount of effort has been dedicated to histology‐based parcellation of discrete regions of the human brain, including the frontal, parietal, temporal, occipital, cingulate, and perirhinal cortices (Hof et al., 1995a ; Van Essen et al., 2001 ; Vogt et al., 2001 ; Öngür et al., 2003 ; Scheperjans et al., 2008 ; Zilles and Amunts, 2009 ; Ding et al., 2009 ; Ding and Van Hoesen, 2010 ; Goebel et al., 2012 ; Petrides and Pandya, 2012 ; Caspers et al., 2013b ), and other regions such as the thalamus, amygdala, hippocampus, and brainstem (e.g., De Olmos, 2004 ; García‐Cabezas et al., 2007 ; Jones, 2007 ; Morel, 2007 ; Mai et al., 2008 ; Ding et al., 2010 ; Paxinos et al., 2012 ; Ding and Van Hoesen, 2015 ). Currently available large‐scale histological reference atlases of the human brain vary substantially in their degree of brain coverage, information content, and structural annotation (Table 1 ), and much of the more recent work is absent in these atlases. The most commonly used cytoarchitecture‐based human brain atlas is Brodmann's cortical map (Brodmann, 1909 ; Talairach and Tournoux, 1988 ; Simić and Hof, 2015 ), particularly for its use in annotating fMRI data, although von Economo's (von Economo and Koskinas, 1925 ) and Sarkisov's (Sarkisov et al., 1955 ) cortical maps are also still referenced. More recently developed large‐scale atlases possess greater anatomical coverage and multimodal information content, but are generally limited by their degree of structural delineation, particularly for neocortical areas that are often referenced only by gyral patterning (Duvernoy, 1999 ; Fischl et al., 2004 ; Damasio, 2005 ; Mai et al., 2008 ; Naidich et al., 2008 ; Destrieux et al., 2010 ; Nowinski and Chua, 2013 ). To overcome these limitations, a 3‐dimensional (3D) model of an adult human brain based on whole‐brain serial sectioning, silver staining, and MRI (Amunts et al., 2013 ) was recently created, and a probabilistic cytoarchitectural atlas (JuBrain; see Caspers et al., 2013a ) is also being generated. However, the staining of these specimens is limited, the imaging of the histology data currently lacks cellular resolution, and detailed annotation or parcellation of all brain regions based on cytoarchitecture remains to be performed. Additional efforts have used ultra‐high‐resolution MRI of ex vivo brains to build intrinsically 3D models of cytoarchitectural boundaries, and quantify the predictive power of macroscopic features for localizing microscopically defined boundaries (Augustinack et al., 2005, 2010, 2012, 2013, 2014 ; Fischl et al., 2008, 2009 ; Iglesias et al., 2015 ). While these latter atlases represent major advances, currently available resources still lack many features of modern atlases available in rodents and non‐human primates such as multimodality, dynamic user interfaces with scalable resolution and topographic interactivity, and brain‐wide anatomic delineation with ordered hierarchical structural ontologies.

The advent and improvement of noninvasive techniques such as magnetic resonance imaging (MRI), functional (f)MRI, and diffusion‐weighted imaging (DWI) have vastly enriched our understanding of the structure, connectivity, and localized function of the human brain in health and disease (Glover and Bowtell, 2009 ; Evans et al., 2012 ; Amunts et al., 2014 ). Interpretation of these data relies heavily on anatomical reference atlases for localization of underlying anatomical partitions, which also provides a common framework for communicating within and across allied disciplines (Mazziotta et al., 2001 ; Toga et al., 2006 ; Bonnici et al., 2012 ; Evans et al., 2012 ; Caspers et al., 2013a ; Annese et al., 2014 ). While neuroimaging data are typically registered to probabilistic reference frameworks (Das et al., 2016 ) to deal with interindividual variation, they lack the cytoarchitectural resolution of single‐brain histological reference atlases (Evans et al., 2012 ; Caspers et al., 2013a ), which is essential for more detailed studies of structural and cellular organization of the brain. There is therefore a strong need to bridge these levels of resolution to understand structure–function relationships in the human brain (Caspers et al., 2013a ; Pascual et al., 2015 ).

Diffusion‐weighted data were acquired over two averages using a 3D steady‐state free precession (SSFP) sequence (TR = 29.9 ms, α = 60°, TE = 24.96 ms, 900‐μm isotropic resolution). Diffusion weighting was applied along 44 directions distributed over the unit sphere (effective b‐value = 3,686s/mm 2 ) (Miller et al., 2012 ) with eight b = 0 images. The two acquisitions were coregistered using FSL's FLIRT to correct for B 0 drift and eddy‐current distortions (Jenkinson and Smith, 2001 ) and then averaged before further processing. DWI analysis was done using Diffusion Toolkit (dtk), and Trackvis was used for visualization of tracts ( http://trackvis.org/ ) (Wang et al., 2007 ). The fiber tracking algorithm is based on the fiber assignment by continuous tracking (FACT) algorithm (Mori et al., 1999 ). Diffusion‐weighted images were rotated to the same orientation as the MRI volume to allow generation of plane‐matched MRI and DWI images for the atlas, and the corresponding transformation was applied to the gradient table used to acquire the images. Tracts were created using a 60° angular threshold, masked so tracts are only contained within the approximate brain volume. The primary eigenvectors of the diffusion tensor were overlaid on the fractional anisotropy (FA) map in Freeview (part of the FreeSurfer software package, http://freesurfer.net ) to create color FA images. Tractography images were generated in TrackVis with a tract threshold of 20 mm and 90% skip applied, using a Y filter to select all tracts that pass through each coronal plane.

where I represents the combined image intensity at a given pixel, Ψ represents the N × N noise covariance matrix, and s represents the N × 1 vector of complex‐valued image intensities at a given pixel across the N coils of the array (Roemer et al., 1990 ; Wright and Wald, 1997 ).

The noise covariance matrix for a coil array is estimated from a noise‐only measurement collected in the absence of any RF excitation. This acquisition lasts about 20 seconds and provides enough thermal noise samples to accurately estimate the noise covariance matrix for the 30‐channel coil and describes the thermal noise coupling between the individual coil channel images for unaccelerated acquisitions. The final combined image is then computed as a noise‐weighted sum of the complex‐valued individual coil channel images and is given by

For the 7 T scans, custom pulse sequence software was used to measure k‐space in “chunks” small enough to be held in the scanner hard disk buffer, and a system was developed to stream each “chunk” of data from the buffer to a multiterabyte RAID array in parallel with it being measured by the scanner. Systems integration and custom software were developed for fast, reliable network and RAID connections and data stream management. Images from each coil channel were reconstructed and combined into a single image using a noise‐weighted combination to optimize SNR.

High‐resolution structural imaging was performed using special coils designed to optimize signal‐to‐noise and contrast‐to‐noise ratios (SNR and CNR, respectively) in fixed specimens by reducing large spacing between the coil elements and the sample. DWI was performed using standard Siemens head coils. Sample packing was performed by vacuum‐sealing the brain specimen in a polyethylene storage bag surrounded by PLP to avoid any artifacts caused by the interface between air and tissue. Diffusion‐weighted images were collected on a 3 T TIM Trio whole‐body scanner (Siemens Medical Solutions, Erlanger, Germany) with a Siemens 32 channel head coil. High‐resolution structural images were acquired using a 7 T scanner (Siemens Medical Solutions) with a custom 30‐channel receive‐array coil designed to image the entire adult brain, utilizing a 36‐cm head gradient coil.

Anatomical delineations were performed on poster‐sized printouts of Nissl‐stained sections and then digitally scanned and registered to the original Nissl images. Structure outlines were converted to digital polygons using Adobe (San Jose, CA) Creative Suite 5, and converted to Scalable Vector Graphics (SVG) format for web utilization. Polygons were linked to the hierarchical structural ontology and color‐coded according to the ontology color scheme such that related structures fall into similar color groups. Furthermore, hues were assigned according to the relative cellular density of the structure: the higher the density, the deeper the shade (i.e., addition of black to hue); the lower the density, the deeper the tint (i.e., addition of white to hue).

For detailed anatomical delineation, 106 Nissl‐stained sections were selected out of 679. Sampling intervals varied from 0.4 to 3.4 mm across the full anterior–posterior (A‐P) extent of the entire left hemisphere. Sparser sampling (3.4 mm) was selectively applied to the most anterior (prefrontal) and posterior (occipital) cortical levels that primarily contain cortex and a few large subcortical structures. Where smaller subcortical structures are more abundant, a much denser sampling was used (0.4–1.0‐mm interval). In total, 862 brain structures were digitally annotated on the 106 whole‐hemisphere images using 11,398 polygons.

A custom‐designed large‐format microscopy system was created to allow digital imaging and processing of all histologically stained sections (Nikon, Melville, NY). The system operates by collecting hundreds of images in lengthwise strips, which are montaged to create a single hemispheric image at 1 μm/pixel resolution. A total of 1,356 sections on 3‐ × 5‐inch slides were digitized for this resource, of which a single section (representative dimension: ∼3.2 × 4.3 m) typically took 6–8 hours to complete. Exposure time, white balance, and flat‐field correction were set independently for each slide. The Nikon NIS‐Elements Advanced Research (AR) microscope imaging software suite (RRID: SCR_014329) was used for acquisition of ND2 format image files that were subsequently converted to TIFF format.

The anti‐PV antibody is a mouse monoclonal IgG1 (Swant, Cat.# 235, RRID: AB_10000343). This antibody was produced by immunizing mice with PV from carp muscle and hybridizing mouse spleen cells with myeloma cell lines. This antibody specifically stained the 1999 Ca‐binding “spot” of PV (MW 12,000) from rat cerebellum on 2D immunoblot assays (Celio et al., 1988 ) (Table 2 ). No staining was observed when the antibody was used to stain cortical tissues from PV knockout mice. This antibody labels subsets of nonpyramidal neurons in cerebral cortex of many species including human (Hof et al., 1999 ; Nimchinsky et al., 1997 ; Ding and Van Hoesen, 2010, 2015 ).

The antibody against NFP (BioLegend, Cat.# SMI‐32, RRID: AB_2314904) is a mouse monoclonal IgG1 recognizing a double band at MW 200,000 and 180,000, which merge into a single neurofilament H line on 2D blots (Sternberger and Sternberger, 1983 ) (Table 2 ). The immunostaining of sections through human temporal cortex produced a pattern of NFP labeling that was identical to previous descriptions (Ding et al., 2009 ). In human and monkey cerebral cortex, the antibody stains a subpopulation of large pyramidal neurons with the labeling largely restricted to dendritic processes and soma (Campbell and Morrison, 1989 ; Hof et al., 1995a, 1995b ; Nimchinsky et al., 1997 ; Ding et al., 2003, 2009 ).

Out of 2,716 total sections, 679 (200‐µm sampling interval) were mounted on gelatin‐coated 3‐ × 5‐inch glass slides, air‐dried, and stained for Nissl substance using 0.05% thionine in acetate buffer (pH 4.5). For immunohistochemistry, 339 sections (400‐µm sampling interval) were immunostained free‐floating for the calcium‐binding protein parvalbumin (PV) and 338 sections (400‐µm sampling interval) for nonphosphorylated neurofilament proteins (NFPs). All incubation solutions, from blocking serum onward, used Tris‐buffered saline (TBS) with Triton X‐100 as the vehicle; all washes were done in TBS after antibody and avidin–biotin–horseradish peroxidase (HRP) incubation. Following treatment with hydrogen peroxide and a blocking serum, tissue sections were immunostained with antibody SMI‐32 (1:3,000, BioLegend, San Diego, CA) and a monoclonal anti‐PV antibody (1:10,000, Swant, Marly, Switzerland) overnight (∼16 hours) at room temperature, with vehicle solutions containing Triton X‐100 for permeabilization. A biotinylated secondary antibody (1:150, Vecta Elite horse anti‐mouse, preabsorbed against rat IgG, Vector Burlingame, CA) and ABC solution (1:200, Vectastain Elite ABC kit, Vector) were then applied for 90 and 45 minutes, respectively. To complete this process, sections were treated with nickel‐diaminobenzidine tetrahydrochloride (DAB) and hydrogen peroxide.

Sectioning was performed by Neuroscience Associates (Knoxville, TN). The slabs were individually thawed rapidly in PBS, treated overnight with 20% glycerol and 2% dimethylsulfoxide to prevent freezing artifacts, and embedded in a gelatin matrix using MultiBrain ® Technology (NeuroScience Associates) to avoid loss of unconnected tissue. After curing in a 2% formaldehyde solution, the blocks were rapidly frozen by immersion in isopentane (chilled by crushed dry ice) and mounted on the frozen stage of a hydraulically driven sliding microtome (Lipshaw model 90A, Pittsburgh, PA). Each block was sectioned coronally in 50‐μm‐thick sections. All sections were collected sequentially (none were discarded) into a 4 × 6 array of containers filled with an antigen preserving solution (50% PBS, pH 7.0, 50% ethylene glycol, 1% polyvinyl pyrrolidone). During sectioning, block‐face images were taken at intervals of 10–12 sections. Due to the challenges of sectioning and mounting thin sections from complete hemispheres, certain artifacts in the tissue sections are present. These artifacts include large cracks through most of the section in some cases as well as smaller tears in white and gray matter structures. In general these artifacts are easily identifiable but should not be confused with structural features of the underlying tissues.

A general workflow for generating this atlas is shown in Figure 1 . After the brain was removed from the skull, 4% periodate‐lysine‐paraformaldehyde (PLP) was injected into the internal carotid and vertebral arteries following a phosphate‐buffered saline (PBS) flush. The brain was then suspended and immersed in 4% PLP at 4°C. This preparation appeared to result in a slight elongation of the brain. Following complete fixation (48 hours), the brain was subjected to MRI and DWI (see details below) and stored in PLP at 4°C until further processing. The fixed brain was bisected through the midline. Following agarose embedding, each hemisphere was cut with a flexi‐slicer in the anterior to posterior direction, resulting in eight 2‐cm‐thick slabs. The slabs were cryoprotected in PBS containing 10%, 20%, and 30% sucrose, respectively and then frozen in a dry ice/isopentane bath (between −50°C and −60°C). Finally, the frozen slabs were placed in plastic bags that were vacuumed sealed, labeled, and stored at −80°C until histological sectioning.

The brain used for this reference atlas was from a 34‐year‐old female donor with no history of neurological diseases or remarkable brain abnormality obtained from the University of Maryland Brain and Tissue Bank, a brain and tissue repository of the NIH NeuroBioBank. All work was performed according to guidelines for the research use of human brain tissue and with approval by the Human Investigation Committees and Institutional Ethics Committees of the University of Maryland, the institution from which the sample was obtained.

RESULTS

Creation of a unified structural brain ontology An essential component of modern interactive digital atlases is a unifying hierarchical structural ontology that provides unique IDs (and colors for representation) for each structure in a parent–child architecture. We created a whole‐brain ontology spanning all adult structures (Table 3) and including a developmental axis for transient structures observed during the specification and cytoarchitectural maturation (Miller et al. 2014). The ontology is fundamentally divided into the basic subdivisions of forebrain, midbrain, and hindbrain, further divided into four major branches comprising gray matter, white matter, ventricles, and surface features. For example, daughter structures of “gray matter of forebrain” (Fig. 2G) include the telencephalon, diencephalon, and transient structures of forebrain (e.g., subplate and ventricular zone of the neocortex), while “white matter of forebrain” includes nearly all commissural and long ipsilateral fiber tracts. “Ventricles of forebrain” includes the lateral and third ventricles and related structures, while “surface structures of forebrain” includes important gross landmark features such as cortical gyri and sulci. For cortical structures, we aimed to accommodate both gyral and sulcal parcellation common to neuroimaging studies as well as cytoarchitectural parcellation based on histology, for which two basic terminologies based on Brodmann (Brodmann, 1909) and von Economo (von Economo and Koskinas, 1925; von Economo, 1927) are in usage. We used Brodmann's nomenclature as the primary reference because it is more commonly used, with modifications based on modern literature (see below) and the combined whole‐brain large‐scale cyto‐ and chemoarchitectural analysis here. Specifically, the following sources were used to modify the Brodmann scheme: for the frontal and cingulate cortex: Hof et al. (1995a), Vogt et al. (1995), Vogt et al. (2001), Öngür et al. (2003), Petrides and Pandya (2012), and Vogt and Palomero‐Gallagher (2012); for parietal, temporal, and occipital cortices (mostly changed to Brodmann's terminology where other nomenclature was used): Caspers et al. (2013b), Ding et al. (2009), Ding and Van Hoesen (2010), Scheperjans et al. (2008), Van Essen et al. (2001), Zilles and Amunts (2009), and Goebel et al. (2012). The terminology for the hippocampal formation is derived from Ding and Van Hoesen (2015) and Ding (2013, 2015). For a few cortical areas that Brodmann (1909) did not parcellate in detail (Simić and Hof, 2015), such as posterior parahippocampal areas (areas TH, TL, and TF), we adopted a modified nomenclature from von Ecomono and Koskinas (1925; see Ding and Van Hoesen, 2010). Another example of modification of Brodmann's areas is the orbitofrontal cortex, where Brodmann's large area 11 was replaced with smaller areas 14, 11, and 13 according to a few modern anatomical studies in human (Hof et al., 1995a; Öngür et al., 2003) and our own investigation of Nissl preparations and PV‐ and NFP‐immunostained sections. In addition, some of Brodmann's areas were further subdivided according to recent literature and the analysis here. For instance, Brodmann's areas 22 and 21 (roughly corresponding to von Economo's areas TA and TEd) were subdivided into rostral, intermediate, and caudal parts based on different staining intensity in PV‐stained sections (Ding et al., 2009). Finally, for the insular cortex that was not numbered by Brodmann in human (1909; see Simić and Hof, 2015), three major subdivisions were delineated and these included agranular, dysgranular, and granular insula (e.g., Bauernfeind et al., 2013; Morel et al., 2013), with the latter two further divided into rostral and caudal parts. Structures from the ontology were delineated as polygons on each Nissl digital image (Fig. 2H), and these structures include both gyral (Fig. 2I1) and modified Brodmann areas (Fig. 2I2) of the neocortex. Together, this comprehensive ontology covers all brain regions and can be used interactively to browse and search delineated structure polygons. It also provides enhanced interlinking capabilities among a broad range of datasets including adult (Hawrylycz et al., 2012) and developing (Miller et al., 2014) human brain transcriptional atlases included in the Allen Brain Atlas (www.brain-map.org).

Delineation of cortical and subcortical gray matter Anatomical delineation for the 106 selected plates (Fig. 2H) was based on a combined analysis of cyto‐ (Nissl stain) and chemoarchitecture (NFP and PV immunohistochemistry). For example, the boundaries between areas 29 and the neighboring suprasplenial subiculum (SuS) and caudal presubiculum (PrSc; also known as the postsubiculum [PoS]) were confidently identified based on staining features revealed in Nissl‐ (Fig. 5A), and adjacent PV‐ and NFP‐ (Fig. 5B and inset) immunostained sections. Dark NFP and PV immunoreactivity highlights SuS and PrSc, respectively, and these complementary and corroborating data allowed a consensus digital annotation of these regions (Fig. 5C). Similarly, in the ventral temporal neocortex, the border between areas 36 and 20 can be more accurately defined with PV immunostaining than Nissl alone, as area 20 (20i) displays significantly stronger PV immunoreactivity than area 36 (Fig. 5D,E). NFP immunoreactivity was in many cases more informative than Nissl stain for delineation of cortical regions based on the selective labeling of pyramidal neuron populations in different layers. For example, many large pyramidal neurons in layer 5 of the primary motor cortex (M1C; Fig. 6A) are NFP‐immunoreactive, while only a small number of medium‐sized neurons are observed in that layer of the primary somatosensory cortex (S1C; Fig. 6B). In contrast, the inferior parietal area (rostrodorsal area 40 [area 40rd], located posterior to S1C; Fig. 6C), has a narrower band of superficial layer labeling and a stronger bilaminar pattern. The combined analysis of Nissl staining and NFP or PV immunolabeling was also useful in defining many subcortical regions and subdivisions such as ventroposterior inferior (VPI), parafascicular (Pf), and centromedian (CM) nuclei in the thalamus (Thal; Fig. 6D) and cranial motor nuclei of the brainstem (Figs. 6E, 7A–C).

Localization and delineation of white matter tracts We also aimed for a comprehensive delineation of white matter tracts and cranial nerves (117 total), aided by NFP and PV fiber immunostaining. Motor roots of the cranial nuclei in the brainstem are clearly delineated by NFP staining (Figs. 6E, G, 7A). PV immunoreactivity shows similar discernment of a variety of fiber tracts and trajectories, such as the commissure of the inferior colliculus (Fig. 6F) and the optic radiation (Fig. 7D,E). A representative fully annotated atlas plate is shown in Figure 8A, with complete cyto‐ and chemoarchitecture‐based parcellation and colorization superimposed on the original Nissl image (Fig. 8C). To relate macroscopic (landmarks) and microscopic (histology) cortical anatomy, parallel plates were created with parcellation by gyri and sulci (Fig. 8B) or modified Brodmann areas (Fig. 8A). The denser sampling of subcortical regions allowed comprehensive detailed annotation of fine nuclear architecture for all major regions, as illustrated for the hypothalamus (Fig. 4) and the amygdala (Fig. 9). Figure 8 Open in figure viewer PowerPoint Alternate schemes for cortical parcellation. Modified Brodmann's areas (A) or sulci and gyri (B) were annotated on the same Nissl‐stained plate (C) to show micro‐ and macrostructural relationships. Examples of how cortical areas were delineated are given in Figures 5 and 6. The markers (*, **, ***) and (#) in A indicate the locations of pictures in Figure 6A–C and Figure 5D,E, respectively. For abbreviations see the ontology in Table 3. Inset is a schematic representation of the whole hemisphere based on MRI, with the red vertical lines in A and B indicating the location of the section plate. Both modified Brodmann's areas and gyral/sulcal mapping of the cerebral cortex are available online at www.branspan.org. Scale bar = 3,108 µm in A–C. Figure 9 Open in figure viewer PowerPoint Detailed parcellation of the human amygdalar complex. Shown are ten of the 18 annotated plates covering the A‐P extent (A–J) of the amygdala. For abbreviations see the amygdalar portion of the ontology in Table 3 Scale bar = 3,102 µm in J (applies to A–J).

Identification of novel brain subregions In addition to confirming previously identified structures, the combination of high image resolution and dense (200‐µm‐interval) Nissl sampling made it possible to reveal or clarify a number of complex or smaller brain structures, while the linkage to the Allen Human Brain Atlas (Hawrylycz et al., 2012) allowed corroboration of these structures with other gene expression data. One example is in the mediodorsal nucleus (MD) of the thalamus, where we observed a group of densely packed larger cells between the paraventricular nucleus (PaV) and the main portion of the MD, which we named the anteromedial subdivision of the MD (MDam in Fig. 10A). In situ hybridization data of both acetylcholinesterase (ACHE) and neurotensin (NTS) supports this partition, as they are selectively enriched in this region compared with the main part of the MD (Fig. 10B and inset). Similarly, we identified a novel subdivision of the basomedial nucleus (BM) of the amygdala. This subdivision is located medial to the dorsal and ventral subdivisions of the BM (BMD and BMV) and was termed BMm (medial subdivision of BM; Fig. 10C and inset). The BMm displays enriched cellular expression of the γ‐aminobutyric acid (GABA) receptor subunit E (GABRE) compared with the neighboring BMD and BMV (Fig. 10D). The homologs of MDam and BMm in other species have not been reported. Figure 10 Open in figure viewer PowerPoint Novel subdivisions of the mediodorsal nucleus (MD) of the thalamus and basomedial nucleus (BM) of the amygdala. A: Nissl staining reveals a group of larger cells (termed MDam, labeled with * in high magnification image and overview atlas plate (inset)) located between the paraventricular nucleus (PaV) and anterior mediodorsal nucleus MDm) of the thalamus distinct from neighboring regions. B: Distinct molecular specificity of MDam is demonstrated by ISH for ACHE and NTS (inset in B). C,D: Novel subdivision of amygdalar basomedial nucleus differentiated by smaller and relatively lightly Nissl‐stained cells (termed BMm, labeled with * in high magnification image and overview atlas plate (inset) in C) and selective enrichment for the GABA receptor subunit E (GABRE, in D) compared with neighboring dorsal and ventral regions (BMD and BMV) and posterior cortical nucleus (CoP). Scale bar = 1,109 µm in B (applies to A,B); 1,550 µm in D (applies to C,D). Another new area was identified running along the side of lateral olfactory stria, situated medially to the piriform cortex (Pir) and laterally to the substantia innominata (SI). This was termed the lateral olfactory area (LOA) and was found to have distinct histological features from the neighboring Pir and SI (Fig. 11). Compared with the Pir, the LOA does not have a dark, densely packed layer 2 on Nissl stain and has much stronger NFP immunoreactivity. In Nissl‐stained materials, the SI contains many cellular patches of differing sizes, packing densities, and staining intensities, with cells of contrasting shapes and sizes, compared with the LOA (Fig. 11A). In sections immunostained for NFP, only the largest neurons are labeled (Fig. 11B). The SI does not display laminar organization, while the LOA has a clear but discontinuous layer 2 and one deep layer. In contrast, the Pir has a dark and continuous layer 2 and a less darkly stained layer 3. Figure 11 Open in figure viewer PowerPoint Identification of the lateral olfactory area (LOA) in the adult human brain. A,B: Adjacent sections stained for Nissl (A) and NFP (B) showing the architectural features of LOA that differ from neighboring substantia nominata (SI) and piriform cortex (Pir). In Nissl‐stained sections, SI contains different types of cell patches (asterisks and arrowhead) while Pir is characterized by a darkly stained and densely packed layer 2 (A). LOA does not have these characteristic features, but shows cell patches that are different from those in SI (A). In NFP‐immunostained sections, Pir is very light throughout while LOA shows strong labeling in the superficial layer (B). Only the large‐celled patch (arrow) and scattered large cells of SI are strongly stained while other patches are negative (B). ac, anterior commissure; NDBh, horizontal part of nucleus of diagonal band; VeP, ventral pallidus; lost, lateral olfactory stria. Scale bar = 430 µm in A (applies to A,B). Two other structures described previously only in non‐human primates were identified as well, such as area prostriata (APro) and the basal interstitial nucleus of the cerebellum (BIcb). APro is a region located at the junction of the retrosplenial, post‐ and parasubiculum, posterior cingulate, and anterior–dorsal primary visual cortices. It has been described in detail in macaque monkey (Morecraft et al., 2000; Ding et al., 2003) and is important for fast procession of peripheral vision (Yu et al., 2012). Although its existence in the human brain was briefly described, its exact location and extent has not been reported in detail so far. Our mapping indicates that APro is much larger in human (Fig. 12) than in macaque monkey (Ding et al., 2003). The BIcb in the human brain is located deep to the medial interpositus nucleus (InPM; globose nucleus) of the cerebellum and consists of scattered large NFP‐immunoractive neurons (Fig. 13). Figure 12 Open in figure viewer PowerPoint Location and topographic relationship of area prostriata (APro). APro (labeled as Pro) is adjoined by the retrosplenial cortex (areas 29 and 30, not shown), postsubiculum (PoS in A), posterior cingulate cortex (area 23 in B–D) anterodorsally, and dorsal secondary visual cortex (V2d in D‐G) posterodorsally. Anteroventrally, APro is adjoined by the ventral secondary visual cortex (V2 in A–D). Posteroventrally and posteriorly, APro is adjoined by the anteroventral part of the primary visual cortex (V1v in E–H). Scale bar = 4,420 µm in H (applies to A–H). Figure 13 Open in figure viewer PowerPoint A novel subregion of the deep nuclei of the cerebellum. This has been named the basal interstitial nucleus of cerebellum (BIcb) and is embedded deep in the white matter medial to the dentate nucleus (DT) and lateral and inferior to the globose nucleus (i.e., the medial interpositus nucleus [InPM in A]). In Nissl stain, the cells in the BIcb are large and darkly stained (B). In NFP stain, these large cells are positively stained (C,D; C is the higher power view of the “*” region in D). Scale bar = 1,554 µm in D (applies to B,D).