Despite their relatively small brains, with only about 100,000 neurons, fruit flies show many complex behaviours. Understanding how these behaviours are generated will require a wiring diagram of the brain, and significant progress is being made towards this goal. One study has labelled 16,000 individual neurons and generated a coarse wiring diagram of the whole fly brain, identifying subnetworks that may carry out local information processing.

Anorexia nervosa is a complex eating disorder with genetic, metabolic, and psychosocial underpinnings. Using genome-wide methods, recent studies have associated many genes with the disorder. We characterized these genes by projecting them into reference transcriptomic atlases of the prenatal and adult human brain to determine where these genes are expressed in fine detail. We found that genes from an induced stem cell study of anorexia nervosa cases are expressed at higher levels in the lateral parabrachial nucleus. Although weaker, expression enrichment of the adult lateral parabrachial is also found with genes from independent genetic studies. Candidate causal genes from the largest genetic study of anorexia nervosa to date were enriched for expression in the arcuate nucleus of the hypothalamus. We also found an enrichment of anorexia nervosa associated genes in the adult and fetal raphe and ventral tegmental areas. Motivated by enrichment of these feeding circuits, we tested if these genes respond to fasting in mice hypothalami, which highlighted the differential expression of Rps26 and Dalrd3. This work improves our understanding of the neurobiology of anorexia nervosa by suggesting disturbances in subcortical appetitive circuits.

The concept of age is a fundamental aspect of mental life. However, it is not clear whether age is more an autobiographical detail we remember, a number indicating the years we live, or an inherent part of our subjective self-perception. An insight may be inferred from the underlying neuroanatomy. To investigate the neuroanatomical basis of age perception, we used lesion analysis in 7 patients with age-disorientation due to acute stroke, as compared to a control group of 9 age-oriented patients. Age-disoriented patients underestimated their age by 17.8±5.0 years. Lesion analysis indicated main regions of overlap in the insula, as well as the rolandic operculum and the supramarginal gyrus, predominantly in the left hemisphere, as compared to stroke patients without age-disorientation. Since these regions are involved in the cognitive functions of self-referenced time-processing, including its emotional aspects, our data suggest that these functions are intimately related to age perception.

Mania, the diagnostic hallmark of bipolar disorder, is an episodic disturbance of mood, sleep, behavior, and perception. Improved understanding of the neurobiology of mania is expected to allow for novel avenues to address current challenges in its diagnosis and treatment. Previous research focusing on the impairment of functional neuronal circuits and brain networks has resulted in heterogenous findings, possibly due to a focus on bipolar disorder and its several phases, rather than on the unique context of mania. Here we present a comprehensive overview of the evidence regarding the functional neuroanatomy of mania. Our interpretation of the best available evidence is consistent with a convergent model of lateralized circuit dysfunction in mania, with hypoactivity of the ventral prefrontal cortex in the right hemisphere, and hyperactivity of the amygdala, basal ganglia, and anterior cingulate cortex in the left hemisphere of the brain. Clarification of dysfunctional neuroanatomic substrates of mania may contribute not only to improve understanding of the neurobiology of bipolar disorder overall, but also highlights potential avenues for new circuit-based therapeutic approaches in the treatment of mania.

Neuronal circuit mapping using electron microscopy demands laborious proofreading or reconciliation of multiple independent reconstructions. Here, we describe new methods to apply quantitative arbor and network context to iteratively proofread and reconstruct circuits and create anatomically enriched wiring diagrams. We measured the morphological underpinnings of connectivity in new and existing reconstructions of Drosophila sensorimotor (larva) and visual (adult) systems. Synaptic inputs were preferentially located on numerous small, microtubule-free 'twigs' which branch off a single microtubule-containing 'backbone'. Omission of individual twigs accounted for 96% of errors. However, the synapses of highly connected neurons were distributed across multiple twigs. Thus, the robustness of a strong connection to detailed twig anatomy was associated with robustness to reconstruction error. By comparing iterative reconstruction to the consensus of multiple reconstructions, we show that our method overcomes the need for redundant effort through the discovery and application of relationships between cellular neuroanatomy and synaptic connectivity.

The neuroscience research landscape has changed dramatically over the past decade. Specifically, an impressive array of new tools and technologies have been generated, including but not limited to: brain gene expression atlases, genetically encoded proteins to monitor and manipulate neuronal activity, and new methods for imaging and mapping circuits. However, despite these technological advances, several significant challenges must be overcome to enable a better understanding of brain function and to develop cell type-targeted therapeutics to treat brain disorders. This review provides an overview of some of the tools and technologies currently being used to advance the field of molecular neuroanatomy, and also discusses emerging technologies that may enable neuroscientists to address these crucial scientific challenges over the coming decade.

Advancing our understanding of how nervous systems work will require the ability to store and annotate 3D anatomical datasets, recording morphology, partonomy and connectivity at multiple levels of granularity from subcellular to gross anatomy. It will also require the ability to integrate this data with other data-types including functional, genetic and electrophysiological data. The web ontology language OWL2 provides the means to solve many of these problems. Using it, one can rigorously define and relate classes of anatomical structure using multiple criteria. The resulting classes can be used to annotate datasets recording, for example, gene expression or electrophysiology. Reasoning software can be used to automate classification and error checking and to construct and answer sophisticated combinatorial queries. But for such queries to give consistent and biologically meaningful results, it is important that both classes and the terms (relations) used to relate them are carefully defined.

The complexity of the nervous system requires high-resolution microscopy to resolve the detailed 3D structure of nerve cells and supracellular domains. The analysis of such imaging data to extract cellular surfaces and cell components often requires the combination of expert human knowledge with carefully engineered software tools. In an effort to make better tools to assist humans in this endeavor, create a more accessible and permanent record of their data, and to aid the process of constructing complex and detailed computational models, we have created a core of formalized knowledge about the structure of the nervous system and have integrated that core into several software applications. In this paper, we describe the structure and content of a formal ontology whose scope is the subcellular anatomy of the nervous system (SAO), covering nerve cells, their parts, and interactions between these parts. Many applications of this ontology to image annotation, content-based retrieval of structural data, and integration of shared data across scales and researchers are also described.

To investigate the entire nerve architecture and content of the two main sensory neuropeptides in mouse cornea to determine if it is a good model with similarities to human corneal innervation.

Perceiving another person's emotional expression often sparks a corresponding signal in the observer. Shared conversational laughter is a familiar example. Prior studies of shared laughter have made use of task-based functional neuroimaging. While these methods offer insight in a controlled setting, the ecological validity of such controlled tasks has limitations. Here, we investigate the neural correlates of shared laughter in patients with one of a variety of neurodegenerative disease syndromes (N = 75), including Alzheimer's disease (AD), behavioral variant frontotemporal dementia (bvFTD), right and left temporal variants of semantic dementia (rtvFTD, svPPA), nonfluent/agrammatic primary progressive aphasia (nfvPPA), corticobasal syndrome (CBS), and progressive supranuclear palsy (PSP). Patients were recorded in a brief unrehearsed conversation with a partner (e.g., a friend or family member). Laughter was manually labeled, and an automated system was used to assess the timing of that laughter relative to the partner's laughter. The probability of each participant with neurodegenerative disease laughing during or shortly after his or her partners' laughter was compared to differences in brain morphology using voxel-based morphometry, thresholded based on cluster size and a permutation method and including age, sex, magnet strength, disease-specific atrophy and total intracranial volumes as covariates. While no significant correlations were found at the critical T value, at a corrected voxelwise threshold of p < 0.005, a cluster in the left posterior cingulate gyrus demonstrated a trend at p = 0.08 (T = 4.54). Exploratory analysis with a voxelwise threshold of p = 0.001 also suggests involvement of the left precuneus (T = 3.91) and right fusiform gyrus (T = 3.86). The precuneus has been previously implicated in the detection of socially complex laughter, and the fusiform gyrus has a well-described role in the recognition and processing of others' emotional cues. This study is limited by a relatively small sample size given the number of covariates. While further investigation is needed, these results support our understanding of the neural underpinnings of shared conversational laughter.

Methods for resolving the three-dimensional (3D) microstructure of the brain typically start by thinly slicing and staining the brain, followed by imaging numerous individual sections with visible light photons or electrons. In contrast, X-rays can be used to image thick samples, providing a rapid approach for producing large 3D brain maps without sectioning. Here we demonstrate the use of synchrotron X-ray microtomography (µCT) for producing mesoscale (∼1 µm 3 resolution) brain maps from millimeter-scale volumes of mouse brain. We introduce a pipeline for µCT-based brain mapping that develops and integrates methods for sample preparation, imaging, and automated segmentation of cells, blood vessels, and myelinated axons, in addition to statistical analyses of these brain structures. Our results demonstrate that X-ray tomography achieves rapid quantification of large brain volumes, complementing other brain mapping and connectomics efforts.

The asymmetrical positioning of neural structures on the left or right side of the brain in vertebrates and in invertebrates may be correlated with brain laterality, which is associated with cognitive skills. But until now this has not been illustrated experimentally. Here we describe an asymmetrically positioned brain structure in the fruitfly Drosophila and find that the small proportion of wild-type flies that have symmetrical brains with two such structures lack a normal long-term memory, although their short-term memory is intact. Our results indicate that brain asymmetry may be required for generating or retrieving long-term memory.

Nematodes are considered excellent models for understanding fundamental aspects of neuron function. However, nematodes are less frequently used as models for examining the evolution of nervous systems. While the habitats and behaviors of nematodes are diverse, the neuroanatomy of nematodes is often considered highly conserved. A small number of nematode species greatly influences our understanding of nematode neurobiology. The free-living species Caenorhabditis elegans and, to a lesser extent, the mammalian gastrointestinal parasite Ascaris suum are, historically, the primary sources of knowledge regarding nematode neurobiology. Despite differences in size and habitat, C. elegans and A. suum share a surprisingly similar neuroanatomy. Here, we examined species across several clades in the phylum Nematoda and show that there is a surprising degree of neuroanatomical variation both within and among nematode clades when compared to C. elegans and Ascaris. We found variation in the numbers of neurons in the ventral nerve cord and dye-filling pattern of sensory neurons. For example, we found that Pristionchus pacificus, a bacterial feeding species used for comparative developmental research had 20% fewer ventral cord neurons compared to C. elegans. Steinernema carpocapsae, an insect-parasitic nematode capable of jumping behavior, had 40% more ventral cord neurons than C. elegans. Interestingly, the non-jumping congeneric nematode, S. glaseri showed an identical number of ventral cord neurons as S. carpocapsae. There was also variability in the timing of neurodevelopment of the ventral cord with two of five species that hatch as second-stage juveniles showing delayed neurodevelopment. We also found unexpected variation in the dye-filling of sensory neurons among examined species. Again, sensory neuron dye-filling pattern did not strictly correlate with phylogeny. Our results demonstrate that variation in nematode neuroanatomy is more prevalent than previously assumed and recommend this diverse phylum for future "evo-devo-neuro" studies.

The autonomic nervous system regulates normal cardiovascular function and plays a critical role in the pathophysiology of cardiovascular disease. Further understanding of the interplay between the autonomic nervous system and cardiovascular system holds promise for the development of neuroscience-based cardiovascular therapeutics. To this end, techniques to image myocardial innervation will help provide a basis for understanding the fundamental underpinnings of cardiac neural control. In this review, we detail the evolution of gross and microscopic anatomical studies for functional mapping of cardiac neuroanatomy.

Vertical representation is central to posture control, as well as to spatial perception and navigation. This representation has been studied for a long time in patients with vestibular disorders and more recently in patients with hemispheric damage, in particular in those with right lesions causing spatial or postural deficits. The aim of the study was to determine the brain areas involved in the visual perception of the vertical. Sixteen right-handed healthy participants were evaluated using fMRI while they were judging the verticality of lines or, in a control task, the color of the same lines. The brain bases of the vertical perception proved to involve a bilateral temporo-occipital and parieto-occipital cortical network, with a right dominance tendency, associated with cerebellar and brainstem areas. Consistent with the outcomes of neuroanatomical studies in stroke patients, The data of this original fMRI study in healthy subjects provides new insights into brain networks associated with vertical perception which is typically impaired in both vestibular and spatial neglect patients. Interestingly, these networks include not only brain areas associated with postural control but also areas implied in body representation.

We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future.

Auditory scene analysis is a demanding computational process that is performed automatically and efficiently by the healthy brain but vulnerable to the neurodegenerative pathology of Alzheimer's disease. Here we assessed the functional neuroanatomy of auditory scene analysis in Alzheimer's disease using the well-known 'cocktail party effect' as a model paradigm whereby stored templates for auditory objects (e.g., hearing one's spoken name) are used to segregate auditory 'foreground' and 'background'. Patients with typical amnestic Alzheimer's disease (n = 13) and age-matched healthy individuals (n = 17) underwent functional 3T-MRI using a sparse acquisition protocol with passive listening to auditory stimulus conditions comprising the participant's own name interleaved with or superimposed on multi-talker babble, and spectrally rotated (unrecognisable) analogues of these conditions. Name identification (conditions containing the participant's own name contrasted with spectrally rotated analogues) produced extensive bilateral activation involving superior temporal cortex in both the AD and healthy control groups, with no significant differences between groups. Auditory object segregation (conditions with interleaved name sounds contrasted with superimposed name sounds) produced activation of right posterior superior temporal cortex in both groups, again with no differences between groups. However, the cocktail party effect (interaction of own name identification with auditory object segregation processing) produced activation of right supramarginal gyrus in the AD group that was significantly enhanced compared with the healthy control group. The findings delineate an altered functional neuroanatomical profile of auditory scene analysis in Alzheimer's disease that may constitute a novel computational signature of this neurodegenerative pathology.

The Sustained Attention to Response task is a classical neuropsychological test that has been used by many centres to characterize the attentional deficits in traumatic brain injury, ADHD, autism and other disorders. During the SART a random series of digits 1-9 is presented repeatedly and subjects have to respond to each digit (go trial) except the digit '3' (no-go trial). Using voxel-based lesion symptom mapping (VLSM) in a consecutive series of 44 ischemic unifocal non-lacunar hemispheric stroke patients we determined the neuroanatomy of 4 SART parameters: commission and omission error rate, reaction time variability and post-error slowing. Lesions of the right inferior frontal gyrus significantly increased commission error rate. Lesions of the middle third of the right inferior frontal sulcus (IFS) reduced post-error slowing, a measure of how well subjects can utilize errors to adjust cognitive resource allocation. Omissions and reaction time variability had less localising value in our sample. To conclude, commission errors and post-error slowing in the SART mainly probe right inferior frontal integrity.

Recent advances in diffusion-weighted MRI (dMRI) and tractography have enabled identification of major long-range white matter tracts in the human brain. Yet, our understanding of shorter tracts, such as those within the parietal lobe, remains limited. Over a century ago, a tract connecting the superior and inferior parts of the parietal cortex was identified in a post-mortem study: stratum proprium of interparietal sulcus (SIPS; Sachs, Das hemisphärenmark des menschlichen grosshirns. Verlag von georg thieme, Leipzig, 1892). The tract has since been replicated in another fibre dissection study (Vergani et al., Cortex 56:145-156, 2014), however, it has not been fully investigated in the living human brain and its precise anatomical properties are yet to be described. We used dMRI and tractography to identify and characterise SIPS in vivo, and explored its spatial proximity to the cortical areas associated with optic-flow processing using fMRI. SIPS was identified bilaterally in all subjects, and its anatomical position and trajectory are consistent with previous post-mortem studies. Subsequent evaluation of the tractography results using the linear fascicle evaluation and virtual lesion analysis yielded strong statistical evidence for SIPS. We also found that the SIPS endpoints are adjacent to the optic-flow selective areas. In sum, we show that SIPS is a short-range tract connecting the superior and inferior parts of the parietal cortex, wrapping around the intraparietal sulcus, and that it may be a crucial anatomy underlying optic-flow processing. In vivo identification and characterisation of SIPS will facilitate further research on SIPS in relation to cortical functions, their development, and diseases that affect them.

Existing neuroanatomical ontologies, databases and information systems, such as the Foundational Model of Anatomy (FMA), represent outgoing connections from brain structures, but cannot represent the "internal wiring" of structures and as such, cannot distinguish between different independent connections from the same structure. Thus, a fundamental aspect of Neuroanatomy, the functional pathways and functional systems of the brain such as the pupillary light reflex system, is not adequately represented. This article identifies underlying anatomical objects which are the source of independent connections (collections of neurons) and uses these as basic building blocks to construct a model of functional neuroanatomy and its functional pathways.