Interoperable atlases of the human brain

Should one go for individual- or group-level brain parcellations? A deep-phenotyping benchmark

The analysis and understanding of brain characteristics often require considering region-level information rather than voxel-sampled data. Subject-specific parcellations have been put forward in recent years, as they can adapt to individual brain organization and thus offer more accurate individual summaries than standard atlases. However, the price to pay for adaptability is the lack of group-level consistency of the data representation. Here, we investigate whether the good representations brought by individualized models are merely an effect of circular analysis, in which individual brain features are better represented by subject-specific summaries, or whether this carries over to new individuals, i.e., whether one can actually adapt an existing parcellation to new individuals and still obtain good summaries in these individuals. For this, we adapt a dictionary-learning method to produce brain parcellations. We use it on a deep-phenotyping dataset to assess quantitatively the patterns of activity obtained under naturalistic and controlled-task-based settings. We show that the benefits of individual parcellations are substantial, but that they vary a lot across brain systems.

Characterizing functional modules in the human thalamus: coactivation-based parcellation and systems-level functional decoding

The human thalamus relays sensory signals to the cortex and facilitates brain-wide communication. The thalamus is also more directly involved in sensorimotor and various cognitive functions but a full characterization of its functional repertoire, particularly in regard to its internal anatomical structure, is still outstanding. As a putative hub in the human connectome, the thalamus might reveal its functional profile only in conjunction with interconnected brain areas. We therefore developed a novel systems-level Bayesian reverse inference decoding that complements the traditional neuroinformatics approach towards a network account of thalamic function. The systems-level decoding considers the functional repertoire (i.e., the terms associated with a brain region) of all regions showing co-activations with a predefined seed region in a brain-wide fashion. Here, we used task-constrained meta-analytic connectivity-based parcellation (MACM-CBP) to identify thalamic subregions as seed regions and applied the systems-level decoding to these subregions in conjunction with functionally connected cortical regions. Our results confirm thalamic structure-function relationships known from animal and clinical studies and revealed further associations with language, memory, and locomotion that have not been detailed in the cognitive neuroscience literature before. The systems-level decoding further uncovered large systems engaged in autobiographical memory and nociception. We propose this novel decoding approach as a useful tool to detect previously unknown structure-function relationships at the brain network level, and to build viable starting points for future studies.

AtOM, an ontology model to standardize use of brain atlases in tools, workflows, and data infrastructures

Brain atlases are important reference resources for accurate anatomical description of neuroscience data. Open access, three-dimensional atlases serve as spatial frameworks for integrating experimental data and defining regions-of-interest in analytic workflows. However, naming conventions, parcellation criteria, area definitions, and underlying mapping methodologies differ considerably between atlases and across atlas versions. This lack of standardized description impedes use of atlases in analytic tools and registration of data to different atlases. To establish a machine-readable standard for representing brain atlases, we identified four fundamental atlas elements, defined their relations, and created an ontology model. Here we present our Atlas Ontology Model (AtOM) and exemplify its use by applying it to mouse, rat, and human brain atlases. We discuss how AtOM can facilitate atlas interoperability and data integration, thereby increasing compliance with the FAIR guiding principles. AtOM provides a standardized framework for communication and use of brain atlases to create, use, and refer to specific atlas elements and versions. We argue that AtOM will accelerate analysis, sharing, and reuse of neuroscience data.

Neuromorphological Atlas of Human Prenatal Brain Development: White Paper

Recent morphological data on human brain development are quite fragmentary. However, they are highly requested for a number of medical practices, educational programs, and fundamental research in the fields of embryology, cytology and histology, neurology, physiology, path anatomy, neonatology, and others. This paper provides the initial information on the new online Human Prenatal Brain Development Atlas (HBDA). The Atlas will start with forebrain annotated hemisphere maps, based on human fetal brain serial sections at the different stages of prenatal ontogenesis. Spatiotemporal changes in the regional-specific immunophenotype profiles will also be demonstrated on virtual serial sections. The HBDA can serve as a reference database for the neurological research, which provides opportunity to compare the data obtained by noninvasive techniques, such as neurosonography, X-ray computed tomography and magnetic resonance imaging, functional magnetic resonance imaging, 3D high-resolution phase-contrast computed tomography visualization techniques, as well as spatial transcriptomics data. It could also become a database for the qualitative and quantitative analysis of individual variability in the human brain. Systemized data on the mechanisms and pathways of prenatal human glio- and neurogenesis could also contribute to the search for new therapy methods for a large spectrum of neurological pathologies, including neurodegenerative and cancer diseases. The preliminary data are now accessible on the special HBDA website.

Brain connectomics: time for a molecular imaging perspective?

In the past two decades brain connectomics has evolved into a major concept in neuroscience. However, the current perspective on brain connectivity and how it underpins brain function relies mainly on the hemodynamic signal of functional magnetic resonance imaging (MRI). Molecular imaging provides unique information inaccessible to MRI-based and electrophysiological techniques. Thus, positron emission tomography (PET) has been successfully applied to measure neural activity, neurotransmission, and proteinopathies in normal and pathological cognition. Here, we position molecular imaging within the brain connectivity framework from the perspective of timeliness, validity, reproducibility, and resolution. We encourage the neuroscientific community to take an integrative approach whereby MRI-based, electrophysiological techniques, and molecular imaging contribute to our understanding of the brain connectome.

Mapping Cytoarchitectonics and Receptor Architectonics to Understand Brain Function and Connectivity

This review focuses on cytoarchitectonics and receptor architectonics as biological correlates of function and connectivity. It introduces the 3-dimensional cytoarchitectonic probabilistic maps of cortical areas and nuclei of the Julich-Brain Atlas, available at EBRAINS, to study structure-function relationships. The maps are linked to the BigBrain as microanatomical reference model and template space. The siibra software tool suite enables programmatic access to the maps and to receptor architectonic data that are anchored to brain areas. Such cellular and molecular data are tools for studying magnetic resonance connectivity including modeling and simulation. At the end, we highlight perspectives of the Julich-Brain as well as methodological considerations. Thus, microstructural maps as part of a multimodal atlas help elucidate the biological correlates of large-scale networks and brain function with a high level of anatomical detail, which provides a basis to study brains of patients with psychiatric disorders.

The quest for multiscale brain modeling

Addressing the multiscale organization of the brain, which is fundamental to the dynamic repertoire of the organ, remains challenging. In principle, it should be possible to model neurons and synapses in detail and then connect them into large neuronal assemblies to explain the relationship between microscopic phenomena, large-scale brain functions, and behavior. It is more difficult to infer neuronal functions from ensemble measurements such as those currently obtained with brain activity recordings. In this article we consider theories and strategies for combining bottom-up models, generated from principles of neuronal biophysics, with top-down models based on ensemble representations of network activity and on functional principles. These integrative approaches are hoped to provide effective multiscale simulations in virtual brains and neurorobots, and pave the way to future applications in medicine and information technologies.

Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy

Brain mapping studies often need to identify brain structures or functional circuits into a set of individual brains. To this end, multiple atlases have been published to represent such structures based on different modalities, subject sets, and techniques. The mainstream approach to exploit these atlases consists in spatially deforming each individual data onto a given atlas using dense deformation fields, which supposes the existence of a continuous mapping between atlases and individuals. However, this continuity is not always verified, and this "iconic" approach has limits. We present in this study an alternative, complementary, "structural" approach, which consists in extracting structures from the individual data, and comparing them without deformation. A "structural atlas" is thus a collection of annotated individual data with a common structure nomenclature. It may be used to characterize structure shape variability across individuals or species, or to train machine learning systems. This study exhibits Anatomist, a powerful structural 3D visualization software dedicated to building, exploring, and editing structural atlases involving a large number of subjects. It has been developed primarily to decipher the cortical folding variability; cortical sulci vary enormously in both size and shape, and some may be missing or have various topologies, which makes iconic approaches inefficient to study them. We, therefore, had to build structural atlases for cortical sulci, and use them to train sulci identification algorithms. Anatomist can display multiple subject data in multiple views, supports all kinds of neuroimaging data, including compound structural object graphs, handles arbitrary coordinate transformation chains between data, and has multiple display features. It is designed as a programming library in both C++ and Python languages, and may be extended or used to build dedicated custom applications. Its generic design makes all the display and structural aspects used to explore the variability of the cortical folding pattern work in other applications, for instance, to browse axonal fiber bundles, deep nuclei, functional activations, or other kinds of cortical parcellations. Multimodal, multi-individual, or inter-species display is supported, and adaptations to large scale screen walls have been developed. These very original features make it a unique viewer for structural atlas browsing.

Three-dimensional virtual histology of the human hippocampus based on phase-contrast computed tomography

We demonstrate multiscale phase-contrast X-ray computed tomography (CT) of postmortem human brain tissue. Large tissue volumes can be covered by parallel-beam CT and combined with subcellular detail for selected regions scanned at high magnification. This has been repeated identically for a larger number of individuals, including both Alzheimer’s-diseased patients and a control group. Optimized phase retrieval, followed by automated segmentation based on machine learning, as well as feature identification and classification based on optimal transport theory, indicates a pathway from healthy to pathological structure without prior hypothesis. This study provides a blueprint for studying the cytoarchitecture of the human brain and its alterations associated with neurodegenerative diseases.

We have studied the three-dimensional (3D) cytoarchitecture of the human hippocampus in neuropathologically healthy and Alzheimer’s disease (AD) individuals, based on phase-contrast X-ray computed tomography of postmortem human tissue punch biopsies. In view of recent findings suggesting a nuclear origin of AD, we target in particular the nuclear structure of the dentate gyrus (DG) granule cells. Tissue samples of 20 individuals were scanned and evaluated using a highly automated approach of measurement and analysis, combining multiscale recordings, optimized phase retrieval, segmentation by machine learning, representation of structural properties in a feature space, and classification based on the theory of optimal transport. Accordingly, we find that the prototypical transformation between a structure representing healthy granule cells and the pathological state involves a decrease in the volume of granule cell nuclei, as well as an increase in the electron density and its spatial heterogeneity. The latter can be explained by a higher ratio of heterochromatin to euchromatin. Similarly, many other structural properties can be derived from the data, reflecting both the natural polydispersity of the hippocampal cytoarchitecture between different individuals in the physiological context and the structural effects associated with AD pathology.

Towards an Architecture of a Multi-purpose, User-Extendable Reference Human Brain Atlas

Human brain atlas development is predominantly research-oriented and the use of atlases in clinical practice is limited. Here I introduce a new definition of a reference human brain atlas that serves education, research and clinical applications, and is extendable by its user. Subsequently, an architecture of a multi-purpose, user-extendable reference human brain atlas is proposed and its implementation discussed. The human brain atlas is defined as a vehicle to gather, present, use, share, and discover knowledge about the human brain with highly organized content, tools enabling a wide range of its applications, massive and heterogeneous knowledge database, and means for content and knowledge growing by its users. The proposed architecture determines major components of the atlas, their mutual relationships, and functional roles. It contains four functional units, core cerebral models, knowledge database, research and clinical data input and conversion, and toolkit (supporting processing, content extension, atlas individualization, navigation, exploration, and display), all united by a user interface. Each unit is described in terms of its function, component modules and sub-modules, data handling, and implementation aspects. This novel architecture supports brain knowledge gathering, presentation, use, sharing, and discovery and is broadly applicable and useful in student- and educator-oriented neuroeducation for knowledge presentation and communication, research for knowledge acquisition, aggregation and discovery, and clinical applications in decision making support for prevention, diagnosis, treatment, monitoring, and prediction. It establishes a backbone for designing and developing new, multi-purpose and user-extendable brain atlas platforms, serving as a potential standard across labs, hospitals, and medical schools.

Applications of optimal nonlinear control to a whole-brain network of FitzHugh-Nagumo oscillators

We apply the framework of optimal nonlinear control to steer the dynamics of a whole-brain network of FitzHugh-Nagumo oscillators. Its nodes correspond to the cortical areas of an atlas-based segmentation of the human cerebral cortex, and the internode coupling strengths are derived from diffusion tensor imaging data of the connectome of the human brain. Nodes are coupled using an additive scheme without delays and are driven by background inputs with fixed mean and additive Gaussian noise. Optimal control inputs to nodes are determined by minimizing a cost functional that penalizes the deviations from a desired network dynamic, the control energy, and spatially nonsparse control inputs. Using the strength of the background input and the overall coupling strength as order parameters, the network's state-space decomposes into regions of low- and high-activity fixed points separated by a high-amplitude limit cycle, all of which qualitatively correspond to the states of an isolated network node. Along the borders, however, additional limit cycles, asynchronous states, and multistability can be observed. Optimal control is applied to several state-switching and network synchronization tasks, and the results are compared to controllability measures from linear control theory for the same connectome. We find that intuitions from the latter about the roles of nodes in steering the network dynamics, which are solely based on connectome features, do not generally carry over to nonlinear systems, as had been previously implied. Instead, the role of nodes under optimal nonlinear control critically depends on the specified task and the system's location in state space. Our results shed new light on the controllability of brain network states and may serve as an inspiration for the design of new paradigms for noninvasive brain stimulation.

Data-point-wise spatiotemporal mapping of human ventral visual areas: Use of spatial frequency/luminance-modulated chromatic faces

Visual information involving facial identity and expression is crucial for social communication. Although the influence of facial features such as spatial frequency (SF) and luminance on face processing in visual areas has been studied extensively using grayscale stimuli, the combined effects of other features in this process have not been characterized. To determine the combined effects of different SFs and color, we created chromatic stimuli with low, high or no SF components, which bring distinct SF and color information into the ventral stream simultaneously. To obtain neural activity data with high spatiotemporal resolution we recorded face-selective responses (M170) using magnetoencephalography. We used a permutation test procedure with threshold-free cluster enhancement to assess statistical significance while resolving problems related to multiple comparisons and arbitrariness found in traditional statistical methods. We found that time windows with statistically significant threshold levels were distributed differently among the stimulus conditions. Face stimuli containing any SF components evoked M170 in the fusiform gyrus (FG), whereas a significant emotional effect on M170 was only observed with the original images. Low SF faces elicited larger activation of the FG and the inferior occipital gyrus than the original images, suggesting an interaction between low and high SF information processing. Interestingly, chromatic face stimuli without SF first activated color-selective regions and then the FG, indicating that facial color was processed according to a hierarchy in the ventral stream. These findings suggest complex effects of SFs in the presence of color information, reflected in M170, and unveil the detailed spatiotemporal dynamics of face processing in the human brain.

Enrichment of Disease-Associated Genes in Cortical Areas Defined by Transcriptome-Based Parcellation

BACKGROUND

Parcellation of the cerebral cortex serves the investigation of the emergence of uniquely human brain functions and disorders. Transcriptome data enable the characterization of the molecular properties of cortical areas in unprecedented detail. Previously, we predicted the expression of 18,686 genes in the entire human brain based on microarray data. Here, we employed these data to parcellate the cortex and study the regional enrichment of disease-associated genes.

METHODS

We performed agglomerative hierarchical clustering based on normalized transcriptome data to delineate areas with distinct gene expression profiles. Subsequently, we tested these profiles for the enrichment of gene sets associated with brain disorders by genome-wide association studies and expert-curated databases using gene set enrichment analysis.

RESULTS

Transcriptome-based parcellation identified borders in line with major anatomical landmarks and the functional differentiation of primary motor, somatosensory, visual, and auditory areas. Gene set enrichment analysis based on curated databases suggested new roles of specific areas in psychiatric and neurological disorders while reproducing well-established links for movement and neurodegenerative disorders, for example, amyotrophic lateral sclerosis (motor cortex) and Alzheimer's disease (entorhinal cortex). Meanwhile, gene sets derived from genome-wide association studies on psychiatric disorders exhibited similar enrichment patterns driven by pleiotropic genes expressed in the posterior fusiform gyrus and inferior parietal lobule.

CONCLUSIONS

The identified enrichment patterns suggest the vulnerability of specific cortical areas to various influences that might alter the risk of developing one or several brain disorders. For several diseases, specific genes were highlighted, which could lead to the discovery of novel disease mechanisms and urgently needed treatments.

Brain topography beyond parcellations: Local gradients of functional maps

Functional neuroimaging provides the unique opportunity to characterize brain regions based on their response to tasks or ongoing activity. As such, it holds the premise to capture brain spatial organization. Yet, the conceptual framework to describe this organization has remained elusive: on the one hand, parcellations build implicitly on a piecewise constant organization, i.e. flat regions separated by sharp boundaries; on the other hand, the recently popularized concept of functional gradient hints instead at a smooth structure. Noting that both views converge to a topographic scheme that pieces together local variations of functional features, we perform a quantitative assessment of local gradient-based models. Using as a driving case the prediction of functional Magnetic Resonance Imaging (fMRI) data -concretely, the prediction of task-fMRI from rest-fMRI maps across subjects- we develop a parcel-wise linear regression model based on a dictionary of reference topographies. Our method uses multiple random parcellations -as opposed to a single fixed parcellation- and aggregates estimates across these parcellations to predict functional features in left-out subjects. Our experiments demonstrate the existence of an optimal cardinality of the parcellation to capture local gradients of functional maps.

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 neurosurgery, 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.

Human Brain Atlases in Stroke Management

Stroke is a leading cause of death and a major cause of permanent disability. Its management is demanding because of variety of protocols, imaging modalities, pulse sequences, hemodynamic maps, criteria for treatment, and time constraints to promptly evaluate and treat. To cope with some of these issues, we propose novel, patented solutions in stroke management by employing multiple brain atlases for diagnosis, treatment, and prediction. Numerous and diverse CT and MRI scans are used: ARIC cohort, ischemic and hemorrhagic stroke CT cases, MRI cases with multiple pulse sequences, and 128 stroke CT patients, each with 170 variables and one year follow-up. The method employs brain atlases of anatomy, blood supply territories, and probabilistic stroke atlas. It rapidly maps an atlas to scan and provides atlas-assisted scan processing. Atlas-to-scan mapping is application-dependent and handles three types of regions of interest (ROIs): atlas-defined ROIs, atlas-quantified ROIs, and ROIs creating an atlas. An ROI is defined by atlas-guided anatomy or scan-derived pathology. The atlas defines ROI or quantifies it. A brain atlas potential has been illustrated in four atlas-assisted applications for stroke occurrence prediction and screening, rapid and automatic stroke diagnosis in emergency room, quantitative decision support in thrombolysis in ischemic stroke, and stroke outcome prediction and treatment assessment. The use of brain atlases in stroke has many potential advantages, including rapid processing, automated and robust handling, wide range of applications, and quantitative assessment. Further work is needed to enhance the developed prototypes, clinically validate proposed solutions, and introduce them to clinical practice.

Cytoarchitectonic Mapping of MRI Detects Rapid Changes in Alzheimer's Disease

The clinical and pathological progression of Alzheimer's disease often proceeds rapidly, but little is understood about its structural characteristics over short intervals. This study evaluated the short temporal characteristics of the brain structure in Alzheimer's disease through the application of cytoarchitectonic probabilistic brain mapping to measurements of gray matter density, a technique which may provide advantages over standard volumetric MRI techniques. Gray matter density was calculated using voxel-based morphometry of T1-weighted MRI obtained from Alzheimer's disease patients and healthy controls evaluated at intervals of 0.5, 1.5, 3.5, 6.5, 9.5, 12, 18, and 24 months by the MIRIAD study. The Alzheimer's disease patients had 19.1% less gray matter at 1st MRI, and this declined 81.6% faster than in healthy controls. Atrophy in the hippocampus, amygdala, and basal forebrain distinguished the Alzheimer's disease patients. Notably, the CA2 of the hippocampus was found to have atrophied significantly within 1 month. Gray matter density measurements were reliable, with intraclass correlation coefficients exceeding 0.8. Comparative atrophy in the Alzheimer's disease group agreed with manual tracing MRI studies of Alzheimer's disease while identifying atrophy on a shorter time scale than has previously been reported. Cytoarchitectonic mapping of gray matter density is reliable and sensitive to small-scale neurodegeneration, indicating its use in the future study of Alzheimer's disease.

The Blind Men and the Elephant: The Quest for Open Data Repositories

Nonhuman primate imaging is maturing into a solid subfield within basic and translational neurosciences. In the present issue of Neuron, Milham et al. (2018) present a data repository to openly share anatomical, functional, and diffusion-based neuroimaging data from monkeys.

Scalable Labeling for Cytoarchitectonic Characterization of Large Optically Cleared Human Neocortex Samples

Optical clearing techniques and light sheet microscopy have transformed fluorescent imaging of rodent brains, and have provided a crucial alternative to traditional confocal or bright field techniques for thin sections. However, clearing and labeling human brain tissue through all cortical layers and significant portions of a cortical area, has so far remained extremely challenging, especially for formalin fixed adult cortical tissue. Here, we present MASH (Multiscale Architectonic Staining of Human cortex): a simple, fast and low-cost cytoarchitectonic labeling approach for optically cleared human cortex samples, which can be applied to large (up to 5 mm thick) formalin fixed adult brain samples. A suite of small-molecule fluorescent nuclear and cytoplasmic dye protocols in combination with new refractive index matching solutions allows deep volume imaging. This greatly reduces time and cost of imaging cytoarchitecture in thick samples and enables classification of cytoarchitectonic layers over the full cortical depth. We demonstrate application of MASH to large archival samples of human visual areas, characterizing cortical architecture in 3D from the scale of cortical areas to that of single cells. In combination with scalable light sheet imaging and data analysis, MASH could open the door to investigation of large human cortical systems at cellular resolution and in the context of their complex 3-dimensional geometry.

The Human Brain Project-Synergy between neuroscience, computing, informatics, and brain-inspired technologies

The Human Brain Project (HBP) is a European flagship project with a 10-year horizon aiming to understand the human brain and to translate neuroscience knowledge into medicine and technology. To achieve such aims, the HBP explores the multilevel complexity of the brain in space and time; transfers the acquired knowledge to brain-derived applications in health, computing, and technology; and provides shared and open computing tools and data through the HBP European brain research infrastructure. We discuss how the HBP creates a transdisciplinary community of researchers united by the quest to understand the brain, with fascinating perspectives on societal benefits.

This Community Page article presents the Human Brain Project; a European Flagship project with a ten-year horizon aiming to understand the human brain and translate neuroscience knowledge into medicine and technology.

Human Pregenual Anterior Cingulate Cortex: Structural, Functional, and Connectional Heterogeneity

The human pregenual anterior cingulate cortex (pACC) encompasses 7 distinct cyto- and receptorarchitectonic areas. We lack a detailed understanding of the functions in which they are involved, and stereotaxic maps are not available. We present an integrated structural/functional map of pACC based on probabilistic cytoarchitectonic mapping and meta-analytic connectivity modeling and quantitative functional decoding. Due to the restricted spatial resolution of functional imaging data relative to the microstructural parcellation, areas p24a of the callosal sulcus and p24b on the surface of the cingulate gyrus were merged into a "gyral component" (p24ab) of area p24, and areas pv24c, pd24cv, and pd24cd, located within the cingulate sulcus were merged into a "sulcal component" (p24c) for meta-analytic analysis. Area p24ab was specifically associated with interoception, p24c with the inhibition of action, and p32, which was also activated by emotion induction tasks pertaining negatively valenced stimuli, with the ability to experience empathy. Thus, area p32 could be classified as cingulate association cortex playing a crucial role in the cognitive regulation of emotion. By this spectrum of functions, pACC is a structurally and functionally heterogeneous region, clearly differing from other parts of the anterior and middle cingulate cortex.

The functional database of the ARCHI project: Potential and perspectives

More than two decades of functional magnetic resonance imaging (fMRI) of the human brain have succeeded to identify, with a growing level of precision, the neural basis of multiple cognitive skills within various domains (perception, sensorimotor processes, language, emotion and social cognition …). Progress has been made in the comprehension of the functional organization of localized brain areas. However, the long time required for fMRI acquisition limits the number of experimental conditions performed in a single individual. As a consequence, distinct brain localizations have mostly been studied in separate groups of participants, and their functional relationships at the individual level remain poorly understood. To address this issue, we report here preliminary results on a database of fMRI data acquired on 78 individuals who each performed a total of 29 experimental conditions, grouped in 4 cross-domains functional localizers. This protocol has been designed to efficiently isolate, in a single session, the brain activity associated with language, numerical representation, social perception and reasoning, premotor and visuomotor representations. Analyses are reported at the group and at the individual level, to establish the ability of our protocol to selectively capture distinct regions of interest in a very short time. Test-retest reliability was assessed in a subset of participants. The activity evoked by the different contrasts of the protocol is located in distinct brain networks that, individually, largely replicate previous findings and, taken together, cover a large proportion of the cortical surface. We provide detailed analyses of a subset of regions of relevance: the left frontal, left temporal and middle frontal cortices. These preliminary analyses highlight how combining such a large set of functional contrasts may contribute to establish a finer-grained brain atlas of cognitive functions, especially in regions of high functional overlap. Detailed structural images (structural connectivity, micro-structures, axonal diameter) acquired in the same individuals in the context of the ARCHI database provide a promising situation to explore functional/structural interdependence. Additionally, this protocol might also be used as a way to establish individual neurofunctional signatures in large cohorts.

Online histological atlas of the Göttingen minipig brain

Background

The cytoarchitecture of the Göttingen minipig telencephalon has recently been elucidated in the published article (Bjarkam et al., 2017). The aim of the current paper is to describe how such data can be presented in an online histological atlas of the Gottingen minipig brain and how this atlas was constructed.

Methods

Two sets of histological sections were used. One set was photographed in high resolution and labelled, the other set in low resolution (resized first set) was used for reference on the computer screen. The two sets of microphotographs enable, using the freely available JQuery Image Zoom Plugin, the subsequent construction of a simple HTML-based atlas web page with a “virtual microscope like” style, which allowed magnifying of the base image (low-resolution image) up to the maximum resolution of the high-resolution image. In addition, we describe how the established histological atlas can be accompanied by a set of similar T1-weighted MRI pictures.

Results and conclusion

Histological and MRI pictures are presented in atlas form on www.cense.dk/minipig_atlas/index.html. The described pipeline represent a cheap and freely available way to present histological images, in online virtual microscopic atlas form, and may thus be of general interest to anyone who would like to present histological data accordingly.

Parcellation of the Human Cerebral Cortex Based on Molecular Targets in the Serotonin System Quantified by Positron Emission Tomography In vivo

Parcellation of distinct areas in the cerebral cortex has a long history in neuroscience and is of great value for the study of brain function, specialization, and alterations in neuropsychiatric disorders. Analysis of cytoarchitectonical features has revealed their close association with molecular profiles based on protein density. This provides a rationale for the use of in vivo molecular imaging data for parcellation of the cortex with the advantage of whole-brain coverage. In the current work, parcellation was based on expression of key players of the serotonin neurotransmitter system. Positron emission tomography was carried out for the quantification of serotonin 1A (5-HT1A, n = 30) and 5-HT2A receptors (n = 22), the serotonin-degrading enzyme monoamine oxidase A (MAO-A, n = 32) and the serotonin transporter (5-HTT, n = 24) in healthy participants. Cortical protein distribution maps were obtained using surface-based quantification. Based on k-means clustering, silhouette criterion and bootstrapping, five distinct clusters were identified as the optimal solution. The defined clusters proved of high explanatory value for the effects of psychotropic drugs acting on the serotonin system, such as antidepressants and psychedelics. Therefore, the proposed method constitutes a sensible approach towards integration of multimodal imaging data for research and development in neuropharmacology and psychiatry.

Navigating the Murine Brain: Toward Best Practices for Determining and Documenting Neuroanatomical Locations in Experimental Studies

In experimental neuroscientific research, anatomical location is a key attribute of experimental observations and critical for interpretation of results, replication of findings, and comparison of data across studies. With steadily rising numbers of publications reporting basic experimental results, there is an increasing need for integration and synthesis of data. Since comparison of data relies on consistently defined anatomical locations, it is a major concern that practices and precision in the reporting of location of observations from different types of experimental studies seem to vary considerably. To elucidate and possibly meet this challenge, we have evaluated and compared current practices for interpreting and documenting the anatomical location of measurements acquired from murine brains with different experimental methods. Our observations show substantial differences in approach, interpretation and reproducibility of anatomical locations among reports of different categories of experimental research, and strongly indicate that ambiguous reports of anatomical location can be attributed to missing descriptions. Based on these findings, we suggest a set of minimum requirements for documentation of anatomical location in experimental murine brain research. We furthermore demonstrate how these requirements have been applied in the EU Human Brain Project to optimize workflows for integration of heterogeneous data in common reference atlases. We propose broad adoption of some straightforward steps for improving the precision of location metadata and thereby facilitating interpretation, reuse and integration of data.

Generation of Individual Whole-Brain Atlases With Resting-State fMRI Data Using Simultaneous Graph Computation and Parcellation

The human brain can be characterized as functional networks. Therefore, it is important to subdivide the brain appropriately in order to construct reliable networks. Resting-state functional connectivity-based parcellation is a commonly used technique to fulfill this goal. Here we propose a novel individual subject-level parcellation approach based on whole-brain resting-state functional magnetic resonance imaging (fMRI) data. We first used a supervoxel method known as simple linear iterative clustering directly on resting-state fMRI time series to generate supervoxels, and then combined similar supervoxels to generate clusters using a clustering method known as graph-without-cut (GWC). The GWC approach incorporates spatial information and multiple features of the supervoxels by energy minimization, simultaneously yielding an optimal graph and brain parcellation. Meanwhile, it theoretically guarantees that the actual cluster number is exactly equal to the initialized cluster number. By comparing the results of the GWC approach and those of the random GWC approach, we demonstrated that GWC does not rely heavily on spatial structures, thus avoiding the challenges encountered in some previous whole-brain parcellation approaches. In addition, by comparing the GWC approach to two competing approaches, we showed that GWC achieved better parcellation performances in terms of different evaluation metrics. The proposed approach can be used to generate individualized brain atlases for applications related to cognition, development, aging, disease, personalized medicine, etc. The major source codes of this study have been made publicly available at https://github.com/yuzhounh/GWC.

Extracting orthogonal subject- and condition-specific signatures from fMRI data using whole-brain effective connectivity

The study of brain communication based on fMRI data is often limited because such measurements are a mixture of session-to-session variability with subject- and condition-related information. Disentangling these contributions is crucial for real-life applications, in particular when only a few recording sessions are available. The present study aims to define a reliable standard for the extraction of multiple signatures from fMRI data, while verifying that they do not mix information about the different modalities (e.g., subjects and conditions such as tasks performed by them). In particular, condition-specific signatures should not be contaminated by subject-related information, since they aim to generalize over subjects. Practically, signatures correspond to subnetworks of directed interactions between brain regions (typically 100 covering the whole brain) supporting the subject and condition identification for single fMRI sessions. The key for robust prediction is using effective connectivity instead of functional connectivity. Our method demonstrates excellent generalization capabilities for subject identification in two datasets, using only a few sessions per subject as reference. Using another dataset with resting state and movie viewing, we show that the two signatures related to subjects and tasks correspond to distinct subnetworks, which are thus topologically orthogonal. Our results set solid foundations for applications tailored to individual subjects, such as clinical diagnostic.

Data integration through brain atlasing: Human Brain Project tools and strategies

The Human Brain Project (HBP), an EU Flagship Initiative, is currently building an infrastructure that will allow integration of large amounts of heterogeneous neuroscience data. The ultimate goal of the project is to develop a unified multi-level understanding of the brain and its diseases, and beyond this to emulate the computational capabilities of the brain. Reference atlases of the brain are one of the key components in this infrastructure. Based on a new generation of three-dimensional (3D) reference atlases, new solutions for analyzing and integrating brain data are being developed. HBP will build services for spatial query and analysis of brain data comparable to current online services for geospatial data. The services will provide interactive access to a wide range of data types that have information about anatomical location tied to them. The 3D volumetric nature of the brain, however, introduces a new level of complexity that requires a range of tools for making use of and interacting with the atlases. With such new tools, neuroscience research groups will be able to connect their data to atlas space, share their data through online data systems, and search and find other relevant data through the same systems. This new approach partly replaces earlier attempts to organize research data based only on a set of semantic terminologies describing the brain and its subdivisions.

How to Characterize the Function of a Brain Region

Many brain regions have been defined, but a comprehensive formalization of each region's function in relation to human behavior is still lacking. Current knowledge comes from various fields, which have diverse conceptions of 'functions'. We briefly review these fields and outline how the heterogeneity of associations could be harnessed to disclose the computational function of any region. Aggregating activation data from neuroimaging studies allows us to characterize the functional engagement of a region across a range of experimental conditions. Furthermore, large-sample data can disclose covariation between brain region features and ecological behavioral phenotyping. Combining these two approaches opens a new perspective to determine the behavioral associations of a brain region, and hence its function and broader role within large-scale functional networks.

Integration of transcriptomic and cytoarchitectonic data implicates a role for MAOA and TAC1 in the limbic-cortical network

Decoding the chain from genes to cognition requires detailed insights how areas with specific gene activities and microanatomical architectures contribute to brain function and dysfunction. The Allen Human Brain Atlas contains regional gene expression data, while the JuBrain Atlas offers three-dimensional cytoarchitectonic maps reflecting interindividual variability. To date, an integrated framework that combines the analytical benefits of both scientific platforms towards a multi-level brain atlas of adult humans was not available. We have, therefore, developed JuGEx, a new method for integrating tissue transcriptome and cytoarchitectonic segregation. We investigated differential gene expression in two JuBrain areas of the frontal pole that we have structurally and functionally characterized in previous studies. Our results show a significant upregulation of MAOA and TAC1 in the medial area frontopolaris which is a node in the limbic-cortical network and known to be susceptible for gray matter loss and behavioral dysfunction in patients with depression. The MAOA gene encodes an enzyme which is involved in the catabolism of dopamine, norepinephrine, serotonin, and other monoaminergic neurotransmitters. The TAC1 locus generates hormones that play a role in neuron excitations and behavioral responses. Overall, JuGEx provides a new tool for the scientific community that empowers research from basic, cognitive and clinical neuroscience in brain regions and disease models with regard to gene expression.

Inter-subject Registration of Functional Images: Do We Need Anatomical Images?

In Echo-Planar Imaging (EPI)-based Magnetic Resonance Imaging (MRI), inter-subject registration typically uses the subject's T1-weighted (T1w) anatomical image to learn deformations of the subject's brain onto a template. The estimated deformation fields are then applied to the subject's EPI scans (functional or diffusion-weighted images) to warp the latter to a template space. Historically, such indirect T1w-based registration was motivated by the lack of clear anatomical details in low-resolution EPI images: a direct registration of the EPI scans to template space would be futile. A central prerequisite in such indirect methods is that the anatomical (aka the T1w) image of each subject is well aligned with their EPI images via rigid coregistration. We provide experimental evidence that things have changed: nowadays, there is a decent amount of anatomical contrast in high-resolution EPI data. That notwithstanding, EPI distortions due to B0 inhomogeneities cannot be fully corrected. Residual uncorrected distortions induce non-rigid deformations between the EPI scans and the same subject's anatomical scan. In this manuscript, we contribute a computationally cheap pipeline that leverages the high spatial resolution of modern EPI scans for direct inter-subject matching. Our pipeline is direct and does not rely on the T1w scan to estimate the inter-subject deformation. Results on a large dataset show that this new pipeline outperforms the classical indirect T1w-based registration scheme, across a variety of post-registration quality-assessment metrics including: Normalized Mutual Information, relative variance (variance-to-mean ratio), and to a lesser extent, improved peaks of group-level General Linear Model (GLM) activation maps.

The Human Brain Project: Creating a European Research Infrastructure to Decode the Human Brain

Decoding the human brain is perhaps the most fascinating scientific challenge in the 21st century. The Human Brain Project (HBP), a 10-year European Flagship, targets the reconstruction of the brain's multi-scale organization. It uses productive loops of experiments, medical, data, data analytics, and simulation on all levels that will eventually bridge the scales. The HBP IT architecture is unique, utilizing cloud-based collaboration and development platforms with databases, workflow systems, petabyte storage, and supercomputers. The HBP is developing toward a European research infrastructure advancing brain research, medicine, and brain-inspired information technology.

Comprehensive cellular-resolution atlas of the adult human brain

Detailed anatomical understanding of the human brain is essential for unraveling its functional architecture, yet current reference atlases have major limitations such as lack of whole‐brain coverage, relatively low image resolution, and sparse structural annotation. We present the first digital human brain atlas to incorporate neuroimaging, high‐resolution histology, and chemoarchitecture across a complete adult female brain, consisting of magnetic resonance imaging (MRI), diffusion‐weighted imaging (DWI), and 1,356 large‐format cellular resolution (1 µm/pixel) Nissl and immunohistochemistry anatomical plates. The atlas is comprehensively annotated for 862 structures, including 117 white matter tracts and several novel cyto‐ and chemoarchitecturally defined structures, and these annotations were transferred onto the matching MRI dataset. Neocortical delineations were done for sulci, gyri, and modified Brodmann areas to link macroscopic anatomical and microscopic cytoarchitectural parcellations. Correlated neuroimaging and histological structural delineation allowed fine feature identification in MRI data and subsequent structural identification in MRI data from other brains. This interactive online digital atlas is integrated with existing Allen Institute for Brain Science gene expression atlases and is publicly accessible as a resource for the neuroscience community. J. Comp. Neurol. 524:3127–3481, 2016. © 2016 The Authors The Journal of Comparative Neurology Published by Wiley Periodicals, Inc.

Seed Location Impacts Whole-Brain Structural Network Comparisons between Healthy Elderly and Individuals with Alzheimer's Disease

Whole-brain networks derived from diffusion tensor imaging (DTI) data require the identification of seed and target regions of interest (ROIs) to assess connectivity patterns. This study investigated how initiating tracts from gray matter (GM) or white matter (WM) seed ROIs impacts (1) structural networks constructed from DTI data from healthy elderly (control) and individuals with Alzheimer’s disease (AD) and (2) between-group comparisons using these networks. DTI datasets were obtained from the Alzheimer’s disease Neuroimaging Initiative database. Deterministic tractography was used to build two whole-brain networks for each subject; one in which tracts were initiated from WM ROIs and another in which they were initiated from GM ROIs. With respect to the first goal, in both groups, WM-seeded networks had approximately 400 more connections and stronger connections (as measured by number of streamlines per connection) than GM-seeded networks, but shared 94% of the connections found in the GM-seed networks. With respect to the second goal, between-group comparisons revealed a stronger subnetwork (as measured by number of streamlines per connection) in controls compared to AD using both WM-seeded and GM-seeded networks. The comparison using WM-seeded networks produced a larger (i.e., a greater number of connections) and more significant subnetwork in controls versus AD. Global, local, and nodal efficiency were greater in controls compared to AD, and between-group comparisons of these measures using WM-seeded networks had larger effect sizes than those using GM-seeded networks. These findings affirm that seed location significantly affects the ability to detect between-group differences in structural networks.

Topographic organization of the cerebral cortex and brain cartography

One of the most specific but also challenging properties of the brain is its topographic organization into distinct modules or cortical areas. In this paper, we first review the concept of topographic organization and its historical development. Next, we provide a critical discussion of the current definition of what constitutes a cortical area, why the concept has been so central to the field of neuroimaging and the challenges that arise from this view. A key aspect in this discussion is the issue of spatial scale and hierarchy in the brain. Focusing on in-vivo brain parcellation as a rapidly expanding field of research, we highlight potential limitations of the classical concept of cortical areas in the context of multi-modal parcellation and propose a revised interpretation of cortical areas building on the concept of neurobiological atoms that may be aggregated into larger units within and across modalities. We conclude by presenting an outlook on the implication of this revised concept for future mapping studies and raise some open questions in the context of brain parcellation.

A Supervoxel-Based Method for Groupwise Whole Brain Parcellation with Resting-State fMRI Data

Node definition is a very important issue in human brain network analysis and functional connectivity studies. Typically, the atlases generated from meta-analysis, random criteria, and structural criteria are utilized as nodes in related applications. However, these atlases are not originally designed for such purposes and may not be suitable. In this study, we combined normalized cut (Ncut) and a supervoxel method called simple linear iterative clustering (SLIC) to parcellate whole brain resting-state fMRI data in order to generate appropriate brain atlases. Specifically, Ncut was employed to extract features from connectivity matrices, and then SLIC was applied on the extracted features to generate parcellations. To obtain group level parcellations, two approaches named mean SLIC and two-level SLIC were proposed. The cluster number varied in a wide range in order to generate parcellations with multiple granularities. The two SLIC approaches were compared with three state-of-the-art approaches under different evaluation metrics, which include spatial contiguity, functional homogeneity, and reproducibility. Both the group-to-group reproducibility and the group-to-subject reproducibility were evaluated in our study. The experimental results showed that the proposed approaches obtained relatively good overall clustering performances in different conditions that included different weighting functions, different sparsifying schemes, and several confounding factors. Therefore, the generated atlases are appropriate to be utilized as nodes for network analysis. The generated atlases and major source codes of this study have been made publicly available at http://www.nitrc.org/projects/slic/.

The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture

The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states.

Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation

Given the increasing number of neuroimaging publications, the automated knowledge extraction on brain-behavior associations by quantitative meta-analyses has become a highly important and rapidly growing field of research. Among several methods to perform coordinate-based neuroimaging meta-analyses, Activation Likelihood Estimation (ALE) has been widely adopted. In this paper, we addressed two pressing questions related to ALE meta-analysis: i) Which thresholding method is most appropriate to perform statistical inference? ii) Which sample size, i.e., number of experiments, is needed to perform robust meta-analyses? We provided quantitative answers to these questions by simulating more than 120,000 meta-analysis datasets using empirical parameters (i.e., number of subjects, number of reported foci, distribution of activation foci) derived from the BrainMap database. This allowed to characterize the behavior of ALE analyses, to derive first power estimates for neuroimaging meta-analyses, and to thus formulate recommendations for future ALE studies. We could show as a first consequence that cluster-level family-wise error (FWE) correction represents the most appropriate method for statistical inference, while voxel-level FWE correction is valid but more conservative. In contrast, uncorrected inference and false-discovery rate correction should be avoided. As a second consequence, researchers should aim to include at least 20 experiments into an ALE meta-analysis to achieve sufficient power for moderate effects. We would like to note, though, that these calculations and recommendations are specific to ALE and may not be extrapolated to other approaches for (neuroimaging) meta-analysis.

Building the Ferretome

Databases of structural connections of the mammalian brain, such as CoCoMac (cocomac.g-node.org) or BAMS (https://bams1.org), are valuable resources for the analysis of brain connectivity and the modeling of brain dynamics in species such as the non-human primate or the rodent, and have also contributed to the computational modeling of the human brain. Another animal model that is widely used in electrophysiological or developmental studies is the ferret; however, no systematic compilation of brain connectivity is currently available for this species. Thus, we have started developing a database of anatomical connections and architectonic features of the ferret brain, the Ferret(connect)ome, www.Ferretome.org. The Ferretome database has adapted essential features of the CoCoMac methodology and legacy, such as the CoCoMac data model. This data model was simplified and extended in order to accommodate new data modalities that were not represented previously, such as the cytoarchitecture of brain areas. The Ferretome uses a semantic parcellation of brain regions as well as a logical brain map transformation algorithm (objective relational transformation, ORT). The ORT algorithm was also adopted for the transformation of architecture data. The database is being developed in MySQL and has been populated with literature reports on tract-tracing observations in the ferret brain using a custom-designed web interface that allows efficient and validated simultaneous input and proofreading by multiple curators. The database is equipped with a non-specialist web interface. This interface can be extended to produce connectivity matrices in several formats, including a graphical representation superimposed on established ferret brain maps. An important feature of the Ferretome database is the possibility to trace back entries in connectivity matrices to the original studies archived in the system. Currently, the Ferretome contains 50 reports on connections comprising 20 injection reports with more than 150 labeled source and target areas, the majority reflecting connectivity of subcortical nuclei and 15 descriptions of regional brain architecture. We hope that the Ferretome database will become a useful resource for neuroinformatics and neural modeling, and will support studies of the ferret brain as well as facilitate advances in comparative studies of mesoscopic brain connectivity.

Medial Prefrontal Aberrations in Major Depressive Disorder Revealed by Cytoarchitectonically Informed Voxel-Based Morphometry

OBJECTIVE

The heterogeneous human frontal pole has been identified as a node in the dysfunctional network of major depressive disorder. The contribution of the medial (socio-affective) versus lateral (cognitive) frontal pole to major depression pathogenesis is currently unclear. The authors performed morphometric comparison of the microstructurally informed subdivisions of human frontal pole between depressed patients and comparison subjects using both uni- and multivariate statistics.

METHOD

Multisite voxel- and region-based morphometric MRI analysis was conducted in 73 depressed patients and 73 matched comparison subjects without psychiatric history. Frontal pole volume was first compared between depressed patients and comparison subjects by subdivision-wise classical morphometric analysis. In a second approach, frontal pole volume was compared by subdivision-naive multivariate searchlight analysis based on support vector machines.

RESULTS

Subdivision-wise morphometric analysis found a significantly smaller medial frontal pole in depressed patients, with a negative correlation of disease severity and duration. Histologically uninformed multivariate voxel-wise statistics provided converging evidence for structural aberrations specific to the microstructurally defined medial area of the frontal pole in depressed patients.

CONCLUSIONS

Across disparate methods, subregion specificity in the left medial frontal pole volume in depressed patients was demonstrated. Indeed, the frontal pole was shown to structurally and functionally connect to other key regions in major depression pathology, such as the anterior cingulate cortex and the amygdala via the uncinate fasciculus. Present and previous findings consolidate the left medial portion of the frontal pole as particularly altered in major depression.

A collaborative environment for shared classification of neuroimages: The experience of the Colibri project

The Colibri project is introduced, whose aim is setting up a shared database of Magnetic Resonance images concerning pediatric patients affected by neurological rare disorders. The project involves 19 Italian centers of excellence in pediatric neuro-radiology and is supported by the nationwide coordinating center for the Information and Communication Technology research infrastructure. After the first year devoted to the design and the implementation, in November 2014 the system finally went into service at the centers involved in the project. This paper illustrates the initial assessment of the user perception and provides some preliminary statistics about its use.

The BRAIN Initiative: developing technology to catalyse neuroscience discovery

The evolution of the field of neuroscience has been propelled by the advent of novel technological capabilities, and the pace at which these capabilities are being developed has accelerated dramatically in the past decade. Capitalizing on this momentum, the United States launched the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative to develop and apply new tools and technologies for revolutionizing our understanding of the brain. In this article, we review the scientific vision for this initiative set forth by the National Institutes of Health and discuss its implications for the future of neuroscience research. Particular emphasis is given to its potential impact on the mapping and study of neural circuits, and how this knowledge will transform our understanding of the complexity of the human brain and its diverse array of behaviours, perceptions, thoughts and emotions.

Connectivity-based parcellation: Critique and implications

Regional specialization and functional integration are often viewed as two fundamental principles of human brain organization. They are closely intertwined because each functionally specialized brain region is probably characterized by a distinct set of long-range connections. This notion has prompted the quickly developing family of connectivity-based parcellation (CBP) methods in neuroimaging research. CBP assumes that there is a latent structure of parcels in a region of interest (ROI). First, connectivity strengths are computed to other parts of the brain for each voxel/vertex within the ROI. These features are then used to identify functionally distinct groups of ROI voxels/vertices. CBP enjoys increasing popularity for the in-vivo mapping of regional specialization in the human brain. Due to the requirements of different applications and datasets, CBP has diverged into a heterogeneous family of methods. This broad overview critically discusses the current state as well as the commonalities and idiosyncrasies of the main CBP methods. We target frequent concerns faced by novices and veterans to provide a reference for the investigation and review of CBP studies.

Multimodal connectivity of motor learning-related dorsal premotor cortex

The dorsal premotor cortex (dPMC) is a key region for motor learning and sensorimotor integration, yet we have limited understanding of its functional interactions with other regions. Previous work has started to examine functional connectivity in several brain areas using resting state functional connectivity (RSFC) and meta-analytical connectivity modelling (MACM). More recently, structural covariance (SC) has been proposed as a technique that may also allow delineation of functional connectivity. Here, we applied these three approaches to provide a comprehensive characterization of functional connectivity with a seed in the left dPMC that a previous meta-analysis of functional neuroimaging studies has identified as playing a key role in motor learning. Using data from two sources (the Rockland sample, containing resting state data and anatomical scans from 132 participants, and the BrainMap database, which contains peak activation foci from over 10,000 experiments), we conducted independent whole-brain functional connectivity mapping analyses of a dPMC seed. RSFC and MACM revealed similar connectivity maps spanning prefrontal, premotor, and parietal regions, while the SC map identified more widespread frontal regions. Analyses indicated a relatively consistent pattern of functional connectivity between RSFC and MACM that was distinct from that identified by SC. Notably, results indicate that the seed is functionally connected to areas involved in visuomotor control and executive functions, suggesting that the dPMC acts as an interface between motor control and cognition.

The influence of construction methodology on structural brain network measures: A review

Structural brain networks based on diffusion MRI and tractography show robust attributes such as small-worldness, hierarchical modularity, and rich-club organization. However, there are large discrepancies in the reports about specific network measures. It is hypothesized that these discrepancies result from the influence of construction methodology. We surveyed the methodological options and their influences on network measures. It is found that most network measures are sensitive to the scale of brain parcellation, MRI gradient schemes and orientation model, and the tractography algorithm, which is in accordance with the theoretical analysis of the small-world network model. Different network weighting schemes represent different attributes of brain networks, which makes these schemes incomparable between studies. Methodology choice depends on the specific study objectives and a clear understanding of the pros and cons of a particular methodology. Because there is no way to eliminate these influences, it seems more practical to quantify them, optimize the methodologies, and construct structural brain networks with multiple spatial resolutions, multiple edge densities, and multiple weighting schemes.