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A novel self-supervised deep learning (DL) method is developed for computing bias-free, personalized brain functional networks (FNs) that provide unique opportunities to better understand brain function, behavior, and disease. Specifically, convolutional neural networks with an encoder-decoder architecture are employed to compute personalized FNs from resting-state fMRI data without utilizing any external supervision by optimizing functional homogeneity of personalized FNs in a self-supervised setting. We demonstrate that a DL model trained on fMRI scans from the Human Connectome Project can identify canonical FNs and generalizes well across four different datasets. We further demonstrate that the identified personalized FNs are informative for predicting individual differences in behavior, brain development, and schizophrenia status. Taken together, self-supervised DL allows for rapid, generalizable computation of personalized FNs.

The interaction of the cerebellum with cerebral cortical dynamics is still poorly understood. In this paper, dynamical causal modeling is used to examine the interaction between cerebellum and cerebral cortex as indexed by MRI resting-state functional connectivity in three large-scale networks on healthy young adults (N = 200; Human Connectome Project dataset). These networks correspond roughly to default mode, task positive, and motor as determined by prior cerebellar functional gradient analyses. We find uniform interactions within all considered networks from cerebellum to cerebral cortex, providing support for the notion of a universal cerebellar transform. Our results provide a foundation for future analyses to quantify and further investigate whether this is a property that is unique to the interactions from cerebellum to cerebral cortex.

Connectomics is the production and study of detailed “connection” maps within the nervous system. With unprecedented advances in imaging and high-performance computing, the construction of individualized connectomes for routine neurosurgical use is on the horizon. Multiple projects, including the Human Connectome Project (HCP), have unraveled new and exciting data describing the functional and structural connectivity of the brain. However, the abstraction from much of these data to clinical relevance remains elusive. In the context of preserving neurological function after supratentorial surgery, abstracting surgically salient points from the vast computational data in connectomics is of paramount importance. Herein, the authors discuss four interesting observations from the HCP data that have surgical relevance, with an emphasis on the cortical organization of language: 1) the existence of a motor speech area outside of Broca’s area, 2) the eloquence of the frontal aslant tract, 3) the explanation of the medial frontal cognitive control networks, and 4) the establishment of the second ventral stream of language processing. From these connectome observations, the authors discuss the anatomical basis of their insights as well as relevant clinical applications. Together, these observations provide a firm platform for neurosurgeons to advance their knowledge of the cortical networks involved in language and to ultimately improve surgical outcomes. It is hoped that this report encourages neurosurgeons to explore new vistas in connectome-based neurosurgery.

The objective of this study was to identify the decussating dentato-rubro-thalamic tract (d-DRTT) and its afferent and efferent connections in healthy humans using diffusion spectrum imaging (DSI) techniques. In the present study, the trajectory and lateralization of the d-DRTT was explored using data from subjects in the Massachusetts General Hospital-Human Connectome Project adult diffusion dataset. The afferent and efferent networks that compose the cerebello-thalamo-cerebral pathways were also reconstructed. Correlation analysis was performed to identify interrelationships between subdivisions of the cerebello-dentato-rubro-thalamic and thalamo-cerebral connections. The d-DRTT was visualized bilaterally in 28 subjects. According to a normalized quantitative anisotropy and lateralization index evaluation, the left and right d-DRTT were relatively symmetric. Afferent regions were found mainly in the posterior cerebellum, especially the entire lobule VII (crus I, II and VIIb). Efferent fibers mainly are projected to the contralateral frontal cortex, including the motor and nonmotor regions. Correlations between cerebello-thalamic connections and thalamo-cerebral connections were positive, including the lobule VIIa (crus I and II) to the medial prefrontal cortex (MPFC) and the dorsolateral prefrontal cortex and lobules VI, VIIb, VIII, and IX, to the MPFC and motor and premotor areas. These results provide DSI-based tratographic evidence showing segregated and parallel cerebellar outputs to cerebral regions. The posterior cerebellum may play an important role in supporting and handling cognitive activities through d-DRTT. Future studies will allow for a more comprehensive understanding of cerebello-cerebral connections.

The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. The RGVP has four subdivisions, including two decussating and two nondecussating pathways that cannot be identified on conventional structural magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for RGVP reconstruction. In this study, four tractography methods are compared, including constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD‐Stream) methods, and multi‐fiber (UKF‐2T) and single‐fiber (UKF‐1T) unscented Kalman filter (UKF) methods. Experiments use diffusion MRI data from 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Quantitative anatomical measurements and expert anatomical judgment are used to assess the advantages and limitations of the four tractography methods. Overall, we conclude that UKF‐2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF‐2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy and have the highest spatial overlap across subjects. Overall, we find that it is challenging for current tractography methods to both accurately track RGVP fibers that correspond to known anatomy and produce an approximately correct percentage of decussating fibers. We suggest that future algorithm development for RGVP tractography should take consideration of both of these two points., In this work, we investigate the performance of four tractography methods (SD‐Stream, iFOD1, UKF‐1T, and UKF‐2T) for reconstruction of the complete retinogeniculate visual pathway (RGVP). Anatomical measurement and expert rating assessments shows that the iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF‐2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy. However, it is challenging for current tractography methods to both accurately track RGVP fibers that correspond to known anatomy and produce an approximately correct percentage of decussating fibers.

Summary Functional connectivity is defined as the undirected association between two or more functional magnetic resonance imaging (fMRI) time series. Increasingly, subject-level functional connectivity data have been used to predict and classify clinical outcomes and subject attributes. We propose a single-index model wherein response variables and sparse functional connectivity network valued predictors are linked by an unspecified smooth function in order to accommodate potentially nonlinear relationships. We exploit the network structure of functional connectivity by imposing meaningful sparsity constraints, which lead not only to the identification of association of interactions between regions with the response but also the assessment of whether or not the functional connectivity associated with a brain region is related to the response variable. We demonstrate the effectiveness of the proposed model in simulation studies and in an application to a resting-state fMRI data set from the Human Connectome Project to model fluid intelligence and sex and to identify predictive links between brain regions.

Sex is a biological variable that contributes to individual variability in brain structure and behavior. Neuroimaging studies of population‐based samples have identified normative differences in brain structure between males and females, many of which are exacerbated in psychiatric and neurological conditions. Still, sex differences in MRI outcomes are understudied, particularly in clinical samples with known sex differences in disease risk, prevalence, and expression of clinical symptoms. Here we review the existing literature on sex differences in adult brain structure in normative samples and in 14 distinct psychiatric and neurological disorders. We discuss commonalities and sources of variance in study designs, analysis procedures, disease subtype effects, and the impact of these factors on MRI interpretation. Lastly, we identify key problems in the neuroimaging literature on sex differences and offer potential recommendations to address current barriers and optimize rigor and reproducibility. In particular, we emphasize the importance of large‐scale neuroimaging initiatives such as the Enhancing NeuroImaging Genetics through Meta‐Analyses consortium, the UK Biobank, Human Connectome Project, and others to provide unprecedented power to evaluate sex‐specific phenotypes in major brain diseases., Here, we review the human neuroimaging literature examining sex effects on adult brain structure using structural and diffusion MRI. We discuss normative sex differences based on population‐based studies as well as sex differences in 14 major brain diseases. Finally, we identify key barriers to advancing the science on neuroimaging sex effects and offer recommendations to mitigate these challenges, particularly through large‐scale neuroimaging.

The pairwise maximum entropy model (MEM) for resting state functional MRI (rsfMRI) has been used to generate energy landscape of brain states and to explore nonlinear brain state dynamics. Researches using MEM, however, has mostly been restricted to fixed‐effect group‐level analyses, using concatenated time series across individuals, due to the need for large samples in the parameter estimation of MEM. To mitigate the small sample problem in analyzing energy landscapes for individuals, we propose a Bayesian estimation of individual MEM using variational Bayes approximation (BMEM). We evaluated the performances of BMEM with respect to sample sizes and prior information using simulation. BMEM showed advantages over conventional maximum likelihood estimation in reliably estimating model parameters for individuals with small sample data, particularly utilizing the empirical priors derived from group data. We then analyzed individual rsfMRI of the Human Connectome Project to show the usefulness of MEM in differentiating individuals and in exploring neural correlates for human behavior. MEM and its energy landscape properties showed high subject specificity comparable to that of functional connectivity. Canonical correlation analysis identified canonical variables for MEM highly associated with cognitive scores. Inter‐individual variations of cognitive scores were also reflected in energy landscape properties such as energies, occupation times, and basin sizes at local minima. We conclude that BMEM provides an efficient method to characterize dynamic properties of individuals using energy landscape analysis of individual brain states., We propose a Bayesian maximum entropy model estimation (BMEM) to characterize nonlinear brain state dynamics in each individual. BMEM showed advantages over conventional maximum likelihood estimation in reliably estimating model parameters for individuals with small sample data. BMEM and its energy landscape properties showed high subject specificity.

There has been increasing interest in jointly studying structural connectivity (SC) and functional connectivity (FC) derived from diffusion and functional MRI. Previous connectome integration studies almost exclusively required predefined atlases. However, there are many potential atlases to choose from and this choice heavily affects all subsequent analyses. To avoid such an arbitrary choice, we propose a novel atlas‐free approach, named Surface‐Based Connectivity Integration (SBCI), to more accurately study the relationships between SC and FC throughout the intra‐cortical gray matter. SBCI represents both SC and FC in a continuous manner on the white surface, avoiding the need for prespecified atlases. The continuous SC is represented as a probability density function and is smoothed for better facilitation of its integration with FC. To infer the relationship between SC and FC, three novel sets of SC‐FC coupling (SFC) measures are derived. Using data from the Human Connectome Project, we introduce the high‐quality SFC measures produced by SBCI and demonstrate the use of these measures to study sex differences in a cohort of young adults. Compared with atlas‐based methods, this atlas‐free framework produces more reproducible SFC features and shows greater predictive power in distinguishing biological sex. This opens promising new directions for all connectomics studies., There has been increasing interest in jointly studying structural connectivity (SC) and functional connectivity (FC) derived from diffusion and functional MRI. In the following article, we propose a novel atlas‐free approach, named Surface‐Based Connectivity Integration (SBCI), to more accurately study the relationships between SC and FC throughout the intra‐cortical gray matter. Compared with atlas‐based methods, the atlas‐free framework produces more reproducible SC‐FC coupling features and shows greater predictive power in distinguishing biological sex. This opens promising new directions for all connectomics studies.

Working memory is a basic human cognitive function. However, the genetic signatures and their biological pathway remain poorly understood. In the present study, we tried to clarify this issue by exploring the potential associations and pathways among genetic variants, brain morphometry and working memory performance. We first carried out association analyses between 2‐back accuracy and 212 image‐derived phenotypes from 1141 Human Connectome Project (HCP) subjects using a linear mixed model (LMM). We found a significantly positive correlation between the left cuneus volume and 2‐back accuracy (T = 3.615, p = 3.150e−4, Cohen's d = 0.226, corrected using family‐wise error [FWE] method). Based on the LMM‐based genome‐wide association study (GWAS) on the HCP dataset and UK Biobank 33 k GWAS summary statistics, we identified eight independent single nucleotide polymorphisms (SNPs) that were reliably associated with left cuneus volume in both UKB and HCP dataset. Within the eight SNPs, we found a negative correlation between the rs76119478 polymorphism and 2‐back accuracy accuracy (T = −2.045, p = .041, Cohen's d = −0.129). Finally, an LMM‐based mediation analysis elucidated a significant effect of left cuneus volume in mediating rs76119478 polymorphism on the 2‐back accuracy (indirect effect = −0.007, 95% BCa CI = [−0.045, −0.003]). These results were also replicated in a subgroup of Caucasians in the HCP population. Further fine mapping demonstrated that rs76119478 maps on intergene CTD‐2315A10.2 adjacent to protein‐encoding gene DAAM1, and is significantly associated with L3HYPDH mRNA expression. Our study suggested this new variant rs76119478 may regulate the working memory through exerting influence on the left cuneus volume., Working memory's genetic signatures and biological pathways remain poorly understood. Using the UK Biobank GWAS summary statistic and the HCP dataset, new variant rs76119478 may regulate the working memory through exerting influence on the left cuneus volume.

First published: 30 March 2021 Many neuroimaging studies have shown that the hippocampus participates in a resting-state network called the default mode network. However, how the hippocampus connects to the default mode network, whether the hippocampus connects to other resting-state networks and how the different hippocampal subfields take part in resting-state networks remains poorly understood. Here, we examined these issues using the high spatial-resolution 7T resting-state fMRI dataset from the Human Connectome Project. We used data-driven techniques that relied on spatially-restricted Independent Component Analysis, Dual Regression and linear mixed-effect group-analyses based on participant-specific brain morphology. The results revealed two main activity hotspots inside the hippocampus. The first hotspot was located in an anterior location and was correlated with the somatomotor network. This network was subserved by co-activity in the CA1, CA3, CA4 and Dentate Gyrus fields. In addition, there was an activity hotspot that extended from middle to posterior locations along the hippocampal long-axis and correlated with the default mode network. This network reflected activity in the Subiculum, CA4 and Dentate Gyrus fields. These results show how different sections of the hippocampus participate in two known resting-state networks and how these two resting-state networks depend on different configurations of hippocampal subfield co-activity. Agencia Canaria de Investigación, Innovación y Sociedad de la Información; Ministerio de Ciencia, Innovación y Universidades, Grant/Award Number: PSI2017-84933- P, PSI2017-91955- EXP and TEC2016-80063- C3- 2- R; NIH Blueprint for Neuroscience Research, Grant/Award Number: 1U54MH091657; McDonnell Center for Systems Neuroscience; European Social Fund (ESF)

The complexity and variability of human brain activity, such as quantified from Functional Magnetic Resonance Imaging (fMRI) time series, have been widely studied as potential markers of healthy and pathological states. However, the extent to which fMRI temporal features exhibit stable markers of inter-individual differences in brain function across healthy young adults is currently an open question. In this study, we draw upon two widely used time-series measures—a nonlinear complexity measure (sample entropy; SampEn) and a spectral measure of low-frequency content (fALFF)—to capture dynamic properties of resting-state fMRI in a large sample of young adults from the Human Connectome Project. We observe that these two measures are closely related, and that both generate reproducible patterns across brain regions over four different fMRI runs, with intra-class correlations of up to 0.8. Moreover, we find that both metrics can uniquely differentiate subjects with high identification rates (ca. 89%). Canonical correlation analysis revealed a significant relationship between multivariate brain temporal features and behavioral measures. Overall, these findings suggest that regional profiles of fMRI temporal characteristics may provide stable markers of individual differences, and motivate future studies to further probe relationships between fMRI time series metrics and behavior.

Background Current registration methods for diffusion-MRI (dMRI) data mostly focus on white matter (WM) areas. Recently, dMRI has been employed for the characterization of gray matter (GM) microstructure, emphasizing the need for registration methods that consider all tissue types. Purpose To develop a dMRI registration method based on GM, WM, and cerebrospinal fluid (CSF) tissue probability maps (TPMs). Study type Retrospective longitudinal study. Population Thirty-two healthy participants were scanned twice (legacy data), divided into a training-set (n = 16) and a test-set (n = 16), and 35 randomly-selected participants from the Human Connectome Project. Field strength/sequence 3.0T, diffusion-weighted spin-echo echo-planar sequence; T1-weighted spoiled gradient-recalled echo (SPGR) sequence. Assessment A joint segmentation-registration approach was implemented: Diffusion tensor imaging (DTI) maps were classified into TPMs using machine-learning approaches. The resulting GM, WM, and CSF probability maps were employed as features for image alignment. Validation was performed on the test dataset and the HCP dataset. Registration performance was compared with current mainstream registration tools. Statistical tests Classifiers used for segmentation were evaluated using leave-one-out cross-validation and scored using Dice-index. Registration success was evaluated by voxel-wise variance, normalized cross-correlation of registered DTI maps, intra- and inter-subject similarity of the registered TPMs, and region-based intra-subject similarity using an anatomical atlas. One-way ANOVAs were performed to compare between our method and other registration tools. Results The proposed method outperformed mainstream registration tools as indicated by lower voxel-wise variance of registered DTI maps (SD decrease of 10%) and higher similarity between registered TPMs within and across participants, for all tissue types (Dice increase of 0.1-0.2; P Data conclusion A joint segmentation-registration approach based on diffusion-driven TPMs provides a more accurate registration of dMRI data, outperforming other registration tools. Our method offers a "translation" of diffusion data into structural information in the form of TPMs, allowing to directly align diffusion and structural images. Level of evidence 1 Technical Efficacy Stage: 1.

White matter pathways between neurons facilitate neuronal coactivation patterns in the brain. Insight into how these structural and functional connections underlie complex cognitive functions provides an important foundation with which to delineate disease‐related changes in cognitive functioning. Here, we integrate neuroimaging, connectomics, and machine learning approaches to explore how functional and structural brain connectivity relate to cognition. Specifically, we evaluate the extent to which functional and structural connectivity predict individual crystallised and fluid cognitive abilities in 415 unrelated healthy young adults (202 females) from the Human Connectome Project. We report three main findings. First, we demonstrate functional connectivity is more predictive of cognitive scores than structural connectivity, and, furthermore, integrating the two modalities does not increase explained variance. Second, we show the quality of cognitive prediction from connectome measures is influenced by the choice of grey matter parcellation, and, possibly, how that parcellation is derived. Third, we find that distinct functional and structural connections predict crystallised and fluid abilities. Taken together, our results suggest that functional and structural connectivity have unique relationships with crystallised and fluid cognition and, furthermore, studying both modalities provides a more comprehensive insight into the neural correlates of cognition., In this study, we investigate the relationship between multimodal connectivity and cognition. First, we demonstrate functional connectivity is more predictive of cognitive scores than structural connectivity, and, furthermore, integrating the two modalities does not increase explained variance. Second, we show the quality of cognitive prediction from connectome measures is influenced by the choice of grey matter parcellation, and, possibly, how that parcellation is derived. Third, we find that distinct functional and structural connections predict crystallised and fluid abilities.

In the field of neuroimaging and cognitive neuroscience, functional Magnetic Resonance Imaging (fMRI) has been widely used to study the functional localization and connectivity of the brain. However, the inherently low signal-to-noise ratio (SNR) of the fMRI signals greatly limits the accuracy and resolution of current studies. In addressing this fundamental challenge in fMRI analytics, in this work we develop and implement a denoising method for task fMRI (tfMRI) data in order to delineate the high-resolution spatial pattern of the brain activation and functional connectivity via dictionary learning and sparse coding (DLSC). In addition to the traditional unsupervised dictionary learning model which has shown success in image denoising, we further utilize the prior knowledge of task paradigm to learn a dictionary consisting of both data-driven and model-driven terms for a more stable sparse representation of the data. The proposed method is applied to preprocess the motor tfMRI dataset from Human Connectome Project (HCP) for the purpose of brain activation detection and functional connectivity estimation. Comparison between the results from original and denoised fMRI data shows that the disruptive brain activation and functional connectivity patterns can be recovered, and the prominence of such patterns is improved through denoising. The proposed method is then compared with the temporal non-local means (tNLM)-based denoising method and shows consistently superior performance in various experimental settings. The promising results show that the proposed DLSC-based fMRI denoising method can effectively reduce the noise level of the fMRI signals and increase the interpretability of the inferred results, therefore constituting a crucial part of the preprocessing pipeline and provide the foundation for further high-resolution functional analysis.

The activity of functional brain networks is responsible for the emergence of time-varying cognition and behaviour. Accordingly, time-varying correlations (Functional Connectivity) in resting fMRI have been shown to be predictive of behavioural traits, and psychiatric and neurological conditions. Typically, methods that measure time varying Functional Connectivity (FC), such as sliding windows approaches, do not separately model when changes occur in the mean activity levels from when changes occur in the FC, therefore conflating these two distinct types of modulation. We show that this can bias the estimation of time-varying FC to appear more stable over time than it actually is. Here, we propose an alternative approach that models changes in the mean brain activity and in the FC as being able to occur at different times to each other. We refer to this method as the Multi-dynamic Adversarial Generator Encoder (MAGE) model, which includes a model of the network dynamics that captures long-range time dependencies, and is estimated on fMRI data using principles of Generative Adversarial Networks. We evaluated the approach across several simulation studies and resting fMRI data from the Human Connectome Project (1003 subjects), as well as from UK Biobank (13301 subjects). Importantly, we find that separating fluctuations in the mean activity levels from those in the FC reveals much stronger changes in FC over time, and is a better predictor of individual behavioural variabilityStatement of SignificanceMAGE is multi-dynamic in that it models temporal fluctuations in FC independently from fluctuations in the mean of the activity.MAGE reveals stronger changes in FC over time than single-dynamic approaches, such as sliding window correlations.Multi-dynamic modelling provides an explanation and a solution as to why resting fMRI FC has previously looked so stable.MAGE models fMRI data as a set of reoccurring brain states, and importantly, these states do not have to be binary and mutually exclusive (e.g., multiple states can be active at one time-point).MAGE estimated time-varying FC is a better predictor of behavioural variability in the resting-state fMRI data than established methods.

Exploring the spatial patterns and temporal dynamics of human brain activity has been of great interest, in the quest to better understand connectome-scale brain networks. Though modeling spatial and temporal patterns of functional brain networks have been researched for a long time, the development of a unified and simultaneous spatial-temporal model has yet to be realized. For instance, although some deep learning methods have been proposed recently in order to model functional brain networks, most of them can only represent either spatial or temporal perspective of functional Magnetic Resonance Imaging (fMRI) data and rarely model both domains simultaneously. Due to the recent success in applying sequential auto-encoders for brain decoding, in this paper, we propose a deep sparse recurrent auto-encoder (DSRAE) to be applied unsupervised to learn spatial patterns and temporal fluctuations of brain networks at the same time. The proposed DSRAE was evaluated and validated based on three tasks of the publicly available Human Connectome Project (HCP) fMRI dataset, resulting with promising evidence. To the best of our knowledge, the proposed DSRAE is among the early efforts in developing unified models that can extract connectome-scale spatial-temporal networks from 4D fMRI data simultaneously.

It is thought that brain structure is the primary determinant of functions of brain regions. For example, cortical areas with functional differences also have different structural connectivity (SC) patterns. We used SCs derived from diffusion tensor imaging (DTI) data in 100 healthy adults included in the Human Connectome Project (HCP) to successfully predict cortical activation responses across distinct cognitive tasks and found that predictive performance varied among tasks. We also observed that predictive performance could be used to characterize task load in both relational reasoning and N-back working memory tasks and was significantly positively associated with behavioral performance. Moreover, we found that the default mode network (DMN) played a more dominant role in both activation prediction and behavioral performance than was found for other functional networks. These results support our hypothesis that individuals who performed tasks better might exhibit a more accurate predicted activation pattern as task-evoked activities are more inclined to flow over inherent structural networks than over more flexible paths. In the high difficulty condition, the decreased correlation between predicted and empirical activation may be associated with the more random brain activity in these conditions/participants due to the lack of engagement. Together, our findings highlight the feasibility of using SCs to estimate various cognitive task activations and thus further facilitate the exploration of the relationship between the brain and behavior by providing strong evidence for the relevance of structure to function in the human brain.

We describe advances in the understanding of brain dynamics that are important for understanding the operation of the cerebral cortex in health and disease. Peaks in the resting state fMRI BOLD signal in many different brain areas can become synchronized. In data from 1,017 participants from the Human Connectome Project, we show that early visual and connected areas have the highest probability of synchronized peaks. We show that these cortical areas also have low temporal variability of their functional connectivity. We show that there is an approximately reciprocal relation between the probability that a brain region will be involved in synchronized peaks and the temporal variability of the connectivity of a brain region. We show that a high probability of synchronized peaks and a low temporal variability of the connectivity of cortical areas are related to high mean functional connectivity, and provide an account of how these dynamics with some of the properties of avalanches arise. These discoveries help to advance our understanding of cortical operation in health, and in some mental disorders including schizophrenia., Peaks in the resting state BOLD signal of different brain areas can become synchronized and the size of these synchronized events follows a power law distribution, consistent with avalanche dynamics that are found when a system is close to a critical point. We show that a high probability of synchronization events and a low temporal variability of the connectivity of cortical areas are related to high mean functional connectivity, and provide an account of how these dynamics arise, and where they start in the brain.

The human brain is the most complex object of study we encounter today. Mapping the neuronal-level connections between the more than 80 billion neurons in the brain is a hopeless task for science. By the recent advancement of magnetic resonance imaging (MRI), we are able to map the macroscopic connections between about 1000 brain areas. The MRI data acquisition and the subsequent algorithmic workflow contain several complex steps, where errors can occur. In the present contribution we describe and publish 1064 human connectomes, computed from the public release of the Human Connectome Project. Each connectome is available in 5 resolutions, with 83, 129, 234, 463 and 1015 anatomically labeled nodes. For error correction we follow an averaging and extreme value deleting strategy for each edge and for each connectome. The resulting 5320 braingraphs can be downloaded from the https://braingraph.org site. This dataset makes possible the access to this graphs for scientists unfamiliar with neuroimaging- and connectome-related tools: mathematicians, physicists and engineers can use their expertize and ideas in the analysis of the connections of the human brain. Brain scientists and computational neuroscientists also have a robust and large, multi-resolution set for connectomical studies.

Objective To investigate whether varied or repeated b-values provide better diffusion MRI data for discriminating cortical areas with a data-driven approach. Methods Data were acquired from three volunteers at 1.5T with b-values of 800, 1400, 2000 s/mm2 along 64 diffusion-encoding directions. The diffusion signal was sampled from gray matter in seven regions of interest (ROIs). Rotational invariants of the local diffusion profile were extracted as features that characterize local tissue properties. Random forest classification experiments assessed whether classification accuracy improved when data with multiple b-values were used over repeated acquisition of the same (1400 s/mm2) b-value to compare all possible pairs of the seven ROIs. Three data sets from the Human Connectome Project were subjected to similar processing and analysis pipelines in eight ROIs. Results Three different b-values showed an average improvement in correct classification rates of 5.6% and 4.6%, respectively, in the local and HCP data over repeated measurements of the same b-value. The improvement in correct classification rate reached as high as 16% for individual binary classification experiments between two ROIs. Often using only two of the available three b-values were adequate to make such an improvement in classification rates. Conclusion Acquisitions with varying b-values are more suitable for discriminating cortical areas., Magnetic Resonance Materials in Physics, Biology, and Medicine, 34 (5), ISSN:0968-5243, ISSN:1352-8661

Here we report on the heritability and Intraclass Correlation Coefficients (ICCs) of brain volumes in 1,103 young healthy adults with mean age 29.2 years. Among them are: 153 monozygotic (MZ) twin pairs and 86 dizygotic (DZ) twin pairs, 133 non-twin siblings of MZ twins, 76 non-twin siblings of DZ twins, 335 siblings, and 81 unrelated individuals. ICCs were calculated between pairs of the following genetic groups: (1) MZ twins; (2) DZ twins; (3) MZ twins—their singleton siblings; (4) DZ twins—their singleton siblings; (5) siblings (SB); and (6) unrelated individuals (NR). We studied 4 brain groups: global, lobar, subcortical, and cortical brain regions. For each of 4 brain groups we found the same order of ICCs ranging from the highest values for MZ twins, statistically significantly smaller for the DZ twins and 3 sibling groups, and practically zero for NR. The DZ twins and 3 sibling groups were not different. No hemispheric difference was found in any genetic group. Among brain groups, the highest heritability was for the global regions, followed by lobar and subcortical groups. Only the cortical brain group heritability was statistically lower than other brain groups. We found less genetic control on the left hemisphere than on the right but no significant difference between hemispheres, and no hemispheric lateralization of heritability for any of the brain groups. These findings document substantial and systematic heritability of global and regional brain volumes.

Resting‐state functional connectivity in the human brain is heritable, and previous studies have investigated the genetic basis underlying functional connectivity. However, at present, the molecular mechanisms associated with functional network centrality are still largely unknown. In this study, functional networks were constructed, and the graph‐theory method was employed to calculate network centrality in 100 healthy young adults from the Human Connectome Project. Specifically, functional connectivity strength (FCS), also known as the “degree centrality” of weighted networks, is calculated to measure functional network centrality. A multivariate technique of partial least squares regression (PLSR) was then conducted to identify genes whose spatial expression profiles best predicted the FCS distribution. We found that FCS spatial distribution was significantly positively correlated with the expression of genes defined by the first PLSR component. The FCS‐related genes we identified were significantly enriched for ion channels, axon guidance, and synaptic transmission. Moreover, FCS‐related genes were preferentially expressed in cortical neurons and young adulthood and were enriched in numerous neurodegenerative and neuropsychiatric disorders. Furthermore, a series of validation and robustness analyses demonstrated the reliability of the results. Overall, our results suggest that the spatial distribution of FCS is modulated by the expression of a set of genes associated with ion channels, axon guidance, and synaptic transmission., This transcription‐neuroimaging association study identified that the expression profiles of a set of genes, especially those involved in processes related to ion channels, the axon guidance pathway and synaptic transmission, are related to FCS. These findings provide new insight into the biological substrates underlying intrinsic functional network centrality.

Resting-state functional connectivity has provided substantial insight into intrinsic brain network organization, yet the functional importance of task-related change from that intrinsic network organization remains unclear. Indeed, such task-related changes are known to be small, suggesting they may have only minimal functional relevance. Alternatively, despite their small amplitude, these task-related changes may be essential for the human brain’s ability to adaptively alter its functionality via rapid changes in inter-regional relationships. We utilized activity flow mapping – an approach for building empirically-derived network models – to quantify the functional importance of task-state functional connectivity (above and beyond resting-state functional connectivity) in shaping cognitive task activations in the (female and male) human brain. We found that task-state functional connectivity could be used to better predict independent fMRI activations across all 24 task conditions and all 360 cortical regions tested. Further, we found that prediction accuracy was strongly driven by individual-specific functional connectivity patterns, while functional connectivity patterns from other tasks (task-general functional connectivity) still improved predictions beyond resting-state functional connectivity. Additionally, since activity flow models simulate how task-evoked activations (which underlie behavior) are generated, these results may provide mechanistic insight into why prior studies found correlations between task-state functional connectivity and individual differences in behavior. These findings suggest that task-related changes to functional connections play an important role in dynamically reshaping brain network organization, shifting the flow of neural activity during task performance.Significance StatementHuman cognition is highly dynamic, yet the human brain’s functional network organization is highly similar across rest and task states. We hypothesized that, despite this overall network stability, task-related changes from the brain’s intrinsic (resting-state) network organization strongly contribute to brain activations during cognitive task performance. Given that cognitive task activations emerge through network interactions, we leveraged connectivity-based models to predict independent cognitive task activations using resting-state versus task-state functional connectivity. This revealed that task-related changes in functional network organization increased prediction accuracy of cognitive task activations substantially, demonstrating their likely functional relevance for dynamic cognitive processes despite the small size of these task-related network changes.

The amygdala has been implicated in processing threat and learning fear. However, the amygdala also responds to motivationally relevant stimuli even in the absence of explicit emotional content. We investigated the relationship among amygdala activation, cognitive and emotional factors, and fMRI task data in participants from the Young Adult Human Connectome Project. We expected to see variation in amygdala activation that corresponded with variation in traits that could affect the salience of task related stimuli (i.e. internalizing symptoms and fearful faces). We found no relationship between amygdala activation during face viewing and emotion related traits. However, amygdala activation under working memory load was negatively correlated with fluid intelligence and reading level. There was also a negative relationship between task performance and activation in the amygdala. The observed relationship suggests that the role of amygdala is not limited to the processing of emotional content of incoming information, but is instead related to salience, which can be influenced by individual differences.

BACKGROUND: Inter-individual variability is an inherent and ineradicable feature of group-level brain atlases that undermines their reliability for clinical and other applications. To date, there have been no reports quantifying inter-individual variability in brain atlases. METHODS: In the present study, we compared inter-individual variability in nine brain atlases by task-based functional magnetic resonance imaging (MRI) mapping of motor and temporal lobe language regions in both cerebral hemispheres. We analyzed complete motor and language task-based fMRI and T1 data for 893 young, healthy subjects in the Human Connectome Project database. Euclidean distances (EDs) between hotspots in specific brain regions were calculated from task-based fMRI and brain atlas data. General linear model parameters were used to investigate the influence of different brain atlases on signal extraction. Finally, the inter-individual variability of ED and extracted signals and interdependence of relevant indicators were statistically evaluated. RESULTS: We found that inter-individual variability of ED varied across the nine brain atlases (P

In humans, the occipital middle-temporal region (hMT+/V5) specializes in the processing of visual motion, while the planum temporale (hPT) specializes in auditory motion processing. It has been hypothesized that these regions might communicate directly to achieve fast and optimal exchange of multisensory motion information. Here we investigated, for the first time in humans (male and female), the presence of direct white matter connections between visual and auditory motion-selective regions using a combined fMRI and diffusion MRI approach. We found evidence supporting the potential existence of direct white matter connections between individually and functionally defined hMT+/V5 and hPT. We show that projections between hMT+/V5 and hPT do not overlap with large white matter bundles, such as the inferior longitudinal fasciculus and the inferior frontal occipital fasciculus. Moreover, we did not find evidence suggesting the presence of projections between the fusiform face area and hPT, supporting the functional specificity of hMT+/V5–hPT connections. Finally, the potential presence of hMT+/V5–hPT connections was corroborated in a large sample of participants (n= 114) from the human connectome project. Together, this study provides a first indication for potential direct occipitotemporal projections between hMT+/V5 and hPT, which may support the exchange of motion information between functionally specialized auditory and visual regions.SIGNIFICANCE STATEMENTPerceiving and integrating moving signal across the senses is arguably one of the most important perceptual skills for the survival of living organisms. In order to create a unified representation of movement, the brain must therefore integrate motion information from separate senses. Our study provides support for the potential existence of direct connections between motion-selective regions in the occipital/visual (hMT+/V5) and temporal/auditory (hPT) cortices in humans. This connection could represent the structural scaffolding for the rapid and optimal exchange and integration of multisensory motion information. These findings suggest the existence of computationally specific pathways that allow information flow between areas that share a similar computational goal.

The subcallosal cingulate (SCC) area is a putative hub in the brain network underlying depression. Deep brain stimulation (DBS) targeting a particular subregion of SCC, identified as the intersection of forceps minor (FM), uncinate fasciculus (UCF), cingulum and fronto‐striatal fiber bundles, may be critical to a therapeutic response in patients with severe, treatment‐resistant forms of major depressive disorder (MDD). The pattern and variability of the white matter anatomy and organization within SCC has not been extensively characterized across individuals. The goal of this study is to investigate the variability of white matter bundles within the SCC that structurally connect this region with critical nodes in the depression network. Structural and diffusion data from 100 healthy subjects from the Human Connectome Project database were analyzed. Anatomically defined SCC regions were used as seeds to perform probabilistic tractography and to estimate the connectivity from the SCC to subject‐specific target areas believed to be involved in the pathology of MDD including ventral striatum (VS), UCF, anterior cingulate cortex (ACC), and medial prefrontal cortex (mPFC). Four distinct areas of connectivity were identified within SCC across subjects: (a) postero‐lateral SCC connectivity to medial temporal regions via UCF, (b) postero‐medial connectivity to VS, (c) superior‐medial connectivity to ACC via cingulum bundle, and (d) antero‐lateral connectivity to mPFC regions via forceps minor. Assuming white matter connectivity is critical to therapeutic response, the improved anatomic understanding of SCC as well as an appreciation of the intersubject variability are critical to developing optimized therapeutic targeting for SCC DBS., The SCC demonstrates consistent connectivity across subjects to brain regions critical in mood regulation. Tractography‐based parcellation demonstrates a relative consistent pattern of white matter bundle organization within SCC. Despite relative consistency, variability in white matter anatomy within SCC was still significant. Understanding the pattern and variability of white matter organization within SCC across individuals may have significant implications for DBS targeting in future clinical trials.

Recent studies found low test–retest reliability in functional magnetic resonance imaging (fMRI), raising serious concerns among researchers, but these studies mostly focused on the reliability of individual fMRI features (e.g., individual connections in resting state connectivity maps). Meanwhile, neuroimaging researchers increasingly employ multivariate predictive models that aggregate information across a large number of features to predict outcomes of interest, but the test–retest reliability of predicted outcomes of these models has not previously been systematically studied. Here we apply 10 predictive modeling methods to resting state connectivity maps from the Human Connectome Project dataset to predict 61 outcome variables. Compared with mean reliability of individual resting state connections, we find mean reliability of the predicted outcomes of predictive models is substantially higher for all 10 modeling methods assessed. Moreover, improvement was consistently observed across all scanning and processing choices (i.e., scan lengths, censoring thresholds, volume- vs. surface-based processing). For the most reliable methods, the reliability of predicted outcomes was mostly, though not exclusively, in the “good” range (above 0.60). Finally, we identified three mechanisms that help to explain why predicted outcomes of predictive models have higher reliability than individual imaging features. We conclude that researchers can potentially achieve higher test–retest reliability by making greater use of predictive models.

The fornix is the primary efferent pathway of the hippocampus and plays a central role in memory circuitry. Diffusion tensor imaging has shown changes in the fornix with typical development and aging. Here, the fornix was investigated in 903 healthy young adult participants aged 22–36 years old from the high-spatial resolution 1.25 mm isotropic Human Connectome Project (HCP) diffusion dataset. Manual deterministic tractography was used to assess relationships between fornix diffusion parameters and age, sex, laterality, hippocampus volume, memory scores, and genetic effects in a subgroup of mono- and dizygotic twins. Fornix diffusion metrics were weakly correlated with age over the given age span. While significant hemispheric and sex differences were observed (greater fractional anisotropy (FA) and volume in the right hemisphere; greater FA and volume in females), there was great overlap between the groups. Hippocampus volume measurements showed greater volume in the right hemisphere, were found to be larger in males, and were weakly correlated with fornix FA and volume. Interestingly, all fornix diffusion measurements correlated strongly with fornix volume, suggesting the presence of partial volume effects despite the high-spatial resolution of the data. Both fornix diffusion parameters and hippocampal volumes were able to explain some variance (0.6–5.5%) in the memory tests evaluated. The fornix diffusion parameters were influenced by both genetic and shared environmental factors, displaying greater variability in dizygotic than in monozygotic twins. These findings provide a comprehensive depiction of the fornix in healthy, young individuals, upon which future typical development/aging and pathological studies could anchor.

Language localization has been an evolving concept over the past 150 years, with the emergence of several important yet conflicting ideologies. The classical theory, starting from the phrenologic work of Gall to the identification of specific regions of language function by Broca, Wernicke, and others, proposed that discrete subcomponents of language were organized into separate anatomic structural regions. The holism theory was postulated in an attempt to disclose that language function was instead attributed to a larger region of the cortex, in which cerebral regions may have the capability of assuming the function of damaged areas. However, this theory was largely abandoned in favor of discrete structural localizationist viewpoints. The subsequent cortical stimulatory work of Penfield led to the development of maps of localization, assigning an eloquent designation to specific regions. The expanding knowledge of cortical and subcortical anatomy allowed for the development of anatomically and functionally integrative language models. In particular, the dual stream model revisited the concept of regional interconnectivity and expanded the concept of eloquence. Advancements in cortical-subcortical stimulation, neurophysiologic monitoring, magnetic resonance diffusion tensor imaging/functional magnetic resonance imaging, awake neurosurgical technique, and knowledge gained by white matter tract anatomy and the Human Connectome Project, shed new light on the dynamic interconnectivity of the cerebrum. New studies are progressively opening doors to this paradigm, showing the dynamic and interdependent nature of language function. In this review, the evolution of language toward the evolving paradigm of dynamic language function and interconnectivity and its impact on shaping the neurosurgical paradigm are outlined.

The process of valuation assists in determining if an object or course of action is rewarding. Delay discounting is the observed decay of a rewards' subjective value over time. Encoding the subjective value of rewards across a spectrum has been attributed to brain regions belonging to the valuation and executive control systems. The valuation system (VS) encodes reward value over short and long delays, influencing reinforcement learning and reward representation. The executive control system (ECS) becomes more active as choice difficulty increases, integrating contextual and mnemonic information with salience signals in the modulation of decision-making. Here, we aimed to identify resting-state functional connectivity-based patterns of the VS and ECS correlated with value-setting and delay discounting (outside-scanner paradigm) in a large (n = 992) cohort of healthy young adults from the Human Connectome Project (HCP). Results suggest the VS may be involved in value-setting of small, immediate rewards while the ECS may be involved in value-setting and delay discounting for large and small rewards over a range of delays. We observed magnitude sensitive connections involving the posterior cingulate cortex, time-sensitive connections with the ventromedial and lateral prefrontal cortex while connections involving the posterior parietal cortex appeared both magnitude- and time-sensitive. The ventromedial prefrontal cortex and posterior parietal cortex could act as "comparator" regions, weighing the value of small rewards against large rewards across various delay duration to aid in decision-making.

An fMRI decoder aims to infer the corresponding type of task stimulus from the given fMRI data. Recently, deep learning techniques have attracted fMRI decoding attention. Yet, it has not demonstrated an outstanding decoding performance because of ultra-high dimensional data, extremely complex calculations, and subtle differences between different tasks. In this work, we propose a parameter-free attention module called Skip Attention Module (SAM) consisted of weight branch and skip branch, which can pay attention to areas with more information to enhance data features. SAM does not contain any parameters that need to be trained, and does not increase any burden of training. Thus, it can stack on any Convolutional Neural Networks (CNN) architecture or even on a pre-trained model. Our experiments on seven tasks of the large-scale Human Connectome Project (HCP) S1200 data set containing about 1200 subjects show that the architecture with SAM achieves a significant performance improvement compared with the non-attention architecture. We have conducted many experiments, and the average decoding accuracy is up to 88.7%. Besides, the average decoding error of the architecture using SAM is 1.2%~3.1% lower than the architecture without SAM. For a single task, the architecture decoding accuracy using SAM has the highest increase of 11.1%. In addition, the proposed method also shows excellent performance on the ADHD-200 dataset, indicating the universality of the method. These results establish that the proposed SAM can be superimposed on any architecture and can effectively improve fMRI decoding accuracy.

The role of massa intermedia (MI) is poorly understood in humans. Recent studies suggest its presence may play a role in normal human neurocognitive function while prior studies have shown the absence of MI correlated with psychiatric disorders. There is growing evidence that MI is likely a midline white matter conduit, responsible for interhemispheric connectivity, similar to other midline commissures. MI presence was identified in an unrelated sample using the Human Connectome Project database. MI structural connectivity maps were created and gray matter target regions were identified using probabilistic tractography of the whole brain. Probabilistic tractography revealed an extensive network of connections between MI and limbic, frontal and temporal lobes as well as insula and pericalcarine cortices. Women compared to men had stronger connectivity via their MI. The presented results support the role of MI as a midline commissure with strong connectivity to the amygdala, hippocampus, and entorhinal cortex., Structural connectivity of human massa intermedia (MI) was studied with probabilsitc tractography. The presented results support the role of MI as a midline commissure with strong connectivity to the amygdala, hippocampus, and entorhinal cortex.

We analysed features relevant for differentiation between males and females based on the data available from the Human Connectome Project (HCP) S1200 dataset. We used 354 features containing cognitive and emotional measures as well as measures derived from task functional magnetic resonance imaging (MRI) and structural brain MRI. The paper presents a thorough analysis of this extensive set of features using a machine learning approach with a goal of identifying features that have the ability to differentiate between males and females. We used two state of the art classification algorithms with different properties: support vector machine (SVM) and random forest classifier (RFC). For each classifier the hyperparameters were obtained and classifiers were optimized using nested cross validation and grid search. This resulted in the classification performance of 91% and 89% accuracy using SVM and RFC, respectively. Using SHAP (SHapley Additive exPlanations) method we obtained relevance of features as indicators of sex differences and identified features with high discriminative power for sex classification. The majority of top features were brain morphological measures, and only a small proportion were features related to cognitive performance. Our results demonstrate the importance and advantages of using a machine learning approach when analysing sex differences.

Network neuroscience employs graph theory to investigate the human brain as a complex network, and derive generalizable insights about the brain’s network properties. However, graph-theoretical results obtained from network construction pipelines that produce idiosyncratic networks may not generalize when alternative pipelines are employed. This issue is especially pressing because a wide variety of network construction pipelines have been employed in the human network neuroscience literature, making comparisons between studies problematic. Here, we investigate how to produce networks that are maximally representative of the broader set of brain networks obtained from the same neuroimaging data. We do so by minimizing an information-theoretic measure of divergence between network topologies, known as the portrait divergence. Based on functional and diffusion MRI data from the Human Connectome Project, we consider anatomical, functional, and multimodal parcellations at three different scales, and 48 distinct ways of defining network edges. We show that the highest representativeness can be obtained by using parcellations in the order of 200 regions and filtering functional networks based on efficiency-cost optimization—though suitable alternatives are also highlighted. Overall, we identify specific node definition and thresholding procedures that neuroscientists can follow in order to derive representative networks from their human neuroimaging data.

Generative Adversarial Networks (GAN) are emerging as an exciting training paradigm which promises a step improvement to the impressive feature learning capabilities of deep neural networks. Unlike supervised learning approaches, GAN learns generalizable features without requiring labeled images to achieve new capabilities like distinguishing previously unseen anomalies, creating novel instances of data and factorizing learned features into explainable dimensions in fully unsupervised fashion. The advanced feature learning property of GAN will enable the next generation of computational image understanding tasks. However, GAN models are difficult to train to converge towards good models, especially for high resolution and high dimensional datasets like image volumes. We develop a GAN approach to learn a generative model of T1-contrast 3D MRI image volumes of the healthy human brain by training on 1112 MRI images from the Human Connectome Project. Our method utilizes a first unconditional Super-Resolution GAN, dubbed the shape network, to learn the 3D shape variations in adult brains and a second conditional pix2pix GAN, dubbed the texture network, to upgrade image slices with realistic local contrast patterns. Novel 3D MRI images are synthesized by first applying the 3D voxel-wise deformation map which is generated from the shape network to deform the Montreal Neurological Institute (MNI) brain template and subsequently performing style transfer on axial-wise slices using the texture network. The Maximum Mean Discrepancy (MMD) and Multi-scale Structural Similarity Index Measure (MS-SSIM) scores of MRI image volumes synthesized using our GAN approach are competitive with state-of-art GAN methods. Our work establishes the feasibility of an alternative approach to high-dimensional GAN learning - splitting the type of information content learned among several GANs can be an effective form of regularization and complementary to latent code shaping or super-resolution approaches in state-of-the-art methods.

Open data initiatives such as the UK Biobank and Human Connectome Project provide researchers with access to neuroimaging, genetic, and other data for large samples of left-and right-handed participants, allowing for more robust investigations of handedness than ever before. Handedness inventories are universal tools for assessing participant handedness in these large-scale neuroimaging contexts. These self-report measures are typically used to screen and recruit subjects, but they are also widely used as variables in statistical analyses of fMRI and other data. Recent investigations into the validity of handedness inventories, however, suggest that self-report data from these inventories might not reflect hand preference/performance as faithfully as previously thought. Using data from the Human Connectome Project, we assessed correspondence between three handedness measures – the Edinburgh Handedness Inventory (EHI), the Rolyan 9-hole pegboard, and grip strength – in 1179 healthy subjects. We show poor association between the different handedness measures, with roughly 10% of the sample having at least one behavioral measure which indicates hand-performance bias opposite to the EHI score, and over 65% of left-handers having one or more mismatched handedness scores. We discuss implications for future work, urging researchers to critically consider direction, degree, and consistency of handedness in their data.

Memory deficits are observed in a range of psychiatric disorders, but it is unclear whether memory deficits arise from a shared brain correlate across disorders or from various dysfunctions unique to each disorder. Connectome-based predictive modeling is a computational method that captures individual differences in functional connectomes associated with behavioral phenotypes such as memory. We used publicly available task-based functional MRI data from patients with schizophrenia (n = 33), bipolar disorder (n = 34), attention deficit hyper-activity disorder (n = 32), and healthy controls (n = 73) to model the macroscale brain networks associated with working, short- and long-term memory. First, we use 10-fold and leave-group-out analyses to demonstrate that the same macroscale brain networks subserve memory across diagnostic groups and that individual differences in memory performance are related to individual differences within networks distributed throughout the brain, including the subcortex, default mode network, limbic network, and cerebellum. Next, we show that diagnostic groups are associated with significant differences in whole-brain functional connectivity that are distinct from the predictive models of memory. Finally, we show that models trained on the transdiagnostic sample generalize to novel, healthy participants (n = 515) from the Human Connectome Project. These results suggest that despite significant differences in whole-brain patterns of functional connectivity between diagnostic groups, the core macroscale brain networks that subserve memory are shared.

Pre‐clinical and human neuroimaging research implicates the extended‐amygdala (ExtA) (including the bed nucleus of the stria terminalis [BST] and central nucleus of the amygdala [CeA]) in networks mediating negative emotional states associated with stress and substance‐use behaviours. The extent to which individual ExtA structures form a functionally integrated unit is controversial. We utilised a large sample (n > 1,000 healthy young adult humans) to compare the intrinsic functional connectivity networks (ICNs) of the BST and CeA using task‐free functional magnetic resonance imaging (fMRI) data from the Human Connectome Project. We assessed whether inter‐individual differences within these ICNs were related to two principal components representing negative disposition and alcohol use. Building on recent primate evidence, we tested whether within BST‐CeA intrinsic functional connectivity (iFC) was heritable and further examined co‐heritability with our principal components. We demonstrate the BST and CeA to have discrete, but largely overlapping ICNs similar to previous findings. We found no evidence that within BST—CeA iFC was heritable; however, post hoc analyses found significant BST iFC heritability with the broader superficial and centromedial amygdala regions. There were no significant correlations or co‐heritability associations with our principal components either across the ICNs or for specific BST‐Amygdala iFC. Possible differences in phenotype associations across task‐free, task‐based, and clinical fMRI are discussed, along with suggestions for more causal investigative paradigms that make use of the now well‐established ExtA ICNs., We examine the intrinsic connectivity networks of two key nodes within the extended amygdala in a large human sample. We find no associations within this network with principal components related to negative disposition or alcohol use traits. We report heritable intrinsic functional connections between the bed nucleus of the stria terminalis (BST) and the centromedial and superficial amygdala regions; however, do not repeat a recent non‐human primate finding of a heritable connection between the BST and central nucleus of the amygdala.

Background The Papez circuit was first described as the anatomic basis of emotion. Subsequent studies consolidated recognition of its limbic activities but showed a more important role in memory. Anatomic dissections and advanced neuroimaging have deepened our understanding of the various interconnections and white matter tracts present in this circuit. The aim of our study is to describe the anatomy of the Papez circuit through cadaveric dissection with correlation to ultrahigh-field magnetic resonance imaging (MRI) and MRI tractography. Methods White fiber dissection was performed on 5 cadaveric human brain hemispheres. The Papez circuit was dissected mediolaterally to show its anatomy and relation to other nuclei and tracts. Open-source MRI tractography data from the Human Connectome Project is combined with ultrahigh resolution, 7T structural MRI, and 17.6T diffusion tractography to further show the anatomy. Results The network connecting the anterior and posterior cingulate, entorhinal cortex, hippocampus, fimbria, dentate gyrus, fornix, mammillary bodies, and anterior thalamus was described using white matter fiber dissection and compared with MRI tractography and ultrahigh-field structural and diffusion MRI. We showed for the first time (through portmortem dissection) fibers directly connecting the anterior thalamic nucleus and the subgenual cingulate via the septal area. Conclusions The description of the anatomy of the Papez circuit through cadaveric dissection and comparisons with advanced neuroimaging studies allow a better understanding of its three-dimensional spatial layout, in addition to showing new areas of connectivity with adjacent structures and possibilities for surgical approaches or stimulation.

Early course schizophrenia is associated with reduced gray matter. The specific structures affected first and how deficits impact symptoms and cognition remain unresolved. We used the Human Connectome Project multimodal parcellation (HCP‐MMP) to precisely identify cortical areas and investigate thickness abnormalities in discovery and replication samples of first‐episode schizophrenia spectrum individuals (FESz). In the discovery sample, T1w scans were acquired from 31 FESz and 31 matched healthy controls (HC). Thickness was calculated for 360 regions in Freesurfer. In the replication sample, high‐resolution T1w, T2w, and BOLD‐rest scans were acquired from 23 FESz and 32 HC and processed with HCP protocols. Thickness was calculated for regions significant in the discovery sample. After FDR correction (q, The early course schizophrenia is associated with reduced gray matter, but the specific structures affected first and how gray matter deficits impact symptoms and cognition remain unresolved. The novel use of the Human Connectome Project multimodal parcellation in two independent first episode schizophrenia‐spectrum samples revealed thinner bilateral parahippocampal area 3, suggesting this byway between cortical and limbic processing is a critical site of pathology near the emergence of psychosis.

Background The inferior temporal gyrus (ITG) is known to be involved in high-cognitive functions, including visual and language comprehensions and emotion regulation. A detailed understanding of the nature of association fibers could significantly improve postoperative morbidity related to declining capacity. Through diffusion spectrum imaging−based fiber tracking, we have characterized these connections on the basis of their relationships to other cortical areas. Methods Diffusion spectrum images from 10 healthy adults of the Human Connectome Project were randomly selected and used for tractography analysis. We evaluated the ITG as a whole based on connectivity with other regions. All ITG tracts were mapped in both hemispheres, and a lateralization index was calculated with resultant tract volumes. Results We identified 5 major connections of the ITG: U-fiber, inferior longitudinal fasciculus, vertical occipital fasciculus, arcuate fasciculus, and uncinate fasciculus. There was no fiber lateralization detected. Conclusions This study highlights the principal white-matter pathways of the ITG and demonstrates key underlying connections. We present a summary of the relevant clinical anatomy for this region of the cerebrum as part of a larger effort to understand it in its entirety.