Parcellating an individual subject's cortical and subcortical brain structures using snowball sampling of resting-state correlations

Variation in brain aging: A review and perspective on the utility of individualized approaches to the study of functional networks in aging

Healthy aging is associated with cognitive decline across multiple domains, including executive function, memory, and attention. These cognitive changes can often influence an individual's ability to function and quality of life. However, the degree to which individuals experience cognitive decline, as well as the trajectory of these changes, exhibits wide variability across people. These cognitive abilities are thought to depend on the coordinated activity of large-scale networks. Like behavioral effects, large variation can be seen in brain structure and function with aging, including in large-scale functional networks. However, tracking this variation requires methods that reliably measure individual brain networks and their changes over time. Here, we review the literature on age-related cognitive decline and on age-related differences in brain structure and function. We focus particularly on functional networks and the individual variation that exists in these measures. We propose that novel individual-centered fMRI approaches can shed new light on patterns of inter- and intra-individual variability in aging. These approaches may be instrumental in understanding the neural bases of cognitive decline.

Connectional axis of individual functional variability: Patterns, structural correlates, and relevance for development and cognition

The human cerebral cortex exhibits intricate interareal functional synchronization at the macroscale, with substantial individual variability in these functional connections. However, the spatial organization of functional connectivity (FC) variability across the human connectome edges and its significance in cognitive development remain unclear. Here, we identified a connectional axis in the edge-level FC variability. The variability declined continuously along this axis from within-network to between-network connections, and from the edges linking association networks to those linking the sensorimotor and association networks. This connectional axis of functional variability is associated with spatial pattern of structural connectivity variability. Moreover, the connectional variability axis evolves in youth with an increasing flatter axis slope. We also observed that the slope of connectional variability axis was positively related to the performance in the higher-order cognition. Together, our results reveal a connectional axis in functional variability that is linked with structural connectome variability, refines during development, and is relevant to cognition.

Classification of Major Depressive Disorder Using Vertex-Wise Brain Sulcal Depth, Curvature, and Thickness with a Deep and a Shallow Learning Model

Major depressive disorder (MDD) is a complex psychiatric disorder that affects the lives of hundreds of millions of individuals around the globe. Even today, researchers debate if morphological alterations in the brain are linked to MDD, likely due to the heterogeneity of this disorder. The application of deep learning tools to neuroimaging data, capable of capturing complex non-linear patterns, has the potential to provide diagnostic and predictive biomarkers for MDD. However, previous attempts to demarcate MDD patients and healthy controls (HC) based on segmented cortical features via linear machine learning approaches have reported low accuracies. In this study, we used globally representative data from the ENIGMA-MDD working group containing 7,012 participants from 30 sites (N=2,772 MDD and N=4,240 HC), which allows a comprehensive analysis with generalizable results. Based on the hypothesis that integration of vertex-wise cortical features can improve classification performance, we evaluated the classification of a DenseNet and a Support Vector Machine (SVM), with the expectation that the former would outperform the latter. As we analyzed a multi-site sample, we additionally applied the ComBat harmonization tool to remove potential nuisance effects of site. We found that both classifiers exhibited close to chance performance (balanced accuracy DenseNet: 51%; SVM: 53%), when estimated on unseen sites. Slightly higher classification performance (balanced accuracy DenseNet: 58%; SVM: 55%) was found when the cross-validation folds contained subjects from all sites, indicating site effect. In conclusion, the integration of vertex-wise morphometric features and the use of the non-linear classifier did not lead to the differentiability between MDD and HC. Our results support the notion that MDD classification on this combination of features and classifiers is unfeasible. Future studies are needed to determine whether more sophisticated integration of information from other MRI modalities such as fMRI and DWI will lead to a higher performance in this diagnostic task.

Functional architecture of cerebral cortex during naturalistic movie watching

Characterizing the functional organization of cerebral cortex is a fundamental step in understanding how different kinds of information are processed in the brain. However, it is still unclear how these areas are organized during naturalistic visual and auditory stimulation. Here, we used high-resolution functional MRI data from 176 human subjects to map the macro-architecture of the entire cerebral cortex based on responses to a 60-min audiovisual movie stimulus. A data-driven clustering approach revealed a map of 24 functional areas/networks, each explicitly linked to a specific aspect of sensory or cognitive processing. Novel features of this map included an extended scene-selective network in the lateral prefrontal cortex, separate clusters responsive to human-object and human-human interaction, and a push-pull interaction between three executive control (domain-general) networks and domain-specific regions of the visual, auditory, and language cortex. Our cortical parcellation provides a comprehensive and unified map of functionally defined areas in the human cerebral cortex.

CoCoNest: A continuous structural connectivity-based nested family of parcellations of the human cerebral cortex

Despite the widespread exploration and availability of parcellations for the functional connectome, parcellations designed for the structural connectome are comparatively limited. Current research suggests that there may be no single "correct" parcellation and that the human brain is intrinsically a multiresolution entity. In this work, we propose the Continuous Structural Connectivitity-based, Nested (CoCoNest) family of parcellations-a fully data-driven, multiresolution family of parcellations derived from structural connectome data. The CoCoNest family is created using agglomerative (bottom-up) clustering and error-complexity pruning, which strikes a balance between the complexity of each parcellation and how well it preserves patterns in vertex-level, high-resolution connectivity data. We draw on a comprehensive battery of internal and external evaluation metrics to show that the CoCoNest family is competitive with or outperforms widely used parcellations in the literature. Additionally, we show how the CoCoNest family can serve as an exploratory tool for researchers to investigate the multiresolution organization of the structural connectome.

Large-scale meta-analyses and network analyses of neural substrates underlying human escalated aggression

Escalated aggression represents a frequent and severe form of violence, sometimes manifesting as antisocial behavior. Driven by the pressures of modern life, escalated aggression is of particular concern due to its rising prevalence and its destructive impact on both individual well-being and socioeconomic stability. However, a consistent neural circuitry underpinning it remains to be definitively identified. Here, we addressed this issue by comparing brain alterations between individuals with escalated aggression and those without such behavioral manifestations. We first conducted a meta-analysis to synthesize previous neuroimaging studies on functional and structural alterations of escalated aggression (325 experiments, 2997 foci, 16,529 subjects). Following-up network and functional decoding analyses were conducted to provide quantitative characterizations of the identified brain regions. Our results revealed that brain regions constantly involved in escalated aggression were localized in the subcortical network (amygdala and lateral orbitofrontal cortex) associated with emotion processing, the default mode network (dorsal medial prefrontal cortex and middle temporal gyrus) associated with mentalizing, and the salience network (anterior cingulate cortex and anterior insula) associated with cognitive control. These findings were further supported by additional meta-analyses on emotion processing, mentalizing, and cognitive control, all of which showed conjunction with the brain regions identified in the escalated aggression. Together, these findings advance the understanding of the risk biomarkers of escalated aggressive populations and refine theoretical models of human aggression.

Individual connectivity-based parcellations reflect functional properties of human auditory cortex

Neuroimaging studies of the functional organization of human auditory cortex have focused on group-level analyses to identify tendencies that represent the typical brain. Here, we mapped auditory areas of the human superior temporal cortex (STC) in 30 participants by combining functional network analysis and 1-mm isotropic resolution 7T functional magnetic resonance imaging (fMRI). Two resting-state fMRI sessions, and one or two auditory and audiovisual speech localizer sessions, were collected on 3-4 separate days. We generated a set of functional network-based parcellations from these data. Solutions with 4, 6, and 11 networks were selected for closer examination based on local maxima of Dice and Silhouette values. The resulting parcellation of auditory cortices showed high intraindividual reproducibility both between resting state sessions (Dice coefficient: 69-78%) and between resting state and task sessions (Dice coefficient: 62-73%). This demonstrates that auditory areas in STC can be reliably segmented into functional subareas. The interindividual variability was significantly larger than intraindividual variability (Dice coefficient: 57%-68%, p<0.001), indicating that the parcellations also captured meaningful interindividual variability. The individual-specific parcellations yielded the highest alignment with task response topographies, suggesting that individual variability in parcellations reflects individual variability in auditory function. Connectional homogeneity within networks was also highest for the individual-specific parcellations. Furthermore, the similarity in the functional parcellations was not explainable by the similarity of macroanatomical properties of auditory cortex. Our findings suggest that individual-level parcellations capture meaningful idiosyncrasies in auditory cortex organization.

pNet: A toolbox for personalized functional networks modeling

Personalized functional networks (FNs) derived from functional magnetic resonance imaging (fMRI) data are useful for characterizing individual variations in the brain functional topography associated with the brain development, aging, and disorders. To facilitate applications of the personalized FNs with enhanced reliability and reproducibility, we develop an open-source toolbox that is user-friendly, extendable, and includes rigorous quality control (QC), featuring multiple user interfaces (graphics, command line, and a step-by-step guideline) and job-scheduling for high performance computing (HPC) clusters. Particularly, the toolbox, named personalized functional network modeling (pNet), takes fMRI inputs in either volumetric or surface type, ensuring compatibility with multiple fMRI data formats, and computes personalized FNs using two distinct modeling methods: one method optimizes the functional coherence of FNs, while the other enhances their independence. Additionally, the toolbox provides HTML-based reports for QC and visualization of personalized FNs. The toolbox is developed in both MATLAB and Python platforms with a modular design to facilitate extension and modification by users familiar with either programming language. We have evaluated the toolbox on two fMRI datasets and demonstrated its effectiveness and user-friendliness with interactive and scripting examples. pNet is publicly available at https://github.com/MLDataAnalytics/pNet.

Reconfiguration of the costly punishment network architecture in punishment decision-making

Human costly punishment is rooted in multiple regions across large-scale functional systems, a collection of which constitutes the costly punishment network (CPN). Our previous study found that the CPN is intrinsically organized in an optimized and reliable manner to support individual costly punishment propensity. However, it remains unknown how the CPN is reconfigured in response to external cognitive demands in punishment decision-making. Here, we combined resting-state and task-functional magnetic resonance imaging to examine the task-related reconfigurations of intrinsic organizations of the CPN when participants made decisions of costly punishment in the Ultimatum Game. Although a strong consistency was observed in the overall pattern and each nodal profile between the intrinsic (task-free) and extrinsic (task-evoked) functional connectivity of the CPN, condition-general and condition-specific reconfigurations were also evident. Specifically, both unfair and fair conditions induced increases in functional connectivity between a few specific pairs of regions, and the unfair condition additionally induced increases in network efficiency of the CPN. Intriguingly, the specific changes in global efficiency of the CPN in the unfair condition were associated with individual differences in costly punishment after adjusting for the corresponding results in the fair condition, which were further identified for females but not for males. These findings were largely reproducible on independent samples. Collectively, our findings provide novel insights into how the CPN adaptively reconfigures its network architecture to support costly punishment.

Concordant dynamic changes of global network properties in the frontoparietal and limbic compartments: An EEG study

INTRODUCTION

Despite its complexity, deciphering nodal interaction is imperative to understanding a neural network. Network interaction is an even more complicated topic that must be addressed. This study aimed to examine the relationship between the brain waves of two canonical brain structures, i.e., the frontoparietal and limbic compartments, during a resting state.

METHODS

Electroencephalography (EEG) of 51 subjects in eye-closed condition was analyzed, and the eLORETA method was applied to convert the signals from the scalp to the brain. By way of community detection, representative neural nodes and the associated mean activities were retrieved. Total and lagged coherences were computed to indicate functional connectivity between those neural nodes. Two global network properties were elucidated based on the connectivity measures, i.e., global efficiency and mean functional connectivity strength. The temporal correlation of the global network indices between the two studied networks was explored.

RESULTS

It was found that there was a significant trend of positive correlation across the four metrics (lagged vs. total coherence x global efficiency vs. average connectivity). In other words, when the neural interaction in the FP network was stronger, so did that in the limbic network, and vice versa. Notably, the above interaction was not spectrally specific and only existed at a finer temporal scale (under hundreds of milliseconds level).

CONCLUSION

The concordant change in network properties indicates an intricate balance between FP and LM compartments. Possible mechanisms and implications for the findings are discussed.

Top-down threat bias in pain perception is predicted by higher segregation between resting-state networks

Top-down processes such as expectations have a strong influence on pain perception. Predicted threat of impending pain can affect perceived pain even more than the actual intensity of a noxious event. This type of threat bias in pain perception is associated with fear of pain and low pain tolerance, and hence the extent of bias varies between individuals. Large-scale patterns of functional brain connectivity are important for integrating expectations with sensory data. Greater integration is necessary for sensory integration; therefore, here we investigate the association between system segregation and top-down threat bias in healthy individuals. We show that top-down threat bias is predicted by less functional connectivity between resting-state networks. This effect was significant at a wide range of network thresholds and specifically in predefined parcellations of resting-state networks. Greater system segregation in brain networks also predicted higher anxiety and pain catastrophizing. These findings highlight the role of integration in brain networks in mediating threat bias in pain perception.

A personalized cortical atlas for functional regions of interest

Advances in functional MRI (fMRI) allow mapping an individual's brain function in vivo. Task fMRI can localize domain-specific regions of cognitive processing or functional regions of interest (fROIs) within an individual. Moreover, data from resting state (no task) fMRI can be used to define an individual's connectome, which can characterize that individual's functional organization via connectivity-based parcellations. However, can connectivity-based parcellations alone predict an individual's fROIs? Here, we describe an approach to compute individualized rs-fROIs (i.e., regions that correspond to given fROI constructed using only resting state data) for motor control, working memory, high-level vision, and language comprehension. The rs-fROIs were computed and validated using a large sample of young adults (n = 1,018) with resting state and task fMRI from the Human Connectome Project. First, resting state parcellations were defined across a sequence of resolutions from broadscale to fine-grained networks in a training group of 500 individuals. Second, 21 rs-fROIs were defined from the training group by identifying the rs network that most closely matched task-defined fROIs across all individuals. Third, the selectivity of rs-fROIs was investigated in a training set of the remaining 518 individuals. All computed rs-fROIs were indeed selective for their preferred category. Critically, the rs-fROIs had higher selectivity than probabilistic atlas parcels for nearly all fROIs. In conclusion, we present a potential approach to define selective fROIs on an individual-level circumventing the need for multiple task-based localizers.NEW & NOTEWORTHY We compute individualized resting state parcels that identify an individual's own functional regions of interest (fROIs) for high-level vision, language comprehension, motor control, and working memory, using only their functional connectome. This approach demonstrates a rapid and powerful alternative for finding a large set of fROIs in an individual, using only their unique connectivity pattern, which does not require the costly acquisition of multiple fMRI localizer tasks.

Dissociable contributions of the medial parietal cortex to recognition memory

Human neuroimaging studies of episodic memory retrieval routinely observe the engagement of specific cortical regions beyond the medial temporal lobe. Of these, medial parietal cortex (MPC) is of particular interest given its ubiquitous, and yet distinct, functional characteristics during different types of retrieval tasks. Specifically, while recognition memory and autobiographical recall tasks are both used to probe episodic retrieval, these paradigms consistently drive distinct patterns of response within MPC. This dissociation adds to growing evidence suggesting a common principle of functional organization across memory related brain structures, specifically regarding the control or content demands of memory-based decisions. To carefully examine this putative organization, we used a high-resolution fMRI dataset collected at ultra-high field (7T) while subjects performed thousands of recognition-memory trials to identify MPC regions responsive to recognition-decisions or semantic content of stimuli within and across individuals. We observed interleaving, though distinct, functional subregions of MPC where responses were sensitive to either recognition decisions or the semantic representation of stimuli, but rarely both. In addition, this functional dissociation within MPC was further accentuated by distinct profiles of connectivity bias with the hippocampus during task and rest. Finally, we show that recent observations of person and place selectivity within MPC reflect category specific responses from within identified semantic regions that are sensitive to mnemonic demands. Together, these data better account for how distinct patterns of MPC responses can occur as a result of task demands during episodic retrieval and may reflect a common principle of organization throughout hippocampal-neocortical memory systems.

Subject-specific whole-brain parcellations of nodes and boundaries are modulated differently under 10 Hz rTMS

Repetitive transcranial magnetic stimulation (rTMS) has gained considerable importance in the treatment of neuropsychiatric disorders, including major depression. However, it is not yet understood how rTMS alters brain's functional connectivity. Here we report changes in functional connectivity captured by resting state functional magnetic resonance imaging (rsfMRI) within the first hour after 10 Hz rTMS. We apply subject-specific parcellation schemes to detect changes (1) in network nodes, where the strongest functional connectivity of regions is observed, and (2) in network boundaries, where functional transitions between regions occur. We use support vector machine (SVM), a widely used machine learning algorithm that is robust and effective, for the classification and characterization of time intervals of changes in node and boundary maps. Our results reveal that changes in connectivity at the boundaries are slower and more complex than in those observed in the nodes, but of similar magnitude according to accuracy confidence intervals. These results were strongest in the posterior cingulate cortex and precuneus. As network boundaries are indeed under-investigated in comparison to nodes in connectomics research, our results highlight their contribution to functional adjustments to rTMS.

Graph-matching distance between individuals' functional connectomes varies with relatedness, age, and cognitive score

Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter-individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome ProjectN = 997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher-order networks, that is, default-mode and fronto-parietal, that underlie executive function and memory. These higher-order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter-subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior.

Individual-specific functional connectivity improves prediction of Alzheimer's disease's symptoms in elderly people regardless of APOE ε4 genotype

To date, reliable biomarkers remain unclear that could link functional connectivity to patients' symptoms for detecting and predicting the process from normal aging to Alzheimer's disease (AD) in elderly people with specific genotypes. To address this, individual-specific functional connectivity is constructed for elderly participants with/without APOE ε4 allele. Then, we utilize recursive feature selection-based machine learning to reveal individual brain-behavior relationships and to predict the symptom transition in different genotypes. Our findings reveal that compared with conventional atlas-based functional connectivity, individual-specific functional connectivity exhibits higher classification and prediction performance from normal aging to AD in both APOE ε4 groups, while no significant performance is detected when the data of two genotyping groups are combined. Furthermore, individual-specific between-network connectivity constitutes a major contributor to assessing cognitive symptoms. This study highlights the essential role of individual variation in cortical functional anatomy and the integration of brain and behavior in predicting individualized symptoms.

Reorganized Hubs of Brain Functional Networks after Acute Mild Traumatic Brain Injury

Mild traumatic brain injury (mTBI)-associated damage to hub regions can lead to disrupted modular structures of functional brain networks and may result in widespread cognitive and behavioral deficits. The spatial layout of brain connections and modules is essential for understanding the reorganization of brain networks to trauma. We investigated the roles of hubs in inter-subnetwork information coordination and integration using participation coefficients (PCs) in 74 patients with acute mTBI and 51 matched healthy controls. In some brain networks, such as default mode network (DMN) and frontoparietal network (FPN), mild TBI patients had decreased PC levels, while this measure was saliently increased in patients in other networks, such as the visual network. The hub disruption index was defined as the gradient of a straight line fitted to scatterplots of individual mTBI in participation coefficient versus mean participation coefficient of healthy groups. There was a trend of radical reorganization of some efficient "hub" nodes in patients (κ = -0.15), compared with controls (κ close to 0). The PC of brain hubs can also differentiate mTBI patients from controls with an 88% accuracy, and decreased PC levels in FPN can predict patient' s worse cognitive information processing speed (r = 0.36, p < 0.002) and working memory performance (r = 0.35, p < 0.002). Reduced PC within the DMN was associated with patients' complaints of post-concussion symptoms (r = -0.35, p < 0.002). This evidence suggests a trend of spatial transition of hub profiles in acute mTBI, and graph metrics of PC measures can be used as potential diagnostic biomarkers.

Regional Topological Aberrances of White Matter- and Gray Matter-Based Functional Networks for Attention Processing May Foster Traumatic Brain Injury-Related Attention Deficits in Adults

Traumatic brain injury (TBI) is highly prevalent in adults. TBI-related functional brain alterations have been linked with common post-TBI neurobehavioral sequelae, with unknown neural substrates. This study examined the systems-level functional brain alterations in white matter (WM) and gray matter (GM) for visual sustained-attention processing, and their interactions and contributions to post-TBI attention deficits. Task-based functional MRI data were collected from 42 adults with TBI and 43 group-matched normal controls (NCs), and analyzed using the graph theoretic technique. Global and nodal topological properties were calculated and compared between the two groups. Correlation analyses were conducted between the neuroimaging measures that showed significant between-group differences and the behavioral symptom measures in attention domain in the groups of TBI and NCs, respectively. Significantly altered nodal efficiencies and/or degrees in several WM and GM nodes were reported in the TBI group, including the posterior corona radiata (PCR), posterior thalamic radiation (PTR), postcentral gyrus (PoG), and superior temporal sulcus (STS). Subjects with TBI also demonstrated abnormal systems-level functional synchronization between the PTR and STS in the right hemisphere, hypo-interaction between the PCR and PoG in the left hemisphere, as well as the involvement of systems-level functional aberrances in the PCR in TBI-related behavioral impairments in the attention domain. The findings of the current study suggest that TBI-related systems-level functional alterations associated with these two major-association WM tracts, and their anatomically connected GM regions may play critical role in TBI-related behavioral deficits in attention domains.

Automatic parcellation of resting-state cortical dynamics by iterative community detection and similarity measurements

To investigate the properties of a large-scale brain network, it is a common practice to reduce the dimension of resting state functional magnetic resonance imaging (rs-fMRI) data to tens to hundreds of nodes. This study presents an analytic streamline that incorporates modular analysis and similarity measurements (MOSI) to fulfill functional parcellation (FP) of the cortex. MOSI is carried out by iteratively dividing a module into sub-modules (via the Louvain community detection method) and unifying similar neighboring sub-modules into a new module (adjacent sub-modules with a similarity index <0.05) until the brain modular structures of successive runs become constant. By adjusting the gamma value, a parameter in the Louvain algorithm, MOSI may segment the cortex with different resolutions. rs-fMRI scans of 33 healthy subjects were selected from the dataset of the Rockland sample. MOSI was applied to the rs-fMRI data after standardized pre-processing steps. The results indicate that the parcellated modules by MOSI are more homogeneous in content. After reducing the grouped voxels to representative neural nodes, the network structures were explored. The resultant network components were comparable with previous reports. The validity of MOSI in achieving data reduction has been confirmed. MOSI may provide a novel starting point for further investigation of the network properties of rs-fMRI data. Potential applications of MOSI are discussed.

A sub+cortical fMRI-based surface parcellation

Both cortical and subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Mapping these areas is of fundamental importance to neuroscience. A central obstacle to this task is the inaccuracy associated with bringing results from individuals into a common space. The vast individual differences in morphology pose a serious problem for volumetric registration. Surface‐based approaches fare substantially better, but have thus far been used only for cortical parcellation, leaving subcortical parcellation in volumetric space. We extend the surface‐based approach to include also the subcortical deep gray‐matter structures, thus achieving a uniform representation across both cortex and subcortex, suitable for use with surface‐based metrics that span these structures, for example, white/gray contrast. Using data from the Enhanced Nathan Klein Institute—Rockland Sample, limited to individuals between 19 and 69 years of age, we generate a functional parcellation of both the cortical and subcortical surfaces. To assess this extended parcellation, we show that (a) our parcellation provides greater homogeneity of functional connectivity patterns than do arbitrary parcellations matching in the number and size of parcels; (b) our parcels align with known cortical and subcortical architecture; and (c) our extended functional parcellation provides an improved fit to the complexity of life‐span (6–85 years) changes in white/gray contrast data compared to arbitrary parcellations matching in the number and size of parcels, supporting its use with surface‐based measures. We provide our extended functional parcellation for the use of the neuroimaging community.

Big Data-Driven Brain Parcellation from fMRI: Impact of Cohort Heterogeneity on Functional Connectivity Maps

Ongoing large-scale human brain studies are generating complex neuroimaging data from thousands of individuals that can be leveraged to derive data-driven, anatomically accurate brain parcellations. However, despite their promise and many strengths, these data are highly heterogeneous, a characteristic that may affect the anatomical accuracy and generalization of the template but has received relatively little attention. Using multiple similarity measures and thresholding approaches, this study investigated the topological intra- and inter-individual variability of restingstate (rs) functional edge maps (often used for brain parcellation), estimated from rs-fMRI connectivity in n = 5878 children from the Adolescent Brain Cognitive Development (ABCD) study. Findings from this initial investigation indicate that choosing a subject- vs cohort-based threshold for estimating edge maps from connectivity matrices does not significantly impact the map topology. In contrast, the choice of similarity measure and non-linear relationship between similarity and edge map sparsity may have a significant impact on map classification and the generation of parcellation atlases. Multi-level classification revealed multiple clusters with a potentially complex mapping onto biological variables beyond simple demographics.Clinical Relevance- Case-control neuroimaging studies should use domain-specific (e.g., demographics-specific) atlases for parcellating the brain, to improve accuracy and rigor of cohort comparisons. To be generalizable, such atlases need to be derived from large datasets, which are inherently heterogeneous. In a cohort of 5878 children (age ~9-10 years), this study systematically assessed the impact of heterogeneity and similarity of edge maps, which are derived from rs-fMRI connectivity and typically used to generate parcellation atlases.

Constructing Connectome Atlas by Graph Laplacian Learning

The recent development of neuroimaging technology and network theory allows us to visualize and characterize the whole-brain functional connectivity in vivo. The importance of conventional structural image atlas widely used in population-based neuroimaging studies has been well verified. Similarly, a "common" brain connectivity map (also called connectome atlas) across individuals can open a new pathway to interpreting disorder-related brain cognition and behaviors. However, the main obstacle of applying the classic image atlas construction approaches to the connectome data is that a regular data structure (such as a grid) in such methods breaks down the intrinsic geometry of the network connectivity derived from the irregular data domain (in the setting of a graph). To tackle this hurdle, we first embed the brain network into a set of graph signals in the Euclidean space via the diffusion mapping technique. Furthermore, we cast the problem of connectome atlas construction into a novel learning-based graph inference model. It can be constructed by iterating the following processes: (1) align all individual brain networks to a common space spanned by the graph spectrum bases of the latent common network, and (2) learn graph Laplacian of the common network that is in consensus with all aligned brain networks. We have evaluated our novel method for connectome atlas construction in comparison with non-learning-based counterparts. Based on experiments using network connectivity data from populations with neurodegenerative and neuropediatric disorders, our approach has demonstrated statistically meaningful improvement over existing methods.

Standardizing human brain parcellations

Using brain atlases to localize regions of interest is a requirement for making neuroscientifically valid statistical inferences. These atlases, represented in volumetric or surface coordinate spaces, can describe brain topology from a variety of perspectives. Although many human brain atlases have circulated the field over the past fifty years, limited effort has been devoted to their standardization. Standardization can facilitate consistency and transparency with respect to orientation, resolution, labeling scheme, file storage format, and coordinate space designation. Our group has worked to consolidate an extensive selection of popular human brain atlases into a single, curated, open-source library, where they are stored following a standardized protocol with accompanying metadata, which can serve as the basis for future atlases. The repository containing the atlases, the specification, as well as relevant transformation functions is available in the neuroparc OSF registered repository or https://github.com/neurodata/neuroparc .

Integrative frontal-parietal dynamics supporting cognitive control

Coordinating among the demands of the external environment and internal plans requires cognitive control supported by a fronto-parietal control network (FPCN). Evidence suggests that multiple control systems span the FPCN whose operations are poorly understood. Previously (Nee and D'Esposito, 2016; 2017), we detailed frontal dynamics that support control processing, but left open their role in broader cortical function. Here, I show that the FPCN consists of an external/present-oriented to internal/future-oriented cortical gradient extending outwardly from sensory-motor cortices. Areas at the ends of this gradient act in a segregative manner, exciting areas at the same level, but suppressing areas at different levels. By contrast, areas in the middle of the gradient excite areas at all levels, promoting integration of control processing. Individual differences in integrative dynamics predict higher level cognitive ability and amenability to neuromodulation. These data suggest that an intermediary zone within the FPCN underlies integrative processing that supports cognitive control.

Deep Learning-based Classification of Resting-state fMRI Independent-component Analysis

Functional connectivity analyses of fMRI data have shown that the activity of the brain at rest is spatially organized into resting-state networks (RSNs). RSNs appear as groups of anatomically distant but functionally tightly connected brain regions. Inter-RSN intrinsic connectivity analyses may provide an optimal spatial level of integration to analyze the variability of the functional connectome. Here we propose a deep learning approach to enable the automated classification of individual independent-component (IC) decompositions into a set of predefined RSNs. Two databases were used in this work, BIL&GIN and MRi-Share, with 427 and 1811 participants, respectively. We trained a multilayer perceptron (MLP) to classify each IC as one of 45 RSNs, using the IC classification of 282 participants in BIL&GIN for training and a 5-dimensional parameter grid search for hyperparameter optimization. It reached an accuracy of 92 %. Predictions for the remaining individuals in BIL&GIN were tested against the original classification and demonstrated good spatial overlap between the cortical RSNs. As a first application, we created an RSN atlas based on MRi-Share. This atlas defined a brain parcellation in 29 RSNs covering 96 % of the gray matter. Second, we proposed an individual-based analysis of the subdivision of the default-mode network into 4 networks. Minimal overlap between RSNs was found except in the angular gyrus and potentially in the precuneus. We thus provide the community with an individual IC classifier that can be used to analyze one dataset or to statistically compare different datasets for RSN spatial definitions.

Quantitative assessment of inter-individual variability in fMRI-based human brain atlas

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<0.0001 for motor regions and P<0.0001 for language regions). There was no correlation between parcel number and inter-individual variability in left to right (LtoR; P=0.7959 for motor regions and P=0.2002 for language regions) and right to left (RtoL; P=0.7654 for motor regions and P=0.3544 for language regions) ED; however, LtoR (P≤0.0001) and RtoL (P≤0.0001) inter-individual variability differed according to brain region: the LtoR (P=0.0008) and RtoL (P=0.0004) inter-individual variability was greater for the right hand than for the left hand, the LtoR (P=0.0019) and RtoL (P=0.0179) inter-individual variability was greater for the right language than for the left language, but there was no such difference between the right foot and left foot (LtoR, P=0.2469 and RtoL, P=0.6140). Inter-individual variability in one motor region was positively correlated with mean values in the other three motor regions (left hand, P=0.0145; left foot, P=0.0103; right hand, P=0.1318; right foot, P=0.3785). Inter-individual variability in language region was positively correlated with mean values in the four motor regions (left language, P=0.0422; right language, P=0.0514). Signal extraction for LtoR (P<0.0001) and RtoL (P<0.0001) varied across the nine brain atlases, which also showed differences in inter-individual variability.

Conclusions

These results underscore the importance of quantitatively assessing the inter-individual variability of a brain atlas prior to use, and demonstrate that mapping motor regions by task-based fMRI is an effective method for quantitatively assessing the inter-individual variability in a brain atlas.

Connectivity-based localization of human hypothalamic nuclei in functional images of standard voxel size

Despite their critical roles in autonomic functions, individual hypothalamic nuclei have not been extensively investigated in humans using functional magnetic resonance imaging, partly due to the difficulty in resolving individual nuclei contained in the small structure of the hypothalamus. Areal parcellation analyses enable discrimination of individual hypothalamic nuclei but require a higher spatial resolution, which necessitates long scanning time or large amounts of data to compensate for the low signal-to-noise ratio in 3T or 1.5T scanners. In this study, we present analytic procedures to estimate likely locations of individual nuclei in the standard 2-mm resolution based on our higher resolution dataset. The spatial profiles of functional connectivity with the cerebral cortex for each nucleus in the medial hypothalamus were calculated using our higher resolution dataset. Voxels in the hypothalamus in standard resolution images from the Human Connectome Project (HCP) database that predominantly shared connectivity profiles with the same nucleus were subsequently identified. Voxels representing individual nuclei, as identified with the analytic procedures, were reproducible across 20 HCP datasets of 20 subjects each. Furthermore, the identified voxels were spatially separate. These results suggest that these analytic procedures are capable of refining voxels that represent individual hypothalamic nuclei in standard resolution. Our results highlight the potential utility of these procedures in various settings such as patient studies, where lengthy scans are infeasible.

Individual-specific functional connectivity markers track dimensional and categorical features of psychotic illness

Neuroimaging studies of psychotic disorders have demonstrated abnormalities in structural and functional connectivity involving widespread brain networks. However, these group-level observations have failed to yield any biomarkers that can provide confirmatory evidence of a patient's current symptoms, predict future symptoms, or predict a treatment response. Lack of precision in both neuroanatomical and clinical boundaries have likely contributed to the inability of even well-powered studies to resolve these key relationships. Here, we employed a novel approach to defining individual-specific functional connectivity in 158 patients diagnosed with schizophrenia (n = 49), schizoaffective disorder (n = 37), or bipolar disorder with psychosis (n = 72), and identified neuroimaging features that track psychotic symptoms in a dimension- or disorder-specific fashion. Using individually specified functional connectivity, we were able to estimate positive, negative, and manic symptoms that showed correlations ranging from r = 0.35 to r = 0.51 with the observed symptom scores. Comparing optimized estimation models among schizophrenia spectrum patients, positive and negative symptoms were associated with largely non-overlapping sets of cortical connections. Comparing between schizophrenia spectrum and bipolar disorder patients, the models for positive symptoms were largely non-overlapping between the two disorder classes. Finally, models derived using conventional region definition strategies performed at chance levels for most symptom domains. Individual-specific functional connectivity analyses revealed important new distinctions among cortical circuits responsible for the positive and negative symptoms, as well as key new information about how circuits underlying symptom expressions may vary depending on the underlying etiology and illness syndrome from which they manifest.

Functional Organization for Response Inhibition in the Right Inferior Frontal Cortex of Individual Human Brains

The right inferior frontal cortex (IFC) is critical to response inhibition. The right IFC referred in the human studies of response inhibition is located in the posterior part of the inferior frontal gyrus and the surrounding regions and consists of multiple areas that implement distinct functions. Recent studies using resting-state functional connectivity have parcellated the cerebral cortex and revealed across-subject variability of parcel-based cerebrocortical networks. However, how the right IFC of individual brains is functionally organized and what functional properties the IFC parcels possess regarding response inhibition remain elusive. In the present functional magnetic resonance imaging study, precision functional mapping of individual human brains was adopted to the parcels in the right IFC to evaluate their functional properties related to response inhibition. The right IFC consisted of six modules or subsets of subregions, and the spatial organization of the modules varied considerably across subjects. Each module revealed unique characteristics of brain activity and its correlation to behavior related to response inhibition. These results provide updated functional features of the IFC and demonstrate the importance of individual-focused approaches in studying response inhibition in the right IFC.

Bagging improves reproducibility of functional parcellation of the human brain

Increasing the reproducibility of neuroimaging measurement addresses a central impediment to the advancement of human neuroscience and its clinical applications. Recent efforts demonstrating variance in functional brain organization within and between individuals shows a need for improving reproducibility of functional parcellations without long scan times. We apply bootstrap aggregation, or bagging, to the problem of improving reproducibility in functional parcellation. We use two large datasets to demonstrate that compared to a standard clustering framework, bagging improves the reproducibility and test-retest reliability of both cortical and subcortical functional parcellations across a range of sites, scanners, samples, scan lengths, clustering algorithms, and clustering parameters (e.g., number of clusters, spatial constraints). With as little as 6 ​min of scan time, bagging creates more reproducible group and individual level parcellations than standard approaches with twice as much data. This suggests that regardless of the specific parcellation strategy employed, bagging may be a key method for improving functional parcellation and bringing functional neuroimaging-based measurement closer to clinical impact.

Functional Parcellation of Individual Cerebral Cortex Based on Functional MRI

The human brain atlas assists us to enhance our scientific understanding of brain structure and function. The typical anatomical atlases are mainly based on brain morphometry which cannot ensure the consistency of structure and function, and are also hard to cover individual functional differences especially in cerebral cortex. Thus, in recent years, functional atlases for individuals have captured great attention, since they are essential not only for identifying the unique functional organization of individual brains, but also to explore individual variations in behaviors. In this study, a novel approach was proposed to accurately parcellate the whole cerebral cortex at the individual level using resting-state functional magnetic resonance image (rs-fMRI). To examine the functional homogeneity in parcellation, a new evaluation criterion, similarity of cluster (SC) coefficient, was proposed. The parcellation results demonstrated the high consistency between two resting-state sessions (Dice >0.72). The most consistent parcellation appeared in the frontal cortex and the least consistent parcellation appeared in the occipital cortex. The functional homogeneity of subregions was high in frontal cortex and insula whereas low in precentral gyrus. According to SC value, the optimal clustering number was about 1600 per hemisphere. Identification accuracy was 100% between two rs-fMRI sessions, and it was also above 0.97 for rest-task and task-task sessions.

Resting-state network mapping in neurosurgical practice: a review

Resting-state functional MRI (rs-fMRI) is a well-established method for studying intrinsic connectivity and mapping the topography of functional networks in the human brain. In the clinical setting, rs-fMRI has been used to define functional topography, typically language and motor systems, in the context of preoperative planning for neurosurgery. Intraoperative mapping of critical speech and motor areas with electrocortical stimulation (ECS) remains standard practice, but preoperative noninvasive mapping has the potential to reduce operative time and provide functional localization when awake mapping is not feasible. Task-based fMRI has historically been used for this purpose, but it can be limited by the young age of the patient, cognitive impairment, poor cooperation, and need for sedation. Resting-state fMRI allows reliable analysis of all functional networks with a single study and is inherently independent of factors affecting task performance. In this review, the authors provide a summary of the theory and methods for resting-state network mapping. They provide case examples illustrating clinical implementation and discuss limitations of rs-fMRI and review available data regarding performance in comparison to ECS. Finally, they discuss novel opportunities for future clinical applications and prospects for rs-fMRI beyond mapping of regions to avoid during surgery but, instead, as a tool to guide novel network-based therapies.

Regions and Connections: Complementary Approaches to Characterize Brain Organization and Function

Functional magnetic resonance imaging has proved to be a powerful tool to characterize spatiotemporal patterns of human brain activity. Analysis methods broadly fall into two camps: those summarizing properties of a region and those measuring interactions among regions. Here we pose an unappreciated question in the field: What are the strengths and limitations of each approach to study fundamental neural processes? We explore the relative utility of region- and connection-based measures in the context of three topics of interest: neurobiological relevance, brain-behavior relationships, and individual differences in brain organization. In each section, we offer illustrative examples. We hope that this discussion offers a novel and useful framework to support efforts to better understand the macroscale functional organization of the brain and how it relates to behavior.

Tracking mood fluctuations with functional network patterns

Subjective mood is a psychophysiological property that depends on complex interactions among the central and peripheral nervous systems. How network interactions in the brain drive temporal fluctuations in mood is unknown. Here we investigate how functional network configuration relates to mood profiles in a single individual over the course of 1 year. Using data from the 'MyConnectome Project', we construct a comprehensive mapping between resting-state functional connectivity (FC) patterns and subjective mood scales using an associative multivariate technique (partial least squares). We report three principal findings. First, FC patterns reliably tracked daily fluctuations in mood. Second, positive mood was marked by an integrated architecture, with prominent interactions between canonical resting-state networks. Finally, one of the top-ranked nodes in mood-related network reconfiguration was the subgenual anterior cingulate cortex, an area commonly associated with mood regulation and dysregulation. Altogether, these results showcase the utility of highly sampled individual-focused data sets for affective neuroscience.

Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion

Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.

Performing group-level functional image analyses based on homologous functional regions mapped in individuals

Functional MRI (fMRI) studies have traditionally relied on intersubject normalization based on global brain morphology, which cannot establish proper functional correspondence between subjects due to substantial intersubject variability in functional organization. Here, we reliably identified a set of discrete, homologous functional regions in individuals to improve intersubject alignment of fMRI data. These functional regions demonstrated marked intersubject variability in size, position, and connectivity. We found that previously reported intersubject variability in functional connectivity maps could be partially explained by variability in size and position of the functional regions. Importantly, individual differences in network topography are associated with individual differences in task-evoked activations, suggesting that these individually specified regions may serve as the "localizer" to improve the alignment of task-fMRI data. We demonstrated that aligning task-fMRI data using the regions derived from resting state fMRI may lead to increased statistical power of task-fMRI analyses. In addition, resting state functional connectivity among these homologous regions is able to capture the idiosyncrasies of subjects and better predict fluid intelligence (gF) than connectivity measures derived from group-level brain atlases. Critically, we showed that not only the connectivity but also the size and position of functional regions are related to human behavior. Collectively, these findings suggest that identifying homologous functional regions across individuals can benefit a wide range of studies in the investigation of connectivity, task activation, and brain-behavior associations.

An Essential Role of the Intraparietal Sulcus in Response Inhibition Predicted by Parcellation-Based Network

The posterior parietal cortex (PPC) features close anatomical and functional relationships with the prefrontal cortex. However, the necessity of the PPC in executive functions has been questioned. The present study used the stop-signal task to examine response inhibition, an executive function that inhibits prepotent response tendency. The brain activity and resting-state functional connectivity were measured to analyze a parcellation-based network that was aimed at identifying a candidate PPC region essential for response inhibition in humans. The intraparietal sulcus (IPS) was activated during response inhibition and connected with the inferior frontal cortex and the presupplementary motor area, the two frontal regions known to be necessary for response inhibition. Next, transcranial magnetic stimulation (TMS) was used to test the essential role of the IPS region for response inhibition. TMS over the IPS region prolonged the stop-signal reaction time (SSRT), the standard behavioral index used to evaluate stopping performance, when stimulation was applied 30-0 ms before stopping. On the contrary, stimulation over the temporoparietal junction region, an area activated during response inhibition but lacking connectivity with the two frontal regions, did not show changes in SSRT. These results indicate that the IPS identified using the parcellation-based network plays an essential role in executive functions.SIGNIFICANCE STATEMENT Based on the previous neuropsychological studies reporting no impairment in executive functions after lesions in the posterior parietal cortex (PPC), the necessity of PPC in executive functions has been questioned. Here, contrary to the long-lasting view, by using recently developed analysis in functional MRI ("parcellation-based network analysis"), we identified the intraparietal sulcus (IPS) region in the PPC as essential for response inhibition: one executive function to stop actions that are inaccurate in a given context. The necessity of IPS for response inhibition was further tested by an interventional technique of transcranial magnetic stimulation. Stimulation to the IPS disrupted the performance of stopping. Our findings suggest that the IPS plays essential roles in executive functions.

Functional Parcellation of the Cerebral Cortex Across the Human Adult Lifespan

Adult aging is associated with differences in structure, function, and connectivity of brain areas. Age-based brain comparisons have typically rested on the assumption that brain areas exhibit a similar spatial organization across age; we evaluate this hypothesis directly. Area parcellation methods that identify locations where resting-state functional correlations (RSFC) exhibit abrupt transitions (boundary-mapping) are used to define cortical areas in cohorts of individuals sampled across a large range of the human adult lifespan (20-93 years). Most of the strongest areal boundaries are spatially consistent across age. Differences in parcellation boundaries are largely explained by differences in cortical thickness and anatomical alignment in older relative to younger adults. Despite the parcellation similarities, age-specific parcellations exhibit better internal validity relative to a young-adult parcellation applied to older adults' data, and age-specific parcels are better able to capture variability in task-evoked functional activity. Incorporating age-specific parcels as nodes in RSFC network analysis reveals that the spatial topography of the brain's large-scale system organization is comparable throughout aging, but confirms that the segregation of systems declines with increasing age. These observations demonstrate that many features of areal organization are consistent across adulthood, and reveal sources of age-related brain variation that contribute to the differences.

Multimodal Brain Parcellation Based on Functional and Anatomical Connectivity

Brain parcellation is often a prerequisite for network analysis due to the statistical challenges, computational burdens, and interpretation difficulties arising from the high dimensionality of neuroimaging data. Predominant approaches are largely unimodal with functional magnetic resonance imaging (fMRI) being the primary modality used. These approaches thus neglect other brain attributes that relate to brain organization. In this paper, we propose an approach for integrating fMRI and diffusion MRI (dMRI) data. Our approach introduces a nonlinear mapping between the connectivity values of two modalities, and adaptively balances their weighting based on their voxel-wise test-retest reliability. An efficient region level extension that additionally incorporates structural information on gyri and sulci is further presented. To validate, we compare multimodal parcellations with unimodal parcellations and existing atlases on the Human Connectome Project data. We show that multimodal parcellations achieve higher reproducibility, comparable/higher functional homogeneity, and comparable/higher leftout data likelihood. The boundaries of multimodal parcels are observed to align to those based on cyto-architecture, and subnetworks extracted from multimodal parcels matched well with established brain systems. Our results thus show that multimodal information improves brain parcellation.

Distinct topological properties of cue-evoked attention processing network in persisters and remitters of childhood ADHD

Attention deficit/hyperactivity disorder (ADHD) is a highly prevalent and impairing neurodevelopmental disorder that persists into adulthood in a sizeable portion of afflicted children. The persistence of ADHD elevates the risk for adverse outcomes that result in substantial individual and societal burden. The objective of this study is to assess neurobiological substrates associated with variability of clinical outcomes in childhood ADHD, which has considerable value for the development of novel interventions that target mechanisms associated with recovery. A total of 36 young adults who were diagnosed with ADHD combined-type during childhood and 33 group-matched controls were involved in the study. Adults with childhood ADHD were further divided into 17 persisters and 19 remitters based on DSM-5 criteria. Functional magnetic resonance imaging data during a cue-evoked attention task were collected from each subject. The cue-evoked attention processing network was constructed using graph theoretic techniques. Network properties, including global-, local-, and nodal-efficiency, and network hubs were computed. Group comparisons of the network properties were conducted. Significantly lower nodal efficiency in right inferior frontal gyrus and reduced left side frontal-parietal functional interactions were observed in both remitters and persisters relative to the controls. The ADHD persisters showed a unique pattern of significantly lower nodal efficiency in right middle frontal gyrus (MFG) and hyper-interactions between bilateral MFG. This study suggests that right MFG functional impairments may relate to inactive fronto-parietal functional interactions for sensory and cognitive information processing and symptom persistence in young adults with childhood ADHD.

Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI

A central goal in systems neuroscience is the parcellation of the cerebral cortex into discrete neurobiological "atoms". Resting-state functional magnetic resonance imaging (rs-fMRI) offers the possibility of in vivo human cortical parcellation. Almost all previous parcellations relied on 1 of 2 approaches. The local gradient approach detects abrupt transitions in functional connectivity patterns. These transitions potentially reflect cortical areal boundaries defined by histology or visuotopic fMRI. By contrast, the global similarity approach clusters similar functional connectivity patterns regardless of spatial proximity, resulting in parcels with homogeneous (similar) rs-fMRI signals. Here, we propose a gradient-weighted Markov Random Field (gwMRF) model integrating local gradient and global similarity approaches. Using task-fMRI and rs-fMRI across diverse acquisition protocols, we found gwMRF parcellations to be more homogeneous than 4 previously published parcellations. Furthermore, gwMRF parcellations agreed with the boundaries of certain cortical areas defined using histology and visuotopic fMRI. Some parcels captured subareal (somatotopic and visuotopic) features that likely reflect distinct computational units within known cortical areas. These results suggest that gwMRF parcellations reveal neurobiologically meaningful features of brain organization and are potentially useful for future applications requiring dimensionality reduction of voxel-wise fMRI data. Multiresolution parcellations generated from 1489 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Schaefer2018_LocalGlobal).

Striatal subdivisions that coherently interact with multiple cerebrocortical networks

The striatum constitutes the cortical‐basal ganglia loop and receives input from the cerebral cortex. Previous MRI studies have parcellated the human striatum using clustering analyses of structural/functional connectivity with the cerebral cortex. However, it is currently unclear how the striatal regions functionally interact with the cerebral cortex to organize cortical functions in the temporal domain. In the present human functional MRI study, the striatum was parcellated using boundary mapping analyses to reveal the fine architecture of the striatum by focusing on local gradient of functional connectivity. Boundary mapping analyses revealed approximately 100 subdivisions of the striatum. Many of the striatal subdivisions were functionally connected with specific combinations of cerebrocortical functional networks, such as somato‐motor (SM) and ventral attention (VA) networks. Time‐resolved functional connectivity analyses further revealed coherent interactions of multiple connectivities between each striatal subdivision and the cerebrocortical networks (i.e., a striatal subdivision‐SM connectivity and the same striatal subdivision‐VA connectivity). These results suggest that the striatum contains a large number of subdivisions that mediate functional coupling between specific combinations of cerebrocortical networks.

A computational model of shared fine-scale structure in the human connectome

Variation in cortical connectivity profiles is typically modeled as having a coarse spatial scale parcellated into interconnected brain areas. We created a high-dimensional common model of the human connectome to search for fine-scale structure that is shared across brains. Projecting individual connectivity data into this new common model connectome accounts for substantially more variance in the human connectome than do previous models. This newly discovered shared structure is closely related to fine-scale distinctions in representations of information. These results reveal a shared fine-scale structure that is a major component of the human connectome that coexists with coarse-scale, areal structure. This shared fine-scale structure was not captured in previous models and was, therefore, inaccessible to analysis and study.

Resting state fMRI has become a ubiquitous tool for measuring connectivity in normal and diseased brains. Current dominant models of connectivity are based on coarse-scale connectivity among brain regions, ignoring fine-scale structure within those regions. We developed a high-dimensional common model of the human connectome that captures both coarse and fine-scale structure of connectivity shared across brains. We showed that this shared fine-scale structure is related to fine-scale distinctions in representation of information, and our model accounts for substantially more shared variance of connectivity compared to previous models. Our model opens new territory—shared fine-scale structure, a dominant but mostly unexplored component of the human connectome—for analysis and study.

Big Data and Neuroimaging

Big Data are of increasing importance in a variety of areas, especially in the biosciences. There is an emerging critical need for Big Data tools and methods, because of the potential impact of advancements in these areas. Importantly, statisticians and statistical thinking have a major role to play in creating meaningful progress in this arena. We would like to emphasize this point in this special issue, as it highlights both the dramatic need for statistical input for Big Data analysis and for a greater number of statisticians working on Big Data problems. We use the field of statistical neuroimaging to demonstrate these points. As such, this paper covers several applications and novel methodological developments of Big Data tools applied to neuroimaging data.