Large-scale functional network overlap is a general property of brain functional organization: Reconciling inconsistent fMRI findings from general-linear-model-based analyses

Learnable Brain Connectivity Structures for Identifying Neurological Disorders

Brain networks/graphs have been widely recognized as powerful and efficient tools for identifying neurological disorders. In recent years, various graph neural network models have been developed to automatically extract features from brain networks. However, a key limitation of these models is that the inputs, namely brain networks/graphs, are constructed using predefined statistical metrics (e.g., Pearson correlation) and are not learnable. The lack of learnability restricts the flexibility of these approaches. While statistically-specific brain networks can be highly effective in recognizing certain diseases, their performance may not exhibit robustness when applied to other types of brain disorders. To address this issue, we propose a novel module called Brain Structure Inference (termed BSI), which can be seamlessly integrated with multiple downstream tasks within a unified framework, enabling end-to-end training. It is highly flexible to learn the most beneficial underlying graph structures directly for specific downstream tasks. The proposed method achieves classification accuracies of 74.83% and 79.18% on two publicly available datasets, respectively. This suggests an improvement of at least 3% over the best-performing existing methods for both tasks. In addition to its excellent performance, the proposed method is highly interpretable, and the results are generally consistent with previous findings.

Abnormal Dynamic Reconstruction of Overlapping Communities in Schizophrenia Patients

OBJECTIVE

This study aims to explore the changes in dynamic overlapping communities in the brains of schizophrenia (SZ) patients and further investigate the dynamic restructuring patterns of overlapping communities in SZ patients.

MATERIALS AND METHODS

A total of 43 SZ patients and 49 normal controls (NC) were selected for resting-state functional MRI (rs-fMRI) scans. Dynamic functional connectivity analysis was conducted separately on SZ patients and NC using rs-fMRI and Jackknife Correlation techniques to construct dynamic brain network models. Based on these models, a dynamic overlapping community detection method was utilized to explore the abnormal overlapping community structure in SZ patients using evaluation metrics such as the structural stability of overlapping communities, nodes' functional diversity, and activity level of overlapping communities.

RESULTS

The stability of communities in SZ patients showed a decreasing trend. The changes in the overlapping community structure of SZ patients may be related to a decrease in the diversity of overlapping node functions. Additionally, compared to the NC group, the activity level of overlapping communities of SZ patients was significantly reduced.

CONCLUSION

The structure or organization of the brain functional network in SZ patients is abnormal or disrupted, and the activity of the brain network in information processing and transmission is weakened in SZ patients.

Inferring brain causal and temporal-lag networks for recognizing abnormal patterns of dementia

Brain functional network analysis has become a popular method to explore the laws of brain organization and identify biomarkers of neurological diseases. However, it is still a challenging task to construct an ideal brain network due to the limited understanding of the human brain. Existing methods often ignore the impact of temporal-lag on the results of brain network modeling, which may lead to some unreliable conclusions. To overcome this issue, we propose a novel brain functional network estimation method, which can simultaneously infer the causal mechanisms and temporal-lag values among brain regions. Specifically, our method converts the lag learning into an instantaneous effect estimation problem, and further embeds the search objectives into a deep neural network model as parameters to be learned. To verify the effectiveness of the proposed estimation method, we perform experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database by comparing the proposed model with several existing methods, including correlation-based and causality-based methods. The experimental results show that our brain networks constructed by the proposed estimation method can not only achieve promising classification performance, but also exhibit some characteristics of physiological mechanisms. Our approach provides a new perspective for understanding the pathogenesis of brain diseases. The source code is released at https://github.com/NJUSTxiazw/CTLN.

The effect of the apolipoprotein E ε4 allele and olfactory function on odor identification networks

INTRODUCTION

The combination of apolipoprotein E ε4 (ApoE ε4) status, odor identification, and odor familiarity predicts conversion to mild cognitive impairment (MCI) and Alzheimer's disease (AD).

METHODS

To further understand olfactory disturbances and AD risk, ApoE ε4 carrier (mean age 76.38 ± 5.21) and ε4 non-carrier (mean age 76.8 ± 3.35) adults were given odor familiarity and identification tests and performed an odor identification task during fMRI scanning. Five task-related functional networks were detected using independent components analysis. Main and interaction effects of mean odor familiarity ratings, odor identification scores, and ε4 status on network activation and task-modulation of network functional connectivity (FC) during correct and incorrect odor identification (hits and misses), controlling for age and sex, were explored using multiple linear regression.

RESULTS

Findings suggested that sensory-olfactory network activation was positively associated with odor identification scores in ε4 carriers with intact odor familiarity. The FC of sensory-olfactory, multisensory-semantic integration, and occipitoparietal networks was altered in ε4 carriers with poorer odor familiarity and identification. In ε4 carriers with poorer familiarity, connectivity between superior frontal areas and the sensory-olfactory network was negatively associated with odor identification scores.

CONCLUSIONS

The results contribute to the clarification of the neurocognitive structure of odor identification processing and suggest that poorer odor familiarity and identification in ε4 carriers may signal multi-network dysfunction. Odor familiarity and identification assessment in ε4 carriers may contribute to the predictive value of risk for MCI and AD due to the breakdown of sensory-cognitive network integration. Additional research on olfactory processing in those at risk for AD is warranted.

Interpretable Cognitive Ability Prediction: A Comprehensive Gated Graph Transformer Framework for Analyzing Functional Brain Networks

Graph convolutional deep learning has emerged as a promising method to explore the functional organization of the human brain in neuroscience research. This paper presents a novel framework that utilizes the gated graph transformer (GGT) model to predict individuals' cognitive ability based on functional connectivity (FC) derived from fMRI. Our framework incorporates prior spatial knowledge and uses a random-walk diffusion strategy that captures the intricate structural and functional relationships between different brain regions. Specifically, our approach employs learnable structural and positional encodings (LSPE) in conjunction with a gating mechanism to efficiently disentangle the learning of positional encoding (PE) and graph embeddings. Additionally, we utilize the attention mechanism to derive multi-view node feature embeddings and dynamically distribute propagation weights between each node and its neighbors, which facilitates the identification of significant biomarkers from functional brain networks and thus enhances the interpretability of the findings. To evaluate our proposed model in cognitive ability prediction, we conduct experiments on two large-scale brain imaging datasets: the Philadelphia Neurodevelopmental Cohort (PNC) and the Human Connectome Project (HCP). The results show that our approach not only outperforms existing methods in prediction accuracy but also provides superior explainability, which can be used to identify important FCs underlying cognitive behaviors.

Modeling default mode network patterns via a universal spatio-temporal brain attention skip network

Designing a comprehensive four-dimensional resting-state functional magnetic resonance imaging (4D Rs-fMRI) based default mode network (DMN) modeling methodology to reveal the spatio-temporal patterns of individual DMN, is crucial for understanding the cognitive mechanisms of the brain and the pathogenesis of psychiatric disorders. However, there are still two limitations of existing approaches for DMN modeling. The approaches either (1) simply split the spatio-temporal components and ignore the overall character of the spatio-temporal patterns or (2) are biased in the process of feature extraction for DMN modeling, and their spatio-temporal accuracy is thus not warranted. To this end, we propose a novel Spatio-Temporal Brain Attention Skip Network (STBAS-Net) to model the personalized spatio-temporal patterns of the DMN. STBAS-Net consists of spatial and temporal components, where the multi-head attention skip connection block in the spatial component achieves detailed feature extraction and enhancement in the shallow stage. Under the guidance of spatial information, we technically fuse multiple spatio-temporal information in the temporal component, which dexterously exploits the overall spatio-temporal features and achieves mutual constraints of spatio-temporal patterns to characterize the spatio-temporal patterns of the DMN. We verify the proposed STBAS-Net on a publicly released 4D Rs-fMRI dataset and an EMCI dataset. The experimental results show that compared with existing advanced methods, the proposed network can more accurately model the personalized spatio-temporal patterns of the human brain DMN and successfully identify abnormal spatio-temporal patterns in EMCI patients. This study provides a potential tool for revealing the spatio-temporal patterns of the human brain DMN and is expected to provide an effective methodological framework for future exploration of abnormal brain spatio-temporal patterns and modeling of other functional brain networks.

Abnormal Spatial and Temporal Overlap of Time-Varying Brain Functional Networks in Patients with Schizophrenia

Schizophrenia (SZ) is a complex psychiatric disorder with unclear etiology and pathological features. Neuroscientists are increasingly proposing that schizophrenia is an abnormality in the dynamic organization of brain networks. Previous studies have found that the dynamic brain networks of people with SZ are abnormal in both space and time. However, little is known about the interactions and overlaps between hubs of the brain underlying spatiotemporal dynamics. In this study, we aimed to investigate different patterns of spatial and temporal overlap of hubs between SZ patients and healthy individuals. Specifically, we obtained resting-state functional magnetic resonance imaging data from the public dataset for 43 SZ patients and 49 healthy individuals. We derived a representation of time-varying functional connectivity using the Jackknife Correlation (JC) method. We employed the Betweenness Centrality (BC) method to identify the hubs of the brain's functional connectivity network. We then applied measures of temporal overlap, spatial overlap, and hierarchical clustering to investigate differences in the organization of brain hubs between SZ patients and healthy controls. Our findings suggest significant differences between SZ patients and healthy controls at the whole-brain and subnetwork levels. Furthermore, spatial overlap and hierarchical clustering analysis showed that quasi-periodic patterns were disrupted in SZ patients. Analyses of temporal overlap revealed abnormal pairwise engagement preferences in the hubs of SZ patients. These results provide new insights into the dynamic characteristics of the network organization of the SZ brain.

A deep learning method for autism spectrum disorder identification based on interactions of hierarchical brain networks

BACKGROUND

It has been recently shown that deep learning models exhibited remarkable performance of representing functional Magnetic Resonance Imaging (fMRI) data for the understanding of brain functional activities. With hierarchical structure, deep learning models can infer hierarchical functional brain networks (FBN) from fMRI. However, the applications of the hierarchical FBNs have been rarely studied.

METHODS

In this work, we proposed a hierarchical recurrent variational auto-encoder (HRVAE) to unsupervisedly model the fMRI data. The trained HRVAE encoder can predict hierarchical temporal features from its three hidden layers, and thus can be regarded as a hierarchical feature extractor. Then LASSO (least absolute shrinkage and selection operator) regression was applied to estimate the corresponding hierarchical FBNs. Based on the hierarchical FBNs from each subject, we constructed a novel classification framework for brain disorder identification and test it on the Autism Brain Imaging Data Exchange (ABIDE) dataset, a world-wide multi-site database of autism spectrum disorder (ASD). We analyzed the hierarchy organization of FBNs, and finally used the overlaps of hierarchical FBNs as features to differentiate ASD from typically developing controls (TDC).

RESULTS

The experimental results on 871 subjects from ABIDE dataset showed that the HRVAE model can effectively derive hierarchical FBNs including many well-known resting state networks (RSN). Moreover, the classification result improved the state-of-the-art by achieving a very high accuracy of 82.1 %.

CONCLUSIONS

This work presents a novel data-driven deep learning method using fMRI data for ASD identification, which could provide valuable reference for clinical diagnosis. The classification results suggest that the interactions of hierarchical FBNs have association with brain disorder, which promotes the understanding of FBN hierarchy and could be applied to other brain disorder analysis.

Identification of overlapping and interacting networks reveals intrinsic spatiotemporal organization of the human brain

The human brain is a complex network that exhibits dynamic fluctuations in activity across space and time. Depending on the analysis method, canonical brain networks identified from resting-state fMRI (rs-fMRI) are typically constrained to be either orthogonal or statistically independent in their spatial and/or temporal domains. We avoid imposing these potentially unnatural constraints through the combination of a temporal synchronization process ("BrainSync") and a three-way tensor decomposition method ("NASCAR") to jointly analyze rs-fMRI data from multiple subjects. The resulting set of interacting networks comprises minimally constrained spatiotemporal distributions, each representing one component of functionally coherent activity across the brain. We show that these networks can be clustered into six distinct functional categories and naturally form a representative functional network atlas for a healthy population. This functional network atlas could help explore group and individual differences in neurocognitive function, as we demonstrate in the context of ADHD and IQ prediction.

Distance Correlation-Based Brain Functional Connectivity Estimation and Non-Convex Multi-Task Learning for Developmental fMRI Studies

OBJECTIVE

Resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional connectivity (FC) patterns have been extensively used to delineate global functional organization of the human brain in healthy development and neuropsychiatric disorders. In this paper, we investigate how FC in males and females differs in an age prediction framework.

METHODS

We first estimate FC between regions-of-interest (ROIs) using distance correlation instead of Pearson's correlation. Distance correlation, as a multivariate statistical method, explores spatial relations of voxel-wise time courses within individual ROIs and measures both linear and nonlinear dependence, capturing more complex between-ROI interactions. Then, we propose a novel non-convex multi-task learning (NC-MTL) model to study age-related gender differences in FC, where age prediction for each gender group is viewed as one task, and a composite regularizer with a combination of the non-convex l2,1-2 and l1-2 terms is introduced for selecting both common and task-specific features.

RESULTS AND CONCLUSION

We validate the effectiveness of our NC-MTL model with distance correlation-based FC derived from rs-fMRI for predicting ages of both genders. The experimental results on the Philadelphia Neurodevelopmental Cohort demonstrate that our NC-MTL model outperforms several other competing MTL models in age prediction. We also compare the age prediction performance of our NC-MTL model using FC estimated by Pearson's correlation and distance correlation, which shows that distance correlation-based FC is more discriminative for age prediction than Pearson's correlation-based FC.

SIGNIFICANCE

This paper presents a novel framework for functional connectome developmental studies, characterizing developmental gender differences in FC patterns.

Task-specific network interactions across key cognitive domains

Human cognition is organized in distributed networks in the brain. Although distinct specialized networks have been identified for different cognitive functions, previous work also emphasizes the overlap of key cognitive domains in higher level association areas. The majority of previous studies focused on network overlap and dissociation during resting states whereas task-related network interactions across cognitive domains remain largely unexplored. A better understanding of network overlap and dissociation during different cognitive tasks may elucidate flexible (re-)distribution of resources during human cognition. The present study addresses this issue by providing a broad characterization of large-scale network dynamics in three key cognitive domains. Combining prototypical tasks of the larger domains of attention, language, and social cognition with whole-brain multivariate activity and connectivity approaches, we provide a spatiotemporal characterization of multiple large-scale, overlapping networks that differentially interact across cognitive domains. We show that network activity and interactions increase with increased cognitive complexity across domains. Interaction patterns reveal a common core structure across domains as well as dissociable domain-specific network activity. The observed patterns of activation and deactivation of overlapping and strongly coupled networks provide insight beyond region-specific activity within a particular cognitive domain toward a network perspective approach across diverse key cognitive functions.

A novel 5D brain parcellation approach based on spatio-temporal encoding of resting fMRI data from deep residual learning

OBJECTIVE

Brain parcellation is an essential aspect of computational neuroimaging research and deals with segmenting the brain into (possibly overlapping) sub-regions employed to study brain anatomy or function. In the context of functional parcellation, brain organization which is often measured via temporal metrics such as coherence, is highly dynamic. This dynamic aspect is ignored in most research, which typically applies anatomically based, fixed regions for each individual, and can produce misleading results.

METHODS

In this work, we propose a novel spatio-temporal-network (5D) brain parcellation scheme utilizing a deep residual network to predict the probability of each voxel belonging to a brain network at each point in time.

RESULTS

We trained 53 4D brain networks and evaluate the ability of these networks to capture spatial and temporal dynamics as well as to show sensitivity to individual or group-level variation (in our case with age).

CONCLUSION

The proposed system generates informative spatio-temporal networks that vary not only across individuals but also over time and space.

SIGNIFICANCE

The dynamic 5D nature of the developed approach provides a powerful framework that expands on existing work and has potential to identify novel and typically ignored findings when studying the healthy and disordered brain.

Low-Rank Tucker-2 Model for Multi-Subject fMRI Data Decomposition With Spatial Sparsity Constraint

Tucker decomposition can provide an intuitive summary to understand brain function by decomposing multi-subject fMRI data into a core tensor and multiple factor matrices, and was mostly used to extract functional connectivity patterns across time/subjects using orthogonality constraints. However, these algorithms are unsuitable for extracting common spatial and temporal patterns across subjects due to distinct characteristics such as high-level noise. Motivated by a successful application of Tucker decomposition to image denoising and the intrinsic sparsity of spatial activations in fMRI, we propose a low-rank Tucker-2 model with spatial sparsity constraint to analyze multi-subject fMRI data. More precisely, we propose to impose a sparsity constraint on spatial maps by using an l_p norm ( ), in addition to adding low-rank constraints on factor matrices via the Frobenius norm. We solve the constrained Tucker-2 model using alternating direction method of multipliers, and propose to update both sparsity and low-rank constrained spatial maps using half quadratic splitting. Moreover, we extract new spatial and temporal features in addition to subject-specific intensities from the core tensor, and use these features to classify multiple subjects. The results from both simulated and experimental fMRI data verify the improvement of the proposed method, compared with four related algorithms including robust Kronecker component analysis, Tucker decomposition with orthogonality constraints, canonical polyadic decomposition, and block term decomposition in extracting common spatial and temporal components across subjects. The spatial and temporal features extracted from the core tensor show promise for characterizing subjects within the same group of patients or healthy controls as well.

Control energy assessment of spatial interactions among macro‐scale brain networks

Many recent studies have revealed that spatial interactions of functional brain networks derived from fMRI data can well model functional connectomes of the human brain. However, it has been rarely explored what the energy consumption characteristics are for such spatial interactions of macro‐scale functional networks, which remains crucial for the understanding of brain organization, behavior, and dynamics. To explore this unanswered question, this article presents a novel framework for quantitative assessment of energy consumptions of macro‐scale functional brain network's spatial interactions via two main effective computational methodologies. First, we designed a novel scheme combining dictionary learning and hierarchical clustering to derive macro‐scale consistent brain network templates that can be used to define a common reference space for brain network interactions and energy assessments. Second, the control energy consumption for driving the brain networks during their spatial interactions is computed from the viewpoint of the linear network control theory. Especially, the energetically favorable brain networks were identified and their energy characteristics were comprehensively analyzed. Experimental results on the Human Connectome Project (HCP) task‐based fMRI (tfMRI) data showed that the proposed methods can reveal meaningful, diverse energy consumption patterns of macro‐scale network interactions. In particular, those networks present remarkable differences in energy consumption. The energetically least favorable brain networks are stable and consistent across HCP tasks such as motor, language, social, and working memory tasks. In general, our framework provides a new perspective to characterize human brain functional connectomes by quantitative assessment for the energy consumption of spatial interactions of macro‐scale brain networks.

Enhanced design matrix for task-related fMRI data analysis

In this paper, we introduce a novel methodology for the analysis of task-related fMRI data. In particular, we propose an alternative way for constructing the design matrix, based on the newly suggested Information-Assisted Dictionary Learning (IADL) method. This technique offers an enhanced potential, within the conventional GLM framework, (a) to efficiently cope with uncertainties in the modeling of the hemodynamic response function, (b) to accommodate unmodeled brain-induced sources, beyond the task-related ones, as well as potential interfering scanner-induced artifacts, uncorrected head-motion residuals and other unmodeled physiological signals, and (c) to integrate external knowledge regarding the natural sparsity of the brain activity that is associated with both the experimental design and brain atlases. The capabilities of the proposed methodology are evaluated via a realistic synthetic fMRI-like dataset, and demonstrated using a test case of a challenging fMRI study, which verifies that the proposed approach produces substantially more consistent results compared to the standard design matrix method. A toolbox extension for SPM is also provided, to facilitate the use and reproducibility of the proposed methodology.

Hierarchical Extraction of Functional Connectivity Components in Human Brain Using Resting-State fMRI

The study of functional networks of the human brain has been of significant interest in cognitive neuroscience for over two decades, albeit they are typically extracted at a single scale using various methods, including decompositions like ICA. However, since numerous studies have suggested that the functional organization of the brain is hierarchical, analogous decompositions might better capture functional connectivity patterns. Moreover, hierarchical decompositions can efficiently reduce the very high dimensionality of functional connectivity data. This paper provides a novel method for the extraction of hierarchical connectivity components in the human brain using resting-state fMRI. The method builds upon prior work of Sparse Connectivity Patterns (SCPs) by introducing a hierarchy of sparse, potentially overlapping patterns. The components are estimated by cascaded factorization of correlation matrices generated from fMRI. The goal of the paper is to extract sparse interpretable hierarchically-organized patterns using correlation matrices where a low rank decomposition is formed by a linear combination of a higher rank decomposition. We formulate the decomposition as a non-convex optimization problem and solve it using gradient descent algorithms with adaptive step size. Along with the hierarchy, our method aims to capture the heterogeneity of the set of common patterns across individuals. We first validate our model through simulated experiments. We then demonstrate the effectiveness of the developed method on two different real-world datasets by showing that multi-scale hierarchical SCPs are reproducible between sub-samples and are more reproducible as compared to single scale patterns. We also compare our method with an existing hierarchical community detection approach.

Brain topography beyond parcellations: Local gradients of functional maps

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

Overarching Principles and Dimensions of the Functional Organization in the Inferior Parietal Cortex

The parietal cortex (PC) is implicated in a confusing myriad of different cognitive processes/tasks. Consequently, understanding the nature and organization of the core underlying neurocomputations is challenging. According to the Parietal Unified Connectivity-biased Computation model, two properties underpin PC function and organization. Firstly, PC is a multidomain, context-dependent buffer of time- and space-varying input, the function of which, over time, becomes sensitive to the statistical temporal/spatial structure of events. Secondly, over and above this core buffering computation, differences in long-range connectivity will generate graded variations in task engagement across subregions. The current study tested these hypotheses using a group independent component analysis technique with two independent functional magnetic resonance imaging datasets (task and resting state data). Three functional organizational principles were revealed: Factor 1, inferior PC was sensitive to the statistical structure of sequences for all stimulus types (pictures, sentences, numbers); Factor 2, a dorsal–ventral variation in generally task-positive versus task-negative (variable) engagement; and Factor 3, an anterior–posterior dimension in inferior PC reflecting different engagement in verbal versus visual tasks, respectively. Together, the data suggest that the core neurocomputation implemented by PC is common across domains, with graded task engagement across regions reflecting variations in the connectivity of task-specific networks that interact with PC.

Multi-Hypergraph Learning-Based Brain Functional Connectivity Analysis in fMRI Data

Recently, a hypergraph constructed from functional magnetic resonance imaging (fMRI) was utilized to explore brain functional connectivity networks (FCNs) for the classification of neurodegenerative diseases. Each edge of a hypergraph (called hyperedge) can connect any number of brain regions-of-interest (ROIs) instead of only two ROIs, and thus characterizes high-order relations among multiple ROIs that cannot be uncovered by a simple graph in the traditional graph based FCN construction methods. Unlike the existing hypergraph based methods where all hyperedges are assumed to have equal weights and only certain topological features are extracted from the hypergraphs, we propose a hypergraph learning based method for FCN construction in this paper. Specifically, we first generate hyperedges from fMRI time series based on sparse representation, then employ hypergraph learning to adaptively learn hyperedge weights, and finally define a hypergraph similarity matrix to represent the FCN. In our proposed method, weighting hyperedges results in better discriminative FCNs across subjects, and the defined hypergraph similarity matrix can better reveal the overall structure of brain network than using those hypergraph topological features. Moreover, we propose a multi-hypergraph learning based method by integrating multi-paradigm fMRI data, where the hyperedge weights associated with each fMRI paradigm are jointly learned and then a unified hypergraph similarity matrix is computed to represent the FCN. We validate the effectiveness of the proposed method on the Philadelphia Neurodevelopmental Cohort dataset for the classification of individuals' learning ability from three paradigms of fMRI data. Experimental results demonstrate that our proposed approach outperforms the traditional graph based methods (i.e., Pearson's correlation and partial correlation with the graphical Lasso) and the existing unweighted hypergraph based methods, which sheds light on how to optimize estimation of FCNs for cognitive and behavioral study.

Resting-state EEG activity predicts frontoparietal network reconfiguration and improved attentional performance

Mounting evidence indicates that resting-state EEG activity is related to various cognitive functions. To trace physiological underpinnings of this relationship, we investigated EEG and behavioral performance of 36 healthy adults recorded at rest and during visual attention tasks: visual search and gun shooting. All measures were repeated two months later to determine stability of the results. Correlation analyses revealed that within the range of 2–45 Hz, at rest, beta-2 band power correlated with the strength of frontoparietal connectivity and behavioral performance in both sessions. Participants with lower global beta-2 resting-state power (gB2rest) showed weaker frontoparietal connectivity and greater capacity for its modifications, as indicated by changes in phase correlations of the EEG signals. At the same time shorter reaction times and improved shooting accuracy were found, in both test and retest, in participants with low gB2rest compared to higher gB2rest values. We posit that weak frontoparietal connectivity permits flexible network reconfigurations required for improved performance in everyday tasks.

Spiritual experiences are related to engagement of a ventral frontotemporal functional brain network: Implications for prevention and treatment of behavioral and substance addictions

BACKGROUND AND AIMS

Spirituality is an important component of 12-step programs for behavioral and substance addictions and has been linked to recovery processes. Understanding the neural correlates of spiritual experiences may help to promote efforts to enhance recovery processes in behavioral addictions. We recently used general linear model (GLM) analyses of functional magnetic resonance imaging data to examine neural correlates of spiritual experiences, with findings implicating cortical and subcortical brain regions. Although informative, the GLM-based approach does not provide insight into brain circuits that may underlie spiritual experiences.

METHODS

Spatial independent component analysis (sICA) was used to identify functional brain networks specifically linked to spiritual (vs. stressful or neutral-relaxing) conditions using a previously validated guided imagery task in 27 young adults.

RESULTS

Using sICA, engagement of a ventral frontotemporal network was identified that was engaged at the onset and conclusion of the spiritual condition in a manner distinct from engagement during the stress or neutral-relaxing conditions. Degree of engagement correlated with subjective reports of spirituality in the scanner (r = .71, p < .001) and an out-of-the-magnet measure of spirituality (r = .48, p < .018).

DISCUSSION AND CONCLUSION

The current findings suggest a distributed functional neural network associated with spiritual experiences and provide a foundation for investigating brain mechanisms underlying the role of spirituality in recovery from behavioral addictions.

Gradient theories of brain activation: A novel application to studying the parental brain

PURPOSE OF REVIEW

Parental brain research primarily employs general-linear-model-based (GLM-based) analyses to assess blood-oxygenation-level-dependent responses to infant auditory and visual cues, reporting common responses in shared cortical and subcortical structures. However, this approach does not reveal intermixed neural substrates related to different sensory modalities. We consider this notion in studying the parental brain.

RECENT FINDINGS

Spatial independent component analysis (sICA) has been used to separate mixed source signals from overlapping functional networks. We explore relative differences between GLM-based analysis and sICA as applied to an fMRI dataset acquired from women while they listened to infant cries or viewed infant sad faces.

SUMMARY

There is growing appreciation for the value of moving beyond GLM-based analyses to consider brain functional organization as continuous, distributive, and overlapping gradients of neural substrates related to different sensory modalities. Preliminary findings suggest sICA can be applied to the study of the parental brain.

Gambling disorder

Gambling disorder is characterized by a persistent, recurrent pattern of gambling that is associated with substantial distress or impairment. The prevalence of gambling disorder has been estimated at 0.5% of the adult population in the United States, with comparable or slightly higher estimates in other countries. The aetiology of gambling disorder is complex, with implicated genetic and environmental factors. Neurobiological studies have implicated cortico-striato-limbic structures and circuits in the pathophysiology of this disorder. Individuals with gambling disorder often go unrecognized and untreated, including within clinical settings. Gambling disorder frequently co-occurs with other conditions, particularly other psychiatric disorders. Behavioural interventions, particularly cognitive-behavioural therapy but also motivational interviewing and Gamblers Anonymous, are supported in the treatment of gambling disorder. No pharmacological therapy has a formal indication for the treatment of gambling disorder, although placebo-controlled trials suggest that some medications, such as opioid-receptor antagonists, may be helpful. Given the associations with poor quality of life and suicide, improved identification, prevention, policy and treatment efforts are needed to help people with gambling disorder.

Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization

fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework.

Discovering and characterizing dynamic functional brain networks in task FMRI

Many existing studies for the mapping of function brain networks impose an implicit assumption that the networks' spatial distributions are constant over time. However, the latest research reports reveal that functional brain networks are dynamical and have time-varying spatial patterns. Furthermore, how these functional networks evolve over time has not been elaborated and explained in sufficient details yet. In this paper, we aim to discover and characterize the dynamics of functional brain networks via a windowed group-wise dictionary learning and sparse coding approach. First, we aggregated the sampled subjects' fMRI signals into one big data matrix, and learned a common dictionary for all individuals via a group-wise dictionary learning step. Second, we obtained the dynamic time-varying functional networks by using the windowed time-varying sparse coding approach. Experimental results demonstrated that our windowed group-wise dictionary learning and sparse coding method can effectively detect the task-evoked networks and also characterize how these networks evolve over time. This work sheds novel insights on the dynamics mechanism of functional brain networks.

Response inhibition and fronto-striatal-thalamic circuit dysfunction in cocaine addiction

BACKGROUND

Many studies have investigated how cognitive control may be compromised in cocaine addiction. Here, we extend this literature by employing spatial Independent Component Analysis (ICA) to describe circuit dysfunction in relation to impairment in response inhibition in cocaine addiction.

METHODS

Fifty-five cocaine-dependent (CD) and 55 age- and sex-matched non-drug-using healthy control individuals (HC) participated in the study. Task-relatedness of 40 independent components (ICs) was assessed using multiple regression analyses of component time courses with the modeled time courses of hemodynamic activity convolved with go success (GS), stop success (SS) and stop error (SE). This procedure produced beta-weights that represented the degree to which each IC was temporally associated with, or 'engaged', by each task event.

RESULTS

Behaviorally, CD participants showed prolonged stop signal reaction times (SSRTs) as compared to HC participants (p < 0.01). ICA identified two networks that showed differences in engagement related to SS between CD and HC (p < 0.05, FDR-corrected). The activity of the fronto-striatal-thalamic network was negatively correlated with SSRTs in HC but not in CD, suggesting a specific role of this network in mediating deficits of response inhibition in CD individuals. In contrast, the engagement of the fronto-parietal-temporal network did not relate to SSRTs, was similarly less engaged for both SS and SE trials, and may reflect attentional dysfunction in cocaine addiction.

CONCLUSIONS

This study highlights the utility of ICA in identifying neural circuitry engagement related to SST performance and suggests that specific networks may represent important targets in remedying executive-control impairment in cocaine addiction.

The Motivation-Based Promotion of Proactive Control: The Role of Salience Network

It has been shown that reward motivation can facilitate proactive control, a cognitive control mode that is characterized of prior preparation and sustained holding of the goal-relevant information in working memory. However, it remains to be established the neural networks that may be involved in this promotion effect. In this study, participants underwent the AX-Continuous Performance Task (AX-CPT) that measures relative proactive control during functional magnetic resonance imaging (fMRI) scanning. We employed independent component analysis to decompose multiple brain networks and identified the task related network. Results showed that the salience network (SN) was engaged in the AX-CPT protocol. Importantly, our data demonstrated that reward modulated the association between task engagement of SN and proactive control, whereby the positive correlation was particularly observed in the reward condition. Moreover, reward modulated task engagement of the SN in a proactive manner, which may contribute to the behavioral proactive performance. Overall, our data suggest the involvement of SN in the reward facilitation effect of proactive control.

Different Behavioral Experiences Produce Distinctive Parallel Changes in, and Correlate With, Frontal Cortex and Hippocampal Global Post-translational Histone Levels

While it is clear that behavioral experience modulates epigenetic profiles, it is less evident how the nature of that experience influences outcomes and whether epigenetic/genetic "biomarkers" could be extracted to classify different types of behavioral experience. To begin to address this question, male and female mice were subjected to either a Fixed Interval (FI) schedule of food reward, or a single episode of forced swim followed by restraint stress, or no explicit behavioral experience after which global expression levels of two activating (H3K9ac and H3K4me3) and two repressive (H3K9me2 and H3k27me3) post-translational histone modifications (PTHMs), were measured in hippocampus (HIPP) and frontal cortex (FC). The specific nature of the behavioral experience differentiated profiles of PTHMs in a sex- and brain region-dependent manner, with all 4 PTHMs changing in parallel in response to different behavioral experiences. These different behavioral experiences also modified the pattern of correlations of PTHMs both within and across FC and HIPP. Unexpectedly, highly robust correlations were found between global PTHM levels and behavioral performances, suggesting that global PTHMs may provide a higher-order pattern recognition function. Further efforts are needed to determine the generality of such findings and what characteristics of behavioral experience are critical for modulating PTHM responses.

Application of Graph Theory to Assess Static and Dynamic Brain Connectivity: Approaches for Building Brain Graphs

Human brain connectivity is complex. Graph theory based analysis has become a powerful and popular approach for analyzing brain imaging data, largely because of its potential to quantitatively illuminate the networks, the static architecture in structure and function, the organization of dynamic behavior over time, and disease related brain changes. The first step in creating brain graphs is to define the nodes and edges connecting them. We review a number of approaches for defining brain nodes including fixed versus data-driven nodes. Expanding the narrow view of most studies which focus on static and/or single modality brain connectivity, we also survey advanced approaches and their performances in building dynamic and multi-modal brain graphs. We show results from both simulated and real data from healthy controls and patients with mental illnesse. We outline the advantages and challenges of these various techniques. By summarizing and inspecting recent studies which analyzed brain imaging data based on graph theory, this article provides a guide for developing new powerful tools to explore complex brain networks.

Is Neural Processing of Negative Stimuli Altered in Addiction Independent of Drug Effects? Findings From Drug-Naïve Youth with Internet Gaming Disorder

Difficulties in emotion regulation are commonly reported among individuals with alcohol and drug addictions and contribute to the acquisition and maintenance of addictive behaviors. Alterations in neural processing of negative affective stimuli have further been demonstrated among individuals with addictions. However, it is unclear whether these alterations are a general feature of addictions or are a result of prolonged exposure to drugs of abuse. To test the hypothesis of altered negative affect processing independent of drug effects, this study assessed neural function among drug-naïve youth with a behavioral addiction-Internet gaming disorder (IGD). Fifty-six young adults (28 with IGD, 28 matched controls) participated in fMRI scanning during performance of a well-validated emotion regulation task. Between-group differences in neural activity during task performance were assessed using a whole-brain, mixed-effects ANOVA with correction for multiple comparisons at currently recommended thresholds (voxel-level p<0.001, pFWE<0.05). Compared to controls, youth with IGD exhibited significantly blunted neural responses within distributed subcortical and cortical regions including the striatum, insula, lateral prefrontal cortex and anterior cingulate in response to negative affective cues, as well as during emotion regulation. Independent component analysis (ICA) further identified between-group differences in engagement of a fronto-cingulo-parietal network, involving decreased engagement in IGD youth relative to controls. Study findings are largely consistent with those from prior neuroimaging studies in substance-use disorders, thus raising the possibility that neural processing of negative affect may be blunted across drug and behavioral addictions independent of acute or chronic drug effects.

Identification of Two Distinct Working Memory-Related Brain Networks in Healthy Young Adults

Working memory (WM) is an important cognitive domain for everyday life functioning and is often disturbed in neuropsychiatric disorders. Functional magnetic resonance imaging (fMRI) studies in humans show that distributed brain areas typically described as fronto-parietal regions are implicated in WM tasks. Based on data from a large sample of healthy young adults (N = 1369), we applied independent component analysis (ICA) to the WM-fMRI signal and identified two distinct networks that were relevant for differences in individual WM task performance. A parietally-centered network was particularly relevant for individual differences in task measures related to WM performance (“WM dependent”) and a frontally-centered network was relevant for differences in attention-dependent task performance. Importantly, frontal areas that are typically considered as key regions for WM were either involved in both WM-dependent and attention-dependent performance, or in attention-dependent performance only. The networks identified here are provided as publicly available datasets. These networks can be applied in future studies to derive a low-dimensional representation of the overall WM brain activation.

Affective neuroimaging in generalized anxiety disorder: an integrated review

Affective neuroimaging has contributed to our knowledge of generalized anxiety disorder (GAD) through measurement of blood oxygenation level-dependent (BOLD) responses, which facilitate inference on neural responses to emotional stimuli during task-based functional magnetic resonance imaging (fMRI). In this article, the authors provide an integrated review of the task-based affective fMRI literature in GAD. Studies provide evidence for variable presence and directionality of BOLD abnormalities in limbic and prefrontal regions during reactivity to, regulation of, and learning from emotional cues. We conclude that understanding the sources of this variability is key to accelerating progress in this area. We propose that the cardinal symptom of GAD—worry—predominantly reflects stimulus-independent mental processes that impose abnormal, inflexible functional brain configurations, ie, the overall pattern of information transfer among behaviorally relevant neural circuits at a given point in time. These configurations that are inflexible to change from the incoming flux of environmental stimuli may underlie inconsistent task-based findings.

Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder

State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data.

Spatio-temporal modeling of connectome-scale brain network interactions via time-evolving graphs

Many recent literature studies have revealed interesting dynamics patterns of functional brain networks derived from fMRI data. However, it has been rarely explored how functional networks spatially overlap (or interact) and how such connectome-scale network interactions temporally evolve. To explore these unanswered questions, this paper presents a novel framework for spatio-temporal modeling of connectome-scale functional brain network interactions via two main effective computational methodologies. First, to integrate, pool and compare brain networks across individuals and their cognitive states under task performances, we designed a novel group-wise dictionary learning scheme to derive connectome-scale consistent brain network templates that can be used to define the common reference space of brain network interactions. Second, the temporal dynamics of spatial network interactions is modeled by a weighted time-evolving graph, and then a data-driven unsupervised learning algorithm based on the dynamic behavioral mixed-membership model (DBMM) is adopted to identify behavioral patterns of brain networks during the temporal evolution process of spatial overlaps/interactions. Experimental results on the Human Connectome Project (HCP) task fMRI data showed that our methods can reveal meaningful, diverse behavior patterns of connectome-scale network interactions. In particular, those networks' behavior patterns are distinct across HCP tasks such as motor, working memory, language and social tasks, and their dynamics well correspond to the temporal changes of specific task designs. In general, our framework offers a new approach to characterizing human brain function by quantitative description for the temporal evolution of spatial overlaps/interactions of connectome-scale brain networks in a standard reference space.

Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis

For more than 20 years, the powerful, flexible family of independent component analysis (ICA) techniques has been used to examine spatial, temporal, and subject variation in functional magnetic resonance (fMR) imaging data. This article provides an overview of 10 key principles in the basic and advanced application of ICA to resting-state fMR imaging. ICA's core advantages include robustness to artifact; false-positives and autocorrelation; adaptability to variant study designs; agnosticism to the temporal evolution of fMR imaging signals; and ability to extract, identify, and analyze neural networks. ICA remains in the vanguard of fMRI methods development.

Developmental Lead and/or Prenatal Stress Exposures Followed by Different Types of Behavioral Experience Result in the Divergence of Brain Epigenetic Profiles in a Sex, Brain Region, and Time-Dependent Manner: Implications for Neurotoxicology

Over a lifetime, early developmental exposures to neurocognitive risk factors, such as lead (Pb) exposures and prenatal stress (PS), will be followed by multiple varied behavioral experiences. Pb, PS and behavioral experience can each influence brain epigenetic profiles. Our recent studies show a greater level of complexity, however, as all three factors interact within each sex to generate differential adult variation in global post-translational histone modifications (PTHMs), which may result in fundamentally different consequences for life-long learning and behavioral function. We have reported that PTHM profiles differ by sex, brain region and time point of measurement following developmental exposures to Pb±PS, resulting in different profiles for each unique combination of these parameters. Imposing differing behavioral experience following developmental Pb±PS results in additional divergence of PTHM profiles, again in a sex, brain region and time-dependent manner, further increasing complexity. Such findings underscore the need to link highly localized and variable epigenetic changes along single genes to the highly-integrated brain functional connectome that is ultimately responsible for governing behavioral function. Here we advance the idea that increased understanding may be achieved through iterative reductionist and holistic approaches. Implications for experimental design of animal studies of developmental exposures to neurotoxicants include the necessity of a 'no behavioral experience' group, given that epigenetic changes in response to behavioral testing can confound effects of the neurotoxicant itself. They also suggest the potential utility of the inclusion of salient behavioral experiences as a potential effect modifier in epidemiological studies.

Making group inferences using sparse representation of resting-state functional mRI data with application to sleep deprivation

Past studies on drawing group inferences for functional magnetic resonance imaging (fMRI) data usually assume that a brain region is involved in only one functional brain network. However, recent evidence has demonstrated that some brain regions might simultaneously participate in multiple functional networks. Here, we presented a novel approach for making group inferences using sparse representation of resting-state fMRI data and its application to the identification of changes in functional networks in the brains of 37 healthy young adult participants after 36 h of sleep deprivation (SD) in contrast to the rested wakefulness (RW) stage. Our analysis based on group-level sparse representation revealed that multiple functional networks involved in memory, emotion, attention, and vigilance processing were impaired by SD. Of particular interest, the thalamus was observed to contribute to multiple functional networks in which differentiated response patterns were exhibited. These results not only further elucidate the impact of SD on brain function but also demonstrate the ability of the proposed approach to provide new insights into the functional organization of the resting-state brain by permitting spatial overlap between networks and facilitating the description of the varied relationships of the overlapping regions with other regions of the brain in the context of different functional systems. Hum Brain Mapp 38:4671-4689, 2017. © 2017 Wiley Periodicals, Inc.

Alterations in functional brain networks associated with loss-chasing in gambling disorder and cocaine-use disorder

BACKGROUND

Continued, persistent gambling to recover accumulating losses, or 'loss-chasing', is a behavioral pattern linked particularly closely to gambling disorder (GD) but may reflect impaired decision-making processes relevant to drug addictions like cocaine-use disorder (CUD). However, little is known regarding the neurocognitive mechanisms of this complex, maladaptive behavior, particularly in individuals with addictive disorders.

METHODS

Seventy participants (25 GD, 18 CUD, and 27 healthy comparison (HC)) completed a loss-chase task during fMRI. Engagement of functional brain networks in response to losing outcomes and during decision-making periods preceding choices to loss-chase or to quit chasing losses were investigated using independent component analysis (ICA). An exploratory factor analysis was performed to examine patterns of coordinated engagement across identified networks.

RESULTS

In GD relative to HC and CUD participants, choices to quit chasing were associated with greater engagement of a medial frontal executive-processing network. By comparison, CUD participants exhibited altered engagement of a striato-amygdala motivational network in response to losing outcomes as compared to HC, and during decision-making as compared to GD. Several other networks were differentially engaged during loss-chase relative to quit-chasing choices, but did not differ across participant groups. Exploratory factor analysis identified a system of coordinated activity across prefrontal executive-control networks that was greater in GD and CUD relative to HC participants and was associated with increased chasing persistence across all participants.

CONCLUSIONS

Results provide evidence of shared and distinct neurobiological mechanisms in substance and behavioral addictions, and lend insight into potential cognitive interventions targeting loss-chasing behavior in GD.

Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis

For more than 20 years, the powerful, flexible family of independent component analysis (ICA) techniques has been used to examine spatial, temporal, and subject variation in functional magnetic resonance (fMR) imaging data. This article provides an overview of 10 key principles in the basic and advanced application of ICA to resting-state fMR imaging. ICA's core advantages include robustness to artifact; false-positives and autocorrelation; adaptability to variant study designs; agnosticism to the temporal evolution of fMR imaging signals; and ability to extract, identify, and analyze neural networks. ICA remains in the vanguard of fMRI methods development.

Automatic Recognition of fMRI-Derived Functional Networks Using 3-D Convolutional Neural Networks

Current functional magnetic resonance imaging (fMRI) data modeling techniques, such as independent component analysis and sparse coding methods, can effectively reconstruct dozens or hundreds of concurrent interacting functional brain networks simultaneously from the whole brain fMRI signals. However, such reconstructed networks have no correspondences across different subjects. Thus, automatic, effective, and accurate classification and recognition of these large numbers of fMRI-derived functional brain networks are very important for subsequent steps of functional brain analysis in cognitive and clinical neuroscience applications. However, this task is still a challenging and open problem due to the tremendous variability of various types of functional brain networks and the presence of various sources of noises. In recognition of the fact that convolutional neural networks (CNN) has superior capability of representing spatial patterns with huge variability and dealing with large noises, in this paper, we design, apply, and evaluate a deep 3-D CNN framework for automatic, effective, and accurate classification and recognition of large number of functional brain networks reconstructed by sparse representation of whole-brain fMRI signals. Our extensive experimental results based on the Human Connectome Project fMRI data showed that the proposed deep 3-D CNN can effectively and robustly perform functional networks classification and recognition tasks, while maintaining a high tolerance for mistakenly labeled training instances. This study provides a new deep learning approach for modeling functional connectomes based on fMRI data.