Task-induced brain state manipulation improves prediction of individual traits

The Human Connectome Project: A retrospective

The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium.

BrainGNN: Interpretable Brain Graph Neural Network for fMRI Analysis

Understanding which brain regions are related to a specific neurological disorder or cognitive stimuli has been an important area of neuroimaging research. We propose BrainGNN, a graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI) and discover neurological biomarkers. Considering the special property of brain graphs, we design novel ROI-aware graph convolutional (Ra-GConv) layers that leverage the topological and functional information of fMRI. Motivated by the need for transparency in medical image analysis, our BrainGNN contains ROI-selection pooling layers (R-pool) that highlight salient ROIs (nodes in the graph), so that we can infer which ROIs are important for prediction. Furthermore, we propose regularization terms-unit loss, topK pooling (TPK) loss and group-level consistency (GLC) loss-on pooling results to encourage reasonable ROI-selection and provide flexibility to encourage either fully individual- or patterns that agree with group-level data. We apply the BrainGNN framework on two independent fMRI datasets: an Autism Spectrum Disorder (ASD) fMRI dataset and data from the Human Connectome Project (HCP) 900 Subject Release. We investigate different choices of the hyper-parameters and show that BrainGNN outperforms the alternative fMRI image analysis methods in terms of four different evaluation metrics. The obtained community clustering and salient ROI detection results show a high correspondence with the previous neuroimaging-derived evidence of biomarkers for ASD and specific task states decoded for HCP. Our code is available at https://github.com/xxlya/BrainGNN_Pytorch.

Theory-driven classification of reading difficulties from fMRI data using Bayesian latent-mixture models

Decades of research have led to several competing theories regarding the neural contributors to impaired reading. But how can we know which theory (or theories) identifies the types of markers that indeed differentiate between individuals with reading disabilities (RD) and their typically developing (TD) peers? To answer this question, we propose a new analytical tool for theory evaluation and comparison, grounded in the Bayesian latent-mixture modeling framework. We start by constructing a series of latent-mixture classification models, each reflecting one existing theoretical claim regarding the neurofunctional markers of RD (highlighting network-level differences in either mean activation, inter-subject heterogeneity, inter-region variability, or connectivity). Then, we run each model on fMRI data alone (i.e., while models are blind to participants' behavioral status), which enables us to interpret the fit between a model's classification of participants and their behavioral (known) RD/TD status as an estimate of its explanatory power. Results from n=127 adolescents and young adults (RD: n=59; TD: n=68) show that models based on network-level differences in mean activation and heterogeneity failed to differentiate between TD and RD individuals. In contrast, classifications based on variability and connectivity were significantly associated with participants' behavioral status. These findings suggest that differences in inter-region variability and connectivity may be better network-level markers of RD than mean activation or heterogeneity (at least in some populations and tasks). More broadly, the results demonstrate the promise of latent-mixture modeling as a theory-driven tool for evaluating different theoretical claims regarding neural contributors to language disorders and other cognitive traits.

Brain-wide functional connectivity patterns support general cognitive ability and mediate effects of socioeconomic status in youth

General cognitive ability (GCA) is an individual difference dimension linked to important academic, occupational, and health-related outcomes and its development is strongly linked to differences in socioeconomic status (SES). Complex abilities of the human brain are realized through interconnections among distributed brain regions, but brain-wide connectivity patterns associated with GCA in youth, and the influence of SES on these connectivity patterns, are poorly understood. The present study examined functional connectomes from 5937 9- and 10-year-olds in the Adolescent Brain Cognitive Development (ABCD) multi-site study. Using multivariate predictive modeling methods, we identified whole-brain functional connectivity patterns linked to GCA. In leave-one-site-out cross-validation, we found these connectivity patterns exhibited strong and statistically reliable generalization at 19 out of 19 held-out sites accounting for 18.0% of the variance in GCA scores (cross-validated partial η²). GCA-related connections were remarkably dispersed across brain networks: across 120 sets of connections linking pairs of large-scale networks, significantly elevated GCA-related connectivity was found in 110 of them, and differences in levels of GCA-related connectivity across brain networks were notably modest. Consistent with prior work, socioeconomic status was a strong predictor of GCA in this sample, and we found that distributed GCA-related brain connectivity patterns significantly statistically mediated this relationship (mean proportion mediated: 15.6%, p < 2 × 10^-16). These results demonstrate that socioeconomic status and GCA are related to broad and diffuse differences in functional connectivity architecture during early adolescence, potentially suggesting a mechanism through which socioeconomic status influences cognitive development.

Refined prefrontal working memory network as a neuromarker for Alzheimer's disease

Detecting Alzheimer's disease (AD) is an important step in preventing pathological brain damage. Working memory (WM)-related network modulation can be a pathological feature of AD, but is usually modulated by untargeted cognitive processes and individual variance, resulting in the concealment of this key information. Therefore, in this study, we comprehensively investigated a new neuromarker, named "refined network," in a prefrontal cortex (PFC) that revealed the pathological features of AD. A refined network was acquired by removing unnecessary variance from the WM-related network. By using a functional near-infrared spectroscopy (fNIRS) device, we evaluated the reliability of the refined network, which was identified from the three groups classified by AD progression: healthy people (N=31), mild cognitive impairment (N=11), and patients with AD (N=18). As a result, we identified edges with significant correlations between cognitive functions and groups in the dorsolateral PFC. Moreover, the refined network achieved a significantly correlating metric with neuropsychological test scores, and a remarkable three-class classification accuracy (95.0%). These results implicate the refined PFC WM-related network as a powerful neuromarker for AD screening.

Visuomotor brain network activation and functional connectivity among individuals with autism spectrum disorder

Sensorimotor abnormalities are common in autism spectrum disorder (ASD) and predictive of functional outcomes, though their neural underpinnings remain poorly understood. Using functional magnetic resonance imaging, we examined both brain activation and functional connectivity during visuomotor behavior in 27 individuals with ASD and 30 typically developing (TD) controls (ages 9–35 years). Participants maintained a constant grip force while receiving visual feedback at three different visual gain levels. Relative to controls, ASD participants showed increased force variability, especially at high gain, and reduced entropy. Brain activation was greater in individuals with ASD than controls in supplementary motor area, bilateral superior parietal lobules, and contralateral middle frontal gyrus at high gain. During motor action, functional connectivity was reduced between parietal‐premotor and parietal‐putamen in individuals with ASD compared to controls. Individuals with ASD also showed greater age‐associated increases in functional connectivity between cerebellum and visual, motor, and prefrontal cortical areas relative to controls. These results indicate that visuomotor deficits in ASD are associated with atypical activation and functional connectivity of posterior parietal, premotor, and striatal circuits involved in translating sensory feedback information into precision motor behaviors, and that functional connectivity of cerebellar–cortical sensorimotor and nonsensorimotor networks show delayed maturation.

Machine learning prediction of cognition from functional connectivity: Are feature weights reliable?

Cognitive performance can be predicted from an individual's functional brain connectivity with modest accuracy using machine learning approaches. As yet, however, predictive models have arguably yielded limited insight into the neurobiological processes supporting cognition. To do so, feature selection and feature weight estimation need to be reliable to ensure that important connections and circuits with high predictive utility can be reliably identified. We comprehensively investigate feature weight test-retest reliability for various predictive models of cognitive performance built from resting-state functional connectivity networks in healthy young adults (n=400). Despite achieving modest prediction accuracies (r=0.2-0.4), we find that feature weight reliability is generally poor for all predictive models (ICC< 0.3), and significantly poorer than predictive models for overt biological attributes such as sex (ICC≈0.5). Larger sample sizes (n=800), the Haufe transformation, non-sparse feature selection/regularization and smaller feature spaces marginally improve reliability (ICC< 0.4). We elucidate a tradeoff between feature weight reliability and prediction accuracy and find that univariate statistics are marginally more reliable than feature weights from predictive models. Finally, we show that measuring agreement in feature weights between cross-validation folds provides inflated estimates of feature weight reliability. We thus recommend for reliability to be estimated out-of-sample, if possible. We argue that rebalancing focus from prediction accuracy to model reliability may facilitate mechanistic understanding of cognition with machine learning approaches.

Functional Connectivity during Encoding Predicts Individual Differences in Long-Term Memory

What is the neural basis of individual differences in the ability to hold information in long-term memory (LTM)? Here, we first characterize two whole-brain functional connectivity networks based on fMRI data acquired during an n-back task that robustly predict individual differences in two important forms of LTM, recognition and recollection. We then focus on the recognition memory model and contrast it with a working memory model. Although functional connectivity during the n-back task also predicts working memory performance and the two networks have some shared components, they are also largely distinct from each other: The recognition memory model performance remains robust when we control for working memory, and vice versa. Functional connectivity only within regions traditionally associated with LTM formation, such as the medial temporal lobe and those that show univariate subsequent memory effect, have little predictive power for both forms of LTM. Interestingly, the interactions between these regions and other brain regions play a more substantial role in predicting recollection memory than recognition memory. These results demonstrate that individual differences in LTM are dependent on the configuration of a whole-brain functional network including but not limited to regions associated with LTM during encoding and that such a network is separable from what supports the retention of information in working memory.

Brain Knows Who Is on the Same Wavelength: Resting-State Connectivity Can Predict Compatibility of a Female-Male Relationship

Prediction of the initial compatibility of heterosexual individuals based on self-reported traits and preferences has not been successful, even with significantly developed information technology. To overcome the limitations of self-reported measures and predict compatibility, we used functional connectivity profiles from resting-state functional magnetic resonance imaging (fMRI) data that carry rich individual-specific information sufficient to predict psychological constructs and activation patterns during social cognitive tasks. Several days after collecting data from resting-state fMRIs, participants undertook a speed-dating experiment in which they had a 3-min speed date with every other opposite-sex participant. Our machine learning algorithm successfully predicted whether pairs in the experiment were compatible or not using (dis)similarity of functional connectivity profiles obtained before the experiment. The similarity and dissimilarity of functional connectivity between individuals and these multivariate relationships contributed to the prediction, hence suggesting the importance of complementarity (observed as dissimilarity) as well as the similarity between an individual and a potential partner during the initial attraction phase. The result indicates that the salience network, limbic areas, and cerebellum are especially important for the feeling of compatibility. This research emphasizes the utility of neural information to predict complex phenomena in a social environment that behavioral measures alone cannot predict.

Nonlinear manifold learning in functional magnetic resonance imaging uncovers a low-dimensional space of brain dynamics

Large-scale brain dynamics are believed to lie in a latent, low-dimensional space. Typically, the embeddings of brain scans are derived independently from different cognitive tasks or resting-state data, ignoring a potentially large-and shared-portion of this space. Here, we establish that a shared, robust, and interpretable low-dimensional space of brain dynamics can be recovered from a rich repertoire of task-based functional magnetic resonance imaging (fMRI) data. This occurs when relying on nonlinear approaches as opposed to traditional linear methods. The embedding maintains proper temporal progression of the tasks, revealing brain states and the dynamics of network integration. We demonstrate that resting-state data embeds fully onto the same task embedding, indicating similar brain states are present in both task and resting-state data. Our findings suggest analysis of fMRI data from multiple cognitive tasks in a low-dimensional space is possible and desirable.

Brief segments of neurophysiological activity enable individual differentiation

Large, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. Recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully distinguish individual participants within a cohort and predict some individual traits, yielding the notion of an individual's neural fingerprint. Here, we aim to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of resting-state brain activity using magnetoencephalography (MEG) in 158 participants. We show that akin to fMRI approaches, neurophysiological functional connectomes enable the differentiation of individuals, with rates similar to those seen with fMRI. We also show that individual differentiation is equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data further indicate that differentiation can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: the neural fingerprint is present in recordings performed weeks after their baseline reference data was collected. This work, thus, extends the notion of a neural or brain fingerprint to fast and large-scale resting-state electrophysiological dynamics.

Genetic variation in endocannabinoid signaling is associated with differential network-level functional connectivity in youth

The endocannabinoid system is an important regulator of emotional responses such as fear, and a number of studies have implicated endocannabinoid signaling in anxiety. The fatty acid amide hydrolase (FAAH) C385A polymorphism, which is associated with enhanced endocannabinoid signaling in the brain, has been identified across species as a potential protective factor from anxiety. In particular, adults with the variant FAAH 385A allele have greater fronto-amygdala connectivity and lower anxiety symptoms. Whether broader network-level differences in connectivity exist, and when during development this neural phenotype emerges, remains unknown and represents an important next step in understanding how the FAAH C385A polymorphism impacts neurodevelopment and risk for anxiety disorders. Here, we leveraged data from 3,109 participants in the nationwide Adolescent Brain Cognitive Development Study℠ (10.04 ± 0.62 years old; 44.23% female, 55.77% male) and a cross-validated, data-driven approach to examine associations between genetic variation and large-scale resting-state brain networks. Our findings revealed a distributed brain network, comprising functional connections that were both significantly greater (95% CI for p values = [<0.001, <0.001]) and lesser (95% CI for p values = [0.006, <0.001]) in A-allele carriers relative to non-carriers. Furthermore, there was a significant interaction between genotype and the summarized connectivity of functional connections that were greater in A-allele carriers, such that non-carriers with connectivity more similar to A-allele carriers (i.e., greater connectivity) had lower anxiety symptoms (β = -0.041, p = 0.030). These findings provide novel evidence of network-level changes in neural connectivity associated with genetic variation in endocannabinoid signaling and suggest that genotype-associated neural differences may emerge at a younger age than genotype-associated differences in anxiety.

Phylogenomic Subsampling and the Search for Phylogenetically Reliable Loci

Phylogenomic subsampling is a procedure by which small sets of loci are selected from large genome-scale data sets and used for phylogenetic inference. This step is often motivated by either computational limitations associated with the use of complex inference methods or as a means of testing the robustness of phylogenetic results by discarding loci that are deemed potentially misleading. Although many alternative methods of phylogenomic subsampling have been proposed, little effort has gone into comparing their behavior across different data sets. Here, I calculate multiple gene properties for a range of phylogenomic data sets spanning animal, fungal, and plant clades, uncovering a remarkable predictability in their patterns of covariance. I also show how these patterns provide a means for ordering loci by both their rate of evolution and their relative phylogenetic usefulness. This method of retrieving phylogenetically useful loci is found to be among the top performing when compared with alternative subsampling protocols. Relatively common approaches such as minimizing potential sources of systematic bias or increasing the clock-likeness of the data are found to fare worse than selecting loci at random. Likewise, the general utility of rate-based subsampling is found to be limited: loci evolving at both low and high rates are among the least effective, and even those evolving at optimal rates can still widely differ in usefulness. This study shows that many common subsampling approaches introduce unintended effects in off-target gene properties and proposes an alternative multivariate method that simultaneously optimizes phylogenetic signal while controlling for known sources of bias.

Multiview Diffusion Map Improves Prediction of Fluid Intelligence With Two Paradigms of fMRI Analysis

OBJECTIVE

To understand the association between brain networks and behaviors of an individual, most studies build predictive models based on functional connectivity (FC) from a single dataset with linear analysis techniques. Such approaches may fail to capture the nonlinear structure of brain networks and neglect the complementary information contained in FC networks (FCNs) from multiple datasets. To address this challenging issue, we use multiview dimensionality reduction to extract a coherent low-dimensional representation of the FCNs from resting-state and emotion identification task-based functional magnetic resonance imaging (fMRI) datasets.

METHODS

We propose a scheme based on multiview diffusion map to extract intrinsic features while preserving the underlying geometric structure of high dimensional datasets. This method is robust to noise and small variations in the data.

RESULTS

After validation on the Philadelphia Neurodevelopmental Cohort data, the predictive model built from both resting-state and emotion identification task-based fMRI datasets outperforms the one using each individual fMRI dataset. In addition, the proposed model achieves better prediction performance than principal component analysis (PCA) and three other competing data fusion methods.

CONCLUSION

Our framework for combing multiple FCNs in one predictive model exhibits improved prediction performance.

SIGNIFICANCE

To our knowledge, we demonstrate a first application of multiview diffusion map to successfully fuse different types of fMRI data for predicting fluid intelligence (gF).

Dissociable neural substrates of opioid and cocaine use identified via connectome-based modelling

Opioid use disorder is a major public health crisis. While effective treatments are available, outcomes vary widely across individuals and relapse rates remain high. Understanding neural mechanisms of treatment response may facilitate the development of personalized and/or novel treatment approaches. Methadone-maintained, polysubstance-using individuals (n = 53) participated in fMRI scanning before and after substance-use treatment. Connectome-based predictive modeling (CPM)-a recently developed, whole-brain approach-was used to identify pretreatment connections associated with abstinence during the 3-month treatment. Follow-up analyses were conducted to determine the specificity of the identified opioid abstinence network across different brain states (cognitive vs. reward task vs. resting-state) and different substance use outcomes (opioid vs. cocaine abstinence). Posttreatment fMRI data were used to assess network changes over time and within-subject replication. To determine further clinical relevance, opioid abstinence network strength was compared with healthy subjects (n = 38). CPM identified an opioid abstinence network (p = 0.018), characterized by stronger within-network motor/sensory connectivity, and reduced connectivity between the motor/sensory network and medial frontal, default mode, and frontoparietal networks. This opioid abstinence network was anatomically distinct from a previously identified cocaine abstinence network. Relationships between abstinence and opioid and cocaine abstinence networks replicated across multiple brain states but did not generalize across substances. Network connectivity measured at posttreatment related to abstinence at 6-month follow-up (p < 0.009). Healthy comparison subjects displayed intermediate network strengths relative to treatment responders and nonresponders. These data indicate dissociable anatomical substrates of opioid vs. cocaine abstinence. Results may inform the development of novel opioid-specific treatment approaches to combat the opioid epidemic.

Using functional connectivity models to characterize relationships between working and episodic memory

INTRODUCTION

Working memory is a critical cognitive ability that affects our daily functioning and relates to many cognitive processes and clinical conditions. Episodic memory is vital because it enables individuals to form and maintain their self-identities. Our study analyzes the extent to which whole-brain functional connectivity observed during completion of an N-back memory task, a common measure of working memory, can predict both working memory and episodic memory.

METHODS

We used connectome-based predictive models (CPMs) to predict 502 Human Connectome Project (HCP) participants' in-scanner 2-back memory test scores and out-of-scanner working memory test (List Sorting) and episodic memory test (Picture Sequence and Penn Word) scores based on functional magnetic resonance imaging (fMRI) data collected both during rest and N-back task performance. We also analyzed the functional brain connections that contributed to prediction for each of these models.

RESULTS

Functional connectivity observed during N-back task performance predicted out-of-scanner List Sorting scores and to a lesser extent out-of-scanner Picture Sequence scores, but did not predict out-of-scanner Penn Word scores. Additionally, the functional connections predicting 2-back scores overlapped to a greater degree with those predicting List Sorting scores than with those predicting Picture Sequence or Penn Word scores. Functional connections with the insula, including connections between insular and parietal regions, predicted scores across the 2-back, List Sorting, and Picture Sequence tasks.

CONCLUSIONS

Our findings validate functional connectivity observed during the N-back task as a measure of working memory, which generalizes to predict episodic memory to a lesser extent. By building on our understanding of the predictive power of N-back task functional connectivity, this work enhances our knowledge of relationships between working memory and episodic memory.

Characterizing the Network Architecture of Emotion Regulation Neurodevelopment

The ability to regulate emotions is key to goal attainment and well-being. Although much has been discovered about neurodevelopment and the acquisition of emotion regulation, very little of this work has leveraged information encoded in whole-brain networks. Here we employed a network neuroscience framework to parse the neural underpinnings of emotion regulation skill acquisition, while accounting for age, in a sample of children and adolescents (N = 70, 34 female, aged 8-17 years). Focusing on three key network metrics-network differentiation, modularity, and community number differences between active regulation and a passive emotional baseline-we found that the control network, the default mode network, and limbic network were each related to emotion regulation ability while controlling for age. Greater network differentiation in the control and limbic networks was related to better emotion regulation ability. With regards to network community structure (modularity and community number), more communities and more crosstalk between modules (i.e., less modularity) in the control network were associated with better regulatory ability. By contrast, less crosstalk (i.e., greater modularity) between modules in the default mode network was associated with better regulatory ability. Together, these findings highlight whole-brain connectome features that support the acquisition of emotion regulation in youth.

Deep learning for sex classification in resting-state and task functional brain networks from the UK Biobank

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Applied deep learning to sex classification in UK BioBank fMRI connectomes.

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Deep learning classifies sex better in resting-state than in task fMRI.

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Algorithm to balance out multiple confounds from an fMRI dataset.

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Adapted two deep learning visualization methods to fMRI connectome classification.

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Analyzed role of three brain a priori networks in sex classification.

Classification of whole-brain functional connectivity MRI data with convolutional neural networks (CNNs) has shown promise, but the complexity of these models impedes understanding of which aspects of brain activity contribute to classification. While visualization techniques have been developed to interpret CNNs, bias inherent in the method of encoding abstract input data, as well as the natural variance of deep learning models, detract from the accuracy of these techniques. We introduce a stochastic encoding method in an ensemble of CNNs to classify functional connectomes by sex. We applied our method to resting-state and task data from the UK BioBank, using two visualization techniques to measure the salience of three brain networks involved in task- and resting-states, and their interaction. To regress confounding factors such as head motion, age, and intracranial volume, we introduced a multivariate balancing algorithm to ensure equal distributions of such covariates between classes in our data. We achieved a final AUROC of 0.8459. We found that resting-state data classifies more accurately than task data, with the inner salience network playing the most important role of the three networks overall in classification of resting-state data and connections to the central executive network in task data.

Connectome-based individual prediction of cognitive behaviors via graph propagation network reveals directed brain network topology

Objective. Brain connectivity network supports the information flow underlying human cognitions and should reflect the individual variability in human cognitive behaviors. Various studies have utilized brain connectivity to predict individual differences in human behaviors. However, traditional studies viewed brain connectivity network as a one-dimensional vector, a method which neglects topological properties of brain connectivity network.Approach. To utilize these topological properties, we proposed that graph neural network (GNN) which combines graph theory and neural network can be adopted. Different from previous node-driven GNNs that parameterize on the node feature transformation, we designed an edge-driven GNN named graph propagation network (GPN) that parameterizes on the information propagation within brain connectivity network.Main results.Edge-driven GPN outperforms various baseline models such as node-driven GNN and traditional partial least square regression in predicting the individual total cognition based on the resting-state functional connectome. GPN also reveals a directed network topology encoding the information flow, indicating that higher-order association cortices such as dorsolateral prefrontal, inferior frontal and inferior parietal cortices are responsible for the information integration underlying total cognition.Significance. These results suggest that edge-driven GPN can better explore topological structures of brain connectivity network and can serve as a new method to associate brain connectome and human behaviors.

Systemic inflammation is associated with differential neural reactivity and connectivity to affective images

Systemic inflammation is increasingly appreciated as a predictor of health and well-being. Further, inflammation has been shown to influence and be influenced by affective experiences. Although prior work has substantiated associations between inflammatory and affective processes, fewer studies have investigated the neurobiological correlates that underlie links between systemic, low-grade inflammation and affective reactivity. Thus, the current study examined whether markers of systemic inflammation (i.e. interleukin-6, C-reactive protein) are associated with differential patterns of neural activation and connectivity in corticolimbic regions in response to affective images. We investigated this question in a sample of 66 adults (44 women, M age = 54.98 years, range = 35–76) from the Midlife in the United States study. Higher levels of inflammation were associated with lower activity in limbic regions (i.e. amygdala, hippocampus, anterior insula, temporal pole) when viewing positive (vs neutral) images. Higher levels of inflammation were also associated with greater connectivity between the hippocampus and the medial prefrontal cortex in response to positive images. Inflammatory markers were not associated with significant differences in activation or connectivity to negative images. These findings highlight the utility of health neuroscience approaches in demonstrating that physiological processes such as inflammation are related to how our brains respond to affective information.

Distributed functional connectivity predicts neuropsychological test performance among older adults

Neuropsychological test is an essential tool in assessing cognitive and functional changes associated with late-life neurocognitive disorders. Despite the utility of the neuropsychological test, the brain-wide neural basis of the test performance remains unclear. Using the predictive modeling approach, we aimed to identify the optimal combination of functional connectivities that predicts neuropsychological test scores of novel individuals. Resting-state functional connectivity and neuropsychological tests included in the OASIS-3 dataset (n = 428) were used to train the predictive models, and the identified models were iteratively applied to the holdout internal test set (n = 216) and external test set (KSHAP, n = 151). We found that the connectivity-based predicted score tracked the actual behavioral test scores (r = 0.08-0.44). The predictive models utilizing most of the connectivity features showed better accuracy than those composed of focal connectivity features, suggesting that its neural basis is largely distributed across multiple brain systems. The discriminant and clinical validity of the predictive models were further assessed. Our results suggest that late-life neuropsychological test performance can be formally characterized with distributed connectome-based predictive models, and further translational evidence is needed when developing theoretically valid and clinically incremental predictive models.

Conditional Functional Graphical Models

Graphical modeling of multivariate functional data is becoming increasingly important in a wide variety of applications. The changes of graph structure can often be attributed to external variables, such as the diagnosis status or time, the latter of which gives rise to the problem of dynamic graphical modeling. Most existing methods focus on estimating the graph by aggregating samples, but largely ignore the subject-level heterogeneity due to the external variables. In this article, we introduce a conditional graphical model for multivariate random functions, where we treat the external variables as conditioning set, and allow the graph structure to vary with the external variables. Our method is built on two new linear operators, the conditional precision operator and the conditional partial correlation operator, which extend the precision matrix and the partial correlation matrix to both the conditional and functional settings. We show that their nonzero elements can be used to characterize the conditional graphs, and develop the corresponding estimators. We establish the uniform convergence of the proposed estimators and the consistency of the estimated graph, while allowing the graph size to grow with the sample size, and accommodating both completely and partially observed data. We demonstrate the efficacy of the method through both simulations and a study of brain functional connectivity network.

The Application of Connectome-Based Predictive Modeling to the Maternal Brain: Implications for Mother-Infant Bonding

Maternal bonding early postpartum lays an important foundation for child development. Changing brain structure and function during pregnancy and postpartum may underscore maternal bonding. We employed connectome-based predictive modeling (CPM) to measure brain functional connectivity and predict self-reported maternal bonding in mothers at 2 and 8 months postpartum. At 2 months, CPM predicted maternal anxiety in the bonding relationship: Greater integration between cerebellar and motor-sensory-auditory networks and between frontoparietal and motor-sensory-auditory networks were associated with more maternal anxiety toward their infant. Furthermore, greater segregation between the cerebellar and frontoparietal, and within the motor-sensory-auditory networks, was associated with more maternal anxiety regarding their infant. We did not observe CPM prediction of maternal bonding impairments or rejection/anger toward the infant. Finally, considering 2 and 8 months of data, changes in network connectivity were associated with changes in maternal anxiety in the bonding relationship. Our results suggest that changing connectivity among maternal brain networks may provide insight into the mother-infant bond, specifically in the context of anxiety and the representation of the infant in the mother's mind. These findings provide an opportunity to mechanistically investigate approaches to enhance the connectivity of these networks to optimize the representational and behavioral quality of the caregiving relationship.

Perturbation of resting-state network nodes preferentially propagates to structurally rather than functionally connected regions

Combining Transcranial Magnetic Stimulation (TMS) with electroencephalography (EEG) offers the opportunity to study signal propagation dynamics at high temporal resolution in the human brain. TMS pulse induces a local effect which propagates across cortical networks engaging distant cortical and subcortical sites. However, the degree of propagation supported by the structural compared to functional connectome remains unclear. Clarifying this issue would help tailor TMS interventions to maximize target engagement. The goal of this study was to establish the contribution of functional and structural connectivity in predicting TMSinduced signal propagation after perturbation of two distinct brain networks. For this purpose, 24 healthy individuals underwent two identical TMS-EEG visits where neuronavigated TMS pulses were delivered to nodes of the default mode network (DMN) and the dorsal attention network (DAN). The functional and structural connectivity derived from each individual stimulation spot were characterized via functional magnetic resonance imaging (fMRI) and Diffusion Weighted Imaging (DWI), and signal propagation across these two metrics was compared. Direct comparison between the signal extracted from brain regions either functionally or structurally connected to the stimulation sites, shows a stronger activation over cortical areas connected via white matter pathways, with a minor contribution of functional projections. This pattern was not observed when analyzing spontaneous resting state EEG activity. Overall, results suggest that structural links can predict network-level response to perturbation more accurately than functional connectivity. Additionally, DWI-based estimation of propagation patterns can be used to estimate off-target engagement of other networks and possibly guide target selection to maximize specificity.

Detecting functional connectivity disruptions in a translational pediatric traumatic brain injury porcine model using resting-state and task-based fMRI

Functional magnetic resonance imaging (fMRI) has significant potential to evaluate changes in brain network activity after traumatic brain injury (TBI) and enable early prognosis of potential functional (e.g., motor, cognitive, behavior) deficits. In this study, resting-state and task-based fMRI (rs- and tb-fMRI) were utilized to examine network changes in a pediatric porcine TBI model that has increased predictive potential in the development of novel therapies. rs- and tb-fMRI were performed one day post-TBI in piglets. Activation maps were generated using group independent component analysis (ICA) and sparse dictionary learning (sDL). Activation maps were compared to pig reference functional connectivity atlases and evaluated using Pearson spatial correlation coefficients and mean ratios. Nonparametric permutation analyses were used to determine significantly different activation areas between the TBI and healthy control groups. Significantly lower Pearson values and mean ratios were observed in the visual, executive control, and sensorimotor networks for TBI piglets compared to controls. Significant differences were also observed within several specific individual anatomical structures within each network. In conclusion, both rs- and tb-fMRI demonstrate the ability to detect functional connectivity disruptions in a translational TBI piglet model, and these disruptions can be traced to specific affected anatomical structures.

Memory Suppression Ability can be Robustly Predicted by the Internetwork Communication of Frontoparietal Control Network

Memory suppression (MS) is essential for mental well-being. However, no studies have explored how intrinsic resting-state functional connectivity (rs-FC) predicts this ability. Here, we adopted the connectome-based predictive modeling (CPM) based on the resting-state fMRI data to investigate whether and how rs-FC profiles in predefined brain networks (the frontoparietal control networks or FPCN) can predict MS in healthy individuals with 497 participants. The MS ability was assessed by MS-induced forgetting during the think/no-think paradigm. The results showed that FPCN network was especially informative for generating the prediction model for MS. Some regions of FPCN, such as middle frontal gyrus, superior frontal gyrus and inferior parietal lobe were critical in predicting MS. Moreover, functional interplay between FPCN and multiple networks, such as dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN), the limbic system and subcortical regions, enabled prediction of MS. Crucially, the predictive FPCN networks were stable and specific to MS. These results indicated that FPCN flexibility interacts with other networks to underpin the ability of MS. These would also be beneficial for understanding how compromises in these functional networks may have led to the intrusive thoughts and memories characterized in some mental disorders.

Volume of β-Bursts, But Not Their Rate, Predicts Successful Response Inhibition

In humans, impaired response inhibition is characteristic of a wide range of psychiatric diseases and of normal aging. It is hypothesized that the right inferior frontal cortex (rIFC) plays a key role by inhibiting the motor cortex via the basal ganglia. The electroencephalography (EEG)-derived β-rhythm (15-29 Hz) is thought to reflect communication within this network, with increased right frontal β-power often observed before successful response inhibition. Recent literature suggests that averaging spectral power obscures the transient, burst-like nature of β-activity. There is evidence that the rate of β-bursts following a Stop signal is higher when a motor response is successfully inhibited. However, other characteristics of β-burst events, and their topographical properties, have not yet been examined. Here, we used a large human (male and female) EEG Stop Signal task (SST) dataset (n = 218) to examine averaged normalized β-power, β-burst rate, and β-burst "volume" (which we defined as burst duration × frequency span × amplitude). We first sought to optimize the β-burst detection method. In order to find predictors across the whole scalp, and with high temporal precision, we then used machine learning to (1) classify successful versus failed stopping and to (2) predict individual stop signal reaction time (SSRT). β-burst volume was significantly more predictive of successful and fast stopping than β-burst rate and normalized β-power. The classification model generalized to an external dataset (n = 201). We suggest β-burst volume is a sensitive and reliable measure for investigation of human response inhibition.SIGNIFICANCE STATEMENT The electroencephalography (EEG)-derived β-rhythm (15-29 Hz) is associated with the ability to inhibit ongoing actions. In this study, we sought to identify the specific characteristics of β-activity that contribute to successful and fast inhibition. In order to search for the most relevant features of β-activity, across the whole scalp and with high temporal precision, we employed machine learning on two large datasets. Spatial and temporal features of β-burst "volume" (duration × frequency span × amplitude) predicted response inhibition outcomes in our data significantly better than β-burst rate and normalized β-power. These findings suggest that multidimensional measures of β-bursts, such as burst volume, can add to our understanding of human response inhibition.

Beyond fingerprinting: Choosing predictive connectomes over reliable connectomes

Recent years have seen a surge of research on variability in functional brain connectivity within and between individuals, with encouraging progress toward understanding the consequences of this variability for cognition and behavior. At the same time, well-founded concerns over rigor and reproducibility in psychology and neuroscience have led many to question whether functional connectivity is sufficiently reliable, and call for methods to improve its reliability. The thesis of this opinion piece is that when studying variability in functional connectivity-both across individuals and within individuals over time-we should use behavior prediction as our benchmark rather than optimize reliability for its own sake. We discuss theoretical and empirical evidence to compel this perspective, both when the goal is to study stable, trait-level differences between people, as well as when the goal is to study state-related changes within individuals. We hope that this piece will be useful to the neuroimaging community as we continue efforts to characterize inter- and intra-subject variability in brain function and build predictive models with an eye toward eventual real-world applications.

Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method

Resting-state functional connectivity (RSFC) can be used for mapping large-scale human brain networks during rest. There is considerable interest in distinguishing the individual-shared and individual-specific components in RSFC for the better identification of individuals and prediction of behavior. Therefore, we propose a multi-task learning based sparse convex alternating structure optimization (MTL-sCASO) method to decompose RSFC into individual-specific connectivity and individual-shared connectivity. We used synthetic data to validate the efficacy of the MTL-sCASO method. In addition, we verified that individual-specific connectivity achieves higher identification rates than the Pearson correlation (PC) method, and the individual-specific components observed in 886 individuals from the Human Connectome Project (HCP) examined in two sessions over two consecutive days might serve as individual fingerprints. Individual-specific connectivity has low inter-subject similarity (-0.005±0.023), while individual-shared connectivity has high inter-subject similarity (0.822±0.061). We also determined the anatomical locations (region or subsystem) related to individual attributes and common features. We find that individual-specific connectivity exhibits low degree centrality in the sensorimotor processing system but high degree centrality in the control system. Importantly, the individual-specific connectivity estimated by the MTL-sCASO method accurately predicts behavioral scores (improved by 9.4% compared to the PC method) in the cognitive dimension. The decomposition of individual-specific and individual-shared components from RSFC provides a new approach for tracing individual traits and group analysis using functional brain networks.

Dopamine-related striatal neurophysiology is associated with specialization of frontostriatal reward circuitry through adolescence

Characterizing developmental changes in frontostriatal circuitry is critical to understanding adolescent development and can clarify neurobiological mechanisms underlying increased reward sensitivity and risk-taking and the emergence of psychopathology during this period. However, the role of striatal neurobiology in the development of frontostriatal circuitry through human adolescence remains largely unknown. We examined background connectivity during a reward-guided decision-making task ("reward-state"), in addition to resting-state, and assessed the association between age-related changes in frontostriatal connectivity and age-related changes in reward learning and risk-taking through adolescence. Further, we examined the contribution of dopaminergic processes to changes in frontostriatal circuitry and decision-making using MR-based assessments of striatal tissue-iron as a correlate of dopamine-related neurobiology. Connectivity between the nucleus accumbens (NAcc) and ventral anterior cingulate, subgenual cingulate, and orbitofrontal cortices decreased through adolescence into adulthood, and decreases in reward-state connectivity were associated with improvements reward-guided decision-making as well as with decreases in risk-taking. Finally, NAcc tissue-iron mediated age-related changes and was associated with variability in connectivity, and developmental increases in NAcc R2' corresponded with developmental decreases in connectivity. Our results provide evidence that dopamine-related striatal properties contribute to the specialization of frontostriatal circuitry, potentially underlying changes in risk-taking and reward sensitivity into adulthood.

Functional connectivity during frustration: a preliminary study of predictive modeling of irritability in youth

Irritability cuts across many pediatric disorders and is a common presenting complaint in child psychiatry; however, its neural mechanisms remain unclear. One core pathophysiological deficit of irritability is aberrant responses to frustrative nonreward. Here, we conducted a preliminary fMRI study to examine the ability of functional connectivity during frustrative nonreward to predict irritability in a transdiagnostic sample. This study included 69 youths (mean age = 14.55 years) with varying levels of irritability across diagnostic groups: disruptive mood dysregulation disorder (n = 20), attention-deficit/hyperactivity disorder (n = 14), anxiety disorder (n = 12), and controls (n = 23). During fMRI, participants completed a frustrating cognitive flexibility task. Frustration was evoked by manipulating task difficulty such that, on trials requiring cognitive flexibility, "frustration" blocks had a 50% error rate and some rigged feedback, while "nonfrustration" blocks had a 10% error rate. Frustration and nonfrustration blocks were randomly interspersed. Child and parent reports of the affective reactivity index were used as dimensional measures of irritability. Connectome-based predictive modeling, a machine learning approach, with tenfold cross-validation was conducted to identify networks predicting irritability. Connectivity during frustration (but not nonfrustration) blocks predicted child-reported irritability (ρ = 0.24, root mean square error = 2.02, p = 0.03, permutation testing, 1000 iterations, one-tailed). Results were adjusted for age, sex, medications, motion, ADHD, and anxiety symptoms. The predictive networks of irritability were primarily within motor-sensory networks; among motor-sensory, subcortical, and salience networks; and between these networks and frontoparietal and medial frontal networks. This study provides preliminary evidence that individual differences in irritability may be associated with functional connectivity during frustration, a phenotype-relevant state.

Decoding different working memory states during an operation span task from prefrontal fNIRS signals

We propose an effective and practical decoding method of different mental states for potential applications for the design of brain-computer interfaces, prediction of cognitive behaviour, and investigation of cognitive mechanism. Functional near infrared spectroscopy (fNIRS) signals that interrogated the prefrontal and parietal cortices and were evaluated by generalized linear model were recorded when nineteen healthy adults performed the operation span (OSPAN) task. The oxygenated hemoglobin changes during OSPAN, response, and rest periods were classified with a support vector machine (SVM). The relevance vector regression algorithm was utilized for prediction of cognitive performance based on multidomain features of fNIRS signals from the OSPAN task. We acquired decent classification accuracies for OSPAN vs. response (above 91.2%) and for OSPAN vs. rest (above 94.7%). Eight of the ten cognitive testing scores could be predicted from the combination of OSPAN and response features, which indicated the brain hemodynamic responses contain meaningful information suitable for predicting cognitive performance.

Boost in Test-Retest Reliability in Resting State fMRI with Predictive Modeling

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

Multi-Paradigm fMRI Fusion via Sparse Tensor Decomposition in Brain Functional Connectivity Study

Functional magnetic resonance imaging (fMRI) is a powerful technique with the potential to estimate individual variations in behavioral and cognitive traits. Joint learning of multiple datasets can utilize their complementary information so as to improve learning performance, but it also gives rise to the challenge for data fusion to effectively integrate brain patterns elicited by multiple fMRI data. However, most of the current data fusion methods analyze each single dataset separately and further infer the relationship among them, which fail to utilize the multidimensional structure inherent across modalities and may ignore complex but important interactions. To address this issue, we propose a novel sparse tensor decomposition method to integrate multiple task-stimulus (paradigm) fMRI data. Seeing each paradigm fMRI as one modality, our proposed method considers the relationships across subjects and modalities simultaneously. In specific, a third-order tensor is first modeled by using the functional network connectivity (FNC) of subjects in multiple fMRI paradigms. A novel sparse tensor decomposition with the regularization terms is designed to factorize the tensor into a series of rank-one components, which can extract the shared components across modalities as the embedded features. The L2,1-norm regularizer (i.e., group sparsity) is enforced to select a few common features among multiple subjects. Validation of the proposed method is performed on realistic three paradigm fMRI datasets from the Philadelphia Neurodevelopmental Cohort (PNC) study, for the study of the relationship between the FNC and human cognitive abilities. Experimental results show our method outperforms several other competing methods in the prediction of individuals with different cognitive behaviors via the wide range achievement test (WRAT). Furthermore, our method discovers the FNC related to the cognitive behaviors, such as the connectivity associated with the default mode network (DMN) for three paradigms, and the connectivity between DMN and visual (VIS) domains within the emotion task.

Interactions between methodological and interindividual variability: How Monetary Incentive Delay (MID) task contrast maps vary and impact associations with behavior

INTRODUCTION

Phenomena related to reward responsiveness have been extensively studied in their associations with substance use and socioemotional functioning. One important task in this literature is the Monetary Incentive Delay (MID) task. By cueing and delivering performance-contingent reward, the MID task has been demonstrated to elicit robust activation of neural circuits involved in different phases of reward responsiveness. However, systematic evaluations of common MID task contrasts have been limited to between-study comparisons of group-level activation maps, limiting their ability to directly evaluate how researchers' choice of contrasts impacts conclusions about individual differences in reward responsiveness or brain-behavior associations.

METHODS

In a sample of 104 participants (Age Mean = 19.3, SD = 1.3), we evaluate similarities and differences between contrasts in: group- and individual-level activation maps using Jaccard's similarity index, region of interest (ROI) mean signal intensities using Pearson's r, and associations between ROI mean signal intensity and psychological measures using Bayesian correlation.

RESULTS

Our findings demonstrate more similarities than differences between win and loss cues during the anticipation contrast, dissimilarity between some win anticipation contrasts, an apparent deactivation effect in the outcome phase, likely stemming from the blood oxygen level-dependent undershoot, and behavioral associations that are less robust than previously reported.

CONCLUSION

Consistent with recent empirical findings, this work has practical implications for helping researchers interpret prior MID studies and make more informed a priori decisions about how their contrast choices may modify results.

Multimodal data revealed different neurobiological correlates of intelligence between males and females

Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separately, using resting-state functional connectivity, grey matter cortical thickness or both. The identified multimodal, IQ-predictive imaging features were then compared between genders. CPM showed high out-of-sample prediction accuracy (r > 0.34), and integrating both functional and structural features further improved prediction accuracy by capturing complementary information (r = 0.45). Male IQ demonstrated higher correlations with cortical thickness in the left inferior parietal lobule, and with functional connectivity in left parahippocampus and default mode network, regions previously implicated in spatial cognition and logical thinking. In contrast, female IQ was more correlated with cortical thickness in the right inferior parietal lobule, and with functional connectivity in putamen and cerebellar networks, regions previously implicated in verbal learning and item memory. Results suggest that the intelligence generation of males and females may rely on opposite cerebral lateralized key brain regions and distinct functional networks consistent with their respective superiority in cognitive domains. Promisingly, understanding the neural basis of gender differences underlying intelligence may potentially lead to optimized personal cognitive developmental programs and facilitate advancements in unbiased educational test design.

Resample aggregating improves the generalizability of connectome predictive modeling

It is a longstanding goal of neuroimaging to produce reliable, generalizable models of brain behavior relationships. More recently, data driven predictive models have become popular. However, overfitting is a common problem with statistical models, which impedes model generalization. Cross validation (CV) is often used to estimate expected model performance within sample. Yet, the best way to generate brain behavior models, and apply them out-of-sample, on an unseen dataset, is unclear. As a solution, this study proposes an ensemble learning method, in this case resample aggregating, encompassing both model parameter estimation and feature selection. Here we investigate the use of resampled aggregated models when used to estimate fluid intelligence (fIQ) from fMRI based functional connectivity (FC) data. We take advantage of two large openly available datasets, the Human Connectome Project (HCP), and the Philadelphia Neurodevelopmental Cohort (PNC). We generate aggregated and non-aggregated models of fIQ in the HCP, using the Connectome Prediction Modelling (CPM) framework. Over various test-train splits, these models are evaluated in sample, on left-out HCP data, and out-of-sample, on PNC data. We find that a resample aggregated model performs best both within- and out-of-sample. We also find that feature selection can vary substantially within-sample. More robust feature selection methods, as detailed here, are needed to improve cross sample performance of CPM based brain behavior models.

Movie-watching outperforms rest for functional connectivity-based prediction of behavior

A major goal of human neuroscience is to relate differences in brain function to differences in behavior across people. Recent work has established that whole-brain functional connectivity patterns are relatively stable within individuals and unique across individuals, and that features of these patterns predict various traits. However, while functional connectivity is most often measured at rest, certain tasks may enhance individual signals and improve sensitivity to behavior differences. Here, we show that compared to the resting state, functional connectivity measured during naturalistic viewing—i.e., movie watching—yields more accurate predictions of trait-like phenotypes in the domains of both cognition and emotion. Traits could be predicted using less than three minutes of data from single video clips, and clips with highly social content gave the most accurate predictions. Results suggest that naturalistic stimuli amplify individual differences in behaviorally relevant brain networks.

Network-level macroscale structural connectivity predicts propagation of transcranial magnetic stimulation

Information processing in the brain is mediated by structural white matter pathways and is highly dependent on topological brain properties. Here we combined transcranial magnetic stimulation (TMS) with high-density electroencephalography (EEG) and Diffusion Weighted Imaging (DWI), specifically looking at macroscale connectivity to understand whether regional, network-level or whole-brain structural properties are more responsible for stimulus propagation. Neuronavigated TMS pulses were delivered over two individually defined nodes of the default mode (DMN) and dorsal attention (DAN) networks in a group of healthy subjects, with test-retest reliability assessed 1-month apart. TMS-evoked activity was predicted by the modularity and structural integrity of the stimulated network rather than the targeted region(s) or the whole-brain connectivity, suggesting network-level structural connectivity as more relevant than local and global brain properties in shaping TMS signal propagation. The importance of network structural connectome was unveiled only by evoked activity, but not resting-state data. Future clinicals interventions might enhance target engagement by adopting DWI-guided, network-focused TMS.

Behavioural relevance of spontaneous, transient brain network interactions in fMRI

How spontaneously fluctuating functional magnetic resonance imaging (fMRI) signals in different brain regions relate to behaviour has been an open question for decades. Correlations in these signals, known as functional connectivity, can be averaged over several minutes of data to provide a stable representation of the functional network architecture for an individual. However, associations between these stable features and behavioural traits have been shown to be dominated by individual differences in anatomy. Here, using kernel learning tools, we propose methods to assess and compare the relation between time-varying functional connectivity, time-averaged functional connectivity, structural brain data, and non-imaging subject behavioural traits. We applied these methods to Human Connectome Project resting-state fMRI data to show that time-varying fMRI functional connectivity, detected at time-scales of a few seconds, has associations with some behavioural traits that are not dominated by anatomy. Despite time-averaged functional connectivity accounting for the largest proportion of variability in the fMRI signal between individuals, we found that some aspects of intelligence could only be explained by time-varying functional connectivity. The finding that time-varying fMRI functional connectivity has a unique relationship to population behavioural variability suggests that it might reflect transient neuronal communication fluctuating around a stable neural architecture.

Functional network estimation using multigraph learning with application to brain maturation study

Although most dramatic structural changes occur in the perinatal period, a growing body of evidences demonstrates that adolescence and early adulthood are also important for substantial neurodevelopment. We were thus motivated to explore brain development during puberty by evaluating functional connectivity network (FCN) differences between childhood and young adulthood using multi-paradigm task-based functional magnetic resonance imaging (fMRI) measurements. Different from conventional multigraph based FCN construction methods where the graph network was built independently for each modality/paradigm, we proposed a multigraph learning model in this work. It promises a better fitting to FCN construction by jointly estimating brain network from multi-paradigm fMRI time series, which may share common graph structures. To investigate the hub regions of the brain, we further conducted graph Fourier transform (GFT) to divide the fMRI BOLD time series of a node within the brain network into a range of frequencies. Then we identified the hub regions characterizing brain maturity through eigen-analysis of the low frequency components, which were believed to represent the organized structures shared by a large population. The proposed method was evaluated using both synthetic and real data, which demonstrated its effectiveness in extracting informative brain connectivity patterns. We detected 14 hub regions from the child group and 12 hub regions from the young adult group. We show the significance of these findings with a discussion of their functions and activation patterns as a function of age. In summary, our proposed method can extract brain connectivity network more accurately by considering the latent common structures between different fMRI paradigms, which are significant for both understanding brain development and recognizing population groups of different ages.

An expanding manifold in transmodal regions characterizes adolescent reconfiguration of structural connectome organization

Adolescence is a critical time for the continued maturation of brain networks. Here, we assessed structural connectome development in a large longitudinal sample ranging from childhood to young adulthood. By projecting high-dimensional connectomes into compact manifold spaces, we identified a marked expansion of structural connectomes, with strongest effects in transmodal regions during adolescence. Findings reflected increased within-module connectivity together with increased segregation, indicating increasing differentiation of higher-order association networks from the rest of the brain. Projection of subcortico-cortical connectivity patterns into these manifolds showed parallel alterations in pathways centered on the caudate and thalamus. Connectome findings were contextualized via spatial transcriptome association analysis, highlighting genes enriched in cortex, thalamus, and striatum. Statistical learning of cortical and subcortical manifold features at baseline and their maturational change predicted measures of intelligence at follow-up. Our findings demonstrate that connectome manifold learning can bridge the conceptual and empirical gaps between macroscale network reconfigurations, microscale processes, and cognitive outcomes in adolescent development.

The neural basis of intelligence in fine-grained cortical topographies

Intelligent thought is the product of efficient neural information processing, which is embedded in fine-grained, topographically organized population responses and supported by fine-grained patterns of connectivity among cortical fields. Previous work on the neural basis of intelligence, however, has focused on coarse-grained features of brain anatomy and function because cortical topographies are highly idiosyncratic at a finer scale, obscuring individual differences in fine-grained connectivity patterns. We used a computational algorithm, hyperalignment, to resolve these topographic idiosyncrasies and found that predictions of general intelligence based on fine-grained (vertex-by-vertex) connectivity patterns were markedly stronger than predictions based on coarse-grained (region-by-region) patterns. Intelligence was best predicted by fine-grained connectivity in the default and frontoparietal cortical systems, both of which are associated with self-generated thought. Previous work overlooked fine-grained architecture because existing methods could not resolve idiosyncratic topographies, preventing investigation where the keys to the neural basis of intelligence are more likely to be found.

A structural enriched functional network: An application to predict brain cognitive performance

The structure-function coupling in brain networks has emerged as an important research topic in modern neuroscience. The structural network could provide the backbone of the functional network. The integration of the functional network with structural information can help us better understand functional communication in the brain. This paper proposed a method to accurately estimate the brain functional network enriched by the structural network from diffusion magnetic resonance imaging. First, we adopted a simplex regression model with graph-constrained Elastic Net to construct the functional networks enriched by the structural network. Then, we compared the constructed network characteristics of this approach with several state-of-the-art competing functional network models. Furthermore, we evaluated whether the structural enriched functional network model improves the performance for predicting the cognitive-behavioral outcomes. The experiments have been performed on 218 participants from the Human Connectome Project database. The results demonstrated that our network model improves network consistency and its predictive performance compared with several state-of-the-art competing functional network models.

A Joint Analysis of Multi-Paradigm fMRI Data With Its Application to Cognitive Study

With the development of neuroimaging techniques, a growing amount of multi-modal brain imaging data are collected, facilitating comprehensive study of the brain. In this paper, we jointly analyzed functional magnetic resonance imaging (fMRI) collected under different paradigms in order to understand cognitive behaviors of an individual. To this end, we proposed a novel multi-view learning algorithm called structure-enforced collaborative regression (SCoRe) to extract co-expressed discriminative brain regions under the guidance of anatomical structure of the brain. An advantage of SCoRe over its predecessor collaborative regression (CoRe) lies in its incorporation of group structures in the brain imaging data, which makes the model biologically more meaningful. Results from real data analysis has confirmed that by incorporating prior knowledge of brain structure, SCoRe can deliver better prediction performance and is less sensitive to hyper-parameters than CoRe. After validation with simulation experiments, we applied SCoRe to fMRI data collected from the Philadelphia Neurodevelopmental Cohort and adopted the scores from the wide range achievement test (WRAT) to evaluate an individual's cognitive skills. We located 14 relevant brain regions that can efficiently predict WRAT scores and these brain regions were further confirmed by other independent studies.

How to Interpret Resting-State fMRI: Ask Your Participants

Resting-state fMRI (rsfMRI) reveals brain dynamics in a task-unconstrained environment as subjects let their minds wander freely. Consequently, resting subjects navigate a rich space of cognitive and perceptual states (i.e., ongoing experience). How this ongoing experience shapes rsfMRI summary metrics (e.g., functional connectivity) is unknown, yet likely to contribute uniquely to within- and between-subject differences. Here we argue that understanding the role of ongoing experience in rsfMRI requires access to standardized, temporally resolved, scientifically validated first-person descriptions of those experiences. We suggest best practices for obtaining those descriptions via introspective methods appropriately adapted for use in fMRI research. We conclude with a set of guidelines for fusing these two data types to answer pressing questions about the etiology of rsfMRI.

General Intelligence Is Associated with Working Memory-Related Functional Connectivity Change: Evidence from a Large-Sample Study

Background/Purpose: Psychometric intelligence is closely related to working memory (WM) and the associated brain activity. We aimed to clarify the associations between psychometric intelligence and WM-induced functional connectivity changes. Materials and Methods: Here we determined the associations between psychometric intelligence measured by nonverbal reasoning (using the Raven's Advanced Progressive Matrices) and WM-induced changes in functional connectivity during the N-back paradigm, in a large cohort of 1221 young adults. Results: We observed that the measures of general intelligence showed a significant positive correlation with WM-induced changes in the functional connectivity with the key nodes of the frontoparietal network, such as the bilateral premotor cortices and the presupplementary motor area. Those significant correlations were observed for (1) areas showing a WM-induced increase of the functional connectivity with the abovementioned key nodes, such as the lateral parietal cortex; (2) areas showing a WM-induced decrease of the functional connectivity with the abovementioned key nodes (2-a) such as left perisylvian areas and cuneus, the fusiform gyrus, and the lingual gyrus, which play key roles in language processing, (2-b) hippocampus and parahippocampal gyrus, which play key roles in memory processing, and (2-c) the key node of the default mode network such as the medial prefrontal cortex; as well as (3) the border areas between (1) and (2). Conclusion: Psychometric intelligence is associated with WM-induced changes in functional connectivity, influencing the way in which WM key nodes dynamically modulate the interaction with other brain nodes in response to WM.

The Functional Relevance of Task-State Functional Connectivity

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