Search

Pediatric obesity rates have quadrupled in the United States, and deficits in higher-order cognition have been linked to obesity, though it remains poorly understood how deviations from normal body mass are related to the neural dynamics serving cognition in youth. Herein, we determine how age- and sex-adjusted measures of body mass index (zBMI) scale with neural activity in brain regions underlying fluid intelligence. Seventy-two youth aged 9–16 years underwent high-density magnetoencephalography while performing an abstract reasoning task. The resulting data were transformed into the time-frequency domain and significant oscillatory responses were imaged using a beamformer. Whole-brain correlations with zBMI were subsequently conducted to quantify relationships between zBMI and neural activity serving abstract reasoning. Our results reveal that participants with higher zBMI exhibit attenuated theta (4–8 Hz) responses in both the left dorsolateral prefrontal cortex and left temporoparietal junction, and that weaker temporoparietal responses scale with slower reaction times. These findings suggest that higher zBMI values are associated with weaker theta oscillations in key brain regions and altered performance during an abstract reasoning task. Thus, future investigations should evaluate neurobehavioral function during abstract reasoning in youth with more severe obesity to identify the potential impact. Standardized indices of obesity are associated with alterations in neural oscillatory activity and functional connectivity in brain regions supporting abstract reasoning and fluid intelligence in typically developing youth. [ABSTRACT FROM AUTHOR]

Background: Cannabis is the most widely used psychoactive drug in the United States. While multiple studies have associated acute cannabis consumption with alterations in cognitive function (e.g., visual and spatial attention), far less is known regarding the effects of chronic consumption on the neural dynamics supporting these cognitive functions. Methods: We used magnetoencephalography (MEG) and an established visuospatial processing task to elicit multi-spectral neuronal responses in 44 regular cannabis users and 53 demographically matched non-user controls. To examine the effects of chronic cannabis use on the oscillatory dynamics underlying visuospatial processing, neural responses were imaged using a time-frequency resolved beamformer and compared across groups. Results: Neuronal oscillations serving visuospatial processing were identified in the theta (4–8 Hz), alpha (8–14 Hz), and gamma range (56–76 Hz), and these were imaged and examined for group differences. Our key results indicated that users exhibited weaker theta oscillations in occipital and cerebellar regions and weaker gamma responses in the left temporal cortices compared to non-users. Lastly, alpha oscillations did not differ, but alpha connectivity among higher-order attention areas was weaker in cannabis users relative to non-users and correlated with performance. Conclusions: Overall, these results suggest that chronic cannabis users have alterations in the oscillatory dynamics and neural connectivity serving visuospatial attention. Such alterations were observed across multiple cortical areas critical for higher-order processing and may reflect compensatory activity and/or the initial emergence of aberrant dynamics. Future work is needed to fully understand the implications of altered multispectral oscillations and neural connectivity in cannabis users. [ABSTRACT FROM AUTHOR]

Fluid intelligence (Gf) involves rational thinking skills and requires the integration of information from different cortical regions to resolve novel complex problems. The effects of non‐invasive brain stimulation on Gf have been studied in attempts to improve Gf, but such studies are rare and the few existing have reached conflicting conclusions. The parieto‐frontal integration theory of intelligence (P‐FIT) postulates that the parietal and frontal lobes play a critical role in Gf. To investigate the suggested role of parietal cortices, we applied high‐definition transcranial direct current stimulation (HD‐tDCS) to the left and right parietal cortices of 39 healthy adults (age 19–33 years) for 20 min in three separate sessions (left active, right active and sham). After completing the stimulation session, the participants completed a logical reasoning task based on Raven's Progressive Matrices during magnetoencephalography. Significant neural responses at the sensor level across all stimulation conditions were imaged using a beamformer. Whole‐brain, spectrally constrained functional connectivity was then computed to examine the network‐level activity. Behaviourally, we found that participants were significantly more accurate following left compared to right parietal stimulation. Regarding neural findings, we found significant HD‐tDCS montage‐related effects in brain networks thought to be critical for P‐FIT, including parieto‐occipital, fronto‐occipital, fronto‐parietal and occipito‐cerebellar connectivity during task performance. In conclusion, our findings showed that left parietal stimulation improved abstract reasoning abilities relative to right parietal stimulation and support both P‐FIT and the neural efficiency hypothesis. Key points: Abstract reasoning is a critical component of fluid intelligence and is known to be served by multispectral oscillatory activity in the fronto‐parietal cortices.Recent studies have aimed to improve abstract reasoning abilities and fluid intelligence overall through behavioural training, but the results have been mixed.High‐definition transcranial direct‐current stimulation (HD‐tDCS) applied to the parietal cortices modulated task performance and neural oscillations during abstract reasoning.Left parietal stimulation resulted in increased accuracy and decreased functional connectivity between occipital regions and frontal, parietal, and cerebellar regions.Future studies should investigate whether HD‐tDCS alters abstract reasoning abilities in those who exhibit declines in performance, such as healthy ageing populations. [ABSTRACT FROM AUTHOR]

Fluid intelligence (Gf) involves logical reasoning and novel problem‐solving abilities. Often, abstract reasoning tasks like Raven's progressive matrices are used to assess Gf. Prior work has shown an age‐related decline in fluid intelligence capabilities, and although many studies have sought to identify the underlying mechanisms, our understanding of the critical brain regions and dynamics remains largely incomplete. In this study, we utilized magnetoencephalography (MEG) to investigate 78 individuals, ages 20–65 years, as they completed an abstract reasoning task. MEG data was co‐registered with structural MRI data, transformed into the time–frequency domain, and the resulting neural oscillations were imaged using a beamformer. We found worsening behavioral performance with age, including prolonged reaction times and reduced accuracy. MEG analyses indicated robust oscillations in the theta, alpha/beta, and gamma range during the task. Whole brain correlation analyses with age revealed relationships in the theta and alpha/beta frequency bands, such that theta oscillations became stronger with increasing age in a right prefrontal region and alpha/beta oscillations became stronger with increasing age in parietal and right motor cortices. Follow‐up connectivity analyses revealed increasing parieto‐frontal connectivity with increasing age in the alpha/beta frequency range. Importantly, our findings are consistent with the parieto‐frontal integration theory of intelligence (P‐FIT). These results further suggest that as people age, there may be alterations in neural responses that are spectrally specific, such that older people exhibit stronger alpha/beta oscillations across the parieto‐frontal network during abstract reasoning tasks. [ABSTRACT FROM AUTHOR]

The increasing incidence of age‐related comorbidities in people with HIV (PWH) has led to accelerated aging theories. Functional neuroimaging research, including functional connectivity (FC) using resting‐state functional magnetic resonance imaging (rs‐fMRI), has identified neural aberrations related to HIV infection. Yet little is known about the relationship between aging and resting‐state FC in PWH. This study included 86 virally suppressed PWH and 99 demographically matched controls spanning 22–72 years old who underwent rs‐fMRI. The independent and interactive effects of HIV and aging on FC were investigated both within‐ and between‐network using a 7‐network atlas. The relationship between HIV‐related cognitive deficits and FC was also examined. We also conducted network‐based statistical analyses using a brain anatomical atlas (n = 512 regions) to ensure similar results across independent approaches. We found independent effects of age and HIV in between‐network FC. The age‐related increases in FC were widespread, while PWH displayed further increases above and beyond aging, particularly between‐network FC of the default‐mode and executive control networks. The results were overall similar using the regional approach. Since both HIV infection and aging are associated with independent increases in between‐network FC, HIV infection may be associated with a reorganization of the major brain networks and their functional interactions in a manner similar to aging. [ABSTRACT FROM AUTHOR]

The brain's functional architecture and organization undergo continual development and modification throughout adolescence. While it is well known that multiple factors govern brain maturation, the constantly evolving patterns of time‐resolved functional connectivity are still unclear and understudied. We systematically evaluated over 47,000 youth and adult brains to bridge this gap, highlighting replicable time‐resolved developmental and aging functional brain patterns. The largest difference between the two life stages was captured in a brain state that indicated coherent strengthening and modularization of functional coupling within the auditory, visual, and motor subdomains, supplemented by anticorrelation with other subdomains in adults. This distinctive pattern, which we replicated in independent data, was consistently less modular or absent in children and presented a negative association with age in adults, thus indicating an overall inverted U‐shaped trajectory. This indicates greater synchrony, strengthening, modularization, and integration of the brain's functional connections beyond adolescence, and gradual decline of this pattern during the healthy aging process. We also found evidence that the developmental changes may also bring along a departure from the canonical static functional connectivity pattern in favor of more efficient and modularized utilization of the vast brain interconnections. State‐based statistical summary measures presented robust and significant group differences that also showed significant age‐related associations. The findings reported in this article support the idea of gradual developmental and aging brain state adaptation processes in different phases of life and warrant future research via lifespan studies to further authenticate the projected time‐resolved brain state trajectories. [ABSTRACT FROM AUTHOR]

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 $\ell _{2,1-2}$ and $\ell _{1-2}$ 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. [ABSTRACT FROM AUTHOR]

Fluid intelligence (Gƒ) includes logical reasoning abilities and is an essential component of normative cognition. Despite the broad consensus that parieto‐prefrontal connectivity is critical for Gƒ (e.g. the parieto‐frontal integration theory of intelligence, P‐FIT), the dynamics of such functional connectivity during logical reasoning remains poorly understood. Further, given the known importance of these brain regions for Gƒ, numerous studies have targeted one or both of these areas with non‐invasive stimulation with the goal of improving Gƒ, but to date there remains little consensus on the overall stimulation‐related effects. To examine this, we applied high‐definition direct current anodal stimulation to the left and right dorsolateral prefrontal cortex (DLPFC) of 24 healthy adults for 20 min in three separate sessions (sham, left, and right active). Following stimulation, participants completed a logical reasoning task during magnetoencephalography (MEG). Significant neural responses at the sensor‐level were imaged using a beamformer, and peak task‐induced activity was subjected to dynamic functional connectivity analyses to evaluate the impact of distinct stimulation montages on network activity. We found that participants responded faster following right DLPFC stimulation vs. sham. Moreover, our neural findings followed a similar trajectory of effects such that left parieto‐frontal connectivity decreased following right and left DLPFC stimulation compared to sham, with connectivity following right stimulation being significantly correlated with the faster reaction times. Importantly, our findings are consistent with P‐FIT, as well as the neural efficiency hypothesis (NEH) of intelligence. In sum, this study provides evidence for beneficial effects of right DLPFC stimulation on logical reasoning. Key points: Logical reasoning is an indispensable component of fluid intelligence and involves multispectral oscillatory activity in parietal and frontal regions.Parieto‐frontal integration is well characterized in logical reasoning; however, its direct neural quantification and neuromodulation by brain stimulation remain poorly understood.High‐definition transcranial direct current stimulation of dorsolateral prefrontal cortex (DLPFC) had modulatory effects on task performance and neural interactions serving logical reasoning, with right stimulation showing beneficial effects.Right DLPFC stimulation led to a decrease in the response time (i.e. better task performance) and left parieto‐frontal connectivity with a marginal positive association between behavioural and neural metrics.Other modes of targeted stimulation of DLPFC (e.g. frequency‐specific) can be employed in future studies. [ABSTRACT FROM AUTHOR]

Background: Cannabis is one of the most commonly used substances in the United States. Prior literature using task-based functional magnetic resonance imaging (fMRI) has identified that individuals with Cannabis use disorder (CUD) show impairments in emotion processing circuitry. However, whether the functional networks involving these regions are also altered in CUD remains poorly understood. Aims: Investigate changes in resting-state functional connectivity (rsFC) in regions related to emotional processing in CUD. Methods: Sixty-two participants completed resting-state fMRI, including 21 with CUD, 20 with histories of illicit substance use but no current CUD diagnosis, and 21 with no history of illicit substance use. Whole-brain seed-based connectivity analyses were performed and one-way analyses of covariance (ANCOVAs) were conducted to detect group differences in the bilateral amygdalae, hippocampi, and the anterior and posterior cingulate cortices. Results: The CUD group exhibited significant reductions in rsFC between the amygdala and the cuneus, paracentral lobule, and supplementary motor area, and between the cingulate cortices and the occipital and temporal lobes. There were no significant group differences in hippocampal functional connectivity. In addition, CUD symptom counts based on the Structured Clinical Interview for DSM-5 (SCID) and the Cannabis Use Disorders Identification Test (CUDIT) significantly correlated with multiple connectivity metrics. Conclusion: These data expand on emerging literature indicating that CUD is associated with dysfunction in the neural circuits underlying emotion processing. Dysfunction in emotion processing circuits may play a role in the behavioral impairments seen in emotion processing tasks in individuals with CUD, and the severity of CUD symptoms appears to be directly related to the degree of dysfunction in these circuits. [ABSTRACT FROM AUTHOR]

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). [ABSTRACT FROM AUTHOR]

The hippocampus is necessary for declarative (relational) memory, and the ability to form hippocampal‐dependent memories develops through late adolescence. This developmental trajectory of hippocampal‐dependent memory could reflect maturation of intrinsic functional brain networks, but resting‐state functional connectivity (rs‐FC) of the human hippocampus is not well‐characterized for periadolescent children. Measuring hippocampal rs‐FC in periadolescence would thus fill a gap, and testing covariance of hippocampal rs‐FC with age and memory could inform theories of cognitive development. Here, we studied hippocampal rs‐FC in a cross‐sectional sample of healthy children (N = 96; 59 F; age 9–15 years) using a seed‐based approach, and linked these data with NIH Toolbox measures, the Picture‐Sequence Memory Test (PSMT) and the List Sorting Working Memory Test (LSWMT). The PSMT was expected to rely more on hippocampal‐dependent memory than the LSWMT. We observed hippocampal rs‐FC with an extensive brain network including temporal, parietal, and frontal regions. This pattern was consistent with prior work measuring hippocampal rs‐FC in younger and older samples. We also observed novel, regionally specific variation in hippocampal rs‐FC with age and hippocampal‐dependent memory but not working memory. Evidence consistent with these findings was observed in a second, validation dataset of similar‐age healthy children drawn from the Philadelphia Neurodevelopment Cohort. Further, a cross‐dataset analysis suggested generalizable properties of hippocampal rs‐FC and covariance with age and memory. Our findings connect prior work by describing hippocampal rs‐FC and covariance with age and memory in typically developing periadolescent children, and our observations suggest a developmental trajectory for brain networks that support hippocampal‐dependent memory. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Functional network connectivity has been widely acknowledged to characterize brain functions, which can be regarded as "brain fingerprinting" to identify an individual from a pool of subjects. Both common and unique information has been shown to exist in the connectomes across individuals. However, very little is known about whether and how this information can be used to predict the individual variability of the brain. In this paper, we propose to enhance the uniqueness of individual connectome based on an autoencoder network. Specifically, we hypothesize that the common neural activities shared across individuals may reduce the individual identification. By removing contributions from shared activities, inter‐subject variability can be enhanced. Our experimental results on HCP data show that the refined connectomes obtained by utilizing autoencoder with sparse dictionary learning can distinguish an individual from the remaining participants with high accuracy (up to 99.5% for the rest–rest pair). Furthermore, high‐level cognitive behaviors (e.g., fluid intelligence, executive function, and language comprehension) can also be better predicted with the obtained refined connectomes. We also find that high‐order association cortices contribute more to both individual discrimination and behavior prediction. In summary, our proposed framework provides a promising way to leverage functional connectivity networks for cognition and behavior study, in addition to a better understanding of brain functions. [ABSTRACT FROM AUTHOR]

Rationale and objectives: Cognitive processing impairments have been associated with acute cannabis use, but there is mixed evidence regarding the cognitive effects of chronic cannabis use. Several neuroimaging studies have noted selective-attention processing differences in those who chronically use cannabis, but the neural dynamics governing the altered processing is unclear. Methods: Twenty-four adults reporting at least weekly cannabis use in the past 6 months on the Cannabis Use Disorder Identification Test – Revised were compared to 24 demographically matched controls who reported no prior cannabis use. All participants completed a visual selective attention processing task while undergoing magnetoencephalography. Time-frequency windows of interest were identified using a data-driven method, and spectrally specific neural activity was imaged using a beamforming approach. Results: All participants performed within normal range on the cognitive task. Regular cannabis users displayed an aberrant cognitive interference effect in the theta (4–8 Hz) frequency range shortly after stimulus onset (i.e., 0–250 ms) in the right occipital cortex. Cannabis users also exhibited altered functional connectivity between the right prefrontal cortex and right occipital cortices in comparison to controls. Conclusions: Individuals with a history of regular cannabis use exhibited abnormal theta interference activity in the occipital cortices, as well as altered prefrontal-occipital functional connectivity in the theta range during a visual selective attention task. Such differences may reflect compensatory processing, as these participants performed within normal range on the task. Understanding the neural dynamics in chronic, regular cannabis users may provide insight on how long-term and/or frequent use may affect neural networks underlying cognitive processes. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: Recently, functional magnetic resonance imaging (fMRI)-derived brain functional connectivity (FC) patterns have been used as fingerprints to predict individual differences in phenotypic measures, and cognitive dysfunction associated with brain diseases. In these applications, how to accurately estimate FC patterns is crucial yet technically challenging. Methods: In this article, we propose a correlation guided graph learning (CGGL) method to estimate FC patterns for establishing brain-behavior relationships. Different from the existing graph learning methods which only consider the graph structure across brain regions-of-interest (ROIs), our proposed CGGL takes into account both the temporal correlation of ROIs across time points, and the graph structure across ROIs. The resulting FC patterns reflect substantial inter-individual variations related to the behavioral measure of interest. Results: We validate the effectiveness of our proposed CGGL on the Philadelphia Neurodevelopmental Cohort data for separately predicting three behavioral measures based on resting-state fMRI. Experimental results demonstrate that the proposed CGGL outperforms other competing FC pattern estimation methods. Conclusion: Our method increases the predictive power of the constructed FC patterns when establishing brain-behavior relationships, and gains meaningful insights into relevant biological mechanisms. Significance: The proposed CGGL offers a more powerful, and reliable method to estimate FC patterns, which can be used as fingerprints in many brain network studies. [ABSTRACT FROM AUTHOR]

Human brain development is a complex and dynamic process caused by several factors such as genetics, sex hormones, and environmental changes. A number of recent studies on brain development have examined functional connectivity (FC) defined by the temporal correlation between time series of different brain regions. We propose to add the directional flow of information during brain maturation. To do so, we extract effective connectivity (EC) through Granger causality (GC) for two different groups of subjects, i.e., children and young adults. The motivation is that the inclusion of causal interaction may further discriminate brain connections between two age groups and help to discover new connections between brain regions. The contributions of this study are threefold. First, there has been a lack of attention to EC-based feature extraction in the context of brain development. To this end, we propose a new kernel-based GC (KGC) method to learn nonlinearity of complex brain network, where a reduced Sine hyperbolic polynomial (RSP) neural network was used as our proposed learner. Second, we used causality values as the weight for the directional connectivity between brain regions. Our findings indicated that the strength of connections was significantly higher in young adults relative to children. In addition, our new EC-based feature outperformed FC-based analysis from Philadelphia neurocohort (PNC) study with better discrimination of different age groups. Moreover, the fusion of these two sets of features (FC + EC) improved brain age prediction accuracy by more than 4%, indicating that they should be used together for brain development studies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Objective: Multi-modal brain functional connectivity (FC) data have shown great potential for providing insights into individual variations in behavioral and cognitive traits. The joint learning of multi-modal data can utilize intrinsic association, and thus can boost learning performance. Although several multi-task based learning models have already been proposed by viewing feature learning on each modality as one task, most of them ignore the structural information inherent across the modalities, which may play an important role in extracting discriminative features. Methods: In this paper, we propose a new manifold regularized multi-task learning model by simultaneously considering between-subject and between-modality relationships. Specifically, the $l_{2,1}$ -norm (i.e., group-sparsity) regularizer is enforced to jointly select a few common features across different modalities. A novelly designed manifold regularizer is further imposed as a crucial underpinning to preserve the structural information both within and between modalities. Such designed regularizers will make our model more adaptive to realistic neuroimaging data, which are usually of small sample size but high dimensional features. Results: Our model is validated on the Philadelphia Neurodevelopmental Cohort dataset, where our modalities are regarded as two types of functional MRI (fMRI) data collected under two paradigms. We conduct experimental studies on fMRI-based FC network data in two task conditions for intelligence quotient (IQ) prediction. The results show that our proposed model can not only achieve improved prediction performance, but also yield a set of IQ-relevant biomarkers. Conclusion and Significance: This paper develops a new multi-task learning model, enabling the discovery of significant biomarkers that may account for a proportion of the variance in human intelligence. [ABSTRACT FROM AUTHOR]

Key points: Visual attention involves discrete multispectral oscillatory responses in visual and 'higher‐order' prefrontal cortices.Prefrontal cortex laterality effects during visual selective attention are poorly characterized.High‐definition transcranial direct current stimulation dynamically modulated right‐lateralized fronto‐visual theta oscillations compared to those observed in left fronto‐visual pathways.Increased connectivity in right fronto‐visual networks after stimulation of the left dorsolateral prefrontal cortex resulted in faster task performance in the context of distractors.Our findings show clear laterality effects in theta oscillatory activity along prefrontal–visual cortical pathways during visual selective attention. Studies of visual attention have implicated oscillatory activity in the recognition, protection and temporal organization of attended representations in visual cortices. These studies have also shown that higher‐order regions such as the prefrontal cortex are critical to attentional processing, but far less is understood regarding prefrontal laterality differences in attention processing. To examine this, we selectively applied high‐definition transcranial direct current stimulation (HD‐tDCS) to the left or right dorsolateral prefrontal cortex (DLPFC). We predicted that HD‐tDCS of the left versus right prefrontal cortex would differentially modulate performance on a visual selective attention task, and alter the underlying oscillatory network dynamics. Our randomized crossover design included 27 healthy adults that underwent three separate sessions of HD‐tDCS (sham, left DLPFC and right DLPFC) for 20 min. Following stimulation, participants completed an attention protocol during magnetoencephalography. The resulting oscillatory dynamics were imaged using beamforming, and peak task‐related neural activity was subjected to dynamic functional connectivity analyses to evaluate the impact of stimulation site (i.e. left and right DLPFC) on neural interactions. Our results indicated that HD‐tDCS over the left DLPFC differentially modulated right fronto‐visual functional connectivity within the theta band compared to HD‐tDCS of the right DLPFC and further, specifically modulated the oscillatory response for detecting targets among an array of distractors. Importantly, these findings provide network‐specific insight into the complex oscillatory mechanisms serving visual selective attention. Key points: Visual attention involves discrete multispectral oscillatory responses in visual and 'higher‐order' prefrontal cortices.Prefrontal cortex laterality effects during visual selective attention are poorly characterized.High‐definition transcranial direct current stimulation dynamically modulated right‐lateralized fronto‐visual theta oscillations compared to those observed in left fronto‐visual pathways.Increased connectivity in right fronto‐visual networks after stimulation of the left dorsolateral prefrontal cortex resulted in faster task performance in the context of distractors.Our findings show clear laterality effects in theta oscillatory activity along prefrontal–visual cortical pathways during visual selective attention. [ABSTRACT FROM AUTHOR]

Brain functional connectome analysis is commonly based on population‐wise inference. However, in this way precious information provided at the individual subject level may be overlooked. Recently, several studies have shown that individual differences contribute strongly to the functional connectivity patterns. In particular, functional connectomes have been proven to offer a fingerprint measure, which can reliably identify a given individual from a pool of participants. In this work, we propose to refine the standard measure of individual functional connectomes using dictionary learning. More specifically, we rely on the assumption that each functional connectivity is dominated by stable group and individual factors. By subtracting population‐wise contributions from connectivity patterns facilitated by dictionary representation, intersubject variability should be increased within the group. We validate our approach using several types of analyses. For example, we observe that refined connectivity profiles significantly increase subject‐specific identifiability across functional magnetic resonance imaging (fMRI) session combinations. Besides, refined connectomes can also improve the prediction power for cognitive behaviors. In accordance with results from the literature, we find that individual distinctiveness is closely linked with differences in neurocognitive activity within the brain. In summary, our results indicate that individual connectivity analysis benefits from the group‐wise inferences and refined connectomes are indeed desirable for brain mapping. [ABSTRACT FROM AUTHOR]

Objective: To explain individual differences in development, behavior, and cognition, most previous studies focused on projecting resting-state functional MRI (fMRI) based functional connectivity (FC) data into a low-dimensional space via linear dimensionality reduction techniques, followed by executing analysis operations. However, linear dimensionality analysis techniques may fail to capture the nonlinearity of brain neuroactivity. Moreover, besides resting-state FC, the FC based on task fMRI can be expected to provide complementary information. Motivated by these considerations, we nonlinearly fuse resting-state and task-based FC networks (FCNs) to seek a better representation in this paper. Methods: We propose a framework based on alternating diffusion map (ADM), which extracts geometry-preserving low-dimensional embeddings that successfully parameterize the intrinsic variables driving the phenomenon of interest. Specifically, we first separately build resting-state and task-based FCNs by symmetric positive definite matrices using sparse inverse covariance estimation for each subject, and then utilize the ADM to fuse them in order to extract significant low-dimensional embeddings, which are used as fingerprints to identify individuals. Results: The proposed framework is validated on the Philadelphia Neurodevelopmental Cohort data, where we conduct extensive experimental study on resting-state and fractal $n$ -back task fMRI for the classification of intelligence quotient (IQ). The fusion of resting-state and $n$ -back task fMRI by the proposed framework achieves better classification accuracy than any single fMRI, and the proposed framework is shown to outperform several other data fusion methods. Conclusion and Significance: To our knowledge, this paper is the first to demonstrate a successful extension of the ADM to fuse resting-state and task-based fMRI data for accurate prediction of IQ. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of estimating multiple sparse, co-activated brain regions from functional magnetic resonance imaging (fMRI) observations belonging to different classes. More precisely, we propose a method to analyze similarities and differences in functional connectivity between children and young adults. Often, analysis is conducted on each class separately, and differences across classes are identified with an additional postprocessing step using adequate statistical tools. Here, we propose to rely on a generalized fused Lasso penalty, which allows us to make use of the entire data set in order to estimate connectivity patterns that are either shared across classes, or specific to a given group. By using the entire population during the estimation, we hope to increase the power of our analysis. The proposed model falls in the category of population-wise matrix decomposition, and a simple and efficient alternating direction method of multipliers algorithm is introduced to solve the associated optimization problem. After validating our approach on simulated data, experiments are performed on resting-state fMRI imaging from the Philadelphia neurodevelopmental cohort data set, comprised of normally developing children from ages 8 to 21. Developmental differences were observed in various brain regions, as a total of three class-specific resting-state components were identified. Statistical analysis of the estimated subject-specific features, as well as classification results (based on age groups, up to 81% accuracy, $n = 583$ samples) related to these components demonstrate that the proposed method is able to properly extract meaningful shared and class-specific sub-networks. [ABSTRACT FROM AUTHOR]

Abstract: The ability to reorient attention within the visual field is central to daily functioning, and numerous fMRI studies have shown that the dorsal and ventral attention networks (DAN, VAN) are critical to such processes. However, despite the instantaneous nature of attentional shifts, the dynamics of oscillatory activity serving attentional reorientation remain poorly characterized. In this study, we utilized magnetoencephalography (MEG) and a Posner task to probe the dynamics of attentional reorienting in 29 healthy adults. MEG data were transformed into the time‐frequency domain and significant oscillatory responses were imaged using a beamformer. Voxel time series were then extracted from peak voxels in the functional beamformer images. These time series were used to quantify the dynamics of attentional reorienting, and to compute dynamic functional connectivity. Our results indicated strong increases in theta and decreases in alpha and beta activity across many nodes in the DAN and VAN. Interestingly, theta responses were generally stronger during trials that required attentional reorienting relative to those that did not, while alpha and beta oscillations were more dynamic, with many regions exhibiting significantly stronger responses during non‐reorienting trials initially, and the opposite pattern during later processing. Finally, stronger functional connectivity was found following target presentation (575‐700 ms) between bilateral superior parietal lobules during attentional reorienting. In sum, these data show that visual attention is served by multiple cortical regions within the DAN and VAN, and that attentional reorienting processes are often associated with spectrally‐specific oscillations that have largely distinct spatiotemporal dynamics. [ABSTRACT FROM AUTHOR]

Multi-paradigm deep learning models show great potential for dynamic functional connectivity (dFC) analysis by integrating complementary information. However, many of them cannot use information from different paradigms effectively and have poor explainability, that is, the ability to identify significant features that contribute to decision making. In this paper, we propose a multi-paradigm fusion-based explainable deep sparse autoencoder (MF-EDSAE) to address these issues. Considering explainability, the MF-EDSAE is constructed based on a deep sparse autoencoder (DSAE). For integrating information effectively, the MF-EDASE contains the nonlinear fusion layer and multi-paradigm hypergraph regularization. We apply the model to the Philadelphia Neurodevelopmental Cohort and demonstrate it achieves better performance in detecting dynamic FC (dFC) that differ significantly during brain development than the single-paradigm DSAE. The experimental results show that children have more dispersive dFC patterns than adults. The function of the brain transits from undifferentiated systems to specialized networks during brain development. Meanwhile, adults have stronger connectivities between task-related functional networks for a given task than children. As the brain develops, the patterns of the global dFC change more quickly when stimulated by a task. [ABSTRACT FROM AUTHOR]

Modeling dynamic interactions among network components is crucial to uncovering the evolution mechanisms of complex networks. Recently, spatio-temporal graph learning methods have achieved noteworthy results in characterizing the dynamic changes of inter-node relations (INRs). However, challenges remain: The spatial neighborhood of an INR is underexploited, and the spatio-temporal dependencies in INRs' dynamic changes are overlooked, ignoring the influence of historical states and local information. In addition, the model's explainability has been understudied. To address these issues, we propose an explainable spatio-temporal graph evolution learning (ESTGEL) model to model the dynamic evolution of INRs. Specifically, an edge attention module is proposed to utilize the spatial neighborhood of an INR at multi-level, i.e., a hierarchy of nested subgraphs derived from decomposing the initial node-relation graph. Subsequently, a dynamic relation learning module is proposed to capture the spatio-temporal dependencies of INRs. The INRs are then used as adjacent information to improve the node representation, resulting in comprehensive delineation of dynamic evolution of the network. Finally, the approach is validated with real data on brain development study. Experimental results on dynamic brain networks analysis reveal that brain functional networks transition from dispersed to more convergent and modular structures throughout development. Significant changes are observed in the dynamic functional connectivity (dFC) associated with functions including emotional control, decision-making, and language processing. • Dynamic evolution of brain networks with spatio-temporal dependencies are revealed. • Multi-level neighborhood information of functional connection is explored. • Brain developmental differences with dynamic functional connectivity are revealed. [ABSTRACT FROM AUTHOR]

• Functional connectivity serving cognitive control in healthy aging are understudied. • 78 participants performed the multisource interference task during MEG. • Weaker age-related fronto-visual coupling during flanker interference were observed. • Parietal gamma power differed between Simon and flanker interference conditions. • The study suggests age-related inefficiency in top-down attentional control. The presence of conflicting stimuli adversely affects behavioral outcomes, which could either be at the level of stimulus (Flanker), response (Simon), or both (Multisource). Briefly, flanker interference involves conflicting stimuli requiring selective attention, Simon interference is caused by an incongruity between the spatial location of the task-relevant stimulus and prepotent motor mapping, and multisource is combination of both. Irrespective of the variant, interference resolution necessitates cognitive control to filter irrelevant information and allocate neural resources to task-related goals. Though previously studied in healthy young adults, the direct quantification of changes in oscillatory activity serving such cognitive control and associated inter-regional interactions in healthy aging are poorly understood. Herein, we used an adapted version of the multisource interference task and magnetoencephalography to investigate age-related alterations in the neural dynamics governing both divergent and convergent cognitive interference in 78 healthy participants (age range: 20-66 years). We identified weaker alpha connectivity between bilateral visual and right dorsolateral prefrontal cortices (DLPFC) and left dorsomedial prefrontal cortices (dmPFC), as well as weaker gamma connectivity between bilateral occipital regions and the right dmPFC during flanker interference with advancing age. Further, an age-related decrease in gamma power was observed in the left cerebellum and parietal region for Simon and differential interference effects (i.e., flanker-Simon), respectively. Moreover, the superadditivity model showed decreased gamma power in the right temporoparietal junction (TPJ) with increasing age. Overall, our findings suggest age-related declines in the engagement of top-down attentional control secondary to reduced alpha and gamma coupling between prefrontal and occipital cortices. [ABSTRACT FROM AUTHOR]

Functional connectomes have been proven to be able to predict an individual's traits, acting as a fingerprint. A majority of studies use the amplitude information of fMRI signals to construct the connectivity but it remains unknown whether phase synchronization can be incorporated for improved prediction of individual cognitive behaviors. In this paper, we address the issue by extracting phase information from the fMRI time series with a phase locking approach, followed by the construction of functional connectomes. We first examine the identification and prediction performance using phase-based profiles in comparison with amplitude-based connectomes. We then combine both phase-based and amplitude-based connectivity to extract subject-specific information enabled by the phase synchronization. Results show that high individual identification rates (from 82.7% to 92.6%) can be achieved by phase-based connectomes. Phase-based connectivity offers unique information complementary to amplitude-based signals. Intra-network phase-locking appears more informative for individual prediction. In addition, phase synchronization can be used to predict cognitive behaviors. Comparison with existing method: The amplitude-based connectivity cannot capture the subject-specific information due to neural synchronization. The comparison with other phase-based methods has been involved in the discussion session. Our findings suggest that neural synchronization carries subject-specific information, which can be captured by phase locking value. The incorporation of phase information into connectomes presents a promising approach to understand each individual brain's uniqueness. • Examine the identification and prediction performance using phase-based profiles. • Propose a pipeline to combine the amplitude and phase information to reveal FC's the fingerprinting ability. • Validate GICA-TVGL using two independent neuroimaging datasets (e.g., HCP and PNC). [ABSTRACT FROM AUTHOR]

Adolescence is a critical period of structural and functional neural maturation among regions serving the cognitive control of emotion. Evidence suggests that this process is guided by developmental changes in amygdala and striatum structure and shifts in functional connectivity between subcortical (SC) and cognitive control (CC) networks. Herein, we investigate the extent to which such developmental shifts in structure and function reciprocally predict one another over time. 179 youth (9-15 years-old) completed annual MRI scans for three years. Amygdala and striatum volumes and connectivity within and between SC and CC resting state networks were measured for each year. We tested for reciprocal predictability of within-person and between-person changes in structure and function using random-intercept cross-lagged panel models. Within-person shifts in amygdala volumes in a given year significantly and specifically predicted deviations in SC-CC connectivity in the following year, such that an increase in volume was associated with decreased SC-CC connectivity the following year. Deviations in connectivity did not predict changes in amygdala volumes over time. Conversely, broader group-level shifts in SC-CC connectivity were predictive of subsequent deviations in striatal volumes. We did not see any cross-predictability among amygdala or striatum volumes and within-network connectivity measures. Within-person shifts in amygdala structure year-to-year robustly predicted weaker SC-CC connectivity in subsequent years, whereas broader increases in SC-CC connectivity predicted smaller striatal volumes over time. These specific structure function relationships may contribute to the development of emotional control across adolescence. [ABSTRACT FROM AUTHOR]

• fMRI matrix randomness decreases in lower BOLD frequencies compared to higher frequencies. • BOLD signal appears least random in the 0.31−0.46 Hz range of those examined. • Eyes closed vs open rsfMRI produce different results. • Eyes closed vs open differences are greater above the 0.46 Hz range. Resting-state fMRI (rs-fMRI) is employed to assess "functional connections" of signal between brain regions. However, multiple rs-fMRI paradigms and data-filtering strategies have been used, highlighting the need to explore BOLD signal across the spectrum. Rs-fMRI data is typically filtered at frequencies ranging between 0.008∼0.2 Hz to mitigate nuisance signal (e.g. cardiac, respiratory) and maximize neuronal BOLD signal. However, some argue neuronal BOLD signal may be parsed at higher frequencies. To assess the contributions of rs-fMRI along the BOLD spectra on functional network connectivity (FNC) matrices, a spatially constrained independent component analysis (ICA) was performed at seven different frequency "bins", after which FNC values and FNC measures of matrix-randomness were assessed using linear mixed models. Results show FNCs at higher-frequency bins display similar randomness to those from the typical frequency bins (0.01−0.15), while the largest values are in the 0.31−0.46 Hz bin. Eyes open (EO) vs eyes closed (EC) comparison found EC was less random than EO across most frequency bins. Further, FNC was greater in EC across auditory and cognitive control pairings while EO values were greater in somatomotor, visual, and default mode FNC. Effect sizes of frequency and resting-state paradigm vary from small to large, but the most notable results are specific to frequency ranges and resting-state paradigm with artifacts like motion displaying negligible effect sizes. These suggest unique information may be derived from FNC matrices across frequencies and paradigms, but additional data is necessary prior to any definitive conclusions. [ABSTRACT FROM AUTHOR]