Multiple Functional Networks Research Articles

Mapping and modeling age-related changes in intrinsic neural timescales

Intrinsic timescales of brain regions exhibit heterogeneity, escalating with hierarchical levels, and are crucial for the temporal integration of external stimuli. Aging, often associated with cognitive decline, involves progressive neuronal and synaptic loss, reshaping brain structure and dynamics. However, the impact of these structural changes on temporal coding in the aging brain remains unclear. We mapped intrinsic timescales and gray matter volume (GMV) using magnetic resonance imaging (MRI) in young and elderly adults. We found shorter intrinsic timescales across multiple large-scale functional networks in the elderly cohort, and a significant positive association between intrinsic timescales and GMV. Additionally, age-related decline in performance on visual discrimination tasks was linked to a reduction in intrinsic timescales in the cuneus. To explain these age-related shifts, we developed an age-dependent spiking neuron network model. In younger subjects, brain regions were near a critical branching regime, while regions in elderly subjects had fewer neurons and synapses, pushing the dynamics toward a subcritical regime. The model accurately reproduced the empirical results, showing longer intrinsic timescales in young adults due to critical slowing down. Our findings reveal how age-related structural brain changes may drive alterations in brain dynamics, offering testable predictions and informing possible interventions targeting cognitive decline.

A functional parcellation of the whole brain in high-functioning individuals with autism spectrum disorder reveals atypical patterns of network organization.

Researchers studying autism spectrum disorder (ASD) lack a comprehensive map of the functional network topography in the ASD brain. We used high-quality resting state functional MRI (rs-fMRI) connectivity data and a robust parcellation routine to provide a whole-brain map of functional networks in a group of seventy high-functioning individuals with ASD and a group of seventy typically developing (TD) individuals. The rs-fMRI data were collected using an imaging sequence optimized to achieve high temporal signal-to-noise ratio (tSNR) across the whole-brain. We identified functional networks using a parcellation routine that intrinsically incorporates internal consistency and repeatability of the networks by keeping only network distinctions that agree across halves of the data over multiple random iterations in each group. The groups were tightly matched on tSNR, in-scanner motion, age, and IQ. We compared the maps from each group and found that functional networks in the ASD group are atypical in three seemingly related ways: (1) whole-brain connectivity patterns are less stable across voxels within multiple functional networks, (2) the cerebellum, subcortex, and hippocampus show weaker differentiation of functional subnetworks, and (3) subcortical structures and the hippocampus are atypically integrated with the neocortex. These results were statistically robust and suggest that patterns of network connectivity between the neocortex and the cerebellum, subcortical structures, and hippocampus are atypical in ASD individuals.

Anterior and posterior thalamic volumes differentially correlate with memory, attention, and motor processes in HIV infection and alcohol use disorder comorbidity

The thalamus, with its reciprocal connections to and from cortical, subcortical, and cerebellar regions, is a central active participant in multiple functional brain networks. Structural MRI studies measuring the entire thalamus without respect to its regional or nuclear divisions report volume shrinkage in diseases including HIV infection, alcohol use disorder (AUD), and their comorbidity (HIV+AUD). Here, we examined relations between thalamic subregions (anterior, ventral, medial, and posterior) and neuropsychological functions (attention/working memory, executive functioning, episodic memory, and motor skills). Volumes of thalamic subregions were derived from automatic segmentations of standard T1 weighted MRIs of 65 individuals with HIV, 189 with AUD, 80 with HIV+AUD comorbidity, and 141 healthy controls (CTRL). Total thalamic volume was smaller and cognitive and motor composite scores were lower in the three diagnostic groups relative to the CTRL group. The AUD and HIV+AUD groups had significantly smaller thalamic subregional volumes than the CTRL group. The HIV+AUD group had smaller anterior thalamic volume than the HIV-only group and smaller ventral thalamic volume than the AUD-only group. In the HIV+AUD group, memory scores correlated with anterior thalamic volumes, attention/working memory scores correlated with posterior and medial thalamic volumes, and motor skill scores correlated with posterior thalamic volumes. Exploratory analyses focused on the HIV+AUD group indicated that within the posterior thalamic region, the pulvinar and medial geniculate nuclei were related to attention/working memory scores, and the pulvinar was related to motor skills scores. This study is novel in locating volume deficits in specific thalamic subregions, in addition to the thalamus as a whole, in HIV, AUD, and their comorbidity and in identifying functional ramifications of these deficits. Taken together, this study highlights the relevance of thalamic subregional volume deficits to dissociable cognitive and motor processes.

Functional Network Connectivity for Components of Depression-Related Psychological Fragility.

Psychological resilience (PR) is known to be inversely associated with depression. While there is a growing body of research examining how depression alters activity across multiple functional neural networks, how differences in PR affect these networks is largely unexplored. This study examines the relationship between PR and functional connectivity in the alpha and beta bands within (and between) eighteen established cortical nodes in the default mode network, the central executive network, and the salience network. Resting-state EEG data from 99 adult participants (32 depressed, 67 non-depressed) were used to measure the correlation between the five factors of PR sourced from the Connor-Davidson Resilience Scale and eLORETA-based measures of coherence and phase synchronisation. Distinct functional connectivity patterns were seen across each resilience factor, with a notable absence of overlapping positive results across the depressed and non-depressed samples. These results indicate that depression may modulate how resilience is expressed in terms of fundamental neural activity.

Distinct homotopic functional connectivity patterns of the amygdalar sub-regions as biomarkers in major depressive disorder

BackgroundMajor depressive disorder (MDD) affects multiple functional neural networks. Neuroimaging studies using resting-state functional connectivity (FC) have focused on the amygdala but did not assess changes in connectivity between the left and right amygdala. The current study aimed to examine the inter-hemispheric functional connectivity (homotopic FC, HoFC) between different amygdalar sub-regions in patients with MDD compared to healthy controls, and to examine whether amygdalar sub-regions' HoFC also predicts response to Serotonin Selective Reuptake Inhibitors (SSRIs). MethodSixty-seven patients with MDD and 64 matched healthy controls were recruited. An MRI scan focusing on resting state fMRI and clinical and cognitive evaluations were performed. An atlas seed-based approach was used to identify the lateral and medial sub-regions of the amygdala. HoFC of these sub-regions was compared between groups and correlated with severity of depression, and emotional processing performance. Baseline HoFC levels were used to predict response to SSRIs after 2 months of treatment. ResultsPatients with MDD demonstrated decreased inter-hemispheric FC in the medial (F3,120 = 4.11, p = 0.008, η2 = 0.096) but not in the lateral (F3,119 = 0.29, p = 0.82, η2 = 0.008) amygdala compared with healthy controls. The inter-hemispheric FC of the medial sub-region correlated with symptoms severity (r = −0.33, p < 0.001) and emotional processing performance (r = 0.38, p < 0.001). Moreover, it predicted treatment response to SSRIs 65.4 % of the cases. LimitationsThe current study did not address FC changes in MDD biotypes. In addition, structural connectivity was not examined. ConclusionsUsing a unique perspective of the amygdalar distinct areas elucidated differential inter-hemispheric FC patterns in MDD patients, emphasizing the role of interhemispheric communication in depression.

The abnormalities of brain function in females with primary insomnia: a resting-state functional magnetic resonance imaging study.

The neuropathologic mechanism of primary insomnia (PI) of females remains unclear. This study aims to investigate the features of amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) in females with PI using functional magnetic resonance imaging (fMRI), and then explore the abnormalities of functional connectivity (FC). A total of 39 female PI patients and 31 female healthy controls (HC) were enrolled in the study. The sleep condition was assessed using the Pittsburgh Sleep Quality Index (PSQI), and Insomnia Severity Index (ISI), and their depressive symptom was evaluated using the Hamilton Depression Scale (HAMD-24). The rs-fMRI was once conducted for every subject. ReHo, ALFF, and ROI-based FC were used to analyze the changes of brain function. ALFF values were increased in the Cerebelum_4_5_L, as well as decreased ALFF in the bilateral Frontal_Sup_Medial (SFGmed), Angular_L (ANG.L), Parietal_Inf_R (IPL.R), SupraMarginal_R (SMG.R), and Postcentral_R (PoCG.R). ReHo values were increased in the Temporal_Pole_Mid_R (TPOsup.R), as well as decreased ReHo in the Insula_R (INS.R), Frontal_Inf_Oper_R (ORBinf.R), Putamen_R (PUT.R), Rolandic_Oper_R (ROL.R), bilateral Cingulum_Post (PCG), bilateral Frontal_Sup_Medial (SFGmed), bilateral anterior cingulate and paracingulate gyri (ACG), and the bilateral precuneus (PCUN). Across the entire brain, there was a decline in the FC between Angular_R and Frontal_Sup_Medial_L. Alterations in brain regions of female patients with PI involved multiple functional networks, including the default mode network, the salience network, the central executive network, and the limbic network. Reduced coordination between functional networks may be an important mechanism for insomnia and may lead to reduced cognitive function and decision-making ability.

FMRI signals in white matter rewire gray matter community organization

Human brain gray matter (GM) has usually been clustered into multiple functional networks. The white matter (WM) fiber bundles are known to interconnect these networks simultaneously, engaging in numerous cognitive functions. However, the exact interconnections between GM and WM are still unclear, whether functional signals in WM rewires GM community organization remains to be explored. In this study, we divided brain functional connections into three types by using edge-centric method, including intra-GM, intra-WM and GM-WM connections, and calculated the edge community evaluation indexes for quantifying GM community engagement. The results showed that the involvement of WM significantly enhanced community entropy in the heteromodal system, while the sensory-attention system remained barely changed. In addition, delta community entropy showed a significant correlation with clinical cognitive scale. Our results suggested that WM rewired GM community organization, enhancing the community engagement of brain regions in the heteromodal system. This involvement was observed to be disrupted in disease groups. Our study revealed that considering the functional signals of GM and WM simultaneously could better understand the brain's functional organization.

Broadening the scope: Multiple functional connectivity networks underlying threat conditioning and extinction

Abstract Threat learning processes are thought to be foundational to anxiety and fear-related disorders. However, the study of these processes in the human brain has largely focused on specific brain regions, owing partly to the ease of translating between these regions in human and nonhuman animals. Moving beyond analyzing focal regions of interest to whole-brain dynamics and connectivity during threat learning is essential for understanding the neuropathology of fear-related disorders in humans. In this study, 223 participants completed a 2-day Pavlovian threat conditioning paradigm while undergoing fMRI. Participants completed threat acquisition and extinction. Extinction recall was assessed 48 hours later. Using a data-driven group independent component analysis (ICA), we examined large-scale functional connectivity networks during each phase of threat learning. Connectivity networks were tested to see how they responded to conditioned stimuli during early and late phases of threat acquisition and extinction as well as during early trials of extinction recall. A network overlapping with the default mode network involving hippocampus, ventromedial prefrontal cortex (vmPFC), and posterior cingulate was implicated in threat acquisition and extinction. Another network overlapping with the salience network involving dorsal anterior cingulate cortex (dACC), mPFC, and inferior frontal gyrus was implicated both in threat acquisition and in extinction recall. Other networks overlapping with parts of the salience, somatomotor, visual, and frontoparietal networks were involved in the acquisition or in the extinction of learned threat responses. These findings help support the functional cooperation of specific brain regions during threat learning in a model-free fashion while introducing new findings of spatially independent functional connectivity networks during threat and safety learning. Rather than being a single process in a core network of regions, threat learning involves multiple brain networks operating in parallel performing different functions at different timescales. Understanding the nature and interplay of these dynamics will be critical for comprehensive understanding of the multiple processes that may be at play in the neuropathology of anxiety and fear-related disorders.

Sex differences in trajectories of cortical development in autistic children from 2–13 years of age

Previous studies have reported alterations in cortical thickness in autism. However, few have included enough autistic females to determine if there are sex specific differences in cortical structure in autism. This longitudinal study aimed to investigate autistic sex differences in cortical thickness and trajectory of cortical thinning across childhood. Participants included 290 autistic (88 females) and 139 nonautistic (60 females) individuals assessed at up to 4 timepoints spanning ~2–13 years of age (918 total MRI timepoints). Estimates of cortical thickness in early and late childhood as well as the trajectory of cortical thinning were modeled using spatiotemporal linear mixed effects models of age-by-sex-by-diagnosis. Additionally, the spatial correspondence between cortical maps of sex-by-diagnosis differences and neurotypical sex differences were evaluated. Relative to their nonautistic peers, autistic females had more extensive cortical differences than autistic males. These differences involved multiple functional networks, and were mainly characterized by thicker cortex at ~3 years of age and faster cortical thinning in autistic females. Cortical regions in which autistic alterations were different between the sexes significantly overlapped with regions that differed by sex in neurotypical development. Autistic females and males demonstrated some shared differences in cortical thickness and rate of cortical thinning across childhood relative to their nonautistic peers, however these areas were relatively small compared to the widespread differences observed across the sexes. These results support evidence of sex-specific neurobiology in autism and suggest that processes that regulate sex differentiation in the neurotypical brain contribute to sex differences in the etiology of autism.

Reduced lateralization of multiple functional brain networks in autistic males

BackgroundAutism spectrum disorder has been linked to a variety of organizational and developmental deviations in the brain. One such organizational difference involves hemispheric lateralization, which may be localized to language-relevant regions of the brain or distributed more broadly.Methods In the present study, we estimated brain hemispheric lateralization in autism based on each participant’s unique functional neuroanatomy rather than relying on group-averaged data. Additionally, we explored potential relationships between the lateralization of the language network and behavioral phenotypes including verbal ability, language delay, and autism symptom severity. We hypothesized that differences in hemispheric asymmetries in autism would be limited to the language network, with the alternative hypothesis of pervasive differences in lateralization. We tested this and other hypotheses by employing a cross-sectional dataset of 118 individuals (48 autistic, 70 neurotypical). Using resting-state fMRI, we generated individual network parcellations and estimated network asymmetries using a surface area-based approach. A series of multiple regressions were then used to compare network asymmetries for eight significantly lateralized networks between groups.ResultsWe found significant group differences in lateralization for the left-lateralized Language (d = -0.89), right-lateralized Salience/Ventral Attention-A (d = 0.55), and right-lateralized Control-B (d = 0.51) networks, with the direction of these group differences indicating less asymmetry in autistic males. These differences were robust across different datasets from the same participants. Furthermore, we found that language delay stratified language lateralization, with the greatest group differences in language lateralization occurring between autistic males with language delay and neurotypical individuals.ConclusionsThese findings evidence a complex pattern of functional lateralization differences in autism, extending beyond the Language network to the Salience/Ventral Attention-A and Control-B networks, yet not encompassing all networks, indicating a selective divergence rather than a pervasive one. Moreover, we observed an association between Language network lateralization and language delay in autistic males.

Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis.

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.

Mild cognitive impairment in Parkinson's disease: current view

Parkinson's disease (PD), the most common motor movement disorder and second most common neurodegenerative disorder after Alzheimer's disease (AD), is often preceded by a period of mild cognitive impairment (MCI), which is associated with impairment of a variety of cognitive domains including executive function, attention, visuospatial abilities and memory. MCI, a risk factor for developing dementia, affects around 30% of de novo PD patients and can increase to 75% after more than 10 years. While 30–40% remain in the MCI state, up to 60% will convert to dementia. Characteristic findings are slowing of EEG rhythms, frontotemporal hypoperfusion, decreased functional connectivity in the default mode and attentional networks, prefrontal and basal-ganglia-cortical circuits, which often manifests prior to clinical symptoms and overt brain atrophy. The heterogeneity of cognitive phenotypes suggests that a common neurodegenerative process affects multiple functional neuronal networks and neuromodulatory systems that may be superimposed by Lewy body and Alzheimer's-related or other co-pathologies. Sparse neuropathological data for PD-MCI revealed a heterogenous picture with various morphological changes similar to MCI in other diseases. This review highlights the essential epidemiological, clinical, neuroimaging and morphological changes in PD-MCI, available biomarkers, and discusses the heterogenous pathobiological mechanisms involved in its development. In view of its complex pathogenesis, well-designed longitudinal clinico-pathological studies are warranted to clarify the alterations leading to MCI in PD, which may be supported by fluid and neuroimaging biomarkers as a basis for early diagnosis and future adequate treatment modalities of this debilitating disorder.

Altered resting-state functional connectivity and dynamic network properties in cognitive impairment: an independent component and dominant-coactivation pattern analyses study.

Cognitive impairment (CI) due to Alzheimer's disease (AD) encompasses a decline in cognitive abilities and can significantly impact an individual's quality of life. Early detection and intervention are crucial in managing CI, both in the preclinical and prodromal stages of AD prior to dementia. In this preliminary study, we investigated differences in resting-state functional connectivity and dynamic network properties between 23 individual with CI due to AD based on clinical assessment and 15 healthy controls (HC) using Independent Component Analysis (ICA) and Dominant-Coactivation Pattern (d-CAP) analysis. The cognitive status of the two groups was also compared, and correlations between cognitive scores and d-CAP switching probability were examined. Results showed comparable numbers of d-CAPs in the Default Mode Network (DMN), Executive Control Network (ECN), and Frontoparietal Network (FPN) between HC and CI groups. However, the Visual Network (VN) exhibited fewer d-CAPs in the CI group, suggesting altered dynamic properties of this network for the CI group. Additionally, ICA revealed significant connectivity differences for all networks. Spatial maps and effect size analyses indicated increased coactivation and more synchronized activity within the DMN in HC compared to CI. Furthermore, reduced switching probabilities were observed for the CI group in DMN, VN, and FPN networks, indicating less dynamic and flexible functional interactions. The findings highlight altered connectivity patterns within the DMN, VN, ECN, and FPN, suggesting the involvement of multiple functional networks in CI. Understanding these brain processes may contribute to developing targeted diagnostic and therapeutic strategies for CI due to AD.

Alterations of resting-state functional network connectivity in patients with noise-induced hearing loss: A study based on independent component analysis.

Functional reorganization is a response to auditory deficits or deprivation, and less is known about the overall brain network alterations involving resting-state networks (RSNs) and multiple functional networks in patients with occupational noise-induced hearing loss (NIHL). So this study evaluated resting-state functional network connectivity (FNC) alterations in occupational NIHL using an independent component analysis (ICA). In total, 79 mild NIHL patients (MP), 32 relatively severe NIHL patients (RSP), and 84 age- and education- matched healthy controls (HC) were recruited. All subjects were tested using the Mini-mental State Examination scale, the tinnitus Handicap Inventory scale, the Hamilton Anxiety scale (HAMA) and scanned by T1-3DFSPGR, resting-state functional magnetic resonance imaging sequence in 3.0 T and analysed by the ICA. Seven RSNs were identified, compared with the HC, the MP showed increased FNC within the executive control network (ECN) and enhanced FNC within the default mode network (DMN) and the visual network (VN); compared with the HC, the RSP showed decreased FNC within the ECN and auditory network (AUN), DMN and VN; no significant changes in FNC were found in the MP compared with the RSP. Furthermore, the correlation analysis between the noise exposure time and hearing loss level, HAMA were both negative, and there were no significant correlations between the abnormal RSNs and the hearing level, noise exposure time and HAMA. These findings indicate that different degrees of NIHL involve different alterations in RSNs connectivity and may reveal the neural mechanisms related to emotion-related features and functional abnormalities following long-term NIHL.

Abnormal intrinsic brain functional network dynamics in first-episode drug-naïve adolescent major depressive disorder.

Alterations in brain functional connectivity (FC) have been frequently reported in adolescent major depressive disorder (MDD). However, there are few studies of dynamic FC analysis, which can provide information about fluctuations in neural activity related to cognition and behavior. The goal of the present study was therefore to investigate the dynamic aspects of FC in adolescent MDD patients. Resting-state functional magnetic resonance imaging data were acquired from 94 adolescents with MDD and 78 healthy controls. Independent component analysis, a sliding-window approach, and graph-theory methods were used to investigate the potential differences in dynamic FC properties between the adolescent MDD patients and controls. Three main FC states were identified, State 1 which was predominant, and State 2 and State 3 which occurred less frequently. Adolescent MDD patients spent significantly more time in the weakly-connected and relatively highly-modularized State 1, spent significantly less time in the strongly-connected and low-modularized State 2, and had significantly higher variability of both global and local efficiency, compared to the controls. Classification of patients with adolescent MDD was most readily performed based on State 1 which exhibited disrupted intra- and inter-network FC involving multiple functional networks. Our study suggests local segregation and global integration impairments and segregation-integration imbalance of functional networks in adolescent MDD patients from the perspectives of dynamic FC. These findings may provide new insights into the neurobiology of adolescent MDD.

Pathobiology of Cognitive Impairment in Parkinson Disease: Challenges and Outlooks.

Cognitive impairment (CI) is a characteristic non-motor feature of Parkinson disease (PD) that poses a severe burden on the patients and caregivers, yet relatively little is known about its pathobiology. Cognitive deficits are evident throughout the course of PD, with around 25% of subtle cognitive decline and mild CI (MCI) at the time of diagnosis and up to 83% of patients developing dementia after 20 years. The heterogeneity of cognitive phenotypes suggests that a common neuropathological process, characterized by progressive degeneration of the dopaminergic striatonigral system and of many other neuronal systems, results not only in structural deficits but also extensive changes of functional neuronal network activities and neurotransmitter dysfunctions. Modern neuroimaging studies revealed multilocular cortical and subcortical atrophies and alterations in intrinsic neuronal connectivities. The decreased functional connectivity (FC) of the default mode network (DMN) in the bilateral prefrontal cortex is affected already before the development of clinical CI and in the absence of structural changes. Longitudinal cognitive decline is associated with frontostriatal and limbic affections, white matter microlesions and changes between multiple functional neuronal networks, including thalamo-insular, frontoparietal and attention networks, the cholinergic forebrain and the noradrenergic system. Superimposed Alzheimer-related (and other concomitant) pathologies due to interactions between α-synuclein, tau-protein and β-amyloid contribute to dementia pathogenesis in both PD and dementia with Lewy bodies (DLB). To further elucidate the interaction of the pathomechanisms responsible for CI in PD, well-designed longitudinal clinico-pathological studies are warranted that are supported by fluid and sophisticated imaging biomarkers as a basis for better early diagnosis and future disease-modifying therapies.

Learning to Fuse Multiple Brain Functional Networks for Automated Autism Identification.

Functional connectivity network (FCN) has become a popular tool to identify potential biomarkers for brain dysfunction, such as autism spectrum disorder (ASD). Due to its importance, researchers have proposed many methods to estimate FCNs from resting-state functional MRI (rs-fMRI) data. However, the existing FCN estimation methods usually only capture a single relationship between brain regions of interest (ROIs), e.g., linear correlation, nonlinear correlation, or higher-order correlation, thus failing to model the complex interaction among ROIs in the brain. Additionally, such traditional methods estimate FCNs in an unsupervised way, and the estimation process is independent of the downstream tasks, which makes it difficult to guarantee the optimal performance for ASD identification. To address these issues, in this paper, we propose a multi-FCN fusion framework for rs-fMRI-based ASD classification. Specifically, for each subject, we first estimate multiple FCNs using different methods to encode rich interactions among ROIs from different perspectives. Then, we use the label information (ASD vs. healthy control (HC)) to learn a set of fusion weights for measuring the importance/discrimination of those estimated FCNs. Finally, we apply the adaptively weighted fused FCN on the ABIDE dataset to identify subjects with ASD from HCs. The proposed FCN fusion framework is straightforward to implement and can significantly improve diagnostic accuracy compared to traditional and state-of-the-art methods.

Few temporally distributed brain connectivity states predict human cognitive abilities

Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10 min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual's network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.

A deep residual model for characterization of 5D spatiotemporal network dynamics reveals widespread spatiodynamic changes in schizophrenia.

Schizophrenia is a severe brain disorder with serious symptoms including delusions, disorganized speech, and hallucinations that can have a long-term detrimental impact on different aspects of a patient's life. It is still unclear what the main cause of schizophrenia is, but a combination of altered brain connectivity and structure may play a role. Neuroimaging data has been useful in characterizing schizophrenia, but there has been very little work focused on voxel-wise changes in multiple brain networks over time, despite evidence that functional networks exhibit complex spatiotemporal changes over time within individual subjects. Recent studies have primarily focused on static (average) features of functional data or on temporal variations between fixed networks; however, such approaches are not able to capture multiple overlapping networks which change at the voxel level. In this work, we employ a deep residual convolutional neural network (CNN) model to extract 53 different spatiotemporal networks each of which captures dynamism within various domains including subcortical, cerebellar, visual, sensori-motor, auditory, cognitive control, and default mode. We apply this approach to study spatiotemporal brain dynamism at the voxel level within multiple functional networks extracted from a large functional magnetic resonance imaging (fMRI) dataset of individuals with schizophrenia (N = 708) and controls (N = 510). Our analysis reveals widespread group level differences across multiple networks and spatiotemporal features including voxel-wise variability, magnitude, and temporal functional network connectivity in widespread regions expected to be impacted by the disorder. We compare with typical average spatial amplitude and show highly structured and neuroanatomically relevant results are missed if one does not consider the voxel-wise spatial dynamics. Importantly, our approach can summarize static, temporal dynamic, spatial dynamic, and spatiotemporal dynamics features, thus proving a powerful approach to unify and compare these various perspectives. In sum, we show the proposed approach highlights the importance of accounting for both temporal and spatial dynamism in whole brain neuroimaging data generally, shows a high-level of sensitivity to schizophrenia highlighting global but spatially unique dynamics showing group differences, and may be especially important in studies focused on the development of brain-based biomarkers.

Amplitudes of resting-state functional networks – investigation into their correlates and biophysical properties

Resting-state fMRI studies have shown that multiple functional networks, which consist of distributed brain regions that share synchronised spontaneous activity, co-exist in the brain. As these resting-state networks (RSNs) have been thought to reflect the brain's intrinsic functional organization, intersubject variability in the networks’ spontaneous fluctuations may be associated with individuals’ clinical, physiological, cognitive, and genetic traits. Here, we investigated resting-state fMRI data along with extensive clinical, lifestyle, and genetic data collected from 37,842 UK Biobank participants, with the object of elucidating intersubject variability in the fluctuation amplitudes of RSNs. Functional properties of the RSN amplitudes were first examined by analyzing correlations with the well-established between-network functional connectivity. It was found that a network amplitude is highly correlated with the mean strength of the functional connectivity that the network has with the other networks. Intersubject clustering analysis showed the amplitudes are most strongly correlated with age, cardiovascular factors, body composition, blood cell counts, lung function, and sex, with some differences in the correlation strengths between sensory and cognitive RSNs. Genome-wide association studies (GWASs) of RSN amplitudes identified several significant genetic variants reported in previous GWASs for their implications in sleep duration. We provide insight into key factors determining RSN amplitudes and demonstrate that intersubject variability of the amplitudes primarily originates from differences in temporal synchrony between functionally linked brain regions, rather than differences in the magnitude of raw voxelwise BOLD signal changes. This finding additionally revealed intriguing differences between sensory and cognitive RSNs with respect to sex effects on temporal synchrony and provided evidence suggesting that synchronous coactivations of functionally linked brain regions, and magnitudes of BOLD signal changes, may be related to different genetic mechanisms. These results underscore that intersubject variability of the amplitudes in health and disease need to be interpreted largely as a measure of the sum of within-network temporal synchrony and amplitudes of BOLD signals, with a dominant contribution from the former.