fMRI Networks Research Articles

Functional connectivity of key resting state networks and objectively measured physical activity in older adults with joint pain: A pilot study.

Greater brain network integrity may associate with physically active lifestyles. Three resting state networks may provide unique insights into known physical activity-mediated brain health benefits: the default mode network (involved with self-monitoring), the salience network (involved in orienting oneself to salient external and internal stimuli), and the central executive network (responsible for higher level cognitive task). The current study explored relationships between system-wide neural network integrity measured by functional magnetic resonance imaging and objectively-measured physical activity. We hypothesize connectivity patterns as measured by fMRI networks will relate to actigraphy markers such that 1) there will be higher connectivity within the central executive network in more physically active individuals, and 2) there will be higher connectivity within the default mode network and salience network in those with higher levels of physical activity. Eighteen non-demented older adults with orthopedic pain (age 67.11±5.61, 50% female, education 15.94±2.51years) completed brain magnetic resonance imaging, and wore an actigraphy device to objectively measure types of physical and sedentary engagement. Results showed a negative relationship between central executive network connectivity and sedentary time (β= -0.108, p=.039), and a positive relationship with both moderate-to-vigorous physical activity (β= 0.629, p=.029) and total activity time (β=0.645, p=.039). Results also showed positive relationships for the default mode network (β=0.588, p=.033) and the salience network (β= 0.608, p=.037) with mean cadence (i.e. steps per minute). Our work adds to the existing literature on specific types of activity measurement (i.e. sedentary time, cadence and moderate-to-vigorous physical activity) which will be useful for interventions aimed at improving the integrity of underlying neural networks.

Dysconnectivity of a brain functional network was associated with blood inflammatory markers in depression

ObjectiveThere is increasing evidence for a subgroup of major depressive disorder (MDD) associated with heightened peripheral blood inflammatory markers. In this study, we aimed to understand the mechanistic brain-immune axis in inflammation-linked depression by investigating associations between functional connectivity (FC) of brain networks and peripheral blood immune markers in depression. MethodsResting-state functional magnetic resonance imaging (fMRI) and peripheral blood inflammatory markers (C-reactive protein; CRP, interleukin-6; IL-6 and immune cells) were collected on N = 46 healthy controls (HC; CRP ≤ 3 mg/L) and N = 83 cases of depression, stratified further into low CRP cases (loCRP cases; ≤ 3 mg/L; N = 50) and high CRP cases (hiCRP cases; > 3 mg/L; N = 33). In a two-part analysis, network-based statistics (NBS) was firstly used to ascertain whole-brain FC differences in HC vs hiCRP cases. Secondly, we investigated the association between this network of interconnected brain regions and continuous measures of peripheral CRP (N = 83), IL-6 (N = 72), neutrophils and CD4+ T-cells (N = 36) in depression cases only. ResultsCase-control NBS testing revealed a single network of abnormally attenuated FC in the high CRP depression cases compared to healthy controls. Connections within this network were mainly between brain regions located in the left insula/frontal operculum and posterior cingulate cortex, which were assigned to ventral attention and default mode canonical fMRI networks respectively. Within-group analysis across all depression cases, secondarily demonstrated that FC within the identified network significantly negatively scaled with CRP, IL-6 and neutrophils. ConclusionsThe findings suggest that inflammation is associated with disruption of functional connectivity within a brain network deemed critical for interoceptive signalling, e.g. accurate communication of peripheral bodily signals such as immune states to the brain, with implications for the pathogenesis of inflammation-linked depression.

Effect of AoA-L2 on L1 and L2 networks in early and late bilinguals

Aim: The age of acquisition of second language (AoA-L2) affects the neural representation of the first language (L1) and L2. Although previous task-fMRI studies showed different activation patterns of language networks in early and late bilinguals, little is known about the effect of AoA-L2 on L1 and L2 resting-state fMRI (rs-fMRI) networks in bilinguals. In this study, we attempted to reveal the differences in resting-state functional connectivity (RSFC) and topological properties of L1 and L2 networks related to AoA-L2 in bilinguals. Design: We pooled 10 early bilinguals and 11 late bilinguals with high proficiency level in L2 (PL-L2) and acquired their brain rs-fMRI data. By taking the brain regions obtained from a previous meta-analysis study as the seeds, we constructed L1 and L2 networks, and then estimated the RSFC and topological properties of L1 and L2 networks. Findings: Significantly higher RSFC between the inferior frontal gyrus (IFG) and lateral occipital gyrus (LOG) in the L1 network, and significantly higher RSFC between the IFG and angular gyrus (ANG) in the L2 network were observed in early bilinguals than in late bilinguals. Early bilinguals showed significantly higher clustering coefficient, global and local efficiency, but lower characteristic path length, than late bilinguals in the L2 network. Originality: Through analyzing the RSFC and topological properties of L1 and L2 networks, we observed the influence of AoA-L2 on the brain neuroplasticity in bilinguals. The findings may provide a new perspective for understanding the neuroplasticity in bilinguals with different L2 experience. Implications: The AoA-L2 affects the RSFC and topological properties of the L2 network more obviously relative to the L1 network in bilinguals. The effect of the L2 experience on the L1 network is hardly detected in bilinguals with high PL-L2.

Integrating multi-omics with neuroimaging and behavior: A preliminary model of dysfunction in football athletes

Contact sports affect measures at multiple scales such as transcriptomics, metabolomics, brain function, and behavior, but studies have not yet studied the statistical structure of how they are integrated. This preliminary study, examining collegiate American football players, integrated across-season changes (Δ) from transcriptomic and metabolomic variables (neuroinflammatory miRNAs and metabolites), neuroimaging (resting-state fMRI network fingerprint similarity), and virtual reality (VR)-based motor control. These findings were then assessed against head acceleration events (HAE). Using permutation-based moderation analysis (all pFperm,pβ3perm≤0.05), we observed that (1) Δtridecenedioate, a mono-unsaturated fatty acid, interacted with ΔmiR-505 to predict default mode network (DMN) fingerprint similarity, meaning the interaction between two molecular biology measures predicted a neuroimaging measure. Further, (2) Δtridecenedioate and DMN fingerprint similarity interacted to predict motor control, indicating the interaction of a molecular and a neuroimaging measure predicted behavior. ΔmiR-505 was positively related to HAE and DMN fingerprint similarity was negatively related to HAE and reduced relative to non-athlete subjects. These multi-scale, moderating relationships between a potential ROS scavenger, neuroinflammatory miRNA, reduced brain connectivity, and diminished motor control argue that seemingly healthy athletes with frequent HAE may experience chronic neuroinflammation. This imaging-omics framework using permutation-based mediation/moderation analysis has general applicability for human-animal translational studies.

Intra-session test-retest reliability of functional connectivity in infants

Resting functional MRI studies of the infant brain are increasingly becoming an important tool in developmental neuroscience. Whereas the test-retest reliability of functional connectivity (FC) measures derived from resting fMRI data have been characterized in the adult and child brain, similar assessments have not been conducted in infants. In this study, we examined the intra-session test-retest reliability of FC measures from 119 infant brain MRI scans from four neurodevelopmental studies. We investigated edge-level and subject-level reliability within one MRI session (between and within runs) measured by the Intraclass correlation coefficient (ICC). First, using an atlas-based approach, we examined whole-brain connectivity as well as connectivity within two common resting fMRI networks – the default mode network (DMN) and the sensorimotor network (SMN). Second, we examined the influence of run duration, study site, and scanning manufacturer (e.g., Philips and General Electric) on ICCs. Lastly, we tested spatial similarity using the Jaccard Index from networks derived from independent component analysis (ICA). Consistent with resting fMRI studies from adults, our findings indicated poor edge-level reliability (ICC = 0.14–0.18), but moderate-to-good subject-level intra-session reliability for whole-brain, DMN, and SMN connectivity (ICC = 0.40–0.78). We also found significant effects of run duration, site, and scanning manufacturer on reliability estimates. Some ICA-derived networks showed strong spatial reproducibility (e.g., DMN, SMN, and Visual Network), and were labelled based on their spatial similarity to analogous networks measured in adults. These networks were reproducibly found across different study sites. However, other ICA-networks (e.g. Executive Control Network) did not show strong spatial reproducibility, suggesting that the reliability and/or maturational course of functional connectivity may vary by network. In sum, our findings suggest that developmental scientists may be on safe ground examining the functional organization of some major neural networks (e.g. DMN and SMN), but judicious interpretation of functional connectivity is essential to its ongoing success.

An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis

Alcohol use disorder (AUD) has high prevalence and adverse societal impacts, but our understanding of the factors driving AUD is hampered by a lack of studies that describe the complex neurobehavioral mechanisms driving AUD. We analyzed causal pathways to AUD severity using Causal Discovery Analysis (CDA) with data from the Human Connectome Project (HCP; n = 926 [54% female], 22% AUD [37% female]). We applied exploratory factor analysis to parse the wide HCP phenotypic space (100 measures) into 18 underlying domains, and we assessed functional connectivity within 12 resting-state brain networks. We then employed data-driven CDA to generate a causal model relating phenotypic factors, fMRI network connectivity, and AUD symptom severity, which highlighted a limited set of causes of AUD. The model proposed a hierarchy with causal influence propagating from brain connectivity to cognition (fluid/crystalized cognition, language/math ability, & working memory) to social (agreeableness/social support) to affective/psychiatric function (negative affect, low conscientiousness/attention, externalizing symptoms) and ultimately AUD severity. Our data-driven model confirmed hypothesized influences of cognitive and affective factors on AUD, while underscoring that addiction models need to be expanded to highlight the importance of social factors, amongst others.

Resting-State Functional Connectivity in Mathematical Expertise.

To what extent are different levels of expertise reflected in the functional connectivity of the brain? We addressed this question by using resting-state functional magnetic resonance imaging (fMRI) in mathematicians versus non-mathematicians. To this end, we investigated how the two groups of participants differ in the correlation of their spontaneous blood oxygen level-dependent fluctuations across the whole brain regions during resting state. Moreover, by using the classification algorithm in machine learning, we investigated whether the resting-state fMRI networks between mathematicians and non-mathematicians were distinguished depending on features of functional connectivity. We showed diverging involvement of the frontal–thalamic–temporal connections for mathematicians and the medial–frontal areas to precuneus and the lateral orbital gyrus to thalamus connections for non-mathematicians. Moreover, mathematicians who had higher scores in mathematical knowledge showed a weaker connection strength between the left and right caudate nucleus, demonstrating the connections’ characteristics related to mathematical expertise. Separate functional networks between the two groups were validated with a maximum classification accuracy of 91.19% using the distinct resting-state fMRI-based functional connectivity features. We suggest the advantageous role of preconfigured resting-state functional connectivity, as well as the neural efficiency for experts’ successful performance.

Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters

Brains demonstrate varying spatial scales of nested hierarchical clustering. Identifying the brain’s neuronal cluster size to be presented as nodes in a network computation is critical to both neuroscience and artificial intelligence, as these define the cognitive blocks capable of building intelligent computation. Experiments support various forms and sizes of neural clustering, from handfuls of dendrites to thousands of neurons, and hint at their behavior. Here, we use computational simulations with a brain-derived fMRI network to show that not only do brain networks remain structurally self-similar across scales but also neuron-like signal integration functionality (“integrate and fire”) is preserved at particular clustering scales. As such, we propose a coarse-graining of neuronal networks to ensemble-nodes, with multiple spikes making up its ensemble-spike and time re-scaling factor defining its ensemble-time step. This fractal-like spatiotemporal property, observed in both structure and function, permits strategic choice in bridging across experimental scales for computational modeling while also suggesting regulatory constraints on developmental and evolutionary “growth spurts” in brain size, as per punctuated equilibrium theories in evolutionary biology.

Microcanonical and Canonical Ensembles for fMRI Brain Networks in Alzheimer's Disease.

This paper seeks to advance the state-of-the-art in analysing fMRI data to detect onset of Alzheimer’s disease and identify stages in the disease progression. We employ methods of network neuroscience to represent correlation across fMRI data arrays, and introduce novel techniques for network construction and analysis. In network construction, we vary thresholds in establishing BOLD time series correlation between nodes, yielding variations in topological and other network characteristics. For network analysis, we employ methods developed for modelling statistical ensembles of virtual particles in thermal systems. The microcanonical ensemble and the canonical ensemble are analogous to two different fMRI network representations. In the former case, there is zero variance in the number of edges in each network, while in the latter case the set of networks have a variance in the number of edges. Ensemble methods describe the macroscopic properties of a network by considering the underlying microscopic characterisations which are in turn closely related to the degree configuration and network entropy. When applied to fMRI data in populations of Alzheimer’s patients and controls, our methods demonstrated levels of sensitivity adequate for clinical purposes in both identifying brain regions undergoing pathological changes and in revealing the dynamics of such changes.

Excitatory\u2013inhibitory balance within EEG microstates and resting-state fMRI networks: assessed via simultaneous trimodal PET\u2013MR\u2013EEG imaging

The symbiosis of neuronal activities and glucose energy metabolism is reflected in the generation of functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) signals. However, their association with the balance between neuronal excitation and inhibition (E/I-B), which is closely related to the activities of glutamate and γ-aminobutyric acid (GABA) and the receptor availability (RA) of GABAA and mGluR5, remains unexplored. This research investigates these associations during the resting state (RS) condition using simultaneously recorded PET/MR/EEG (trimodal) data. The trimodal data were acquired from three studies using different radio-tracers such as, [11C]ABP688 (ABP) (N = 9), [11C]Flumazenil (FMZ) (N = 10) and 2-[18F]fluoro-2-deoxy-d-glucose (FDG) (N = 10) targeted to study the mGluR5, GABAA receptors and glucose metabolism respectively. Glucose metabolism and neuroreceptor binding availability (non-displaceable binding potential (BPND)) of GABAA and mGluR5 were found to be significantly higher and closely linked within core resting-state networks (RSNs). The neuronal generators of EEG microstates and the fMRI measures were most tightly associated with the BPND of GABAA relative to mGluR5 BPND and the glucose metabolism, emphasising a predominance of inhibitory processes within in the core RSNs at rest. Changes in the neuroreceptors leading to an altered coupling with glucose metabolism may render the RSNs vulnerable to psychiatric conditions. The paradigm employed here will likely help identify the precise neurobiological mechanisms behind these alterations in fMRI functional connectivity and EEG oscillations, potentially benefitting individualised healthcare treatment measures.

A Review of the Default Mode Network in Autism Spectrum Disorders and Attention Deficit Hyperactivity Disorder.

Functional magnetic resonance imaging (fMRI) has been widely used to examine the relationships between brain function and phenotypic features in neurodevelopmental disorders. Techniques such as resting-state functional connectivity (FC) have enabled the identification of the primary networks of the brain. One fMRI network, in particular, the default mode network (DMN), has been implicated in social-cognitive deficits in autism spectrum disorders (ASD) and attentional deficits in attention deficit hyperactivity disorder (ADHD). Given the significant clinical and genetic overlap between ASD and ADHD, surprisingly, no reviews have compared the clinical, developmental, and genetic correlates of DMN in ASD and ADHD and here we address this knowledge gap. We find that, compared with matched controls, ASD studies show a mixed pattern of both stronger and weaker FC in the DMN and ADHD studies mostly show stronger FC. Factors such as age, intelligence quotient, medication status, and heredity affect DMN FC in both ASD and ADHD. We also note that most DMN studies make ASD versus ADHD group comparisons and fail to consider ASD+ADHD comorbidity. We conclude, by identifying areas for improvement and by discussing the importance of using transdiagnostic approaches such as the Research Domain Criteria (RDoC) to fully account for the phenotypic and genotypic heterogeneity and overlap of ASD and ADHD.Impact statementIn this work, we review the default mode network in autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD), as well as comorbid ASD+ADHD literature. Such a review has not been constructed in the field of cognitive neuroscience at this time, and it would greatly aid other behavioral and cognitive neuroscientists in identifying gaps in the field. In addition, the need to consider disorders to be on a continuum, as suggested by the Research Domain Criteria (RDoC), is important while identifying abnormal patterns in resting-state functional connectivity. This timely review will impact the field in a meaningful way, such that more research on the overlaps between ASD and ADHD is conducted along a spectrum.

BrainNET: Inference of Brain Network Topology Using Machine Learning.

Background: To develop a new functional magnetic resonance image (fMRI) network inference method, BrainNET, that utilizes an efficient machine learning algorithm to quantify contributions of various regions of interests (ROIs) in the brain to a specific ROI. Methods: BrainNET is based on extremely randomized trees to estimate network topology from fMRI data and modified to generate an adjacency matrix representing brain network topology, without reliance on arbitrary thresholds. Open-source simulated fMRI data of 50 subjects in 28 different simulations under various confounding conditions with known ground truth were used to validate the method. Performance was compared with correlation and partial correlation (PC). The real-world performance was then evaluated in a publicly available attention-deficit/hyperactivity disorder (ADHD) data set, including 134 typically developing children (mean age: 12.03, males: 83), 75 ADHD inattentive (mean age: 11.46, males: 56), and 93 ADHD combined (mean age: 11.86, males: 77) subjects. Network topologies in ADHD were inferred using BrainNET, correlation, and PC. Graph metrics were extracted to determine differences between the ADHD groups. Results: BrainNET demonstrated excellent performance across all simulations and varying confounders in identifying the true presence of connections. In the ADHD data set, BrainNET was able to identify significant changes (p < 0.05) in graph metrics between groups. No significant changes in graph metrics between ADHD groups were identified using correlation and PC. Conclusion: We describe BrainNET, a new network inference method to estimate fMRI connectivity that was adapted from gene regulatory methods. BrainNET out-performed Pearson correlation and PC in fMRI simulation data and real-world ADHD data. BrainNET can be used independently or combined with other existing methods as a useful tool to understand network changes and to determine the true network topology of the brain under various conditions and disease states. Impact statement Developed a new functional magnetic resonance image (fMRI) network inference method named as BrainNET using machine learning. BrainNET out-performed Pearson correlation and partial correlation in fMRI simulation data and real-world attention-deficit/hyperactivity disorder data. BrainNET does not need to be pretrained and can be applied to infer fMRI network topology independently on individual subjects and for varying number of nodes.

Complete hemispherotomy leads to lateralized functional organization and lower level of consciousness in the isolated hemisphere.

ObjectiveTo quantify whole‐brain functional organization after complete hemispherotomy, characterizing unexplored plasticity pathways and the conscious level of the dissected hemispheres.MethodsEvaluation with multimodal magnetic resonance imaging in two pediatric patients undergoing right hemispherotomy including complete callosotomy with a perithalamic section. Regional cerebral blood flow and fMRI network connectivity assessed the functional integrity of both hemispheres after surgery. The level of consciousness was tested by means of a support vector machine classifier which compared the intrinsic organization of the dissected hemispheres with those of patients suffering from disorders of consciousness.ResultsAfter hemispherotomy, both patients showed typical daily functionality. We found no interhemispheric transfer of functional connectivity in either patient as predicted by the operation. The healthy left hemispheres displayed focal blood hyperperfusion in motor and limbic areas, with preserved network‐level organization. Unexpectedly, the disconnected right hemispheres showed sustained network organization despite low regional cerebral blood flow. Subcortically, functional connectivity was increased in the left thalamo‐cortical loop and between the cerebelli. One patient further showed unusual ipsilateral right cerebello‐cortical connectivity, which was explained by the mediation of the vascular system. The healthy left hemisphere had higher probability to be classified as in a minimally conscious state compared to the isolated right hemisphere.SignificanceComplete hemispherotomy leads to a lateralized whole‐brain organization, with the remaining hemisphere claiming most of the brain's energetic reserves supported by subcortical structures. Our results further underline the contribution of nonneuronal vascular signals on contralateral connectivity, shedding light on the nature of network organization in the isolated tissue. The disconnected hemisphere is characterized by a level of consciousness which is necessary but insufficient for conscious processing, paving the way for more specific inquiries about its role in awareness in the absence of behavioral output.

Altered resting-state fMRI signals and network topological properties of bipolar depression patients with anxiety symptoms

BackgroundThis study aims to explore the changes in functional neuroimaging in bipolar depression patients with anxiety symptoms (BDP-A). MethodsForty-five BDP-A patients, 22 bipolar depression patients without anxiety symptoms (BDP-NA), and 48 healthy controls (HC) were finally involved. The low-frequency oscillation characteristics, functional connectivity (FC), and network properties among the three groups of participants were analyzed. ResultsCompared with the BDP-NA group, BDP-A patients exhibited significantly decreased amplitude of low-frequency fluctuation (ALFF) in the left middle frontal gyrus (MFG), superior occipital gyrus, and inferior parietal, but supramarginal and angular gyri (IPL). Enhanced FC from left IPL to middle temporal gyrus, from left precentral gyrus (PreCG) to bilateral angular gyri, medial superior frontal gyrus, and left superior frontal gyrus (SFG)/MFG were also revealed. Compared with HC, the BDP-A group showed remarkably increased ALFF in the left MFG/PreCG, right superior parietal gyrus, while decreased ALFF in the left inferior frontal gyrus, opercular part, and SFG. In addition, higher regional homogeneity in the left MFG/PreCG was found. LimitationsThe limitations are as follows: (1) relatively small sample size; (2) not all the patients were drug-naive; (3) lack of pure anxiety disorder patients as a controlled group; (4) mental health conditions of HC were not systemic evaluated. ConclusionsBDP-A patients showed significant differences in resting-state fMRI properties when compared with BDP-NA or HC group. These results may infer the dysfunction of the dorsal attention network, the default network, and the fronto-limbic system as well as disrupted brain network efficiency in BDP-A patients.

Resting-state functional dynamic connectivity and healthy aging: A sliding-window network analysis.

Graph theory has been widely used to study structural and functional brain connectivity changes in healthy aging, and occasionally with clinical samples; in both cases, during task-related and resting-state experiments. Recent studies have focused their interest on dynamic changes during a resting-state fMRI register in order to identify differences in non-stationary patterns associated with the aging process. The objective of this study was to characterize resting-state fMRI network dynamics in order to study the healthy aging process. 114 healthy older adults were measured in a resting-state paradigm using fMRI. A sliding-window approach to graph theory was used to measure the mean degree, average path length, clustering coefficient, and small-worldness of each subnetwork, and the impact of age and time in each graph measure was assessed. A combined effect of age and time was detected in mean degree, average path length, and small-worldness, where participants aged 75 to 79 showed a curvilinear trend with reduced network density and increased small-world coefficient in the middle of the register. An effect of age was observed on average path length, with younger participants showing slightly lower scores.

Multi‐subject stochastic blockmodels with mixed effects for adaptive analysis of individual differences in human brain network cluster structure

Recently, there has been a renewed interest in the class of stochastic blockmodels (SBM) and their applications to multi‐subject brain networks. In our most recent work, we have considered an extension of the classical SBM, termed heterogeneous SBM (Het‐SBM), that models subject variability in the cluster‐connectivity profiles through the addition of a logistic regression model with subject‐specific covariates on the level of each block. Although this model has proved to be useful in both the clustering and inference aspects of multi‐subject brain network data, including fleshing out differences in connectivity between patients and controls, it does not account for dependencies that may exist within subjects. To overcome this limitation, we propose an extension of Het‐SBM, termed Het‐Mixed‐SBM, in which we model the within‐subject dependencies by adding subject‐ and block‐level random intercepts in the embedded logistic regression model. Using synthetic data, we investigate the accuracy of the partitions estimated by our proposed model as well as the validity of inference procedures based on the Wald and permutation tests. Finally, we illustrate the model by analyzing the resting‐state fMRI networks of 99 healthy volunteers from the Human Connectome Project (HCP) using covariates like age, gender, and IQ to explain the clustering patterns observed in the data.

Toward Establishing Internal Validity for Correlated Gene Expression Measures in Imaging Genomics of Functional Networks: Why Distance Corrections and External Face Validity Alone Fall Short. Reply to "Distance Is Not Everything in Imaging Genomics of Functional Networks: Reply to a Commentary on Correlated Gene Expression Supports Synchronous Activity in Brain Networks".

The primary claim of the Richiardi et al. (2015) Science article is that a measure of correlated gene expression, significant strength fraction (SSF), is related to resting state fMRI (rsfMRI) networks. However, there is still debate about this claim and whether spatial proximity, in the form of contiguous clusters, accounts entirely, or only partially, for SSF (Pantazatos and Li, 2017; Richiardi et al., 2017). Here, 13 distributed networks were simulated by combining 34 contiguous clusters randomly placed throughout cortex, with resulting edge distance distributions similar to rsfMRI networks. Cluster size was modulated (6–15 mm radius) to test its influence on SSF false positive rate (SSF-FPR) among the simulated “noise” networks. The contribution of rsfMRI networks on SSF-FPR was examined by comparing simulated networks whose clusters were sampled from: (1) all 1,777 cortical tissue samples, (2) all samples, but with non-rsfMRI cluster centers, and (3) only 1,276 non-rsfMRI samples. Results show that SSF-FPR is influenced only by cluster size (r > 0.9, p < 0.001), not by rsfMRI samples. Simulations using 14 mm radius clusters most resembled rsfMRI networks. When thresholding at p < 10–4, the SSF-FPR was 0.47. Genes that maximize SF have high global spatial autocorrelation. In conclusion, SSF is unrelated to rsfMRI networks. The main conclusion of Richiardi et al. (2015) is based on a finding that is ∼50% likely to be a false positive, not <0.01% as originally reported in the article (Richiardi et al., 2015). We discuss why distance corrections alone and external face validity are insufficient to establish a trustworthy relationship between correlated gene expression measures and rsfMRI networks, and propose more rigorous approaches to preclude common pitfalls in related studies.

EEG microstates are correlated with brain functional networks during slow-wave sleep

Electroencephalography (EEG) microstates have been extensively studied in wakefulness and have been described as the “atoms of thought”. Previous studies of EEG have found four microstates, i.e., microstates A, B, C and D, that are consistent among participants across the lifespan during the resting state. Studies using simultaneous EEG and functional magnetic resonance imaging (fMRI) have provided evidence for correlations between EEG microstates and fMRI networks during the resting state. Microstates have also been found during non-rapid eye movement (NREM) sleep. Slow-wave sleep (SWS) is considered the most restorative sleep stage and has been associated with the maintenance of sleep. However, the relationship between EEG microstates and brain functional networks during SWS has not yet been investigated. In this study, simultaneous EEG-fMRI data were collected during SWS to test the correspondence between EEG microstates and fMRI networks. EEG microstate-informed fMRI analysis revealed that three out of the four microstates showed significant correlations with fMRI data: 1) fMRI fluctuations in the insula and posterior temporal gyrus positively correlated with microstate B, 2) fMRI signals in the middle temporal gyrus and fusiform gyrus negatively correlated with microstate C, and 3) fMRI fluctuations in the occipital lobe negatively correlated with microstate D, while fMRI signals in the anterior cingulate and cingulate gyrus positively correlated with this microstate. Functional brain networks were then assessed using group independent component analysis based on the fMRI data. The group-level spatial correlation analysis showed that the fMRI auditory network overlapped the fMRI activation map of microstate B, the executive control network overlapped the fMRI deactivation of microstate C, and the visual and salience networks overlapped the fMRI deactivation and activation maps of microstate D. In addition, the subject-level spatial correlations between the general linear model (GLM) beta map of each microstate and the individual maps of each component yielded by dual regression also showed that EEG microstates were closely associated with brain functional networks measured using fMRI during SWS. Overall, the results showed that EEG microstates were closely related to brain functional networks during SWS, which suggested that EEG microstates provide an important electrophysiological basis underlying brain functional networks.

Anomalous intrinsic connectivity within and between visual and auditory networks in major depressive disorder

ObjectiveMajor depressive disorder (MDD) is a ubiquitous mental illness with heterogeneous symptoms, however, the pathophysiology mechanisms are still not fully understood. Clinical and preclinical studies suggested that depression could cause disturbances in sensory perception systems, disruptions in auditory and visual functions may serve as an essential clinical features underlying MDD. MethodsThe current study investigated the abnormal intrinsic connectivity within and between visual and auditory networks in 95 MDD patients and 97 age-, gender-, education level-matched healthy controls (HCs) by using resting-state functional magnetic resonance imaging (fMRI). One auditory network (AN) and three visual components including visual component 1 (VC1), VC2, and VC3 were identified by using independent component analysis method based on the fMRI networks during the resting state with the largest spatial correlations, combining with brain regions and specific network templates. ResultsWe found that MDD could be characterized by the following disrupted network model relative to HCs: (i) reduced within-network connectivity in the AN, VC2, and VC3; (ii) reduced between-network connectivity between the AN and the VC3. Furthermore, aberrant functional connectivity (FC) within the visual network was linked to the clinical symptoms. ConclusionsOverall, our results demonstrated that abnormalities of FC in perception systems including intrinsic visual and auditory networks may explain neurobiological mechanisms underlying MDD and could serve as a potential effective biomarker.

Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure

There is considerable interest in elucidating the cluster structure of brain networks in terms of modules, blocks or clusters of similar nodes. However, it is currently challenging to handle data on multiple subjects since most of the existing methods are applicable only on a subject-by-subject basis or for analysis of an average group network. The main limitation of per-subject models is that there is no obvious way to combine the results for group comparisons, and of group-averaged models that they do not reflect the variability between subjects. Here, we propose two new extensions of the classical Stochastic Blockmodel (SBM) that use a mixture model to estimate blocks or clusters of connected nodes, combined with a regression model to capture the effects of subject-level covariates on individual differences in cluster structure. The proposed Multi-Subject Stochastic Blockmodels (MS-SBMs) can flexibly account for between-subject variability in terms of homogeneous or heterogeneous covariate effects on connectivity using subject demographics such as age or diagnostic status. Using synthetic data, representing a range of block sizes and cluster structures, we investigate the accuracy of the estimated MS-SBM parameters as well as the validity of inference procedures based on the Wald, likelihood ratio and permutation tests. We show that the proposed multi-subject SBMs recover the true cluster structure of synthetic networks more accurately and adaptively than standard methods for modular decomposition (i.e. the Fast Louvain and Newman Spectral algorithms). Permutation tests of MS-SBM parameters were more robustly valid for statistical inference and Type I error control than tests based on standard asymptotic assumptions. Applied to analysis of multi-subject resting-state fMRI networks (13 healthy volunteers; 12 people with schizophrenia; n=268 brain regions), we show that Heterogeneous Stochastic Blockmodel (Het-SBM) identifies a range of network topologies simultaneously, including modular and core structures.