Intrinsic Functional Networks Research Articles

Resting-state functional connectivity in children cooled for neonatal encephalopathy.

Therapeutic hypothermia improves outcomes following neonatal hypoxic-ischaemic encephalopathy, reducing cases of death and severe disability such as cerebral palsy compared with normothermia management. However, when cooled children reach early school-age, they have cognitive and motor impairments which are associated with underlying alterations to brain structure and white matter connectivity. It is unknown whether these differences in structural connectivity are associated with differences in functional connectivity between cooled children and healthy controls. Resting-state functional MRI has been used to characterize static and dynamic functional connectivity in children, both with typical development and those with neurodevelopmental disorders. Previous studies of resting-state brain networks in children with hypoxic-ischaemic encephalopathy have focussed on the neonatal period. In this study, we used resting-state fMRI to investigate static and dynamic functional connectivity in children aged 6-8 years who were cooled for neonatal hypoxic-ischaemic without cerebral palsy [n = 22, median age (interquartile range) 7.08 (6.85-7.52) years] and healthy controls matched for age, sex and socioeconomic status [n = 20, median age (interquartile range) 6.75 (6.48-7.25) years]. Using group independent component analysis, we identified 31 intrinsic functional connectivity networks consistent with those previously reported in children and adults. We found no case-control differences in the spatial maps of these intrinsic connectivity networks. We constructed subject-specific static functional connectivity networks by measuring pairwise Pearson correlations between component time courses and found no case-control differences in functional connectivity after false discovery rate correction. To study the time-varying organization of resting-state networks, we used sliding window correlations and deep clustering to investigate dynamic functional connectivity characteristics. We found k = 4 repetitively occurring functional connectivity states, which exhibited no case-control differences in dwell time, fractional occupancy or state functional connectivity matrices. In this small cohort, the spatiotemporal characteristics of resting-state brain networks in cooled children without severe disability were too subtle to be differentiated from healthy controls at early school-age, despite underlying differences in brain structure and white matter connectivity, possibly reflecting a level of recovery of healthy resting-state brain function. To our knowledge, this is the first study to investigate resting-state functional connectivity in children with hypoxic-ischaemic encephalopathy beyond the neonatal period and the first to investigate dynamic functional connectivity in any children with hypoxic-ischaemic encephalopathy.

Early altered directionality of resting brain network state transitions in the TgF344-AD rat model of Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disease resulting in memory loss and cognitive decline. Synaptic dysfunction is an early hallmark of the disease whose effects on whole-brain functional architecture can be identified using resting-state functional MRI (rsfMRI). Insights into mechanisms of early, whole-brain network alterations can help our understanding of the functional impact of AD's pathophysiology. Here, we obtained rsfMRI data in the TgF344-AD rat model at the pre- and early-plaque stages. This model recapitulates the major pathological and behavioral hallmarks of AD. We used co-activation pattern (CAP) analysis to investigate if and how the dynamic organization of intrinsic brain functional networks states, undetectable by earlier methods, is altered at these early stages. We identified and characterized six intrinsic brain states as CAPs, their spatial and temporal features, and the transitions between the different states. At the pre-plaque stage, the TgF344-AD rats showed reduced co-activation of hub regions in the CAPs corresponding to the default mode-like and lateral cortical network. Default mode-like network activity segregated into two distinct brain states, with one state characterized by high co-activation of the basal forebrain. This basal forebrain co-activation was reduced in TgF344-AD animals mainly at the pre-plaque stage. Brain state transition probabilities were altered at the pre-plaque stage between states involving the default mode-like network, lateral cortical network, and basal forebrain regions. Additionally, while the directionality preference in the network-state transitions observed in the wild-type animals at the pre-plaque stage had diminished at the early-plaque stage, TgF344-AD animals continued to show directionality preference at both stages. Our study enhances the understanding of intrinsic brain state dynamics and how they are impacted at the early stages of AD, providing a nuanced characterization of the early, functional impact of the disease's neurodegenerative process.

Uncovering brain functional connectivity disruption patterns of lung cancer-related pain.

Pain is a pervasive symptom in lung cancer patients during the onset of the disease. This study aims to investigate the connectivity disruption patterns of the whole-brain functional network in lung cancer patients with cancer pain (CP+). We constructed individual whole-brain, region of interest (ROI)-level functional connectivity (FC) networks for 50 CP+ patients, 34 lung cancer patients without pain-related complaints (CP-), and 31 matched healthy controls (HC). Then, a ROI-based FC analysis was used to determine the disruptions of FC among the three groups. The relationships between aberrant FCs and clinical parameters were also characterized. The ROI-based FC analysis demonstrated that hypo-connectivity was present both in CP+ and CP- patients compared to HC, which were particularly clustered in the somatomotor and ventral attention, frontoparietal control, and default mode modules. Notably, compared to CP- patients, CP+ patients had hyper-connectivity in several brain regions mainly distributed in the somatomotor and visual modules, suggesting these abnormal FC patterns may be significant for cancer pain. Moreover, CP+ patients also showed increased intramodular and intermodular connectivity strength of the functional network, which could be replicated in cancer stage IV and lung adenocarcinoma. Finally, abnormal FCs within the prefrontal cortex and somatomotor cortex were positively correlated with pain intensity and pain duration, respectively. These findings suggested that lung cancer patients with cancer pain had disrupted connectivity in the intrinsic brain functional network, which may be the underlying neuroimaging mechanisms.

Investigating robust associations between functional connectivity based on graph theory and general intelligence

Previous research investigating relations between general intelligence and graph-theoretical properties of the brain’s intrinsic functional network has yielded contradictory results. A promising approach to tackle such mixed findings is multi-center analysis. For this study, we analyzed data from four independent data sets (total N > 2000) to identify robust associations amongst samples between g factor scores and global as well as node-specific graph metrics. On the global level, g showed no significant associations with global efficiency or small-world propensity in any sample, but significant positive associations with global clustering coefficient in two samples. On the node-specific level, elastic-net regressions for nodal efficiency and local clustering yielded no brain areas that exhibited consistent associations amongst data sets. Using the areas identified via elastic-net regression in one sample to predict g in other samples was not successful for local clustering and only led to one significant, one-way prediction across data sets for nodal efficiency. Thus, using conventional graph theoretical measures based on resting-state imaging did not result in replicable associations between functional connectivity and general intelligence.

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.

Reorganization of intrinsic functional connectivity in early-stage Parkinson’s disease patients with probable REM sleep behavior disorder

REM sleep behavior disorder (RBD) symptoms in Parkinson’s disease (PD) suggest both a clinically and pathologically malignant subtype. However, whether RBD symptoms are associated with alterations in the organization of whole-brain intrinsic functional networks in PD, especially at early disease stages, remains unclear. Here we use resting-state functional MRI, coupled with graph-theoretical approaches and network-based statistics analyses, and validated with large-scale network analyses, to characterize functional brain networks and their relationship with clinical measures in early PD patients with probable RBD (PD+pRBD), early PD patients without probable RBD (PD-pRBD) and healthy controls. Thirty-six PD+pRBD, 57 PD-pRBD and 71 healthy controls were included in the final analyses. The PD+pRBD group demonstrated decreased global efficiency (t = -2.036, P = 0.0432) compared to PD-pRBD, and decreased network efficiency, as well as comprehensively disrupted nodal efficiency and whole-brain networks (all eight networks, but especially in the sensorimotor, default mode and visual networks) compared to healthy controls. The PD-pRBD group showed decreased nodal degree in right ventral frontal cortex and more affected edges in the frontoparietal and ventral attention networks compared to healthy controls. Furthermore, the assortativity coefficient was negatively correlated with Montreal cognitive assessment scores in the PD+pRBD group (r = -0.365, P = 0.026, d = 0.154). The observation of altered whole-brain functional networks and its correlation with cognitive function in PD+pRBD suggest reorganization of the intrinsic functional connectivity to maintain the brain function in the early stage of the disease. Future longitudinal studies following these alterations along disease progression are warranted.

Abnormal dynamics of functional brain network in Apolipoprotein E ε4 carriers with mild cognitive impairment

<abstract> <p>As is well known, the Apolipoprotein E (APOE) ε4 allele is the most pertinent genetic hazardous element for Alzheimer's disease (AD). Mild cognitive impairment (MCI) is considered a prodromal stage of AD. How the APOE ε4 allele modulates functional connectivity of brain network in MCI group is a question worth exploring. At present, some studies have evaluated the relationship between APOE ε4 allele and static functional network connectivity (sFNC) for MCI individuals, while the relationship of dynamic FNC (dFNC) with APOE ε4 allele still remained puzzled. Thus, we aim to detect aberrant dFNC for APOE ε4 carriers in the MCI group. On the basis of the resting-state functional magnetic resonance imaging (rs-fMRI) data, seven intrinsic brain functional networks were first recognized by the group independent component analysis. Then, the technique of sliding window was employed to determine the dFNC, and two dFNC states were detected by the k-means clustering algorithm. Finally, three temporal properties of fraction time, mean dwell time as well as transition numbers in the dFNC states were investigated. The results found that the dFNC and temporal properties in APOE ε4 carriers were abnormal compared with those in APOE ε4 noncarriers. In detail, in the MCI group, compared with APOE ε4 noncarriers, carriers had 9 pairs of abnormal dFNC and had significant differences in all the three temporal properties of the two dFNC states. In addition, two pairs of dFNC were found significantly correlated with clinical measure. This detected abnormal dynamics of temporal properties and dFNC in APOE ε4 carriers were similar with that reported for AD patients in previous studies. These results may suggest that in the MCI group, APOE carriers are more at risk for AD compared to noncarriers. Our findings may offer novel insights into the mechanisms of abnormal brain reconfiguration for individuals at genetic risk for AD, which could also be regarded as biomarkers for the early identification of AD.</p> </abstract>

Individual differences in the language task-evoked and resting-state functional networks.

The resting state functional network is highly variable across individuals. However, inter-individual differences in functional networks evoked by language tasks and their comparison with resting state are still unclear. To address these two questions, we used T1 anatomical data and functional brain imaging data of resting state and a story comprehension task from the Human Connectome Project (HCP) to characterize functional network variability and investigate the uniqueness of the functional network in both task and resting states. We first demonstrated that intrinsic and task-induced functional networks exhibited remarkable differences across individuals, and language tasks can constrain inter-individual variability in the functional brain network. Furthermore, we found that the inter-individual variability of functional networks in two states was broadly consistent and spatially heterogeneous, with high-level association areas manifesting more significant variability than primary visual processing areas. Our results suggested that the functional network underlying language comprehension is unique at the individual level, and the inter-individual variability architecture of the functional network is broadly consistent in language task and resting state.

Inside the mindful moment: The effects of brief mindfulness practice on large-scale network organization and intimate partner aggression.

Mindfulness can produce neuroplastic changes that support adaptive cognitive and emotional functioning. Recently interest in single-exercise mindfulness instruction has grown considerably because of the advent of mobile health technology. Accordingly, the current study sought to extend neural models of mindfulness by investigating transient states of mindfulness during single-dose exposure to focused attention meditation. Specifically, we examined the ability of a brief mindfulness induction to attenuate intimate partner aggression via adaptive changes to intrinsic functional brain networks. We employed a dual-regression approach to examine a large-scale functional network organization in 50 intimate partner dyads (total n = 100) while they received either mindfulness (n = 50) or relaxation (n = 50) instruction. Mindfulness instruction reduced coherence within the Default Mode Network and increased functional connectivity within the Frontoparietal Control and Salience Networks. Additionally, mindfulness decoupled primary visual and attention-linked networks. Yet, this induction was unable to elicit changes in subsequent intimate partner aggression, and such aggression was broadly unassociated with any of our network indices. These findings suggest that minimal doses of focused attention-based mindfulness can promote transient changes in large-scale brain networks that have uncertain implications for aggressive behavior.

Multifractal long-range dependence pattern of functional magnetic resonance imaging in the human brain at rest.

Long-range dependence is a prevalent phenomenon in various biological systems that characterizes the long-memory effect of temporal fluctuations. While recent research suggests that functional magnetic resonance imaging signal has fractal property, it remains unknown about the multifractal long-range dependence pattern of resting-state functional magnetic resonance imaging signals. The current study adopted the multifractal detrended fluctuation analysis on highly sampled resting-state functional magnetic resonance imaging scans to investigate long-range dependence profile associated with the whole-brain voxels as specific functional networks. Our findings revealed the long-range dependence's multifractal properties. Moreover, long-term persistent fluctuations are found for all stations with stronger persistency in whole-brain regions. Subsets with large fluctuations contribute more to the multifractal spectrum in the whole brain. Additionally, we found that the preprocessing with band-pass filtering provided significantly higher reliability for estimating long-range dependence. Our validation analysis confirmed that the optimal pipeline of long-range dependence analysis should include band-pass filtering and removal of daily temporal dependence. Furthermore, multifractal long-range dependence characteristics in healthy control and schizophrenia are different significantly. This work has provided an analytical pipeline for the multifractal long-range dependence in the resting-state functional magnetic resonance imaging signal. The findings suggest differential long-memory effects in the intrinsic functional networks, which may offer a neural marker finding for understanding brain function and pathology.

Optimizing network neuroscience computation of individual differences in human spontaneous brain activity for test-retest reliability.

A rapidly emerging application of network neuroscience in neuroimaging studies has provided useful tools to understand individual differences in intrinsic brain function by mapping spontaneous brain activity, namely intrinsic functional network neuroscience (ifNN). However, the variability of methodologies applied across the ifNN studies-with respect to node definition, edge construction, and graph measurements-makes it difficult to directly compare findings and also challenging for end users to select the optimal strategies for mapping individual differences in brain networks. Here, we aim to provide a benchmark for best ifNN practices by systematically comparing the measurement reliability of individual differences under different ifNN analytical strategies using the test-retest design of the Human Connectome Project. The results uncovered four essential principles to guide ifNN studies: (1) use a whole brain parcellation to define network nodes, including subcortical and cerebellar regions; (2) construct functional networks using spontaneous brain activity in multiple slow bands; and (3) optimize topological economy of networks at individual level; and (4) characterize information flow with specific metrics of integration and segregation. We built an interactive online resource of reliability assessments for future ifNN (https://ibraindata.com/research/ifNN).

Integrating multimodal and multiscale connectivity blueprints of the human cerebral cortex in health and disease.

The brain is composed of disparate neural populations that communicate and interact with one another. Although fiber bundles, similarities in molecular architecture, and synchronized neural activity all reflect how brain regions potentially interact with one another, a comprehensive study of how all these interregional relationships jointly reflect brain structure and function remains missing. Here, we systematically integrate 7 multimodal, multiscale types of interregional similarity ("connectivity modes") derived from gene expression, neurotransmitter receptor density, cellular morphology, glucose metabolism, haemodynamic activity, and electrophysiology in humans. We first show that for all connectivity modes, feature similarity decreases with distance and increases when regions are structurally connected. Next, we show that connectivity modes exhibit unique and diverse connection patterns, hub profiles, spatial gradients, and modular organization. Throughout, we observe a consistent primacy of molecular connectivity modes-namely correlated gene expression and receptor similarity-that map onto multiple phenomena, including the rich club and patterns of abnormal cortical thickness across 13 neurological, psychiatric, and neurodevelopmental disorders. Finally, to construct a single multimodal wiring map of the human cortex, we fuse all 7 connectivity modes and show that the fused network maps onto major organizational features of the cortex including structural connectivity, intrinsic functional networks, and cytoarchitectonic classes. Altogether, this work contributes to the integrative study of interregional relationships in the human cerebral cortex.

A neural mechanism of cognitive reserve: The case of bilingualism

Cognitive Reserve (CR) refers to the preservation of cognitive function in the face of age- or disease-related neuroanatomical decline. While bilingualism has been shown to contribute to CR, the extent to which, and what particular aspect of, second language experience contributes to CR are debated, and the underlying neural mechanism(s) unknown. Intrinsic functional connectivity reflects experience-dependent neuroplasticity that occurs across timescales ranging from minutes to decades, and may be a neural mechanism underlying CR. To test this hypothesis, we used voxel-based morphometry and resting-state functional connectivity analyses of MRI data to compare structural and functional brain integrity between monolingual and bilingual older adults, matched on cognitive performance, and across levels of second language proficiency measured as a continuous variable. Bilingualism, and degree of second language proficiency specifically, were associated with lower gray matter integrity in a hub of the default mode network – a region that is particularly vulnerable to decline in aging and dementia – but preserved intrinsic functional network organization. Bilingualism moderated the association between neuroanatomical differences and cognitive decline, such that lower gray matter integrity was associated with lower executive function in monolinguals, but not bilinguals. Intrinsic functional network integrity predicted executive function when controlling for group differences in gray matter integrity and language status. Our findings confirm that lifelong bilingualism is a CR factor, as bilingual older adults performed just as well as their monolingual peers on tasks of executive function, despite showing signs of more advanced neuroanatomical aging, and that this is a consequence of preserved intrinsic functional network organization.

Abnormal functional connectivity in the habenula is associated with subjective hyperarousal state in chronic insomnia disorder.

The hyperarousal process model plays a central role in the physiology of chronic insomnia disorder (CID). Recent evidence has demonstrated that the habenula is involved in the arousal and sleep-wake cycle. However, whether the intrinsic habenular functional network contributes to the underlying mechanism of CID and its relationship to the arousal state in CID remains unclear. This single-centered study included 34 patients with subjective CID and 22 matched good sleep control (GSC), and underwent a series of neuropsychological tests and resting-state functional magnetic resonance imaging scans. The habenular functional network was assessed using seed-based functional connectivity (FC) analysis. The subjective arousal state was evaluated with the hyperarousal scale (HAS). Alterations in the habenular FC network and their clinical significance in patients with CID were explored. Compared with the GSC group, the CID group showed decreased habenular FC in the left caudate nucleus and right inferior parietal lobule and increased FC in the right habenula, bilateral calcarine cortex, and posterior cingulate cortex. The decreased FC between the left habenula and caudate nucleus was associated with an increased arousal state in the CID group. The present results provide evidence for a dysfunctional habenular network in patients with CID. These findings extend our understanding of the neuropathological mechanisms underlying the hyperarousal model in chronic insomnia.

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.

Altered intrinsic functional network connectivity is associated with impulsivity and emotion dysregulation in drug-naïve young patients with borderline personality disorder

BackgroundDespite impulse control and emotion regulation being altered in borderline personality disorder (BPD), the specific mechanism of these clinical features remains unclear. This study investigated the functional connectivity (FC) abnormalities within- and between- default mode network (DMN), salience network (SN), and central executive network (CEN) in BPD, and examined the association between aberrant FC and clinical features. We aimed to explore whether the abnormal large-scale networks underlie the pathophysiology of impulsivity and emotion dysregulation in BPD.MethodsForty-one young, drug-naïve patients with BPD (24.98 ± 3.12 years, 20 males) and 42 healthy controls (HCs; 24.74 ± 1.29 years, 17 males) were included in resting-state functional magnetic resonance imaging analyses. Independent component analysis was performed to extract subnetworks of the DMN, CEN, and SN. Additionally, partial correlation was performed to explore the association between brain imaging variables and clinical features in BPD.ResultsCompared with HCs, BPD showed significant decreased intra-network FC of right medial prefrontal cortex in the anterior DMN and of right angular gyrus in the right CEN. Intra-network FC of right angular gyrus in the anterior DMN was significantly negatively correlated with attention impulsivity in BPD. The patients also showed decreased inter-network FC between the posterior DMN and left CEN, which was significantly negatively correlated with emotion dysregulation.ConclusionThese findings suggest that impaired intra-network FC may underlie the neurophysiological mechanism of impulsivity, and abnormal inter-network FC may elucidate the neurophysiological mechanism of emotion dysregulation in BPD.

Association of Intrinsic Functional Brain Network and Longitudinal Development of Cognitive Behavioral Symptoms in Young Girls With Fragile X Syndrome

BackgroundFragile X syndrome (FXS) is an X chromosome–linked genetic disorder characterized by increased risk for behavioral, social, and neurocognitive deficits. Because males express a more severe phenotype than females, research has focused largely on identifying neural abnormalities in all-male or both-sex populations with FXS. Therefore, very little is known about the neural alterations that contribute to cognitive behavioral symptoms in females with FXS. This cross-sectional study aimed to elucidate the large-scale resting-state brain networks associated with the multidomain cognitive behavioral phenotype in girls with FXS. MethodsWe recruited 38 girls with full-mutation FXS (11.58 ± 3.15 years) and 32 girls without FXS (11.66 ± 2.27 years). Both groups were matched on age, verbal IQ, and multidomain cognitive behavioral symptoms. Resting-state functional magnetic resonance imaging data were collected. ResultsCompared with the control group, girls with FXS showed significantly greater resting-state functional connectivity of the default mode network, lower nodal strength at the right middle temporal gyrus, stronger nodal strength at the left caudate, and higher global efficiency of the default mode network. These aberrant brain network characteristics map directly onto the cognitive behavioral symptoms commonly observed in girls with FXS. An exploratory analysis suggested that brain network patterns at a prior time point (time 1) were predictive of the longitudinal development of participants’ multidomain cognitive behavioral symptoms. ConclusionsThese findings represent the first examination of large-scale brain network alterations in a large sample of girls with FXS, expanding our knowledge of potential neural mechanisms underlying the development of cognitive behavioral symptoms in girls with FXS.

Reliability and clinical utility of spatially constrained estimates of intrinsic functional networks from very short fMRI scans.

Resting-state functional network connectivity (rsFNC) has shown utility for identifying characteristic functional brain patterns in individuals with psychiatric and mood disorders, providing a promising avenue for biomarker development. However, several factors have precluded widespread clinical adoption of rsFNC diagnostics, namely a lack of standardized approaches for capturing comparable and reproducible imaging markers across individuals, as well as the disagreement on the amount of data required to robustly detect intrinsic connectivity networks (ICNs) and diagnostically relevant patterns of rsFNC at the individual subject level. Recently, spatially constrained independent component analysis (scICA) has been proposed as an automated method for extracting ICNs standardized to a chosen network template while still preserving individual variation. Leveraging the scICA methodology, which solves the former challenge of standardized neuroimaging markers, we investigate the latter challenge of identifying a minimally sufficient data length for clinical applications of resting-state fMRI (rsfMRI). Using a dataset containing rsfMRIscansofindividuals with schizophrenia and controls (M= 310) as well as simulated rsfMRI, we evaluated the robustness of ICN and rsFNC estimates at both the subject- and group-level, as well as the performance of diagnostic classification, with respect to the length of the rsfMRI time course. We found individual estimates of ICNs and rsFNC from the full-length (5min) reference time course were sufficiently approximated with just 3-3.5min of data (r= 0.85, 0.88, respectively), and significant differences in group-average rsFNC could be sufficiently approximated with even less data, just 2min (r= 0.86). These results from the shorter clinical data were largely consistent with the results from validation experiments using longer time series from both simulated (30 min) and real-world (14 min) datasets, in which estimates of subject-level FNC were reliably estimated with 3-5min of data. Moreover, in the real-world data we found rsFNC and ICN estimates generated across the full range of data lengths (0.5-14 min) more reliably matched those generated from the first 5min of scan time than those generated from the last 5min, suggesting increased influence of "late scan" noise factors such as fatigue or drowsiness may limit the reliability of FNC from data collected after 10+ minof scan time, further supporting the notion of shorter scans. Lastly, a diagnostic classification model trained on just 2min of data retained 97%-98% classification accuracy relative to that of the full-length reference model. Our results suggest that, when decomposed with scICA, rsfMRI scans of just 2-5min show good clinical utility without significant loss of individual FNC information of longer scan lengths.

Brain network mapping and glioma pathophysiology.

Adult diffuse gliomas are among the most difficult brain disorders to treat in part due to a lack of clarity regarding the anatomical origins and mechanisms of migration of the tumours. While the importance of studying networks of glioma spread has been recognized for at least 80 years, the ability to carry out such investigations in humans has emerged only recently. Here, we comprehensively review the fields of brain network mapping and glioma biology to provide a primer for investigators interested in merging these areas of inquiry for the purposes of translational research. Specifically, we trace the historical development of ideas in both brain network mapping and glioma biology, highlighting studies that explore clinical applications of network neuroscience, cells-of-origin of diffuse glioma and glioma-neuronal interactions. We discuss recent research that has merged neuro-oncology and network neuroscience, finding that the spatial distribution patterns of gliomas follow intrinsic functional and structural brain networks. Ultimately, we call for more contributions from network neuroimaging to realize the translational potential of cancer neuroscience.

Differences in intrinsic functional networks in patients with essential tremor who had good and poor long-term responses after thalamotomy performed using MR-guided ultrasound.

Thalamotomy at the nucleus ventralis intermedius using MR-guided focused ultrasound has been an effective treatment method for essential tremor (ET). However, this is not true for all cases, even for successful ablation. How the brain differs in patients with ET between those with long-term good and poor outcomes is not clear. To analyze the functional connectivity difference between patients in whom thalamotomy was effective and those in whom thalamotomy was ineffective and its prognostic role in ET treatment, the authors evaluated preoperative resting-state functional MRI in thalamotomy-treated patients. Preoperative resting-state functional MRI data in 85 patients with ET, who were experiencing tremor relief at the time of treatment and were followed up for a minimum of 6 months after the procedure, were collected for the study. The authors conducted a graph independent component analysis of the functional connectivity matrices of tremor-related networks. The patients were divided into thalamotomy-effective and thalamotomy-ineffective groups (thalamotomy-effective group, ≥ 50% motor symptom reduction; thalamotomy-ineffective group, < 50% motor symptom reduction at 6 months after treatment) and the authors compared network components between groups. Seventy-two (84.7%) of the 85 patients showed ≥ 50% tremor reduction from baseline at 6 months after thalamotomy. The network analysis shows significant suppression of functional network components with connections between the areas of the cerebellum and the basal ganglia and thalamus, but enhancement of those between the premotor cortex and supplementary motor area in the noneffective group compared to the effective group. The present study demonstrates that patients in the noneffective group have suppressed functional subnetworks in the cerebellum and subcortex regions and have enhanced functional subnetworks among motor-sensory cortical networks compared to the thalamotomy-effective group. Therefore, the authors suggest that the functional connectivity pattern might be a possible predictive factor for outcomes of MR-guided focused ultrasound thalamotomy.