The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity

Functional brain network dynamics of brooding in depression: Insights from real-time fMRI neurofeedback

BACKGROUND

Brooding is a critical symptom and prognostic factor of major depressive disorder (MDD), which involves passively dwelling on self-referential dysphoria and related abstractions. The neurobiology of brooding remains under characterized. We aimed to elucidate neural dynamics underlying brooding, and explore their responses to neurofeedback intervention in MDD.

METHODS

We investigated functional MRI (fMRI) dynamic functional network connectivity (dFNC) in 36 MDD subjects and 26 healthy controls (HCs) during rest and brooding. Rest was measured before and after fMRI neurofeedback (MDD-active/sham: n = 18/18, HC-active/sham: n = 13/13). Baseline brooding severity was recorded using Ruminative Response Scale - Brooding subscale (RRS-B).

RESULTS

Four recurrent dFNC states were identified. Measures of time spent were not significantly different between MDD and HC for any of these states during brooding or rest. RRS-B scores in MDD showed significant negative correlation with measures of time spent in dFNC state 3 during brooding (r = -0.4, p = 0.002, FDR-significant). This state comprises strong connections spanning several brain systems involved in sensory, attentional and cognitive processing. Time spent in this anti-brooding dFNC state significantly increased following neurofeedback only in the MDD active group (z = -2.09, FWE-p = 0.034).

LIMITATIONS

The sample size was small and imbalanced between groups. Brooding condition was not examined post-neurofeedback.

CONCLUSION

We identified a densely connected anti-brooding dFNC brain state in MDD. MDD subjects spent significantly longer time in this state after active neurofeedback intervention, highlighting neurofeedback's potential for modulating dysfunctional brain dynamics to treat MDD.

Characterizing the effects of age, puberty, and sex on variability in resting-state functional connectivity in late childhood and early adolescence

Understanding the relative influences of age, pubertal development, and sex assigned at birth on brain development is a key priority of developmental neuroscience given the complex interplay of these factors in the onset of psychopathology. Previous research has investigated how these factors relate to static (time-averaged) functional connectivity (FC), but little is known about their relationship with dynamic (time-varying) FC. The present study aimed to investigate the unique and overlapping roles of these factors on dynamic FC in children aged approximately 9 to 14 in the ABCD Study using a sample of 5122 low-motion resting-state scans (from 4136 unique participants). Time-varying correlations in the frontolimbic, default mode, and dorsal and ventral corticostriatal networks, estimated using the Dynamic Conditional Correlations (DCC) method, were used to calculate variability of within- and between-network connectivity and of graph theoretical measures of segregation and integration. We found decreased variability in global efficiency across the age range, and increased variability within the frontolimbic network driven primarily by those assigned female at birth (AFAB). AFAB youth specifically also showed increased variability in several other networks. Controlling for age, both advanced pubertal development and being AFAB were associated with decreased variability in all within- and between-network correlations and increased variability in measures of network segregation. These results potentially suggest advanced brain maturation in AFAB youth, particularly in key networks related to psychopathology, and lay the foundation for future investigations of dynamic FC.

Methylphenidate differentially alters corticostriatal connectivity after traumatic brain injury

Traumatic brain injury commonly impairs attention and executive function and disrupts the large-scale brain networks that support these cognitive functions. Abnormalities of functional connectivity are seen in corticostriatal networks, which are associated with executive dysfunction and damage to neuromodulatory catecholaminergic systems caused by head injury. Methylphenidate, a stimulant medication that increases extracellular dopamine and noradrenaline, can improve cognitive function following traumatic brain injury. In this experimental medicine add-on study to a randomized, double-blind, placebo-controlled clinical trial, we test whether administration of methylphenidate alters corticostriatal network function and influences drug response. Forty-three moderate-severe traumatic brain injury patients received 0.3 mg/kg of methylphenidate or placebo twice a day in 2-week blocks. Twenty-eight patients were included in the neuropsychological and functional imaging analysis (four females, mean age 40.9 ± 12.7 years, range 20-65 years) and underwent functional MRI and neuropsychological assessment after each block. 123I-Ioflupane single-photon emission computed tomography dopamine transporter scans were performed, and specific binding ratios were extracted from caudate subdivisions. Functional connectivity and the relationship to cognition were compared between drug and placebo conditions. Methylphenidate increased caudate to anterior cingulate cortex functional connectivity compared with placebo and decreased connectivity from the caudate to the default mode network. Connectivity within the default mode network was also decreased by methylphenidate administration, and there was a significant relationship between caudate functional connectivity and dopamine transporter binding during methylphenidate administration. Methylphenidate significantly improved executive function in traumatic brain injury patients, and this was associated with alterations in the relationship between executive function and right anterior caudate functional connectivity. Functional connectivity is strengthened to brain regions, including the anterior cingulate, that are activated when attention is focused externally. These results show that methylphenidate alters caudate interactions with cortical brain networks involved in executive control. In contrast, caudate functional connectivity reduces to default mode network regions involved in internally focused attention and that deactivate during tasks that require externally focused attention. These results suggest that the beneficial cognitive effects of methylphenidate might be mediated through its impact on the caudate. Methylphenidate differentially influences how the caudate interacts with large-scale functional brain networks that exhibit co-ordinated but distinct patterns of activity required for attentionally demanding tasks.

Static and temporal dynamic changes in brain activity in patients with post-stroke balance dysfunction: a pilot resting state fMRI

Objective

The aim of this study was to investigate the characteristics of brain activity changes in patients with post-stroke balance dysfunction and their relationship with clinical assessment, and to construct a classification model based on the extreme Gradient Boosting (XGBoost) algorithm to discriminate between stroke patients and healthy controls (HCs).

Methods

In the current study, twenty-six patients with post-stroke balance dysfunction and twenty-four HCs were examined by resting-state functional magnetic resonance imaging (rs-fMRI). Static amplitude of low frequency fluctuation (sALFF), static fractional ALFF (sfALFF), static regional homogeneity (sReHo), dynamic ALFF (dALFF), dynamic fALFF (dfALFF) and dynamic ReHo (dReHo) values were calculated and compared between the two groups. The values of the imaging metrics for the brain regions with significant differences were used in Pearson correlation analyses with the Berg Balance Scale (BBS) scores and as features in the construction of the XGBoost model.

Results

Compared to HCs, the brain regions with significant functional abnormalities in patients with post-stroke balance dysfunction were mainly involved bilateral insula, right fusiform gyrus, right lingual gyrus, left thalamus, left inferior occipital gyrus, left inferior temporal gyrus, right calcarine fissure and surrounding cortex, left precuneus, right median cingulate and paracingulate gyri, right anterior cingulate and paracingulate gyri, bilateral supplementary motor area, right putamen, and left cerebellar crus II. XGBoost results show that the model constructed based on static imaging features has the best classification prediction performance.

Conclusion

In conclusion, this study provided evidence of functional abnormalities in local brain regions in patients with post-stroke balance dysfunction. The results suggested that the abnormal brain regions were mainly related to visual processing, motor execution, motor coordination, sensorimotor control and cognitive function, which contributed to our understanding of the neuropathological mechanisms of post-stroke balance dysfunction. XGBoost is a promising machine learning method to explore these changes.

Dynamic alterations in spontaneous neural activity in patients with attention-deficit/hyperactivity disorder: A resting-state fMRI study

BACKGROUND

To investigate the change of dynamic amplitude of low-frequency fluctuation (dALFF) and dynamic fractional amplitude of low-frequency fluctuation (dfALFF) in patients with attention-deficit/hyperactivity disorder (ADHD), and to explore whether dALFF/dfALFF can be used to distinguish ADHD from health controls (HCs).

METHODS

Forty-eight cases of clinically confirmed ADHD and forty-four cases of HCs were included in the present study. It was compared to the amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF), as well as the dynamic indicators dALFF and dfALFF. We investigated the relationship between clinical and dynamic indicators, and additionally performed voxel-based functional connectivity (FC) analysis. Finally, we developed an auxiliary diagnosis model.

RESULTS

Brain regions with increased dALFF variability of ADHD were located in right middle frontal gyrus (MFG), left inferior parietal lobe (IPL) and superior parietal gyrus (SPG) compared with HCs. Meanwhile, increased dfALFF variability was also observed in left lingual gyrus (LING), right MFG and left middle occipital gyrus (MOG) in ADHD compared to HCs. Neuropsychological scale scores correlated with some dALFF and dfALFF indicators. Reduced FC was found between the left IPL and right cerebellum crus II in ADHD compared with HCs. With dALFF and dfALFF variability as features, we achieved a good area under the curve and an accurate classification.

CONCLUSION

This study offers new valuable insights into the cerebral dysfunction associated with ADHD from the standpoint of dynamic local brain activity. The understanding of dALFF/dfALFF variability can contribute to the comprehension of neurophysiological mechanisms and potentially aid in the diagnosis of ADHD.

Cognitive impairment assessed by static and dynamic changes of spontaneous brain activity during end stage renal disease patients on early hemodialysis

Background

Compared with the general population, patients with end-stage renal disease (ESRD) undergoing maintenance hemodialysis (ESHD) exhibit a higher incidence of cognitive impairment. Early identification of cognitive impairment in these patients is crucial for reducing disability and mortality rates. Examining the characteristics of static and dynamic regional spontaneous activities in ESHD cases may provide insights into neuropathological damage in these patients.

Methods

Resting-state functional magnetic resonance images were acquired from 40 patients with early ESHD (3 or 4 times/week for more than 30 days but less than 12 months) and 31 healthy matched controls. Group differences in regional static and dynamic regional homogeneity (ReHo) were identified, and correlations examined with clinical variables, including neuropsychological scale scores, while controlling for covariates. Receiving operating characteristic (ROC) curve analyses were conducted to assess the accuracy of ReHo abnormalities for predicting cognitive decline among early ESHD.

Results

The ESHD group exhibited significantly reduced static and dynamic ReHo in the temporal and parietal lobes, including regions involved in basal ganglia-thalamus-cortex circuits, the default mode network, and ventral attentional network. Several static and dynamic ReHo abnormalities (including those in the right parietal and left middle temporal gyrus) were significantly correlated with neurocognitive scale scores. In addition, the dynamic ReHo value of the left superior temporal gyrus was positively correlated with depression scale scores. Comparing the ROC curve area revealed that numerous brain regions with altered ReHo can effectively distinguish between patients with ESHD and those without cognitive impairment.

Conclusion

Our study found that spontaneous activity alterations located in the basal ganglia-thalamus-cortex circuit, default mode network, and ventral attentional network are associated with the severity of cognitive deficits and negative emotion in early ESHD patients. These findings provide further insight into the relationship between cognitive impairment and underlying neuropathophysiological mechanisms underlying the interplay between the kidneys and the nervous system in ESRD patients, and provide further possibilities for developing effective clinical intervention measures.

Network state dynamics underpin basal craving in a transdiagnostic population

Emerging fMRI methods quantifying brain dynamics present an opportunity to capture how fluctuations in brain responses give rise to individual variations in affective and motivation states. Although the experience and regulation of affective states affect psychopathology, their underlying time-varying brain responses remain unclear. Here, we present a novel framework to identify network states matched to an affective experience and examine how the dynamic engagement of these network states contributes to this experience. We apply this framework to investigate network state dynamics underlying basal craving, an affective experience with important clinical implications. In a transdiagnostic sample of healthy controls and individuals diagnosed with or at risk for craving-related disorders (total N = 252), we utilized connectome-based predictive modeling (CPM) to identify brain networks predictive of basal craving. An edge-centric timeseries approach was leveraged to quantify the moment-to-moment engagement of the craving-positive and craving-negative subnetworks during independent scan runs. We found that dynamic markers of network engagement, namely more persistence in a craving-positive network state and less dwelling in a craving-negative network state, characterized individuals with higher craving. We replicated the latter results in a separate dataset, incorporating distinct participants (N = 173) and experimental stimuli. The associations between basal craving and network state dynamics were consistently observed even when craving-predictive networks were defined in the replication dataset. These robust findings suggest that network state dynamics underpin individual differences in basal craving. Our framework additionally presents a new avenue to explore how the moment-to-moment engagement of behaviorally meaningful network states supports our affective experiences.

Cognitive and psychiatric relevance of dynamic functional connectivity states in a large (N > 10,000) children population

Children's brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9~11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age. A hybrid independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state with the most within-network synchrony and the anticorrelations between networks, especially between the sensory networks and between the cerebellum and other networks, was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, opposite relationships were observed for a DFC state showing integration of sensory networks and antagonism between default-mode and sensorimotor networks but weak segregation of the cerebellum. The mediation analysis further showed that attention problems mediated the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC states, which suggests that tracking the transient dynamic connectivity may help to characterize cognitive and mental problems in children and guide people to provide early intervention to buffer adverse influences.

Aberrant Modular Dynamics of Functional Networks in Schizophrenia and Their Relationship with Neurotransmitter and Gene Expression Profiles

Introduction

Numerous studies have emphasized the time-varying modular architecture of functional brain networks and its relevance to cognitive functions in healthy participants. However, how brain modular dynamics change in schizophrenia and how these alterations relate to neurotransmitter and transcriptomic signatures have not been well elucidated.

Methods

We harmonized resting-state fMRI data from a multi-site sample including 223 patients and 279 healthy controls and applied the multilayer network method to estimate the regional module switching rate (flexibility) of functional brain connectomes. We examined aberrant flexibility in patients relative to controls and explored its relations to neurotransmitter systems and postmortem gene expression.

Results

Compared with controls, patients with schizophrenia had significantly higher flexibility in the somatomotor and right visual regions, and lower flexibility in the left parahippocampal gyrus, right supramarginal gyrus, right frontal-operculum-insula, bilateral precuneus posterior cingulate cortex, and bilateral inferior parietal gyrus. These alterations were associated with multiple neurotransmitter systems and weighted gene transcriptomic profiles. The most relevant genes were preferentially enriched for biological processes of transmembrane transport and brain development, specific cell types, and previously identified schizophrenia-related genes.

Conclusions

This study reveals aberrant modular dynamics in schizophrenia and its relations to neurotransmitter systems and schizophrenia-related transcriptomic profiles, providing insights into the understanding of the pathophysiology underlying schizophrenia.

Dynamic switching between brain networks predicts creative ability

Creativity is hypothesized to arise from a mental state which balances spontaneous thought and cognitive control, corresponding to functional connectivity between the brain's Default Mode (DMN) and Executive Control (ECN) Networks. Here, we conduct a large-scale, multi-center examination of this hypothesis. Employing a meta-analytic network neuroscience approach, we analyze resting-state fMRI and creative task performance across 10 independent samples from Austria, Canada, China, Japan, and the United States (N = 2433)-constituting the largest and most ethnically diverse creativity neuroscience study to date. Using time-resolved network analysis, we investigate the relationship between creativity (i.e., divergent thinking ability) and dynamic switching between DMN and ECN. We find that creativity, but not general intelligence, can be reliably predicted by the number of DMN-ECN switches. Importantly, we identify an inverted-U relationship between creativity and the degree of balance between DMN-ECN switching, suggesting that optimal creative performance requires balanced brain network dynamics. Furthermore, an independent task-fMRI validation study (N = 31) demonstrates higher DMN-ECN switching during creative idea generation (compared to a control condition) and replicates the inverted-U relationship. Therefore, we provide robust evidence across multi-center datasets that creativity is tied to the capacity to dynamically switch between brain networks supporting spontaneous and controlled cognition.

Individual differences in functional connectivity during suppression of imagined threat

Functional magnetic resonance imaging studies typically rely on between-person analyses. To examine individual differences in functional connectivity, we used Group Iterative Multiple Model Estimation and its subgrouping function to analyze functional magnetic resonance imaging data of 54 participants who were suppressing imagined future threat. A two-stage random-effects meta-analytic approach was employed to examine individual differences. In addition to generalizable connections between brain regions, we identified individual differences in personalized models suggesting different pathways through which individuals suppress future threat. Two subgroups with distinct connectivity patterns emerged: One subgroup (n = 29; 53.70%), characterized by an additional lagged connection from the right to the left posterior cingulate cortex, exhibited comparatively higher anxiety and less brain connectivity, whereas the other subgroup (n = 25; 46.30%), showing an additional connection from the left posterior cingulate cortex to the ventromedial prefrontal cortex, was associated with lower anxiety levels and greater connectivity. This study points to individual differences in functional connectivity during emotion regulation.

Reorganization of Dynamic Network in Stroke Patients and Its Potential for Predicting Motor Recovery

Objective: The investigation of brain functional network dynamics offers a promising approach to understanding network reorganization poststroke. This study aims to explore the dynamic network configurations associated with motor recovery in stroke patients and assess their predictive potential using multilayer network analysis. Methods: Resting-state functional magnetic resonance imaging data were collected from patients with subacute stroke within 2 weeks of onset and from matched healthy controls (HCs). Group-independent component analysis and a sliding window approach were utilized to construct dynamic functional networks. A multilayer network model was applied to quantify the switching rates of individual nodes, subnetworks, and the global network across the dynamic network. Correlation analyses assessed the relationship between switching rates and motor function recovery, while linear regression models evaluated the predictive potential of global network switching rate on motor recovery outcomes. Results: Stroke patients exhibited a significant increase in the switching rates of specific brain regions, including the medial frontal gyrus, precentral gyrus, inferior parietal lobule, anterior cingulate, superior frontal gyrus, and postcentral gyrus, compared to HCs. Additionally, elevated switching rates were observed in the frontoparietal network, default mode network, cerebellar network, and in the global network. These increased switching rates were positively correlated with baseline Fugl-Meyer assessment (FMA) scores and changes in FMA scores at 90 days poststroke. Importantly, the global network's switching rate emerged as a significant predictor of motor recovery in stroke patients. Conclusions: The reorganization of dynamic network configurations in stroke patients reveals crucial insights into the mechanisms of motor recovery. These findings suggest that metrics of dynamic network reorganization, particularly global network switching rate, may offer a robust predictor of motor recovery.

Resting-state dynamic functional connectivity in major depressive disorder: A systematic review

As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.

Abnormal intrinsic brain functional network dynamics in patients with retinal detachment based on graph theory and machine learning

Background

and purpose: The investigation of functional plasticity and remodeling of the brain in patients with retinal detachment (RD) has gained increasing attention and validation. However, the precise alterations in the topological configuration of dynamic functional networks are still not fully understood. This study aimed to investigate the topological structure of dynamic brain functional networks in RD patients.

Methods

We recruited 32 patients with RD and 33 healthy controls (HCs) to participate in resting-state fMRI. Employing the sliding time window analysis and K-means clustering method, we sought to identify dynamic functional connectivity (dFC) variability patterns in both groups. The investigation into the topological structure of whole-brain functional networks utilized a graph theoretical approach. Furthermore, we employed machine learning analysis, selecting altered topological properties as classification features to distinguish RD patients from HCs.

Results

All participants exhibited four distinct states of dynamic functional connectivity. Compared to the healthy control (HC) group, patients with RD experienced a significant reduction in the number of transitions among these four states. Additionally, the dynamic topological properties of RD patients demonstrated notable changes in both global and node-specific characteristics, with these changes correlating with clinical parameters. The support vector machine (SVM) model used for classification achieved an accuracy of 0.938, an area under the curve (AUC) of 0.988, and both sensitivity and specificity of 0.937.

Conclusion

The alterations in the topological properties of the brain in RD patients may indicate the integration function and information exchange efficiency of the whole brain network were reduced. In addition, the topological properties hold considerable promise for distinguishing between RD and HCs.

Hemodynamic cortical ripples through cyclicity analysis

A fine-grained understanding of dynamics in cortical networks is crucial to unpacking brain function. Resting-state functional magnetic resonance imaging (fMRI) gives rise to time series recordings of the activity of different brain regions, which are aperiodic and lack a base frequency. Cyclicity analysis, a novel technique robust under time reparametrizations, is effective in recovering the temporal ordering of such time series, collectively considered components of a multidimensional trajectory. Here, we extend this analytical method for characterizing the dynamic interaction between distant brain regions and apply it to the data from the Human Connectome Project. Our analysis detected cortical traveling waves of activity propagating along a spatial axis, resembling cortical hierarchical organization with consistent lead-lag relationships between specific brain regions in resting-state scans. In fMRI scans involving tasks, we observed short bursts of task-modulated strong temporal ordering that dominate overall lead-lag relationships between pairs of regions in the brain that align temporally with stimuli from the tasks. Our results suggest a possible role played by waves of excitation sweeping through brain regions that underlie emergent cognitive functions.

Cognitive abilities are associated with rapid dynamics of electrophysiological connectome states

Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, hold significant implications for cognition. However, connectome dynamics at fast (>1 Hz) timescales highly relevant to cognition are poorly understood due to the dominance of inherently slow fMRI in connectome studies. Here, we investigated the behavioral significance of rapid electrophysiological connectome dynamics using source-localized EEG connectomes during resting state (N = 926, 473 females). We focused on dynamic connectome features pertinent to individual differences, specifically those with established heritability: Fractional Occupancy (i.e., the overall duration spent in each recurrent connectome state) in beta and gamma bands and Transition Probability (i.e., the frequency of state switches) in theta, alpha, beta, and gamma bands. Canonical correlation analysis found a significant relationship between the heritable phenotypes of subsecond connectome dynamics and cognition. Specifically, principal components of Transition Probabilities in alpha (followed by theta and gamma bands) and a cognitive factor representing visuospatial processing (followed by verbal and auditory working memory) most notably contributed to the relationship. We conclude that rapid connectome state transitions shape individuals' cognitive abilities and traits. Such subsecond connectome dynamics may inform about behavioral function and dysfunction and serve as endophenotypes for cognitive abilities.

Rapid dynamics of electrophysiological connectome states are heritable

Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, are a prominent feature of brain activity with broad functional implications. While infraslow (<0.1 Hz) connectome dynamics have been extensively studied with fMRI, rapid dynamics highly relevant for cognition are poorly understood. Here, we asked whether rapid electrophysiological connectome dynamics constitute subject-specific brain traits and to what extent they are under genetic influence. Using source-localized EEG connectomes during resting state (N = 928, 473 females), we quantified the heritability of multivariate (multistate) features describing temporal or spatial characteristics of connectome dynamics. States switched rapidly every ∼60-500 ms. Temporal features were heritable, particularly Fractional Occupancy (in theta, alpha, beta, and gamma bands) and Transition Probability (in theta, alpha, and gamma bands), representing the duration spent in each state and the frequency of state switches, respectively. Genetic effects explained a substantial proportion of the phenotypic variance of these features: Fractional Occupancy in beta (44.3%) and gamma (39.8%) bands and Transition Probability in theta (38.4%), alpha (63.3%), beta (22.6%), and gamma (40%) bands. However, we found no evidence for the heritability of dynamic spatial features, specifically states' Modularity and connectivity pattern. We conclude that genetic effects shape individuals' connectome dynamics at rapid timescales, specifically states' overall occurrence and sequencing.

Static and temporal dynamic alterations of local functional connectivity in chronic insomnia

Several studies have revealed altered intrinsic neural activity in chronic insomnia (CI). However, the temporal variability of intrinsic neural activity in CI is rarely mentioned. This study aimed to explore static and temporal dynamic alterations of regional homogeneity (ReHo) in CI and excavate the potential associations between these changes and clinical characteristics. Eighty-seven patients with CI and seventy-eight healthy controls (HCs) were included. Resting-state functional magnetic resonance imaging was performed on all subjects and both static and dynamic ReHo were used to detect local functional connectivity. We then tested the relationship between altered brain regions, disease duration, and clinical scales. The receiver operating characteristic curve analysis was used to reveal the potential capability of these indicators to screen CI patients from HCs. CI showed increased dynamic ReHo in the right precuneus and decreased static ReHo in the right cerebellum_6. The dynamic ReHo values of the right precuneus were negatively correlated with the self-rating depression score and the static ReHo values of the right cerebellum_6 were positively correlated with the Montreal Cognitive Assessment-Naming score. In addition, the combination of the two metrics showed a potential capacity to distinguish CI patients from HCs, which was better than a single metric alone. The present study has revealed the altered local functional connectivity under static and temporal dynamic conditions in patients with CI, and found the relationships between these changes, mood-related scales, and cognitive-related scales. These may be useful in elucidating the neurological mechanisms of CI and accompanying symptoms.

Reconfiguration of Functional Brain Network Organization and Dynamics With Changing Cognitive Demands in Children With Attention-Deficit/Hyperactivity Disorder

BACKGROUND

The pathophysiology of attention-deficit/hyperactivity disorder (ADHD) is characterized by atypical brain network organization and dynamics. Although functional brain networks adaptively reconfigure across cognitive contexts, previous studies have largely focused on network dysfunction during the resting state. In this preliminary study, we examined how functional brain network organization and dynamics flexibly reconfigure across rest and 2 cognitive control tasks with different cognitive demands in 30 children with ADHD and 36 typically developing children (ages 8-12 years).

METHODS

We leveraged graph theoretical analyses to interrogate the segregation (modularity, within-module degree) and integration (global efficiency, node dissociation index) of frontoparietal, cingulo-opercular/salience, default mode, somatomotor, and visual networks. We also conducted edge time series analyses to quantify connectivity dynamics within and between these networks.

RESULTS

Across resting and task-based states, children with ADHD demonstrated significantly lower whole-graph modularity and a greater node dissociation index between default mode and visual networks. Furthermore, a significant task-by-diagnosis interaction was observed for frontoparietal network within-module degree, which decreased from rest to task in children with ADHD but increased in typically developing children. Finally, children with ADHD displayed significantly more dynamic connectivity within and across cingulo-opercular/salience, default mode, and somatomotor networks, especially during task performance. Exploratory analyses revealed associations between network dynamics, cognitive performance, and ADHD symptoms.

CONCLUSIONS

By integrating static and dynamic network analyses across changing cognitive demands, this study provides novel insight into how context-specific, context-general, and timescale-dependent network connectivity is altered in children with ADHD. Our findings highlight the involvement and clinical relevance of both association and sensory/motor systems in ADHD.

Syncing the brain's networks: dynamic functional connectivity shifts from temporal interference

Background

Temporal interference (TI) stimulation, an innovative non-invasive brain stimulation approach, has the potential to activate neurons in deep brain regions. However, the dynamic mechanisms underlying its neuromodulatory effects are not fully understood. This study aims to investigate the effects of TI stimulation on dynamic functional connectivity (dFC) in the motor cortex.

Methods

40 healthy adults underwent both TI and tDCS in a double-blind, randomized crossover design, with sessions separated by at least 48 h. The total stimulation intensity of TI is 4 mA, with each channel's intensity set at 2 mA and a 20 Hz frequency difference (2 kHz and 2.02 kHz). The tDCS stimulation intensity is 2 mA. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected before, during, and after stimulation. dFC was calculated using the left primary motor cortex (M1) as the region of interest (ROI) and analyzed using a sliding time-window method. A two-way repeated measures ANOVA (group × time) was conducted to evaluate the effects of TI and tDCS on changes in dFC.

Results

For CV of dFC, significant main effects of stimulation type (P = 0.004) and time (P < 0.001) were observed. TI showed lower CV of dFC than tDCS in the left postcentral gyrus (P < 0.001). TI-T2 displayed lower CV of dFC than TI-T1 in the left precentral gyrus (P < 0.001). For mean dFC, a significant main effect of time was found (P < 0.001). TI-T2 showed higher mean dFC than tDCS-T2 in the left postcentral gyrus (P = 0.018). Within-group comparisons revealed significant differences between time points in both TI and tDCS groups, primarily in the left precentral and postcentral gyri (all P < 0.001). Results were consistent across different window sizes.

Conclusion

20 Hz TI stimulation altered dFC in the primary motor cortex, leading to a significant decreasing variability and increasing mean connectivity strength in dFC. This outcome indicates that the 20 Hz TI frequency interacted with the motor cortex's natural resonance.

Insights on cognitive reorganization after hemispherectomy in Rasmussen's encephalitis. A narrative review

Rasmussen's encephalitis is a rare neurological pathology affecting one cerebral hemisphere, therefore, posing unique challenges. Patients may undergo hemispherectomy, a surgical procedure after which cognitive development occurs in the isolated contralateral hemisphere. This rare situation provides an excellent opportunity to evaluate brain plasticity and cognitive recovery at a hemispheric level. This literature review synthesizes the existing body of research on cognitive recovery following hemispherectomy in Rasmussen patients, considering cognitive domains and modulatory factors that influence cognitive outcomes. While language function has traditionally been the focus of postoperative assessments, there is a growing acknowledgment of the need to broaden the scope of language investigation in interaction with other cognitive domains and to consider cognitive scaffolding in development and recovery. By synthesizing findings reported in the literature, we delineate how language functions may find support from the right hemisphere after left hemispherectomy, but also how, beyond language, global cognitive functioning is affected. We highlight the critical influence of several factors on postoperative cognitive outcomes, including the timing of hemispherectomy and the baseline preoperative cognitive status, pointing to early surgical intervention as predictive of better cognitive outcomes. However, further specific studies are needed to confirm this correlation. This review aims to emphasize a better understanding of mechanisms underlying hemispheric specialization and plasticity in humans, which are particularly important for both clinical and research advancements. This narrative review underscores the need for an integrative approach based on cognitive scaffolding to provide a comprehensive understanding of mechanisms underlying the reorganization in Rasmussen patients after hemispherectomy.

Dynamic functional connectivity and gene expression correlates in temporal lobe epilepsy: insights from hidden markov models

BACKGROUD

Temporal lobe epilepsy (TLE) is associated with abnormal dynamic functional connectivity patterns, but the dynamic changes in brain activity at each time point remain unclear, as does the potential molecular mechanisms associated with the dynamic temporal characteristics of TLE.

METHODS

Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired for 84 TLE patients and 35 healthy controls (HCs). The data was then used to conduct HMM analysis on rs-fMRI data from TLE patients and an HC group in order to explore the intricate temporal dynamics of brain activity in TLE patients with cognitive impairment (TLE-CI). Additionally, we aim to examine the gene expression profiles associated with the dynamic modular characteristics in TLE patients using the Allen Human Brain Atlas (AHBA) database.

RESULTS

Five HMM states were identified in this study. Compared with HCs, TLE and TLE-CI patients exhibited distinct changes in dynamics, including fractional occupancy, lifetimes, mean dwell time and switch rate. Furthermore, transition probability across HMM states were significantly different between TLE and TLE-CI patients (p < 0.05). The temporal reconfiguration of states in TLE and TLE-CI patients was associated with several brain networks (including the high-order default mode network (DMN), subcortical network (SCN), and cerebellum network (CN). Furthermore, a total of 1580 genes were revealed to be significantly associated with dynamic brain states of TLE, mainly enriched in neuronal signaling and synaptic function.

CONCLUSIONS

This study provides new insights into characterizing dynamic neural activity in TLE. The brain network dynamics defined by HMM analysis may deepen our understanding of the neurobiological underpinnings of TLE and TLE-CI, indicating a linkage between neural configuration and gene expression in TLE.

Dynamicity of brain network organization & their community architecture as characterizing features for classification of common mental disorders from whole-brain connectome

The urgency of addressing common mental disorders (bipolar disorder, attention-deficit hyperactivity disorder (ADHD), and schizophrenia) arises from their significant societal impact. Developing strategies to support psychiatrists is crucial. Previous studies focused on the relationship between these disorders and changes in the resting-state functional connectome's modularity, often using static functional connectivity (sFC) estimation. However, understanding the dynamic reconfiguration of resting-state brain networks with rich temporal structure is essential for comprehending neural activity and addressing mental health disorders. This study proposes an unsupervised approach combining spatial and temporal characterization of brain networks to classify common mental disorders using fMRI timeseries data from two cohorts (N = 408 participants). We employ the weighted stochastic block model to uncover mesoscale community architecture differences, providing insights into network organization. Our approach overcomes sFC limitations and biases in community detection algorithms by modelling the functional connectome's temporal dynamics as a landscape, quantifying temporal stability at whole-brain and network levels. Findings reveal individuals with schizophrenia exhibit less assortative community structure and participate in multiple motif classes, indicating less specialized network organization. Patients with schizophrenia and ADHD demonstrate significantly reduced temporal stability compared to healthy controls. This study offers insights into functional connectivity (FC) patterns' spatiotemporal organization and their alterations in common mental disorders, highlighting the potential of temporal stability as a biomarker.

A new model for dynamic mapping of effective connectivity in task fMRI

Whole-brain dynamic functional connectivity is a growing area in neuroimaging research, encompassing data-driven methods for investigating how large-scale brain networks dynamically reorganize during resting states. However, this approach has been rarely applied to functional magnetic resonance imaging (fMRI) data acquired during task performance. In this study, we first combined the psychophysiological interactions (PPI) and sliding-window methods to analyze dynamic effective connectivity of fMRI data obtained from subjects performing the N-back task within the Human Connectome Project dataset. We then proposed a hypothetical model called Condition Activated Specific Trajectory (CAST) to represent a series of spatiotemporal synchronous changes in significantly activated connections across time windows, which we refer to as a trajectory. Our finding demonstrate that the CAST model outperforms other models in terms of intra-group consistency of individual spatial pattern of PPI connectivity, overall representational ability of temporal variability and hierarchy for individual task performance and cognitive traits. This dynamic view afforded by the CAST model reflects the intrinsic nature of coherent brain activities.

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.

The brain entropy dynamics in resting state

As a novel measure for irregularity and complexity of the spontaneous fluctuations of brain activities, brain entropy (BEN) has attracted much attention in resting-state functional magnetic resonance imaging (rs-fMRI) studies during the last decade. Previous studies have shown its associations with cognitive and mental functions. While most previous research assumes BEN is approximately stationary during scan sessions, the brain, even at its resting state, is a highly dynamic system. Such dynamics could be characterized by a series of reoccurring whole-brain patterns related to cognitive and mental processes. The present study aims to explore the time-varying feature of BEN and its potential links with general cognitive ability. We adopted a sliding window approach to derive the dynamical brain entropy (dBEN) of the whole-brain functional networks from the HCP (Human Connectome Project) rs-fMRI dataset that includes 812 young healthy adults. The dBEN was further clustered into 4 reoccurring BEN states by the k-means clustering method. The fraction window (FW) and mean dwell time (MDT) of one BEN state, characterized by the extremely low overall BEN, were found to be negatively correlated with general cognitive abilities (i.e., cognitive flexibility, inhibitory control, and processing speed). Another BEN state, characterized by intermediate overall BEN and low within-state BEN located in DMN, ECN, and part of SAN, its FW, and MDT were positively correlated with the above cognitive abilities. The results of our study advance our understanding of the underlying mechanism of BEN dynamics and provide a potential framework for future investigations in clinical populations.

EiDA: A lossless approach for dynamic functional connectivity; application to fMRI data of a model of ageing

Dynamic Functional Connectivity (dFC) is the study of the dynamic patterns of interaction that characterise brain function. Numerous numerical methods are available to compute and analyse dFC from high-dimensional data. In fMRI, a number of them rely on the computation of the instantaneous Phase Alignment (iPA) matrix (also known as instantaneous Phase Locking). Their limitations are the high computational cost and the concomitant need to introduce approximations with ensuing information loss. Here, we introduce the analytical decomposition of the iPA. This has two advantages. Firstly, we achieve an up to 1000-fold reduction in computing time without information loss. Secondly, we can formally introduce two alternative approaches to the analysis of the resulting time-varying instantaneous connectivity patterns, Discrete and Continuous EiDA (Eigenvector Dynamic Analysis), and a related set of metrics to quantify the total amount of instantaneous connectivity, drawn from dynamical systems and information theory. We applied EiDA to a dataset from 48 rats that underwent functional magnetic resonance imaging (fMRI) at four stages during a longitudinal study of ageing. Using EiDA, we found that the metrics we introduce provided robust markers of ageing with decreases in total connectivity and metastability, and an increase in informational complexity over the life span. This suggests that ageing reduces the available functional repertoire that is postulated to support cognitive functions and overt behaviours, slows down the exploration of this reduced repertoire, and decreases the coherence of its structure. In summary, EiDA is a method to extract lossless connectivity information that requires significantly less computational time, and provides robust and analytically principled metrics for brain dynamics. These metrics are interpretable and promising for studies on neurodevelopmental and neurodegenerative disorders.

Investigating brain dynamics and their association with cognitive control in opioid use disorder using naturalistic and drug cue paradigms

Objectives

Opioid use disorder (OUD) impacts millions of people worldwide. The prevalence and debilitating effects of OUD present a pressing need to understand its neural mechanisms to provide more targeted interventions. Prior studies have linked altered functioning in large-scale brain networks with clinical symptoms and outcomes in OUD. However, these investigations often do not consider how brain responses change over time. Time-varying brain network engagement can convey clinically relevant information not captured by static brain measures.

Methods

We investigated brain dynamic alterations in individuals with OUD by applying a new multivariate computational framework to movie-watching (i.e., naturalistic; N=76) and task-based (N=70) fMRI. We further probed the associations between cognitive control and brain dynamics during a separate drug cue paradigm in individuals with OUD.

Results

Compared to healthy controls (N=97), individuals with OUD showed decreased variability in the engagement of recurring brain states during movie-watching. We also found that worse cognitive control was linked to decreased variability during the rest period when no opioid-related stimuli were present.

Conclusions

These findings suggest that individuals with OUD may experience greater difficulty in effectively engaging brain networks in response to evolving internal or external demands. Such inflexibility may contribute to aberrant response inhibition and biased attention toward opioid-related stimuli, two hallmark characteristics of OUD. By incorporating temporal information, the current study introduces novel information about how brain dynamics are altered in individuals with OUD and their behavioral implications.

Edge time series components of functional connectivity and cognitive function in Alzheimer's disease

Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer's disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer's Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer's disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.

Rapid dynamics of electrophysiological connectome states are heritable

Time-varying changes in whole-brain connectivity patterns, or connectome state dynamics, are a prominent feature of brain activity with broad functional implications. While infra-slow (<0.1Hz) connectome dynamics have been extensively studied with fMRI, rapid dynamics highly relevant for cognition are poorly understood. Here, we asked whether rapid electrophysiological connectome dynamics constitute subject-specific brain traits and to what extent they are under genetic influence. Using source-localized EEG connectomes during resting-state (N=928, 473 females), we quantified heritability of multivariate (multi-state) features describing temporal or spatial characteristics of connectome dynamics. States switched rapidly every ~60-500ms. Temporal features were heritable, particularly, Fractional Occupancy (in theta, alpha, beta, and gamma bands) and Transition Probability (in theta, alpha, and gamma bands), representing the duration spent in each state and the frequency of state switches, respectively. Genetic effects explained a substantial proportion of phenotypic variance of these features: Fractional Occupancy in beta (44.3%) and gamma (39.8%) bands and Transition Probability in theta (38.4%), alpha (63.3%), beta (22.6%), and gamma (40%) bands. However, we found no evidence for heritability of spatial features, specifically states' Modularity and connectivity pattern. We conclude that genetic effects strongly shape individuals' connectome dynamics at rapid timescales, specifically states' overall occurrence and sequencing.

Cognitive and Psychiatric Relevance of Dynamic Functional Connectivity States in a Large (N>10,000) Children Population

Children's brains dynamically adapt to the stimuli from the internal state and the external environment, allowing for changes in cognitive and mental behavior. In this work, we performed a large-scale analysis of dynamic functional connectivity (DFC) in children aged 9 ~ 11 years, investigating how brain dynamics relate to cognitive performance and mental health at an early age. A hybrid independent component analysis framework was applied to the Adolescent Brain Cognitive Development (ABCD) data containing 10,988 children. We combined a sliding-window approach with k-means clustering to identify five brain states with distinct DFC patterns. Interestingly, the occurrence of a strongly connected state was negatively correlated with cognitive performance and positively correlated with dimensional psychopathology in children. Meanwhile, opposite relationships were observed for a sparsely connected state. The composite cognitive score and the ADHD score were the most significantly correlated with the DFC states. The mediation analysis further showed that attention problems mediated the effect of DFC states on cognitive performance. This investigation unveils the neurological underpinnings of DFC states, which suggests that tracking the transient dynamic connectivity may help to characterize cognitive and mental problems in children and guide people to provide early intervention to buffer adverse influences.

On the variability of dynamic functional connectivity assessment methods

BACKGROUND

Dynamic functional connectivity (dFC) has become an important measure for understanding brain function and as a potential biomarker. However, various methodologies have been developed for assessing dFC, and it is unclear how the choice of method affects the results. In this work, we aimed to study the results variability of commonly used dFC methods.

METHODS

We implemented 7 dFC assessment methods in Python and used them to analyze the functional magnetic resonance imaging data of 395 subjects from the Human Connectome Project. We measured the similarity of dFC results yielded by different methods using several metrics to quantify overall, temporal, spatial, and intersubject similarity.

RESULTS

Our results showed a range of weak to strong similarity between the results of different methods, indicating considerable overall variability. Somewhat surprisingly, the observed variability in dFC estimates was found to be comparable to the expected functional connectivity variation over time, emphasizing the impact of methodological choices on the final results. Our findings revealed 3 distinct groups of methods with significant intergroup variability, each exhibiting distinct assumptions and advantages.

CONCLUSIONS

Overall, our findings shed light on the impact of dFC assessment analytical flexibility and highlight the need for multianalysis approaches and careful method selection to capture the full range of dFC variation. They also emphasize the importance of distinguishing neural-driven dFC variations from physiological confounds and developing validation frameworks under a known ground truth. To facilitate such investigations, we provide an open-source Python toolbox, PydFC, which facilitates multianalysis dFC assessment, with the goal of enhancing the reliability and interpretability of dFC studies.

Atypical instantaneous spatio-temporal patterns of neural dynamics in Alzheimer's disease

Cognitive functions produced by large-scale neural integrations are the most representative 'emergence phenomena' in complex systems. A novel approach focusing on the instantaneous phase difference of brain oscillations across brain regions has succeeded in detecting moment-to-moment dynamic functional connectivity. However, it is restricted to pairwise observations of two brain regions, contrary to large-scale spatial neural integration in the whole-brain. In this study, we introduce a microstate analysis to capture whole-brain instantaneous phase distributions instead of pairwise differences. Upon applying this method to electroencephalography signals of Alzheimer's disease (AD), which is characterised by progressive cognitive decline, the AD-specific state transition among the four states defined as the leading phase location due to the loss of brain regional interactions could be promptly characterised. In conclusion, our synthetic analysis approach, focusing on the microstate and instantaneous phase, enables the capture of the instantaneous spatiotemporal neural dynamics of brain activity and characterises its pathological conditions.

Dynamic Functional Connectivity in Pediatric Mild Traumatic Brain Injury

Resting-state fMRI can be used to identify recurrent oscillatory patterns of functional connectivity within the human brain, also known as dynamic brain states. Alterations in dynamic brain states are highly likely to occur following pediatric mild traumatic brain injury (pmTBI) due to the active developmental changes. The current study used resting-state fMRI to investigate dynamic brain states in 200 patients with pmTBI (ages 8-18 years, median = 14 years) at the subacute (∼1-week post-injury) and early chronic (∼ 4 months post-injury) stages, and in 179 age- and sex-matched healthy controls (HC). A k-means clustering analysis was applied to the dominant time-varying phase coherence patterns to obtain dynamic brain states. In addition, correlations between brain signals were computed as measures of static functional connectivity. Dynamic connectivity analyses showed that patients with pmTBI spend less time in a frontotemporal default mode/limbic brain state, with no evidence of change as a function of recovery post-injury. Consistent with models showing traumatic strain convergence in deep grey matter and midline regions, static interhemispheric connectivity was affected between the left and right precuneus and thalamus, and between the right supplementary motor area and contralateral cerebellum. Changes in static or dynamic connectivity were not related to symptom burden or injury severity measures, such as loss of consciousness and post-traumatic amnesia. In aggregate, our study shows that brain dynamics are altered up to 4 months after pmTBI, in brain areas that are known to be vulnerable to TBI. Future longitudinal studies are warranted to examine the significance of our findings in terms of long-term neurodevelopment.

Static and dynamic fMRI-derived functional connectomes represent largely similar information

Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.

Individual-level functional connectivity predicts cognitive control efficiency

Cognitive control (CC) is essential for problem-solving in everyday life, and CC-related deficits occur alongside costly and debilitating disorders. The tri-partite model suggests that CC comprises multiple behaviors, including switching, inhibiting, and updating. Activity within the fronto-parietal control network B (FPCN-B), the dorsal attention network (DAN), the cingulo-opercular network (CON), and the lateral default-mode network (L-DMN) is related to switching and inhibiting behaviors. However, our understanding of how these brain regions interact to bring about cognitive switching and inhibiting in individuals is unclear. In the current study, subjects performed two in-scanner tasks that required switching and inhibiting. We used support vector regression (SVR) models containing individually-estimated functional connectivity between the FPCN-B, DAN, CON and L-DMN to predict switching and inhibiting behaviors. We observed that: inter-network connectivity can predict inhibiting and switching behaviors in individuals, and the L-DMN plays a role in switching and inhibiting behaviors. Therefore, individually estimated inter-network connections are markers of CC behaviors, and CC behaviors may arise due to interactions between a set of networks.

Edge Time Series Components of Functional Connectivity and Cognitive Function in Alzheimer's Disease

Understanding the interrelationships of brain function as measured by resting-state magnetic resonance imaging and neuropsychological/behavioral measures in Alzheimer's disease is key for advancement of neuroimaging analysis methods in clinical research. The edge time-series framework recently developed in the field of network neuroscience, in combination with other network science methods, allows for investigations of brain-behavior relationships that are not possible with conventional functional connectivity methods. Data from the Indiana Alzheimer's Disease Research Center sample (53 cognitively normal control, 47 subjective cognitive decline, 32 mild cognitive impairment, and 20 Alzheimer's disease participants) were used to investigate relationships between functional connectivity components, each derived from a subset of time points based on co-fluctuation of regional signals, and measures of domain-specific neuropsychological functions. Multiple relationships were identified with the component approach that were not found with conventional functional connectivity. These involved attentional, limbic, frontoparietal, and default mode systems and their interactions, which were shown to couple with cognitive, executive, language, and attention neuropsychological domains. Additionally, overlapping results were obtained with two different statistical strategies (network contingency correlation analysis and network-based statistics correlation). Results demonstrate that connectivity components derived from edge time-series based on co-fluctuation reveal disease-relevant relationships not observed with conventional static functional connectivity.

Socioeconomic resources in youth are linked to divergent patterns of network integration and segregation across the brain's transmodal axis

Socioeconomic resources (SER) calibrate the developing brain to the current context, which can confer or attenuate risk for psychopathology across the lifespan. Recent multivariate work indicates that SER levels powerfully influence intrinsic functional connectivity patterns across the entire brain. Nevertheless, the neurobiological meaning of these widespread alterations remains poorly understood, despite its translational promise for early risk identification, targeted intervention, and policy reform. In the present study, we leverage the resources of graph theory to precisely characterize multivariate and univariate associations between household SER and the functional integration and segregation (i.e., participation coefficient, within-module degree) of brain regions across major cognitive, affective, and sensorimotor systems during the resting state in 5,821 youth (ages 9-10 years) from the Adolescent Brain Cognitive Development (ABCD) Study. First, we establish that decomposing the brain into profiles of integration and segregation captures more than half of the multivariate association between SER and functional connectivity with greater parsimony (100-fold reduction in number of features) and interpretability. Second, we show that the topological effects of SER are not uniform across the brain; rather, higher SER levels are related to greater integration of somatomotor and subcortical systems, but greater segregation of default mode, orbitofrontal, and cerebellar systems. Finally, we demonstrate that the effects of SER are spatially patterned along the unimodal-transmodal gradient of brain organization. These findings provide critical interpretive context for the established and widespread effects of SER on brain organization, indicating that SER levels differentially configure the intrinsic functional architecture of developing unimodal and transmodal systems. This study highlights both sensorimotor and higher-order networks that may serve as neural markers of environmental stress and opportunity, and which may guide efforts to scaffold healthy neurobehavioral development among disadvantaged communities of youth.

Network state dynamics underpin craving in a transdiagnostic population

Emerging fMRI brain dynamic methods present a unique opportunity to capture how brain region interactions across time give rise to evolving affective and motivational states. As the unfolding experience and regulation of affective states affect psychopathology and well-being, it is important to elucidate their underlying time-varying brain responses. Here, we developed a novel framework to identify network states specific to an affective state of interest and examine how their instantaneous engagement contributed to its experience. This framework investigated network state dynamics underlying craving, a clinically meaningful and changeable state. In a transdiagnostic sample of healthy controls and individuals diagnosed with or at risk for craving-related disorders (N=252), we utilized connectome-based predictive modeling (CPM) to identify craving-predictive edges. An edge-centric timeseries approach was leveraged to quantify the instantaneous engagement of the craving-positive and craving-negative networks during independent scan runs. Individuals with higher craving persisted longer in a craving-positive network state while dwelling less in a craving-negative network state. We replicated the latter results externally in an independent group of healthy controls and individuals with alcohol use disorder exposed to different stimuli during the scan (N=173). The associations between craving and network state dynamics can still be consistently observed even when craving-predictive edges were instead identified in the replication dataset. These robust findings suggest that variations in craving-specific network state recruitment underpin individual differences in craving. Our framework additionally presents a new avenue to explore how the moment-to-moment engagement of behaviorally meaningful network states supports our changing affective experiences.

Controversies and progress on standardization of large-scale brain network nomenclature

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.

Dynamic Functional Connectome Harmonics

Functional connectivity (FC) "gradients" enable investigation of connection topography in relation to cognitive hierarchy, and yield the primary axes along which FC is organized. In this work, we employ a variant of the "gradient" approach wherein we solve for the normal modes of FC, yielding functional connectome harmonics. Until now, research in this vein has only considered static FC, neglecting the possibility that the principal axes of FC may depend on the timescale at which they are computed. Recent work suggests that momentary activation patterns, or brain states, mediate the dominant components of functional connectivity, suggesting that the principal axes may be invariant to change in timescale. In light of this, we compute functional connectome harmonics using time windows of varying lengths and demonstrate that they are stable across timescales. Our connectome harmonics correspond to meaningful brain states. The activation strength of the brain states, as well as their inter-relationships, are found to be reproducible for individuals. Further, we utilize our time-varying functional connectome harmonics to formulate a simple and elegant method for computing cortical flexibility at vertex resolution and demonstrate qualitative similarity between flexibility maps from our method and a method standard in the literature.

Abnormal intrinsic brain functional network dynamics in patients with cervical spondylotic myelopathy

The specific topological changes in dynamic functional networks and their role in cervical spondylotic myelopathy (CSM) brain function reorganization remain unclear. This study aimed to investigate the dynamic functional connection (dFC) of patients with CSM, focusing on the temporal characteristics of the functional connection state patterns and the variability of network topological organization. Eighty-eight patients with CSM and 77 healthy controls (HCs) were recruited for resting-state functional magnetic resonance imaging. We applied the sliding time window analysis method and K-means clustering analysis to capture the dFC variability patterns of the two groups. The graph-theoretical approach was used to investigate the variance in the topological organization of whole-brain functional networks. All participants showed four types of dynamic functional connection states. The mean dwell time in state 2 was significantly different between the two groups. Particularly, the mean dwell time in state 2 was significantly longer in the CSM group than in the healthy control group. Among the four states, switching of relative brain networks mainly included the executive control network (ECN), salience network (SN), default mode network (DMN), language network (LN), visual network (VN), auditory network (AN), precuneus network (PN), and sensorimotor network (SMN). Additionally, the topological properties of the dynamic network were variable in patients with CSM. Dynamic functional connection states may offer new insights into intrinsic functional activities in CSM brain networks. The variance of topological organization may suggest instability of the brain networks in patients with CSM.

Modeling the role of the thalamus in resting-state functional connectivity: Nature or structure

The thalamus is a central brain structure that serves as a relay station for sensory inputs from the periphery to the cortex and regulates cortical arousal. Traditionally, it has been regarded as a passive relay that transmits information between brain regions. However, recent studies have suggested that the thalamus may also play a role in shaping functional connectivity (FC) in a task-based context. Based on this idea, we hypothesized that due to its centrality in the network and its involvement in cortical activation, the thalamus may also contribute to resting-state FC, a key neurological biomarker widely used to characterize brain function in health and disease. To investigate this hypothesis, we constructed ten in-silico brain network models based on neuroimaging data (MEG, MRI, and dwMRI), and simulated them including and excluding the thalamus, and raising the noise into thalamus to represent the afferences related to the reticular activating system (RAS) and the relay of peripheral sensory inputs. We simulated brain activity and compared the resulting FC to their empirical MEG counterparts to evaluate model's performance. Results showed that a parceled version of the thalamus with higher noise, able to drive damped cortical oscillators, enhanced the match to empirical FC. However, with an already active self-oscillatory cortex, no impact on the dynamics was observed when introducing the thalamus. We also demonstrated that the enhanced performance was not related to the structural connectivity of the thalamus, but to its higher noisy inputs. Additionally, we highlighted the relevance of a balanced signal-to-noise ratio in thalamus to allow it to propagate its own dynamics. In conclusion, our study sheds light on the role of the thalamus in shaping brain dynamics and FC in resting-state and allowed us to discuss the general role of criticality in the brain at the mesoscale level.

Why is everyone talking about brain state?

The rapid and coordinated propagation of neural activity across the brain provides the foundation for complex behavior and cognition. Technical advances across neuroscience subfields have advanced understanding of these dynamics, but points of convergence are often obscured by semantic differences, creating silos of subfield-specific findings. In this review we describe how a parsimonious conceptualization of brain state as the fundamental building block of whole-brain activity offers a common framework to relate findings across scales and species. We present examples of the diverse techniques commonly used to study brain states associated with physiology and higher-order cognitive processes, and discuss how integration across them will enable a more comprehensive and mechanistic characterization of the neural dynamics that are crucial to survival but are disrupted in disease.

Larger lesion volume in people with multiple sclerosis is associated with increased transition energies between brain states and decreased entropy of brain activity

Quantifying the relationship between the brain's functional activity patterns and its structural backbone is crucial when relating the severity of brain pathology to disability in multiple sclerosis (MS). Network control theory (NCT) characterizes the brain's energetic landscape using the structural connectome and patterns of brain activity over time. We applied NCT to investigate brain-state dynamics and energy landscapes in controls and people with MS (pwMS). We also computed entropy of brain activity and investigated its association with the dynamic landscape's transition energy and lesion volume. Brain states were identified by clustering regional brain activity vectors, and NCT was applied to compute the energy required to transition between these brain states. We found that entropy was negatively correlated with lesion volume and transition energy, and that larger transition energies were associated with pwMS with disability. This work supports the notion that shifts in the pattern of brain activity in pwMS without disability results in decreased transition energies compared to controls, but, as this shift evolves over the disease, transition energies increase beyond controls and disability occurs. Our results provide the first evidence in pwMS that larger lesion volumes result in greater transition energy between brain states and decreased entropy of brain activity.

Altered large-scale dynamic connectivity patterns in Alzheimer's disease and mild cognitive impairment patients: A machine learning study

Alzheimer's disease (AD) is a common neurodegeneration disease associated with substantial disruptions in the brain network. However, most studies investigated static resting-state functional connections, while the alteration of dynamic functional connectivity in AD remains largely unknown. This study used group independent component analysis and the sliding-window method to estimate the subject-specific dynamic connectivity states in 1704 individuals from three data sets. Informative inherent states were identified by the multivariate pattern classification method, and classifiers were built to distinguish ADs from normal controls (NCs) and to classify mild cognitive impairment (MCI) patients with informative inherent states similar to ADs or not. In addition, MCI subgroups with heterogeneous functional states were examined in the context of different cognition decline trajectories. Five informative states were identified by feature selection, mainly involving functional connectivity belonging to the default mode network and working memory network. The classifiers discriminating AD and NC achieved the mean area under the receiver operating characteristic curve of 0.87 with leave-one-site-out cross-validation. Alterations in connectivity strength, fluctuation, and inter-synchronization were found in AD and MCIs. Moreover, individuals with MCI were clustered into two subgroups, which had different degrees of atrophy and different trajectories of cognition decline progression. The present study uncovered the alteration of dynamic functional connectivity in AD and highlighted that the dynamic states could be powerful features to discriminate patients from NCs. Furthermore, it demonstrated that these states help to identify MCIs with faster cognition decline and might contribute to the early prevention of AD.

Abnormal dynamic functional connectivity of thalamic subregions in patients with first-episode, drug-naïve major depressive disorder

Background

Recent studies have shown that major depressive disorder (MDD) is associated with altered intrinsic functional connectivity (FC) of the thalamus; however, investigations of these alterations at a finer time scale and the level of thalamic subregions are still lacking.

Methods

We collected resting-state functional MRI data from 100 treatment-naïve, first-episode MDD patients and 99 age-, gender- and education-matched healthy controls (HCs). Seed-based whole-brain sliding window-based dFC analyses were performed for 16 thalamic subregions. Between-group differences in the mean and variance of dFC were determined using threshold-free cluster enhancement algorithm. For significant alterations, there relationships with clinical and neuropsychological variables were further examined via bivariate and multivariate correlation analyses.

Results

Of all thalamic subregions, only the left sensory thalamus (Stha) showed altered variance of dFC in the patients characterized by increases with the left inferior parietal lobule, left superior frontal gyrus, left inferior temporal gyrus, and left precuneus, and decreases with multiple frontal, temporal, parietal, and subcortical regions. These alterations accounted for, to a great extent, clinical, and neuropsychological characteristics of the patients as revealed by the multivariate correlation analysis. In addition, the bivariate correlation analysis revealed a positive correlation between the variance of dFC between the left Stha and right inferior temporal gurus/fusiform and childhood trauma questionnaires scores (r = 0.562, P < 0.001).

Conclusion

These findings suggest that the left Stha is the most vulnerable thalamic subregion to MDD, whose dFC alterations may serve as potential biomarkers for the diagnosis of the disease.

The domain-separation language network dynamics in resting state support its flexible functional segregation and integration during language and speech processing

Modern linguistic theories and network science propose that language and speech processing are organized into hierarchical, segregated large-scale subnetworks, with a core of dorsal (phonological) stream and ventral (semantic) stream. The two streams are asymmetrically recruited in receptive and expressive language or speech tasks, which showed flexible functional segregation and integration. We hypothesized that the functional segregation of the two streams was supported by the underlying network segregation. A dynamic conditional correlation approach was employed to construct framewise time-varying language networks and k-means clustering was employed to investigate the temporal-reoccurring patterns. We found that the framewise language network dynamics in resting state were robustly clustered into four states, which dynamically reconfigured following a domain-separation manner. Spatially, the hub distributions of the first three states highly resembled the neurobiology of speech perception and lexical-phonological processing, speech production, and semantic processing, respectively. The fourth state was characterized by the weakest functional connectivity and was regarded as a baseline state. Temporally, the first three states appeared exclusively in limited time bins (∼15%), and most of the time (> 55%), state 4 was dominant. Machine learning-based dFC-linguistics prediction analyses showed that dFCs of the four states significantly predicted individual linguistic performance. These findings suggest a domain-separation manner of language network dynamics in resting state, which forms a dynamic "meta-network" framework to support flexible functional segregation and integration during language and speech processing.