Variability In Functional Connectivity Research Articles

Effects of repetitive transcranial magnetic stimulation on individual variability of resting-state functional connectivity in major depressive disorder.

Repetitive transcranial magnetic stimulation (rTMS) is an effective treatment for major depressive disorder (MDD), but substantial heterogeneity in outcomes remains. We examined a potential mechanism of action of rTMS to normalize individual variability in resting-state functional connectivity (rs-fc) before and after a course of treatment. Variability in rs-fc was examined in healthy controls (baseline) and individuals with MDD (baseline and after 4-6 weeks of rTMS). Seed-based connectivity was calculated to 4 regions associated with MDD: left dorsolateral prefrontal cortex (DLPFC), right subgenual anterior cingulate cortex (sgACC), bilateral insula, and bilateral precuneus. Individual variability was quantified for each region by calculating the mean correlational distance of connectivity maps relative to the healthy controls; a higher variability score indicated a more atypical/idiosyncratic connectivity pattern. We included data from 66 healthy controls and 252 individuals with MDD in our analyses. Patients with MDD did not show significant differences in baseline variability of rs-fc compared with controls. Treatment with rTMS increased rs-fc variability from the right sgACC and precuneus, but the increased variability was not associated with clinical outcomes. Interestingly, higher baseline variability of the right sgACC was significantly associated with less clinical improvement (p = 0.037, uncorrected; did not survive false discovery rate correction).Limitations: The linear model was constructed separately for each region of interest. This was, to our knowledge, the first study to examine individual variability of rs-fc related to rTMS in individuals with MDD. In contrast to our hypotheses, we found that rTMS increased the individual variability of rs-fc. Our results suggest that individual variability of the right sgACC and bilateral precuneus connectivity may be a potential mechanism of rTMS.

Intoxication due to Δ9-tetrahydrocannabinol is characterized by disrupted prefrontal cortex activity.

Neural states of impairment from intoxicating substances, including cannabis, are poorly understood. Cannabinoid 1 receptors, the main target of Δ9-tetrahydrocannabinol (THC), the primary intoxicating cannabinoid in cannabis, are densely localized within prefrontal cortex; therefore, prefrontal brain regions are key locations to examine brain changes that characterize acute intoxication. We conducted a double-blind, randomized, cross-over study in adults, aged 18-55 years, who use cannabis regularly, to determine the effects of acute intoxication on prefrontal cortex resting-state measures, assessed with portable functional near-infrared spectroscopy. Participants received oral THC (10-80 mg, individually dosed to overcome tolerance and achieve acute intoxication) and identical placebo, randomized for order;185 adults were randomized and 128 completed both study days and had usable data. THC was associated with expected increases in subjective intoxication ratings (ES = 35.30, p < 0.001) and heart rate (ES = 11.15, p = 0.001). THC was associated with decreased correlations and anticorrelations in static resting-state functional connectivity within the prefrontal cortex relative to placebo, with weakest correlations and anticorrelations among those who reported greater severity of intoxication (RSFC between medial PFC-ventromedial PFC and DEQ scores, r = 0.32, p < 0.001; RSFC between bilateral mPFC and DEQ scores, r = -0.28, p = 0.001). Relative to placebo, THC was associated with increased variability (or reduced stability) in dynamic resting-state functional connectivity of the prefrontal cortex at p = 0.001, consistent across a range of window sizes. Finally, using frequency power spectrum analyses, we observed that relative to placebo, THC was associated with widespread reduced spectral power within the prefrontal cortex across the 0.073-0.1 Hz frequency range at p < 0.039. These neural features suggest a disruptive influence of THC on the neural dynamics of the prefrontal cortex and may underlie cognitive impairing effects of THC that are detectable with portable imaging. This study is registered in Clinicaltrials.gov (NCT03655717).

Altered static and dynamic cerebellar-cerebral functional connectivity in acute pontine infarction.

This study investigates abnormalities in cerebellar-cerebral static and dynamic functional connectivity among patients with acute pontine infarction, examining the relationship between these connectivity changes and behavioral dysfunction. Resting-state functional magnetic resonance imaging was utilized to collect data from 45 patients within seven days post-pontine infarction and 34 normal controls. Seed-based static and dynamic functional connectivity analyses identified divergences in cerebellar-cerebral connectivity features between pontine infarction patients and normal controls. Correlations between abnormal functional connectivity features and behavioral scores were explored. Compared to normal controls, left pontine infarction patients exhibited significantly increased static functional connectivity within the executive, affective-limbic, and motor networks. Conversely, right pontine infarction patients demonstrated decreased static functional connectivity in the executive, affective-limbic, and default mode networks, alongside an increase in the executive and motor networks. Decreased temporal variability of dynamic functional connectivity was observed in the executive and default mode networks among left pontine infarction patients. Furthermore, abnormalities in static and dynamic functional connectivity within the executive network correlated with motor and working memory performance in patients. These findings suggest that alterations in cerebellar-cerebral static and dynamic functional connectivity could underpin the behavioral dysfunctions observed in acute pontine infarction patients.

The associations among glycemic control, heart variability, and autonomic brain function in healthy individuals: Age- and sex-related differences

IntroductionThe purpose of this study was to clarify the relationships between glycemia and function of the autonomic nervous system (ANS), assessed via resting-state functional connectivity (FC) and heart-rate variability (HRV). MethodsData for this study were extracted from the Leipzig Study for Mind-Body-Emotion Interactions, including 146 healthy adults (114 young, 32 older). Variables of interest were glycated hemoglobin (HbA1c), resting-state FC in the salience aspect of the central-autonomic (S-CAN) and salience network (SN) and HRV (RMSSD and high-frequency HRV (HF-HRV)). ResultsHbA1c was inversely correlated with FC in the S-CAN but not SN. HbA1c was also inversely correlated with HRV (RMSSD and log(HF-HRV)). Both RMSSD and log(HF-HRV) were correlated with FC in the S-CAN and SN. Age- (not sex-related) differences were observed in the Hb1Ac-FC associations while sex- (not age-related) differences were observed in the HRV-FC. ConclusionsThese findings extend the diabetes literature to healthy adults in relating glycemia and brain function. The age- and sex-related differences in these relationships highlight the need to account for the potential effects of age and sex in future investigations.

Altered intersubject functional variability of brain white-matter in major depressive disorder and its association with gene expression profiles.

Major depressive disorder (MDD) is a clinically heterogeneous disorder. Its mechanism is still unknown. Although the altered intersubject variability in functional connectivity (IVFC) within gray-matter has been reported in MDD, the alterations to IVFC within white-matter (WM-IVFC) remain unknown. Based on the resting-state functional MRI data of discovery (145 MDD patients and 119 healthy controls [HCs]) and validation cohorts (54 MDD patients, and 78 HCs), we compared the WM-IVFC between the two groups. We further assessed the meta-analytic cognitive functions related to the alterations. The discriminant WM-IVFC values were used to classify MDD patients and predict clinical symptoms in patients. In combination with the Allen Human Brain Atlas, transcriptome-neuroimaging association analyses were further conducted to investigate gene expression profiles associated with WM-IVFC alterations in MDD, followed by a set of gene functional characteristic analyses. We found extensive WM-IVFC alterations in MDD compared to HCs, which were associated with multiple behavioral domains, including sensorimotor processes and higher-order functions. The discriminant WM-IVFC could not only effectively distinguish MDD patients from HCs with an area under curve ranging from 0.889 to 0.901 across three classifiers, but significantly predict depression severity (r = 0.575, p = 0.002) and suicide risk (r = 0.384, p = 0.040) in patients. Furthermore, the variability-related genes were enriched for synapse, neuronal system, and ion channel, and predominantly expressed in excitatory and inhibitory neurons. Our results obtained good reproducibility in the validation cohort. These findings revealed intersubject functional variability changes of brain WM in MDD and its linkage with gene expression profiles, providing potential implications for understanding the high clinical heterogeneity of MDD.

Heterogeneity in functional connectivity: Dimensional predictors of individual variability during rest and task fMRI in psychosis

BackgroundIndividuals with schizophrenia spectrum disorders (SSD) often demonstrate cognitive impairments, associated with poor functional outcomes. While neurobiological heterogeneity has posed challenges when examining social cognition in SSD, it provides a unique opportunity to explore brain-behavior relationships. The aim of this study was to investigate the relationship between individual variability in functional connectivity during resting state and the performance of a social task and social and non-social cognition in a large sample of controls and individuals diagnosed with SSD. MethodsNeuroimaging and behavioral data were analyzed for 193 individuals with SSD and 155 controls (total n = 348). Individual variability was quantified through mean correlational distance (MCD) of functional connectivity between participants; MCD was defined as a global ‘variability score’. Pairwise correlational distance was calculated as 1 – the correlation coefficient between a given pair of participants, and averaging distance from one participant to all other participants provided the mean correlational distance metric. Hierarchical regressions were performed on variability scores derived from resting state and Empathic Accuracy (EA) task functional connectivity data to determine potential predictors (e.g., age, sex, neurocognitive and social cognitive scores) of individual variability. ResultsGroup comparison between SSD and controls showed greater SSD MCD during rest (p = 0.00038), while no diagnostic differences were observed during task (p = 0.063). Hierarchical regression analyses demonstrated the persistence of a significant diagnostic effect during rest (p = 0.008), contrasting with its non-significance during the task (p = 0.50), after social cognition was added to the model. Notably, social cognition exhibited significance in both resting state and task conditions (both p = 0.01). ConclusionsDiagnostic differences were more prevalent during unconstrained resting scans, whereas the task pushed participants into a more common pattern which better emphasized transdiagnostic differences in cognitive abilities. Focusing on variability may provide new opportunities for interventions targeting specific cognitive impairments to improve functional outcomes.

Differences in the expression of cortex-wide neural dynamics are related to behavioral phenotype

Behavior differs across individuals, ranging from typical to atypical phenotypes.1 Understanding how differences in behavior relate to differences in neural activity is critical for developing treatments of neuropsychiatric and neurodevelopmental disorders. One hypothesis is that differences in behavior reflect individual differences in the dynamics of how information flows through the brain. In support of this, the correlation of neural activity between brain areas, termed "functional connectivity," varies across individuals2 and is disrupted in autism,3 schizophrenia,4 and depression.5 However, the changes in neural activity that underlie altered behavior and functional connectivity remain unclear. Here, we show that individual differences in the expression of different patterns of cortical neural dynamics explain variability in both functional connectivity and behavior. Using mesoscale imaging, we recorded neural activity across the dorsal cortex of behaviorally "typical" and "atypical" mice. All mice shared the same recurring cortex-wide spatiotemporal motifs of neural activity, and these motifs explained the large majority of variance in cortical activity (>75%). However, individuals differed in how frequently different motifs were expressed. These differences in motif expression explained differences in functional connectivity and behavior across both typical and atypical mice. Our results suggest that differences in behavior and functional connectivity are due to changes in the processes that select which pattern of neural activity is expressed at each moment in time.

Abnormal temporal variability of thalamo-cortical circuit in patients with moderate-to-severe obstructive sleep apnea.

This study investigated the abnormal dynamic functional connectivity (dFC) variability of the thalamo-cortical circuit in patients with obstructive sleep apnea (OSA) and explored the relationship between these changes and the clinical characteristics of patients with OSA. A total of 91 newly diagnosed patients with moderate-to-severe OSA and 84 education-matched healthy controls (HCs) were included. All participants underwent neuropsychological testing and a functional magnetic resonance imaging scan. We explored the thalamo-cortical dFC changes by dividing the thalamus into 16 subregions and combining them using a sliding-window approach. Correlation analysis assessed the relationship between dFC variability and clinical features, and the support vector machine method was used for classification. The OSA group exhibited increased dFC variability between the thalamic subregions and extensive cortical areas, compared with the HCs group. Decreased dFC variability was observed in some frontal-occipital-temporal cortical regions. These dFC changes positively correlated with daytime sleepiness, disease severity, and cognitive scores. Altered dFC variability contributed to the discrimination between patients with OSA and HCs, with a classification accuracy of 77.8%. Our findings show thalamo-cortical overactivation and disconnection in patients with OSA, disrupting information flow within the brain networks. These results enhance understanding of the temporal variability of thalamo-cortical circuits in patients with OSA.

Individual variability in functional connectivity of human auditory cortex.

Individual variability in functional connectivity underlies individual differences in cognition and behaviors, yet its association with functional specialization in the auditory cortex remains elusive. Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, this study was designed to investigate the spatial distribution of auditory cortex individual variability in its whole-brain functional network architecture. An inherent hierarchical axis of the variability was discerned, which radiates from the medial to lateral orientation, with the left auditory cortex demonstrating more pronounced variations than the right. This variability exhibited a significant correlation with the variations in structural and functional metrics in the auditory cortex. Four auditory cortex subregions, which were identified from a clustering analysis based on this variability, exhibited unique connectional fingerprints and cognitive maps, with certain subregions showing specificity to speech perception functional activation. Moreover, the lateralization of the connectional fingerprint exhibited a U-shaped trajectory across the subregions. These findings emphasize the role of individual variability in functional connectivity in understanding cortical functional organization, as well as in revealing its association with functional specialization from the activation, connectome, and cognition perspectives.

CHANGES IN FUNCTIONAL CONNECTIVITY FOLLOWING INTENSIVE ATTENTION TRAINING IN PATIENTS WITH TRAUMATIC BRAIN INJURY. A PILOT STUDY.

To explore functional connectivity after intensive attention training in the chronic phase after traumatic brain injury as clinical evidence indicates that intensive attention training improves attention dysfunction in persons with traumatic brain injury. A case series study. Two young adults, 13- and 18-months post traumatic brain injury, with traumatic brain injury induced attention deficits were assigned to 20 h of intensive attention training and neuroimaging. Functional magnetic resonance imaging during a psychomotor vigilance test was conducted pre- and post-intervention. The neuroimaging indicated both increased and decreased connectivity density in frontal, posterior and subcortical brain regions, for some regions with separate change patterns for left and right hemisphere respectively, and an overall reduction in variability in functional connectivity. The changed and decreased variability of functional connectivity in various brain regions, captured by fMRI during a psychomotor vigilance test after direct attention training in a small sample of persons with traumatic brain injury, suggests further studies of functional connectivity changes in neural networks.

EEG brain network variability is correlated with other pathophysiological indicators of critical patients in neurology intensive care unit

Continuous electroencephalogram (cEEG) plays a crucial role in monitoring and postoperative evaluation of critical patients with extensive EEG abnormalities. Recently, the temporal variability of dynamic resting-state functional connectivity has emerged as a novel approach to understanding the pathophysiological mechanisms underlying diseases. However, little is known about the underlying temporal variability of functional connections in critical patients admitted to neurology intensive care unit (NICU). Furthermore, considering the emerging field of network physiology that emphasizes the integrated nature of human organisms, we hypothesize that this temporal variability in brain activity may be potentially linked to other physiological functions. Therefore, this study aimed to investigate network variability using fuzzy entropy in 24-hour dynamic resting-state networks of critical patients in NICU, with an emphasis on exploring spatial topology changes over time. Our findings revealed both atypical flexible and robust architectures in critical patients. Specifically, the former exhibited denser functional connectivity across the left frontal and left parietal lobes, while the latter showed predominantly short-range connections within anterior regions. These patterns of network variability deviating from normality may underlie the altered network integrity leading to loss of consciousness and cognitive impairment observed in these patients. Additionally, we explored changes in 24-hour network properties and found simultaneous decreases in brain efficiency, heart rate, and blood pressure between approximately 1 pm and 5 pm. Moreover, we observed a close relationship between temporal variability of resting-state network properties and other physiological indicators including heart rate as well as liver and kidney function. These findings suggest that the application of a temporal variability-based cEEG analysis method offers valuable insights into underlying pathophysiological mechanisms of critical patients in NICU, and may present novel avenues for their condition monitoring, intervention, and treatment.

On the variability of dynamic functional connectivity assessment methods.

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. 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. 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. 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.

Anatomy-Guided Spatio-Temporal Graph Convolutional Networks (AG-STGCNs) for Modeling Functional Connectivity Between Gyri and Sulci Across Multiple Task Domains.

The cerebral cortex is folded as gyri and sulci, which provide the foundation to unveil anatomo-functional relationship of brain. Previous studies have extensively demonstrated that gyri and sulci exhibit intrinsic functional difference, which is further supported by morphological, genetic, and structural evidences. Therefore, systematically investigating the gyro-sulcal (G-S) functional difference can help deeply understand the functional mechanism of brain. By integrating functional magnetic resonance imaging (fMRI) with advanced deep learning models, recent studies have unveiled the temporal difference in functional activity between gyri and sulci. However, the potential difference of functional connectivity, which represents functional dependency between gyri and sulci, is much unknown. Moreover, the regularity and variability of the G-S functional connectivity difference across multiple task domains remains to be explored. To address the two concerns, this study developed new anatomy-guided spatio-temporal graph convolutional networks (AG-STGCNs) to investigate the regularity and variability of functional connectivity differences between gyri and sulci across multiple task domains. Based on 830 subjects with seven different task-based and one resting state fMRI (rs-fMRI) datasets from the public Human Connectome Project (HCP), we consistently found that there are significant differences of functional connectivity between gyral and sulcal regions within task domains compared with resting state (RS). Furthermore, there is considerable variability of such functional connectivity and information flow between gyri and sulci across different task domains, which are correlated with individual cognitive behaviors. Our study helps better understand the functional segregation of gyri and sulci within task domains as well as the anatomo-functional-behavioral relationship of the human brain.

Dissociation of Reliability, Heritability, and Predictivity inCoarse- and Fine-Scale Functional Connectomes duringDevelopment.

The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9-10 years; n = 1,115 individuals; both sexes; 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex-wise) connectivity information and compare the fine-scale connectome with the traditional parcel-wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition.

Blood pressure variability and functional network connectivity in older adults

AbstractBackgroundBlood pressure variability is an emerging risk indicator for cerebrovascular disease and Alzheimer’s disease, independent of traditionally studied mean blood pressure levels. Recent studies suggest elevated blood pressure variability in older adults is also associated with attenuated cerebrovascular reactivity, which may have implications for functional hyperemia underpinning brain network connectivity. However, it remains unclear whether blood pressure variability is related to functional network connectivity. We examined relationships between beat‐to‐beat blood pressure variability in older adults and functional connectivity in multiple brain networks vulnerable to Alzheimer’s disease and related disorders (AD/ADRD).Method32 community‐dwelling older adults (mean [SD] age = 69.6 [7.5] years) without history of dementia or clinical stroke underwent continuous blood pressure monitoring during a 5‐minute period of rest and resting state fMRI. Blood pressure variability was calculated as variability independent of mean, an increasingly used index of variability uncorrelated with mean blood pressure levels. Functional connectivity was determined by resting state fMRI for several brain networks: default mode, salience, dorsal attention, fronto‐parietal, and language. Multiple linear regression examined relationships between blood pressure variability and functional network connectivity, controlling for age, sex, and mean blood pressure. Associations with mean blood pressure were also explored to directly compare potential effects with blood pressure variability.ResultElevated blood pressure variability was associated with lower functional connectivity in the default mode network (ß = ‐.55 [‐1.02, ‐.08]; p = .02). There were no significant associations between blood pressure variability and connectivity in other functional networks (p’s = .10 ‐ .62). Mean blood pressure was not related to functional connectivity in any network (p’s = .08 ‐ .98).ConclusionOlder adults with elevated blood pressure variability exhibit resting state functional connectivity deficits in the default mode network. Findings add to literature supporting the role of blood pressure variability in neurovascular dysfunction and AD/ADRD. Blood pressure variability may represent an important but understudied early vascular risk factor for neurovascular dysfunction relevant to AD/ADRD, with potential therapeutic implications.

Anterior and posterior default mode network functional connectivity and segregation in aging

AbstractBackgroundThe default mode network (DMN) supports memory performance and has been shown to be vulnerable to the effects of aging. The posterior DMN (pDMN) is an early region of amyloid‐β (Aβ) accumulation, which may lead to disrupted communication within/between subnetworks of the DMN. Using resting state functional connectivity (FC), we tested whether communication within and between the pDMN and anterior DMN (aDMN) were altered by aging and Aβ pathology and related to memory performance. We also tested segregation FC between the aDMN and pDMN.MethodData from 27 cognitively normal adults (69.6 years, 59% females) with resting state fMRI (rsfMRI), 18F‐florbetapir (FBP) PET to measure Aβ, and the Rey Auditory Verbal LearningTest (RAVLT) delayed recall to assess memory were analyzed. The rsfMRI data were preprocessed using CONN toolbox. ROI‐to‐ROI FC matrices were constructed for regions included in the aDMN and pDMN. We calculated: (1) the average FC within each network (Zw), (2) average FC between the aDMN and pDMN (Zb), and (3) network segregation [(Zw‐Zb)/Zw] for each DMN network. Global FBP SUVR was calculated as the mean across association cortical regions, and a cutoff of 1.11 was used to determine Aβ positivity. Analyses included the FC variables and their associations with age, Aβ, and memory.ResultOlder age was associated with reduced network segregation of the pDMN (r = ‐0.53, p = 0.004), and a trend level association for reduced within‐pDMN FC (r = ‐0.37, p = 0.057). Global FBP SUVR was not associated with FC or network segregation, and there was no significant difference in FC between Aβ groups (controlling for age). There was a trend‐level association between better memory performance and stronger FC between aDMN and pDMN (r = 0.37, p = 0.066; controlling for age and education).ConclusionResults demonstrated that aging is associated with specific FC disruptions of pDMN, leading to reduced specialization of processing as indexed by network segregation. Moreover, increased communication between DMN subnetworks may support memory in aging. Future analyses in a larger sample will further test the association between Alzheimer’s pathology and functional changes to DMN subnetworks.

Increased brain nucleus accumbens functional connectivity in melancholic depression

BackgroundMelancholic depression, marked by typical symptoms of anhedonia, is regarded as a homogeneous subtype of major depressive disorder (MDD). However, little attention was paid to underlying mechanisms of melancholic depression. This study aims to examine functional connectivity of the reward circuit associated with anhedonia symptoms in melancholic depression. MethodsFifty-nine patients with first-episode drug- naive MDD, including 31 melancholic patients and 28 non-melancholic patients, were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Thirty-two healthy volunteers were recruited as controls. Bilateral nucleus accumbens (NAc) were selected as seed points to form functional NAc network. Then support vector machine (SVM) was used to distinguish melancholic patients from non-melancholic patients. ResultsRelative to non-melancholic patients, melancholic patients displayed increased functional connectivity (FC) between bilateral NAc and right middle frontal gyrus (MFG) and between right NAc and left cerebellum lobule VIII. Compared to healthy controls, melancholic patients showed increased FC between right NAc and right lingual gyrus and between left NAc and left postcentral gyrus; non-melancholic patients had increased FC between bilateral NAc and right lingual gyrus. No significant correlations were observed between altered FC and clinical variables in melancholic patients. SVM results showed that FC between left NAc and right MFG could accurately distinguish melancholic patients from non-melancholic patients. ConclusionMelancholic depression exhibited different patterns of functional connectivity of the reward circuit relative to non-melancholic patients. This study highlights the significance of the reward circuit in the neuropathology of melancholic depression.

Hippocampal Resting State Functional Connectivity Associated with Physical Activity in Periadolescent Children.

Periadolescence is a neurodevelopmental period characterized by structural and functional brain changes that are associated with cognitive maturation. The development of the functional connectivity of the hippocampus contributes to cognitive maturation, especially memory processes. Notably, hippocampal development is influenced by lifestyle factors, including physical activity. Physical activity has been associated with individual variability in hippocampal functional connectivity. However, this relationship has not been characterized in a developmental cohort. In this study, we aimed to fill this gap by investigating the relationship between physical activity and the functional connectivity of the hippocampus in a cohort of periadolescents aged 8-13 years (N = 117). The participants completed a physical activity questionnaire, reporting the number of days per week they performed 60 min of physical activity; then, they completed a resting-state functional MRI scan. We observed that greater physical activity was significantly associated with differences in hippocampal functional connectivity in frontal and temporal regions. Greater physical activity was associated with decreased connectivity between the hippocampus and the right superior frontal gyrus and increased connectivity between the hippocampus and the left superior temporal sulcus. Capturing changes in hippocampal functional connectivity during key developmental periods may elucidate how lifestyle factors including physical activity influence brain network connectivity trajectories, cognitive development, and future disease risk.

Functional connectivity uniqueness and variability? Linkages with cognitive and psychiatric problems in children

Brain functional connectivity (FC) derived from functional magnetic resonance imaging has been serving as a potential ‘fingerprint’ for adults. However, cross-scan variation of FC can be substantial and carries biological information, especially during childhood. Here we performed a large-scale cross-sectional analysis on cross-scan FC stability and its associations with a diverse range of health measures in children. Functional network connectivity (FNC) was extracted via a hybrid independent component analysis framework on 9,071 participants and compared across four scans. We found that FNC can identify a given child from a large group with high accuracy (maximum >94%) and replicated the results across multiple scans. We then performed a linear mixed-effects model to investigate how cross-scan FNC stability was predictive of children’s behaviour. Although we could not find strong relationships between FNC stability and children’s behaviour, we observed significant but small associations between them (maximum r = 0.1070), with higher stability correlated with better cognitive performance, longer sleep duration and less psychotic expression. Via a multivariate analysis method, we captured larger effects between FNC stability and children’s cognitive performance (maximum r = 0.2932), which further proved the relevance of FNC stability to neurocognitive development. Overall, our findings show that a child’s connectivity profile is not only intrinsic but also exhibits reliable variability across scans, regardless of brain growth and development. Cross-scan connectivity stability may serve as a valuable neuroimaging feature to draw inferences on early cognitive and psychiatric behaviours in children.

Dynamic alterations of striatal-related functional networks in juvenile absence epilepsy

PurposeTo explore the features of dynamic functional connectivity (dFC) variability of striatal-cortical/subcortical networks in juvenile absence epilepsy (JAE). MethodsWe collected resting-state functional magnetic imaging data from 18 JAE patients and 28 healthy controls. The striatum was divided into six pairs of regions: the inferior-ventral striatum (VSi), superior-ventral striatum (VSs), dorsal-caudal putamen, dorsal-rostral putamen, dorsal-caudate (DC) and ventral-rostral putamen. We assessed the dFC variability of each subdivision in the whole brain using the sliding-window method, and correlated altered circuit with clinical variables in JAE patients. ResultsWe found altered dFC variability of striatal-cortical/subcortical networks in patients with JAE. The VSs exhibited decreased dFC variability with subcortical regions, and dFC variability between VSs and thalamus was negatively correlated with epilepsy duration. For the striatal-cortical networks, the dFC variability was decreased in VSi-affective network but increased in DC-executive network. The altered dynamics of striatal-cortical networks involved crucial nodes of the default mode network (DMN). ConclusionJAE patients exhibit excessive stability in the striatal-subcortical networks. For striatal-cortical networks in JAE, the striatal-affective circuit was more stable, while the striatal-executive circuit was more variable. Furthermore, crucial nodes of DMN were changed in striatal-cortical networks in JAE.