Dynamic functional network connectivity impairment in bipolar disorder and its relationship with global functioning.

Sex is a powerful modulator of disease susceptibility, course and outcome. The gene CACNA1C is among the best replicated vulnerability genes of bipolar disorder and schizophrenia. The aim of the present study was to investigate whether sex and a variant in CACNA1C (rs10774035 as a proxy for the well-acknowledged risk variant rs1006737) influence psychosocial adaptation in a large German patient sample with schizophrenia-spectrum (n=297) and bipolar (n=516) disorders. We analyzed Global Assessment of Functioning (GAF) scores, retrospectively collected for different time points during disease course. We investigated whether CACNA1C sex-dependently modulates longitudinal GAF scores and recovery from episodes of psychiatric disturbance in the above mentioned disorders. Psychosocial recovery was measured as difference score between the current GAF score (assessing the last remission) and the worst GAF score ever during an illness episode. Covariate- adjusted association analyses revealed a sex × rs10774035 genotype interaction on longitudinal GAF and recovery from illness episodes only in schizophrenia-spectrum but not in bipolar disorders. In schizophrenia-spectrum affected males, rs10774035 minor allele (T) carriers had higher GAF scores at three time points (premorbid, worst ever, current). In contrast, females carrying rs10774035 minor alleles had impaired recovery from schizophrenia-spectrum episodes. These results encourage further investigations of gene × sex interactions and longitudinal quantitative phenotypes to unravel the rich variety of behavioral consequences of genetic individuality.

Previous resting-state functional magnetic resonance imaging studies on intracerebral hemorrhage patients have focused more on the static characteristics of brain activity, while the time-varying effects during scanning have received less attention. Therefore, the current study aimed to explore the dynamic functional network connectivity changes of intracerebral hemorrhage patients. Using independent component analysis, the sliding window approach, and the k-means clustering analysis method, different dynamic functional network connectivity states were detected from resting-state functional magnetic resonance imaging data of 37 intracerebral hemorrhage patients and 44 healthy controls. The inter-group differences in dynamic functional network connectivity patterns and temporal properties were investigated, followed by correlation analyses between clinical scales and abnormal functional indexes. Ten resting-state networks were identified, and the dynamic functional network connectivity matrices were clustered into four different states. The transition numbers were decreased in the intracerebral hemorrhage patients compared with healthy controls, which was associated with trail making test scores in patients. The cerebellar network and executive control network connectivity in State 1 was reduced in patients, and this abnormal dynamic functional connectivity was positively correlated with the animal fluency test scores of patients. The current study demonstrated the characteristics of dynamic functional network connectivity in intracerebral hemorrhage patients and revealed that abnormal temporal properties and functional connectivity may be related to the performance of different cognitive domains after ictus. These results may provide new insights into exploring the neurocognitive mechanisms of intracerebral hemorrhage.

The current study integrated static (sFNC) and dynamic (dFNC) functional network connectivity to investigate the neurobiological mechanisms underlying alterations in static and dynamic functional network connectivity in subcortical vascular cognitive impairment (SVCI). We recruited 80 patients with SVCI (39 males, 41 females) and 83 healthy controls (32 males, 51 females). Clinical and imaging data, including clinical history, neuropsychological assessments, and Magnetic Resonance Imaging (MRI) scans, were collected. We extracted network independent components for sFNC and dFNC using independent component analysis with resting-state functional MRI data. Firstly, changes in sFNC in SVCI were comparatively analyzed. Subsequently, dynamic connectivity was examined using the sliding time window technique and cluster analysis to assess brain functional activity states and temporal properties. Differences in dFNC temporal properties (fractional occupancy, mean dwell time, and number of transitions) and functional connectivity across different time domains between groups were assessed with two sample t-tests. Spearman correlation analyses were performed to explore relationships between sFNC and dFNC changes and cognitive function. In the sFNC analysis, the SVCI group showed significantly decreased interactions between the sensorimotor network and lateral visual network, which was negatively associated with executive function (r = − 0.248, p = 0.027). In the dFNC analysis, brain functional activity was grouped into four highly structured functional connection states. The results revealed one connected state dominated by an increased connectivity pattern, two moderately connected states primarily characterized by increased connectivity with moderate decreases, and one weakly connected state exhibiting a modular connectivity pattern. These findings illustrate the progression in SVCI from connectivity disruption to compensation, eventually leading to a diminished compensatory response. Fractional occupancy and mean dwell time of states were correlated with cognitive function (all p < 0.05). SVCI patients exhibit impairments in both sFNC and dFNC, linked to cognitive decline. Connectivity dynamics reflect the brain’s adaptive capacity in response to cognitive impairment.

ObjectiveThe purpose of this study was to investigate the dynamic functional network connectivity (FNC) and its relationship with cognitive function in obstructive sleep apnea (OSA) patients from normal cognition (OSA-NC) to mild cognitive impairment (OSA-MCI).Materials and methodsEighty-two male OSA patients and 48 male healthy controls (HC) were included in this study. OSA patients were classified to OSA-MCI (n = 41) and OSA-NC (n = 41) based on cognitive assessments. The independent component analysis was used to determine resting-state functional networks. Then, a sliding-window approach was used to construct the dynamic FNC, and differences in temporal properties of dynamic FNC and functional connectivity strength were compared between OSA patients and the HC. Furthermore, the relationship between temporal properties and clinical assessments were analyzed in OSA patients.ResultsTwo different connectivity states were identified, namely, State I with stronger connectivity and lower frequency, and State II with lower connectivity and relatively higher frequency. Compared to HC, OSA patients had a longer mean dwell time and higher fractional window in stronger connectivity State I, and opposite result were found in State II, which was mainly reflected in OSA-MCI patients. The number of transitions was an increasing trend and positively correlated with cognitive assessment in OSA-MCI patients. Compared with HC, OSA patients showed extensive abnormal functional connectivity in stronger connected State I and less reduced functional connectivity in lower connected State II, which were mainly located in the salience network, default mode network, and executive control network.ConclusionOur study found that OSA patients showed abnormal dynamic FNC properties, which was a continuous trend from HC, and OSA-NC to OSA-MCI, and OSA patients showed abnormal dynamic functional connectivity strength. The number of transformations was associated with cognitive impairment in OSA-MCI patients, which may provide new insights into the neural mechanisms in OSA patients.

Reconfigurations of dynamic functional network connectivity after 1HZ repetitive transcranial magnetic stimulation in insomnia disorder.

Low back pain (LBP) is a prevalent pain condition whose persistence can lead to changes in the brain regions responsible for sensory, cognitive, attentional, and emotional processing. Previous neuroimaging studies have identified various structural and functional abnormalities in patients with LBP; however, how the static and dynamic large-scale functional network connectivity (FNC) of the brain is affected in these patients remains unclear. Forty-one patients with chronic low back pain (cLBP) and 42 healthy controls underwent resting-state functional MRI scanning. The independent component analysis method was employed to extract the resting-state networks. Subsequently, we calculate and compare between groups for static intra- and inter-network functional connectivity. In addition, we investigated the differences between dynamic functional network connectivity and dynamic temporal metrics between cLBP patients and healthy controls. Finally, we tried to distinguish cLBP patients from healthy controls by support vector machine method. The results showed that significant reductions in functional connectivity within the network were found within the DMN,DAN, and ECN in cLBP patients. Significant between-group differences were also found in static FNC and in each state of dynamic FNC. In addition, in terms of dynamic temporal metrics, fraction time and mean dwell time were significantly altered in cLBP patients. In conclusion, our study suggests the existence of static and dynamic large-scale brain network alterations in patients with cLBP. The findings provide insights into the neural mechanisms underlying various brain function abnormalities and altered pain experiences in patients with cLBP.

Background and HypothesisAltered functional connectivity (FC) has been frequently reported in psychosis. Studying FC and its time-varying patterns in early-stage psychosis allows the investigation of the neural mechanisms of this disorder without the confounding effects of drug treatment or illness-related factors.Study DesignWe employed resting-state functional magnetic resonance imaging (rs-fMRI) to explore FC in individuals with early psychosis (EP), who also underwent clinical and neuropsychological assessments. 96 EP and 56 demographically matched healthy controls (HC) from the Human Connectome Project for Early Psychosis database were included. Multivariate analyses using spatial group independent component analysis were used to compute static FC and dynamic functional network connectivity (dFNC). Partial correlations between FC measures and clinical and cognitive variables were performed to test brain-behavior associations.Study ResultsCompared to HC, EP showed higher static FC in the striatum and temporal, frontal, and parietal cortex, as well as lower FC in the frontal, parietal, and occipital gyrus. We found a negative correlation in EP between cognitive function and FC in the right striatum FC (pFWE = 0.009). All dFNC parameters, including dynamism and fluidity measures, were altered in EP, and positive symptoms were negatively correlated with the meta-state changes and the total distance (pFWE = 0.040 and pFWE = 0.049).ConclusionsOur findings support the view that psychosis is characterized from the early stages by complex alterations in intrinsic static and dynamic FC, that may ultimately result in positive symptoms and cognitive deficits.

ObjectiveMany studies have explored the neural mechanisms of cognitive impairment in chronic obstructive pulmonary disease (COPD) patients using the functional MRI. However, the dynamic properties of brain functional networks are still unclear. The purpose of this study was to explore the changes in dynamic functional network attributes and their relationship with cognitive impairment in stable COPD patients.Materials and methodsThe resting-state functional MRI and cognitive assessments were performed on 19 stable COPD patients and 19 age-, sex-, and education-matched healthy controls (HC). We conducted the independent component analysis (ICA) method on the resting-state fMRI data, and obtained seven resting-state networks (RSNs). After that, the static and dynamic functional network connectivity (sFNC and dFNC) were respectively constructed, and the differences of functional connectivity (FC) were compared between the COPD patients and the HC groups. In addition, the correlation between the dynamic functional network attributes and cognitive assessments was analyzed in COPD patients.ResultsCompared to HC, there were significant differences in sFNC among COPD patients between and within networks. COPD patients showed significantly longer mean dwell time and higher fractional windows in weaker connected State I than that in HC. Besides, in comparison to HC, COPD patients had more extensive abnormal FC in weaker connected State I and State IV, and less abnormal FC in stronger connected State II and State III, which were mainly located in the default mode network, executive control network, and visual network. In addition, the dFNC properties including mean dwell time and fractional windows, were significantly correlated with some essential clinical indicators such as FEV1, FEV1/FVC, and c-reactive protein (CRP) in COPD patients.ConclusionThese findings emphasized the differences in sFNC and dFNC of COPD patients, which provided a new perspective for understanding the cognitive neural mechanisms, and these indexes may serve as neuroimaging biomarkers of cognitive performance in COPD patients.

The transition from early adulthood to the elder is marked by functional and structural brain transformations. Many previous studies examined the correlation between the functional connectivity (FC) and aging using resting-state fMRI. Results showed that the changes in FC are linked to aging as well as the cognitive ability changes. However, some researchers proposed that the FC is not static but dynamic changes during the resting-state fMRI scan. In this study, we examined the correlation between the resting-state dynamic functional network connectivity and age using resting scan data of 434 subjects. The results suggested: (a) age is negatively associated with variability of dynamic functional network connectivity state; (b) the dwell time of each age range spends in each state is different; (c) the dynamic graph metrics curve of each age ranges is different and 19-30 age range has the largest average global efficiency and average local efficiency; (d) some dynamic functional network connectivity measures were correlated to the certain cognitive ability. Overall, the results suggested the changes in dynamic functional network connectivity measures may be a characteristic of the aging process and in further investigations may provide a deep understanding of the aging process.

Abnormal intrinsic brain functional network dynamics in unmedicated depressed bipolar II disorder

The aim of the current study was to determine whether the Global Assessment of Functioning (GAF) score is associated with the functional exercise capacity among in- and outpatients with bipolar disorder. Sixty-five (36♀) persons with bipolar disorder performed a 6-minute walk test (6MWT) and were assessed with the GAF, Quick Inventory of Depressive Symptomatology Self Report (QIDS), and the International Physical Activity Questionnaire (IPAQ). The mean GAF-score was 55.0 ± 15.0, whereas the mean distance achieved on the 6MWT was 615.6 ± 118.6 m. There was a positive association between the GAF score and 6MWT score (r = 0.60, p < 0.001). A backward regression analysis demonstrated that an inpatient status, illness duration (16.1 ± 10.7 years), and the QIDS score (7.7 ± 5.7) explained 72.4% of the GAF-score variance. The GAF, QIDS score, and age explained 74.1% of the 6MWT-score variance. Our results indicate that a bidirectional relationship is evident between the exercise capacity and global functioning among people with bipolar disorder.

BackgroundDiabetic retinopathy (DR) is a prevalent microvascular complication of diabetes. Prior neuroimaging research has indicated that patients with DR exhibit diverse levels of disrupted brain function alongside a variety of ocular symptoms. Nevertheless, past investigations have predominantly focused on static brain activity changes, leaving uncertainties regarding the modifications in dynamic large-scale brain networks among DR patients.PurposeThe aim of this study was to investigate the alterations in dynamic large-scale functional network connectivity in DR patients and its medical significance.MethodsForty-six patients with DR (type 2 diabetes mellitus) and 46 healthy controls, matched for age, gender, and education level, were enrolled in this study. Initial application of Independent Component Analysis (ICA) methods was used to extract the resting state network (RSN) from resting state functional magnetic resonance imaging (fMRI) data. Subsequently, sliding time window and k-means cluster analysis were employed to derive five stable repetitions of the dynamic functional network connectivity (dFNC) states and compare the differences in dFNC between the two cohorts for each state. Finally, the study investigated between-group variances in three dynamic temporal metrics.ResultsSignificant between-group differences in dFNC were observed in states 1 and 2. Patients with DR, compared to healthy controls, exhibited reduced functional connectivity within the visual network (VN) and between the dorsal attention network (DAN) and VN, coupled with higher functional connectivity between the default mode network (DMN) and VN, cerebellum network (CN) and VN, and DMN-executive control network (ECN). Regarding the three dynamic temporal metrics, the study findings indicated that DR patients experienced a notable decline in the fraction of time and mean dwell time in state 1, while showing an increase in these metrics for state 3.ConclusionOur study reveals extensive dynamic functional network connectivity alterations among patients with DR, potentially linked to visual impairment and cognitive deficits. These discoveries offer valuable insights into the neural mechanisms that drive changes in dynamic large-scale brain networks in individuals with DR.

The ability to distinguish myelin oligodendrocyte glycoprotein antibody-seropositive optic neuritis (MOG-ON) from seronegative-ON is critical in clinical practice. We investigate potential neural mechanisms and differentiation biomarkers via large-scale functional network connectivity (FNC) using resting-state functional magnetic resonance imaging (RS-fMRI). RS-fMRI-based independent component analysis (ICA) was performed in 79 subjects, including 23 with MOG-ON, 30 with seronegative-ON and 26 healthy controls (HCs). The resting-state networks (RSNs) extracted from the ICA were used to investigate static FNC (sFNC) changes within and between groups. In addition, 5 dynamic FNC (dFNC) states were identified using k-means cluster analysis, and several state-related properties were calculated. Receiver operating characteristic (ROC) curve analysis was also performed to determine its value in differential diagnosis. In the sFNC analysis, the patient groups showed decreased intranetwork functional connectivity (FC) within several RSNs compared to the HC group. The MOG-ON group presented significantly altered intranetwork FC in the medial visual network (mVN) and posterior default mode network (pDMN) compared with the seronegative-ON group. Compared with the HCs, the patient groups also presented abnormal internetwork FC between RSNs. In the dFNC analysis, the patient groups presented altered fractional occupancy and dwell times in states 1 and 5 compared with HCs, and the changes in state-related metrics were also distinct between the MOG-ON and seronegative-ON groups. In terms of ROC curve analysis, optimal diagnostic performance was achieved by combining static and dynamic approaches. Abnormal large-scale static and dynamic brain functional networks may help to better understand the neural mechanisms of MOG-ON and seronegative-ON and their differentiation.

The human insular cortex consists of functionally diverse subdivisions that engage during tasks ranging from interoception to cognitive control. The multiplicity of functions subserved by insular subdivisions calls for a nuanced investigation of their functional connectivity profiles. Four insula subdivisions (dorsal anterior, dAI; ventral, VI; posterior, PI; middle, MI) derived using a data-driven approach were subjected to static- and dynamic functional network connectivity (s-FNC and d-FNC) analyses. Static-FNC analyses replicated previous work demonstrating a cognition-emotion-interoception division of the insula, where the dAI is functionally connected to frontal areas, the VI to limbic areas, and the PI and MI to sensorimotor areas. Dynamic-FNC analyses consisted of k-means clustering of sliding windows to identify variable insula connectivity states. The d-FNC analysis revealed that the most frequently occurring dynamic state mirrored the cognition-emotion-interoception division observed from the s-FNC analysis, with less frequently occurring states showing overlapping and unique subdivision connectivity profiles. In two of the states, all subdivisions exhibited largely overlapping profiles, consisting of subcortical, sensory, motor, and frontal connections. Two other states showed the dAI exhibited a unique connectivity profile compared with other insula subdivisions. Additionally, the dAI exhibited the most variable functional connections across the s-FNC and d-FNC analyses, and was the only subdivision to exhibit dynamic functional connections with regions of the default mode network. These results highlight how a d-FNC approach can capture functional dynamics masked by s-FNC approaches, and reveal dynamic functional connections enabling the functional flexibility of the insula across time. Hum Brain Mapp 37:1770-1787, 2016. © 2016 Wiley Periodicals, Inc.

Introduction Disruptions in functional connectivity (FC) within the default mode network (DMN) are well established as a key neuropathology underlying cognitive impairment in type 2 diabetes mellitus (T2DM). Subcortical nuclei, including the basal forebrain (BF) and mediodorsal thalamus, play critical roles in regulating DMN-associated cognitive processes and are particularly vulnerable to hyperglycemia and brain insulin resistance. However, the specific FC patterns between these subcortical nuclei and DMN cortical regions in patients with T2DM, as well as their potential associations with cognitive impairment, remain incompletely elucidated. Methods Eighty-two patients with T2DM and 79 healthy controls (HCs) were enrolled in this study. Clinical data, neuropsychological assessments, and resting-state functional magnetic resonance imaging were collected from all participants. Resting-state (rs-FNC) and dynamic (dFNC) functional network connectivity analyses were performed to characterize connectivity between subcortical nuclei and DMN cortical regions. Correlation analyses explored associations between FNC metrics showing significant intergroup differences and participants’ clinical and cognitive parameters. Results rs-FNC analysis revealed decreased FC between the BF and the dorsomedial prefrontal cortex (dMPFC), the BF and the temporal pole, and the dMPFC and the anteromedial prefrontal cortex in patients with T2DM (network-based statistic correction; edge p &amp;lt; 0.001, component p &amp;lt; 0.05). dFNC analyses indicated increased frequency and prolonged mean dwell time (MDT) of State 1 (high-frequency low-connectivity), as well as decreased frequency and shortened MDT of State 2 (high-frequency high-connectivity) compared with HCs (all p &amp;lt; 0.05). Reduced FC between the dMPFC and BF was positively correlated with Montreal Cognitive Assessment scores ( r = 0.353, p = 0.001), whereas frequency ( r = −0.434, p &amp;lt; 0.001) and MDT ( r = −0.376, p = 0.001) of State 2 were negatively correlated with T2DM disease duration after Bonferroni correction. Conclusion These findings indicate that T2DM duration correlates with reduced highly efficient DMN connectivity, and that the BF may regulate cognitive function via the dMPFC subsystem. The results reveal temporal and functional specificity in abnormal DMN connectivity in patients with T2DM and enrich the neural atlas of DMN dysfunction in this population.