Static Functional Network Connectivity Research Articles

Altered static and dynamic functional brain network in knee osteoarthritis: A resting-state functional magnetic resonance imaging study: Static and dynamic FNC in KOA

This study aimed to investigate altered static and dynamic functional network connectivity (FNC) and its correlation with clinical symptoms in patients with knee osteoarthritis (KOA). One hundred and fifty-nine patients with KOA and 73 age- and gender-matched healthy subjects (HS) underwent resting-state functional magnetic resonance imaging (rs-fMRI) and clinical evaluations. Group independent component analysis (GICA) was applied, and seven resting-state networks were identified. Patients with KOA had decreased static FNC within the default mode network (DM), visual network (VS), and cerebellar network (CB) and increased static FNC between the subcortical network (SC) and VS (p < 0.05, FDR corrected). Four reoccurring FNC states were identified using k-means clustering analysis. Although abnormalities in dynamic FNCs of KOA patients have been found using the common window size (22 TR, 44 s), but the results of the clustering analysis were inconsistent when using different window sizes, suggesting dynamic FNCs might be an unstable method to compare brain function between KOA patients and HS. These recent findings illustrate that patients with KOA have a wide range of abnormalities in the static and dynamic FNCs, which provided a reference for the identification of potential central nervous therapeutic targets for KOA treatment and might shed light on the other musculoskeletal pain neuroimaging studies.

The overlap across psychotic disorders: A functional network connectivity analysis

Functional network connectivity (FNC) has previously been shown to distinguish patient groups from healthy controls (HC). However, the overlap across psychiatric disorders such as schizophrenia (SZ), bipolar (BP), and schizoaffective disorder (SAD) is not evident yet. This study focuses on studying the overlap across these three psychotic disorders in both dynamic and static FNC (dFNC/sFNC). We used resting-state fMRI, demographics, and clinical information from the Bipolar–Schizophrenia Network on Intermediate Phenotypes cohort (BSNIP). The data includes three groups of patients with schizophrenia (SZ, N = 181), bipolar (BP, N = 163), and schizoaffective (SAD, N = 130) and HC (N = 238) groups. After estimating each individual's dFNC, we group them into three distinct states. We evaluated two dFNC features, including occupancy rate (OCR) and distance travelled over time. Finally, the extracted features, including both sFNC and dFNC, are tested statistically across patients and HC groups. In addition, we explored the link between the clinical scores and the extracted features. We evaluated the connectivity patterns and their overlap among SZ, BP, and SAD disorders (false discovery rate or FDR corrected p < 0.05). Results showed dFNC captured unique information about overlap across disorders where all disorder groups showed similar pattern of activity in state 2. Moreover, the results showed similar patterns between SZ and SAD in state 1 which was different than BP. Finally, the distance travelled feature of SZ (average R = 0.245, p < 0.01) and combined distance travelled from all disorders was predictive of the PANSS symptoms scores (average R = 0.147, p < 0.01).

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

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

Non-alcoholic fatty liver disease is associated with brain function disruption in type 2 diabetes patients without cognitive impairment.

To investigate the neural static and dynamic intrinsic activity of intra-/inter-network topology among patients with type 2 diabetes (T2D) with non-alcoholic fatty liver disease (NAFLD) and those without NAFLD (T2NAFLD group and T2noNAFLD group, respectively) and to assess the relationship with metabolism. Fifty-six patients with T2NAFLD, 78 with T2noNAFLD, and 55 healthy controls (HCs) were recruited to the study. Participants had normal cognition and underwent functional magnetic resonance imaging scans, clinical measurements, and global cognition evaluation. Independent component analysis was used to identify frequency spectrum parameters, static functional network connectivity, and temporal properties of dynamic functional network connectivity (P < 0.05, false discovery rate-corrected). Statistical analysis involved one-way analysis of covariance with post hoc, partial correlation and canonical correlation analyses. Our findings showed that: (i) T2NAFLD patients had more disordered glucose and lipid metabolism, had more severe insulin resistance, and were more obese than T2noNAFLD patients; (ii) T2D patients exhibited disrupted brain function, as evidenced by alterations in intra-/inter-network topology, even without clinically measurable cognitive impairment; (iii) T2NAFLD patients had more significant reductions in the frequency spectrum parameters of cognitive executive and visual networks than those with T2noNAFLD; and (iv) altered brain function in T2D patients was correlated with postprandial glucose, high-density lipoprotein cholesterol, and waist-hip ratio. This study may provide novel insights into neuroimaging correlates for underlying pathophysiological processes inducing brain damage in T2NAFLD. Thus, controlling blood glucose levels, lipid levels and abdominal obesity may reduce brain damage risk in such patients.

Functional network connectivity patterns predicting the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease

BackgroundNeuro-navigated repetitive transcranial magnetic stimulation (rTMS) is potentially effective in enhancing cognitive performance in the spectrum of Alzheimer’s disease (AD). We explored the effect of rTMS-induced network reorganization and its predictive value for individual treatment response.MethodsSixty-two amnestic mild cognitive impairment (aMCI) and AD patients were recruited. These subjects were assigned to multimodal magnetic resonance imaging scanning before and after a 4-week stimulation. Then, we investigated the neural mechanism underlying rTMS treatment based on static functional network connectivity (sFNC) and dynamic functional network connectivity (dFNC) analyses. Finally, the support vector regression was used to predict the individual rTMS treatment response through these functional features at baseline.ResultsWe found that rTMS at the left angular gyrus significantly induced cognitive improvement in multiple cognitive domains. Participants after rTMS treatment exhibited significantly the increased sFNC between the right frontoparietal network (rFPN) and left frontoparietal network (lFPN) and decreased sFNC between posterior visual network and medial visual network. We revealed remarkable dFNC characteristics of brain connectivity, which was increased mainly in higher-order cognitive networks and decreased in primary networks or between primary networks and higher-order cognitive networks. dFNC characteristics in state 1 and state 4 could further predict individual higher memory improvement after rTMS treatment (state 1, R = 0.58; state 4, R = 0.54).ConclusionOur findings highlight that neuro-navigated rTMS could suppress primary network connections to compensate for higher-order cognitive networks. Crucially, dynamic regulation of brain networks at baseline may serve as an individualized predictor of rTMS treatment response.Relevance statementDynamic reorganization of brain networks could predict the efficacy of repetitive transcranial magnetic stimulation in the spectrum of Alzheimer’s disease.Key points• rTMS at the left angular gyrus could induce cognitive improvement.• rTMS could suppress primary network connections to compensate for higher-order networks.• Dynamic reorganization of brain networks could predict individual treatment response to rTMS.Graphical

Male and female are not the same: a multicenter study of static and dynamic functional connectivity in relapse-remitting multiple sclerosis in China.

Sex-related effects have been observed in relapsing-remitting multiple sclerosis (RRMS), but their impact on functional networks remains unclear. To investigate the sex-related differences in connectivity strength and time variability within large-scale networks in RRMS. This is a multi-center retrospective study. A total of 208 RRMS patients (135 females; 37.55 ± 11.47 years old) and 228 healthy controls (123 females; 36.94 ± 12.17 years old) were included. All participants underwent clinical and MRI assessments. Independent component analysis was used to extract resting-state networks (RSNs). We assessed the connectivity strength using spatial maps (SMs) and static functional network connectivity (sFNC), evaluated temporal properties and dynamic functional network connectivity (dFNC) patterns of RSNs using dFNC, and investigated their associations with structural damage or clinical variables. For static connectivity, only male RRMS patients displayed decreased SMs in the attention network and reduced sFNC between the sensorimotor network and visual or frontoparietal networks compared with healthy controls [P<0.05, false discovery rate (FDR) corrected]. For dynamic connectivity, three recurring states were identified for all participants: State 1 (sparse connected state; 42%), State 2 (middle-high connected state; 36%), and State 3 (high connected state; 16%). dFNC analyses suggested that altered temporal properties and dFNC patterns only occurred in females: female patients showed a higher fractional time (P<0.001) and more dwell time in State 1 (P<0.001) with higher transitions (P=0.004) compared with healthy females. Receiver operating characteristic curves revealed that the fraction time and mean dwell time of State 1 could significantly distinguish female patients from controls (area under the curve: 0.838-0.896). In addition, female patients with RRMS also mainly showed decreased dFNC in all states, particularly within cognitive networks such as the default mode, frontoparietal, and visual networks compared with healthy females (P < 0.05, FDR corrected). Our results observed alterations in connectivity strength only in male patients and time variability in female patients, suggesting that sex-related effects may play an important role in the functional impairment and reorganization of RRMS.

Network Differential in Gaussian Graphical Models from Multimodal Neuroimaging Data.

Multimodal brain network analysis has the potential to provide insights into the mechanisms of brain disorders. Most previous studies have analyzed either unimodal brain graphs or focused on local/global graphic metrics with little consideration of details of disrupted paths in the patient group. As we show, the combination of multimodal brain graphs and disrupted path-based analysis can be highly illuminating to recognize path-based disease biomarkers. In this study, we first propose a way to estimate multimodal brain graphs using static functional network connectivity (sFNC) and gray matter features using a Gaussian graphical model of schizophrenia versus controls. Next, applying the graph theory approach we identify disconnectors or connectors in the patient group graph that create additional paths or cause absent paths compared to the control graph. Results showed several edges in the schizophrenia group graph that trigger missing or additional paths. Identified edges associated with these disrupted paths were identified both within and between dFNC and gray matter which highlights the importance of considering multimodal studies and moving beyond pairwise edges to provide a more comprehensive understanding of brain disorders.Clinical Relevance- We identified a path-based biomarker in schizophrenia, by imitating the structure of paths in a multimodal (sMIR+fMRI) brain graph of the control group. Identified cross-modal edges associated with disrupted paths were related to the middle temporal gyrus and cerebellar regions.

Predictive signature of static and dynamic functional connectivity for ECT clinical outcomes.

Introduction: Electroconvulsive therapy (ECT) remains one of the most effective approaches for treatment-resistant depressive episodes, despite the potential cognitive impairment associated with this treatment. As a potent stimulator of neuroplasticity, ECT might normalize aberrant depression-related brain function via the brain's reconstruction by forming new neural connections. Multiple lines of evidence have demonstrated that functional connectivity (FC) changes are reliable indicators of antidepressant efficacy and cognitive changes from static and dynamic perspectives. However, no previous studies have directly ascertained whether and how different aspects of FC provide complementary information in terms of neuroimaging-based prediction of clinical outcomes. Methods: In this study, we implemented a fully automated independent component analysis framework to an ECT dataset with subjects (n = 50, age = 65.54 ± 8.92) randomized to three treatment amplitudes (600, 700, or 800 milliamperes [mA]). We extracted the static functional network connectivity (sFNC) and dynamic FNC (dFNC) features and employed a partial least square regression to build predictive models for antidepressant outcomes and cognitive changes. Results: We found that both antidepressant outcomes and memory changes can be robustly predicted by the changes in sFNC (permutation test p < 5.0 × 10-3). More interestingly, by adding dFNC information, the model achieved higher accuracy for predicting changes in the Hamilton Depression Rating Scale 24-item (HDRS24, t = 9.6434, p = 1.5 × 10-21). The predictive maps of clinical outcomes show a weakly negative correlation, indicating that the ECT-induced antidepressant outcomes and cognitive changes might be associated with different functional brain neuroplasticity. Discussion: The overall results reveal that dynamic FC is not redundant but reflects mechanisms of ECT that cannot be captured by its static counterpart, especially for the prediction of antidepressant efficacy. Tracking the predictive signatures of static and dynamic FC will help maximize antidepressant outcomes and cognitive safety with individualized ECT dosing.

The link between static and dynamic brain functional network connectivity and genetic risk of Alzheimer's disease

Apolipoprotein E (APOE) polymorphic alleles are genetic factors associated with Alzheimer’s disease (AD) risk. Although previous studies have explored the link between AD genetic risk and static functional network connectivity (sFNC), to the best of our knowledge, no previous studies have evaluated the association between dynamic FNC (dFNC) and AD genetic risk. Here, we examined the link between sFNC, dFNC, and AD genetic risk with a data-driven approach. We used rs-fMRI, demographic, and APOE data from cognitively normal individuals (N = 886) between 42 and 95 years of age (mean = 70 years). We separated individuals into low, moderate, and high-risk groups. Using Pearson correlation, we calculated sFNC across seven brain networks. We also calculated dFNC with a sliding window and Pearson correlation. The dFNC windows were partitioned into three distinct states with k-means clustering. Next, we calculated the proportion of time each subject spent in each state, called occupancy rate or OCR and frequency of visits. We compared both sFNC and dFNC features across individuals with different genetic risks and found that both sFNC and dFNC are related to AD genetic risk. We found that higher AD risk reduces within-visual sensory network (VSN) sFNC and that individuals with higher AD risk spend more time in a state with lower within-VSN dFNC. We also found that AD genetic risk affects whole-brain sFNC and dFNC in women but not men. In conclusion, we presented novel insights into the links between sFNC, dFNC, and AD genetic risk.

Exploring the longitudinal associations of functional network connectivity and psychiatric symptom changes in youth

BackgroundFunctional connectivity has been associated with psychiatric problems, both in children and adults, but inconsistencies are present across studies. Prior research has mostly focused on small clinical samples with cross-sectional designs. MethodsWe adopted a longitudinal design with repeated assessments to investigate associations between functional network connectivity (FNC) and psychiatric problems in youth (9- to 17-year-olds, two time points) from the general population. The largest single-site study of pediatric neurodevelopment was used: Generation R (N = 3,131 with data at either time point). Psychiatric symptoms were measured with the Child Behavioral Checklist as broadband internalizing and externalizing problems, and its eight specific syndrome scales (e.g., anxious-depressed). FNC was assessed with two complementary approaches. First, static FNC (sFNC) was measured with graph theory-based metrics. Second, dynamic FNC (dFNC), where connectivity is allowed to vary over time, was summarized into 5 states that participants spent time in. Cross-lagged panel models were used to investigate the longitudinal bidirectional relationships of sFNC with internalizing and externalizing problems. Similar cross-lagged panel models were run for dFNC. ResultsSmall longitudinal relationships between dFNC and certain syndrome scales were observed, especially for baseline syndrome scales (i.e., rule-breaking, somatic complaints, thought problems, and attention problems) predicting connectivity changes. However, no association between any of the psychiatric problems (broadband and syndrome scales) with either measure of FNC survived correction for multiple testing. ConclusionWe found no or very modest evidence for longitudinal associations between psychiatric problems with dynamic and static FNC in this population-based sample. Differences in findings may stem from the population drawn, study design, developmental timing, and sample sizes.

Impact of low-frequency repetitive transcranial magnetic stimulation on functional network connectivity in schizophrenia patients with auditory verbal hallucinations

Auditory verbal hallucinations (AVH) are a key symptom of schizophrenia. Low-frequency repetitive transcranial magnetic stimulation (rTMS) has shown potential in the treatment of AVH. However, the underlying neural mechanismof rTMS in the treatment of AVH remains largely unknown. In this study, we used a static and dynamic functional network connectivity approach to investigate the connectivity changes among the brain functional networks in schizophrenia patients with AVH receiving 1 Hz rTMS treatment. The static functional network connectivity (sFNC) analysis revealed that patients at baseline had significantly decreased connectivity between the default mode network (DMN) and language network (LAN), and within the executive control network (ECN) as well as within the auditory network (AUD) compared to controls. However, the abnormal network connectivity patterns were normalized or restored after rTMS treatment in patients, instead of increased connectivity between the ECN and LAN, as well as within the AUD. Moreover, the dynamic functional network connectivity (dFNC) analysis showed that the patients at baseline spent more time in this state that was characterized by strongly negative connectivity between the ENC and AUD, as well as within the AUD relative to controls. While after rTMS treatment, the patients showed a higher occurrence rate in this state that was characterized by strongly positive connectivity among the LAN, DMN, and ENC, as well as within the ECN. In addition, the altered static and dynamic connectivity properties were associated with reduced severity of clinical symptoms. Both sFNC and dFNC analyses provided complementary information and suggested that low-frequency rTMS treatment could induce intrinsic functional network alternations and contribute to improvements in clinical symptoms in patients with AVH.

Alterations of static and dynamic functional network connectivity in acute ischemic brainstem stroke.

Prior studies have shown abnormal brain functional network changes in patients with acute ischemic stroke. However, the alterations of dynamic functional network connectivity (FNC) in brainstem strokes have not been elucidated. To assess alterations of static and dynamic FNCs and determine the relationships between these and upper limb movement performance in patients with acute brainstem ischemic stroke. In total, 50 patients with acute brainstem ischemic stroke and 50 age- and sex-matched healthy controls were enrolled in the present study and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Independent component analysis was conducted to assess static and dynamic FNC patterns based on seven resting-state networks, namely, the default mode network (DMN), executive control network (ECN), attention network (AN), somatomotor network (SMN), visual network (VN), auditory network (AUN), and cerebellum network (CN). Compared with controls, patients with acute brainstem ischemic stroke exhibited wide aberrations of static FNC, including increased FNC in DMN-ECN, DMN-VN, ECN-VN, ECN-AN and AN-AUN pairs. Patients with acute brainstem ischemic stroke showed aberrant dynamic FNC in State 1, involving increased FNC aberrance in the DMN with AN, DMN with ECN, and reduced FNC in SMN-VN pairs. In State 5, patients with acute brainstem ischemic stroke showed increased FNC in DMN-VN and AN-AUN, and decreased FNC in AN-SMN pairs. This study suggests that static and dynamic FNC impairment and aberrant connections exist in acute brainstem ischemic stroke, which expands what is known regarding the relationship between stroke and FNC from static and dynamic perspectives.

Altered static functional network connectivity predicts the efficacy of non-steroidal anti-inflammatory drugs in migraineurs without aura.

Brain networks have significant implications for the understanding of migraine pathophysiology and prognosis. This study aimed to investigate whether large-scale network dysfunction in patients with migraine without aura (MwoA) could predict the efficacy of non-steroidal anti-inflammatory drugs (NSAIDs). Seventy patients with episodic MwoA and 33 healthy controls (HCs) were recruited. Patients were divided into MwoA with effective NSAIDs (M-eNSAIDs) and with ineffective NSAIDs (M-ieNSAIDs). Group-level independent component analysis and functional network connectivity (FNC) analysis were used to extract intrinsic networks and detect dysfunction among these networks. The clinical characteristics and FNC abnormalities were considered as features, and a support vector machine (SVM) model with fivefold cross-validation was applied to distinguish the subjects at an individual level. Dysfunctional connections within seven networks were observed, including default mode network (DMN), executive control network (ECN), salience network (SN), sensorimotor network (SMN), dorsal attention network (DAN), visual network (VN), and auditory network (AN). Compared with M-ieNSAIDs and HCs, patients with M-eNSAIDs displayed reduced DMN-VN and SMN-VN, and enhanced VN-AN connections. Moreover, patients with M-eNSAIDs showed increased FNC patterns within ECN, DAN, and SN, relative to HCs. Higher ECN-SN connections than HCs were revealed in patients with M-ieNSAIDs. The SVM model demonstrated that the area under the curve, sensitivity, and specificity were 0.93, 0.88, and 0.89, respectively. The widespread FNC impairment existing in the modulation of medical treatment suggested FNC disruption as a biomarker for advancing the understanding of neurophysiological mechanisms and improving the decision-making of therapeutic strategy.

Mind the gap: functional network connectivity interpolation between schizophrenia patients and controls using a variational autoencoder.

Mental disorders such as schizophrenia have been challenging to characterize due in part to their heterogeneous presentation in individuals. Most studies have focused on identifying groups differences and have typically ignored the heterogeneous patterns within groups. Here we propose a novel approach based on a variational autoencoder (VAE) to interpolate static functional network connectivity (sFNC) across individuals, with group-specific patterns between schizophrenia patients and controls captured simultaneously. We then visualize the original sFNC in a 2D grid according to the samples in the VAE latent space. We observe a high correspondence between the generated and the original sFNC. The proposed framework facilitates data visualization and can potentially be applied to predict the stage that a subject falls within a disorder continuum as well as characterize individual heterogeneity within and between groups.

Static and Dynamic Characteristics of Functional Network Connectivity in Neurologically Asymptomatic Patients Undergoing Maintenance Hemodialysis: A Resting-State Functional MRI Study.

The characteristics of static functional network connectivity (sFNC) and dynamic FNC (dFNC) in neurologically asymptomatic patients undergoing maintenance hemodialysis are unknown. Elucidating these characteristics may improve our understanding of the mechanisms of neuropathological damage in these patients. To explore the static and dynamic characteristics of FNC in neurologically asymptomatic patients undergoing maintenance hemodialysis and the relationship between FNC-related parameters with the neuropsychological scores and blood biomarkers. Retrospective. A total of 23 neurologically asymptomatic patients undergoing maintenance hemodialysis and 25 healthy controls matched for age, sex, and years of education. A 3.0 T MRI/functional MRI and three-dimensional-T1 structural imaging ASSESSMENT: Independent components; spatial map intensity; sFNC and dFNC strengths; and time attribute parameters (mean dwell time, fractional window, and number of transitions) were determined. Neuropsychological tests were performed. Blood biochemical tests were performed for the patients but not healthy controls. Chi-squared test, one-sample t-test, two-sample t-test, partial correlation analysis, and family-wise error and false discovery rate correction. P < 0.05 denoted statistical significance. Significant group differences in the strengths of sFNC and dFNC between networks were found. The sFNC strength between the visual and sensorimotor networks was significantly associated with the global cognitive function score (i.e. the Montreal Cognitive Assessment [MoCA]) (r=0.606). The sFNC strength between the salience and default mode networks was significantly associated with anxiety scores (r=0.458). In state 1, positive correlations were found between the mean dwell time and backward digital span task score (r =0.562), fractional window and MoCA score (r=0.576), and fractional window and backward digital span task score (r=0.592). Neurologically asymptomatic patients undergoing maintenance hemodialysis had defective sFNC and dFNC. Our results provide a new perspective on the mechanism of neuropathological damage in patients undergoing maintenance hemodialysis. 1 TECHNICAL EFFICACY: Stage 1.

Aberrant static and dynamic functional network connectivity in heart failure with preserved ejection fraction.

AimsHeart failure may lead to brain functional alterations related to cognitive impairment. This study aimed to detect alterations of static functional network connectivity (FNC) and dynamic FNC in heart failure with preserved ejection fraction (HFpEF) and to estimate the association between the altered FNC and clinical features related to HFpEF.Methods and resultsThe clinical and resting‐state functional magnetic resonance imaging (fMRI) data of HFpEF patients (n = 35) and healthy controls (HCs) (n = 35) were acquired at baseline. Resting‐state networks (RSNs) were established based on independent component analysis (ICA) and FNC analyses were performed. The associations between the FNC abnormalities and clinical features related to HFpEF were analysed. Compared with HCs, HFpEF patients showed decreased functional connectivity within the default mode network, left frontoparietal network, and right frontoparietal network and increased functional connectivity within the right frontoparietal network and visual network. Negative correlations were observed between decreased dynamic FNC and the left ventricular end‐diastolic diameter (LVDd) (r = −0.435, P = 0.015) as well as the left ventricular end‐systolic diameter (LVDs) (r = −0.443, P = 0.013).ConclusionsThe FNC disruption and altered temporal properties of functional dynamics in HFpEF patients may reflect the neural mechanisms of brain injury after HFpEF, which may deepen our understanding of the disease.

An attention-based hybrid deep learning framework integrating brain connectivity and activity of resting-state functional MRI data.

Functional magnetic resonance imaging (fMRI) as a promising tool to investigate psychotic disorders can be decomposed into useful imaging features such as time courses (TCs) of independent components (ICs) and functional network connectivity (FNC) calculated by TC cross-correlation. TCs reflect the temporal dynamics of brain activity and the FNC characterizes temporal coherence across intrinsic brain networks. Both features have been used as input to deep learning approaches with decent results. However, few studies have tried to leverage their complementary information to learn optimal representations at multiple facets. Motivated by this, we proposed a Hybrid Deep Learning Framework integrating brain Connectivity and Activity (HDLFCA) together by combining convolutional recurrent neural network (C-RNN) and deep neural network (DNN), aiming to improve classification accuracy and interpretability simultaneously. Specifically, C-RNNAM was proposed to extract temporal dynamic dependencies with an attention module (AM) to automatically learn discriminative knowledge from TC nodes, while DNN was applied to identify the most group-discriminative FNC patterns with layer-wise relevance propagation (LRP). Then, both prediction outputs were concatenated to build a new feature matrix, generating the final decision by logistic regression. The effectiveness of HDLFCA was validated on both multi-site schizophrenia (SZ, n ∼ 1100) and public autism datasets (ABIDE, n ∼ 1522) by outperforming 12 alternative models at 2.8-8.9% accuracy, including 8 models using either static FNC or TCs and 4 models using dynamic FNC. Appreciable classification accuracy was achieved for HC vs. SZ (85.3%) and HC vs. Autism (72.4%) respectively. More importantly, the most group-discriminative brain regions can be easily attributed and visualized, providing meaningful biological interpretability and highlighting the great potential of the proposed HDLFCA model in the identification of valid neuroimaging biomarkers.

The link between brain functional network connectivity and genetic risk of Alzheimer's disease

AbstractBackgroundIndividuals carrying the ɛ4 allele have the highest risk of Alzheimer’s disease (AD) compared with those carrying ɛ3 and ɛ2 allele, whereas ɛ2 allele has the lowest risk. Although previous studies explored the link between the genetic risk of AD and static functional network connectivity (sFNC), limited studies have evaluated the association between dynamic FNC (dFNC) and AD risk. Here, we explore how the dFNC differs between individuals with genetic risk for AD.MethodWe used rs‐fMRI data of 991 healthy brains (404 females) and their demographic information from the Open Access Series of Imaging Studies‐3 cohort. The participants' age at scanning time was ranging from 42.46 to 95.39, with a mean of 69.81. We put the data into three groups including group1 (N=135, 63 females) including subjects with ɛ2 allele (i.e., ɛ2/ ɛ2, ɛ2/ ɛ3, and ɛ3/ɛ2), group2 (N=558, 219 females) including subjects with only ɛ3 allele (i.e., ɛ3/ ɛ3), and group3 (N=298, 122 females) including subjects with ɛ4 allele (i.e., ɛ3/ ɛ4, ɛ4/ ɛ3, and ɛ4/ɛ4). Age and gender were not significantly different across groups. Group‐ICA was used to extract 53 components. The sliding window and Pearson correlation were used to measure the dFNC among components K‐means algorithm was applied to partition dFNC windows into three distinct states. We calculated each subject's occupancy rate (OCR) in each state. A two‐sample t‐test was used to compare the OCR of groups in each state (Fig. 1).ResultSubject with a lower AD risk spend more time in state1 with more positive connectivity within cognitive control network (CCN) and between CCN and sensory network (Fig. 2A and Fig. 2B: pcorrected&lt;0.05). Interestingly, in this state, the difference of OCR among subjects with different AD risk was more significant in females (Fig. 2C), while males did not show any significant difference in their OCR across three groups (Fig. 2D). Females with higher AD risk had more OCR in state 3 with relatively lower within CCN connectivity.ConclusionResults support the use of dFNC features as a potential biomarker of AD genetic risk for females. We also showed the role of CCN connectivity associated with the AD risk.

Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder.

Background: Major depressive disorder (MDD) is a severe mental illness marked by a continuous sense of sadness and a loss of interest. The default mode network (DMN) is a group of brain areas that are more active during rest and deactivate when engaged in task-oriented activities. The DMN of MDD has been found to have aberrant static functional network connectivity (FNC) in recent studies. In this work, we extend previous findings by evaluating dynamic functional network connectivity (dFNC) within the DMN subnodes in MDD. Methods: We analyzed resting-state functional magnetic resonance imaging data of 262 patients with MDD and 277 healthy controls (HCs). We estimated dFNCs for seven subnodes of the DMN, including the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and precuneus (PCu), using a sliding window approach, and then clustered the dFNCs into five brain states. Classification of MDD and HC subjects based on state-specific FC was performed using a logistic regression classifier. Transition probabilities between dFNC states were used to identify relationships between symptom severity and dFNC data in MDD patients. Results: By comparing state-specific FNC between HC and MDD, a disrupted connectivity pattern was observed within the DMN. In more detail, we found that the connectivity of ACC is stronger, and the connectivity between PCu and PCC is weaker in individuals with MDD than in those of HC subjects. In addition, MDD showed a higher probability of transitioning from a state with weaker ACC connectivity to a state with stronger ACC connectivity, and this abnormality is associated with symptom severity. This is the first research to look at the dFC of the DMN in MDD with a large sample size. It provides novel evidence of abnormal time-varying DMN configuration in MDD and offers links to symptom severity in MDD subjects. Impact Statement This study is the first attempt that explored the temporal change on default mode network (DMN) connectivity in a relatively large cohort of patients with major depressive disorder (MDD). We also introduced a new hypothesis that explains the inconsistency in DMN functional network connectivity (FNC) comparison between MDD and healthy control based on static FNC in the previous literature. Additionally, our findings suggest that within anterior cingulate cortex connectivity and the connectivity between the precuneus and posterior cingulate cortex are the potential biomarkers for the future intervention of MDD.

Sleep Quality Modulates the Association between Dynamic Functional Network Connectivity and Cognitive Function in Healthy Older Adults

Aging is associated with changes in sleep, brain activity, and cognitive function, as well as the association among these factors; however, the precise nature of these changes has not been elucidated. This study systematically investigated the modulatory effect of sleep on the relationship between brain functional network connectivity (FNC) and cognitive function in older adults. In total, 107 community-dwelling healthy older adults were recruited and assigned into poor sleep and good sleep groups based on the Pittsburgh Sleep Quality Index. The static functional network connectivity (sFNC), the temporal variability of dynamic FNC (dFNC) from variance (dFNC-var), and the dFNC from clustering state (dFNC-state) were calculated. Corresponding cognition-predictive models were constructed for each sleep group. dFNC but not sFNC, was able to significantly predict the cognitive function in older adults. Specifically, sleep played a modulatory role in the association between dFNC and cognitive function, with sleep-specific variations at both microscopic (i.e., specific edges) and macroscopic levels (i.e., specific states) of dFNC.