Functional Connectivity Features Research Articles

Altered resting-state cerebellar-cerebral functional connectivity in patients with panic disorder before and after treatment

The study aimed to investigate the functional connectivity (FC) between the cerebellum and intrinsic cerebral networks in patients with panic disorder (PD), and to observe changes in the cerebellar-cerebral FC following pharmacotherapy. Fifty-four patients with PD and 54 healthy controls (HCs) underwent clinical assessments and functional magnetic resonance imaging scans before and after a 5-week paroxetine treatment. Seed-based cerebellar-cerebral FC was compared between the PD and HC groups, as well as between patients with PD before and after treatment. Additionally, the correlations between FC and clinical features of PD were analyzed. Compared to HCs, patients with PD had altered cerebellar-cerebral FC in the default mode, affective-limbic, and sensorimotor networks. Moreover, a negative correlation between cerebellar-insula disconnection and the severity of depressive symptoms in patients with PD (Pearson correlation, r = −0.424, p = 0.001, Bonferroni corrected) was found. After treatment, most of the enhanced FCs observed in patients with PD at baseline returned to levels similar to those observed in HCs. However, the reduced FC at baseline did not significantly change after treatment. The findings suggest that patients with PD have specific deficits in resting-state cerebellar-cerebral FC and that paroxetine may improve PD by restoring the balance of cerebellar-cerebral FC. These findings emphasize the crucial involvement of cerebellar-cerebral FC in the neuropsychological mechanisms underlying PD and in the potential pharmacological mechanisms of paroxetine for treating PD.

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.

A novel approach for cross-subject unfavorable driving state detection by cooperation between phase-based functional connectivity and convolutional neural network

Driving under unfavorable driving state (UDS) is the primary factor inducing the soaring incidence of road accidents. Recently, Electroencephalography (EEG) with high time resolution is regarded as the “gold standard” for identifying drivers' UDS because it directly reflects the neural activity of human brain. However, EEG recordings are heavily influenced by individuals, resulting in poor generality and fair recognition performance on unseen subject. To handle the issue, an EEG-based cross-subject UDS detection framework is proposed, which combines model-driven and data-driven approaches. Concretely, EEG recordings collected from the driving simulation are first decomposed into six classical frequency bands. Interactive relation of all EEG channels is estimated by the phase transfer entropy (PTE). Subsequently, a novel meta-heuristic algorithm called equilibrium optimizer (EO) is employed as a PTE-based functional connectivity feature selection method. A customized 14-layer CNN model is further developed to extract hidden feature from PTE-EO-based functional connectivity and to perform UDS and NUDS classification tasks. Statistical analysis demonstrates that equilibrium optimizer significantly outperforms binary dragonfly algorithm (BDA) and whale optimization algorithm (WOA) in terms of UDS detection. The best recognition outcome is achieved using PTE-EO-CNN model, yielding a mean ACC of 90.19 %, a mean PRE of 89.20 %, a mean SEN of 91.75 %, and a mean SPE of 88.63 % on 16 subjects using LOSO-CV strategy. Conclusively, our findings suggest that PTE-EO-CNN is a promising framework with excellence reliability and generalizability, which has a new perspective and practical significance to solve the road safety issue.

Multiscale effects of excitatory-inhibitory homeostasis in lesioned cortical networks: A computational study.

Stroke-related disruptions in functional connectivity (FC) often spread beyond lesioned areas and, given the localized nature of lesions, it is unclear how the recovery of FC is orchestrated on a global scale. Since recovery is accompanied by long-term changes in excitability, we propose excitatory-inhibitory (E-I) homeostasis as a driving mechanism. We present a large-scale model of the neocortex, with synaptic scaling of local inhibition, showing how E-I homeostasis can drive the post-lesion restoration of FC and linking it to changes in excitability. We show that functional networks could reorganize to recover disrupted modularity and small-worldness, but not network dynamics, suggesting the need to consider forms of plasticity beyond synaptic scaling of inhibition. On average, we observed widespread increases in excitability, with the emergence of complex lesion-dependent patterns related to biomarkers of relevant side effects of stroke, such as epilepsy, depression and chronic pain. In summary, our results show that the effects of E-I homeostasis extend beyond local E-I balance, driving the restoration of global properties of FC, and relating to post-stroke symptomatology. Therefore, we suggest the framework of E-I homeostasis as a relevant theoretical foundation for the study of stroke recovery and for understanding the emergence of meaningful features of FC from local dynamics.

Reduced loss aversion in value-based decision-making and edge-centric functional connectivity in patients with internet gaming disorder.

Impaired value-based decision-making is a feature of substance and behavioral addictions. Loss aversion is a core of value-based decision-making and its alteration plays an important role in addiction. However, few studies explored it in internet gaming disorder patients (IGD). In this study, IGD patients (PIGD) and healthy controls (Con-PIGD) performed the Iowa gambling task (IGT), under functional magnetic resonance imaging (fMRI). We investigated group differences in loss aversion, brain functional networks of node-centric functional connectivity (nFC) and the overlapping community features of edge-centric functional connectivity (eFC) in IGT. PIGD performed worse with lower average net score in IGT. The computational model results showed that PIGD significantly reduced loss aversion. There was no group difference in nFC. However, there were significant group differences in the overlapping community features of eFC1. Furthermore, in Con-PIGD, loss aversion was positively correlated with the edge community profile similarity of the edge2 between left IFG and right hippocampus at right caudate. This relationship was suppressed by response consistency3 in PIGD. In addition, reduced loss aversion was negatively correlated with the promoted bottom-to-up neuromodulation from the right hippocampus to the left IFG in PIGD. The reduced loss aversion in value-based decision making and their related edge-centric functional connectivity support that the IGD showed the same value-based decision-making deficit as the substance use and other behavioral addictive disorders. These findings may have important significance for understanding the definition and mechanism of IGD in the future.

Multi-modal and multi-model interrogation of large-scale functional brain networks

Existing whole-brain models are generally tailored to the modelling of a particular data modality (e.g., fMRI or MEG/EEG). We propose that despite the differing aspects of neural activity each modality captures, they originate from shared network dynamics. Building on the universal principles of self-organising delay-coupled nonlinear systems, we aim to link distinct features of brain activity - captured across modalities - to the dynamics unfolding on a macroscopic structural connectome.To jointly predict connectivity, spatiotemporal and transient features of distinct signal modalities, we consider two large-scale models - the Stuart Landau and Wilson and Cowan models - which generate short-lived 40 Hz oscillations with varying levels of realism. To this end, we measure features of functional connectivity and metastable oscillatory modes (MOMs) in fMRI and MEG signals - and compare them against simulated data.We show that both models can represent MEG functional connectivity (FC), functional connectivity dynamics (FCD) and generate MOMs to a comparable degree. This is achieved by adjusting the global coupling and mean conduction time delay and, in the WC model, through the inclusion of balance between excitation and inhibition. For both models, the omission of delays dramatically decreased the performance. For fMRI, the SL model performed worse for FCD and MOMs, highlighting the importance of balanced dynamics for the emergence of spatiotemporal and transient patterns of ultra-slow dynamics. Notably, optimal working points varied across modalities and no model was able to achieve a correlation with empirical FC higher than 0.4 across modalities for the same set of parameters. Nonetheless, both displayed the emergence of FC patterns that extended beyond the constraints of the anatomical structure. Finally, we show that both models can generate MOMs with empirical-like properties such as size (number of brain regions engaging in a mode) and duration (continuous time interval during which a mode appears).Our results demonstrate the emergence of static and dynamic properties of neural activity at different timescales from networks of delay-coupled oscillators at 40 Hz. Given the higher dependence of simulated FC on the underlying structural connectivity, we suggest that mesoscale heterogeneities in neural circuitry may be critical for the emergence of parallel cross-modal functional networks and should be accounted for in future modelling endeavours.

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

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

Simple topological task-based functional connectivity features predict longitudinal behavioral change of fluid reasoning in the RANN cohort

Recent attention has been given to topological data analysis (TDA), and more specifically persistent homology (PH), to identify the underlying shape of brain network connectivity beyond simple edge pairings by computing connective components across different connectivity thresholds (see Sizemore et al., 2019). In the present study, we applied PH to task-based functional connectivity, computing 0-dimension Betti (B0) curves and calculating the area under these curves (AUC); AUC indicates how quickly a single connected component is formed across correlation filtration thresholds, with lower values interpreted as potentially analogous to lower whole-brain system segregation (e.g., Gracia-Tabuenca et al., 2020). One hundred sixty-three participants from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (age 20–80 years) were tested in-scanner at baseline and five-year follow-up on a battery of tests comprising four domains of cognition (i.e., Stern et al., 2014). We tested for 1.) age-related change in the AUC of the B0 curve over time, 2.) the predictive utility of AUC in accounting for longitudinal change in behavioral performance and 3.) compared system segregation to the PH approach. Results demonstrated longitudinal age-related decreases in AUC for Fluid Reasoning, with these decreases predicting longitudinal declines in cognition, even after controlling for demographic and brain integrity factors; moreover, change in AUC partially mediated the effect of age on change in cognitive performance. System segregation also significantly decreased with age in three of the four cognitive domains but did not predict change in cognition. These results argue for greater application of TDA to the study of aging.

Distinct hemodynamic and functional connectivity features of fatigue in clinically isolated syndrome and multiple sclerosis: accounting for the confounding effect of concurrent depression symptoms

PurposeThis study aims to identify common and distinct hemodynamic and functional connectivity (FC) features for self-rated fatigue and depression symptoms in patients with clinically isolated syndrome (CIS) and relapsing–remitting multiple sclerosis (RR-MS).MethodsTwenty-four CIS, 29 RR-MS patients, and 39 healthy volunteers were examined using resting-state fMRI (rs-fMRI) to obtain whole-brain maps of (i) hemodynamic response patterns (through time shift analysis), (ii) FC (via intrinsic connectivity contrast maps), and (iii) coupling between hemodynamic response patterns and FC. Each regional map was correlated with fatigue scores, controlling for depression, and with depression scores, controlling for fatigue.ResultsIn CIS patients, the severity of fatigue was associated with accelerated hemodynamic response in the insula, hyperconnectivity of the superior frontal gyrus, and evidence of reduced hemodynamics–FC coupling in the left amygdala. In contrast, depression severity was associated with accelerated hemodynamic response in the right limbic temporal pole, hypoconnectivity of the anterior cingulate gyrus, and increased hemodynamics–FC coupling in the left amygdala. In RR-MS patients, fatigue was associated with accelerated hemodynamic response in the insula and medial superior frontal cortex, increased functional role of the left amygdala, and hypoconnectivity of the dorsal orbitofrontal cortex, while depression symptom severity was linked to delayed hemodynamic response in the medial superior frontal gyrus; hypoconnectivity of the insula, ventromedial thalamus, dorsolateral prefrontal cortex, and posterior cingulate; and decreased hemodynamics–FC coupling of the medial orbitofrontal cortex.ConclusionThere are distinct FC and hemodynamic responses, as well as different magnitude and topography of hemodynamic connectivity coupling, associated with fatigue and depression in early and later stages of MS.

Kernel-based Nonlinear Manifold Learning for EEG-based Functional Connectivity Analysis and Channel Selection with Application to Alzheimer’s Disease

Dynamical, causal, and cross-frequency coupling analysis using the electroencephalogram (EEG) has gained significant attention for diagnosing and characterizing neurological disorders. Selecting important EEG channels is crucial for reducing computational complexity in implementing these methods and improving classification accuracy. In neuroscience, measures of (dis) similarity between EEG channels are often used as functional connectivity (FC) features, and important channels are selected via feature selection. Developing a generic measure of (dis) similarity is important for FC analysis and channel selection. In this study, learning of (dis) similarity information within the EEG is achieved using kernel-based nonlinear manifold learning. The focus is on FC changes and, thereby, EEG channel selection. Isomap and Gaussian Process Latent Variable Model (Isomap-GPLVM) are employed for this purpose. The resulting kernel (dis) similarity matrix is used as a novel measure of linear and nonlinear FC between EEG channels. The analysis of EEG from healthy controls (HC) and patients with mild to moderate Alzheimer’s disease (AD) are presented as a case study. Classification results are compared with other commonly used FC measures. Our analysis shows significant differences in FC between bipolar channels of the occipital region and other regions (i.e. parietal, centro-parietal, and fronto-central) between AD and HC groups. Furthermore, our results indicate that FC changes between channels along the fronto-parietal region and the rest of the EEG are important in diagnosing AD. Our results and its relation to functional networks are consistent with those obtained from previous studies using fMRI, resting-state fMRI and EEG.

Intrusive Traumatic Re-Experiencing Domain: Functional Connectivity Feature Classification by the ENIGMA PTSD Consortium

BackgroundIntrusive traumatic re-experiencing domain (ITRED) was recently introduced as a novel perspective on posttraumatic psychopathology, proposing to focus research of posttraumatic stress disorder (PTSD) on the unique symptoms of intrusive and involuntary re-experiencing of the trauma, namely, intrusive memories, nightmares, and flashbacks. The aim of the present study was to explore ITRED from a neural network connectivity perspective. MethodsData were collected from 9 sites taking part in the ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) PTSD Consortium (n= 584) and included itemized PTSD symptom scores and resting-state functional connectivity (rsFC) data. We assessed the utility of rsFC in classifying PTSD, ITRED-only (no PTSD diagnosis), and trauma-exposed (TE)–only (no PTSD or ITRED) groups using a machine learning approach, examining well-known networks implicated in PTSD. A random forest classification model was built on a training set using cross-validation, and the averaged cross-validation model performance for classification was evaluated using the area under the curve. The model was tested using a fully independent portion of the data (test dataset), and the test area under the curve was evaluated. ResultsrsFC signatures differentiated TE-only participants from PTSD and ITRED-only participants at about 60% accuracy. Conversely, rsFC signatures did not differentiate PTSD from ITRED-only individuals (45% accuracy). Common features differentiating TE-only participants from PTSD and ITRED-only participants mainly involved default mode network–related pathways. Some unique features, such as connectivity within the frontoparietal network, differentiated TE-only participants from one group (PTSD or ITRED-only) but to a lesser extent from the other group. ConclusionsNeural network connectivity supports ITRED as a novel neurobiologically based approach to classifying posttrauma psychopathology.

Diagnosis of Chronic Musculoskeletal Pain by Using Functional Near-Infrared Spectroscopy and Machine Learning.

Chronic pain (CP) has been found to cause significant alternations of the brain's structure and function due to changes in pain processing and disrupted cognitive functions, including with respect to the prefrontal cortex (PFC). However, until now, no studies have used a wearable, low-cost neuroimaging tool capable of performing functional near-infrared spectroscopy (fNIRS) to explore the functional alternations of the PFC and thus automatically achieve a clinical diagnosis of CP. In this case-control study, the pain characteristics of 19 chronic pain patients and 32 healthy controls were measured using fNIRS. Functional connectivity (FC), FC in the PFC, and spontaneous brain activity of the PFC were examined in the CP patients and compared to those of healthy controls (HCs). Then, leave-one-out cross-validation and machine learning algorithms were used to automatically achieve a diagnosis corresponding to a CP patient or an HC. The current study found significantly weaker FC, notably higher small-worldness properties of FC, and increased spontaneous brain activity during resting state within the PFC. Additionally, the resting-state fNIRS measurements exhibited excellent performance in identifying the chronic pain patients via supervised machine learning, achieving F1 score of 0.8229 using only seven features. It is expected that potential FC features can be identified, which can thus serve as a neural marker for the detection of CP using machine learning algorithms. Therefore, the present study will open a new avenue for the diagnosis of chronic musculoskeletal pain by using fNIRS and machine learning techniques.

Reproducibility analysis of functional connectivity measures for application in motor imagery BCIs

Electroencephalography (EEG)-based motor imagery brain-computer interfaces (MI-BCIs) can improve motor rehabilitation processes. Nevertheless, the large variability of intra and inter-subject EEG signals has precluded translation of this technology to the clinical setting. The aim of this work was to evaluate the reproducibility of EEG functional connectivity (FC) features used to discriminate between left- and right-hand MI. Ten subjects underwent 12 EEG-MI-BCI sessions. Two frequency bands, three FC methods, four graph parameters and six electrode sites were evaluated, using statistical and classification analyses. The β band produced the largest number of statistically significantly discriminating and most stable features for the majority of subjects, and also the best classification features, suggesting that engagement of needed brain regions may be more important for stability and distinguishability among MI tasks than inhibition of unneeded cortical regions. The Cz electrode stood out in terms of largest number of statistically significantly discriminating features. The motif synchronization (MS) method produced the largest number of significantly discriminating features, the most stable, most discriminating features and best classification features for most subjects. Since this method ignores amplitude changes, this seems to indicate that signal variation patterns are more important for feature stability and class separability. Eigenvector centrality (EC) was the most stable graph parameter for most subjects while both EC and strength (S) were the most discriminating. In summary, using features from the MS method, β band, EC and/or S parameters, and central electrodes (Cz), result in the best combination for the task of distinguishing left from right-hand MI.

Antidepressant-induced mania in panic disorder: a single-case study of clinical and functional connectivity characteristics.

Mental health issues, including panic disorder (PD), are prevalent and often co-occur with anxiety and bipolar disorders. While panic disorder is characterized by unexpected panic attacks, and its treatment often involves antidepressants, there is a 20-40% risk of inducing mania (antidepressant-induced mania) during treatment, making it crucial to understand mania risk factors. However, research on clinical and neurological characteristics of patients with anxiety disorders who develop mania is limited. In this single case study, we conducted a larger prospective study on panic disorder, comparing baseline data between one patient who developed mania (PD-manic) and others who did not (PD-NM group). We enrolled 27 patients with panic disorder and 30 healthy controls (HCs) and examined alterations in amygdala-based brain connectivity using a seed-based whole-brain approach. We also performed exploratory comparisons with healthy controls using ROI-to-ROI analyses and conducted statistical inferences at a threshold of cluster-level family-wise error-corrected p < 0.05, with the cluster-forming threshold at the voxel level of uncorrected p < 0.001. The patient with PD-mania showed lower connectivity in brain regions related to the default mode network (left precuneous cortex, maximum z-value within the cluster = -6.99) and frontoparietal network (right middle frontal gyrus, maximum z-value within the cluster = -7.38; two regions in left supramarginal gyrus, maximum z-value within the cluster = -5.02 and -5.86), and higher in brain regions associated with visual processing network (right lingual gyrus, maximum z-value within the cluster = 7.86; right lateral occipital cortex, maximum z-value within the cluster = 8.09; right medial temporal gyrus, maximum z-value within the cluster = 8.16) in the patient with PD-mania compared to the PD-NM group. One significantly identified cluster, the left medial temporal gyrus (maximum z-value within the cluster = 5.82), presented higher resting-state functional connectivity with the right amygdala. Additionally, ROI-to-ROI analysis revealed that significant clusters between PD-manic and PD-NM groups differed from HCs in the PD-manic group but not in the PD-NM group. Here, we demonstrate altered amygdala-DMN and amygdala-FPN connectivity in the PD-manic patient, as reported in bipolar disorder (hypo) manic episodes. Our study suggests that amygdala-based resting-state functional connectivity could serve as a potential biomarker for antidepressant-induced mania in panic disorder patients. Our findings provide an advance in understanding the neurological basis of antidepressant-induced mania, but further research with larger cohorts and more cases is necessary for a broader perspective on this issue.

Reliability of self-reported dispositional mindfulness scales and their association with working memory performance and functional connectivity

We systematically investigated the link between trait mindfulness scores and functional connectivity (FC) features or behavioral data, to emphasize the importance of the reliability of self-report mindfulness scores. Sixty healthy young male participants underwent two functional MRI runs with three mindfulness or mind-wandering task blocks with an N-back task (NBT) block. The data from 49 participants (age: 23.3 ± 2.8) for whom two sets of the self-reported Mindfulness Attention Awareness Scale (MAAS) and NBT performance were available were analyzed. We divided participants into two groups based on the consistency level of their MAAS scores (i.e., a “consistent” and an “inconsistent” group). Then, the association between the MAAS scores and FC features or NBT performance was investigated using linear regression analysis with p-value correction and bootstrapping. Meaningful associations (a) between MAAS and NBT accuracy (slope = 0.41, CI = [0.10, 0.73], corrected p < 0.05), (b) between MAAS and the FC edges in the frontoparietal network, and (c) between the FC edges and NBT performance were only observed in the consistent group (n = 26). Our findings demonstrate the importance of appropriate screening mechanisms for self-report-based dispositional mindfulness scores when trait mindfulness scores are combined with neuronal features and behavioral data.

An extensible hierarchical graph convolutional network for early Alzheimer’s disease identification

Background and Objective: For early identification of Alzheimer’s disease (AD) based on multi-modal magnetic resonance imaging (MRI) data, it is important to make comprehensive use of image features and non-image information to analyze the gray matter atrophy and the structural/functional connectivity abnormalities for different courses of AD.Methods: In this study, we propose an extensible hierarchical graph convolutional network (EH-GCN) for early AD identification. Based on the extracted image features from multi-modal MRI data using the presented multi-branch residual network (ResNet), the brain regions-of-interests (ROIs) based GCN is built to extract structural and functional connectivity features between different ROIs of the brain. In order to further improve the performance of AD identification, an optimized spatial GCN is proposed as convolution operator in the population-based GCN to avoid rebuilding the graph network and take advantage of relationships between subjects. Finally, the proposed EH-GCN is built by embedding the image features and internal brain connectivity features into the spatial population-based GCN, which provides an extensible way to improve early AD identification performance by adding imaging features and non-image information from multi-modal data.Results: Experiments are performed on two datasets, which illustrate the effectiveness of the extracted structural/functional connectivity features and the high computational efficiency of the proposed method. The classification accuracy of AD vs NC, AD vs MCI and MCI vs NC classification tasks reaches 88.71%, 82.71% and 79.68% respectively. The extracted connectivity features between ROIs indicate that functional abnormalities are earlier than gray matter atrophy and abnormalities of structural connections, which is consistent with the clinical manifestations. The proposed method allows for the addition of other modal image features and non-image information from multi-modal data to continuously improve the performance of clinical data analysis.Conclusions: The proposed method can help us comprehensively analyze the role of gray matter atrophy, the damage of white matter nerve fiber tracts and the degradation of functional connectivity for different courses of AD, which could be useful for further extraction of clinical biomarkers for early AD identification.

Brain functional connectivity-based prediction of vagus nerve stimulation efficacy in pediatric pharmacoresistant epilepsy.

Although vagus nerve stimulation (VNS) is a common and widely used therapy for pharmacoresistant epilepsy, the reported efficacy of VNS in pediatric patients varies, so it is unclear which children will respond to VNS therapy. This study aimed to identify functional brain network features associated with VNS action to distinguish VNS responders from nonresponders using scalp electroencephalogram (EEG) data. Twenty-three children were included in this study, 16 in the discovery cohort and 7 in the test cohort. Using partial correlation value as a measure of whole-brain functional connectivity, we identified the differential edges between responders and nonresponders. Results derived from this were used as input to generate a support vector machine-learning classifier to predict VNS outcomes. The postcentral gyrus in the left and right parietal lobe regions was identified as the most significant differential brain region between VNS responders and nonresponders (p < 0.001). The resultant classifier demonstrated a mean AUC value of 0.88, a mean sensitivity rate of 91.4%, and a mean specificity rate of 84.3% on fivefold cross-validation in the discovery cohort. In the testing cohort, our study demonstrated an AUC value of 0.91, a sensitivity rate of 86.6%, and a specificity rate of 79.3%. Furthermore, for prediction accuracy, our model can achieve 81.4% accuracy at the epoch level and 100% accuracy at the patient level. This study provides the first treatment response prediction model for VNS using scalp EEG data with ictal recordings and offers new insights into its mechanism of action. Our results suggest that brain functional connectivity features can help predict therapeutic response to VNS therapy. With further validation, our model could facilitate the selection of targeted pediatric patients and help avoid risky and costly procedures for patients who are unlikely to benefit from VNS therapy.

Altered resting-state hippocampal functional connectivity in breast cancer survivors with chemotherapy-induced amenorrhea.

Amenorrhea induced decrease of hormones is associated with cognitive impairment. This study aimed to evaluate hippocampal functional connectivity patterns in chemotherapy-induced amenorrhea (CIA) breast cancer (BC) patients, to evaluate the relationship between the functional connectivity features and hormone levels. Neuropsychological test, functional magnetic resonance imaging, and assessment of hormone levels were conducted in 21 premenopausal BC patients before chemotherapy (t0 ) and 1 week after completing chemotherapy (t1 ). Twenty matched healthy controls (HC) were also included and underwent the same assessments at similar time intervals. Mixed effect analysis and paired t-test were used to compare differences in brain functional connectivity. Voxel-based paired t-tests revealed increased functional connectivity of the right and left hippocampus with the left fusiform gyrus, inferior and middle temporal gyrus, inferior occipital gyrus, left lingual gyrus, and parahippocampal gyrus after chemotherapy (p<.001) in CIA patients. Repeated measures analysis revealed significant group-by-time interactions in the left hippocampus with the bilateral fusiform gyrus, right parahippocampal gyrus, left inferior temporal gyrus, and left inferior occipital gyrus (p<.001). Premenopausal BC patients had no significant differences in cognitive function compared with HC at baseline. However, the CIA patients had high levels of self-rating depression scale, self-rating anxiety scale, total cholesterol, and triglycerides. Further, the CIA patients showed significant differences in hormone and fasting plasma glucose levels and cognitive performances between t0 and t1 (p<.05). Functional connectivity changes between the left hippocampus and the left inferior occipital gyrus was negatively correlated with E2 and luteinizing hormone changes (p<.05). The CIA patients had cognitive dysfunction mainly in memory and visual mobility. Chemotherapy may affect hippocampal-posterior cortical circuit which mediates visual processing in CIA patients. Moreover, E2 may be involved in this process.

The increased functional connectivity between the locus coeruleus and supramarginal gyrus in insomnia disorder with acupuncture modulation.

Insomnia disorder (ID) seriously affects the quality of people's daily life, and acupuncture is an effective therapy for it. As an essential component of the upward activation system, the locus coeruleus (LC) plays a crucial role in sleep-wake regulation, its aberrant functional connectivity (FC) is found to be involved in ID. The purpose of this study was to explore the modulation effect of acupuncture on the resting state FC of LC in ID patients. 60 ID patients were recruited and randomly assigned to real acupuncture (RA) or sham acupuncture (SA) treatment. Resting-state functional magnetic resonance imaging (fMRI) data were collected before and after the treatment. With LC as the region of interest, the FC method was adopted to examine acupuncture-related modulation of intrinsic connectivity in ID patients. The Pittsburgh Sleep Quality Index (PSQI), Hyperarousal Scale (HAS), and actigraphy were used to assess sleep quality and cortical hyperarousal states. Associations between clinical outcomes and FC features were calculated using Pearson's correlation analysis. The improvement in sleep quality and hyperarousal in the RA group was greater than that in the SA group. After treatment, the FC between the LC and left inferior frontal gyrus (IFG) decreased in the RA group. The FC between the LC and left insula and supramarginal gyrus (SMG) was higher in the RA group. The change of LC FC values with the SMG was negatively associated with the change in PSQI scores. Acupuncture can modulate FC between the LC and IFG, insular gyrus, and SMG. This may imply the potential mechanism of acupuncture treatment for insomnia.

Metastability as a candidate neuromechanistic biomarker of schizophrenia pathology.

The disconnection hypothesis of schizophrenia proposes that symptoms of the disorder arise as a result of aberrant functional integration between segregated areas of the brain. The concept of metastability characterizes the coexistence of competing tendencies for functional integration and functional segregation in the brain, and is therefore well suited for the study of schizophrenia. In this study, we investigate metastability as a candidate neuromechanistic biomarker of schizophrenia pathology, including a demonstration of reliability and face validity. Group-level discrimination, individual-level classification, pathophysiological relevance, and explanatory power were assessed using two independent case-control studies of schizophrenia, the Human Connectome Project Early Psychosis (HCPEP) study (controls n = 53, non-affective psychosis n = 82) and the Cobre study (controls n = 71, cases n = 59). In this work we extend Leading Eigenvector Dynamic Analysis (LEiDA) to capture specific features of dynamic functional connectivity and then implement a novel approach to estimate metastability. We used non-parametric testing to evaluate group-level differences and a naïve Bayes classifier to discriminate cases from controls. Our results show that our new approach is capable of discriminating cases from controls with elevated effect sizes relative to published literature, reflected in an up to 76% area under the curve (AUC) in out-of-sample classification analyses. Additionally, our new metric showed explanatory power of between 81-92% for measures of integration and segregation. Furthermore, our analyses demonstrated that patients with early psychosis exhibit intermittent disconnectivity of subcortical regions with frontal cortex and cerebellar regions, introducing new insights about the mechanistic bases of these conditions. Overall, these findings demonstrate reliability and face validity of metastability as a candidate neuromechanistic biomarker of schizophrenia pathology.