Brain Network Connectivity Research Articles

Seizure Pathways Changes at the Subject-Specific Level via Dynamic Step Effective Network Analysis.

The variability in the propagation pathway in epilepsy is a main factor contributing to surgical treatment failure. Ways to accurately capture the brain propagation network and quantitatively assess its evolution remain poorly described. This work aims to develop a dynamic step effective network (dSTE) to obtain the propagation path network of multiple seizures in the same patient and explore the degree of dissimilarity. Multichannel stereo-electroencephalography (sEEG) signals were acquired with ictal processes involving continuous changes in information propagation. We utilized high-order dynamic brain networks to obtain propagation networks through different levels of linking steps. We proposed a dissimilarity index based on singular value decomposition to quantitatively compare seizure pathways. Simulated data were generated through The Virtual Brain, and the reliability of this method was verified through ablation experiments. By applying the proposed method to two datasets consisting of 29 patients total, the evolution processes of each patient's seizure networks was obtained, and the within-patient dissimilarities were quantitatively compared. Finally, three types of brain network connectivity patterns were found. Type I patients have a good prognosis, while type III patients are prone to postoperative recurrence. This method captures the evolution of seizure propagation networks and assesses their dissimilarity more reliably than existing methods, demonstrating good robustness for studying the propagation path differences for multiple seizures in epilepsy patients. The three different patterns will be important considerations when planning epilepsy surgery under sEEG guidance.

A Study Over Brain Connectivity Network of Parkinson's Patients, Using Nonparametric Bayesian Model.

Parkinson disease is a neurodegenerative disease that disrupts functional brain networks. Many neurodegenerative disorders are associated with changes in brain communication patterns. Resting-state functional connectivity studies can distinguish the topological structure of Parkinson patients from healthy individuals by analyzing patterns between different regions of the brain. Accordingly, the present study aimed to determine the brain topological features and functional connectivity in patients with Parkinson disease, using a Bayesian approach. The data of this study were downloaded from the open neuro site. These data include resting-state functional magnetic resonance imaging (rs-fMRI) of 11 healthy individuals and 11 Parkinson patients with mean ages of 64.36 and 63.73, respectively. An advanced nonparametric Bayesian model was used to evaluate topological characteristics, including clustering of brain regions and correlation coefficient of the clusters. The significance of functional relationships based on each edge between the two groups was examined through false discovery rate (FDR) and network-based statistics (NBS) methods. Brain connectivity results showed a major difference in terms of the number of regions in each cluster and the correlation coefficient between the patient and healthy groups. The largest clusters in the patient and control groups were 26 and 53 regions, respectively, with clustering correlation values of 0.36 and 0.26. Although there are 15 common areas across the two clusters, the intensity of the functional relationship between these areas was different in the two groups. Moreover, using NBS and FDR methods, no significant difference was observed for each edge between the patient and healthy groups (P>0.05). The results of this study show a different topological configuration of the brain network between the patient and healthy groups, indicating changes in the functional relationship between a set of areas of the brain.

A Connectivity-Aware Graph Neural Network for Real-Time Drowsiness Classification.

Drowsy driving is one of the primary causes of driving fatalities. Electroencephalography (EEG), a method for detecting drowsiness directly from brain activity, has been widely used for detecting driver drowsiness in real-time. Recent studies have revealed the great potential of using brain connectivity graphs constructed based on EEG data for drowsy state predictions. However, traditional brain connectivity networks are irrelevant to the downstream prediction tasks. This article proposes a connectivity-aware graph neural network (CAGNN) using a self-attention mechanism that can generate task-relevant connectivity networks via end-to-end training. Our method achieved an accuracy of 72.6% and outperformed other convolutional neural networks (CNNs) and graph generation methods based on a drowsy driving dataset. In addition, we introduced a squeeze-and-excitation (SE) block to capture important features and demonstrated that the SE attention score can reveal the most important feature band. We compared our generated connectivity graphs in the drowsy and alert states and found drowsiness connectivity patterns, including significantly reduced occipital connectivity and interregional connectivity. Additionally, we performed a post hoc interpretability analysis and found that our method could identify drowsiness features such as alpha spindles. Our code is available online at https://github.com/ALEX95GOGO/CAGNN.

Predictions of neural control deficits in elders with subjective memory complaints and Alzheimer’s disease

Alzheimer’s disease (AD) is a common disease, affecting over 6 million elders in the U.S in 2024. However, AD remains untreatable due to the absence of an effective biomarker to assess the underlying deficits in cognitive control. Disruptions in the brain's control systems are key factors in the learning and memory impairments that define AD. In this study, we hypothesized that we could predict neural control deficits in elders with subjective memory complaints (SMC) and AD patients using Diffusion Tensor Imaging (DTI) data and brain controllability analysis. DTI is a non-invasive neuroimaging tool that detects how fluid travels along the white matter tracts inside the brain. To test our hypothesis, we used the DTI data of 12 elders with SMC, 12 AD patients, and 12 healthy subjects from the open-source dataset Alzheimer's Disease Neuroimaging Initiative. First, we constructed individual brain connectivity networks. Using graph theory and brain controllability analysis, we assessed node degree and controllability. We then averaged these measures across brain areas to calculate global metrics for each subject. Results illustrated that the node degree could not identify SMC and AD from healthy subjects, while the controllability measure could differentiate SMC and AD from healthy subjects and distinguish between SMC and AD. In conclusion, this study provides a promising biomarker for detecting neural control deficits in elders with SMC and AD patients for future clinical application.

Alterations of Structural Network Efficiency in Early-Onset and Late-Onset Alzheimer's Disease.

Early- and late-onset Alzheimer's disease (EOAD and LOAD, respectively) share the same neuropathological hallmarks of amyloid and neurofibrillary tangles but have distinct cognitive features. We compared structural brain connectivity between the EOAD and LOAD groups using structural network efficiency and evaluated the association of structural network efficiency with the cognitive profile and pathological markers of Alzheimer's disease (AD). The structural brain connectivity networks of 80 AD patients (47 with EOAD and 33 with LOAD) and 57 healthy controls were reconstructed using diffusion-tensor imaging. Graph-theoretic indices were calculated and intergroup differences were evaluated. Correlations between network parameters and neuropsychological test results were analyzed. The correlations of the amyloid and tau burdens with network parameters were evaluated for the patients and controls. Compared with the age-matched control group, the EOAD patients had increased global path length and decreased global efficiency, averaged local efficiency, and averaged clustering coefficient. In contrast, no significant differences were found in the LOAD patients. Locally, the EOAD patients showed decreases in local efficiency and the clustering coefficient over a wide area compared with the control group, whereas LOAD patients showed such decreases only within a limited area. Changes in network parameters were significantly correlated with multiple cognitive domains in EOAD patients, but only with Clinical Dementia Rating Sum-of-Boxes scores in LOAD patients. Finally, the tau burden was correlated with changes in network parameters in AD signature areas in both patient groups, while there was no correlation with the amyloid burden. The impairment of structural network efficiency and its effects on cognition may differ between EOAD and LOAD.

Exploring Multiconnectivity and Subdivision Functions of Brain Network via Heterogeneous Graph Network for Cognitive Disorder Identification.

Brain serves as a critical cornerstone of human intelligence, which involves a series of complex neuropsychological activities that lead to the coordination of various functions in the brain network. In recent years, brain network analysis methods based on graph neural networks (GNNs) have attracted increasing attention for the identification of brain disorders. However, these methods generally assume that the brain network is a homogeneous graph while ignoring its heterogeneity among human brain activities, which is reflected in both the complex connectivity of the brain network and distinctive brain functions. To overcome this problem, we propose a heterogeneous subdivision GNN (HSGNN), which captures the heterogeneous connections and functions of the brain network simultaneously. Specifically, we first employ two fundamental brain connectivity patterns to capture both statistical dependency and directional information flow among different brain regions and construct a heterogeneous brain connectivity network for each subject. Then, we develop a functional subdivision method that encodes brain networks into multiple latent feature subspaces corresponding to heterogeneous brain functions and extracts features of brain networks accordingly. Considering the intricate interactions of brain functions to facilitate cognitive activities within the brain network, we further employ the self-attention mechanism to obtain comprehensive representations of brain networks in a joint latent space. Finally, we propose a composite loss function to train the model for obtaining the heterogeneous brain network representation, which can be utilized for disease classification. The experimental results in the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Autism Brain Imaging Data Exchange (ABIDE) datasets demonstrate that our method outperforms several state-of-the-art (SOTA) methods to identify different types of brain cognitive-related disorders.

Network Localization of Spontaneous Confabulation.

Spontaneous confabulation is a symptom in which false memories are conveyed by the patient as true. The purpose of the study was to identify the neuroanatomical substrate of this complex symptom and evaluate the relationship to related symptoms, such as delusions and amnesia. Twenty-five lesion locations associated with spontaneous confabulation were identified in a systematic literature search. The network of brain regions functionally connected to each lesion location was identified with a large connectome database (N=1,000) and compared with networks derived from lesions associated with nonspecific (i.e., variable) symptoms (N=135), delusions (N=32), or amnesia (N=53). Lesions associated with spontaneous confabulation occurred in multiple brain locations, but they were all part of a single functionally connected brain network. Specifically, 100% of lesions were connected to the mammillary bodies (familywise error rate [FWE]-corrected p<0.05). This connectivity was specific for lesions associated with confabulation compared with lesions associated with nonspecific symptoms or delusions (FWE-corrected p<0.05). Lesions associated with confabulation were more connected to the orbitofrontal cortex than those associated with amnesia (FWE-corrected p<0.05). Spontaneous confabulation maps to a common functionally connected brain network that partially overlaps, but is distinct from, networks associated with delusions or amnesia. These findings lend new insight into the neuroanatomical bases of spontaneous confabulation.

EEG Network Analysis of Depressive Emotion Interference Spatial Cognition Based on a Simulated Robotic Arm Docking Task.

Depressive emotion (DE) refers to clinically relevant depressive symptoms without meeting the diagnostic criteria for depression. Studies have demonstrated that DE can cause spatial cognition impairment. However, the brain network mechanisms underlying DE interference spatial cognition remain unclear. This study aimed to reveal the differences in brain network connections between DE and healthy control (HC) groups during resting state and a spatial cognition task. The longer operation time of the DE group during spatial cognition task indicated DE interference spatial cognition. In the resting state stage, the DE group had weaker network connections in theta and alpha bands than the HC group had. Specifically, the electrodes in parietal regions were hubs of the differential networks, which are related to spatial attention. Moreover, in docking task stages, the left frontoparietal network connections in delta, beta, and gamma bands were stronger in the DE group than those of the HC group. The enhanced left frontoparietal connections in the DE group may be related to brain resource reorganization to compensate for spatial cognition decline and ensure the completion of spatial cognition tasks. Thus, these findings might provide new insights into the neural mechanisms of depressive emotion interference spatial cognition.

Functional connectivity in ADHD children doing Go/No-Go tasks: An fMRI systematic review and meta-analysis.

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders diagnosed in childhood. Two common features of ADHD are impaired behavioural inhibition and sustained attention. The Go/No-Go experimental paradigm with concurrent functional magnetic resonance imaging (fMRI) scanning has previously revealed important neurobiological correlates of ADHD such as the supplementary motor area and the prefrontal cortex. The coordinate-based meta-analysis combined with quantitative techniques, such as activation likelihood estimate (ALE) generation, provides an unbiased and objective method of summarising these data to understand the brain network architecture and connectivity in ADHD children. Go/No-Go task-based fMRI studies involving children and adolescent subjects were selected. Coordinates indicating foci of activation were collected to generate ALEs using threshold values (voxel-level: p < 0.001; cluster-level: p < 0.05). ALEs were matched to one of seven canonical brain networks based on the cortical parcellation scheme derived from the Human Connectome Project. Fourteen studies involving 457 children met the eligibility criteria. No significant convergence of Go/No-Go related brain activation was found for ADHD groups. Three significant ALE clusters were detected for brain activation relating to controls or ADHD < controls. Significant clusters were related to specific areas of the default mode network (DMN). Network-based analysis revealed less extensive DMN, dorsal attention network, and limbic network activation in ADHD children compared to controls. The presence of significant ALE clusters may be due to reduced homogeneity in the selected sample demographic and experimental paradigm. Further investigations regarding hemispheric asymmetry in ADHD subjects would be beneficial.

Puberty, brain network connectivity and neuropsychiatric outcomes following pediatric traumatic brain injury in females: A research protocol.

Examining the role of sex on recovery from pediatric TBI (pTBI) is a complex task, specifically when referring to injuries occurring during critical developmental and maturation periods. The effect of sex hormones on neurological and neuropsychiatric outcomes has been studied among adult TBI females, but not in children. During development, puberty is considered a key milestone accompanied by changes in physical growth, neuronal maturation, sex hormones, and psychological symptoms. Following pTBI, such changes might have a significant effect on brain re-organization and on long-term neuropsychiatric outcomes. While hormonal dysfunction is a common consequence following pTBI, only few studies have systematically evaluated hormonal changes following pTBI. To describe a multimodal protocol aimed to examine the effect of puberty on brain connectivity and long-term neuropsychiatric outcomes following TBI in female girls and adolescents. A case-control longitudinal prospective design will be used. 120 female participants aged 9 to 16 years (N = 60 per group) will be recruited. In the acute phase (T0-1 month), participants will undergo an MRI protocol for brain connectivity, as well as a clinical evaluation for puberty stage and hormonal levels. In the chronic phase (T1-18-24 months), participants will complete a neuropsychiatric assessment in addition to the MRI and puberty evaluations. Hormonal levels will be monitored at T0 and T1. A moderation-mediation model will be used to examine the moderating effects of puberty on the association between pTBI and neuropsychiatric symptoms in female girls and adolescents, through the mediating effect of brain network connectivity. This study will highlight sex-specific factors related to outcomes among females following pTBI and enhance our understanding of the unique challenges they face. Such information has a substantial potential to guide future directions for research, policy and practice.

CONTRAST SENSITIVITY AND BRAIN NETWORK COMMUNITY STRUCTURE IN THE BRAIN NETWORKS AND MOBILITY STUDY

Abstract Contrast sensitivity (CS) is the ability to perceive differences in shades of light and dark, which is important for pattern recognition and depth perception. We have previously shown that impaired CS is associated with poor performance on the expanded short physical performance battery (eSPPB), and that performance on the eSPPB is associated with sensory motor cortex (SMC) connectivity at rest. We hypothesized that brain networks are important to the relationship of CS with eSPPB. Participants (n=192) were cognitively unimpaired older adults (mean age=76.5±4.7 years; 56.5% female, 9.4% black) with good visual acuity who completed CS testing. We applied graph theory to characterize the cross-sectional relationship of binocular CS to functional brain networks generated from fMRI both at rest and during a motor imagery (MI) task and determined the spatial patterns of SMC, visual (VIS) and default mode (DMN) network connectivity. Regression analyses were used to assess associations of CS with brain network connectivity without and with adjustment for sex and eSPPB. Lower CS (p=0.009) was significantly associated with degraded SMC connectivity at rest even in models adjusted for eSPPB and sex. Similarly, lower CS was significantly associated with degraded VIS and DMN connectivity during the MI task (all p&amp;lt; 0.005) when controlling for eSPPB and sex. These results indicate that low CS and poor eSPPB are both associated with connectivity in the SMC at rest and with VIS and DMN during MI in older adults. These findings may be important for understanding the relationship between age-related visual and mobility dysfunction.

Fusing multi-scale fMRI features using a brain-inspired multi-channel graph neural network for major depressive disorder diagnosis

Depression stands as one of the most pernicious mental disorders in contemporary society, characterized by a highly intricate pathological mechanism. Specifically, individuals suffering from Major Depressive Disorder (MDD) exhibit heightened vulnerability to suicidal tendencies. Currently, healthcare practitioners often encounter challenges related to the misdiagnosis and underdiagnosis of depression during clinical assessments. Consequently, it is of paramount importance to develop highly accurate auxiliary diagnostic tools for depression. Unfortunately, traditional machine learning and deep learning methodologies frequently neglect the integration of multi-source data and disregard the intricate topological structure and high-order attributes of brain networks. In this study, a multi-scale feature fusion classification framework is proposed to distinguish between MDD patients and healthy controls. Within the proposed model, a novel method, the Cross-Level High-Order Interaction (CLHOI), is introduced and implemented on a low-order functional connectivity (LOFC) matrix to derive two distinct high-order functional connectivity (HOFC) matrices. Subsequently, a Multi-Channel Fusion Graph Convolutional Network (MFGCN) is trained by integrating high-order and low-order brain graph features along with phenotypic information. The results of 10-fold cross-validation experiments conducted on the publicly available REST-meta-MDD dataset indicate that the fusion of multi-scale features improves the average accuracy by approximately 3%, resulting in an accuracy rate of 77.6%. Simultaneously, the hypothesis asserting the existence of intricate information interactions at various levels within brain connectivity networks is validated. Moreover, our model exhibits strong explanatory capabilities, effectively identifying brain regions closely associated with MDD, including the Precentral gyrus, Superior frontal gyrus, Cuneus, Lingual gyrus, and Fusiform gyrus. In comparison to numerous advanced studies within the same domain, our approach has produced competitive results. Furthermore, our proposed method can be readily extended to facilitate the diagnosis of various neurological diseases.

Brain connectivity networks underlying resting heart rate variability in acute ischemic stroke

Acute strokes can affect heart rate variability (HRV), the mechanisms how are not well understood. We included 42 acute stroke patients (2–7 days after ischemic stroke, mean age 66 years, 16 women). For analysis of HRV, 20 matched controls (mean age 60.7, 10 women) were recruited. HRV was assessed at rest, in a supine position and individual breathing rhythmus for 5 min. The coefficient of variation (VC), the root mean square of successive differences (RMSSD), the powers of low (LF, 0.04–0.14 Hz) and high (HF, 0.15–0.50 Hz) frequency bands were extracted. HRV parameters were z-transformed related to age- and sex-matched normal subjects. Z-values < -1 indicate reduced HRV. Acute stroke lesions were marked on diffusion-weighted images employing MRIcroN and co-registered to a T1-weighted structural volume-dataset. Using independent component analysis (ICA), stroke lesions were related to HRV. Subsequently, we used the ICA-derived lesion pattern as a seed and estimated the connectivity between these brain regions and seven common functional networks, which were obtained from 50 age-matched healthy subjects (mean age 68.9, 27 women). Especially, LF and VC were frequently reduced in patients. ICA revealed one covarying lesion pattern for LF and one similar for VC, predominantly affecting the right hemisphere. Activity in brain areas corresponding to these lesions mainly impact on limbic (r = 0.55 ± 0.08) and salience ventral attention networks (0.61 ± 0.10) in the group with reduced LF power (z-score < -1), but on control and default mode networks in the group with physiological LF power (z-score > -1). No different connectivity could be found for the respective VC groups. Our results suggest that HRV alteration after acute stroke might be due to affecting resting-state brain networks.

Neurophysiological hallmarks of Huntington's disease progression: an EEG and fMRI connectivity study.

Electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can provide corroborative data on neurophysiological alterations in Huntington's disease (HD). However, the alterations in EEG and fMRI resting-state functional connectivity (rsFC), as well as their interrelations, at different stages of HD remain insufficiently investigated. This study aimed to identify neurophysiological alterations in individuals with preclinical HD (preHD) and early manifest HD (EMHD) by analyzing EEG and fMRI rsFC and examining their interrelationships. We found significant differences in EEG power between preHD individuals and healthy controls (HC), with a decrease in power in a specific frequency range at the theta-alpha border and slow alpha activity. In EMHD patients, in addition to the decrease in power in the 7-9 Hz range, a reduction in power within the classic alpha band compared to HC was observed. The fMRI analysis revealed disrupted functional connectivity in various brain networks, particularly within frontal lobe, putamen-cortical, and cortico-cerebellar networks, in individuals with the HD mutation compared to HC. The analysis of the relationship between EEG and fMRI rsFC revealed an association between decreased alpha power, observed in individuals with EMHD, and increased connectivity in large-scale brain networks. These networks include putamen-cortical, DMN-related and cortico-hippocampal circuits. Overall, the findings suggest that EEG and fMRI provide valuable information for monitoring pathological processes during the development of HD. A decrease in inhibitory control within the putamen-cortical, DMN-related and cortico-hippocampal circuits, accompanied by a reduction in alpha and theta-alpha border oscillatory activity, could potentially contribute to cognitive decline in HD.

Multi feature fusion network for schizophrenia classification and abnormal brain network recognition

Schizophrenia classification and abnormal brain network recognition have an important research significance. Researchers have proposed many classification methods based on machine learning and deep learning. However, fewer studies utilized the advantages of complementary information from multi feature to learn the best representation of schizophrenia. In this study, we proposed a multi-feature fusion network (MFFN) using functional network connectivity (FNC) and time courses (TC) to distinguish schizophrenia patients from healthy controls. DNN backbone was adopted to learn the feature map of functional network connectivity, C-RNNAM backbone was designed to learn the feature map of time courses, and Deep SHAP was applied to obtain the most discriminative brain networks. We proved the effectiveness of this proposed model using the combining two public datasets and evaluated this model quantitatively using the evaluation indexes. The results showed that the functional network connectivity generated by independent component analysis has advantage in schizophrenia classification by comparing static and dynamic functional connections. This method obtained the best classification accuracy (ACC=87.30%, SPE=89.28%, SEN=85.71%, F1 =88.23%, and AUC=0.9081), and it demonstrated the superiority of this proposed model by comparing state-of-the-art methods. Ablation experiment also demonstrated that multi feature fusion and attention module can improve classification accuracy. The most discriminative brain networks showed that default mode network and visual network of schizophrenia patients have aberrant connections in brain networks. In conclusion, this method can identify schizophrenia effectively and visualize the abnormal brain network, and it has important clinical application value.

Functional connectivity of cognition-related brain networks in adults with fetal alcohol syndrome

BackgroundFetal alcohol syndrome (FAS) can result in cognitive dysfunction. Cognitive functions affected are subserved by few functional brain networks. Functional connectivity (FC) in these networks can be assessed with resting-state functional MRI (rs-fMRI). Alterations of FC have been reported in children and adolescents prenatally exposed to alcohol. Previous reports varied substantially regarding the exact nature of findings. The purpose of this study was to assess FC of cognition-related networks in young adults with FAS.MethodsCross-sectional rs-fMRI study in participants with FAS (n = 39, age: 20.9 ± 3.4 years) and healthy participants without prenatal alcohol exposure (n = 44, age: 22.2 ± 3.4 years). FC was calculated as correlation between cortical regions in ten cognition-related sub-networks. Subsequent modelling of overall FC was based on linear models comparing FC between FAS and controls. Results were subjected to a hierarchical statistical testing approach, first determining whether there is any alteration of FC in FAS in the full cognitive connectome, subsequently resolving these findings to the level of either FC within each network or between networks based on the Higher Criticism (HC) approach for detecting rare and weak effects in high-dimensional data. Finally, group differences in single connections were assessed using conventional multiple-comparison correction. In an additional exploratory analysis, dynamic FC states were assessed.ResultsComparing FAS participants with controls, we observed altered FC of cognition-related brain regions globally, within 7 out of 10 networks, and between networks employing the HC statistic. This was most obvious in attention-related network components. Findings also spanned across subcomponents of the fronto-parietal control and default mode networks. None of the single FC alterations within these networks yielded statistical significance in the conventional high-resolution analysis. The exploratory time-resolved FC analysis did not show significant group differences of dynamic FC states.ConclusionsFC in cognition-related networks was altered in adults with FAS. Effects were widely distributed across networks, potentially reflecting the diversity of cognitive deficits in FAS. However, no altered single connections could be determined in the most detailed analysis level. Findings were pronounced in networks in line with attentional deficits previously reported.

Distinct resting-state effective connectivity of large-scale networks in first-episode and recurrent major depression disorder: evidence from the REST-meta-MDD consortium.

Previous studies have shown disrupted effective connectivity in the large-scale brain networks of individuals with major depressive disorder (MDD). However, it is unclear whether these changes differ between first-episode drug-naive MDD (FEDN-MDD) and recurrent MDD (R-MDD). This study utilized resting-state fMRI data from 17 sites in the Chinese REST-meta-MDD project, consisting of 839 patients with MDD and 788 normal controls (NCs). All data was preprocessed using a standardized protocol. Then, we performed a granger causality analysis to calculate the effectivity connectivity (EC) within and between brain networks for each participant, and compared the differences between the groups. Our findings revealed that R-MDD exhibited increased EC in the fronto-parietal network (FPN) and decreased EC in the cerebellum network, while FEDN-MDD demonstrated increased EC from the sensorimotor network (SMN) to the FPN compared with the NCs. Importantly, the two MDD subgroups displayed significant differences in EC within the FPN and between the SMN and visual network. Moreover, the EC from the cingulo-opercular network to the SMN showed a significant negative correlation with the Hamilton Rating Scale for Depression (HAMD) score in the FEDN-MDD group. These findings suggest that first-episode and recurrent MDD have distinct effects on the effective connectivity in large-scale brain networks, which could be potential neural mechanisms underlying their different clinical manifestations.

Exploring the propagation pathway in individual patients with epilepsy: A stepwise effective connection approach

ObjectiveThe unclear propagation pathway of seizures is one of the main reasons for failure of surgical treatment, and the propagation process involves the directional brain networks. However, few network analysis techniques have successfully traced specific seizure propagation pathways. This study proposed a stepwise transfer entropy (STE) approach to describe the propagation of effective connections in brain networks. MethodsThe proposed STE technique is applied to stereoelectroencephalography (SEEG) data collected from patients with epilepsy, which can identify characteristic regions connected to specific seed brain regions at different link-step levels. Importantly, the underlying TE-based network construction method can accurately obtain electrode-to-electrode connections, and the stepwise approach can capture the interactions between electrodes at the connection level. Finally, simulation and clinical data were used to evaluate the STE approach according to the similarity, confusion matrix, accuracy and recall. ResultsWe used three datasets: a simulation dataset, a clinical dataset, and a public dataset. We compared the results of different network construction methods with multiple datasets, and the STE approach successfully captured node changes in epilepsy patients, effectively identified early and late propagation nodes, and determined the propagation pathway. Moreover, the STE approach is superior to other methods in all evaluation indices, achieving 97.9% accuracy and 0.93 similarity. SignificanceCompared with the existing methods, the STE approach has significant advantages in accurately tracking propagation pathways. Moreover, the STE approach is simple and quickly performs calculations, making it an easy-to-use promising method for determining propagation pathways in clinical settings.

The Dynamical Biomarkers in Functional Connectivity of Autism Spectrum Disorder Based on Dynamic Graph Embedding.

Autism spectrum disorder (ASD) is a neurological and developmental disorder and its early diagnosis is a challenging task. The dynamic brain network (DBN) offers a wealth of information for the diagnosis and treatment of ASD. Mining the spatio-temporal characteristics of DBN is critical for finding dynamic communication across brain regions and, ultimately, identifying the ASD diagnostic biomarker. We proposed the dgEmbed-KNN and the Aggregation-SVM diagnostic models, which use the spatio-temporal information from DBN and interactive information among brain regions represented by dynamic graph embedding. The classification accuracies show that dgEmbed-KNN model performs slightly better than traditional machine learning and deep learning methods, while the Aggregation-SVM model has a very good capacity to diagnose ASD using aggregation brain network connections as features. We discovered over- and under-connections in ASD at the level of dynamic connections, involving brain regions of the postcentral gyrus, the insula, the cerebellum, the caudate nucleus, and the temporal pole. We also found abnormal dynamic interactions associated with ASD within/between the functional subnetworks, including default mode network, visual network, auditory network and saliency network. These can provide potential DBN biomarkers for ASD identification.

Resting state brain networks activity and association with transfer of cognitive training gains

AbstractBackgroundAge‐related cognitive decline increases the need for cognitive interventions to maintain cognitive function. Cognitive training is considered an effective non‐pharmacological intervention. Transfer of training gains to untrained tasks is a key indicator for the effectiveness of cognitive training. However, the underlying brain mechanisms need to be further investigated. We implemented a cognitive training study to assess functional connectivity determinants of transfer of training gains.MethodA sample of 181 healthy older adults (mean age: 68 years) underwent a 4‐week cognitive training across three sites. The control group consisted of 54 older adults. To evaluate transfer and training effects, participants underwent a neuropsychological assessment before and after the training. A second follow‐up assessment was applied 12 weeks after the training. The training group was divided in subjects who had and who did not have successful transfer, which was defined as higher improvement in cognitive tasks than the control group. We used composite scores representing working memory, memory and executive functions to assess transfer and training effects. Baseline resting‐state functional magnetic resonance imaging was used to investigate the functional connectivity of brain networks associated with cognitive functions. We extracted brain resting state networks using the Independent Component Analysis (ICA) approach.ResultWe observed successful transfer of cognitive training gains in most of the participants. Our results demonstrated spatially restricted effects (p&lt;.01 uncorrected) for the association of transfer of gains with the resting‐state connectivity of brain networks, such as the Default Mode Network and Central Executive Network.ConclusionTransfer of training gains in aging is possible. A strong association between transfer of gains and resting‐state functional connectivity was not identified.