Brain Functional Connectivity Data Research Articles

Identification of autism spectrum disorder using multiple functional connectivity-based graph convolutional network.

Presently, the combination of graph convolutional networks (GCN) with resting-state functional magnetic resonance imaging (rs-fMRI) data is a promising approach for early diagnosis of autism spectrum disorder (ASD). However, the prevalent approach involves exclusively full-brain functional connectivity data for disease classification using GCN, while overlooking the prior information related to the functional connectivity of brain subnetworks associated with ASD. Therefore, in this study, the multiple functional connectivity-based graph convolutional network (MFC-GCN) framework is proposed, using not only full brain functional connectivity data but also the established functional connectivity data from networks of key brain subnetworks associated with ASD, and the GCN is adopted to acquire complementary feature information for the final classification task. Given the heterogeneity within the Autism Brain Imaging Data Exchange (ABIDE) dataset, a novel External Attention Network Readout (EANReadout) is introduced. This design enables the exploration of potential subject associations, effectively addressing the dataset's heterogeneity. Experiments were conducted on the ABIDE dataset using the proposed framework, involving 714 subjects, and the average accuracy of the framework was 70.31%. The experimental results show that the proposed EANReadout outperforms the best traditional readout layer and improves the average accuracy of the framework by 4.32%.

The Use of Functional Magnetic Resonance Imaging (fMRI) and Electroencephalogram (EEG) during Self-Reflection: A Scoping Review

The integration of two fields, which are neuroscience and psychology has allowed a deeper understanding of the mental processes in the human mind, through the observation of biological and chemical processes in the brain and nervous system. Even though the gap between neuroscience and the human behavioral sciences is still large, the advancement of technology has helped narrow the gap, especially over the past two decades. This scoping review is aimed to explore the currently published research on self-reflection using the fMRI and the EEG. The present study describes the scope of existing research, and summarizes findings and recommendations for future work. The scoping review methodology guided by the standards of the PRISMA Extension for Scoping Reviews (PRISMA-ScR) was used. Comprehensive searches on the use of the fMRI and the EEG during self-reflection studies were conducted using several electronic databases including Scopus, Science Direct, Web of Science, Emerald Insights, and PubMed. The results obtained showed that all studies did not justify the sample size. The findings also revealed that various terms were used to describe the cognitive aspect of a self-reflection process. The brain functional connectivity data were taken through several experimental stages, which shows the flexibility of the experimental process. The activation of the prefrontal cortex (PFC) is one of the most commonly found regions in most studies, together with other areas associated with the self-reflection process. Based on the results obtained, this scoping review is believed to provide a comprehensive summary of the existing research on self-reflection using neuroimaging techniques.

Soft-orthogonal constrained dual-stream encoder with self-supervised clustering network for brain functional connectivity data

In many brain network studies, brain functional connectivity data is extracted from neuroimaging data and then used for disease prediction. For now, brain disease data not only has a small sample but also has the problem of high dimensional and nonlinear. Therefore, deep clustering on brain functional connectivity data is very challenging. To solve these problems, we propose a Soft-orthogonal Constrained Dual-stream Encoder with Self-supervised clustering network (SSCDE), which consists of a pretext task and downstream task, which can fully mine the effective information in brain disease data. In the pretext task, we use two brain disease data under the same category to do cross-domain learning to obtain effective information from the same dataset. In the downstream task, to reduce redundancy and avoid negative coding, we propose a soft-orthogonal constrained dual-stream encoder to encode features separately. At the same time, we use the pseudo labels given by the pretext task as prior information for self-supervised learning. We conduct validation on different brain disease recognition tasks, and the result have proved that the proposed framework has achieved good performance compared with the unsupervised clustering analysis algorithms. To our knowledge, this is the first cross-domain assisted recognition study on brain functional connectivity data. The code is available at https://github.com/hulu88/SSCDE.

Acute alcohol induces greater dose-dependent increase in the lateral cortical network functional connectivity in adult than adolescent rats

Alcohol misuse and, particularly adolescent drinking, is a major public health concern. While evidence suggests that adolescent alcohol use affects frontal brain regions that are important for cognitive control over behavior, little is known about how acute alcohol exposure alters large-scale brain networks and how sex and age may moderate such effects. Here, we employ a recently developed functional magnetic resonance imaging (fMRI) protocol to acquire rat brain functional connectivity data and use an established analytical pipeline to examine the effect of sex, age, and alcohol dose on connectivity within and between three major rodent brain networks: default mode, salience, and lateral cortical network. We identify the intra- and inter-network connectivity differences and establish moderation models to reveal significant influences of age on acute alcohol-induced lateral cortical network connectivity. Through this work, we make brain-wide isotropic fMRI data with acute alcohol challenge publicly available, with the hope to facilitate future discovery of brain regions/circuits that are causally relevant to the impact of acute alcohol use.

Deep multi-kernel auto-encoder network for clustering brain functional connectivity data

In this study, we propose a deep-learning network model called the deep multi-kernel auto-encoder clustering network (DMACN) for clustering functional connectivity data for brain diseases. This model is an end-to-end clustering algorithm that can learn potentially advanced features and cluster disease categories. Unlike other auto-encoders, DMACN has an added self-expression layer and standard back-propagation is used to learn the features that are beneficial for clustering brain functional connectivity data. In the self-expression layer, the kernel matrix is constructed to extract effective features and a new loss function is proposed to constrain the clustering portion, which enables the training of a deep neural learning network that tends to cluster. To test the performance of the proposed algorithm, we applied the end-to-end deep unsupervised clustering algorithm to brain connectivity data. We then conducted experiments based on four public brain functional connectivity data sets and our own functional connectivity data set. The DMACN algorithm yielded good results in various evaluations compared with the existing clustering algorithm for brain functional connectivity data, the deep auto-encoder clustering algorithm, and several other relevant clustering algorithms. The deep-learning-based clustering algorithm has great potential for use in the unsupervised recognition of brain diseases.

Brain functional connectivity data enhance prediction of clinical outcome in youth at risk for psychosis.

The first episode of psychosis is typically preceded by a prodromal phase with subthreshold symptoms and functional decline. Improved outcome prediction in this stage is needed to allow targeted early intervention. This study assesses a combined clinical and resting-state fMRI prediction model in 137 adolescents and young adults at Clinical High Risk (CHR) for psychosis from the Shanghai At Risk for Psychosis (SHARP) program. Based on outcome at one-year follow-up, participants were separated into three outcome categories including good outcome (symptom remission, N=71), intermediate outcome (ongoing CHR symptoms, N=30), and poor outcome (conversion to psychosis or treatment-refractory, N=36). Validated clinical predictors from the psychosis-risk calculator were combined with measures of resting-state functional connectivity. Using multinomial logistic regression analysis and leave-one-out cross-validation, a clinical-only prediction model did not achieve a significant level of outcome prediction (F1=0.32, p=.154). An imaging-only model yielded a significant prediction model (F1=0.41, p = .016), but a combined model including both clinical and connectivity measures showed the best performance (F1 = 0.46, p<.001). Influential predictors in this model included functional decline, verbal learning performance, a family history of psychosis, default-mode and frontoparietal within-network connectivity, and between-network connectivity among language, salience, dorsal attention, sensorimotor, and cerebellar networks. These findings suggest that brain changes reflected by alterations in functional connectivity may be useful for outcome prediction in the prodromal stage.

Broca’s region: linking human brain functional connectivity data and non‐human primate tracing anatomy studies

Brodmann areas 6, 44 and 45 in the ventrolateral frontal cortex of the left hemisphere of the human brain constitute the anterior language production zone. The anatomical connectivity of these areas with parietal and temporal cortical regions was recently examined in an autoradiographic tract-tracing study in the macaque monkey. Studies suggest strong correspondence between human resting state functional connectivity (RSFC) based on functional magnetic resonance imaging data and experimentally demonstrated anatomical connections in non-human primates. Accordingly, we hypothesized that areas 6, 44 and 45 of the human brain would exhibit patterns of RSFC consistent with patterns of anatomical connectivity observed in the macaque. In a primary analysis, we examined the RSFC associated with regions-of-interest placed in ventrolateral frontal areas 6, 44 and 45, on the basis of local sulcal and gyral anatomy. We validated the results of the primary hypothesis-driven analysis with a data-driven partitioning of ventrolateral frontal cortex into regions exhibiting distinct RSFC patterns, using a spectral clustering algorithm. The RSFC of ventrolateral frontal areas 6, 44 and 45 was consistent with patterns of anatomical connectivity shown in the macaque. We observed a striking dissociation between RSFC for the ventral part of area 6 that is involved in orofacial motor control and RSFC associated with Broca's region (areas 44 and 45). These findings indicate rich and differential RSFC patterns for the ventrolateral frontal areas controlling language production, consistent with known anatomical connectivity in the macaque brain, and suggest conservation of connectivity during the evolution of the primate brain.