Functional Connectivity Patterns Research Articles

Negative family and interpersonal relationship are associated with centromedial amygdala functional connectivity alterations in adolescent depression.

The amygdala, known for its functional heterogeneity, plays a critical role in the neural mechanism of adolescent major depressive disorder (aMDD). However, changes in its subregional functional networks in relation to stressful factors remain unclear. We recruited 78 comorbidity-free, medication-naive aMDD patients and 40 matched healthy controls (HC) to explore changes in resting-state functional connectivity (FC) across four amygdala subregions: the centromedial nucleus (CM), the basolateral nucleus (LB), the superficial nucleus (SF), and the amygdalostriatal transition area (Astr). Then, we performed partial correlation analysis to investigate the relationship between amygdala subregional FC and stressful factors as measured by the Chinese Version of Family Environment Scale (FES-CV) and the Adolescent Self-Rated Life Events Scale (ASLEC). Compared to HC, aMDD patients demonstrated significantly decreased functional connectivity between the left CM and left precentral gyrus, as well as between left SF and left precentral gyrus, and between left LB and posterior cingulate gyrus (PCC)/precuneus. In aMDD group, left CM-precentral gyrus FC exhibited negative correlation with interpersonal relationship and punishment, and positive correlation with family cohesion and expressiveness. This study reveals distinct patterns of abnormal functional connectivity among amygdala subregions in aMDD. Our findings suggest that the CM network, in particular, may be involved in stress-related factors in aMDD, which provide a potential target for the prevention and treatment of adolescent depression.

Aberrant functional connectivity of the salience network in adult patients with tic disorders: a resting-state fMRI study.

Tic disorders (TD) are characterized by the presence of motor and/or vocal tics. Common neurophysiological frameworks suggest dysregulations of the cortico-striatal-thalamo-cortical (CSTC) brain circuit that controls movement execution. Besides the common tics, there are other "non-tic" symptoms that are primarily related to sensory perception, sensorimotor integration, attention, and social cognition. The existence of these symptoms, the sensory tic triggers and the modifying effect of attention and cognitive control mechanisms on tics may indicate the salience network's involvement in the neurophysiology of TD. Resting-state functional MRI measurements were performed in 26 participants with TD and 25 healthy controls. The group differences in resting-state functional connectivity patterns were measured based on seed-to-voxel connectivity analyses. Compared to healthy controls, patients with TD exhibited altered connectivity between the core regions of the salience network (insula, ACC and TPJ) and sensory, associative, and motor-related cortices. Furthermore, connectivity changes were observed in relation to the severity of tics in the TD group. The salience network, particularly the insula, is likely to be an important site of dysregulation in TD. Our results provide evidence for large-scale neural deviations in TD beyond the CSTC pathologies. These findings may be relevant for developing treatment targets.Significance statement Tic disorders (TD) are associated with a variety of symptoms beyond typical motor and vocal tics that affect sensory perception, attention, and social cognition. The presence of such non-tic symptoms suggests the potential involvement of the salience network in the pathophysiology of TD. While previous studies have predominantly focused on the cortico-striato-thalamo-cortical (CSTC) circuitry, which is known to underlie tic generation and expression, we conducted resting-state fMRI to investigate the functional connectivity of the salience network in TD. Notably, we observed impaired connectivity of the salience network with relations to the tic symptom severity. Our research provided important evidence that the pathophysiology of TD involves the salience network, which is highly relevant for developing treatment strategies.

Network representation of multicellular activity in pancreatic islets: Technical considerations for functional connectivity analysis.

Within the islets of Langerhans, beta cells orchestrate synchronized insulin secretion, a pivotal aspect of metabolic homeostasis. Despite the inherent heterogeneity and multimodal activity of individual cells, intercellular coupling acts as a homogenizing force, enabling coordinated responses through the propagation of intercellular waves. Disruptions in this coordination are implicated in irregular insulin secretion, a hallmark of diabetes. Recently, innovative approaches, such as integrating multicellular calcium imaging with network analysis, have emerged for a quantitative assessment of the cellular activity in islets. However, different groups use distinct experimental preparations, microscopic techniques, apply different methods to process the measured signals and use various methods to derive functional connectivity patterns. This makes comparisons between findings and their integration into a bigger picture difficult and has led to disputes in functional connectivity interpretations. To address these issues, we present here a systematic analysis of how different approaches influence the network representation of islet activity. Our findings show that the choice of methods used to construct networks is not crucial, although care is needed when combining data from different islets. Conversely, the conclusions drawn from network analysis can be heavily affected by the pre-processing of the time series, the type of the oscillatory component in the signals, and by the experimental preparation. Our tutorial-like investigation aims to resolve interpretational issues, reconcile conflicting views, advance functional implications, and encourage researchers to adopt connectivity analysis. As we conclude, we outline challenges for future research, emphasizing the broader applicability of our conclusions to other tissues exhibiting complex multicellular dynamics.

Towards personalized precision functional mapping in infancy

Abstract The precise network topology of functional brain systems is highly specific to individuals and undergoes dramatic changes during critical periods of development. Large amounts of high-quality resting state data are required to investigate these individual differences, but are difficult to obtain in early infancy. Using the template matching method, we generated a set of infant network templates to use as priors for individualized functional resting-state network mapping in two independent neonatal datasets with extended acquisition of resting-state functional MRI (fMRI) data. We show that template matching detects all major adult resting-state networks in individual infants and that the topology of these resting-state network maps is individual-specific. Interestingly, there was no plateau in within-subject network map similarity with up to 25 minutes of resting-state data, suggesting that the amount and/or quality of infant data required to achieve stable or high-precision network maps is higher than adults. These findings are a critical step towards personalized precision functional brain mapping in infants, which opens new avenues for clinical applicability of resting-state fMRI and potential for robust prediction of how early functional connectivity patterns relate to subsequent behavioral phenotypes and health outcomes.

Functional connectivity alterations in the thalamus among patients with bronchial asthma

ObjectiveBronchial Asthma (BA) is a common chronic respiratory disease worldwide. Earlier research has demonstrated abnormal functional connectivity (FC) in multiple cognition-related cortices in asthma patients. The thalamus (Thal) serves as a relay center for transmitting sensory signals, yet the modifications in the thalamic FC among individuals with asthma remain uncertain. This research employed the resting-state functional connectivity (rsFC) approach to explore alterations in thalamic functional connectivity among individuals with BA.Patients and methodsAfter excluding participants who did not meet the criteria, this study finally included 31 patients with BA, with a gender distribution of 16 males and 15 females. Subsequently, we recruited 31 healthy control participants (HC) matched for age, gender, and educational background. All participants underwent the Montreal Cognitive Assessment (MoCA) and the Hamilton Depression Rating Scale (HAMD) assessment. Following this, both groups underwent head magnetic resonance imaging scans, and resting-state functional magnetic resonance imaging (rs-fMRI) data was collected. Based on the AAL (Automated Anatomical Labeling) template, the bilateral thalamic regions were used as seed points (ROI) for subsequent rsFC research. Pearson correlation analysis was used to explore the relationship between thalamic functional connectivity and neuropsychological scales in both groups. After controlling for potential confounding factors such as age, gender, intelligence, and emotional level, a two-sample t-test was further used to explore differences in thalamic functional connectivity between the two groups of participants.ResultCompared to the HC group, the BA group demonstrated heightened functional connectivity (FC) between the left thalamus and the left cerebellar posterior lobe (CPL), left postcentral gyrus (PCG), and right superior frontal gyrus (SFG). Concurrently, there was a decrease in FC with both the Lentiform Nucleus (LN) and the left corpus callosum (CC). Performing FC analysis with the right thalamus as the Region of Interest (ROI) revealed an increase in FC between the right thalamus and the right SFG as well as the left CPL. Conversely, a decrease in FC was observed between the right thalamus and the right LN as well as the left CC.ConclusionIn our study, we have verified the presence of aberrant FC patterns in the thalamus of BA patients. When compared to HCs, BA patients exhibit aberrant alterations in FC between the thalamus and various brain areas connected to vision, hearing, emotional regulation, cognitive control, somatic sensations, and wakefulness. This provides further confirmation of the substantial role played by the thalamus in the advancement of BA.

Abnormal dynamic functional connectivity in young nondisabling intracerebral hemorrhage patients.

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

Functional connectivity of the amygdala subnuclei in various mood states of bipolar disorder.

Amygdala functional dysconnectivity lies at the heart of the pathophysiology of bipolar disorder (BD). Recent preclinical studies suggest that the amygdala is a heterogeneous group of nuclei, whose specific connectivity could drive positive or negative emotional valence. We investigated functional connectivity (FC) changes within these circuits emerging from each amygdala's subdivision in 127 patients with BD in different mood states and 131 healthy controls (HC), who underwent resting-state functional MRI. FC was evaluated between lateral and medial nuclei of amygdalae, and key subcortical regions of the emotion processing network: anterior and posterior parts of the hippocampus, and core and shell parts of the nucleus accumbens. FC was compared across groups, and subgroups of patients depending on their mood states, using linear mixed models. We also tested correlations between FC and depression (MADRS) and mania (YMRS) scores. We found no difference between the whole sample of BD patients vs. HC but a significant correlation between MADRS and right lateral amygdala /right anterior hippocampus, right lateral amygdala/right posterior hippocampus and right lateral amygdala/left anterior hippocampus FC (r = -0.44, r = -0.32, r = -0.27, respectively, all pFDR<0.05). Subgroup analysis revealed decreased right lateral amygdala/right anterior hippocampus and right lateral amygdala/right posterior hippocampus FC in depressed vs. non-depressed patients and increased left medial amygdala/shell part of the left nucleus accumbens FC in manic vs non-manic patients. These results demonstrate that acute mood states in BD concur with FC changes in individual nuclei of the amygdala implicated in distinct emotional valence processing. Overall, our data highlight the importance to consider the amygdala subnuclei separately when studying its FC patterns including patients in distinct homogeneous mood states.

Alterations in spatiotemporal characteristics of dynamic networks in juvenile myoclonic epilepsy.

Juvenile myoclonic epilepsy (JME) is characterized by altered patterns of brain functional connectivity (FC). However, the nature and extent of alterations in the spatiotemporal characteristics of dynamic FC in JME patients remain elusive. Dynamic networks effectively encapsulate temporal variations in brain imaging data, offering insights into brain network abnormalities and contributing to our understanding of the seizure mechanisms and origins. Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 37 JME patients and 37 healthy counterparts. Forty-seven network nodes were identified by group-independent component analysis (ICA) to construct the dynamic network. Ultimately, patients' and controls' spatiotemporal characteristics, encompassing temporal clustering and variability, were contrasted at the whole-brain, large-scale network, and regional levels. Our findings reveal a marked reduction in temporal clustering and an elevation in temporal variability in JME patients at the whole-brain echelon. Perturbations were notably pronounced in the default mode network (DMN) and visual network (VN) at the large-scale level. Nodes exhibiting anomalous were predominantly situated within the DMN and VN. Additionally, there was a significant correlation between the severity of JME symptoms and the temporal clustering of the VN. Our findings suggest that excessive temporal changes in brain FC may affect the temporal structure of dynamic brain networks, leading to disturbances in brain function in patients with JME. The DMN and VN play an important role in the dynamics of brain networks in patients, and their abnormal spatiotemporal properties may underlie abnormal brain function in patients with JME in the early stages of the disease.

Intrinsic functional connectivity of motor and heteromodal association cortex predicts individual differences in regulatory focus.

Regulatory focus theory (RFT) describes two cognitive-motivational systems for goal pursuit-the promotion and prevention systems-important for self-regulation and previously implicated in vulnerability to psychopathology. According to RFT, the promotion system is engaged in attaining ideal goals (e.g. hopes and dreams), whereas the prevention system is associated with accomplishing ought goals (e.g. duties and obligations). Prior task-based functional magnetic resonance imaging (fMRI) studies have mostly explored the mapping of these two systems onto the activity of a priori brain regions supporting motivation and executive control in both healthy and depressed adults. However, complex behavioral processes such as those guided by individual differences in regulatory focus are likely supported by widely distributed patterns of intrinsic functional connectivity. We used data-driven connectome-based predictive modeling to identify patterns of distributed whole-brain intrinsic network connectivity associated with individual differences in promotion and prevention system orientation in 1,307 young university volunteers. Our analyses produced a network model predictive of prevention but not promotion orientation, specifically the subjective experience of successful goal pursuit using prevention strategies. The predictive model of prevention success was highlighted by decreased intrinsic functional connectivity of both heteromodal association cortices in the parietal and limbic networks and the primary motor cortex. We discuss these findings in the context of strategic inaction, which drives individuals with a strong dispositional prevention orientation to inhibit their behavioral tendencies in order to shield the self from potential losses, thus maintaining the safety of the status quo but also leading to trade-offs in goal pursuit success.

Aberrant connectivity in the hippocampus, bilateral insula and temporal poles precedes treatment resistance in first-episode psychosis: a prospective resting-state functional magnetic resonance imaging study with connectivity concordance mapping.

Functional connectivity resting-state functional magnetic resonance imaging has been proposed to predict antipsychotic treatment response in schizophrenia. However, only a few prospective studies have examined baseline resting-state functional magnetic resonance imaging data in drug-naïve first-episode schizophrenia patients with regard to subsequent treatment response. Data-driven approaches to conceptualize and measure functional connectivity patterns vary broadly, and model-free, voxel-wise, whole-brain analysis techniques are scarce. Here, we apply such a method, called connectivity concordance mapping to resting-state functional magnetic resonance imaging data acquired from an Asian sample (n = 60) with first-episode psychosis, prior to pharmaceutical treatment. Using a longitudinal design, 12 months after the resting-state functional magnetic resonance imaging, we measured and classified patients into two groups based on psychometric testing: treatment responsive and treatment resistant. Next, we compared the two groups' connectivity concordance maps that were derived from the resting-state functional magnetic resonance imaging data at baseline. We have identified consistently higher functional connectivity in the treatment-resistant group in a network including the left hippocampus, bilateral insula and temporal poles. These data-driven novel findings can help researchers to consider new regions of interest and facilitate biomarker development in order to identify treatment-resistant schizophrenia patients early, in advance of treatment and at the time of their first psychotic episode.

23. Capturing Dynamic Patterns of Task-Based Functional Connectivity During Drug Cue Reactivity in Fronto-Amygdala Circuit

23. Capturing Dynamic Patterns of Task-Based Functional Connectivity During Drug Cue Reactivity in Fronto-Amygdala Circuit

The risk of cannabis use disorder is mediated by altered brain connectivity: A chronnectome study.

The brain mechanisms underlying the risk of cannabis use disorder (CUD) are poorly understood. Several studies have reported changes in functional connectivity (FC) in CUD, although none have focused on the study of time-varying patterns of FC. To fill this important gap of knowledge, 39 individuals at risk for CUD and 55 controls, stratified by their score on a self-screening questionnaire for cannabis-related problems (CUDIT-R), underwent resting-state functional magnetic resonance imaging. Dynamic functional connectivity (dFNC) was estimated using independent component analysis, sliding-time window correlations, cluster states and meta-state indices of global dynamics and were compared among groups. At-risk individuals stayed longer in a cluster state with higher within and reduced between network dFNC for the subcortical, sensory-motor, visual, cognitive-control and default-mode networks, relative to controls. More globally, at-risk individuals had a greater number of meta-states and transitions between them and a longer state span and total distance between meta-states in the state space. Our findings suggest that the risk of CUD is associated with an increased dynamic fluidity and dynamic range of FC. This may result in altered stability and engagement of the brain networks, which can ultimately translate into altered cortical and subcortical function conveying CUD risk. Identifying these changes in brain function can pave the way for early pharmacological and neurostimulation treatment of CUD, as much as they could facilitate the stratification of high-risk individuals.

490. Functional Connectivity Patterns Associated With Self-Reported Pain Measures in Individuals With Opioid Use Disorder

490. Functional Connectivity Patterns Associated With Self-Reported Pain Measures in Individuals With Opioid Use Disorder

Corolaires en neuro-imagerie des effets des psychédéliques classiques : une revue systématique de la littérature

BackgroundCurrent scientific literature supports classical psychedelic efficacy in many psychiatric disorders. However, less attention has been given to the neurological effects of these substances. The aim of this medical thesis was to conduct a systematic review examining the neuroimaging correlates of the effects of psychedelics. MethodWe performed an electronic research through Medline and Science Direct databases. A comprehensive search yielded 460 articles published up to May 2022. After a cautious screening process, we selected 49 scientific papers for further analysis. ResultsMajor findings included reduced functional network integration, increased between-network functional connectivity, and expansion of functional connectivity patterns repertoire under psychedelics. Thalamic gating and emotional processing were also impaired. These results positively correlated with symptom improvement in pathological populations. ConclusionTo this day, our knowledge concerning psychedelic effects remains partial. Several neurocognitive theories have been developed in recent years to model psychedelic phenomenology, but no unifying theory has emerged. Studies involving larger populations investigating various psychiatric disorders, including several neuroimaging modalities and considering medium- and long-term effects, would be necessary to deepen current knowledge.

The emergence of AI in mental health: A transformative journey

This paper traces the evolution of Artificial Intelligence (AI) in psychiatry, from early attempts like the ELIZA program to the integration of neuroimaging studies and the emergence of digital interventions. Utilizing deep learning models, AI now demonstrates high accuracy in classifying psychiatric disorders, such as schizophrenia based on functional connectivity patterns. The rise of AI chat/therapy bots, exemplified by Woebot and Tess, addresses mental health challenges, providing accessible and effective cognitive behavioral therapy tools. Innovative interventions like avatar therapy and intelligent animal-like robots contribute to broadening therapeutic perspectives. While AI-based interventions offer advantages in reducing stigma and increasing accessibility, challenges including diagnostic accuracy, legal responsibilities, and privacy concerns must be carefully considered. Despite challenges, the significant potential of AI in psychiatry calls for large, controlled trials, national standards, and responsible integration into clinical practice. The convergence of AI and psychiatry represents a technological revolution poised to transform mental health care delivery globally.

The Use of Generative Adversarial Network and Graph Convolution Network for Neuroimaging-Based Diagnostic Classification

Functional connectivity (FC) obtained from resting-state functional magnetic resonance imaging has been integrated with machine learning algorithms to deliver consistent and reliable brain disease classification outcomes. However, in classical learning procedures, custom-built specialized feature selection techniques are typically used to filter out uninformative features from FC patterns to generalize efficiently on the datasets. The ability of convolutional neural networks (CNN) and other deep learning models to extract informative features from data with grid structure (such as images) has led to the surge in popularity of these techniques. However, the designs of many existing CNN models still fail to exploit the relationships between entities of graph-structure data (such as networks). Therefore, graph convolution network (GCN) has been suggested as a means for uncovering the intricate structure of brain network data, which has the potential to substantially improve classification accuracy. Furthermore, overfitting in classifiers can be largely attributed to the limited number of available training samples. Recently, the generative adversarial network (GAN) has been widely used in the medical field for its generative aspect that can generate synthesis images to cope with the problems of data scarcity and patient privacy. In our previous work, GCN and GAN have been designed to investigate FC patterns to perform diagnosis tasks, and their effectiveness has been tested on the ABIDE-I dataset. In this paper, the models will be further applied to FC data derived from more public datasets (ADHD, ABIDE-II, and ADNI) and our in-house dataset (PTSD) to justify their generalization on all types of data. The results of a number of experiments show the powerful characteristic of GAN to mimic FC data to achieve high performance in disease prediction. When employing GAN for data augmentation, the diagnostic accuracy across ADHD-200, ABIDE-II, and ADNI datasets surpasses that of other machine learning models, including results achieved with BrainNetCNN. Specifically, in ADHD, the accuracy increased from 67.74% to 73.96% with GAN, in ABIDE-II from 70.36% to 77.40%, and in ADNI, reaching 52.84% and 88.56% for multiclass and binary classification, respectively. GCN also obtains decent results, with the best accuracy in ADHD datasets at 71.38% for multinomial and 75% for binary classification, respectively, and the second-best accuracy in the ABIDE-II dataset (72.28% and 75.16%, respectively). Both GAN and GCN achieved the highest accuracy for the PTSD dataset, reaching 97.76%. However, there are still some limitations that can be improved. Both methods have many opportunities for the prediction and diagnosis of diseases.

Altered patterns of dynamic functional connectivity of brain networks in deficit and non-deficit schizophrenia.

This study aims to investigate the altered patterns of dynamic functional network connectivity (dFNC) between deficit schizophrenia (DS) and non-deficit schizophrenia (NDS), and further explore the associations with cognitive impairments. 70 DS, 91 NDS, and 120 matched healthy controls (HCs) were enrolled. The independent component analysis was used to segment the whole brain. The fMRI brain atlas was used to identify functional networks, and the dynamic functional connectivity (FC) of each network was detected. Correlation analysis was used to explore the associations between altered dFNC and cognitive functions. Four dynamic states were identified. Compared to NDS, DS showed increased FC between sensorimotor network and default mode network in state 1 and decreased FC within auditory network in state 4. Additionally, DS had a longer mean dwell time of state 2 and a shorter one in state 3 compared to NDS. Correlation analysis showed that fraction time and mean dwell time of states were correlated with cognitive impairments in DS. This study demonstrates the distinctive altered patterns of dFNC between DS and NDS patients. The associations with impaired cognition provide specific neuroimaging evidence for the pathogenesis of DS.

Electroacupuncture stimulation modulates functional brain connectivity in the treatment of pediatric cerebral palsy: a case report

BackgroundPediatric cerebral palsy (CP) is a non-progressive brain injury syndrome characterized by central motor dysfunction and insufficient brain coordination ability. The etiology of CP is complex and often accompanied by diverse complications such as intellectual disability and language disorders, making clinical treatment difficult. Despite the availability of pharmacological interventions, rehabilitation programs, and spasticity relief surgery as treatment options for CP, their effectiveness is still constrained. Electroacupuncture (EA) stimulation has demonstrated great improvements in motor function, but its comprehensive, objective therapeutic effects on pediatric CP remain to be clarified.MethodsWe present a case of a 5-year-old Chinese female child who was diagnosed with CP at the age of 4. The patient exhibited severe impairments in motor, language, social, and cognitive functions. We performed a 3-month period of EA rehabilitation, obtaining resting state functional magnetic resonance imaging (rs-fMRI) of the patient at 0 month, 3 months and 5 months since treatment started, then characterized brain functional connectivity patterns in each phase for comparison.ResultsAfter a 12-month follow-up, notable advancements were observed in the patient’s language and social symptoms. Changes of functional connectivity patterns confirmed this therapeutic effect and showed specific benefits for different recovery phase: starting from language functions then modulating social participation and other developmental behaviors.ConclusionThis is a pioneering report demonstrating the longitudinal effect of EA stimulation on functional brain connectivity in CP patients, suggesting EA an effective intervention for developmental disabilities (especially language and social dysfunctions) associated with pediatric CP.

Altered functional connectivity strength of primary visual cortex in subjects with thyroid-associated ophthalmopathy.

Our objective was to explore the disparities in the intrinsic functional connectivity (FC) patterns of primary visual cortex (V1) between patients with thyroid-associated ophthalmopathy (TAO) and healthy controls (HCs) utilizing resting-state functional MRI. Twenty-one patients with TAO (14 males and 7 females; mean age: 54.17 ± 4.83 years) and 21 well-matched HCs (14 males and 7 females; mean age: 55.17 ± 5.37 years) underwent functional MRI scans in the resting-state. We assessed modifications in the intrinsic FC patterns of the V1 in TAO patients using the FC method. Subsequently, the identified alterations in FC regions in the analysis were selected as classification features to distinguish TAO patients from HCs through the support vector machine (SVM) method. The results indicated that, in comparison to HCs, patients with TAO exhibited notably reduced FC values between the left V1 and the bilateral calcarine (CAL), lingual gyrus (LING) and superior occipital gyrus, as well as between the right V1 and the bilateral CAL/LING and the right cerebellum. Furthermore, the SVM classification model based on FC maps demonstrated effective performance in distinguishing TAO patients from HCs, achieving an accuracy of 61.9% using the FC of the left V1 and 64.29% using the FC of the right V1. Our study revealed that patients with TAO manifested disruptions in FC between the V1 and higher visual regions during rest. This might indicate that TAO patients could present with impaired top-down modulations, visual imagery and vision-motor function. These insights could be valuable in understanding the underlying neurobiological mechanisms of vision impairment in individuals with TAO.

Real-time Classification of Brain States in Functional MRI Using Dynamic Connectivity Patterns and Machine Learning

Understanding real-time brain states facilitates deeper insights into cognitive processes, emotional responses, and neurological phenomena. It provides researchers with a dynamic view of brain function, aiding in the development of novel therapies, advancing neuroscience, and fostering innovations in brain-computer interfaces and artificial intelligence. The research aims to achieve real-time classification of brain states using dynamic connectivity patterns and Convolutional Neural Network (CNN) algorithms. It focuses on how demographic variables, such as brain volume and medication usage, influence classification accuracy. The study employs Python programming with FSL and Nilearn for preprocessing tasks like motion correction and dynamic connectivity pattern extraction. Feature selection and data partitioning are managed using Scikit-learn, ensuring standardized feature values and handling missing data. The CNN architecture is customized to handle spatial and temporal features in functional MRI (fMRI) data, with convolutional layers extracting spatial features representing local connectivity patterns. Dynamic connectivity matrices visualization aids in understanding brain network reconfigurations over time. This approach offers real-time insights into cognitive processes and neurological disorders, guiding personalized interventions for brain health. In result, connectivity matrices and 3D brain network visualizations are pivotal for unraveling brain state dynamics. Connectivity matrices unveil intricate interactions among brain regions across resting, task execution, and stress response states, offering quantitative insights into functional connectivity patterns. Meanwhile, 3D visualizations provide spatial representations, showcasing complex interplays and architectural changes across states.