Abnormal Insula Network Characteristics in Panic Disorder.

BACKGROUND: Panic disorder (PD) is a common disabling condition characterized by recurrent panic attacks. Emotional and behavioral impairments are associated with functional connectivity (FC) and network abnormalities. We used the whole brain FC, modular networks, and graph-theory analysis to investigate extensive network profiles in PD. METHOD: The functional MRI data from 82 PD and 97 controls were included. Intrinsic FC between each pair of 160 regions, 6 intra-networks, and 15 inter-networks were analyzed. The topological properties were explored. RESULTS: PD patients showed altered FCs within the right insula, between frontal cortex-posterior cingulate cortex (PCC), frontal cortex-cerebellum, and PCC-occipital cortex (corrected P values < 0.001). Lower connections within the Sensorimotor Network (SMN) and SMN-Occipital Network (OCN) were detected (P values < 0.05). Various decreased global and local network features were found in PD (P values < 0.05). In addition, significant correlations were found between PD symptoms and nodal efficiency (Ne) in the insula (r = -0.273, P = 0.016), and the FC of the intra-insula (r = -0.226, P = 0.041). CONCLUSIONS: PD patients present with abnormal functional brain networks, especially the decreased FC and Ne within insula, suggesting that dysfunction of information integration plays an important role in PD.

Sports promote brain evolution: a resting-state fMRI study of volleyball athlete.

Background: Long-term skill learning can lead to structure and function changes in the brain. Different sports can trigger neuroplasticity in distinct brain regions. Volleyball, as one of the most popular team sports, heavily relies on individual abilities such as perception and prediction for high-level athletes to excel. However, the specific brain mechanisms that contribute to the superior performance of volleyball athletes compared to non-athletes remain unclear. Method: We conducted a study involving the recruitment of ten female volleyball athletes and ten regular female college students, forming the athlete and novice groups, respectively. Comprehensive behavioral assessments, including Functional Movement Screen and audio-visual reaction time tests, were administered to both groups. Additionally, resting-state magnetic resonance imaging (MRI) data were acquired for both groups. Subsequently, we conducted in-depth analyses, focusing on the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) in the brain for both the athlete and novice groups. Results: No significant differences were observed in the behavioral data between the two groups. However, the athlete group exhibited noteworthy enhancements in both the ALFF and ReHo within the visual cortex compared to the novice group. Moreover, the functional connectivity between the visual cortex and key brain regions, including the left primary sensory cortex, left supplementary motor cortex, right insula, left superior temporal gyrus, and left inferior parietal lobule, was notably stronger in the athlete group than in the novice group. Conclusion: This study has unveiled the remarkable impact of volleyball athletes on various brain functions related to vision, movement, and cognition. It indicates that volleyball, as a team-based competitive activity, fosters the advancement of visual, cognitive, and motor skills. These findings lend additional support to the early cultivation of sports talents and the comprehensive development of adolescents. Furthermore, they offer fresh perspectives on preventing and treating movement-related disorders. Trial registration: Registration number: ChiCTR2400079602. Date of Registration: January 8, 2024.

Resting-State Functional Connectivity of the Primary Visual Cortex in Children with Anisometropia Amblyopia.

INTRODUCTION: This study aimed to explore the functional connectivity of the primary visual cortex (V1) in children with anisometropic amblyopia by using the resting-state functional connectivity analysis method and determine whether anisometropic amblyopia is associated with changes in brain function. METHODS: Functional magnetic resonance imaging (fMRI) data were obtained from 16 children with anisometropia amblyopia (CAA group) and 12 healthy children (HC group) during the resting state. The Brodmann area 17 (BA17) was used as the region of interest, and the functional connection (FC) of V1 was analyzed in both groups. A two-sample t test was used to analyze the FC value between the two groups. Pearson's correlation was used to analyze the correlation between the mean FC value in the brain function change area of the CAA group and the best corrected visual acuity (BCVA) of amblyopia. p < 0.05 was considered statistically significant. RESULTS: There were no significant differences in age and sex between the CAA and HC groups (p > 0.05). Compared to the HC group, the CAA group showed lower FC values in BA17 and the left medial frontal gyrus, as well as BA17 and the left triangle inferior frontal gyrus. Conversely, the CAA group showed higher FC values in BA17 and the left central posterior gyrus. Notably, BCVA in amblyopia did not correlate with the area of change in mean FC in the brain function of the CAA group. CONCLUSION: Resting-state fMRI-based functional connectivity analysis indicates a significant alteration in V1 of children with anisometropic amblyopia. These findings contribute additional insights into the neuropathological mechanisms underlying visual impairment in anisometropic amblyopia.

Sports game intervention aids executive function enhancement in children with autism - An fNIRS study.

Executive dysfunction is a prevalent issue in children diagnosed with autism spectrum disorder (ASD). While the efficacy of physical exercise in enhancing cognitive abilities in these children is well-documented, research exploring the relationship between physical exercise and brain function remains limited. This study aimed to investigate the impact of cognitively stimulating exercise on executive functions (EF) in children with ASD. The study enrolled thirty children with ASD who were randomly allocated into two groups: a sports game learning group (n = 15) and a control group (n = 15). Functional near-infrared spectroscopy was utilized to monitor cerebral function alterations pre- and post- an eight-week intervention program. The study focused on three core components of executive function: working memory, inhibitory control (IC), and cognitive flexibility (CF). Results revealed a significant improvement in the EF in the intervention group. After eight weeks of intervention, neural activity, along with improved EF performance, was enhanced significantly in the prefrontal cortex (PFC). During post-intervention, EF tasks were also significantly activated in the dorsolateral PFC, orbitofrontal cortex, and frontal pole area. Furthermore, an increase in short-distance functional connectivity within the PFC was observed during resting states. These results imply that engagement in sports game training can significantly improve EF information processing, augmenting task-related cortical activations and the efficiency of brain function networks in children with ASD.

Brain optimization with additional study time: potential brain differences between high- and low-performance college students.

This study investigates potential differences in brain function among high-, average-, and low-performance college students using electroencephalography (EEG). We hypothesize that the increased academic engagement of high-performance students will lead to discernible EEG variations due to the brain's structural plasticity. 61 third-year college students from identical majors were divided into high-performance (n = 20), average-performance (n = 21), and low-performance (n = 20) groups based on their academic achievements. We conducted three EEG experiments: resting state, Sternberg working memory task, and Raven progressive matrix task. Comprehensive analyses of the EEG data from the three experiments focused on power spectral density (PSD) and functional connectivity, with coherence (COH) employed as our primary metric for the latter. The results showed that in all experiments, there were no differences in working memory ability and IQ scores among the groups, and there were no significant differences in the power spectral densities of the delta, theta, alpha1, alpha2, beta, and gamma bands among the groups. Notably, on the Raven test, compared to their high-performing peers, low-performing students showed enhanced functional connectivity in the alpha 1 (8-9 Hz) band that connects the frontal and occipital lobes. We explored three potential explanations for this phenomenon: fatigue, anxiety, and greater cognitive effort required for problem-solving due to inefficient self-regulation and increased susceptibility to distraction. In essence, these insights not only deepen our understanding of the neural basis that anchors academic ability, but also hold promise in guiding interventions that address students' diverse academic needs.

Abnormalities in modular connectivity of functional brain networks and cognitive changes in patients with anti -N-methyl-D-aspartate receptor encephalitis.

OBJECTIVE: To explore potential mechanisms of cognitive changes in patients with anti-NMDAR encephalitis (ANMDARE) from intramodule and intermodule effects of brain functional networks. METHODS: Resting-state functional MRI(rs-fMRI) imaging data was collected from 30 ANMDARE and 30 healthy controls (HCs). A brain functional matrix was constructed, and sparsity was established by module similarity. For both groups, changes in functional connectivity (FC) within and between modules was calculated, and whole-brain functional topology was analyzed. Finally, the association of brain functional with cognitive function in ANMDARE was further analyzed. RESULTS: Compared to HCs, ANMDARE had enhanced connectivity within the modules that included the occipito-parietal-temporal and parahippocampal gyri. ANMDARE had significantly higher participation coefficients (PC) in the right inferior frontal gyrus than HCs and significantly lower PC in the left superior parietal lobule, left caudate nucleus, and right putamen. No statistically significant differences in global topological properties were found between the two groups. No correlations were found between functional and structural brain indicators and the Cognitive Assessment Scale and the Emotional Deficit Scale. CONCLUSIONS: Patients with ANMDARE are manifested by enhanced intramodular FC and intermodular connectivity changes in the brain. This may help to understand the pathophysiological mechanisms of the disease from a global perspective.

Lack of functional brain connectivity was associated with poor inhibition in children with attention-deficit/hyperactivity disorder using near-infrared spectroscopy.

Objectives: The present study aimed to evaluate the characteristics of functional brain connectivity in the resting state in children with attention deficit hyperactivity disorder (ADHD) and to assess the association between the connectivity and inhibition function using near-infrared spectroscopy (NIRS). Methods: In total, 34 children aged 6-13 diagnosed with ADHD were recruited from Hangzhou Seventh People's Hospital. In comparison, 37 healthy children were recruited from a local primary school as controls matched by age and sex. We used NIRS to collect information on brain images. The Stroop test assessed inhibition function. We compared the differences in functional brain connectivity in two groups by analyzing the resting-state brain network. Pearson partial correlation analysis was applied to evaluate the correlation between functional brain connectivity and inhibition in all the children. Results: Compared with the control group, results of NIRS images analysis showed that children with ADHD had significantly low functional brain connectivity in regions of the orbitofrontal cortex, left dorsolateral prefrontal cortex, left pre-motor and supplementary motor cortex, inferior prefrontal gyrus, and right middle temporal gyrus (p = 0.006). Inhibition function of children with ADHD was negatively correlated with functional brain connectivity (p = 0.009), while such correlation was not found in the control group. Conclusion: The present study demonstrated that children with ADHD had relatively low connectivity in several brain regions measured at the resting state. Our results supported the evidence that lack of functional brain connectivity was associated with impaired inhibition function in children with ADHD.

Extracting the most discriminating functional connections in mild traumatic brain injury based on machine learning.

BACKGROUND: Mild traumatic brain injury (mTBI) is characterized as brain microstructural damage, which may cause a wide range of brain functional disturbances and emotional problems. Brain network analysis based on machine learning is an important means of neuroimaging research. Obtaining the most discriminating functional connection is of great significance to analyze the pathological mechanism of mTBI. METHODS: To better obtain the most discriminating features of functional connection networks, this study proposes a hierarchical feature selection pipeline (HFSP) composed of Variance Filtering (VF), Lasso, and Principal Component Analysis (PCA). Ablation experiments indicate that each module plays a positive role in classification, validating the robustness and reliability of the HFSP. Furthermore, the HFSP is compared with recursive feature elimination (RFE), elastic net (EN), and locally linear embedding (LLE), verifying its superiority. In addition, this study also utilizes random forest (RF), SVM, Bayesian, linear discriminant analysis (LDA), and logistic regression (LR) as classifiers to evaluate the generalizability of HFSP. RESULTS: The results show that the indexes obtained from RF are the highest, with accuracy = 89.74%, precision = 91.26%, recall = 89.74%, and F1 score = 89.42%. The HFSP selects 25 pairs of the most discriminating functional connections, mainly distributed in the frontal lobe, occipital lobe, and cerebellum. Nine brain regions show the largest node degree. LIMITATIONS: The number of samples is small. This study only includes acute mTBI. CONCLUSIONS: The HFSP is a useful tool for extracting discriminating functional connections and may contribute to the diagnostic processes.

[Effect of transcutaneous auricular vagus nerve stimulation on functional connectivity in the related brain regions of patients with depression based on the resting-state fMRI].

OBJECTIVE: To explore the brain effect mechanism and the correlation between brain functional imaging and cognitive function in treatment of depressive disorder (DD) with transcutaneous auricular vagus nerve stimulation (taVNS) based on the resting-state functional magenetic reasonance imaging (rs-fMRI). METHODS: Thirty-two DD patients were included in a depression group and 32 subjects of healthy condition were enrolled in a normal group. In the depression group, the taVNS was applied to bilateral Xin (CO15) and Shen (CO10), at disperse-dense wave, 4 Hz/20 Hz in frequency and current intensity ≤20 mA depending on patient's tolerance, 30 min each time, twice daily. The duration of treatment consisted of 8 weeks. The patients of two groups were undertaken rs-fMRI scanning. The scores of Hamilton depression scale (HAMD), Hamilton anxiety scale (HAMA) and Wisconsin card sorting test (WCST) were observed in the normal group at baseline and the depression group before and after treatment separately. The differential brain regions were observed before and after treatment in the two groups and the value of degree centrality (DC) of fMRI was obtained. Their correlation was analyzed in terms of HAMD, HAMA and WCST scores. RESULTS: The scores of HAMD and HAMA in the depression group were all higher than those in the normal group (P<0.05). After treatment, the scores of HAMD and HAMA were lower than those before treatment in the depression group; the scores of total responses, response errors and perseverative errors of WCST were all lower than those before treatment (P<0.05). The brain regions with significant differences included the left inferior temporal gyrus, the left cerebellar peduncles region 1, the left insula, the right putamen, the bilateral supplementary motor area and the right middle frontal gyrus. After treatment, the value of DC in left supplementary motor area was negatively correlated to HAMD and HAMA scores respectively (r=-0.324, P=0.012; r=-0.310, P=0.015); the value of DC in left cerebellar peduncles region 1 was negatively correlated to the total responses of WCST (r=-0.322, P=0.013), and the left insula was positively correlated to the total responses of WCST (r=0.271, P=0.036). CONCLUSION: The taVNS can modulate the intensity of the functional activities of some brain regions so as to relieve depressive symptoms and improve cognitive function.

Neuroplasticity of pain processing and motor control in CAI patients: A UK Biobank study with clinical validation.

Background: Pain plays an important role in chronic ankle instability (CAI), and prolonged pain may be associated with ankle dysfunction and abnormal neuroplasticity. Purpose: To investigate the differences in resting-state functional connectivity among the pain-related brain regions and the ankle motor-related brain regions between healthy controls and patients with CAI, and explore the relationship between patients' motor function and pain. Study design: A cross-database, cross-sectional study. Methods: This study included a UK Biobank dataset of 28 patients with ankle pain and 109 healthy controls and a validation dataset of 15 patients with CAI and 15 healthy controls. All participants underwent resting-state functional magnetic resonance imaging scanning, and the functional connectivity (FC) among the pain-related brain regions and the ankle motor-related brain regions were calculated and compared between groups. The correlations between the potentially different functional connectivity and the clinical questionnaires were also explored in patients with CAI. Results: The functional connection between the cingulate motor area and insula significantly differed between groups in both the UK Biobank (p = 0.005) and clinical validation dataset (p = 0.049), which was also significantly correlated with Tegner scores (r = 0.532, p = 0.041) in patients with CAI. Conclusion: A reduced functional connection between the cingulate motor area and the insula was present in patients with CAI, which was also directly correlated with reduction in the level of patient physical activity.

Spatio-temporal dynamics of resting-state brain networks are associated with migraine disability.

OBJECTIVE: The changes in resting-state functional networks and their correlations with clinical traits remain to be clarified in migraine. Here we aim to investigate the brain spatio-temporal dynamics of resting-state networks and their possible correlations with the clinical traits in migraine. METHODS: Twenty Four migraine patients without aura and 26 healthy controls (HC) were enrolled. Each included subject underwent a resting-state EEG and echo planar imaging examination. The disability of migraine patients was evaluated by Migraine Disability Assessment (MIDAS). After data acquisition, EEG microstates (Ms) combining functional connectivity (FC) analysis based on Schafer 400-seven network atlas were performed. Then, the correlation between obtained parameters and clinical traits was investigated. RESULTS: Compared with HC group, the brain temporal dynamics depicted by microstates showed significantly increased activity in functional networks involving MsB and decreased activity in functional networks involving MsD; The spatial dynamics were featured by decreased intra-network FC within the executive control network( ECN) and inter-network FC between dorsal attention network (DAN) and ECN (P < 0.05); Moreover, correlation analysis showed that the MIDAS score was positively correlated with the coverage and duration of MsC, and negatively correlated with the occurrence of MsA; The FC within default mode network (DMN), and the inter-FC of ECN- visual network (VN), ECN- limbic network, VN-limbic network was negatively correlated with MIDAS. However, the FC of DMN-ECN was positively correlated with MIDAS; Furthermore, significant interactions between the temporal and spatial dynamics were also obtained. CONCLUSIONS: Our study confirmed the notion that altered spatio-temporal dynamics exist in migraine patients during resting-state. And the temporal dynamics, the spatial changes and the clinical traits such as migraine disability interact with each other. The spatio-temporal dynamics obtained from EEG microstate and fMRI FC analyses may be potential biomarkers for migraine and with a huge potential to change future clinical practice in migraine.

Effect of transcutaneous auricular vagus nerve stimulation on functional connectivity in the related brain regions of patients with depression based on the resting-state fMRI / 中国针灸

OBJECTIVE@#To explore the brain effect mechanism and the correlation between brain functional imaging and cognitive function in treatment of depressive disorder (DD) with transcutaneous auricular vagus nerve stimulation (taVNS) based on the resting-state functional magenetic reasonance imaging (rs-fMRI).@*METHODS@#Thirty-two DD patients were included in a depression group and 32 subjects of healthy condition were enrolled in a normal group. In the depression group, the taVNS was applied to bilateral Xin (CO15) and Shen (CO10), at disperse-dense wave, 4 Hz/20 Hz in frequency and current intensity ≤20 mA depending on patient's tolerance, 30 min each time, twice daily. The duration of treatment consisted of 8 weeks. The patients of two groups were undertaken rs-fMRI scanning. The scores of Hamilton depression scale (HAMD), Hamilton anxiety scale (HAMA) and Wisconsin card sorting test (WCST) were observed in the normal group at baseline and the depression group before and after treatment separately. The differential brain regions were observed before and after treatment in the two groups and the value of degree centrality (DC) of fMRI was obtained. Their correlation was analyzed in terms of HAMD, HAMA and WCST scores.@*RESULTS@#The scores of HAMD and HAMA in the depression group were all higher than those in the normal group (P<0.05). After treatment, the scores of HAMD and HAMA were lower than those before treatment in the depression group; the scores of total responses, response errors and perseverative errors of WCST were all lower than those before treatment (P<0.05). The brain regions with significant differences included the left inferior temporal gyrus, the left cerebellar peduncles region 1, the left insula, the right putamen, the bilateral supplementary motor area and the right middle frontal gyrus. After treatment, the value of DC in left supplementary motor area was negatively correlated to HAMD and HAMA scores respectively (r=-0.324, P=0.012; r=-0.310, P=0.015); the value of DC in left cerebellar peduncles region 1 was negatively correlated to the total responses of WCST (r=-0.322, P=0.013), and the left insula was positively correlated to the total responses of WCST (r=0.271, P=0.036).@*CONCLUSION@#The taVNS can modulate the intensity of the functional activities of some brain regions so as to relieve depressive symptoms and improve cognitive function.

A study on the combination of functional connection features and Riemannian manifold in EEG emotion recognition.

Introduction: Affective computing is the core for Human-computer interface (HCI) to be more intelligent, where electroencephalogram (EEG) based emotion recognition is one of the primary research orientations. Besides, in the field of brain-computer interface, Riemannian manifold is a highly robust and effective method. However, the symmetric positive definiteness (SPD) of the features limits its application. Methods: In the present work, we introduced the Laplace matrix to transform the functional connection features, i.e., phase locking value (PLV), Pearson correlation coefficient (PCC), spectral coherent (COH), and mutual information (MI), to into semi-positive, and the max operator to ensure the transformed feature be positive. Then the SPD network is employed to extract the deep spatial information and a fully connected layer is employed to validate the effectiveness of the extracted features. Particularly, the decision layer fusion strategy is utilized to achieve more accurate and stable recognition results, and the differences of classification performance of different feature combinations are studied. What's more, the optimal threshold value applied to the functional connection feature is also studied. Results: The public emotional dataset, SEED, is adopted to test the proposed method with subject dependent cross-validation strategy. The result of average accuracies for the four features indicate that PCC outperform others three features. The proposed model achieve best accuracy of 91.05% for the fusion of PLV, PCC, and COH, followed by the fusion of all four features with the accuracy of 90.16%. Discussion: The experimental results demonstrate that the optimal thresholds for the four functional connection features always kept relatively stable within a fixed interval. In conclusion, the experimental results demonstrated the effectiveness of the proposed method.

Functional interactions among neurons within single columns of macaque V1.

Recent developments in high-density neurophysiological tools now make it possible to record from hundreds of single neurons within local, highly interconnected neural networks. Among the many advantages of such recordings is that they dramatically increase the quantity of identifiable, functional interactions between neurons thereby providing an unprecedented view of local circuits. Using high-density, Neuropixels recordings from single neocortical columns of primary visual cortex in nonhuman primates, we identified 1000s of functionally interacting neuronal pairs using established crosscorrelation approaches. Our results reveal clear and systematic variations in the synchrony and strength of functional interactions within single cortical columns. Despite neurons residing within the same column, both measures of interactions depended heavily on the vertical distance separating neuronal pairs, as well as on the similarity of stimulus tuning. In addition, we leveraged the statistical power afforded by the large numbers of functionally interacting pairs to categorize interactions between neurons based on their crosscorrelation functions. These analyses identified distinct, putative classes of functional interactions within the full population. These classes of functional interactions were corroborated by their unique distributions across defined laminar compartments and were consistent with known properties of V1 cortical circuitry, such as the lead-lag relationship between simple and complex cells. Our results provide a clear proof-of-principle for the use of high-density neurophysiological recordings to assess circuit-level interactions within local neuronal networks.

Effects of total sleep deprivation on execution lapses during vigilance tasks.

Total sleep deprivation (TSD) results in reduced efficiency of cognitive resources. Moreover, when the available cognitive resources are less than required, individuals exhibit lapses in responsiveness. Accordingly, this study explored the effects of TSD on executive function and the characteristics of execution lapses. Functional near-infrared spectroscopy was used to monitor the prefrontal cortex's functional connections in resting and tasking states for various sleep deprivation durations. Data from participants' attentional performance test and self-reported fatigue were collected over 30 hours of wakefulness. Task performance was compared based on time of day, time on task, and reaction time. The results show that participants' arousal level significantly decreased post 14 hours (P < .05), while sleepiness increased. The prefrontal cortex connection and attentional performance dropped at the Window of Circadian Low (3:00 ~ 6:00). The number of execution lapses was higher during the initiation, inhibition, and fatigue phases and rose markedly post 14 hours of wakefulness. We conclude that maintaining better inhibition control requires a reasonable extension of the reaction time. Moreover, subjective perception is significantly correlated with task performance and right prefrontal connection strength. This study presents the scientific evidence for measures to address consistently long working hours and disrupted circadian rhythms.

Scalp EEG functional connection and brain network in infants with West syndrome.

The common age-dependent West syndrome can be diagnosed accurately by electroencephalogram (EEG), but its pathogenesis and evolution remain unclear. Existing research mainly aims at the study of West seizure markers in time/frequency domain, while less literature uses a graph-theoretic approach to analyze changes among different brain regions. In this paper, the scalp EEG based functional connectivity (including Correlation, Coherence, Time Frequency Cross Mutual Information, Phase-Locking Value, Phase Lag Index, Weighted Phase Lag Index) and network topology parameters (including Clustering coefficient, Feature path length, Global efficiency, and Local efficiency) are comprehensively studied for the prognostic analysis of the West episode cycle. The scalp EEGs of 15 children with clinically diagnosed string spasticity seizures are used for prospective study, where the signal is divided into pre-seizure, seizure, and post-seizure states in 5 typical brain wave rhythm frequency bands (δ (1-4 Hz), θ (4-8 Hz), α (8-13 Hz), ß (13-30 Hz), and γ (30-80 Hz)) for functional connectivity analysis. The study shows that recurrent West seizures weaken connections between brain regions responsible for cognition and intelligence, while brain regions responsible for information synergy and visual reception have greater variability in connectivity during seizures. It is observed that the changes inßandγfrequency bands of the multiband brain network connectivity patterns calculated by Corr and WPLI can be preliminarily used as judgment of seizure cycle changes in West syndrome.

Differences in Brain Activity Between Dopa-Responsive and -Unresponsive Pain in Parkinson's Disease.

INTRODUCTION: Pain in Parkinson's disease is poorly understood, and most patients with pain do not respond to dopaminergic drugs. We aimed to explore the mechanisms of dopa-responsive and -unresponsive pain by comparing such patients against patients without pain in terms of neural activity and functional connectivity in the brain. METHODS: We prospectively examined 31 Parkinson's patients with dopa-responsive pain, 51 with dopa-unresponsive pain and 93 without pain using resting-state functional magnetic resonance imaging. Neural activity was assessed in terms of the amplitude of low-frequency fluctuation, while functional connectivity was assessed based on analysis of regions of interest. RESULTS: Patients with dopa-unresponsive pain showed significantly higher amplitude of low-frequency fluctuation in the right parahippocampal/lingual region than patients with no pain. However, there was no amplitude difference between the dopa-responsive pain group and the no pain group. Patients with dopa-unresponsive pain also differed significantly from patients with no pain in their functional connections between the superior temporal gyrus and other areas of cerebral cortex, between amygdala and thalamus and between the amygdala and putamen. Patients with dopa-responsive pain differed significantly from patients with no pain in their functional connections between temporal fusiform cortex and cerebellum, between precentral gyrus and temporal fusiform cortex and between precentral gyrus and cerebellum. CONCLUSIONS: Regional neural activity and functional connectivity in the brain differ substantially among Parkinson's patients with dopa-unresponsive pain, dopa-responsive pain or no pain. Our results suggest that dopa-responsive and -unresponsive pain may arise through different mechanisms, which may help guide the development of targeted therapies.

Identifying Boys With Autism Spectrum Disorder Based on Whole-Brain Resting-State Interregional Functional Connections Using a Boruta-Based Support Vector Machine Approach.

An increasing number of resting-state functional magnetic resonance neuroimaging (R-fMRI) studies have used functional connections as discriminative features for machine learning to identify patients with brain diseases. However, it remains unclear which functional connections could serve as highly discriminative features to realize the classification of autism spectrum disorder (ASD). The aim of this study was to find ASD-related functional connectivity patterns and examine whether these patterns had the potential to provide neuroimaging-based information to clinically assist with the diagnosis of ASD by means of machine learning. We investigated the whole-brain interregional functional connections derived from R-fMRI. Data were acquired from 48 boys with ASD and 50 typically developing age-matched controls at NYU Langone Medical Center from the publicly available Autism Brain Imaging Data Exchange I (ABIDE I) dataset; the ASD-related functional connections identified by the Boruta algorithm were used as the features of support vector machine (SVM) to distinguish patients with ASD from typically developing controls (TDC); a permutation test was performed to assess the classification performance. Approximately, 92.9% of participants were correctly classified by a combined SVM and leave-one-out cross-validation (LOOCV) approach, wherein 95.8% of patients with ASD were correctly identified. The default mode network (DMN) exhibited a relatively high network degree and discriminative power. Eight important brain regions showed a high discriminative power, including the posterior cingulate cortex (PCC) and the ventrolateral prefrontal cortex (vlPFC). Significant correlations were found between the classification scores of several functional connections and ASD symptoms (p < 0.05). This study highlights the important role of DMN in ASD identification. Interregional functional connections might provide useful information for the clinical diagnosis of ASD.

Mindfulness-Based Cognitive Therapy Regulates Brain Connectivity in Patients With Late-Life Depression.

Late-life depression (LLD) is an important public health problem among the aging population. Recent studies found that mindfulness-based cognitive therapy (MBCT) can effectively alleviate depressive symptoms in major depressive disorder. The present study explored the clinical effect and potential neuroimaging mechanism of MBCT in the treatment of LLD. We enrolled 60 participants with LLD in an 8-week, randomized, controlled trial (ChiCTR1800017725). Patients were randomized to the treatment-as-usual (TAU) group or a MBCT+TAU group. The Hamilton Depression Scale (HAMD) and Hamilton Anxiety Scale (HAMA) were used to evaluate symptoms. Magnetic resonance imaging (MRI) was used to measure changes in resting-state functional connectivity and structural connectivity. We also measured the relationship between changes in brain connectivity and improvements in clinical symptoms. HAMD total scores in the MBCT+TAU group were significantly lower than in the TAU group after 8 weeks of treatment (p < 0.001) and at the end of the 3-month follow-up (p < 0.001). The increase in functional connections between the amygdala and middle frontal gyrus (MFG) correlated with decreases in HAMA and HAMD scores in the MBCT+TAU group. Diffusion tensor imaging analyses showed that fractional anisotropy of the MFG-amygdala significantly increased in the MBCT+TAU group after 8-week treatment compared with the TAU group. Our study suggested that MBCT improves depression and anxiety symptoms that are associated with LLD. MBCT strengthened functional and structural connections between the amygdala and MFG, and this increase in communication correlated with improvements in clinical symptoms. Randomized Controlled Trial; Follow-Up Study; fMRI; Brain Connectivity.

Development of a classifier for gambling disorder based on functional connections between brain regions.

AIM: Recently, a machine-learning (ML) technique has been used to create generalizable classifiers for psychiatric disorders based on information of functional connections (FCs) between brain regions at resting state. These classifiers predict diagnostic labels by a weighted linear sum (WLS) of the correlation values of a small number of selected FCs. We aimed to develop a generalizable classifier for gambling disorder (GD) from the information of FCs using the ML technique and examine relationships between WLS and clinical data. METHODS: As a training dataset for ML, data from 71 GD patients and 90 healthy controls (HCs) were obtained from two magnetic resonance imaging sites. We used an ML algorithm consisting of a cascade of an L1-regularized sparse canonical correlation analysis and a sparse logistic regression to create the classifier. The generalizability of the classifier was verified using an external dataset. This external dataset consisted of six GD patients and 14 HCs, and was collected at a different site from the sites of the training dataset. Correlations between WLS and South Oaks Gambling Screen (SOGS) and duration of illness were examined. RESULTS: The classifier distinguished between the GD patients and HCs with high accuracy in leave-one-out cross-validation (area under curve (AUC = 0.89)). This performance was confirmed in the external dataset (AUC = 0.81). There was no correlation between WLS, and SOGS and duration of illness in the GD patients. CONCLUSION: We developed a generalizable classifier for GD based on information of functional connections between brain regions at resting state.