The impact of tonsillectomy and/or adenoidectomy on cognitive function and brain structure in pediatric patients with OSAHS.

BACKGROUND: Tonsillectomy and/or adenoidectomy can treat children with obstructive sleep apnea/hypopnea syndrome (OSAHS). OBJECTIVE: This study investigated the effects of tonsillectomy and/or adenoidectomy on cognitive function and brain structure in children with OSAHS. METHODS: This study included 40 obstructive sleep apnea/hypopnea syndrome children and 40 healthy children. The cognitive function and brain structure changes of OSAHS children before and after surgery and 40 healthy children were evaluated by the Swanson, Nolan, and Pelham Rating Scale (SNAP-IV) and the Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), as well as brain resting-state fMRI functional magnetic resonance imaging (rs-fMRI). RESULTS: Children with OSAHS showed higher Swanson, Nolan, and Pelham Rating Scale and lower Integrated Visual and Auditory Continuous Performance Test scores than healthy peers, indicating cognitive impairment. Post-surgery, there was a significant improvement in cognitive function, evidenced by decreased Swanson, Nolan, and Pelham Rating Scale and increased Integrated Visual and Auditory Continuous Performance Test scores. Compared to healthy children, OSAHS children displayed altered ReHo values in certain brain regions, such as decreased values in the right angular gyrus, right precuneus, left parahippocampal gyrus, and left middle frontal gyrus, but increased values in the right posterior cerebellum. After surgery, ReHo values increased in regions like the right precuneus, right temporal lobe, right posterior cingulate gyrus, and left limbic lobe, suggesting neurological changes associated with treatment. CONCLUSIONS: Children with obstructive sleep apnea/hypopnea syndrome had cognitive impairment and abnormal changes in multiple brain regions. Tonsillectomy and/or adenoidectomy could improve cognitive function and contribute to the reconstruction of brain function and structure in children with obstructive sleep apnea/hypopnea syndrome.

Bilinear Perceptual Fusion Algorithm Based on Brain Functional and Structural Data for ASD Diagnosis and Regions of Interest Identification.

Autism spectrum disorder (ASD) is a serious mental disorder with a complex pathogenesis mechanism and variable presentation among individuals. Although many deep learning algorithms have been used to diagnose ASD, most of them focus on a single modality of data, resulting in limited information extraction and poor stability. In this paper, we propose a bilinear perceptual fusion (BPF) algorithm that leverages data from multiple modalities. In our algorithm, different schemes are used to extract features according to the characteristics of functional and structural data. Through bilinear operations, the associations between the functional and structural features of each region of interest (ROI) are captured. Then the associations are used to integrate the feature representation. Graph convolutional neural networks (GCNs) can effectively utilize topology and node features in brain network analysis. Therefore, we design a deep learning framework called BPF-GCN and conduct experiments on publicly available ASD dataset. The results show that the classification accuracy of BPF-GCN reached 82.35%, surpassing existing methods. This demonstrates the superiority of its classification performance, and the framework can extract ROIs related to ASD. Our work provides a valuable reference for the timely diagnosis and treatment of ASD.

Differences in subcortical functional connectivity in patients with epilepsy.

INTRODUCTION: Epilepsy is a disease characterized by abnormal paroxysmal bioelectrical activity in the brain cortex and subcortical structures. Seizures per se change brain metabolism in epileptic focus and in distal parts of the brain. However, interictal phenomena can also affect functional connectivity (FC) and brain metabolism in other parts of the brain. AIM OF STUDY: We hypothesised that epilepsy affects functional connectivity not only among cortical, but also between subcortical, structures of the brain in a resting state condition. CLINICAL RATIONALE FOR STUDY: Investigating functional connectivity in patients with epilepsy could provide insights into the underlying pathophysiological mechanisms. Better understanding may lead to more effective treatment strategies. MATERIAL AND METHODS: Functional connectivity was analysed in 35 patients with epilepsy and in 28 healthy volunteers. The group of patients was divided into generalised and focal epilepsy (temporal and extratemporal subgroups). Each patient and healthy volunteer underwent an fMRI resting-state session. During the study, EEG signals were simultaneously recorded with fMRI to facilitate the subsequent detection of potential interictal epileptiform discharges (IEDs). Their potential impact on BOLD signals was mitigated through linear regression. The data was processed and correlation coefficients (FC values) between the BOLD signal from selected structures of the central nervous system were determined and compared between study groups. The results were presented as significant differences in correlation coefficients between brain/subcortical structures in the epilepsy and control groups. RESULTS: Lower FC values for the epilepsy group compared to the control group were shown for connections related to the MPFC, hippocampus, thalamus, amygdala, and the parahippocampal gyrus. CONCLUSIONS: Epilepsy alters the functional connectivity of resting state subcortical networks. Patterns of pathological changes of FC differ between epilepsy subtypes, with predominant lower FC between the hippocampus, parahippocampal gyrus, amygdala and thalamus in patients with epilepsy. CLINICAL IMPLICATIONS: This study suggests that epilepsy affects subcortical structures. Identifying distinct patterns of altered FC in epilepsy subtypes may help to tailor treatment strategies. Changes in FC detected by fMRI may precede clinical symptoms, aiding in the early diagnosis of cognitive and emotional disorders in focal epilepsy.

Association of synuclein alpha (SNCA) gene polymorphisms with spontaneous brain activity in patients with Parkinson's disease.

Background: The synuclein alpha (SNCA) gene responsible for encoding alpha-synuclein, is believed to play a crucial role in the pathogenesis of Parkinson's disease (PD). However, the specific impact of SNCA gene single-nucleotide polymorphisms (SNPs) on brain function in PD remains unclear. Therefore, this cross-sectional retrospective study, particularly through use of imaging analysis, aimed to characterize the relationship between SNCA gene SNPs and spontaneous brain activity in PD in order to enhance our understanding of the mechanisms underlying PD pathogenesis. Methods: A total of 63 patients with PD and 73 sex- and age-matched healthy control (HC) participants were recruited from outpatient and inpatient clinics at Fujian Medical University Union Hospital from August 2017 to November 2019, and all underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scanning. All participants were also examined to determine the correlation of different genotypes with regional brain activity measured by rs-fMRI using amplitude of low-frequency fluctuation (ALFF) analysis. Multivariate regression analysis was used to calculate the correlation between the brain function data and clinical features. All rs-fMRI data were analyzed with the SPM12 software and adjusted according to the false discovery rate (FDR) at the cluster level. Results: This study included 63 patients with PD and 73 sex- and age-matched healthy participants were included in the study. The spontaneous brain activity in the right superior cerebellum (Cerebelum_Crus1_R), vermis (Vermis_7), and left supplementary motor area (Supp_Motor_Area_L) of patients in the PD group was weak compared to that in the HC group. The z-score ALFF of left central posterior gyrus was positively correlated with the Mini-Mental State Examination score (r=0.542; P<0.001) in the PD group. For rs11931074, the main genotypic effects were found in the left inferior cerebellum (Cerebellum_9_L) and right anterior cingulate and paracingulate gyri (Cingulum_Ant_R); for rs356219 and rs356165, the main genotypic effects were found in the left caudate nucleus (Caudate_L). An interaction effect of disease with genotype was found in the right inferior parietal gyrus (Parietal_Inf_R) only for rs356219. Conclusions: Our study found a correlation of the SNCA SNPs rs11931074, rs356219, and rs356165 with brain functional alterations in patients with PD. Furthermore, an interaction effect was found in the right inferior parietal gyrus only for rs356219. This study may contribute to furthering the understanding of the influence of SNCA gene SNPs on brain function in patients with PD.

PD-ARnet: a deep learning approach for Parkinson's disease diagnosis from resting-state fMRI.

Objective. The clinical diagnosis of Parkinson's disease (PD) relying on medical history, clinical symptoms, and signs is subjective and lacks sensitivity. Resting-state fMRI (rs-fMRI) has been demonstrated to be an effective biomarker for diagnosing PD.Approach.This study proposes a deep learning approach for the automatic diagnosis of PD using rs-fMRI, named PD-ARnet. Specifically, PD-ARnet utilizes Amplitude of Low Frequency Fluctuations and Regional Homogeneity extracted from rs-fMRI as inputs. The inputs are then processed through a developed dual-branch 3D feature extractor to perform advanced feature extraction. During this process, a Correlation-Driven weighting module is applied to capture complementary information from both features. Subsequently, the Attention-Enhanced fusion module is developed to effectively merge two types of features, and the fused features are input into a fully connected layer for automatic diagnosis classification.Main results.Using 145 samples from the PPMI dataset to evaluate the detection performance of PD-ARnet, the results indicated an average classification accuracy of 91.6% (95% confidence interval [CI]: 90.9%, 92.4%), precision of 94.7% (95% CI: 94.2%, 95.1%), recall of 86.2% (95% CI: 84.9%, 87.4%), F1 score of 90.2% (95% CI: 89.3%, 91.1%), and AUC of 92.8% (95% CI: 91.1%, 95.0%).Significance.The proposed method has the potential to become a clinical auxiliary diagnostic tool for PD, reducing subjectivity in the diagnostic process, and enhancing diagnostic efficiency and consistency.

Static and dynamic changes in amplitude of Low-Frequency fluctuations in patients with Self-Limited epilepsy with centrotemporal Spikes (SeLECTS): A Resting-State fMRI study.

OBJECTIVE: This study aims to explore differences in the static and dynamic amplitude of low-frequency fluctuations (sALFF and dALFF) in resting-state functional MRI (rs-fMRI) data between patients with Benign childhood epilepsy with centrotemporal spikes (SeLECTS) and healthy controls (HCs). MATERIALS AND METHODS: We recruited 45 patient with SeLECTS and 55 HCs, employing rs-fMRI to assess brain activity. The analysis utilized a two-sample t-test for primary comparisons, supplemented by stratification and matching based on clinical and demographic characteristics to ensure comparability between groups. Post hoc analyses assessed the relationships between sALFF/dALFF alterations and clinical demographics, incorporating statistical adjustments for potential confounders and performing sensitivity analysis to test the robustness of our findings. RESULTS: Our analysis identified significant differences in sALFF and dALFF between patient with SeLECTS and HCs. Notably, increases in sALFF and dALFF were observed in the right middle temporal gyrus and left superior temporal gyrus among patient with SeLECTS, while a decrease in dALFF was seen in the right cerebellum crus 1. Additionally, a positive correlation was found between abnormal dALFF variability in specific brain regions and various clinical and demographic factors of patient with SeLECTS, with age being one such influential factor. CONCLUSION: This investigation provides insights into the assessment of local brain activity in SeLECTS through both static and dynamic approaches. It highlights the significance of non-invasive neuroimaging techniques in understanding the complexities of epilepsy syndromes like SeLECTS and emphasizes the need to consider a range of clinical and demographic factors in neuroimaging studies of neurological disorders.

Alterations in functional brain connectivity associated with developmental dyscalculia.

BACKGROUND AND PURPOSE: In recent years, there has been a growing interest in the study of resting neural networks in different neurological and mental disorders. While previous studies suggest that the default mode network (DMN) may be altered in dyscalculia, the study of resting-state networks in the development of numerical skills, especially in children with developmental dyscalculia (DD), is scarce and relatively recent. Based on this, this study examines differences in resting-state functional connectivity (rs-FC) data of children with DD using functional connectivity multivariate pattern analysis (fc-MVPA), a data-driven methodology that summarizes properties of the entire connectome. METHODS: We performed fc-MVPA on resting-state images of a sample composed of a group of children with DD (n = 19, 8.06 ± 0.87 years) and an age- and sex-matched control group of typically developing children (n = 23, 7.76 ± 0.46 years). RESULTS: Analysis of fc-MVPA showed significant differences between group connectivity profiles in two clusters allocated in both the right and left medial temporal gyrus. Post hoc effect size results revealed a decreased rs-FC between each temporal pole and the DMN in children with DD and an increased rs-FC between each temporal pole and the sensorimotor network. CONCLUSIONS: Our results suggest an aberrant information flow between resting-state networks in children with DD, demonstrating the importance of these networks for arithmetic development.

Investigating changes of functional brain networks in major depressive disorder by graph theoretical analysis of resting-state fMRI.

BACKGROUND: Major Depressive Disorder (MDD), as a chronic mental disorder, causes changes in mood, thoughts, and behavior. The pathophysiology of the disorder and its treatment are still unknown. One of the most notable changes observed in patients with MDD through fMRI is abnormal functional brain connectivity. METHODS: Preprocessed data from 60 MDD patients and 60 normal controls (NCs) were selected, which has been performed using the DPARSF toolbox. The whole-brain functional networks and topologies were extracted using graph theory-based methods. A two-sample, two-tailed t-test was used to compare the topological features of functional brain networks between the MDD and NCs groups using the DPABI-Net/Statistical Analysis toolbox. RESULTS: The obtained results showed a decrease in both global and local efficiency in MDD patients compared to NCs, and specifically, MDD patients showed significantly higher path length values. Acceptable p-values were obtained with a small sample size and less computational volume compared to the other studies on large datasets. At the node level, MDD patients showed decreased and relatively decreased node degrees in the sensorimotor network (SMN) and the dorsal attention network (DAN), respectively, as well as decreased node efficiency in the SMN, default mode network (DMN), and DAN. Also, MDD patients showed slightly decreased node efficiency in the visual networks (VN) and the ventral attention network (VAN), which were reported after FDR correction with Q < 0.05. LIMITATIONS: All participants were Chinese. CONCLUSIONS: Collectively, increased path length, decreased global and local efficiency, and also decreased nodal degree and efficiency in the SMN, DAN, DAN, VN, and VAN were found in patients compared to NCs.

LRE-MMF: A novel multi-modal fusion algorithm for detecting neurodegeneration in Parkinson's disease among the geriatric population.

Parkinson's disease (PD) is a prevalent neurological disorder characterized by progressive dopaminergic neuron loss, leading to both motor and non-motor symptoms. Early and accurate diagnosis is challenging due to the subtle and variable nature of early symptoms. This study aims to address these diagnostic challenges by proposing a novel method, Localized Region Extraction and Multi-Modal Fusion (LRE-MMF), designed to enhance diagnostic accuracy through the integration of structural MRI (sMRI) and resting-state functional MRI (rs-fMRI) data. The LRE-MMF method utilizes the complementary strengths of sMRI and rs-fMRI: sMRI provides detailed anatomical information, while rs-fMRI captures functional connectivity patterns. We applied this approach to a dataset consisting of 20 PD patients and 20 healthy controls (HC), all scanned with a 3 T MRI. The primary objective was to determine whether the integration of sMRI and rs-fMRI through the LRE-MMF method improves the classification accuracy between PD and HC subjects. LRE-MMF involves the division of imaging data into localized regions, followed by feature extraction and dimensionality reduction using Principal Component Analysis (PCA). The resulting features were fused and processed through a neural network to learn high-level representations. The model achieved an accuracy of 75 %, with a precision of 0.8125, recall of 0.65, and an AUC of 0.8875. The validation accuracy curves indicated good generalization, with significant brain regions identified, including the caudate, putamen, thalamus, supplementary motor area, and precuneus, as per the AAL atlas. These results demonstrate the potential of the LRE-MMF method for improving early diagnosis and understanding of PD by effectively utilizing both sMRI and rs-fMRI data. This approach could contribute to the development of more accurate diagnostic tools.

Altered brain function in treatment-resistant depression patients: A resting-state functional magnetic resonance imaging study.

BACKGROUND: It has been established that there are functional changes in the brain of treatment-resistant depression (TRD) patients, but previous studies of functional connectivity (FC) usually involved selection of regions of interest based on accumulated a priori knowledge of the disorder. In this study, we combine amplitude of low-frequency fluctuation (ALFF) and FC; this approach, based on the abnormal ALFF, may provide some insights into the neural basis of the disease in terms of fMRI signals of low-frequency fluctuations. METHODS: A total of 16 TRD patients, who visited the Qingdao Mental Health Center, Shandong Province, China between March 2023 and January 2024, along with 16 normal subjects, were enrolled into this study for functional imaging. In this study, we first explored the ALFF changes of TRD patients at a baseline resting state. Second, we selected the regions that were significantly changed in the ALFF as seeds and calculated the regional activity and functional connectivity (FC) of these regions using a seed-based approach. We also calculated correlations between the percent change in the PDQ-5D scores and ALFF values in brain regions with differing activity for TRD patients. RESULTS: During the baseline resting state, by using the ALFF, we found a significantly decreased or increased ALFF in the TRD patients relative to the controls. These regions were located in the left/right postcentral gyrus (PoCG.L/PoCG.R), right cuneus(CUN.R). We found that the ALFF values of the right hippocampus (HIP.R) in the TRD group were negatively correlated with the PDQ-5D score. Then, we selected these brain regions as seeds to investigate the FC changes in brains of TRD patients. We found abnormal functional connectivity in left/right middle frontal gyrus(MFG.L/MFG.R), the right Inferior frontal gyrus, opercular part (IFGoperc.R), the left/right Anterior cingulate and paracingulate gyri (ACC.L/ACC.R), the right supramarginal gyrus (SMG.R), and the right Calcarine fissure and surrounding cortex (CAL.R). CONCLUSION: We found a larger range of altered brain regions in TRD patients compared to healthy controls, especially in the central executive network (CEN), salience network (SN) and default mode network (DMN).

Aberrant resting-state voxel-mirrored homotopic connectivity in major depressive disorder with and without anxiety.

OBJECTIVE: Prior researchers have identified distinct differences in functional connectivity neuroimaging characteristics among MDD patients. However, the auxiliary diagnosis and subtype differentiation roles of VMHC values in MDD patients have yet to be fully understood. We aim to explore the separating ability of VMHC values in patients with anxious MDD or with non-anxious MDD and HCs. METHODS: We recruited 90 patients with anxious MDD, 69 patients with non-anxious MDD and 84 HCs. We collected a set of clinical variables included HAMD-17 scores, HAMA scores and rs-fMRI data. The data were analyzed combining difference analysis, SVM, correlation analysis and ROC analysis. RESULTS: Relative to HCs, non-anxious MDD patients displayed significant lower VMHC values in the insula and PCG, and anxious MDD patients displayed a significant decrease in VMHC values in the cerebellum_crus2, STG, postCG, MFG and IFG. Compared with non-anxious MDD patients, the anxious MDD showed significant enhanced VMHC values in the PCG. The VMHC values in the insula and cerebellum_crus2 regions showed a better ability to discriminate HCs from patients with non-anxious MDD or with anxious MDD. The VMHC values in PCG showed a better ability to discriminate patients with anxious MDD and non-anxious MDD patients. CONCLUSION: The VMHC values in the insula and cerebellum_crus2 regions could be served as imaging markers to differentiate HCs from patients with non-anxious MDD or with anxious MDD respectively. And the VMHC values in the PCG could be used to discriminate patients with anxious MDD from the non-anxious MDD patients.

Data-driven analysis of whole-brain intrinsic connectivity in patients with chronic low back pain undergoing osteopathic manipulative treatment.

BACKGROUND: Chronic Low Back Pain (cLBP) poses a significant health challenge, leading to functional disability and reduced quality of life. Osteopathic Manipulative Treatment (OMT) is emerging as a therapeutic option for cLBP, but the brain mechanisms underlying its analgesic effect remain unclear. MATERIALS AND METHODS: Thirty cLBP patients were randomly exposed to either four weekly sessions of OMT (N=16) or Sham treatment (N=14). Resting-state Magnetic Resonance Imaging (rs-MRI) scans and pain perception questionnaires were collected before and after treatment. A voxel-wise, rs-fMRI data-driven analysis was conducted to identify changes in the intrinsic functional connectivity across the whole brain that were associated with the OMT. Spearman's correlations were used to test for the association between changes in intrinsic connectivity and individual reports of pain perception. RESULTS: Compared to the Sham group, participants who received OMT showed significant alterations in the functional connectivity of several regions belonging to the pain matrix. Specifically, OMT was associated with decreased connectivity of a parietal cluster that includes the somatosensory cortex and an increase of connectivity of the right anterior insula and ventral and dorsal anterolateral prefrontal areas. Crucially, the change in connectivity strength observed in the ventral anterolateral prefrontal cortex, a putative region of the affective-reappraisive layer of the pain matrix, correlates with the reduction in pain perception caused by the OMT. CONCLUSIONS: This study offers insights into the brain mechanisms underlying the analgesic effect of OMT. Our findings support a link between OMT-driven functional cortical architecture alterations and improved clinical outcomes.

Increased between-network connectivity: A risk factor for tau elevation and disease progression.

One of the pathologic hallmarks of Alzheimer's disease (AD) is neurofibrillary tau tangles. Despite our knowledge that tau typically initiates in the medial temporal lobe (MTL), the mechanisms driving tau to spread beyond MTL remain unclear. Emerging evidence reveals distinct patterns of functional connectivity change during aging and preclinical AD: while connectivity within-network decreases, connectivity between-network increases. Building upon increased between-network connectivity, our study hypothesizes that this increase may play a critical role in facilitating tau spread in early stages. We conducted a longitudinal study over two to three years intervals on a cohort of 46 healthy elderly participants (mean age 64.23 ± 3.15 years, 26 females). Subjects were examined clinically and utilizing advanced imaging techniques that included resting-state functional MRI (rs-fMRI), structural magnetic resonance imaging (MRI), and a second-generation positron emission tomography (PET) tau tracer, 18F-MK6240. Through unsupervised agglomerative clustering and increase in between-network connectivity, we successfully identified individuals at increased risk of future tau elevation and AD progression. Our analysis revealed that individuals with increased between-network connectivity are more likely to experience more future tau deposition, entorhinal cortex thinning, and lower selective reminding test (SRT) delayed scores. Additionally, in the limbic network, we found a strong association between tau progression and increased between-network connectivity, which was mainly driven by beta-amyloid (Aß) positive participants. These findings provide evidence for the hypothesis that an increase in between-network connectivity predicts future tau deposition and AD progression, also enhancing our understanding of AD pathogenesis in the preclinical stages.

Alteration in early resting­state functional MRI activity in comatose survivors of cardiac arrest: a prospective cohort study.

BACKGROUND: This study aimed to explore the characteristics of abnormal regional resting-state functional magnetic resonance imaging (rs-fMRI) activity in comatose patients in the early period after cardiac arrest (CA), and to investigate their relationships with neurological outcomes. We also explored the correlations between jugular venous oxygen saturation (SjvO2) and rs-fMRI activity in resuscitated comatose patients. We also examined the relationship between the amplitude of the N20-baseline and the rs-fMRI activity within the intracranial conduction pathway of somatosensory evoked potentials (SSEPs). METHODS: Between January 2021 and January 2024, eligible post-resuscitated patients were screened to undergo fMRI examination. The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) of rs-fMRI blood oxygenation level-dependent (BOLD) signals were used to characterize regional neural activity. Neurological outcomes were evaluated using the Glasgow-Pittsburgh cerebral performance category (CPC) scale at 3 months after CA. RESULTS: In total, 20 healthy controls and 31 post-resuscitated patients were enrolled in this study. The rs-fMRI activity of resuscitated patients revealed complex changes, characterized by increased activity in some local brain regions and reduced activity in others compared to healthy controls (P < 0.05). However, the mean ALFF values of the whole brain were significantly greater in CA patients (P = 0.011). Among the clusters of abnormal rs-fMRI activity, the cluster values of ALFF in the left middle temporal gyrus and inferior temporal gyrus and the cluster values of ReHo in the right precentral gyrus, superior frontal gyrus and middle frontal gyrus were strongly correlated with the CPC score (P < 0.001). There was a strong correlation between the mean ALFF and SjvO2 in CA patients (r = 0.910, P < 0.001). The SSEP N20-baseline amplitudes in CA patients were negatively correlated with thalamic rs-fMRI activity (all P < 0.001). CONCLUSIONS: This study revealed that abnormal rs-fMRI BOLD signals in resuscitated patients showed complex changes, characterized by increased activity in some local brain regions and reduced activity in others. Abnormal BOLD signals were associated with neurological outcomes in resuscitated patients. The mean ALFF values of the whole brain were closely related to SjvO2 levels, and changes in the thalamic BOLD signals correlated with the N20-baseline amplitudes of SSEP responses. TRIAL REGISTRATION: NCT05966389 (Registered July 27, 2023).

Towards New Methodology for Cross-Validation of Clinical Evaluation Scales and Functional MRI in Psychiatry.

Objective biomarkers have been a critical challenge for the field of psychiatry, where diagnostic, prognostic, and theranostic assessments are still based on subjective narratives. Psychopathology operates with idiographic knowledge and subjective evaluations incorporated into clinical assessment inventories, but is considered to be a medical discipline and, as such, uses medical intervention methods (e.g., pharmacological, ECT; rTMS; tDCS) and, therefore, is supposed to operate with the language and methods of nomothetic networks. The idiographic assessments are provisionally "quantified" into "structured clinical scales" to in some way resemble nomothetic measures. Instead of fostering data merging and integration, this approach further encapsulates the clinical psychiatric methods, as all other biological tests (molecular, neuroimaging) are performed separately, only after the clinical assessment has provided diagnosis. Translational cross-validation of clinical assessment instruments and fMRI is an attempt to address the gap. The aim of this approach is to investigate whether there exist common and specific neural circuits, which underpin differential item responses to clinical self-rating scales during fMRI sessions in patients suffering from the two main spectra of mental disorders: schizophrenia and major depression. The current status of this research program and future implications to promote the development of psychiatry as a medical discipline are discussed.

ASD-GANNet: A Generative Adversarial Network-Inspired Deep Learning Approach for the Classification of Autism Brain Disorder.

The classification of a pre-processed fMRI dataset using functional connectivity (FC)-based features is considered a challenging task because of the set of high-dimensional FC features and the small dataset size. To tackle this specific set of FC high-dimensional features and a small-sized dataset, we propose here a conditional Generative Adversarial Network (cGAN)-based dataset augmenter to first train the cGAN on computed connectivity features of NYU dataset and use the trained cGAN to generate synthetic connectivity features per category. After obtaining a sufficient number of connectivity features per category, a Multi-Head attention mechanism is used as a head for the classification. We name our proposed approach "ASD-GANNet", which is end-to-end and does not require hand-crafted features, as the Multi-Head attention mechanism focuses on the features that are more relevant. Moreover, we compare our results with the six available state-of-the-art techniques from the literature. Our proposed approach results using the "NYU" site as a training set for generating a cGAN-based synthetic dataset are promising. We achieve an overall 10-fold cross-validation-based accuracy of 82%, sensitivity of 82%, and specificity of 81%, outperforming available state-of-the art approaches. A sitewise comparison of our proposed approach also outperforms the available state-of-the-art, as out of the 17 sites, our proposed approach has better results in the 10 sites.

Altered dynamic large-scale brain networks and combined machine learning in primary angle-closure glaucoma.

Primary angle-closure glaucoma (PACG) is a severe and irreversible blinding eye disease characterized by progressive retinal ganglion cell death. However, prior research has predominantly focused on static brain activity changes, neglecting the exploration of how PACG impacts the dynamic characteristics of functional brain networks. This study enrolled forty-four patients diagnosed with PACG and forty-four age, gender, and education level-matched healthy controls (HCs). The study employed Independent Component Analysis (ICA) techniques to extract resting-state networks (RSNs) from resting-state functional magnetic resonance imaging (rs-fMRI) data. Subsequently, the RSNs was utilized as the basis for examining and comparing the functional connectivity variations within and between the two groups of resting-state networks. To further explore, a combination of sliding time window and k-means cluster analyses identified seven stable and repetitive dynamic functional network connectivity (dFNC) states. This approach facilitated the comparison of dynamic functional network connectivity and temporal metrics between PACG patients and HCs for each state. Subsequently, a support vector machine (SVM) model leveraging functional connectivity (FC) and FNC was applied to differentiate PACG patients from HCs. Our study underscores the presence of modified functional connectivity within large-scale brain networks and abnormalities in dynamic temporal metrics among PACG patients. By elucidating the impact of changes in large-scale brain networks on disease evolution, researchers may enhance the development of targeted therapies and interventions to preserve vision and cognitive function in PACG.

Relationships between the gut microbiome and brain functional alterations in first-episode, drug-naïve patients with major depressive disorder.

OBJECTIVE: Increasing evidence has shown that the microbiota-gut-brain axis (MGB) is involved in the mechanism of major depressive disorder (MDD). However, the relationship between the gut microbiome and brain function in MDD patients has not been determined. Here, we intend to identify specific changes in the gut microbiome and brain function in first-episode, drug-naïve MDD patients and then explore the associations between the two omics to elucidate how the MGB axis plays a role in MDD development. METHODS: We recruited 38 first-episode, drug-naïve MDD patients and 37 healthy controls (HC). The composition of the fecal microbiome and neural spontaneous activity alterations were examined using 16S rRNA gene amplicon sequencing analysis and regional homogeneity (ReHo). Spearman correlation analyses were conducted to assess the associations between the gut microbiome and brain function. RESULTS: Compared with HC, MDD patients exhibited distinct alterations in the gut microbiota and elevated ReHo in the frontal regions. In the MDD group, a positive relationship was noted between the relative abundance of Blautia and the HAMD-17 and HAMA scores, as well as between the relative abundance of Oxalobacteraceae and the HAMD-17 score. The relative abundances of Porphyromonadaceae and Parabacteroides were negatively correlated with the ReHo values of frontal regions. LIMITATIONS: Our study utilized a cross-sectional design, and the number of subjects was relatively small. CONCLUSION: We found that some specific gut microbiomes were associated with frontal function, and others were associated with clinical symptoms in MDD patients, which may support the role of the MGB axis underlying MDD.

Abnormal static and dynamic brain network connectivity associated with chronic tinnitus.

In order to comprehensively understand the changes of brain networks in patients with chronic tinnitus, this study combined static and dynamic analysis methods to explore the abnormalities of brain networks. Thirty-two patients with chronic tinnitus and 30 age-, sex- and education-matched healthy controls (HC) were recruited. Independent component analysis was used to identify resting-state networks (RSNs). Static and dynamic functional network connectivity (FNC) were performed. The temporal properties of brain network including mean dwell time (MDT), fraction time (FT) and numbers of transitions (NT) were calculated. Two-sample t test and Spearman's correlation were used for group compares and correlation analysis. Four RSNs showed abnormal FNC including auditory network (AUN), default mode network (DMN), attention network (AN) and sensorimotor network (SMN). For static analysis, tinnitus patients showed significantly decreased FNC in AUN-DMN, AUN-AN, DMN-AN, and DMN-SMN than HC [p < 0.05, false discovery rate (FDR) corrected]. For dynamic analysis, tinnitus patients showed significantly decreased FNC in DMN-AN in state 3 (p < 0.05, FDR corrected). MDT in state 3 was significantly decreased in tinnitus patients (t = 2.039, P = 0.046). In the tinnitus group, the score of tinnitus functional index (TFI) was negatively correlated with MDT and FT in state 4, and the duration of tinnitus was positively correlated with FT in state 1 and NT. Chronic tinnitus causes abnormal brain network connectivity. These abnormal brain networks help to clarify the mechanism of tinnitus generation and chronicity, and provide a potential basis for the treatment of tinnitus.

Spatiotemporal consistency analysis of cerebral small vessel disease: an rs-fMRI study.

Introduction: Cerebral small vessel disease (SVD) affects older adults, but traditional approaches have limited the understanding of the neural mechanisms of SVD. This study aimed to explore the effects of SVD on brain regions and its association with cognitive decline using the four-dimensional (spatiotemporal) consistency of local neural activity (FOCA) method. Methods: Magnetic resonance imaging data from 42 patients with SVD and 38 healthy controls (HCs) were analyzed using the FOCA values. A two-sample t test was performed to compare the differences in FOCA values in the brain between the HCs and SVD groups. Pearson correlation analysis was conducted to analyze the association of various brain regions with SVD scores. Results: The results revealed that the FOCA values in the right frontal_inf_oper, right temporal_pole_sup, and default mode network decreased, whereas those in the temporal_inf, hippocampus, basal ganglia, and cerebellum increased, in patients with SVD. Most of these varying brain regions were negatively correlated with SVD scores. Discussion: This study suggested that the FOCA approach might have the potential to provide useful insights into the understanding of the neurophysiologic mechanisms of patients with SVD.