Study of a precise treatment protocol for patients with consciousness disorders based on the brain network analysis of functional magnetic resonance imaging.

Objective: How to conduct objective and accurate individualized assessments of patients with disorders of consciousness (DOC) and carry out precision rehabilitation treatment technology is a major rehabilitation problem that needs to be solved urgently. Methods: In this study, a multi-layer brain network was constructed based on functional magnetic resonance imaging (fMRI) to analyze the structural and functional brain networks of patients with DOC at different levels and to find regulatory targets (imaging markers) with recovery potential for DOC. Then repeated transcranial magnetic stimulation (rTMS) was performed in DOC patients to clinically validate. Results: The brain network connectivity of DOC patients with different consciousness states is different, and the most obvious brain regions appeared in the olfactory cortex and precuneus. rTMS stimulation could effectively improve the consciousness level of DOC patients and stimulate the occipital lobe (specific regions found in this study) and the dorsolateral prefrontal cortex (DLPFC), and both parts had a good consciousness recovery effect. Conclusion: In clinical work, personalized stimulation regimen treatment combined with the brain network characteristics of DOC patients can improve the treatment effect.

Brain sodium MRI-derived priors support the estimation of epileptogenic zones using personalized model-based methods in epilepsy.

Patients presenting with drug-resistant epilepsy are eligible for surgery aiming to remove the regions involved in the production of seizure activities, the so-called epileptogenic zone network (EZN). Thus the accurate estimation of the EZN is crucial. Data-driven, personalized virtual brain models derived from patient-specific anatomical and functional data are used in Virtual Epileptic Patient (VEP) to estimate the EZN via optimization methods from Bayesian inference. The Bayesian inference approach used in previous VEP integrates priors, based on the features of stereotactic-electroencephalography (SEEG) seizures' recordings. Here, we propose new priors, based on quantitative 23Na-MRI. The 23Na-MRI data were acquired at 7T and provided several features characterizing the sodium signal decay. The hypothesis is that the sodium features are biomarkers of neuronal excitability related to the EZN and will add additional information to VEP estimation. In this paper, we first proposed the mapping from 23Na-MRI features to predict the EZN via a machine learning approach. Then, we exploited these predictions as priors in the VEP pipeline. The statistical results demonstrated that compared with the results from current VEP, the result from VEP based on 23Na-MRI prior has better balanced accuracy, and the similar weighted harmonic mean of the precision and recall.

For the first time quantitative ²³Na-MRI were used as prior information to improve estimation of epileptogenic network (EZN) using VEP pipeline, a personalized whole-brain network modeling from patient's specific data. The prior information of EZN can be derived from ²³Na-MRI features using logistic regression predictions. The ²³Na-MRI priors inferred EZNs has a better balanced accuracy than the previously used priors or the no-prior condition.

An information-theoretic framework for conditional causality analysis of brain networks.

Identifying directed network models for multivariate time series is a ubiquitous problem in data science. Granger causality measure (GCM) and conditional GCM (cGCM) are widely used methods for identifying directed connections between time series. Both GCM and cGCM have frequency-domain formulations to characterize the dependence of time series in the spectral domain. However, the original methods were developed using a heuristic approach without rigorous theoretical explanations. To overcome the limitation, the minimum-entropy (ME) estimation approach was introduced in our previous work (Ning & Rathi, 2018) to generalize GCM and cGCM with more rigorous frequency-domain formulations. In this work, this information-theoretic framework is further generalized with three formulations for conditional causality analysis using techniques in control theory, such as state-space representations and spectral factorizations. The three conditional causal measures are developed based on different ME estimation procedures that are motivated by equivalent formulations of the classical minimum mean squared error estimation method. The relationship between the three formulations of conditional causality measures is analyzed theoretically. Their performance is evaluated using simulations and real neuroimaging data to analyze brain networks. The results show that the proposed methods provide more accurate network structures than the original approach.

This paper introduces a theoretical framework for causal inference in brain networks using time series measurements based on the principle of minimum-entropy regression. Three types of conditional causality measures are derived based on varying formulations of minimum-entropy regressions. The standard time-domain conditional Granger causality measure is formulated as a special case but with a different expression of the frequency-domain measure. The methods were evaluated using simulations and real resting-state functional MRI data of human brains and compared with standard Granger causality measures and directed transfer functions. Two new formulations of minimum-entropy-based causality measures showed better performance than other methods. The algorithms developed from this work may provide new insights to understand information flow in brain networks.

Network connectivity underlying information processing speed in children: Application of a pediatric brain tumor survivor injury model.

Elucidating how adaptive and maladaptive changes to the structural connectivity of brain networks influences neural synchrony, and how this structure-function coupling impacts cognition is an important question in human neuroscience. This study assesses these links in the default mode and executive control networks during resting state, a visual-motor task, and through computational modeling in the developing brain and in acquired brain injuries. Pediatric brain tumor survivors were used as an injury model as they are known to exhibit cognitive deficits, structural connectivity compromise, and perturbations in neural communication. Focusing on information processing speed to assess cognitive performance, we demonstrate that during the presence and absence of specific task demands, structural connectivity of these critical brain networks directly influences neural communication and information processing speed, and white matter compromise has an indirect adverse impact on reaction time via perturbed neural synchrony. Further, when our experimentally acquired structural connectomes simulated neural activity, the resulting functional simulations aligned with our empirical results and accurately predicted cognitive group differences. Overall, our synergistic findings further our understanding of the neural underpinnings of cognition and when it is perturbed. Further establishing alterations in structural-functional coupling as biomarkers of cognitive impairments could facilitate early intervention and monitoring of these deficits.

MMF-NNs: Multi-modal Multi-granularity Fusion Neural Networks for brain networks and its application to epilepsy identification.

Structural and functional brain networks are generated from two scan sequences of magnetic resonance imaging data, which can provide different perspectives for describing pathological changes caused by brain diseases. Recent studies found that fusing these two types of brain networks improves performance in brain disease identification. However, traditional fusion models combine these brain networks at a single granularity, ignoring the natural multi-granularity structure of brain networks that can be divided into the edge, node, and graph levels. To this end, this paper proposes a Multi-modal Multi-granularity Fusion Neural Networks (MMF-NNs) framework for brain networks, which integrates the features of the multi-modal brain network from global (i.e., graph-level) and local (i.e., edge-level and node-level) granularities to take full advantage of the topological information. Specifically, we design an interactive feature learning module at the local granularity to learn feature maps of structural and functional brain networks at the edge-level and the node-level, respectively. In that way, these two types of brain networks are fused during the feature learning process. At the global granularity, a multi-modal decomposition bilinear pooling module is designed to learn the graph-level joint representation of these brain networks. Experiments on real epilepsy datasets demonstrate that MMF-NNs are superior to several state-of-the-art methods in epilepsy identification.

Behavioral disorders in dementia with Lewy bodies: old and new knowledge.

Dementia with Lewy bodies (DLB), the second most common primary degenerative neurocognitive disorder after Alzheimer disease, is frequently preceded by REM sleep behavior disorders (RBD) and other behavioral symptoms, like anxiety, irritability, agitation or apathy, as well as visual hallucinations and delusions, most of which occurring in 40-60% of DLB patients. Other frequent behavioral symptoms like attention deficits contribute to cognitive impairment, while attention-deficit/hyperactivity disorder (ADHD) is a risk factor for DLB. Behavioral problems in DLB are more frequent, more severe and appear earlier than in other neurodegenerative diseases and, together with other neuropsychiatric symptoms, contribute to impairment of quality of life of the patients, but their pathophysiology is poorly understood. Neuroimaging studies displayed deficits in cholinergic brainstem nuclei and decreased metabolism in frontal, superior parietal regions, cingulate gyrus and amygdala in DLB. Early RBD in autopsy-confirmed DLB is associated with lower Braak neuritic stages, whereas those without RBD has greater atrophy of hippocampus and increased tau burden. αSyn pathology in the amygdala, a central region in the fear circuitry, may contribute to the high prevalence of anxiety, while in attention dysfunctions the default mode and dorsal attention networks displayed diverging activity. These changes suggest that behavioral disorders in DLB are associated with marked impairment in large-scale brain structures and functional connectivity network disruptions. However, many pathobiological mechanisms involved in the development of behavioral disorders in DLB await further elucidation in order to allow an early diagnosis and adequate treatment to prevent progression of these debilitating disorders.

Behavioral disorders in multiple sclerosis: a comprehensive review.

Multiple sclerosis (MS) is a heterogenous autoimmune-mediated disease of the central nervous system (CNS) characterized by inflammation, demyelination and chronic progressive neurodegeneration. Among its broad and unpredictable range of neuropsychiatric symptoms, behavioral changes are common, even from the early stages of the disease, while they are associated with cognitive deficits in advanced MS. According to DSM-5, behavioral disorders include attention deficits, oppositional, defiant and conduct disorders, anxiety, panic, obsessive-compulsive disorders (OCD), disruptive and emotional disorders, while others include also irritability, agitation, aggression and executive dysfunctions. Approximately 30 to 80% of individuals with MS demonstrate behavioral changes associated with disease progression. They are often combined with depression and other neuropsychiatric disorders, but usually not correlated with motor deficits, suggesting different pathomechanisms. These and other alterations contribute to disability in MS. While no specific neuropathological data for behavioral changes in MS are available, those in demyelination animal models share similarities with white matter and neuroinflammatory abnormalities in humans. Neuroimaging revealed prefrontal cortical atrophy, interhemispheric inhibition and disruption of fronto-striato-thalamic and frontoparietal networks. This indicates multi-regional patterns of cerebral disturbances within the MS pathology although their pathogenic mechanisms await further elucidation. Benefits of social, psychological, behavioral interventions and exercise were reported. Based on systematical analysis of PubMed, Google Scholar and Cochrane library, current epidemiological, clinical, neuroimaging and pathogenetic evidence are reviewed that may aid early identification of behavioral symptoms in MS, and promote new therapeutic targets and strategies.

Sex differences in human brain networks in normal and psychiatric populations from the perspective of small-world properties.

Females and males are known to be different in the prevalences of multiple psychiatric disorders, while the underlying neural mechanisms are unclear. Based on non-invasive neuroimaging techniques and graph theory, many researchers have tried to use a small-world network model to elucidate sex differences in the brain. This manuscript aims to compile the related research findings from the past few years and summarize the sex differences in human brain networks in both normal and psychiatric populations from the perspective of small-world properties. We reviewed published reports examining altered small-world properties in both the functional and structural brain networks between males and females. Based on four patterns of altered small-world properties proposed: randomization, regularization, stronger small-worldization, and weaker small-worldization, we found that current results point to a significant trend toward more regularization in normal females and more randomization in normal males in functional brain networks. On the other hand, there seems to be no consensus to date on the sex differences in small-world properties of the structural brain networks in normal populations. Nevertheless, we noticed that the sample sizes in many published studies are small, and future studies with larger samples are warranted to obtain more reliable results. Moreover, the number of related studies conducted in psychiatric populations is still limited and more investigations might be needed. We anticipate that these conclusions will contribute to a deeper understanding of the sex differences in the brain, which may be also valuable for developing new methods in the treatment of psychiatric disorders.

Provincial and connector qualities of somatosensory brain network hubs in bipolar disorder.

Brain network hubs are highly connected brain regions serving as important relay stations for information integration. Recent studies have linked mental disorders to impaired hub function. Provincial hubs mainly integrate information within their own brain network, while connector hubs share information between different brain networks. This study used a novel time-varying analysis to investigate whether hubs aberrantly follow the trajectory of other brain networks than their own. The aim was to characterize brain hub functioning in clinically remitted bipolar patients. We analyzed resting-state functional magnetic resonance imaging data from 96 euthymic individuals with bipolar disorder and 61 healthy control individuals. We characterized different hub qualities within the somatomotor network. We found that the somatomotor network comprised mainly provincial hubs in healthy controls. Conversely, in bipolar disorder patients, hubs in the primary somatosensory cortex displayed weaker provincial and stronger connector hub function. Furthermore, hubs in bipolar disorder showed weaker allegiances with their own brain network and followed the trajectories of the limbic, salience, dorsal attention, and frontoparietal network. We suggest that these hub aberrancies contribute to previously shown functional connectivity alterations in bipolar disorder and may thus constitute the neural substrate to persistently impaired sensory integration despite clinical remission.

A brain structure learning-guided multi-view graph representation learning for brain network analysis.

Background: Resting-state brain networks represent the interconnectivity of different brain regions during rest. Utilizing brain network analysis methods to model these networks can enhance our understanding of how different brain regions collaborate and communicate without explicit external stimuli. However, analyzing resting-state brain networks faces challenges due to high heterogeneity and noise correlation between subjects. This study proposes a brain structure learning-guided multi-view graph representation learning method to address the limitations of current brain network analysis and improve the diagnostic accuracy (ACC) of mental disorders. Methods: We first used multiple thresholds to generate different sparse levels of brain networks. Subsequently, we introduced graph pooling to optimize the brain network representation by reducing noise edges and data inconsistency, thereby providing more reliable input for subsequent graph convolutional networks (GCNs). Following this, we designed a multi-view GCN to comprehensively capture the complexity and variability of brain structure. Finally, we employed an attention-based adaptive module to adjust the contributions of different views, facilitating their fusion. Considering that the Smith atlas offers superior characterization of resting-state brain networks, we utilized the Smith atlas to construct the graph network. Results: Experiments on two mental disorder datasets, the Autism Brain Imaging Data Exchange (ABIDE) dataset and the Mexican Cocaine Use Disorders (SUDMEX CONN) dataset, show that our model outperforms the state-of-the-art methods, achieving nearly 75% ACC and 70% area under the receiver operating characteristic curve (AUC) on both datasets. Conclusions: These findings demonstrate that our method of combining multi-view graph learning and brain structure learning can effectively capture crucial structural information in brain networks while facilitating the acquisition of feature information from diverse perspectives, thereby improving the performance of brain network analysis.

Right-to-left shunt-associated brain functional changes in migraine: evidences from a resting-state FMRI study.

Background: Migraine, a neurological condition perpetually under investigation, remains shrouded in mystery regarding its underlying causes. While a potential link to Right-to-Left Shunt (RLS) has been postulated, the exact nature of this association remains elusive, necessitating further exploration. Methods: The amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), regional homogeneity (ReHo) and functional connectivity (FC) were employed to investigate functional segregation and functional integration across distinct brain regions. Graph theory-based network analysis was utilized to assess functional networks in migraine patients with RLS. Pearson correlation analysis further explored the relationship between RLS severity and various functional metrics. Results: Compared with migraine patients without RLS, patients with RLS exhibited a significant increase in the ALFF within left middle occipital and superior occipital gyrus; In migraine patients with RLS, significantly reduced brain functional connectivity was found, including the connectivity between default mode network and visual network, ventral attention network, as well as the intra-functional connectivity of somatomotor network and its connection with the limbic network, and also the connectivity between the left rolandic operculum and the right middle cingulate gyrus. Notably, a significantly enhanced functional connectivity between the frontoparietal network and the ventral attention network was found in migraine with RLS; Patients with RLS displayed higher values of the normalized clustering coefficient and greater betweenness centrality in specific regions, including the left precuneus, right insula, and right inferior temporal gyrus. Additionally, these patients displayed a diminished nodal degree in the occipital lobe and reduced nodal efficiency within the fusiform gyrus; Further, the study found positive correlations between ALFF in the temporal lobes, thalamus, left middle occipital, and superior occipital gyrus and RLS severity. Conversely, negative correlations emerged between ALFF in the right inferior frontal gyrus, middle frontal gyrus, and insula and RLS grading. Finally, the study identified a positive correlation between angular gyrus betweenness centrality and RLS severity. Conclusion: RLS-associated brain functional alterations in migraine consisted of local brain regions, connectivity, and networks involved in pain conduction and regulation did exist in migraine with RLS.

Abnormal Structural-Functional Coupling and MRI Alterations of Brain Network Topology in Progressive Supranuclear Palsy.

BACKGROUND: Progressive supranuclear palsy (PSP) can cause structural and functional brain reconstruction. There is a lack of knowledge about the consistency between structural-functional (S-F) connection networks in PSP, despite growing evidence of anomalies in various single brain network parameters. PURPOSE: To study the changes in the structural and functional networks of PSP, network's topological properties including degree, and the consistency of S-F coupling. The relationship with clinical scales was examined including the assessment of PSP severity, and so on. STUDY TYPE: Retrospective. SUBJECTS: A total of 51 PSP patients (70.04 ± 7.46, 25 females) and 101 healthy controls (64.58 ± 8.84, 58 females). FIELD STRENGTH/SEQUENCE: 3-T, resting-state functional MRI, diffusion tensor imaging, and T1-weighted images. ASSESSMENT: A graph-theoretic approach was used to evaluate structural and functional network topology metrics. We used the S-F coupling changes to explore the consistency of structural and functional networks. STATISTICAL TESTS: Independent samples t tests were employed for continuous variables, χ2 tests were used for categorical variables. For network analysis, two-sample t tests was used and implied an false discovery rate (FDR) correction. Pearson correlation analysis was used to explore the correlations. A P-value <0.05 was considered statistically significant. RESULTS: PSP showed variations within and between modules. Specifically, PSP had decreased network properties changes (t = -2.0136; t = 2.5409; t = -2.5338; t = -2.4296; t = -2.5338; t = 2.8079). PSP showed a lower coupling in the thalamus and left putamen and a higher coupling in the visual, somatomotor, dorsal attention, and ventral attention network. S-F coupling was related to the number of network connections (r = 0.32, r = 0.22) and information transmission efficiency (r = 0.55, r = 0.28). S-F coupling was related to basic academic ability (r = 0.39) and disinhibition (r = 0.49). DATA CONCLUSION: PSP may show abnormal S-F coupling and intramodular and intermodular connectome in the structural and functional networks. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 3.

Contrastive Graph Pooling for Explainable Classification of Brain Networks.

Functional magnetic resonance imaging (fMRI) is a commonly used technique to measure neural activation. Its application has been particularly important in identifying underlying neurodegenerative conditions such as Parkinson's, Alzheimer's, and Autism. Recent analysis of fMRI data models the brain as a graph and extracts features by graph neural networks (GNNs). However, the unique characteristics of fMRI data require a special design of GNN. Tailoring GNN to generate effective and domain-explainable features remains challenging. In this paper, we propose a contrastive dual-attention block and a differentiable graph pooling method called ContrastPool to better utilize GNN for brain networks, meeting fMRI-specific requirements. We apply our method to 5 resting-state fMRI brain network datasets of 3 diseases and demonstrate its superiority over state-of-the-art baselines. Our case study confirms that the patterns extracted by our method match the domain knowledge in neuroscience literature, and disclose direct and interesting insights. Our contributions underscore the potential of ContrastPool for advancing the understanding of brain networks and neurodegenerative conditions. The source code is available at https://github.com/AngusMonroe/ContrastPool.

Establishing group-level brain structural connectivity incorporating anatomical knowledge under latent space modeling.

Brain structural connectivity, capturing the white matter fiber tracts among brain regions inferred by diffusion MRI (dMRI), provides a unique characterization of brain anatomical organization. One fundamental question to address with structural connectivity is how to properly summarize and perform statistical inference for a group-level connectivity architecture, for instance, under different sex groups, or disease cohorts. Existing analyses commonly summarize group-level brain connectivity by a simple entry-wise sample mean or median across individual brain connectivity matrices. However, such a heuristic approach fully ignores the associations among structural connections and the topological properties of brain networks. In this project, we propose a latent space-based generative network model to estimate group-level brain connectivity. Within our modeling framework, we incorporate the anatomical information of brain regions as the attributes of nodes to enhance the plausibility of our estimation and improve biological interpretation. We name our method the attributes-informed brain connectivity (ABC) model, which compared with existing group-level connectivity estimations, (1) offers an interpretable latent space representation of the group-level connectivity, (2) incorporates the anatomical knowledge of nodes and tests its co-varying relationship with connectivity and (3) quantifies the uncertainty and evaluates the likelihood of the estimated group-level effects against chance. We devise a novel Bayesian MCMC algorithm to estimate the model. We evaluate the performance of our model through extensive simulations. By applying the ABC model to study brain structural connectivity stratified by sex among Alzheimer's Disease (AD) subjects and healthy controls incorporating the anatomical attributes (volume, thickness and area) on nodes, our method shows superior predictive power on out-of-sample structural connectivity and identifies meaningful sex-specific network neuromarkers for AD.

Predictive modeling based on functional connectivity of interictal scalp EEG for infantile epileptic spasms syndrome.

OBJECTIVE: This study aims to delineate the electrophysiological variances between patients with infantile epileptic spasms syndrome (IESS) and healthy controls and to devise a predictive model for long-term seizure outcomes. METHODS: The cohort consisted of 30 individuals in the seizure-free group, 23 in the seizure-residual group, and 20 in the control group. We conducted a comprehensive analysis of pretreatment electroencephalography, including the relative power spectrum (rPS), weighted phase-lag index (wPLI), and network metrics. Follow-up EEGs at 2 years of age were also analyzed to elucidate physiological changes among groups. RESULTS: Infants in the seizure-residual group exhibited increased rPS in theta and alpha bands at IESS onset compared to the other groups (all p < 0.0001). The control group showed higher rPS in fast frequency bands, indicating potentially enhanced cognitive function. The seizure-free group presented increased wPLI across all frequency bands (all p < 0.0001). Our predictive model utilizing wPLI anticipated long-term outcomes at IESS onset (area under the curve 0.75). CONCLUSION: Our findings demonstrated an initial "hypersynchronous state" in the seizure-free group, which was ameliorated following successful treatment. SIGNIFICANCE: This study provides a predictive model utilizing functional connectivity and insights into the diverse electrophysiology observed among outcome groups of IESS.

A Novel Brain Network Analysis Method for Pediatric ADHD Using RFE-GA Feature Selection Strategy.

Attention Deficit Hyperactivity Disorder (ADHD) is a highly prevalent childhood disorder, and related research has been increasing in recent years. However, it remains a challenging issue to accurately identify individuals with ADHD. The research proposes a method for ADHD detection using Recursive Feature Elimination-Genetic Algorithm (RFE-GA) for the feature selection of EEG data. Firstly, this study employed Transfer Entropy (TE) to construct brain networks from the EEG data of the ADHD and Normal groups, conducting an analysis of effective connectivity to unveil causal relationships in the brain's information exchange activities. Subsequently, a dual-layer feature selection method combining Recursive Feature Elimination (RFE) and Genetic Algorithm (GA) was proposed. Using the global search capability of GA and the feature selection ability of RFE, the performance of each feature subset is evaluated to find the optimal feature subset. Finally, a Support Vector Machine (SVM) classifier was employed to classify the ultimate feature set. The results revealed the control group exhibited lower connectivity strength in the left temporal alpha and beta bands, but higher frontal connectivity strength compared to the ADHD group. Additionally, in the gamma frequency band, the control group had higher top lobe connectivity strength than the ADHD group. Through the RFE-GA feature selection method, the optimized feature set was more concise, achieving classification accuracies of 91.3%, 94.1%, and 90.7% for the alpha, beta, and gamma frequency bands, respectively. The proposed RFE-GA feature selection method significantly reduced the number of features, thereby improving classification accuracy. .

Altered functional brain connectivity, efficiency, and information flow associated with brain fog after mild to moderate COVID-19 infection.

COVID-19 is associated with increased risk for cognitive decline but very little is known regarding the neural mechanisms of this risk. We enrolled 49 adults (55% female, mean age = 30.7 ± 8.7), 25 with and 24 without a history of COVID-19 infection. We administered standardized tests of cognitive function and acquired brain connectivity data using MRI. The COVID-19 group demonstrated significantly lower cognitive function (W = 475, p < 0.001, effect size r = 0.58) and lower functional connectivity in multiple brain regions (mean t = 3.47 ±0.36, p = 0.03, corrected, effect size d = 0.92 to 1.5). Hypo-connectivity of these regions was inversely correlated with subjective cognitive function and directly correlated with fatigue (p < 0.05, corrected). These regions demonstrated significantly reduced local efficiency (p < 0.026, corrected) and altered effective connectivity (p < 0.001, corrected). COVID-19 may have a widespread effect on the functional connectome characterized by lower functional connectivity and altered patterns of information processing efficiency and effective information flow. This may serve as an adaptation to the pathology of SARS-CoV-2 wherein the brain can continue functioning at near expected objective levels, but patients experience lowered efficiency as brain fog.

Aberrant Flexibility of Dynamic Brain Network in Patients with Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a collection of neurodevelopmental disorders whose pathobiology remains elusive. This study aimed to investigate the possible neural mechanisms underlying ASD using a dynamic brain network model and a relatively large-sample, multi-site dataset. Resting-state functional magnetic resonance imaging data were acquired from 208 ASD patients and 227 typical development (TD) controls, who were drawn from the multi-site Autism Brain Imaging Data Exchange (ABIDE) database. Brain network flexibilities were estimated and compared between the ASD and TD groups at both global and local levels, after adjusting for sex, age, head motion, and site effects. The results revealed significantly increased brain network flexibilities (indicating a decreased stability) at the global level, as well as at the local level within the default mode and sensorimotor areas in ASD patients than TD participants. Additionally, significant ASD-related decreases in flexibilities were also observed in several occipital regions at the nodal level. Most of these changes were significantly correlated with the Autism Diagnostic Observation Schedule (ADOS) total score in the entire sample. These results suggested that ASD is characterized by significant changes in temporal stabilities of the functional brain network, which can further strengthen our understanding of the pathobiology of ASD.

An Integrated Electroencephalography and Eye-Tracking Analysis Using eXtreme Gradient Boosting for Mental Workload Evaluation in Surgery.

OBJECTIVE: We aimed to develop advanced machine learning models using electroencephalogram (EEG) and eye-tracking data to predict the mental workload associated with engaging in various surgical tasks. BACKGROUND: Traditional methods of evaluating mental workload often involve self-report scales, which are subject to individual biases. Due to the multidimensional nature of mental workload, there is a pressing need to identify factors that contribute to mental workload across different surgical tasks. METHOD: EEG and eye-tracking data from 26 participants performing Matchboard and Ring Walk tasks from the da Vinci simulator and the pattern cut and suturing tasks from the Fundamentals of Laparoscopic Surgery (FLS) program were used to develop an eXtreme Gradient Boosting (XGBoost) model for mental workload evaluation. RESULTS: The developed XGBoost models demonstrated strong predictive performance with R2 values of 0.82, 0.81, 0.82, and 0.83 for the Matchboard, Ring Walk, pattern cut, and suturing tasks, respectively. Key features for predicting mental workload included task average pupil diameter, complexity level, average functional connectivity strength at the temporal lobe, and the total trajectory length of the nondominant eye's pupil. Integrating features from both EEG and eye-tracking data significantly enhanced the performance of mental workload evaluation models, as evidenced by repeated-measures t-tests yielding p-values less than 0.05. However, this enhancement was not observed in the Pattern Cut task (repeated-measures t-tests; p > 0.05). CONCLUSION: The findings underscore the potential for machine learning and multidimensional feature integration to predict mental workload and thereby improve task design and surgical training. APPLICATION: The advanced mental workload prediction models could serve as instrumental tools to enhance our understanding of surgeons' cognitive demands and significantly improve the effectiveness of surgical training programs.

Metabolic network connectivity disturbances in Parkinson's disease: a novel imaging biomarker.

The diagnosis of Parkinson's Disease (PD) presents ongoing challenges. Advances in imaging techniques like 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) have highlighted metabolic alterations in PD, yet the dynamic network interactions within the metabolic connectome remain elusive. To this end, we examined a dataset comprising 49 PD patients and 49 healthy controls. By employing a personalized metabolic connectome approach, we assessed both within- and between-network connectivities using Standard Uptake Value (SUV) and Jensen-Shannon Divergence Similarity Estimation (JSSE). A random forest algorithm was utilized to pinpoint key neuroimaging features differentiating PD from healthy states. Specifically, the results revealed heightened internetwork connectivity in PD, specifically within the somatomotor (SMN) and frontoparietal (FPN) networks, persisting after multiple comparison corrections (P < 0.05, Bonferroni adjusted for 10% and 20% sparsity). This altered connectivity effectively distinguished PD patients from healthy individuals. Notably, this study utilizes 18F-FDG PET imaging to map individual metabolic networks, revealing enhanced connectivity in the SMN and FPN among PD patients. This enhanced connectivity may serve as a promising imaging biomarker, offering a valuable asset for early PD detection.