Neurodynamic observations indicate that the cerebral cortex evolved by self-organizing into functional networks, These networks, or distributed clusters of regions, display various degrees of attention maps based on input. Traditionally, the study of network self-organization relies predominantly on static data, overlooking temporal information in dynamic neuromorphic data. This paper proposes Temporal Self-Organizing (TSO) method for neuromorphic data processing using a spiking neural network. The TSO method incorporates information from multiple time steps into the selection strategy of the Best Matching Unit (BMU) neurons. It enables the coupled BMUs to radiate the weight across the same layer of neurons, ultimately forming a hierarchical self-organizing topographic map of concern. Additionally, we simulate real neuronal dynamics, introduce a glial cell-mediated Glial-LIF (Leaky Integrate-and-fire) model, and adjust multiple levels of BMUs to optimize the attention topological map.Experiments demonstrate that the proposed Self-organizing Glial Spiking Neural Network (SG-SNN) can generate attention topographies for dynamic event data from coarse to fine. A heuristic method based on cognitive science effectively guides the network's distribution of excitatory regions. Furthermore, the SG-SNN shows improved accuracy on three standard neuromorphic datasets: DVS128-Gesture, CIFAR10-DVS, and N-Caltech 101, with accuracy improvements of 0.3%, 2.4%, and 0.54% respectively. Notably, the recognition accuracy on the DVS128-Gesture dataset reaches 99.3%, achieving state-of-the-art (SOTA) performance.

Juvenile myoclonic epilepsy (JME) exhibits abnormal functional connectivity of brain networks at multiple frequencies. We used the multilayer network model to address the heterogeneous features at different frequencies and assess the mechanisms of functional integration and segregation of brain networks in JME patients. To address the possibility of false edges or missing edges during network construction, we combined multilayer networks with link prediction techniques. Resting-state functional magnetic resonance imaging (rs-fMRI) data were procured from 40 JME patients and 40 healthy controls. The Multilayer Network framework is utilized to integrate information from different frequency bands and to fuse similarity metrics for link prediction. Finally, calculate the entropy of the multiplex degree and multilayer clustering coefficient of the reconfigured multilayer frequency network. The results showed that the multilayer brain network of JME patients had significantly reduced ability to integrate and separate information and significantly correlated with severity of JME symptoms. This difference was particularly evident in default mode network (DMN), motor and somatosensory network (SMN), and auditory network (AN). In addition, significant differences were found in the precuneus, suboccipital gyrus, middle temporal gyrus, thalamus, and insula. Results suggest that JME patients have abnormal brain function and reduced cross-frequency interactions. This may be due to changes in the distribution of connections within and between the DMN, SMN, and AN in multiple frequency bands, resulting in unstable connectivity patterns. The generation of these changes is related to the pathological mechanisms of JME and may exacerbate cognitive and behavioral problems in patients.

The online version contains supplementary material available at 10.1007/s11571-024-10191-0.

Individual variability preference is a typical characteristic of alcohol drinking behaviors, with a higher risk for the development of alcohol use disorders (AUDs) in high alcohol preference (HP) populations. Here, we created a map of alcohol-related brain regions through c-Fos profiling, and comparatively investigated the differences of functional neural networks between the HP mice and low alcohol preference (LP) mice. We found that neuronal activity in some brain regions, such as ventral tegmental area (VTA), was altered in both HP and LP mice, indicating that these neurons were universally sensitive to alcohol. Most importantly, several brain regions, such as the prefrontal cortex and insular cortex, exhibited significantly higher c-Fos expression in HP mice than that in LP mice and displayed broader and stronger neural connections across brain networks, suggesting that these brain regions are the potential targets for individual alcohol preference. Graph theory-based analysis unraveled a decrease in brain modularity in HP networks, yet with more centralized connection patterns, and maintained higher communication efficiency and redundancy. Furthermore, LP mice switched the central network hubs, with the key differential network centered on nucleus accumbens shell (NAc Sh), nucleus accumbens core (NAc C), VTA, and anterior insular cortex (AIC), indicating that these brain regions and related neural circuits, such as NAc Sh-AIC may be involved in regulating individual alcohol preference. These results provide novel insights into the neural connections governing individual preferences to alcohol consumption, which may contribute to AUDs prediction and pharmacotherapy.

Long-term exposure to fluoxetine and other selective serotonin reuptake inhibitors alters social and anxiety-related behaviours, including social withdrawal, which is a symptom of several neuropsychiatric disorders. Adaptive changes in serotonergic neurotransmission likely mediate this delayed effect, although the exact mechanisms are still unclear. Here we investigated the functional circuitry underlying the biphasic effects of fluoxetine on social approach-avoidance behaviour and explored the place of serotonergic dorsal raphe nucleus (DR) ensembles in this network, using c-Fos-immunoreactivity as a correlate of activity. Graph theory-based network analysis revealed changes in patterns of functional connectivity and identified neuronal populations in the insular cortex (IC) and serotonergic populations in the DR as central targets to the prosocial effects of chronic fluoxetine. To determine the role of serotonergic projections to the IC, a retrograde tracer was micro-injected in the IC prior to fluoxetine treatment and social behaviour testing. Chronic fluoxetine increased c-Fos immunoreactivity in insula-projecting neurons of the rostral, ventral part of the DR (DRV). Using a virally delivered Tet-Off platform for temporally-controlled marking of neuronal activation, we observed that chronic fluoxetine may affect social behaviour by influencing independent but interconnected populations of serotonergic DR ensembles. These findings suggest that sustained fluoxetine exposure causes adaptive changes in functional connectivity due to altered serotonergic neurotransmission in DR projection targets, and the increased serotonergic signalling to the IC likely mediates some of the therapeutic effects of fluoxetine on social behaviour.

Cognitive sequelae are a concern in glioma patients postradiation therapy. As there is uncertainty regarding which brain regions to spare during radiation therapy to preserve cognition, we explored structural brain network hubs as potential organs at risk.

We conducted a cross-sectional study, involving 39 irradiated adult WHO grade 2 and 3 gliomas along with 50 healthy controls. Cognitive domains (language, memory, attention, motor-, and executive functioning) were assessed ≥1 year postradiation therapy. Using multishell diffusion-weighted imaging, weighted structural graphs were constructed, and graph measures calculated to define hubs. The association between mean radiation therapy (RT) dose in each region and nodal strength and cognitive domains were tested with a linear regression model and Spearman's rho correlations, respectively.

Lower nodal strength was significantly associated with increasing RT dose in 9 brain regions, significantly (McNemar test, P < .01) impacting hubs more often than nonhubs (58% vs 7%). Executive performance (r(37) ≥ -.474, PFDR ≤ .045) and attention (r(37) ≥ -.471, PFDR ≤ .045) were significantly correlated with RT doses to the left pre- and postcentral gyrus and right posterior cingulate cortex, whereas poorer language outcomes were observed in patients receiving higher doses to the left insula, superior frontal, and precentral gyrus (r(37) ≥ -.460, PFDR ≤ .045). These correlations were more prevalent in hubs than nonhubs (P = .33), and higher than those between memory and left (r(37) = -.359) and right (r(37) = .059) hippocampal dose.

Higher RT doses to specific brain regions, particularly left-sided hubs, were associated with reduced nodal strength (ie, lower network centrality) and poorer cognitive performance. Although baseline cognitive testing is unavailable and cognitive functioning is influenced by multiple factors, this study highlights the potential value of network- or hub-sparing RT dose planning. Future longitudinal studies are needed to validate these findings before clinical implementation.

Artificial neural networks (ANNs) were originally modeled after their biological counterparts, but have since conceptually diverged in many ways. The resulting network architectures are not well understood, and furthermore, we lack the quantitative tools to characterize their structures. Network science provides an ideal mathematical framework with which to characterize systems of interacting components, and has transformed our understanding across many domains, including the mammalian brain. Yet, little has been done to bring network science to ANNs. In this work, we propose tools that leverage and adapt network science methods to measure both global- and local-level characteristics of ANNs. Specifically, we focus on the structures of efficient multilayer perceptrons as a case study, which are sparse and systematically pruned such that they share many characteristics with real-world networks. We use adapted network science metrics to show that the pruning process leads to the emergence of a spanning subnetwork (lottery ticket multilayer perceptrons) with complex architecture. This complex network exhibits global and local characteristics, including heavy-tailed nodal degree distributions and dominant weighted pathways, that mirror patterns observed in human neuronal connectivity. Furthermore, alterations in network metrics precede catastrophic decay in performance as the network is heavily pruned. This network science-driven approach to the analysis of artificial neural networks serves as a valuable tool to establish and improve biological fidelity, increase the interpretability, and assess the performance of artificial neural networks. Significance Statement Artificial neural network architectures have become increasingly complex, often diverging from their biological counterparts in many ways. To design plausible "brain-like" architectures, whether to advance neuroscience research or to improve explainability, it is essential that these networks optimally resemble their biological counterparts. Network science tools offer valuable information about interconnected systems, including the brain, but have not attracted much attention for analyzing artificial neural networks. Here, we present the significance of our work: •We adapt network science tools to analyze the structural characteristics of artificial neural networks. •We demonstrate that organizational patterns similar to those observed in the mammalian brain emerge through the pruning process alone. The convergence on these complex network features in both artificial neural networks and biological brain networks is compelling evidence for their optimality in information processing capabilities. •Our approach is a significant first step towards a network science-based understanding of artificial neural networks, and has the potential to shed light on the biological fidelity of artificial neural networks.

This study investigates large-scale brain network alterations in cerebral amyloid angiopathy (CAA) using structural covariance network (SCN) analysis and graph theory based on 7 T MRI.

We employed structural covariance network (SCN) analysis based on cortical thickness data from ultra-high field 7 T MRI to investigate network alterations in CAA patients. Graph theoretical analysis was applied to quantify topological properties, including small-worldness, nodal centrality, and network efficiency. Between-group differences were assessed using permutation tests and false discovery rate (FDR) correction.

CAA patients exhibited significant alterations in small-world properties, with decreased Gamma (p = 0.002) and Sigma (p < 0.001), suggesting a shift toward a less optimal network configuration. Local efficiency was significantly different between groups (p = 0.045), while global efficiency remained unchanged (p = 0.127), indicating regionally disrupted rather than globally impaired network efficiency. At the nodal level, the right superior frontal gyrus exhibited increased betweenness centrality (p = 0.013), whereas the right banks of the superior temporal sulcus, left postcentral gyrus, and left superior temporal gyrus showed significantly reduced centrality (all p < 0.05). Additionally, nodal degree and efficiency were altered in key memory-related and association regions, including the entorhinal cortex, fusiform gyrus, and temporal pole.

SCN analysis combined with graph theory offers a valuable approach for understanding disease-related connectivity disruptions and may contribute to the development of network-based biomarkers for CAA.

This study investigated the relationship between fundus microvascular characteristics and the nodal local efficiency (Nle) of brain functional connectivity (FC), as well as their association with cognitive performance in a community-based cohort. A total of 1532 participants from Lishui City, China, were enrolled between May 2017 and September 2019 as part of the polyvascular evaluation for cognitive impairment and vascular events (PRECISE) study. Cognitive performance was assessed using the Montreal Cognitive Assessment-Beijing (MoCA-Beijing), and Nle was derived from resting-state functional magnetic resonance imaging (rs-fMRI). Fundus photography of the left eye was performed to measure microvascular features, including the central retinal arterial equivalent (CRAE), central retinal vein equivalent (CRVE), and their ratio (AVR). Correlations between fundus microvascular indices, cognitive function scores, and brain FC were analyzed. Notably, a wider CRVE was significantly associated with poorer naming scores on cognitive assessments. Several key brain regions, including the left orbital gyrus, right inferior temporal gyrus, left parahippocampal gyrus, bilateral posterior hippocampus, left fusiform gyrus, and left inferior parietal lobule, demonstrated significant correlations between fundus microvascular indices and brain FC. These regions played a crucial role in cognitive function and neural network connectivity. Overall, fundus microvascular characteristics were correlated with the indicators of brain FC related to cognitive function. Our findings suggest that fundus microvascular characteristics may serve as a potential non-invasive biomarker for detecting brain functional alterations linked to cognitive dysfunction in elderly populations.

Developmental dyslexia (DD) is a common reading disorder with neurological underpinnings; however, it remains unclear whether Chinese children with DD exhibit spectral power or network topology abnormalities. This study investigated spectral power and brain network topology abnormalities using electroencephalography (EEG) during resting states and a one-back Chinese-Korean character task in 85 Hong Kong Chinese children with DD and 51 typically developing peers (ages 7-11). EEG signals were transformed using the Fast Fourier Transform to estimate spectral power. Functional connectivity matrices were derived using the phase-lag index, and network topology was assessed via minimum spanning tree (MST) analysis. The results suggested that children with DD showed reduced alpha power over central, frontal, temporal, parietal, and occipital scalp areas at rest, and over central and frontal areas during the task. MST results revealed decreased beta band integration at rest but increased alpha band integration during the one-back task. Familiar Chinese stimuli elicited greater alpha and beta power and lower beta band integration compared to unfamiliar Korean stimuli. Moreover, resting-state beta band integration correlated positively with reading fluency in children with DD. These findings point to inhibitory control deficits and cortical hyperactivation in Chinese DD, reflected in disrupted large-scale network topology, and highlight the alpha band as a potential biomarker. They also demonstrate that language familiarity modulates neural efficiency and recruits compensatory networks. Overall, the study provides new insights into the neural basis of reading difficulties in Chinese children with DD.

Binge eating (BE), a transdiagnostic feature that occurs across eating disorders and in the general population, carries significant health risks even in the absence of a full-syndrome diagnosis. The limited efficacy of current treatments for binge-type eating disorders highlights the need to better understand the mechanistic heterogeneity underlying BE to optimize treatment allocation, advance personalized medicine, and ultimately improve outcomes. We hypothesized considerable heterogeneity within three neurofunctional domains prevalent across compulsive behaviors and implicated in BE: approach-related behavior, executive function, and negative emotionality. We analyzed data from 612 participants (ages 18-59, 66% female) from the enhanced Nathan Kline Institute-Rockland Sample, including 461 controls and 151 individuals with BE behaviors. Using data-driven statistical modeling of comprehensive, multimodal measures across the three hypothesized domains, we identified subtypes of BE. Subtypes were validated using assessments of eating pathology, substance use, clinical diagnostics, and resting-state functional magnetic resonance imaging. Three distinct and stable subtypes emerged: a 'Negative Emotionality' subtype characterized by greater negative affect, emotion dysregulation and psychiatric comorbidity, an 'Approach' subtype with higher approach-related and impulsive behaviors, and a 'Restrained' subtype that was overcontrolled and harm avoidant. The Approach and Restrained subtypes further demonstrated unique neurobiological profiles, as determined by graph theory analysis of resting-state functional connectivity. All subtypes showed similar proportions of BE episodes meeting clinical-level threshold (≥4 objective binge episodes/month), and no differences in BMI, indicating functionally distinct expressions of BE, beyond clinical severity and diagnostic classification. This study is the first to explore the mechanistic heterogeneity of BE through a comprehensive multi-modal assessment across three neurofunctional domains in a single sample. Findings highlight the need for updated models of BE etiology that integrate approach/reward-related behaviors, impulsivity and overcontrolled behaviors, and negative emotionality, and suggest the potential of these functionally-derived subtypes to inform the development of personalized, targeted interventions.

Developmental dyslexia (DD) affects approximately 5-12% of learners, posing persistent challenges in reading and writing. This study presents a novel electroencephalography (EEG)-based methodology for identifying DD using two auditory stimuli modulated at 4.8[Formula: see text]Hz (prosodic) and 40[Formula: see text]Hz (phonemic). EEG signals were processed to estimate one-to-one Granger causality, yielding directed and weighted connectivity matrices. A novel Mutually Informed Correlation Coefficient (MICC) feature selection method was employed to identify the most relevant causal links, which were visualized using connectograms. Under the 4.8[Formula: see text]Hz stimulus, altered theta-band connectivity between frontal and occipital regions indicated compensatory frontal activation for prosodic processing and visual-auditory integration difficulties, while gamma-band anomalies between occipital and temporal regions suggested impaired visual-prosodic integration. Classification analysis under the 4.8[Formula: see text]Hz stimulus yielded area under the ROC curve (AUC) values of 0.92 (theta) and 0.91 (gamma band). Under the 40[Formula: see text]Hz stimulus, theta abnormalities reflected dysfunctions in integrating auditory phoneme signals with executive and motor regions, and gamma alterations indicated difficulties coordinating visual and auditory inputs for phonological decoding, with AUC values of 0.84 (theta) and 0.89 (gamma). These results support both the Temporal Sampling Framework and the Phonological Core Deficit Hypothesis. Future research should extend the range of stimuli frequencies and include more diverse cohorts to further validate these potential biomarkers.

Advances in MRI techniques continue to open new avenues to investigate the structure and function of the human brain. Radiomics, involving the extraction of quantitative image features, and connectomics, involving the estimation of structural and functional neural connections, from large amounts and different types of MRI data sets, represent two key research areas for advancing neuroimaging while exploiting progress in computational and theoretical modelling applied to MRI. This systematic literature review aimed at exploring the combination of radiomics and connectomics in human brain MRI studies, highlighting how the combination of these approaches can provide novel or additional insights into the human brain under normal and pathological conditions. The review was conducted according to the Preferred Reported Item for Systematic Reviews and Meta-Analyses (PRISMA) statement, seeking documents from Scopus and PubMed archives. Eleven studies (out of the initial 675 records) have met the established criteria and reported combined approaches from radiomics and connectomics. Three subgroups of approaches were identified, based on the MRI modalities used to obtain radiomic and connectomic features. The first group of 3 studies combined radiomics and connectomics applied to structural MRI (sMRI) data sets; the second group of 5 studies combined radiomics applied to sMRI data and connectomics applied to diffusion (dMRI) and/or functional MRI (fMRI) data sets; the third group of 3 studies combined radiomics and connectomics applied to fMRI. This review highlighted the recent growing interest in combining MRI-based radiomics and connectomics to explore the human brain for neurological, psychiatric, and oncological conditions. Current methodologies and challenges were discussed, pointing out future research directions to improve or standardize these approaches and the gaps to be filled to advance the field.

Distinguishing between unipolar depression (UD) and bipolar disorder (BD) during periods of remission presents a significant clinical challenge. To mitigate the potential confounding effects of depressive episodes, our study compares the white matter networks of individuals with UD and BD in remission, aiming to explore the differentiation between these two affective disorders. Our cohort included 69 individuals with remitted UD, 55 with remitted BD, and 78 healthy controls (HC). We employed diffusion tensor imaging (DTI) to assess the white matter (WM) network. Additionally, we utilized a comprehensive set of connectome and five communication models to characterize the alterations within the whole-brain WM network. Compared to HC, both UD and BD patients showed reduced connectivity in the frontal orbital region, with BD patients exhibiting a more pronounced decrease. BD patients demonstrated superior navigation ability and higher shortest path metric values in key brain region connections compared to UD. Conversely, UD patients showed greater diffusion efficiency in certain brain regions. Communicability and search information analyses revealed distinct patterns of connectivity between the two patient groups, with potential implications for emotion regulation and information processing. Our findings highlight distinct brain connectivity patterns in BD and UD during remission, suggesting that these patterns could serve as neuroimaging biomarkers for differentiating between the two disorders. The study provides insights into the enduring effects of mood disorders on brain connectivity and has potential clinical implications for diagnosis and treatment.

The ketogenic diet (KD) is an effective alternative therapy for drug-resistant epilepsy (DRE). However, there are no established predictors for KD effectiveness. We aimed to investigate the impact of 12 months of KD therapy (KDT) on brain connectivity, as measured by functional magnetic resonance imaging (fMRI), and its correlation with seizure control, behavioral/mood alterations, and parental stress.

Children with DRE were enrolled in this single-center, prospective cohort study from February 2020 to October 2021. They were divided into a control group and a KDT group. The Child Behavior Checklist (CBCL) and Parental Stress Index (PSI) were administered to parents at the initiation of KDT (T0) and at 12 months (T1). Resting-state fMRI was performed at T0 and at 6 months of KDT. The primary outcome was the between-group difference in the change of CBCL/PSI scores, and brain connectivity metrics after KDT, and the secondary outcome involved measuring their correlation with seizure reduction rates.

Twenty-two patients with DRE were enrolled. We had 13 patients in the control group and 9 in the KDT group. Our data revealed that 12 months of KDT can reduce monthly seizure frequency. Several subscales of CBCL T-scores were higher at T0 compared with the control group, then becoming comparable at T1. The PSI scores from 'mothers' reports reduced after receiving KDT. The changes in node assortativity (ΔAssortativity) were positively correlated with behavioral problems and negatively with seizure reduction rates in the KD group.

Twelve months of KDT can reduce monthly seizure frequency and improve mood/behavioral disturbances in patients with DRE. Furthermore, KDT could relieve primary caregivers' stress. A lower ΔAssortativity value was associated with better behavioral outcomes and greater seizure reduction. The ΔAssortativity value in fMRI may be a crucial predictor for the effectiveness of KDT.

Although the pathophysiology of pain has been investigated tremendously, there are still many open questions with regard to specific pain entities and their pain-related symptoms. To increase the translational impact of (preclinical) animal neuroimaging pain studies, the use of disease-specific pain models, as well as relevant stimulus modalities, are critical. We developed a comprehensive framework for brain network analysis combining functional magnetic resonance imaging (MRI) with graph-theory (GT) and data classification by linear discriminant analysis. This enabled us to expand our knowledge of stimulus modalities processing under incisional (INC) and pathogen-induced inflammatory (CFA) pain entities compared to acute pain conditions. GT-analysis has uncovered specific features in pain modality processing that align well with those previously identified in humans. These include areas such as S1, M1, CPu, HC, piriform, and cingulate cortex. Additionally, we have identified unique Network Signatures of Pain Hypersensitivity (NSPH) for INC and CFA. This leads to a diminished ability to differentiate between stimulus modalities in both pain models compared to control conditions, while also enhancing aversion processing and descending pain modulation. Our findings further show that different pain entities modulate sensory input through distinct NSPHs. These neuroimaging signatures are an important step toward identifying novel cerebral pain biomarkers for certain diseases and relevant outcomes to evaluate target engagement of novel therapeutic and diagnostic options, which ultimately can be translated to the clinic.

Schizophrenia (SZ) is a severe mental disorder with high heritability. DLG2 encodes the postsynaptic scaffolding protein DLG2 (PSD93, Postsynaptic Density Protein 93), and its variants were associated with an increased risk of SZ. However, the role of DLG2 locus variation in SZ remains elusive. This study aims to investigate the association between DLG2 gene polymorphisms and SZ susceptibility and the relationship between DLG2 and altered brain function and clinical symptoms in SZ patients.

Single nucleotide polymorphisms (SNPs) rs11607886 and rs7479949 were genotyped in 350 SZ patients and 407 healthy controls (HCs). 47 SZ patients and 79 HCs were genotyped into two groups: the risk A allele carrier group and the GG-pure group. Functional magnetic resonance imaging (fMRI) indices were further analyzed. Subsequently, data from different brain regions were correlated with clinical symptom assessment.

DLG2 rs11607886 was significantly associated with SZ. Significant main effects were found in the ALFF and ReHo, especially for the left precuneus gyrus (PCu). A significant interaction between genotype and diagnosis had a significant effect on FC, which was increased between the left PCu and the right middle temporal gyrus in carriers of the A allele with SZ (r = -0.336, Pun-corrected = 0.042) and negatively correlated with spatial breadth scores (r = 0.444, PFDR-corrected = 0.002).

The rs11607886 polymorphism in DLG2 may influence the pathogenesis of SZ and have potential effects on cognitive function. The present study emphasizes DLG2 as a candidate gene for SZ and suggests an important role for PCu in SZ.

Schizophrenia is a complex mental disorder characterised by positive symptoms, negative symptoms, and cognitive deficits, with recent studies suggesting that disruptions in the default mode network (DMN) may underlie many of these symptoms. In this study, we used graph theory analysis of resting-state functional magnetic resonance imaging data to investigate differences in the topological organisation and functional connectivity of the DMN in patients with schizophrenia, using two independent datasets of patients and healthy controls. The findings revealed significant group differences in the DMN of patients with schizophrenia, particularly within the core-medial temporal lobe (MTL) subsystem, characterised by lower shortest path length, clustering coefficient, and small-worldness, indicating less efficient network organisation. Weaker functional connectivity in the core-MTL subsystem was correlated with higher avolition-apathy scores, highlighting the role of DMN connectivity patterns in negative symptoms. These results, validated across two independent datasets, emphasise the robust and generalisable association between schizophrenia and DMN network features, less efficient topological properties, and weaker functional connectivity. This underscores the importance of targeting DMN connectivity to alleviate negative symptoms, improve clinical outcomes, and potentially serve as a biomarker for monitoring symptom severity and guiding treatment.

At the time of this writing, most pediatricians or child psychiatrists will probably have treated a child with early acute-onset obsessive compulsive disorder (OCD) behaviors due to the pediatric autoimmune neuropsychiatric disorder associated with Group A beta-hemolytic streptococcus, abbreviated PANDAS, described more than two decades ago; or Tourette syndrome, incorporating motor and vocal tics, described more than a century ago. One typically self-limited post-infectious OCD resulting from exposure to other putative microbial disease triggers defines PANS, abbreviating pediatric autoimmune neuropsychiatric syndrome. Tourette syndrome, PANDAS and PANS share overlapping neuroimaging features of hypometabolism of the medial temporal lobe and hippocampus on brain 18Fluorodeoxyglucose positron emission tomography fused to magnetic resonance imaging (PET/MRI) consistent with involvement of common central nervous system (CNS) pathways for the shared clinical expression of OCD. The field of pediatric neuropsychiatric disorders manifesting OCD behaviors is at a crossroads commensurate with recent advances in the neurobiology of the medial temporal area, with its wide-ranging connectivity and cortical cross-talk, and CNS immune responsiveness through resident microglia. This review advances the field of pediatric neuropsychiatric disorders and in particular PANS, by providing insights through clinical vignettes and descriptive clinical and neuroimaging correlations from the author's file. Neuroscience collaborations with child psychiatry and infectious disease practitioners are needed to design clinical trials with the necessary rigor to provide meaningful insights into the rational clinical management of PANS with the aim of developing evidence-based guidelines for the clinical management of early, abrupt-onset childhood OCD to avert potentially life-long neuropsychological struggles.

Parkinson's disease (PD) is associated with extensive structural brain changes. Recent work has proposed that the spatial pattern of disease pathology is shaped by both network spread and local vulnerability. However, only few studies assessed these biological frameworks in large patient samples across disease stages. Analyzing the largest imaging cohort in PD to date (N = 3,096 patients), we investigated the roles of network architecture and local brain features by relating regional abnormality maps to normative profiles of connectivity, intrinsic networks, cytoarchitectonics, neurotransmitter receptor densities, and gene expression. We found widespread cortical and subcortical atrophy in PD to be associated with advancing disease stage, longer time since diagnosis, and poorer global cognition. Structural brain connectivity best explained cortical atrophy patterns in PD and across disease stages. These patterns were robust among individual patients. The precuneus, lateral temporal cortex, and amygdala were identified as likely network-based epicentres, with high convergence across disease stages. Individual epicentres varied significantly among patients, yet they consistently localized to the default mode and limbic networks. Furthermore, we showed that regional overexpression of genes implicated in synaptic structure and signalling conferred increased susceptibility to brain atrophy in PD. In summary, this study demonstrates in a well-powered sample that structural brain abnormalities in PD across disease stages and within individual patients are influenced by both network spread and local vulnerability.

Advancements in magnetic resonance imaging (MRI) analysis over the past decades have significantly reshaped the field of neuroradiology. The ability to extract multiple quantitative measures from each MRI scan, alongside the development of extensive data repositories, has been fundamental to the emergence of advanced methodologies such as radiomics and artificial intelligence (AI). This educational review aims to delineate the importance of image analysis, highlight key paradigm shifts, examine their implications, and identify existing constraints that must be addressed to facilitate integration into clinical practice. Particular attention is given to aiding junior neuroradiologists in navigating this complex and evolving landscape.

A comprehensive review of the available analysis toolboxes was conducted, focusing on major technological advancements in MRI analysis, the evolution of data repositories, and the rise of AI and radiomics in neuroradiology. Stakeholders within the field were identified and their roles examined. Additionally, current challenges and barriers to clinical implementation were analyzed.

The analysis revealed several pivotal shifts, including the transition from qualitative to quantitative imaging, the central role of large datasets in developing AI tools, and the growing importance of interdisciplinary collaboration. Key stakeholders-including academic institutions, industry partners, regulatory bodies, and clinical practitioners-were identified, each playing a distinct role in advancing the field. However, significant barriers remain, particularly regarding standardization, data sharing, regulatory approval, and integration into clinical workflows.

While advancements in MRI analysis offer tremendous potential to enhance neuroradiology practice, realizing this potential requires overcoming technical, regulatory, and practical barriers. Education and structured support for junior neuroradiologists are essential to ensure they are well-equipped to participate in and drive future developments. A coordinated effort among stakeholders is crucial to facilitate the seamless translation of these technological innovations into everyday clinical practice.

Previous studies have revealed that the cerebellum is involved in bilingual language control. In the present study, we further examined the cerebellum's role in bilingual language control and the plasticity of the cerebellar network using a training paradigm. Two groups of Chinese-English bilinguals performed the same language switching task in the pre-test and post-test sessions during functional magnetic resonance imaging scanning. After the pre-test, only the training group received an 8-day training in language switching. Results showed that bilingual language control was associated with a cerebellar network including multiple posterior cerebellar subregions as well as the anterior cerebellum (i.e., lobules IV-V). Furthermore, the cerebellar network exhibited adaptive changes with enhanced local neural efficiency and network connectivity after training. For the first time, our study revealed the plasticity of the cerebellar network in bilingual language control.

Rhegmatogenous retinal detachment (RRD) has been linked to abnormal functional changes in visual pathways and gray matter regions; however, alterations in white matter (WM) function and structure remain poorly understood. Using functional clustering networks and TractSeg methodologies, we investigated WM alterations in RRD patients and employed Support Vector Machine (SVM) algorithms for classification. RRD patients demonstrated reduced functional covariance connectivity (FCC) between the Superior Temporal Network and the Cerebellar Network, along with increased WM amplitude in the Anterior Corpus Callosum Network. Distinct differences in WM fiber bundles associated with visual and cognitive functions were observed, with visual acuity negatively correlating with amplitudes in the Occipital Networks. The SVM model based on WM7_amplitude achieved the highest AUC, highlighting its potential as a neurobiological marker for distinguishing RRD patients from healthy controls (HCs). These findings reveal critical disruptions in WM functional and structural integrity linked to cognitive and visual deficits in RRD, offering novel insights into the neural mechanisms underlying these impairments.

Functional neurological disorder (FND) is a neuropsychiatric condition that is framed as a multi-network brain problem. Despite this conceptualization, studies have generally focused on specific regions or connectivity features, under-characterizing the complex and nuanced role of resting-state networks in FND pathophysiology. This study employed three complementary graph theory analyses to delineate the functional network architecture in FND. Specifically, we investigated whole-brain weighted-degree, isocortical integration and isocortical segregation extracted from resting-state functional MRI data prospectively collected from 178 participants: 61 individuals with mixed FND; 58 psychiatric controls matched on age, sex, depression, anxiety and post-traumatic stress disorder severity; and 59 age- and sex-matched healthy controls. All analyses were adjusted for age, sex and antidepressant use and focused on differences between FND versus psychiatric controls, with individual-subject maps normalized to healthy controls. Compared to psychiatric controls, patients with mixed FND exhibited increased weighted-degree in the right dorsal anterior cingulate and superior frontal gyrus and the left inferior frontal gyrus and supplementary motor area. Isocortical integration analyses revealed increased between-network connectivity for somatomotor network areas, with widespread heightened connections to regions of the default mode, frontoparietal and salience networks. Isocortical segregation analyses revealed increased within-network connectivity for the frontoparietal network. Secondary analyses of functional motor disorder (n = 46) and functional seizure (n = 23) subtypes (versus psychiatric controls) revealed both shared and unique patterns of altered connectivity across subtypes, including increased weighted-degree and integrated connectivity in the left posterior insula and anterior/mid-cingulate in functional motor disorder and increased segregated connectivity in the right angular gyrus for functional seizures. In post hoc between-group analyses, findings remained significant adjusting for depression, anxiety and post-traumatic stress disorder severity, as well as for childhood maltreatment. Post hoc correlations revealed significant relationships between connectivity metrics in several of these regions and somatic symptom severity across FND and psychiatric control participants. Notably, individual connectivity values were predominantly within the range of healthy controls (with patients with FND generally showing tendencies for increased connectivity and psychiatric controls showing tendencies towards decreased connectivity), indicating subtle shifts in the network architecture rather than gross abnormalities. This study provides novel mechanistic insights (i.e. increased somatomotor integration) and specificity regarding the neurobiology of FND, highlighting both shared mechanisms across subtypes and subtype-specific patterns. The results support the notion that FND involves aberrant within- and between-network communication, setting the stage for biologically informed treatment development and large-scale replication.

The reward circuits are crucial in treating human methamphetamine (MA) addiction, while the underlying action mechanisms may vary throughout the intervention process. This gap limits the identification of specific modulation targets and results in a "one-size-fits-all" approach. Demonstrating these specific neural signatures can inform tailored therapy and enhance precision medicine for MA addiction.

A total of 62 MA addicts (21 females) and 57 healthy controls (16 females) were recruited. Longitudinal data were collected at the early and later stages of MA abstinence. We used probabilistic metastable substates to investigate macro-scale functional changes and established the digital twin brain model to determine key regions in abstinence from a causal, quantitative perspective. Molecular imaging, gene set, and cell-type enrichment analyses were conducted to provide a multi-scale neurobiological explanation. Computational drug repurposing analysis was performed to identify drug candidates with the potential to treat MA addiction.

We observed that brain regions within the reward circuits were crucial throughout the entire abstinence process. Molecular imaging, transcriptomic data, and cell-type analysis independently revealed that metabolic activities may play a more prominent role in early abstinence, while neuroplasticity is essential in both early and later abstinence. Identified putative drugs included approved medications for psychiatric symptoms, AIDS, and cancer.

Our work provides an integrative perspective on understanding the neural underpinnings of human MA abstinence and may inform future tailored therapies. Particularly, these findings support the stage-dependent nature of in-vivo human MA abstinence.

We previously proposed a neurocognitive model of psychosis in which reduced morphometric hippocampal-cortical connectivity precedes impaired episodic memory, social cognition, negative symptoms, and functional outcome. We provided support for this model in a patient subtype, and aimed to extend these findings to resting-state functional MRI to potentially explain the progression for a broader range of patients.

We used a subsample of our previous analysis consisting of 54 patients with first-episode psychosis and 52 controls and applied the machine-learning algorithm Subtype and Stage Inference, which combines clustering and disease progression modeling, to the patient data for rs-functional hippocampal connectivity, episodic memory, social cognition, negative symptoms and functioning.

We identified three subtypes, with Subtype 0 being unimpaired on the markers, Subtype 1 showing impaired hippocampal connectivity and episodic memory, and Subtype 2 showing impaired memory and a trend for impaired functioning. We identified similar progression patterns to our previously published morphometric results in functional MRI data (hippocampal dysconnectivity preceded cognition, symptoms, and functioning in one subtype and followed these alterations in another subtype). We further show that the impairments in our previously published and current findings across modalities do not necessarily overlap in patients, hinting towards an additive effect of morphometric and resting-state connectivity in explaining the neurocognitive underpinnings of this model.

Our results provide an extension of our previous work and build the foundation for a multimodal neurocognitive model of psychosis, potentially elucidating this aspect of illness progression in psychosis for a broader range of patients.

To explore microstructure changes across brain white matter and gray matter in tinnitus patients and its effect on neuropsychological performance.

The cross-sectional study used Multi-shell Diffusion Weighted Imaging data and neuropsychological assessment from 48 tinnitus patients and 48 healthy controls. Microstructural features across over white matter and gray matter based on Diffusion Tensor Imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI) model using Tract-Based Spatial Statistics (TBSS) and Gray Matter-Based Spatial Statistics (GBSS), as well as topological properties were derived from an advanced tractography model in subjects. Brain-neuropsychological performance correlations were analyzed.

Tinnitus patients showed decreased axial diffusivity in forceps minor and right corticospinal tract, increased orientation dispersion in forceps minor, decreased connection strength between the right caudate and pericalcarine, right caudate and superior temporal lobe, and left putamen and cuneus. Global network efficiency and local network efficiency were significantly less in tinnitus patients while feeder connection strength was significantly less in tinnitus patients. The orientation dispersion value mediated the relationship between tinnitus status and Trail Making Test-Part B scores. However, no obvious microstructural changes in gray matter were observed.

Leveraging multi-shell DWI data, the current study indicated that fiber disruption and internal connectivity organizational changes in brain white matter, rather than gray matter, were more susceptible in tinnitus patients. These microstructural changes in white matter could be associated with changes in cognitive function in tinnitus patients.

Major depressive disorder (MDD) is a prevalent and intricate mental health condition characterized by a wide range of symptoms. A fundamental challenge in understanding MDD lies in elucidating the brain mechanisms underlying the complexity and diversity of these symptoms, particularly the heterogeneity reflected in individual differences and subtype variations within brain networks.

To address this problem, we explored the brain network topology using resting-state functional magnetic resonance imaging (rs-fMRI) data from a cohort of 797 MDD patients and 822 matched healthy controls (HC). Utilizing normative modeling of HC, we quantified individual deviations in brain network degree centrality among MDD patients. Through k-means clustering of these deviation profiles, we identified two clinically meaningful MDD subtypes. Moreover, we employed Neurosynth to analyze the cognitive correlates of these subtypes.

Subtype 1 exhibited positive deviations of degree centrality in the limbic (LIM), frontoparietal (FPN), and default mode networks (DMN), but negative deviations in the visual (VIS) and sensorimotor networks (SMN), positively correlating with higher cognitive functions and negatively with basic perceptual processes. In contrast, subtype 2 demonstrated opposing patterns, characterized by negative deviations in degree centrality of the LIM, FPN, and DMN and positive deviations of the VIS and SMN, along with inverse cognitive associations.

Our findings underscore the heterogeneity within MDD, revealing two distinct patterns of network topology between unimodal and transmodal networks, offering a valuable reference for personalized diagnosis and treatment strategies.

Alcohol use disorder (AUD) induces significant structural alterations in both gray and white matter, contributing to cognitive and functional impairments. This chapter presents a translational neuroimaging approach using diffusion-weighted MRI in humans and rodents to uncover novel pathophysiological mechanisms underlying AUD. Our studies demonstrate that increased mean diffusivity (MD) in gray matter reflects microglial reactivity and reduced extracellular space tortuosity, leading to enhanced volume neurotransmission. In white matter, fractional anisotropy (FA) reductions indicate progressive deterioration of key tracts, particularly the fimbria/fornix, linked to impaired cognitive flexibility. Importantly, longitudinal analyses reveal that white matter degeneration continues during early abstinence, suggesting that neuroinflammation and demyelination persist beyond alcohol cessation. Finally, we discuss how neuromodulatory interventions, such as transcranial magnetic stimulation (TMS), may promote recovery by enhancing myelin plasticity. These findings provide crucial insights into AUD's neurobiological underpinnings and highlight potential therapeutic targets for improving treatment outcomes.

No reliable biomarkers exist for predicting and assessing vagus nerve stimulation (VNS) response. While VNS induces acute electroencephalography (EEG) desynchronization after implantation, longitudinal evaluations of EEG synchronization changes are lacking. This study constitutes the first prospective investigation evaluating EEG synchronization before and after VNS device implantation and correlating it with the clinical response to VNS.

High-density EEG recordings were obtained from 12 adults with drug-resistant epilepsy before and after VNS device implantation (one, three, and six months). EEG resting state (180 seconds), with eyes open and eyes closed (EC), was recorded in VNS ON and OFF conditions. The global weighted phase lag index (wPLI) was computed as an EEG phase-synchronization measure and correlated with the VNS response using various assessment methods, including binary classification (>50% or <50% seizure frequency reduction), percentage of seizure reduction, and the newly developed Clinical-Research Response Scale (CRRS).

We observed a progressive decrease of wPLI in the delta band during the EC VNS OFF condition, which correlated with the VNS response over time, particularly when assessed using the new CRRS compared with other assessment methods. Additionally, a higher preimplant global wPLI predicted a better outcome of VNS, as did an early magnet response.

Overall, VNS may positively influence specific brain states, with a time-dependent evolution of EEG synchronization reflecting therapeutic efficacy. Preimplantation EEG synchronization and an early magnet response may predict VNS response. Moreover, the CRRS could constitute a more sensitive method for characterizing VNS response compared with traditional assessment methods.

Did you know that even though tractography is often considered a computationally expensive and offline process, the latest algorithms can now be performed in real-time without sacrificing accuracy? Interactive real-time tractography has proven to be valuable in surgical planning and has the potential to enhance neuromodulation therapies, highlighting the importance of speed and precision in the generation of tractograms. This demand has driven the development of nearly 50 visualization tools over the past two decades, with advances in interactive real-time tractography offering new possibilities and providing rich insights into brain connectivity.

The brain connectivity network can be represented as a graph to reveal its intrinsic topological properties. While classical graph theory provides a powerful framework for examining brain connectivity patterns, it often focuses on low-order graphical indicators and pays less attention to high-order topological metrics, which are crucial to the comprehensive understanding of brain topology. In this paper, we capture high-order topological features via a graphical topology analysis framework for brain connectivity networks derived from functional Magnetic Resonance Imaging (fMRI). Several high-order metrics are examined across varying sparsity levels of binary graphs to trace the evolution of brain networks. Topological phase transitions are primarily investigated that reflect brain criticality, and a novel indicator called "redundant energy" is proposed to measure the chaos level of the brain. Extensive experiments on diverse datasets from healthy controls validate the reproducibility and generalizability of our framework. The results demonstrate that around critical points, classical graph theoretical indicators change sharply, driven by crucial brain regions that have high node curvatures. Further investigations on fMRI of subjects with and without Parkinson's disease uncover significant alterations in high-order topological features which are further associated with the severity of the disease. This study provides a fresh perspective on studying topological architectures of the brain, with the potential to expand our comprehension on brain function in both healthy and diseased states.

Virtual reality (VR) environments simulating height offer a unique platform to investigate neural adaptations to emotionally salient contexts during locomotion. These simulations allow for controlled analysis of motor-cognitive interactions under perceived threat. This secondary analysis of a previously dataset aimed to explore regional and global brain network adaptations, focusing on connectivity, modularity, and centrality, during gait under neutral and height-induced negative conditions. Seventy-five healthy participants performed a VR task involving a virtual plank at two heights: street level (neutral) and 80 floors up (negative). EEG was recorded using 32 scalp electrodes. Functional connectivity was analyzed using local efficiency, modularity, and eigenvector centrality across frontal, central, parietal, temporal, and occipital regions during two tasks: preparation (elevator) and active walking (plank). Repeated-measures ANOVAs examined the effects of task and condition. Frontal connectivity was significantly higher in the negative condition across tasks, suggesting increased cognitive-emotional regulation. Central connectivity showed a task × condition interaction, with elevated values during walking under threat, indicating increased sensorimotor integration. Occipital connectivity was higher during preparation, independent of condition, likely reflecting greater visual scene processing. Modularity was reduced in the negative condition, consistent with decreased functional segregation, while eigenvector centrality was greater in frontal and parietal regions during walking, highlighting their role as integrative network hubs. Height-related threat in VR modulates both regional and global brain network properties, enhancing integration in cognitive, motor, and visual systems. These findings advance our understanding of adaptive brain responses and support the use of VR in rehabilitation.

Brain aging is associated with a decline in cognitive performance, motor function and sensory perception, even in the absence of neurodegeneration. The underlying pathophysiological mechanisms remain incompletely understood, though alterations in neurogenesis, neuronal senescence and synaptic plasticity are implicated. Recent years have seen advancements in neurophysiological techniques such as electroencephalography (EEG), magnetoencephalography (MEG), event-related potentials (ERP) and transcranial magnetic stimulation (TMS), offering insights into physiological and pathological brain aging. These methods provide real-time information on brain activity, connectivity and network dynamics. Integration of Artificial Intelligence (AI) techniques promise as a tool enhancing the diagnosis and prognosis of age-related cognitive decline. Our review highlights recent advances in these electrophysiological techniques (focusing on EEG, ERP, TMS and TMS-EEG methodologies) and their application in physiological and pathological brain aging. Physiological aging is characterized by changes in EEG spectral power and connectivity, ERP and TMS parameters, indicating alterations in neural activity and network function. Pathological aging, such as in Alzheimer's disease, is associated with further disruptions in EEG rhythms, ERP components and TMS measures, reflecting underlying neurodegenerative processes. Machine learning approaches show promise in classifying cognitive impairment and predicting disease progression. Standardization of neurophysiological methods and integration with other modalities are crucial for a comprehensive understanding of brain aging and neurodegenerative disorders. Advanced network analysis techniques and AI methods hold potential for enhancing diagnostic accuracy and deepening insights into age-related brain changes.

Motor imagery (MI) engages a broad network of brain regions to imagine a specific action. Investigating the mechanism of brain network reorganization during MI after spinal cord injury (SCI) is crucial because it reflects overall brain activity. Using electroencephalogram (EEG) data from SCI patients, we conducted EEG-based coherence analysis to examine different brain network reorganizations across different frequency bands, from resting to MI. Furthermore, we introduced a consistency calculation-based residual graph convolution (C-ResGCN) classification algorithm. The results show that the [Formula: see text]- and [Formula: see text]-band connectivity weakens, and brain activity decreases during the MI task compared to the resting state. In contrast, the [Formula: see text]-band connectivity increases in motor regions while the default mode network activity declines during MI. Our C-ResGCN algorithm showed excellent performance, achieving a maximum classification accuracy of 96.25%, highlighting its reliability and stability. These findings suggest that brain reorganization in SCI patients reallocates relevant brain resources from the resting state to MI, and effective network reorganization correlates with improved MI performance. This study offers new insights into the mechanisms of MI and potential biomarkers for evaluating rehabilitation outcomes in patients with SCI.

Understanding how sleep affects the glymphatic system and human brain networks is crucial for elucidating the neurophysiological mechanism underpinning aging-related memory declines. We analyzed a multimodal dataset collected through magnetic resonance imaging (MRI) and polysomnographic recording from 72 older adults. A proxy of the glymphatic functioning was obtained from the Diffusion Tensor Image Analysis along the Perivascular Space (DTI-ALPS) index. Structural and functional brain networks were constructed based on MRI data, and coupling between the two networks (SC-FC coupling) was also calculated. Correlation analyses revealed that DTI-ALPS was negatively correlated with sleep quality measures [e.g., Pittsburgh Sleep Quality Index (PSQI) and apnea-hypopnea index]. Regarding human brain networks, DTI-ALPS was associated with the strength of both functional connectivity (FC) and structural connectivity (SC) involving regions such as the middle temporal gyrus and parahippocampal gyrus, as well as with the SC-FC coupling of rich-club connections. Furthermore, we found that DTI-ALPS positively mediated the association between sleep quality and rich-club SC-FC coupling. The rich-club SC-FC coupling further mediated the association between DTI-ALPS and memory function in good sleepers but not in poor sleepers. The results suggest a disrupted glymphatic-brain relationship in poor sleepers, which underlies memory decline. Our findings add important evidence that sleep quality affects cognitive health through the underlying neural relationships and the interplay between the glymphatic system and multimodal brain networks.

Attempts have been made to modulate motor sequence learning (MSL) through repetitive transcranial magnetic stimulation, targeting different sites within the sensorimotor network. However, the target with the optimum modulatory effect on neural plasticity associated with MSL remains unclarified. This study was therefore designed to compare the role of the left primary motor cortex and the left supplementary motor area proper (SMAp) in modulating MSL across different complexity levels and for both hands, as well as the associated neuroplasticity by applying intermittent theta burst stimulation together with the electroencephalogram and concurrent transcranial magnetic stimulation. Our data demonstrated the role of SMAp stimulation in modulating neural communication to support MSL, which is achieved by facilitating regional activation and orchestrating neural coupling across distributed brain regions, particularly in interhemispheric connections. These findings may have important clinical implications, particularly for motor rehabilitation in populations such as post-stroke patients.

This study retrospectively analyzed preoperative arterial spin labeling (ASL) perfusion MRI data of patients with the temporal-insula type of temporal plus epilepsy (TI-TPE). We aimed to investigate the differences in presurgical cerebral blood flow (CBF) changes in TI-TPE patients with different surgical outcomes.

A total of 48 TI-TPE patients confirmed by SEEG were meticulously reviewed for this study. Patients were divided into the seizure-free (SF) group (Engel IA) and the non-seizure-free (NSF) group (Engel IB to IV) according to the Engel seizure classification. The 3D-ASL data of all patients before surgery were analyzed using statistical parametric mapping (SPM) and graph theory analysis. These findings were then compared to healthy controls (HC) based on whole-brain voxel-level analysis and covariance network analysis.

At the voxel-level, both SF and NSF groups showed significantly decreased CBF in the ipsilateral transverse temporal gyrus and insula (TTG/insula), contralateral middle cingulate gyrus, precuneus (MCG/precuneus), and increased CBF in the ipsilateral superior temporal gyrus and the superior temporal pole (STG/STP). Wherein the SF group showed more lower CBF in the contralateral MCG/precuneus, with unique increased CBF in the contralateral STG/insula and decreased CBF in the contralateral calcarine as well. In terms of network attributes, the NSF group showed a significantly higher clustering coefficient (Cp), global efficiency (Eglob), local efficiency (Eloc), shorter shortest path length (Lp), and more extensive abnormal nodes compared to the SF and HC groups. While the SF group has higher synchronicity than the HC group.

Both SF and NSF groups had abnormal CBF changes at the voxel and network levels with different patterns. The SF group showed more obvious regional CBF changes, while the NSF group showed more extended network disruption, which might underlie different seizure outcomes after local surgical resection.