Bulimia nervosa (BN) is a severe psychiatric disorder characterized by dysregulated eating behaviors and impaired cognitive-emotional control. Despite increasing recognition of brain network dysfunction in BN, the interplay between structural connectivity (SC) and functional connectivity (FC), termed SC-FC coupling, remains poorly understood. This study aimed to comprehensively characterize SC-FC coupling alterations in BN using multimodal neuroimaging and to evaluate the predictive value for disordered eating behaviors. This study enrolled 79 patients with BN and 69 healthy controls who underwent high-resolution structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and resting-state functional MRI (rs-fMRI). Functional and structural connectomes were constructed using the Schaefer-400 atlas. SC-FC coupling was quantified using eight biologically grounded similarity and communication metrics. A multivariate linear modeling framework was applied to estimate region-specific coupling profiles. Group comparisons and ridge regression-based leave-one-out cross-validation were used to identify altered coupling and predict symptom severity. The global topological properties of the SC and FC networks were preserved in BN. However, patients exhibited significantly reduced degree centrality and nodal efficiency in the inferior frontal gyrus within the FC network. SC-FC coupling, quantified using the matching index (MI), showed widespread regional alterations in BN, particularly within the default mode, control, and attention networks. Seventeen brain parcels demonstrated significant group differences in MI-based coupling (false discovery rate (FDR)-corrected, p < 0.05), with both hypercoupling and hypocoupling observed. Findings were parcellation-robust (Glasser-360 replication; Dice = 0.93 vs. Schaefer-400). Moreover, coupling features moderately predicted binge-eating frequency (r = 0.24, p < 0.001), but not questionnaire-based emotional or behavioral scores. In BN, macroscale white-matter organization is preserved, yet focal prefrontal functional decentralization and widespread, parcellation-robust SC-FC coupling changes invisible to single-modality analyses were observed. Multidimensional SC-FC coupling provides a sensitive neurobiological marker that explains clinically relevant variance in binge-eating behavior, highlighting its potential as a target for personalized diagnosis and intervention in BN.

Thermoregulatory dysfunction—temperature intolerance and/or inappropriate compensation—is an underrecognized feature of Parkinson’s disease (PD) and is linked to poor quality of life. Multiple mechanisms may underlie this dysfunction, including α-synuclein deposition in relevant structures, altered functional connectivity in thermoregulatory networks, and disrupted neurotransmitter modulation, on top of the deleterious consequences of aging. Although multiple advanced tests can confirm this dysfunction, diagnosis is largely based on a detailed history. Once this critical symptom is identified, management focuses on crisis prevention and safety, as PD-specific clinical trials are often lacking. This narrative review of the literature addresses mechanisms, clinical expression, diagnostic evaluation, and management of thermoregulatory dysfunction in PD to help guide care for this underappreciated, yet potentially debilitating, non-motor symptom of PD. Future PD-specific trials are needed to further clarify underlying mechanisms and improve treatment options.

BackgroundResting-state functional magnetic resonance imaging (fMRI) studies in dementia with Lewy bodies (DLB) and Alzheimer's disease (AD) have described connectivity alterations in large-scale brain networks. However, little is known about functional changes across disease stages, particularly in DLB.ObjectiveTo investigate functional connectivity of key brain networks in DLB patients at different stages, compare them to AD patients and healthy controls (HC), and examine associations with core clinical symptoms.MethodsNinety DLB patients (63 with mild cognitive impairment [MCI-DLB] and 27 with dementia [d-DLB]), 25 AD patients (11 MCI-AD and 14 d-AD) and 34 HC underwent clinical, neuropsychological and resting-state fMRI assessments. Region of interest (ROI)-to-ROI analyses were performed using the CONN toolbox (pFDR < 0.05).ResultsThe overall DLB group showed reduced functional connectivity within the salience network (SN) compared to HC, but not to the overall AD group. At the subgroup level, d-DLB patients showed reduced SN and frontoparietal network (FPN) connectivity compared to both HC and the overall AD group, whereas MCI-DLB did not significantly differ from either group. In the overall DLB group, SN connectivity correlated with fluctuation severity and FPN connectivity correlated with both REM sleep behavior disorder and cognitive decline. In the overall AD group, decreased default mode network (DMN) connectivity was associated with lower Mini-Mental State Examination scores.ConclusionsSN and FPN connectivity impairments relate to disease progression and core clinical features in DLB, whereas DMN connectivity is linked to cognitive decline in AD. These distinct patterns highlight divergent paths of network dysfunction in the two diseases.Clinical Trial: This study is part of the AlphaLewyMA cohort, registered on ClinicalTrials.gov (identifier: NCT01876459; registered on June 12, 2013).

Electroencephalography source-space functional connectivity reveals frequency-specific brain network dysfunctions in obsessive-compulsive disorder.

The sacrifice of alerting in active short video users: Evidence from executive control and default mode network functional connectivity.

Developmental dyslexia is a common neurodevelopmental learning disorder that severely impacts children's reading abilities and social adaptation. In recent years, brain network analysis based on functional magnetic resonance imaging has provided new insights into its neural mechanisms, yet it struggles to capture the temporal characteristics of dynamic brain interactions. To address this, this paper proposes a GAT-LSTM framework for high-precision classification of DD. This method first constructs a dynamic functional connectivity network based on the AAL90 brain atlas. It then employs GAT to adaptively learn spatial dependencies between brain regions within each time window, followed by LSTM to model the temporal evolution patterns of node embedding sequences. To further enhance the model's temporal consistency and discriminative power, dynamic graph stability constraints are introduced during training. Experimental results demonstrate that the proposed method achieves an 85.36% classification accuracy, significantly outperforming baseline models. This study not only provides a novel computational paradigm for the objective diagnosis of DD but also offers robust support for the application of brain network modeling in neurodevelopmental disorder research.

Postoperative delirium is a frequent and serious neurocognitive complication in older surgical patients, characterized by acute impairments in attention, awareness, and cognition. It is associated with increased morbidity, prolonged hospitalization, and persistent cognitive decline. In this narrative review, we synthesize translational research on biological mechanisms underlying delirium and emerging targeted interventions. We conducted a comprehensive search of major biomedical databases, with no date restrictions but prioritizing publications from 2018 to 2025. The multifactorial pathophysiology involves dysregulated cholinergic and dopaminergic signaling, systemic and neuroinflammation, oxidative stress, and metabolic disturbances. Pre-existing cognitive impairment and frailty emerge as key clinical risk factors linked to these mechanisms. Aged rodent models replicate delirium-like cognitive deficits and validate mechanistic pathways, while human neuroimaging studies demonstrate disrupted functional connectivity in attentional and consciousness networks. Genomic and proteomic analyses have identified candidate biomarkers for early detection and risk stratification, and genetic variants associated with inflammation and neurodegeneration contribute to individual vulnerability. Emerging therapies targeting inflammation, microglial activation, mitochondrial function, and neurotransmitter balance show promise in preclinical studies, although clinical trials report mixed results. We advocate integrating basic science with clinical care through thorough preoperative assessment, multicomponent non-pharmacological strategies, and mechanism-based preventive measures to reduce the burden of postoperative delirium.

Evidence suggests that subjective cognitive decline (SCD) involves abnormal structures and functional alterations in multiple brain networks, rather than a single brain region. Acupuncture has shown a positive therapeutic effect in treating SCD, although whether and how it can improve cognitive decline by altering large-scale brain network organization is unclear. We utilized resting-state functional magnetic resonance imaging (fMRI) data from 66 individuals with SCD (derived from a previous randomized controlled trial) and explored brain-wide network-level functional connectivity and topological property changes after 12 weeks of acupuncture intervention to examine its therapeutic mechanisms. The Auditory Verbal Learning Test-Huashan version (AVLT-H) test was used to measure objective memory performance. Neuroimaging outcomes included brain network functional connectivity and topological properties obtained from resting-state fMRI. A repeated-measures general linear model and mixed-effect analysis were used to examine group × time interaction effects on cognitive function and neuroimaging outcomes. Correlation analyses were used to examine the relationship between functional connections (FCs) and memory performance. Compared with sham acupuncture, 12 weeks of acupuncture treatment significantly improved the objective memory performance of individuals with SCD. Five FCs within the sensorimotor network (SMN) and between the SMN and the cingulo-opercular network (CON) showed significant alterations after acupuncture. Two intrinsic SMN connections were enhanced by acupuncture, whereas inter-network FCs changed oppositely, negatively correlating with memory improvement. The topological properties of two regions within the SMN were also significantly modulated after acupuncture. The results suggest that 12 weeks of acupuncture may improve objective memory performance in SCD, potentially by reducing FCs between the SMN and CON. Enhancing functional segregation of these networks may be a potential target for acupuncture treatment. No: NCT03444896. https://www. gov/study/NCT03444896.

Intrinsic functional connectivity network abnormalities in C9orf72 hexanucleotide repeat expansion carriers emerge during the asymptomatic phase, yet longitudinal studies remain limited. We examined cross-sectional abnormalities and longitudinal connectivity changes across clinical stages. We analyzed task-free fMRI and structural MRI data in 36 asymptomatic (aSxC9), 17 prodromal (proC9), and 29 symptomatic (SxC9) carriers, and 107 healthy controls (HC). Functional networks previously found altered in C9orf72 , including salience, sensorimotor, default mode, and medial pulvinar thalamic networks, were examined. Associations between longitudinal connectivity and gray matter decline with baseline neurofilament light chain (NfL) concentrations and symptom severity were assessed. Despite lacking detectable gray matter decline, aSxC9 and SxC9 showed longitudinal connectivity changes within specific networks. In aSxC9, connectivity changes correlated with baseline NfL. In proC9 and SxC9, changes in connectivity and gray matter were associated with baseline NfL and symptom severity. C9orf72 expansion carriers demonstrate stage-specific network connectivity changes. - Stage-specific longitudinal connectivity changes were detected in C9orf72 . - Compared to controls, each C9orf72 cohort lacked detectable gray matter decline. - For aSxC9, connectivity network changes correlated with baseline NfL.- Connectivity/GM changes in proC9 and SxC9 correlate with NfL and symptom severity. Systematic review: The authors conducted a systematic review using major databases (e.g., PubMed and Google Scholar). While cross-sectional studies have investigated functional connectivity patterns in C9orf72 expansion carriers ( C9orf72 ), longitudinal studies are scarce. Notably, no study has examined longitudinal functional connectivity changes during the prodromal stage, a critical transition stage from the asymptomatic to symptomatic phases, which was included in this study. Interpretation: Leveraging a large longitudinal neuroimaging cohort of C9orf72 , we comprehensively characterized stage-specific connectivity changes in asymptomatic, prodromal and symptomatic carriers. Associations between longitudinal connectivity, and neurodegeneration and symptom severity highlight the potential of task-free fMRI for tracking disease progression during each clinical stage. Future directions: This study lays important groundwork for tracking disease progression as early as the asymptomatic stage. Future research should establish phenotype- and stage-specific connectivity trajectories in carriers who convert to advanced stages or develop distinct C9orf72 -associated syndromes.

BackgroundComputerized cognitive training (CCT) has been found to improve cognition by altering functional activity and functional connectivity of brain networks in people with and without cognitive impairment. The effects of CCT on functional brain networks in Huntington's disease (HD) have not been comprehensively examined.ObjectiveIn our pilot trial of CCT, we aimed to explore effects of CCT on functional activity and connectivity of fronto-striatal regions during processing speed and cognitive flexibility tasks, and functional connectivity of resting-state networks in HD.MethodsSixteen participants in pre-manifest and early stages of HD were randomised to either a 12-week multi-domain CCT intervention (n = 6) or lifestyle education (n = 10). Participants completed a 1-h magnetic resonance imaging (MRI) scan at baseline and follow-up, which included task-based and resting-state functional MRI. Analyses examined changes in functional activity and connectivity of fronto-striatal regions during processing speed and cognitive flexibility task performance, as well as functional connectivity within default mode and frontoparietal resting-state networks.ResultsWhile there was evidence of benefits to in-scanner task performance, there were no significant effects on functional activity or functional connectivity of fronto-striatal regions during task performance, or resting-state functional connectivity.ConclusionCCT did not generate significant effects on functional activity or connectivity of fronto-striatal networks associated with processing speed or cognitive flexibility, or resting-state networks in HD. A larger study is required to further examine the effects of CCT on functional brain outcomes and potential moderating factors.

Understanding how cellular and molecular architecture underpins the large-scale organization of human brain function is a central challenge in neuroscience. By integrating transcriptomic (microarray and single-nucleus RNA-sequencing), molecular imaging, and neuroimaging datasets, we observe spatial correspondences indicating that the distributions of diverse cell types, neurotransmitter systems, and mitochondrial phenotypes align with intrinsic connectivity networks (ICNs). These associations extend beyond local correspondence to reflect network-level structure: inter-ICN similarity networks derived from cellular and molecular profiles recapitulate static and dynamic patterns of functional network connectivity (FNC), mirroring canonical functional domains. Mediation analyses reveal that specific ICNs mediate the relationship between microscale cell-type architecture and domain-specific cognitive processes, while FNCs capture mediating pathways linking cell-type and neurotransmitter similarity networks to cognitive organization. Together, our findings show that the brain's functional architecture systematically aligns with cellular and molecular organization, which may constrain functional network formation and contribute to the neural basis of cognition.

Background The amygdala-hippocampal complex (AHC) plays a central role in the neural mechanisms underlying Internet Gaming Disorder (IGD), particularly in emotional regulation, memory processing, and reward-related functions. However, the dynamic interactions between the AHC and large-scale brain networks, and their relationship with cognitive performance in IGD, remain poorly understood. Methods A total of 123 adolescents (66 with IGD and 57 healthy controls) underwent resting-state functional magnetic resonance imaging (fMRI). Temporal fluctuations in AHC connectivity were assessed using dynamic functional network connectivity (dFNC) analysis. Correlation and mediation analyses were conducted to investigate the relationship between aberrant AHC-related dFNC and cognitive function. Results Three distinct connectivity states were identified, each characterized by unique network configurations. In State 2, dFNC strength between the AHC and both the attentional network (ATN) and visual network (VN) was positively correlated with T scores of the MATRICS Consensus Cognitive Battery (MCCB). Further mediation analysis revealed that weakened dFNC between the AHC and VN regions, particularly the calcarine sulcus and cuneus, served as a mediator linking cognitive impairment to the internet addiction severity of IGD. Conclusion These findings suggest that aberrant dynamic connectivity of the AHC, particularly its disrupted interaction with VN, may underlie the cognitive impairments in adolescents with IGD. This study provides novel insights into the neurobiological basis of behavioral addiction and highlights the importance of dynamic network analysis in elucidating its underlying pathology.

Timely diagnosis and effective treatment of Lennox-Gastaut syndrome (LGS) improve prognosis and lower health care costs, but the transition from infantile epileptic spasms syndrome (IESS) to LGS is highly variable and insidious. Objective biomarkers are needed to monitor this progression and guide clinical decision-making. We retrospectively collected longitudinal EEG data at the Children's Hospital of Orange County from 15 children who were diagnosed with IESS and later with LGS between 2012 and 2021. Electroencephalography studies were from IESS and LGS diagnoses, between the two diagnoses, and following LGS diagnosis. Functional connectivity networks were calculated using a cross-correlation-based method and assessed relative to diagnostic timepoint, treatment response, and the presence of clinical markers of disease, age, and amplitude of interictal spikes. Connectivity strength was high at LGS diagnosis and decreased after favorable response to treatment, but it remained stable or increased when response was unfavorable. In all subjects, connectivity strength was higher at the time of LGS diagnosis than at the preceding timepoint. The presence of clinical markers of LGS was associated with high connectivity strength, but no single marker predicted connectivity strength. Computational EEG analysis can be used to map the evolution from IESS to LGS. Changes in connectivity may enable prediction of impending LGS and treatment response monitoring, thus facilitating earlier LGS treatment and guiding medical management.

Brain reorganization: altered functional connectivity in reward network after stroke.

In order to proceed the fast detection of depression with EEG (electroencephalogram) signal, this study proposed a so-called AKRCC-KNN model for automatic and accurate diagnosis. Based on the multi-channel EEG signal with pre-processing, there is a novel approach focusing on the feature extraction, in which the PLI (phase lag index) of EEG signals is calculated as the feature; moreover, the feature selection algorithm (so-called AKRCC) is innovatively integrated with AKRC (altered Kendall’s rank correlation coefficient) method for feature re-arrangement and convergence determination for feature selection, in order to improve the selective feature’s accuracy with limited computation expense. Hence the entire process of detection of depression with enhanced performance is listed as follows. Firstly, the PLI of EEG signals is computed to obtain their functional connectivity networks. AKRCC algorithm is then applied to rank PLI matrix elements by their discriminative power and determine optimal feature dimensionality through classification accuracy convergence monitoring. Finally, the selected multidimensional features are input into a KNN classifier for automatic classification. Extensive experiments on the MODMA dataset (24 major depression disorder patients, 29 healthy controls) demonstrate the model’s superior performance. With 1-second full-band EEG features, the AKRCC-KNN model achieves a state-of-the-art identification accuracy of 97.65% (specificity: 96.95%, sensitivity: 98.54%), surpassing existing methods. This indicates that the proposed depression detection model in this paper can achieve intelligent and rapid depression detection, providing an efficient, accurate, and diverse solution for clinical depression detection.

Sensorimotor and cognitive abilities undergo substantial changes throughout the human lifespan, but the corresponding changes in the functional properties of cortical networks remain poorly understood. This can be studied using temporal and spatial scales of functional magnetic resonance imaging (fMRI) signals, which provide a robust description of the topological structure and temporal dynamics of neural activity. For example, timescales of resting-state fMRI signals parsimoniously predict a significant amount of the individual variability in functional connectivity networks identified in adult human brains. In the present study, we quantified and compared temporal and spatial scales in resting-state fMRI data collected from 2,352 subjects of either sex between the ages of 5 and 100 in Developmental, Young Adult, and Aging datasets from the Human Connectome Project. For most cortical regions, we found that both temporal and spatial scales decreased with age throughout the lifespan, with the visual cortex and the limbic network consistently showing the largest and smallest scales, respectively. For some prefrontal regions, however, these two scales displayed non-monotonic trajectories and peaked around the same time during adolescence and decreased throughout the rest of the lifespan. We also found that cortical myelination increased monotonically throughout the lifespan, and its rate of change was significantly correlated with the changes in both temporal and spatial scales across different cortical regions in adulthood. These findings suggest that temporal and spatial scales in fMRI signals, as well as cortical myelination, are closely coordinated during both development and aging.Significance Statement Temporal and spatial scales of resting-state cortical activity in humans measured by fMRI largely decreased throughout the lifespan, except that for some regions in the prefrontal cortex they peaked similarly during adolescence. In addition, whereas cortical myelination consistently increased throughout the lifespan, its variation across different cortical networks and the rate of age-related changes were correlated with the dynamics of temporal and spatial scales of rs-fMRI activity, suggesting that the spatio-temporal scales of cortical activity and cortical myelination might be co-regulated during development and aging.

Processing level of meat modulates functional brain network connectivity during visual evaluation

Alcohol-related cues are a critical factor in relapse among individuals with alcohol use disorder (AUD), yet their neuroelectrophysiological mechanisms at the brain network level remain unclear. This study aims to investigate alterations in functional connectivity and topological properties of brain networks in AUD patients during alcohol cue processing. A three-stimulus oddball paradigm with alcohol-related images was used. After rigorous EEG quality control, 16 AUD patients and 16 matched healthy controls were included in the final analysis. Source-level phase locking value (PLV) was used to construct functional connectivity matrices, followed by graph-theoretical analysis of network topology. Correlations with drinking characteristics and clinical scale scores were also examined. During alcohol cue processing, the AUD group exhibited reduced delta-band (1-4Hz) functional connectivity, primarily involving the default mode, frontoparietal, and cingulo-opercular networks (p = 0.034). In the theta band (4-8Hz), the shortest path length increased (p = 0.01, q = 0.04, Cohen's d = 0.96), with significantly decreased nodal centrality in the right prefrontal cortex, left posterior insula, and left basal ganglia, and increased centrality in the left parietal, bilateral occipital, and right mid-insula regions (all p < 0.01, q < 0.05). Centrality metrics in several key brain regions were significantly correlated with drinking behaviors and clinical scale scores. AUD patients exhibit impaired delta-band network integration, reduced theta-band information transfer efficiency, and disrupted nodal centrality during alcohol cue processing, indicating abnormal neural mechanisms underlying cue-reactivity. This study was registered in the Chinese Clinical Trial Registry (ChiCTR2300076251) on September 28, 2023. Recruitment began on October 1, 2023.

ABSTRACTBackgroundHeroin and methamphetamine are two widely abused drugs that have profound effects on brain morphology and functioning. This study aims to (1) identify brain structural differences between heroin and methamphetamine users; (2) examine how these drugs differentially affect the topology and functional connectivity of key brain networks; and (3) characterize associations between morphological alterations and clinical symptoms, including anxiety and depression.MethodsIn this study, we collected T1‐weighted magnetic resonance imaging data from 26 heroin‐abstinent (HA) patients, 24 methamphetamine‐abstinent (MA) patients, and 32 healthy controls (HC). All participants were in early abstinence (< 6 months) to minimize acute intoxication and withdrawal confounds while capturing residual brain alterations. Four surface‐based morphological features, including cortical thickness (CT), fractal dimension (FD), gyrification index (GI), and sulcal depth (SD), were analyzed, and morphological brain networks were constructed using Jensen‐Shannon divergence with 210 cortical regions from the Brainnetome Atlas.ResultsBoth patient groups showed brain tissue thinning in hearing‐related areas (temporal cortex) and reduced depth in visual processing regions. Heroin users specifically exhibited atrophy in somatosensory cortex regions associated with touch sensation whereas methamphetamine users demonstrated distinctive cortical folding alterations in motor cortex areas related to movement control. Network analysis revealed that heroin users had widespread connection problems affecting brain communication efficiency, while methamphetamine users showed localized damage in specific brain hubs important for memory, attention, and visual processing. Clinical correlations revealed that morphological changes were significantly associated with drug use patterns (frequency and dosage) and psychological symptoms, with anxiety scores negatively correlating with SD in heroin users and depression scores positively correlating with morphological measures in methamphetamine users.ConclusionsOur findings demonstrate distinct neurobiological signatures of heroin and methamphetamine addiction that persist during early abstinence. Heroin primarily causes widespread network disruption, while methamphetamine leads to focal hub damage. The observed associations between brain morphology and clinical symptoms indicate the practical importance of these structural alterations. These distinct patterns may inform the development of substance‐specific treatment approaches.

BackgroundWhile the effects of psychotherapy methods are being intensively researched, little is known about the clinical and neurobiological effects of specific psychotherapeutic interventions. This study examines the effects of experiential emotion-focused and cognitive interventions in schema therapy on emotion regulation in borderline personality disorder.MethodsIn a randomized, single-blinded, parallel group design, clinical effects and effects on resting-state functional connectivity in neural emotion regulation networks and neurotransmitter metabolism (Glx/GABA) in key regions of these networks are compared. The 9-week treatment protocol includes emotion-focused interventions such as chair dialogues, imagery rescripting, or mode role-playing in the test condition; these interventions are omitted in the active control condition (dismantling design). Resting-state functional MR imaging (rsfMRI) and MEGA-sLASER 1 H MR spectroscopy in the pregenual cingulate cortex (pgACC), anteromedial cingulate cortex (aMCC), and dorsolateral prefrontal cortex (DLPFC) are performed before and after the therapy interval and 6 months after the end of therapy and compared with the neurobiological parameters of healthy control subjects. The clinical effects are recorded using a comprehensive test battery and specified using the Reliable Change Index (RCI). Clinical and biological data are examined using mixed model analysis both longitudinally and in terms of their interactions.DiscussionThe aim is to show that different psychotherapeutic interventions have different effects on deficits in emotion regulation associated with specific effects on neural emotion regulation networks. This would contribute to a better understanding of the neurobiological effects and mechanisms underlying psychotherapeutic core interventions and to their more targeted use in BPD and other related disorders in the future.Trial registrationClinicalTrials.gov Identifier: NCT06367907, Retrospectively registered, April 2024.Supplementary InformationThe online version contains supplementary material available at 10.1186/s40479-025-00311-5.