Most recent paper

Neural Netw. 2026 Feb 19;200:108748. doi: 10.1016/j.neunet.2026.108748. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by both structural atrophy and functional dysregulation in the brain, yet its early detection remains elusive. Although recent efforts have leveraged artificial intelligence combined with multimodal neuroimaging to improve diagnostic accuracy, these methods often falter in harmonizing disparate data sources and lack the transparency needed for clinical adoption. In particular, the sheer dimensionality of functional Magnetic Resonance Imaging (fMRI) and T1-weighted Magnetic Resonance Imaging (T1w-MRI) features, together with complex inter-modality relationships, can lead to overfitting and hinder the reliable identification of robust biomarkers. To overcome the aforementioned challenges, we propose a novel Multimodal Graph Fusion Graph Convolutional Network (MGF-GCN) that integrates functional (fMRI) and structural (T1w-MRI) brain features for accurate and interpretable AD diagnosis. We construct brain graphs by incorporating nonlinear Granger causality (NGC) from resting-state fMRI (rs-fMRI) to capture inter-regional functional dependencies, alongside morphological features from T1-weighted MRI to enrich node attributes. To effectively align and enhance multimodal representations while preserving the underlying topological structure, we introduce a cross-attention-based graph fusion strategy. To further improve both performance and interpretability, we develop a Bayesian Self-Attention Graph Convolutional Network (BSAGCN), where attention weights are modeled as probability distributions, allowing for the identification of critical brain regions and minimizing noise sensitivity. All features are extracted based on the BN246 brain atlas, facilitating fine-grained localization of potential biomarkers. Experimental results show that our approach significantly outperforms existing methods in diagnostic accuracy and interpretability, providing new insights into the pathophysiological mechanisms of AD and offering valuable support for clinical decision-making.

PMID:41780284 | DOI:10.1016/j.neunet.2026.108748

PLOS Digit Health. 2026 Mar 4;5(3):e0001261. doi: 10.1371/journal.pdig.0001261. eCollection 2026 Mar.

ABSTRACT

Major depressive disorder (MDD) remains clinically diagnosed based on subjective symptoms rather than objective neurobiological markers, which limits diagnostic accuracy and the ability to tailor treatment. We present an ensemble hybrid framework that integrates graph neural networks (GNN) with unsupervised clustering to classify and subtype MDD using resting-state functional connectivity (rs-fMRI) profiles. A GNN was trained to distinguish MDD from healthy controls using functional connectivity derived brain graphs, and the resulting subject level embeddings were clustered to uncover subtype structure. We evaluated the approach on two public multisite cohorts, REST-meta-MDD (China; N = 1,604; 17 sites) and SRPBS (Japan; N = 446; 4 sites), using leave-one-site-out cross-validation and cross-national transfer. The classifier achieved 0.73 leave-one-site-out accuracy on REST-meta-MDD and retained 0.78 sensitivity when transferred from the Chinese to the Japanese cohort, outperforming BrainIB and CI GNN under the same protocol. To mitigate site related confounds, we applied a standardized preprocessing pipeline and ComBat harmonization. Clustering consistently identified three MDD subtypes with distinct connectivity signatures involving the default mode network and cerebellum, the insula-cingulum temporal circuit, and frontostriatal circuitry. These findings provide a reproducible and biologically interpretable stratification of MDD. Prospective studies will be needed to link these subtypes to treatment response and other clinically meaningful outcomes.

PMID:41779813 | DOI:10.1371/journal.pdig.0001261

Brain Topogr. 2026 Mar 4;39(3):29. doi: 10.1007/s10548-026-01186-7.

ABSTRACT

Sleep critically influences socio-emotional functioning during interpersonal interactions; however, the relationship between poor sleep quality and susceptibility to social exclusion remains unclear. This study aimed to investigate this relationship and its underlying neural mechanisms. A total of 147 healthy sleepers (HS) and 105 individuals with poor sleep quality (PS) completed a social exclusion imagery task, followed by resting-state functional magnetic resonance imaging (fMRI). Negative feelings and reaction times during the task, as well as seed-based functional connectivity (FC) of the left ventral anterior cingulate cortex (vACC) and left inferior frontal gyrus (IFG), were compared between groups. Associations between FC showing group differences and behavioral measures were further examined. After controlling for depressive and anxiety symptoms, the PS group exhibited stronger negative feelings during the task and longer reaction times in neutral conditions. Seed-based FC analysis revealed increased connectivity between the left IFG and left temporal lobe (TL), alongside decreased connectivity between the left IFG and right precentral gyrus (PG) in the PS compared to the HS group. Moreover, FC between the IFG and PG was negatively correlated with negative affect in HS but not in PS. Poor sleep quality is associated with heightened susceptibility to social exclusion, potentially linked to altered functional connectivity between the IFG and PG. These findings underscore the protective role of healthy sleep in social functioning and suggest neural targets for interventions aimed at mitigating social impairments in individuals with poor sleep.

PMID:41779232 | DOI:10.1007/s10548-026-01186-7

Brain Imaging Behav. 2026 Mar 4;20(2):31. doi: 10.1007/s11682-026-01090-5.

ABSTRACT

Tinnitus, characterized by phantom sound perception, exhibits heterogeneous pathophysiology influenced by hearing status. This study investigated dynamic neural activity patterns in 82 participants: 29 healthy controls (HC), 21 tinnitus patients with normal hearing (G1), and 32 tinnitus patients with hearing impairment (G2). Using resting-state fMRI, we computed dynamic amplitude of low-frequency fluctuation (d-ALFF) and dynamic regional homogeneity (d-ReHo) through sliding-window analyses, measuring temporal variability via coefficient of variation. One-way ANOVAs (covarying age/sex) revealed six d-ALFF clusters showing group differences (voxel p < 0.01, cluster p < 0.05 GRF-corrected). Post-hoc analyses demonstrated that G1 exhibited significantly increased d-ALFF variability versus HC and G2 in cerebellar, fusiform, and occipital regions. Conversely, both patient groups showed reduced d-ALFF variability in frontal clusters versus HC. Negative correlations emerged in G2 between fusiform d-ALFF and tinnitus distress/anxiety, while G1 showed positive correlations between temporal d-ALFF and depression. d-ReHo analysis identified reduced variability in the right anterior cingulate in both patient groups versus HC. These findings highlight distinct neural dynamics: tinnitus with normal hearing involves hypervariability in sensory processing regions, while hearing-impaired tinnitus shows distinct clinical correlations. Reduced activity variability in the superior and middle frontal gyri and reduced temporal synchrony in the anterior cingulate suggest a common tinnitus mechanism irrespective of hearing status.

PMID:41779099 | DOI:10.1007/s11682-026-01090-5

Brain Topogr. 2026 Mar 4;39(3):28. doi: 10.1007/s10548-026-01185-8.

ABSTRACT

In this study, we aimed to investigate the intrinsic brain activity alterations in patients with disorders of consciousness (DOC) using multidimensional resting-state functional magnetic resonance imaging (rs-fMRI) metrics at ultra-high field (7 T) MRI. We enrolled 10 patients with DOC, including those with vegetative state/unresponsive wakefulness syndrome and minimally conscious state, and 11 healthy controls (HCs). We applied various rs-fMRI metrics ranging from neuronal activity to synchronization and coordination of whole-brain activity, including amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), percent amplitude of fluctuation (PerAF), regional homogeneity (ReHo), and degree centrality (DC). Patients with DOC exhibited distinct brain activity patterns compared to HCs. The bilateral inferior temporal gyri showed enhanced activity across various metrics (right: ALFF, ReHo, DC; left: ALFF, fALFF, ReHo), while the right precuneus showed decreased activity in patients with DOC (ALFF, DC, PerAF), compared to HCs. Although an initial inverse relationship was observed between the left putamen and CRS-R total scores in DOC patients, this association did not survive multiple comparisons correction (Bonferroni-adjusted threshold: p < 0.0019). Our findings provide new insights into the neural mechanisms underlying DOC, highlighting the importance of the right precuneus and the bilateral inferior temporal gyri in consciousness level. These results can inform the development of diagnostic and therapeutic strategies for DOC.

PMID:41779062 | DOI:10.1007/s10548-026-01185-8

Noro Psikiyatr Ars. 2026 Jan 31;63:192-200. doi: 10.29399/npa.29010. eCollection 2026.

ABSTRACT

INTRODUCTION: This study aimed to investigate the modulation of simple sensory stimuli on brain intrinsic connectivity networks in the Alzheimer's disease continuum (ADC) using functional magnetic resonance imaging (fMRI).

METHODS: fMRI and neuropsychological assessment data of 88 cases in ADC were analysed. fMRI data were recorded in a session including blocks of light stimuli flickering at 20 Hz frequency and in the resting state from 21 Alzheimer's disease dementia (ADD), 34 mild cognitive impairment (MCI) and 33 subjective cognitive impairment (SCI). CONN (functional connectivity toolbox) software was used for functional connectivity analyses of fMRI data. Bonferroni correction was applied according to the number of ROIs in functional connectivity analyses and the significance threshold was determined as pFWE <0.0033.

RESULTS: As a result of the analysis of the resting state data, decreased connectivity was detected between the posterior cingulate cortex seed of the default mode network and the temporal and parietal areas in ADD compared to the SCI and MCI groups. Decreased functional connectivity was detected between the anterior insula and anterior cingulate cortex seeds of the salience network and the temporal, frontal and cingulate cortices in ADD compared to the SCI and MCI groups. However, in the data of flickering light stimulation at a frequency of 20 Hz, increased functional connectivity was detected between the right lateral prefrontal cortex seed of the frontoparietal network, which could not be captured with the resting state data, and the precuneus in the MCI group compared to the SCI group.

CONCLUSIONS: The increase in connectivity between the frontoparietal network and precuneus may be a compensatory response in the early stages of the disease. In addition, it was thought that fMRI images performed using simple sensory stimuli were more sensitive to cognitive decline in the early stages of the disease compared to resting state data and could have biomarker potential.

PMID:41777513 | PMC:PMC12951513 | DOI:10.29399/npa.29010

Sci Rep. 2026 Mar 3;16(1):8152. doi: 10.1038/s41598-026-41665-2.

ABSTRACT

Neurological symptoms are common in post-COVID-19 condition (PCC) and have been linked to underlying brain alterations. However, in individuals with PCC following a mild infection without hospitalization, such alterations are rarely detected using conventional neuroimaging techniques. This study aims to investigate brain connectivity in patients with PCC with cognitive symptoms after mild COVID-19 infection, using resting-state functional magnetic resonance imaging (rs-fMRI). Additional aims were to explore associations between brain connectivity, neuropsychological performance, and self-reported fatigue and emotional status. Patients with PCC (n = 22) and lasting cognitive symptoms and fatigue were consecutively recruited from a regional rehabilitation unit and compared with a convenience sample of non-symptomatic controls (n = 19). The assessments were conducted on average 32 months post-infection and included 3 Tesla rs-fMRI, neuropsychological testing, and self-report measures of fatigue (MFI-20), anxiety, and depression (HADS). Patients with PCC had elevated functional connectivity in brain regions associated with the default mode network (DMN) compared to controls. No significant correlations were found between functional connectivity, neuropsychological test performance, fatigue, anxiety, or depression. Our findings suggest persistent alterations in DMN connectivity in PCC with cognitive symptoms and fatigue, underscoring the need for continued larger studies on brain functioning in this patient group.

Clinical trial registration: No. NCT06042530.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1038/s41598-026-41665-2.

PMID:41776260 | PMC:PMC12960797 | DOI:10.1038/s41598-026-41665-2

J Eat Disord. 2026 Mar 3. doi: 10.1186/s40337-026-01559-0. Online ahead of print.

ABSTRACT

BACKGROUND: Anorexia nervosa (AN) and bulimia nervosa (BN) are two primary subtypes of eating disorders (ED), often presenting with overlapping clinical features that complicate diagnosis. Despite shared symptoms, the underlying neural mechanisms of two subtypes remain incompletely understood. Delineating both shared and unique neural alterations may support biomarker discovery and inform targeted interventions.

METHODS: We recruited 28 patients with AN, 26 with BN, and 31 matched healthy controls (HC), aged from 14 to 40 years old. Resting-state functional magnetic resonance image (Rs-fMRI) data were acquired to investigate alterations in spontaneous brain activity. Four voxel-wise metrics were analyzed: amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), and degree centrality (DC). Symptom severity was assessed using the Eating Disorder Examination-Questionnaire (EDEQ), which includes four subscales: Eating concern (EDEQ_E), Shape concern (EDEQ_S), Weight concern (EDEQ_W), and Restraint (EDEQ_R). Pearson correlation analysis was used to examine associations between altered imaging metrics and clinical variables.

RESULTS: Both AN and BN exhibited convergent alterations, including reduced activity in the bilateral middle frontal gyrus (MFG), insular cortex (INS), superior temporal gyrus (STG), and left parahippocampal gyrus (PHG), alongside increased activity in the bilateral striatum, middle occipital gyrus (MOG), and cerebellum. Disorder-specific alterations in AN included increased activity in the right striatum and right precuneus, increased DC in the right superior frontal gyrus (SFG), and decreased fALFF and DC in the left calcarine. In contrast, patients with BN exhibited elevated fALFF in the right precentral gyrus (PCG_R) and increased DC in the right calcarine. Correlation analyses revealed negative association between the ReHo value of the MOG_L and EDEQ, and positive associations between the DC value of the PCG_R and EDEQ and EDEQ_E in patients with BN.

CONCLUSION: Our findings revealed both shared and diagnosis-specific alterations in intrinsic brain activity within the cortico- striatal-limbic circuit, underscoring its role in the pathophysiology of ED.

PMID:41776705 | DOI:10.1186/s40337-026-01559-0

NPJ Biol Timing Sleep. 2026 Feb 16;3(1):7. doi: 10.1038/s44323-025-00065-x.

ABSTRACT

Circadian robustness is usually cast on a single weak-strong continuum, but multi-system data suggest a different picture. We followed 52 healthy young adults for ~30 days with wearable locomotor (accelerometry; ACC) and autonomic (heart rate; BPM) signals and paired these with structural and resting-state fMRI. From person-level circadian feature vectors (stability, amplitude, acrophase, and ACC-BPM alignment/lag), we uncovered a Control-Salience axis of brain-body organization. A control-anchored archetype showed ACC-dominant rhythms-higher activity stability and amplitude, later BPM acrophase, and a longer ACC → BPM phase lead-together with stronger connectivity in cognitive control networks. A complementary salience-anchored archetype exhibited BPM-dominant rhythms-earlier BPM acrophase, higher BPM relative amplitude, tighter ACC-BPM coupling-and stronger connectivity in salience and attention networks. Across individuals, cross-system alignment (ACC-BPM lag) tracked control-network coherence, whereas rhythm timing and amplitude related selectively to cortical geometry and network strength. These findings recast circadian health as axis-based and system-specific: individuals organize along a spectrum from stability-anchored, locomotor-led profiles to coupling-anchored, autonomic-dominated profiles with distinct neural correlates. The Control-Salience axis refines mechanistic models of circadian risk and points to alignment-aware, network-targeted strategies for monitoring and intervention.

PMID:41775974 | DOI:10.1038/s44323-025-00065-x

NPJ Parkinsons Dis. 2026 Mar 3. doi: 10.1038/s41531-026-01305-y. Online ahead of print.

ABSTRACT

Subthalamic nucleus deep brain stimulation (STN DBS) is an established treatment for advanced Parkinson's disease (PD), whereas the posterior subthalamic area (PSA) has been proposed as an alternative target for tremor-dominant cases. However, their underlying therapeutic mechanisms have not been directly compared. Leveraging the single-trajectory dual-target DBS technique, this work utilizes high-field 3.0 T resting-state functional magnetic resonance imaging data and spectral dynamic causal modeling to investigate the differential modulatory effects of PSA and STN stimulation on effective connectivity within both cortico-basal ganglia and cerebello-thalamo-cortical networks. We show that both PSA and STN stimulation suppress cortico-cerebellar connectivity and cortico-subthalamic hyperdirect connectivity, while enhancing STN self-inhibition. Compared with STN stimulation, PSA stimulation provides a greater reduction in cortico-cerebellar coupling but a greater increase in striato-STN connectivity. Moreover, changes in hyperdirect pathway coupling correlate with motor improvement in response to both PSA and STN stimulation. Furthermore, hyperdirect pathway and cerebellar connectivity were significantly associated with motor impairment and resting tremor severity, respectively, regardless of hemisphere or DBS target. Taken together, these findings suggest that PSA and STN stimulation share common network-level mechanisms but differ in their relative modulation of cortico-cerebellar pathway. The present study may offer theoretical guidance for future individualized DBS targeting in treating tremor-dominant PD.

PMID:41776187 | DOI:10.1038/s41531-026-01305-y

IEEE Trans Biomed Eng. 2026 Mar 3;PP. doi: 10.1109/TBME.2026.3670101. Online ahead of print.

ABSTRACT

Resting state fMRI (rsfMRI) is widely used to differentiate Alzheimer's Disease (AD) and identify biomarkers but its obscure features and noises challenge the present models. Brain graph convolution network (GCN) provides a good interpretation but suffers from the inferior performance due to the insufficient feature representation. Population GCN improves the precision of detection by involving the phenotypic information but fails in the bio logical interpretation. The GCN taking a single generated connectome as input focuses only on the low-level inter regional temporal correlation and is incapable to exploit hierarchical spatial functional features. In this paper, we propose a multi-level connectome-generated GCN (MLC GCN) to enhance the feature extraction for the individual connectome. First, we construct multiple connectomes in parallel through stacked spatiotemporal feature extractors (STFEs), effectively enhancing the hierarchical features and reducing the noise. Each generated connectome is then input into the GCN for further feature extraction, and the output of all GCNs is concatenated for a multilayer percep tron to predict AD. We use independent cohort validations ontwomedicaldatasetsADNIandOASIS-3,andexperiment results demonstrate MLC-GCN obtains better performance for differentiating normal control, mild cognitive impairment and AD than current GCN architectures and other AD classifiers. The proposed MLC-GCNrevealshighinterpreta tion in terms of learning clinically reasonable connectome nodes and connectivity features.

PMID:41774666 | DOI:10.1109/TBME.2026.3670101

J Child Psychol Psychiatry. 2026 Mar 3. doi: 10.1111/jcpp.70143. Online ahead of print.

ABSTRACT

BACKGROUND: Multi-domain dysregulation in adolescence, indexed by co-occurring affective, cognitive, and behavioural difficulties, is a robust transdiagnostic risk factor. However, its developmental course and neural antecedents are poorly understood. Given heightened emotional reactivity and impulsivity in adolescence, alterations in reward-network connectivity may represent an early neural marker of risk.

METHODS: Adolescents completed four assessments approximately two years apart between ages 9-13 and 15-18 years. Multi-domain dysregulation was assessed at each wave using the Youth Self-Report Dysregulation Profile (YSR-DP), computed as the sum of the anxious/depressed, aggressive behaviour, and attention problems subscales. Resting-state fMRI was acquired at baseline (Mage = 11.34 years). Piecewise linear mixed-effects models (N = 211) characterized trajectories of YSR-DP scores across adolescence. Principal component scores indexing a Latent Dysregulation Factor were used to derive residualised change in dysregulation, and regression analyses (N = 94) tested whether baseline reward-network connectivity predicted this change.

RESULTS: YSR-DP scores declined from late childhood to early adolescence, increased from early to mid-adolescence, and then stabilized in late adolescence. Weaker connectivity within the reward network in late childhood predicted greater increases in the latent dysregulation factor from early to mid-adolescence, above and beyond baseline dysregulation. Connectivity in seven large-scale control networks did not predict changes in dysregulation.

CONCLUSIONS: Multi-domain dysregulation follows a nonlinear trajectory across adolescence, and weaker reward-network connectivity in childhood prospectively predicts subsequent escalation of this phenotype. Prevention and intervention efforts may benefit from targeting reward processing and regulatory skills in late childhood and early adolescence.

PMID:41774020 | DOI:10.1111/jcpp.70143

Trials. 2026 Mar 3. doi: 10.1186/s13063-026-09557-y. Online ahead of print.

ABSTRACT

BACKGROUND: While mirror neuron-based rehabilitation approaches demonstrate efficacy in post-stroke upper limb motor recovery. Crucially, whether sequential activation of sensory mirror neurons preceding motor mirror neurons enhances functional outcomes remains unsubstantiated. Furthermore, conventional protocols require auditory-controlled environments and sustained high-attentional engagement for optimal efficacy. This study proposes a novel integrated intervention incorporating somatosensory observation (SO) components into Graded Motor Imagery (GMI), augmented by virtual reality (VR) technology to enhance participant engagement and attentional allocation. This synergistic approach aims to potentiate sensorimotor cortical integration, thereby optimizing upper limb recovery trajectory and clinical outcomes in stroke patients.

METHODS: Sixty patients were randomized into four experimental groups: the conventional GMI Group received standard graded motor imagery; the SO-GMI Group incorporated SO with GMI; the VR-GMI Group implemented GMI through virtual reality; and the VR-SO-GMI Group combined SO and GMI within a VR environment. All interventions followed a standardized 4-week protocol. Resting-state functional MRI (rs-fMRI) assessed neuroplastic changes at baseline and post-intervention. Upper limb functional recovery was evaluated using three validated metrics: Fugl-Meyer Assessment for Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), and Modified Barthel Index (MBI), administered at treatment initiation, week 2 (mid-intervention), and week 4 (conclusion) to track therapeutic efficacy. The study flow diagram is Fig. 1.

DISCUSSION: The purpose of this clinical trial is to observe the efficacy of increased SO in GMI on the recovery of upper limb motor function after cerebral stroke through a randomized controlled clinical trial, and to explore the clinical efficacy after implementing the above therapy using immersive VR technology, as well as to further investigate whether this research protocol can achieve the neurophysiological mechanism of "sensory-motor" linkage in the brain. This research method is widely applicable to patients with poor motor function and those with limitations in active movement. At the same time, VR technology allows for one-to-many training. At the same time, VR technology can conduct one-to-many training. This study aims to improve the efficacy of graded exercise therapy for upper limb rehabilitation in cerebral stroke and provide a new method that requires less physical effort, saves manpower, and has a wide range of applicability.

TRIAL REGISTRATION: China Clinical Trial Registry ChiCTR2400084611. Registered on 21 May 2024.

PMID:41772702 | DOI:10.1186/s13063-026-09557-y

Sci Rep. 2026 Mar 2. doi: 10.1038/s41598-026-41884-7. Online ahead of print.

ABSTRACT

Child maltreatment adversely affects brain development, resulting in vulnerabilities in brain structure and function connectivity, as well as various psychiatric disorders. However, the relationship between structural changes in auditory-related regions and auditory function remains unclear. Therefore, this study investigated the relationship between auditory frequencies associated with brain atypicality and child maltreatment. T1-weighted magnetic resonance imaging (MRI) and functional MRI were used to assess differences in gray matter volume (GMV) and functional connectivity (FC) in maltreated children (n = 19) compared to those of no maltreatment history (n = 38) participants. This case-control study focused on the left middle temporal gyrus (L.MTG), a key region in speech perception, and its connectivity with the right temporal pole (R.TP). Additionally, the study analyzed the relationship between these neural alterations and auditory thresholds at pivotal frequencies relevant to speech perception and measures of speech reception thresholds. Maltreatment-related neurodevelopmental adaptations affected GMV (L.MTG; P < 0.001 for peak level, family-wise error [FWE] corrected P = 0.038 for cluster level) and FC (L.MTG-R.TP; P < 0.001 for peak level, FWE corrected P = 0.013 for the cluster level), potentially influencing how abused children process auditory and emotional information. These alterations may have long-term consequences on speech perception, emotional recognition, and social communication. Elucidating these mechanisms will contribute to developing effective therapeutic strategies to improve social and emotional outcomes in maltreated individuals.

PMID:41772002 | DOI:10.1038/s41598-026-41884-7

Sci Data. 2026 Mar 3. doi: 10.1038/s41597-026-06616-6. Online ahead of print.

ABSTRACT

To study human attentional fluctuations, this study introduces Sustained Attention Task (the gradual onset continuous performance: gradCPT) multimodal dataset combining electroencephalography (EEG), functional magnetic resonance imaging (fMRI), and diffusion-weighted imaging (DWI). The dataset contains neuroimaging data from 28 participants across the attentional tasks (gradCPT, gradCPT with imagery), imagery task, visual task (flickering checkerboard), and resting-states (eyes-open and eyes-closed). We publicly share raw and preprocessed data from each modality to expand the scope of exploring the brain states during attentional fluctuations in the human brain. The accessibility of this dataset will provide opportunities for future research in investigating the relationship between attention dynamics and brain activity across different imaging modalities.

PMID:41771931 | DOI:10.1038/s41597-026-06616-6

J Psychiatr Res. 2026 Feb 24;197:86-96. doi: 10.1016/j.jpsychires.2026.02.043. Online ahead of print.

ABSTRACT

BACKGROUND: Systemic inflammation has emerged as a significant contributor to the pathophysiology of neuropsychiatric disorders, particularly depression. The lipopolysaccharide (LPS)-induced inflammation model in rodents is widely used to study inflammation-related behavioral and neural changes, with a strong emphasis on understanding the mechanisms of LPS-induced depression. However, the effects of systemic inflammation on the dynamic architecture of brain functional networks in the context of depression are not well understood.

METHODS: Using high-field (9.4 T) resting-state functional magnetic resonance imaging (rs-fMRI), we investigated the impact of LPS-induced systemic inflammation on brain functional network organization in mice. Independent component analysis (ICA) was used to extract 15 functional brain networks. Static and dynamic functional network connectivity (sFNC and dFNC) were analyzed, and graph theory-based metrics were applied to evaluate global, local, and nodal efficiency. Behavioral tests (open field, elevated plus maze, tail suspension) and biochemical assays (serum IL-6, CXCL1, and brain regional ATP levels) were performed to assess emotional state, inflammation, and brain metabolism.

RESULTS: LPS administration significantly increased anxiety- and depression-like behaviors, elevated peripheral inflammatory markers, and reduced ATP levels in multiple brain regions. ICA-based analysis revealed significant alterations in both static and dynamic connectivity across cortical, limbic, cerebellar, and basal ganglia networks. Graph theory analysis showed preserved global and local efficiency but a significant reduction in nodal efficiency within the basal ganglia. Moreover, dynamic metrics revealed reduced temporal variability of global and local efficiency following LPS treatment. Several brain network metrics were significantly correlated with behavioral outcomes, serum cytokine levels, and regional ATP concentrations.

CONCLUSIONS: Our findings demonstrate that acute systemic inflammation disrupts both the static structure and dynamic regulation of brain functional networks in mice. These alterations are linked to emotional and metabolic disturbances and highlight the basal ganglia and cortical networks as key nodes of inflammation-related vulnerability. This study provides novel systems-level insights into the neural mechanisms underlying inflammation-associated neuropsychiatric symptoms.

PMID:41771236 | DOI:10.1016/j.jpsychires.2026.02.043

IEEE J Biomed Health Inform. 2026 Mar 2;PP. doi: 10.1109/JBHI.2026.3669067. Online ahead of print.

ABSTRACT

Imaging biomarkers are essential for monitoring multiple sclerosis (MS), wand resting-state functional MRI (rs-fMRI) offers functional insights that complement structural imaging. This study investigates whether a novel co-activation pattern (CAP) approach for dynamic rs-fMRI can function as a dual-purpose biomarker in MS, aiding diagnosis and tracking disease severity. RS-fMRI scans from 25 relapsing-remitting MS patients and 41 healthy controls (HCs) were analyzed using a novel CAP-based approach. CAPs derived from individual time frames to capture dynamic brain activity patterns incorporated a bivariate similarity assessment, eigen volume-based dimensionality reduction, and consensus clustering. We evaluated the framework in two analyses: (1) a diagnostic evaluation, using dynamic CAP features-dwell time, persistence, and transition probabilities-for group comparisons and classification; and (2) a severity-prediction analysis, relating these CAP-derived measures to clinical disability (EDSS) in MS using LASSO regression. Method performance was benchmarked against standard CAP and sliding-window (SW) approaches. It revealed significant differences in brain activity between MS and HCs, within the default mode, sensorimotor, and language networks (p < 0.05), highlighting alterations relevant to motor, cognitive, and sensory functions affected in MS. Transition probabilities showed strong correlations with EDSS (r > 0.75) and yielded better classification performance than standard CAP and SW approaches in classifying MS from HCs. These results suggest that dynamic brain activity patterns are altered in MS and linked to clinical disability. The proposed CAP provided improved performance in distinguishing MS patients, offering enhanced clinical monitoring. Transition probabilities emerged as a potential biomarker for tracking MS progression, with network shifts reflecting disease severity. As MS advances, increased transitions toward sensory, motor, and executive networks suggest compensatory recruitment. Conversely, reduced transitions from default mode and salience networks to sensorimotor and frontoparietal systems were associated with greater disability and diminished adaptive reorganization.

PMID:41770960 | DOI:10.1109/JBHI.2026.3669067

Psychoradiology. 2026 Jan 5;6:kkag001. doi: 10.1093/psyrad/kkag001. eCollection 2026.

ABSTRACT

BACKGROUND: Chronic insomnia disorder (CID) is associated with disrupted functional brain networks, yet prior research has focused primarily on group-level analyses. This study employed personalized functional network mapping to identify connectivity abnormalities in CID.

METHODS: Resting-state functional magentic resonance imaging (rs-fMRI) data were collected from 86 CID patients and 38 good sleeper controls (GSCs). Using non-negative matrix factorization (NMF), we derived individualized large-scale brain networks for each participant to uncover subject-specific connectivity changes in CID. We also constructed functional network connectivity (FNC) matrices using Pearson correlation coefficients and compared global and local graph-theory metrics across groups based on these individualized networks.

RESULTS: FNC analysis revealed significant differences between CID patients and GSCs within the default mode network (DMN), ventral attention network, visual network (VIS), and other key brain regions. CID exhibited altered global network topology and significant differences in local topological properties. At the global level, CID demonstrated significantly higher small-worldness (Sigma) and normalized clustering coefficient (Gamma). At the nodal level, CID showed increased local efficiency and clustering coefficient, as well as decreased nodal efficiency in the DMN, along with increased degree centrality in the VIS.

CONCLUSION: By focusing on individualized functional connectivity, this approach reveals unique "fingerprint" alterations in CID. These findings provide novel insights into CID's neurobiological mechanisms and underscore the value of personalized network approaches for understanding and treating sleep disorders.

PMID:41767428 | PMC:PMC12947161 | DOI:10.1093/psyrad/kkag001

Front Psychiatry. 2026 Feb 12;17:1743024. doi: 10.3389/fpsyt.2026.1743024. eCollection 2026.

ABSTRACT

BACKGROUND: Treatment-resistant insomnia remains a major unmet clinical challenge, as a substantial proportion of patients fail to achieve long-term remission with cognitive behavioral therapy or pharmacotherapy alone. Transcranial direct current stimulation (tDCS) has shown promise in modulating cortical excitability and improving sleep quality through non-invasive neuromodulation, whereas zolpidem (ZOL), a GABA-A receptor agonist, provides rapid but transient symptomatic relief. However, whether their combination offers additive therapeutic benefits and how such effects are represented at the neural level remain unknown.

METHODS: This prospective, double-blind, randomized controlled trial will enroll 165 patients with treatment-resistant insomnia. Participants will be randomly assigned (1:1:1) to one of three groups: (A) active tDCS + ZOL, (B) active tDCS + placebo, and (C) sham tDCS + ZOL. The intervention lasts four weeks, with 20 tDCS sessions (2 mA, 20 min/day, 5 days/week, anode over left and cathode over right dorsolateral prefrontal cortex) and nightly oral administration of ZOL or placebo. The primary outcome is the response rate at week 4, defined as the percentage of those having at least a 50% reduction in insomnia symptoms from baseline as measured via the Pittsburgh Sleep Quality Index (PSQI). Secondary outcomes include response rates at 8 and 12 weeks, clinical remission (PSQI<5), changes in PSQI and Insomnia Severity Index scores, sleep architecture monitored by a wearable device, and mood assessments using Hamilton Depression Rating Scale and Hamilton Anxiety Rating Scale. Resting-state functional MRI (rs-fMRI) will be acquired at baseline and 4 weeks to explore alterations in regional brain activity and functional connectivity.

DISCUSSION: This trial will systematically evaluate the efficacy and neurobiological mechanisms of tDCS combined with zolpidem in treatment-resistant insomnia. By integrating subjective clinical assessments, objective digital sleep monitoring, and neuroimaging biomarkers, it aims to elucidate whether these combined pharmacological and neuromodulatory interventions produce additive effects. The findings are anticipated to establish a mechanistic foundation for personalized, multimodal sleep therapeutics, thereby potentially advancing the management paradigm for treatment-resistant insomnia.

CLINICAL TRIAL REGISTRATION: https://www.chictr.org.cn/showproj.html?proj=288195, identifier ChiCTR2500111601.

PMID:41767140 | PMC:PMC12935941 | DOI:10.3389/fpsyt.2026.1743024

Phys Rev Lett. 2026 Feb 13;136(6):068402. doi: 10.1103/36v9-wtm8.

ABSTRACT

Whether brain dynamics operate near a critical regime remains a central question in neuroscience, with potential implications for information processing and computational flexibility. However, conventional approaches are susceptible to artifacts introduced by temporal correlations, spatial dependencies, and subsampling, which can create the illusion of scaling in noncritical systems. Here we introduce an analytical and numerical framework centered on the covariance matrix and its spectrum, combined with a phenomenological renormalization group (PRG) approach, and extended to incorporate colored inputs, temporal and spatial correlations, and robust inference and control strategies for empirical data. Applying this framework to pooled resting-state fMRI, we find that collective brain activity is slightly subcritical yet close to criticality. The extracted exponents are robust and align with predictions from recurrent firing-rate models in the long-time correlation limit. Beyond these results, our Letter provides methodological tools for more reliable tests of criticality in neuroscience and complex systems.

PMID:41765780 | DOI:10.1103/36v9-wtm8