Most recent paper

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

J Affect Disord. 2026 Feb 27:121523. doi: 10.1016/j.jad.2026.121523. Online ahead of print.

ABSTRACT

BACKGROUND: Bipolar disorder (BD) is often misdiagnosed as major depressive disorder (MDD), especially in late adolescence and young adulthood, due to delayed emergence of distinguishing symptoms. This prospective nested case-control study aimed to identify neurobiological markers for early identification of BD from MDD in this age group.

METHODS: The study comprised 139 patients with bipolar depressive disorder (BDD), 148 patients with unipolar depression (UD) and 128 healthy controls (HC). During follow-up, we additionally identified 62 patients who transitioned from MDD to BD (tBD). All participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) at baseline. Functional network analyses were performed based on large-scale brain networks, along with graph-theoretical analyses.

RESULTS: Patients with depressive episodes showed reduced FC between the sensorimotor network (SMN) and visual network (VN) and within the subcortical network (SubN). Compared to HC, all patient groups showed reduced assortativity. Sigma was elevated in BDD and tBD, with reduced maximum sparsity in BDD and increased gamma in tBD. The UD showed reduced clustering coefficient and local efficiency. Compared to UD, BDD and tBD showed higher sigma and gamma, and tBD also showed higher local efficiency. In tBD, network metrics were associated with depressive severity, anxiety, suicide risk, and aggression.

CONCLUSIONS: BDD and tBD showed highly modular organization in the SMN, VN and SubN, whereas UD was characterized by reduced local efficiency. These patterns may represent neural signatures with diagnostic potential for early identification of BD from depressive episodes in late adolescence and early adulthood.

PMID:41765244 | DOI:10.1016/j.jad.2026.121523

Neuroimage. 2026 Feb 27:121833. doi: 10.1016/j.neuroimage.2026.121833. Online ahead of print.

ABSTRACT

Cerebrospinal fluid (CSF) bulk movement around the brain is mediated by brain hemodynamics and has been linked to the brain´s waste clearance process and the so-called glymphatic system. Factors such as sleep have been demonstrated to influence global brain hemodynamics, including the coupling between global grey matter (gGM) BOLD and CSF signals, a measure to assess macroscopic CSF flow in relation to global oxygen fluctuations. Although diurnal changes in the amplitude of gGM also occur, whether macroscopic CSF flow couples to gGM varies across the day remains unclear. Using publicly available resting-state fMRI data from healthy adults, we examined the coupling of these signals at varying time points throughout the day. We included data from 875 healthy young adults from the Human Connectome Project, together with Pittsburgh Sleep Quality Index (PSQI) scores. Our results show that coupling strength was highest in the morning and decreased significantly across the day (r = -0.18, p < 0.001). While gGM BOLD amplitude also decreased over the day (r = -0.14, p < 0.001), CSF amplitude did not (r = 0.01, p = 0.74). Among PSQI subcomponents, only sleep duration was significantly associated with coupling (r = 0.10, p < 0.006). Furthermore, self-reported sleep duration was negatively correlated with coupling strength (r = -0.11, p < 0.01), indicating stronger coupling in those who reported shorter sleep. These findings highlight the importance of accounting for both the time of scan and individual sleep characteristics when interpreting fMRI-based gGM-CSF coupling measures.

PMID:41765124 | DOI:10.1016/j.neuroimage.2026.121833

Neuroimage Clin. 2026 Feb 26;49:103980. doi: 10.1016/j.nicl.2026.103980. Online ahead of print.

ABSTRACT

The Central Autonomic Network (CAN) crucially maintains the homeostatic integrity of brain-heart communication, yet its directed interactions with cardiac control in anorexia nervosa (AN) remain poorly understood. To this end, we investigate the causal connectivity characterizing the CAN in 26 AN patients and 40 healthy controls, using a cross-spectral Dynamic Causal Modeling applied to resting state functional magnetic resonance imaging (fMRI). A Parametric Empirical Bayes framework was leveraged to estimate CAN connectivity group level differences and their linear association with group diagnosis and heart rate (HR). In patients, group connectivity differences revealed stronger top-down causal signaling from frontal/insular nodes to hypothalamus, alongside weaker connectivity from the anterior cingulate and insula to the hypothalamus and brainstem, respectively. These alterations may reflect bodily and emotional signals maladaptive processing at rest. In controls, HR was positively associated with most of the CAN connectivity, including with amygdala to prefrontal area causal influence: this might reflect a bottom-up role of amygdala in interoceptive signals processing, in absence of emotionally salient demands. In AN group, HR was negatively associated with most of the CAN connectivity, except for the connection from prefrontal to amygdala: in AN, a greater prefrontal control may emerge as a form of top-down compensatory regulation of limbic activity, at rest. Nevertheless, between-group CAN-HR differences were not found in the prefrontal-amygdala circuit. Instead, in patients, stronger top-down modulations encompassing frontal-insular and brainstem circuits resulted in driving maladaptive CAN-heart dynamics, compared to controls.

PMID:41763060 | DOI:10.1016/j.nicl.2026.103980

Dev Cogn Neurosci. 2026 Feb 4;79:101689. doi: 10.1016/j.dcn.2026.101689. Online ahead of print.

ABSTRACT

Understanding the neural mechanisms of adolescent substance use is a critical public health issue, with direct implications for bolstering prevention and treatment strategies. Yet this effort is challenging because substance use is multi-faceted, substance use facets change over time, and commonly used brain network features are not optimized to capture both local and global aspects of intrinsic connectivity. In this study, we aimed to address these issues. We operationalized adolescent substance use along three dimensions-intent, access, and family-developmental history-and trained predictive models of each facet at mulitple timepoints using traditional and emergent (connectome embedding) metrics of resting-state connectivity. Trait impulsivity, a known risk factor, was also examined. Using Baseline and 2 Year Follow-Up data from the ABCD Bids Community Collection (ABCC), we found that prediction was more successful at follow-up than baseline. At baseline, predictive accuracy was modest and intent to use substances was the most accurately predicted facet. Prediction accuracies at follow-up were much higher, with access and family-developmental history being better predicted, signaling a developmental shift in the brain-behavior mapping of substance use vulnerabilities. Tradtional and emergent metrics of connectivity performed similarity. These findings suggest that the neurobiological correlates of substance use are dynamic across adolescence, possibly reflecting changing phenotypes. More broadly, these results underscore the importance of modeling distinct substance use facets and accounting for developmental timing to understand risk trajectories, while contributing to a growing literature that shows early-developing individual differences are predictive of later outcomes.

PMID:41763017 | DOI:10.1016/j.dcn.2026.101689

J Affect Disord. 2026 Feb 26:121484. doi: 10.1016/j.jad.2026.121484. Online ahead of print.

ABSTRACT

BACKGROUND: Previous clinical studies have demonstrated that transcutaneous auricular vagal nerve stimulation (taVNS) alleviates functional dyspepsia (FD) symptoms. But the underlying brain functional mechanism remains unclear. This study aimed to explore the brain modulation effects of taVNS treating FD by using resting-state functional magnetic resonance imaging (rs-fMRI).

METHOD: Twenty-one patients with FD and 30 healthy controls (HCs) were enrolled. The FD subjects (FDs) were treated by taVNS and conducted with brain fMRI scans before and after 8-week treatment, HCs underwent one fMRI scan upon inclusion. Voxel-based functional connectivity (FC) and correlation analyses were used to explore the brain modulation effects of taVNS on FDs.

RESULT: After treatment, FD patients showed significant improvement in symptoms. At baseline, FD patients exhibited significantly increased functional connectivity (FC) between the left dorsal anterior insula (dAI) and the left dorsolateral prefrontal cortex (DLPFC) compared with healthy controls. After taVNS treatment, FC between insular subregions-particularly the anterior insula (AI)-and multiple brain regions, including the DLPFC, amygdala, parahippocampus, and cuneus/lingual/calcarine gyrus, was widely reduced.

CONCLUSION: This study demonstrated that FD patients exhibit abnormal FC between the AI and DLPFC. TaVNS effectively alleviated clinical symptoms in FD, which may be associated with its modulation of FC between the AI with central executive network (CEN), limbic system, and visual network (VN).

PMID:41763327 | DOI:10.1016/j.jad.2026.121484

Psychiatry Res Neuroimaging. 2026 Feb 19;358:112179. doi: 10.1016/j.pscychresns.2026.112179. Online ahead of print.

ABSTRACT

This study aimed to examine functional connectivity and grey matter volume differences in the pulvinar sub-regions between healthy controls (HCs) and Parkinson's disease (PD) patients with dementia. Resting-state functional MRI (rs-fMRI) and T1-weighted images were collected from 20 HCs (10 males, 10 females; mean age 65.45±7.53) and 20 PD patients with dementia (9 males, 11 females; mean age 66.75±7.87). Functional data were pre-processed using SPM12 and CONN software. ROI-based rs-fMRI and grey matter volume analyses were conducted to compare functional connectivity and grey matter volume, respectively. After controlling for age, education, and gender, PD patients with dementia showed significantly lower functional connectivity of the right anterior pulvinar (PuA) to bilateral temporal regions (Cluster 1: p = 0.000919 and Cluster 2: p = 0.038627, FDR-corrected) and reduced right PuA volume compared to HCs (p = 0.044). These functional differences correlated with Unified Parkinson's Disease Rating Scale (UPDRS) scores (cluster 1 r = -0.641, p = 0.006), as well as with right PuA volume loss (cluster 1: r = 0387, p = 0.016 and cluster 2: r = 0.350, p = 0.031). The findings suggest that reduced functional connectivity and volume in the right anterior pulvinar are associated with cognitive symptoms in PD with dementia, highlighting the pulvinar's role in cognitive deficits linked to neurodegeneration.

PMID:41763062 | DOI:10.1016/j.pscychresns.2026.112179

Int J Neural Syst. 2026 Feb 28:2650024. doi: 10.1142/S0129065726500243. Online ahead of print.

ABSTRACT

Major depressive disorder (MDD) is a serious, complex psychiatric condition that affects millions of people worldwide. Early diagnosis and biomarker identification are critical for personalized treatment and effective disease monitoring. While resting-state functional magnetic resonance imaging (rs-fMRI) combined with deep learning has facilitated MDD prediction, existing methods often overlook the dynamic temporal characteristics of blood oxygen level-dependent (BOLD) signals and ignore the strength of inter-regional connections, resulting in brain region updates devoid of biological specificity. To this end, a functional-dynamic synaptic graph neural network (FDSyn-GNN) is proposed, which integrates a bidirectional gated recurrent unit (Bi-GRU) timestamp encoding (BGTE) module for modeling dynamic BOLD signals and a synaptic graph Transformer (SGT) module for connection-aware brain region updates. FDSyn-GNN is validated on two large-scale MDD datasets collected across multiple sites, where it outperforms 12 state-of-the-art (SOTA) baseline methods. In addition, extensive ablation and interpretability analyses highlight its potential for biomarker discovery, offering insights into the neural mechanisms underlying MDD. The code is publicly available at https://github.com/ZHChen-294/FDSyn-GNN.

PMID:41762174 | DOI:10.1142/S0129065726500243

J Integr Neurosci. 2026 Jan 30;25(2):46634. doi: 10.31083/JIN46634.

ABSTRACT

BACKGROUND: The stage-specific dynamics of functional brain networks in early Parkinson's disease cognitive impairment (PD-CI) remain unclear. This study investigated caudate-centric hierarchical functional network reconfiguration across early PD-CI stages using simultaneous [18F]fluoropropyl-(+)-dihydrotetrabenazine positron emission tomography (18F-FP-DTBZ PET) and resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS: Forty-six Parkinson's disease (PD) patients underwent simultaneous 18F-FP-DTBZ PET/MR with rs-fMRI sequences. Patients were categorized as normal cognition (PD-NC, n = 15), subjective cognitive decline (PD-SCD, n = 16), and mild cognitive impairment (PD-MCI, n = 15). PET-identified striatal regions with significant dopaminergic deficits were used as seeds for stepwise functional connectivity (SFC) analysis. Associations with cognitive factors and network coupling in early PD-CI were evaluated.

RESULTS: 18F-FP-DTBZ PET revealed that the caudate nucleus was a critical dopaminergic hub in early PD-CI. Caudate seed-based SFC analysis revealed a triphasic reconfiguration: stable integration in PD-NC, compensatory hyperconnectivity in PD-SCD, and global inefficiency with rigidity in PD-MCI. Key circuits showed reduced connectivity in PD-MCI including caudate linkages with the globus pallidus, thalamus, right superior frontal gyrus, left inferior temporal gyrus, right superior orbitofrontal cortex, supplementary motor area, and right hippocampus. Clinical analysis showed that both global cognitive efficiency and memory control were associated with specific short- and long-range caudate connectivity.

CONCLUSIONS: The caudate nucleus is central to the interplay between dopaminergic metabolic deficits and functional network reconfiguration during early PD-CI progression, shifting from compensatory hyperconnectivity to network rigidity. These findings provide a mechanistic framework for targeted neuromodulation strategies in early PD-CI.

PMID:41762057 | DOI:10.31083/JIN46634

Neuroscience. 2026 Feb 25:S0306-4522(26)00146-6. doi: 10.1016/j.neuroscience.2026.02.043. Online ahead of print.

ABSTRACT

BACKGROUND: Parkinson's disease (PD) shows widespread alterations in intrinsic brain activity, yet the molecular and cellular bases of these disruptions remain unclear. Resting-state fMRI metrics-amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo)-offer complementary views of spontaneous neural activity but have rarely been examined within an integrated biological framework.

METHODS: We studied 31 PD patients and 28 healthy controls using voxel-wise ALFF and ReHo analyses combined with cortical transcriptomic data from the Allen Human Brain Atlas, cell type-specific gene expression, and PET-derived neurotransmitter receptor density maps. Partial least squares regression identified gene expression patterns associated with PD-related imaging alterations. Enrichment analyses, cell type overlap, and spatial correlations with neurotransmitter receptor distributions were subsequently performed.

RESULTS: PD patients showed decreased ALFF in the left putamen, precentral gyrus, and middle frontal gyrus, and decreased ReHo in the left thalamus and cerebellum. ALFF alterations corresponded to negatively loaded PLS1 genes enriched for immune and neuroinflammatory pathways, predominantly expressed in microglia, astrocytes, oligodendrocytes, and endothelial cells. ReHo changes corresponded to positively loaded PLS2 genes enriched for receptor-mediated signaling and transcriptional regulation, mainly expressed by astrocytes. Both ALFF and ReHo patterns showed strong spatial coupling with 5HT2a receptor density, suggesting serotonergic involvement.

CONCLUSION: This multimodal analysis links PD-related ALFF and ReHo alterations to distinct yet converging transcriptomic, cellular, and neurochemical substrates. The findings suggest that PD-related functional alterations spatially align with glial-neurovascular transcriptional gradients and serotonergic receptor distribution, providing convergent but indirect evidence for their involvement in PD-related network reorganization.

PMID:41759987 | DOI:10.1016/j.neuroscience.2026.02.043