Most recent paper

J Clin Endocrinol Metab. 2026 Mar 7:dgag102. doi: 10.1210/clinem/dgag102. Online ahead of print.

ABSTRACT

CONTEXT: The developing brain is particularly vulnerable to glycemic extremes in early-onset type 1 diabetes (T1D). However, how treatment-specific modalities may influence long-term neurodevelopmental trajectories remains poorly understood.

OBJECTIVE: To characterize multimodal MRI neurodevelopmental profiles in pediatric T1D and evaluate treatment-related effects of Multiple Daily Injections (MDI) versus Continuous Subcutaneous Insulin Infusion (CSII) on brain structure, function, and test whether HbA1c-linked imaging features relate to executive-working-memory performance.

METHODS: Sixteen children with T1D (8 MDI, 8 CSII from diagnosis) and eight controls underwent structural MRI, diffusion MRI, and resting-state fMRI. Union Recursive Feature Elimination (U-RFE) selected gray-matter (GM), white-matter (WM), and functional connectivity (rs-FC) features discriminating groups; regression related selected features to long-term age-adjusted mean glycated hemoglobin (HbA1c). NEPSY-II Word List Interference (WI) was administered; control-referenced WI outcomes were examined versus HbA1c and HbA1c-associated structural features, including mediation.

RESULTS: Functional features outperformed structural features (balanced accuracy 0.83 vs 0.67). MDI showed reduced GM/WM integrity and disrupted fronto-temporal and subcortical connectivity versus CSII and controls. Right inferior frontal gyrus (IFG) volume correlated with HbA1c (r=0.71, p<0.05) and predicted HbA1c (β=0.28, p=0.015). Higher HbA1c related to poorer WI repetition (r=-0.60, p=0.013), and right IFG volume related to poorer WI repetition (r=-0.70, p=0.002). Mediation supported an indirect HbA1c effect via right IFG volume (a×b=-0.676; Sobel z=-1.765, one-tailed p=0.0388), explaining ∼64% of the total association. CSII had 30% lower hyperglycemia exposure than MDI and higher WI repetition mean ranks (11.19 vs 5.81; p=0.023).

CONCLUSIONS: Pediatric T1D is associated with multimodal neuroimaging alterations influenced by insulin treatment modality. CSII may confer neuroprotective benefits by improving metabolic control and preserving functional connectivity. Right IFG volume is a candidate imaging marker linking metabolic regulation to interference-sensitive executive-working-memory vulnerability.

PMID:41793758 | DOI:10.1210/clinem/dgag102

BMC Med. 2026 Mar 7. doi: 10.1186/s12916-026-04766-3. Online ahead of print.

ABSTRACT

BACKGROUND: Bipolar depression (BD-D) in adolescents and young adults is associated with disrupted neural circuits underlying affective regulation, particularly those involving the orbitofrontal cortex (OFC). Despite the promise of repetitive transcranial magnetic stimulation (rTMS) as a non-invasive intervention, effective targeting strategies that engage these dysfunctional circuits remain insufficiently explored. This study investigates the clinical efficacy of a novel rTMS protocol targeting the primary visual cortex (V1) node of the V1-OFC functional circuit in adolescents and young adults with BD-D.

METHODS: We conducted a double-blind randomized controlled trial. Fifty-two adolescents and young adults BD-D participants were randomized to active rTMS group (10 Hz, 100% RMT) or sham rTMS group (20% RMT) targeting the V1 region that exhibited the strongest functional connectivity with the OFC (MNI: - 12, - 81, 6). rTMS was administered over 3 weeks (5 sessions/week, 15 sessions in total), with all participants receiving adjunctive lurasidone (40-80 mg/day). The primary outcome was the change in depressive symptoms measured by the Montgomery-Åsberg Depression Rating Scale (MADRS) at baseline, week 3, and week 8. Secondary outcomes included HAMD-24, QIDS-SR, and HAMA. Resting-state fMRI was performed at baseline and after the 3-week intervention to examine changes in functional connectivity related to rTMS.

RESULTS: A total of 43 participants completed a 3-week intervention, and 37 completed the 8-week follow-up. Compared with the sham group, the active rTMS group showed significantly greater reductions in depressive symptoms. Between-group differences were significant on the primary outcome MADRS at week 8 (t(35) = - 3.595, pFDR < 0.01), with a parallel effect detected for the secondary outcome on the QIDS-SR (t(35) = - 3.653, pFDR < 0.01). HAMD-24 scores also differed significantly at week 3 (t(35) = - 3.921, pFDR < 0.01). No significant changes were found in anxiety symptoms. Resting-state fMRI indicated altered connectivity in the anterior cingulate cortex and right superior occipital gyrus, suggesting modulation of mood-related visual circuits. No severe adverse effects were reported in all participants.

CONCLUSIONS: The study preliminarily demonstrated that the navigated rTMS precisely targeting the V1-OFC circuit may be a safe and potentially effective intervention for adolescents and young adults with BD-D.

TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT05929183.

PMID:41792767 | DOI:10.1186/s12916-026-04766-3

Childs Nerv Syst. 2026 Mar 7;42(1):99. doi: 10.1007/s00381-026-07187-y.

ABSTRACT

BACKGROUND: Pediatric posterior fossa lesions are frequent, and surgery in this area often results in cerebellar injury. Despite improved surgical methods, the impact on brain functional networks and their role in motor and cognitive performance remain unclear. Existing research mainly involves adults or children who received additional therapies, obscuring the effects of isolated cerebellar damage. Evidence from pediatric functional neuroimaging after posterior fossa surgery is limited.

METHOD: In this cross-sectional study, we examined 12 postoperative children and 13 healthy peers using resting-state functional magnetic resonance imaging (rs-fMRI) and the CNS Vital Signs (CNS VS) computerized neurocognitive battery. The CNS VS battery provided age-adjusted standard scores across multiple cognitive domains as well as motor speed (MotSpd) and psychomotor speed (PsyMotSpd) indices.

RESULTS: Short-term postoperative results indicated that most cognitive domains assessed by CNS VS did not differ significantly between groups, whereas the patient group showed lower scores on MotSpd, PsyMotSpd, and the neurocognitive index (NCI). Brain imaging analyses revealed increased regional activity and altered seed-based functional connectivity in several cortical regions of the patient group relative to controls. Exploratory correlation analyses conducted across all participants showed that several of these imaging metrics were negatively associated with motor-related scores; however, one functional connectivity measure was positively associated with MotSpd.

CONCLUSION: These findings suggest that children following posterior fossa surgery exhibit measurable alterations in supratentorial brain network organization alongside relatively preserved cognitive performance but reduced motor-related scores on the CNS VS battery. The negative associations between certain imaging metrics and motor performance may reflect heterogeneous or inefficient network reorganization rather than a straightforward compensatory process. Further longitudinal and interventional studies are needed to clarify the functional significance of these network changes and to inform rehabilitation strategies.

PMID:41792257 | DOI:10.1007/s00381-026-07187-y

Transl Psychiatry. 2026 Mar 6. doi: 10.1038/s41398-026-03929-3. Online ahead of print.

ABSTRACT

Postoperative depression adversely influences breast cancer patients' clinical outcomes. Our prior study demonstrated that intraoperative esketamine ameliorated postoperative depression in breast cancer patients, yet the underlying neural mechanism remains incompletely understood. We performed a double-blind randomized controlled trial in 35 breast cancer patients with preoperative depressive symptoms, who were randomly given intraoperative esketamine 0.25 mg·kg⁻¹ (n = 18) or saline placebo (n = 17) over the initial 40 min of anesthesia. Resting-state functional magnetic resonance imaging data were collected at preoperative baseline and postoperative day 1 follow-up to calculate brain functional network measures. In contrast to no significant change in the placebo group, the esketamine group showed increased degree centrality of the left inferior frontal gyrus, opercular part from baseline to follow-up, which was related to improvement in depressive symptoms. Additionally, we found significant associations of baseline network measures at the global, nodal, and edge levels with short-term and long-term improvements in depressive symptoms following esketamine administration. These findings may not only provide novel insights into the neural mechanism by which esketamine exerts its antidepressant efficacy during the perioperative period, but also highlight the prospect of functional network measures as useful predictors of antidepressant response to esketamine in patients with breast cancer.

PMID:41792101 | DOI:10.1038/s41398-026-03929-3

Front Physiol. 2026 Feb 18;17:1750101. doi: 10.3389/fphys.2026.1750101. eCollection 2026.

ABSTRACT

INTRODUCTION: Cerebrospinal fluid (CSF) pulsations are linked to hemodynamics, with autonomic mechanisms, suggested to modulate slow-wave induced pulsations.

METHOD: To explore autonomic regulation's role in neurofluid flow, independent of sleep and neural activity, we hypothesized that modulating basal CO2 (altering vascular tone, cardiac activity and respiration) would highlight this link.

RESULTS: Using resting-state BOLD fMRI in neurofluid regions under different CO2 levels (capnic states), we found: 1) biomechanical modulation does not explain neurofluid dynamic variations across capnias; 2) beyond respiration, heart-rate variability independently drives low-frequency neurofluid flow, indicating autonomic control; 3) altered CO2 primarily affects neurofluid dynamics through the frequency (and not amplitude) of heart-rate and respiratory-volume variability.

DISCUSSION: These results suggest that both hyper- and hypocapnia disrupt how CSF responds to autonomic regulation, seen in deviations from normal cardiac and respiratory responses. Our work reveals neurofluid dynamics' sensitivity to CO2's frequency response, best explained by autonomic modulation. Modulating basal CO2 offers a new way to influence human neurofluid dynamics, independent of sleep or neuronal activity.

PMID:41788877 | PMC:PMC12956733 | DOI:10.3389/fphys.2026.1750101

Front Neurosci. 2026 Feb 18;20:1773151. doi: 10.3389/fnins.2026.1773151. eCollection 2026.

ABSTRACT

BACKGROUND AND PURPOSE: Awake mouse fMRI is a powerful tool for both neuroscience and translational research. To minimize head motion during scanning, habituation under physical restraint is commonly used. However, it remains unclear how stress levels and head motion evolve during habituation, particularly within the MRI environment.

METHODS: To address this, we repeatedly measured plasma corticosterone (CORT) levels in three groups of mice - controls, mice habituated outside the MRI magnet, and mice habituated within the fMRI environment - and acquired longitudinal resting-state fMRI data daily during an eight-day habituation period and again 15 days post-habituation at 15.2 T.

RESULTS: We found that CORT levels initially increased by approximately twofold and gradually decreased during habituation outside the magnet, whereas in mice habituated within the fMRI environment, CORT levels increased two- to fourfold and remained elevated throughout the habituation period. One week after habituation, CORT levels returned to baseline in both groups. Throughout all resting-state fMRI scanning sessions, head motion and functional connectivity remained stable, likely due to the well-designed restraint cradle that permitted paw movement.

CONCLUSION: These results suggest that, for our experimental setup, extending the number of habituation days does not further reduce stress in the MRI environment, provided that head motion remains within acceptable limits.

PMID:41788545 | PMC:PMC12956629 | DOI:10.3389/fnins.2026.1773151

Hum Brain Mapp. 2026 Mar;47(4):e70483. doi: 10.1002/hbm.70483.

ABSTRACT

Cortical parcellation is fundamental to neuroscience, enabling the division of cerebral cortex into distinct, non-overlapping regions to support interpretation and comparison of complex neuroimaging data. Although extensive literature has investigated cortical parcellation and its connection to functional brain networks, the optimal spatial features for deriving parcellations from resting-state fMRI (rsfMRI) remain unclear. Traditional methods such as Independent Component Analysis (ICA) have been widely used to identify large-scale functional networks, while other approaches define disjoint cortical parcellations. However, bridging these perspectives through effective feature extraction remains an open challenge. To address this, we introduce Untamed, a novel framework that integrates unconstrained tensor decomposition using NASCAR to identify functional networks, with state-of-the-art graph node embedding to generate cortical parcellations. Our method produces near-homogeneous, spatially coherent regions aligned with large-scale functional networks, while avoiding strong assumptions like statistical independence required in ICA. Across multiple datasets, Untamed consistently demonstrates improved or comparable performance in functional connectivity homogeneity and task contrast alignment compared to existing atlases. The pipeline is fully automated, allowing for rapid adaptation to new datasets and the generation of custom parcellations. The atlases derived from the Genomics Superstruct Project (GSP) dataset, along with the code for generating customizable parcel numbers, are publicly available at https://untamed-atlas.github.io.

PMID:41787960 | DOI:10.1002/hbm.70483

Neuroscience. 2026 Mar 3:S0306-4522(26)00154-5. doi: 10.1016/j.neuroscience.2026.02.045. Online ahead of print.

ABSTRACT

Autonomic dysfunction is implicated in the manifestation of binge eating (BE) behaviors. Aberrant functional connectivity among brain regions involved in reward processing, affect regulation, and cognitive control have been the focus of resting state functional connectivity studies in BE. However, the influence of brain regions involved in central autonomic control and the relationship between autonomic balance and BE behavior remains unclear. The current study examines whether effective connectivity (EC) patterns in neural networks supporting the afore-mentioned cognitive processes moderates the relationship between heart rate variability (HRV), a physiological marker of autonomic balance, and BE behaviors. Resting-state fMRI data from 158 healthy adults (BE = 30; Control = 128) were analyzed using spectral dynamic causal modeling within a priori-defined eating regulation network. Leave-one-out cross-validation (LOOCV) assessed the predictive utility of EC patterns. Although 20 EC connections were associated with BE (posterior probability > 0.99), EC from the left posterior cingulate cortex to the hypothalamus predicted group membership above chance (AUC = 0.622, 95% CI [0.508, 0.731]). Approximate entropy was a significant predictor of binge eating behavior (ß = -6.625; p < 0.05) in a subset of 114 participants with usable photoplethysmography data (BE = 24; Control = 90). This relationship was significantly moderated by ventral tegmental area to hypothalamus EC (ß = -6.529; p < 0.05). These findings uncover novel directional connectivity patterns and identify brain-autonomic interactions that may underlie BE behavior, offering promising targets for future intervention strategies.

PMID:41786020 | DOI:10.1016/j.neuroscience.2026.02.045

J Affect Disord. 2026 Mar 3:121550. doi: 10.1016/j.jad.2026.121550. Online ahead of print.

ABSTRACT

This study explores the global signal topography of core and periphery brain networks in Major Depressive Disorder (MDD), Bipolar disorder (BD-Dep) and healthy controls (HC) using resting-state fMRI. In a sample of 140 depressed MDD and BD patients, and 70 HC, we observed a significant shift toward increased activity in the transmodal-core regions (e.g., default mode network, frontoparietal network) at the expense of unimodal-periphery regions (e.g., visual, sensory-motor cortices) in both depressed MDD and BD patients compared to HC. Whole brain machine learning analyses further demonstrated that altered global signal dynamics can effectively distinguish MDD and BD from HC (ACC = 79% and 77% respectively). Notably, we identified a significant negative correlation between global signal correlation in unimodal-periphery networks and depressive symptom severity. Additionally, in a smaller sample of BD during mania (N = 22) a distinct topographic pattern was observed, with increased global representation in the unimodal-periphery compared to depressive states, suggesting mood state-dependent shifts in network organization. To assess multivariate discriminability across diagnostic groups, a Partial Least Squares (PLS) analysis revealed that higher Core and related network activity (DMN, FPN) predicted diagnostic assignment to MDD and BD-Dep, whereas higher Periphery and related network (e.g., visual and sensory-motor networks) predicted assignment to BD-Man and HC. The Core-Periphery (C-) ratio emerged as the strongest predictor (VIP = 1.65). These results underscore the critical role of global signal topography in mood disorders, particularly the imbalance between core and peripheral brain networks, as a potential neurobiological marker for depressive states.

PMID:41785933 | DOI:10.1016/j.jad.2026.121550

Psychiatry Res Neuroimaging. 2025 Dec 20;359:112114. doi: 10.1016/j.pscychresns.2025.112114. Online ahead of print.

ABSTRACT

BACKGROUND: Previous studies demonstrated a link between disrupted resting-state functional connectivity (RSFC) in the Default Mode Network (DMN) and cognitive impairment in people with HIV (PWH). Rs-fMRI studies to date have not investigated DMN connectivity with other brain regions that are typically implicated in HIV (i.e., striatum), and how this is related to cognition. We hypothesized that intra-DMN RSFC and DMN-striatal RSFC will be significantly different between PWH and HIV-negative controls (HC), and that these alterations are associated with cognition.

METHODS: We used the posterior cingulate cortex (PCC) as the main node of the DMN and calculated its RSFC to other DMN regions for intra-DMN RSFC and to striatum. We performed an Analysis of Covariance to examine group differences in RSFC between PWH and HC. We conducted linear regression modelling to test the association of cognitive performance and RSFC in PWH.

RESULTS: There were no statistically significant differences between PWH and HC in PCC-ROI RSFC. However, in PWH, we found that higher education and greater RSFC of PCC- right hippocampus were associated with better performance in working memory (p = 0.0007 and 0.03).

CONCLUSION: Our results suggest that altered intra-DMN RSFC may contribute to cognitive dysfunction observed in PWH.

PMID:41785822 | DOI:10.1016/j.pscychresns.2025.112114

Brain Struct Funct. 2026 Mar 5;231(3):35. doi: 10.1007/s00429-026-03092-x.

NO ABSTRACT

PMID:41784842 | DOI:10.1007/s00429-026-03092-x

Adv Sci (Weinh). 2026 Mar 5:e23609. doi: 10.1002/advs.202523609. Online ahead of print.

ABSTRACT

Autoimmune thyroid diseases (AITD) are systemic conditions frequently associated with neurological manifestations, yet the underlying neural and immunological mechanisms remain unclear. This study focuses on thyroid eye disease (TED), a representative AITD, to provide a deeper insight into its neural mechanism. We first combined resting-state functional magnetic resonance imaging (rs-fMRI) data from a retrospective cohort of 116 TED patients with transcriptomic data from the Allen Human Brain Atlas. The analysis of rs-fMRI data demonstrated significant alterations in frontal, parietal, subcortical, and brainstem regions in TED. By integrating rs-fMRI data with regional transcriptomic profiles derived from the postmortem Allen Human Brain Atlas, enabling region-level transcriptional inference, we revealed enriched pathways related to synaptic signaling, neurovascular regulation, and immune activation. Tissue and cellular level enrichment further showed close association with the cortex and neurons. Key neuroimaging findings identified in the retrospective cohort were subsequently validated in an independent prospective cohort of TED patients and healthy controls (TED: 39; HC: 42) using paired rs-fMRI and peripheral blood RNA sequencing data, which identified significant associations between immune cell infiltration and neural activity patterns. Collectively, these findings delineate coordinated brain-immune associations in TED and generate hypotheses regarding neuroimmune interactions in AITD.

PMID:41783917 | DOI:10.1002/advs.202523609

Netw Neurosci. 2026 Jan 28;10(1):158-184. doi: 10.1162/NETN.a.515. eCollection 2026.

ABSTRACT

Edge time series decompose functional connections into their fine-scale, framewise contributions. Previous studies have demonstrated that global high-amplitude "events" in edge time series can be clustered into distinct patterns. However, whether events and their patterns change or persist throughout the human lifespan has not been investigated. Here, we directly address this question by clustering event frames using the Nathan Kline Institute-Rockland sample that includes subjects with ages spanning the human lifespan. We find evidence of two main clusters that appear across subjects and age groups which systematically change in magnitude and frequency with age. Our results also demonstrate that such event clusters have distinct, heterogeneous relationships with structural connectivity-derived communication measures, which change with age. Finally, event clusters were found to outperform nonevents in predicting phenotypes regarding human intelligence and achievement. Collectively, our findings fill several gaps in current knowledge about cofluctuation patterns in edge time series and human aging, setting the stage for future investigation into the causal origins of changes in functional connectivity throughout the human lifespan.

PMID:41782939 | PMC:PMC12956297 | DOI:10.1162/NETN.a.515

Netw Neurosci. 2026 Jan 28;10(1):93-117. doi: 10.1162/NETN.a.505. eCollection 2026.

ABSTRACT

Brain function is inherently dynamic, characterized by transient, overlapping functional states rather than static connectivity patterns. Current clustering-based dynamic functional network connectivity methods often fail to capture overlapping states; meanwhile, independent component analysis (ICA)-based methods typically rely on group-level analysis, limiting subject-specific accuracy. To address this gap, we introduce a novel analytical framework estimating individualized dynamic double functional independent primitives (ddFIPs)-based states. Our methodological innovation includes: (a) a two-stage ICA combining spatially constrained ICA to define group-level intrinsic connectivity networks (ICNs), followed by constrained ICA to estimate subject-specific states and timecourses; (b) calibration ensuring derived states preserve original correlation scales, enabling meaningful cross-subject and group-level comparisons; and (c) novel metrics leveraging this calibrated representation, including amplitude convergence (uniformity of simultaneous state contributions), amplitude divergence (variability of states independent of state dominance), and dynamic state density (number of concurrently active states at any given time). These methodological advances enhance our ability to characterize subtle differences in brain connectivity dynamics, offering deeper insight into healthy and disrupted connectivity patterns. Validating our framework on an extensive resting-state fMRI dataset (N > 5.5K) spanning four neuropsychiatric conditions revealed disorder-specific connectivity signatures: schizophrenia exhibited extensive variability (increased divergence), while autism displayed pronounced stability (increased convergence). In summary, our proposed method uniquely integrates subject-specific ICA estimation, unit-preserving calibration, and novel convergence-divergence metrics, providing data-driven biomarkers differentiating psychiatric disorders.

PMID:41782938 | PMC:PMC12956294 | DOI:10.1162/NETN.a.505

Netw Neurosci. 2026 Jan 28;10(1):185-203. doi: 10.1162/NETN.a.516. eCollection 2026.

ABSTRACT

Alzheimer's disease (AD) is characterized by progressive neural network degradation. In brain functional networks, overlapping module structures provide more accurate representations of brain function than nonoverlapping structures. Since the involvement of overlapping nodes in multiple modules can vary over time, investigating dynamic functional changes in the brain may provide deeper insights into the structural characteristics of these overlapping modules. However, the spatiotemporal dynamics of overlapping modular brain organization remain unclear. We employed resting-state fMRI to explore the overlapping modular organization and dynamic multilayer modules in 64 AD (Agemean = 74.04) and 61 healthy controls (HC, Agemean = 74.86) from the Alzheimer's Disease Neuroimaging Initiative. Compared with HC, AD exhibited increased overlapping modules and decreased modularity, with altered nodal overlapping probability, particularly in the superior frontal cortex and hippocampus. Higher nodal overlapping probability correlated with greater flexibility and was associated with larger amyloid deposits. Lasso regression analysis further revealed strong correlations between overlapping nodal characteristics and cognitive performance. Our findings suggest that overlapping nodes are critical components in AD, demonstrating high amyloid deposition, significant functional flexibility, and strong associations to cognitive behavior. These alterations may enhance the understanding of AD pathology and contribute to the development of biomarkers for improved diagnosis and therapeutic strategies.

PMID:41782936 | PMC:PMC12956295 | DOI:10.1162/NETN.a.516

Med Biol Eng Comput. 2026 Mar 5. doi: 10.1007/s11517-026-03530-2. Online ahead of print.

ABSTRACT

Autism Spectrum Disorder (ASD) and Attention Deficit and Hyperactivity Disorder (ADHD) are two psychiatric disorders frequently encountered in children. ADHD is further categorized into three subtypes. The diagnostic processes for these conditions are complex and often prone to misclassification. We proposed a lightweight deep neural network, ADBrainNet, to differentiate ASD, ADHD combined, ADHD hyperactive/impulsive, ADHD inattentive and neurotypical individuals. Our methodology was benchmarked against prevalent ImageNet transfer learning methods, including AlexNet, MobileNet, ResNet18, and Xception, for training on resting-state fMRI images sourced from ABIDE and ADHD-200 datasets. ADBrainNet achieved superior performance on the independent external testing set through five-fold cross-validation, with a mean (± standard deviation) accuracy, precision, recall, and F1 score of 61.87% (± 5.59%), 65.72% (± 6.98%), 61.87% (± 5.59%), and 62.50% (± 5.78%), respectively. Furthermore, the explainable artificial intelligence algorithm LIME was employed to explore the most significant features during ADBrainNet's decision process. Our model provides an interpretable computational framework for neuroimaging-based classification between ASD and ADHD subtypes. This approach may inform future research and, upon further validation and comparison with clinician performance, could potentially aid in patient assessment, stratification, and management of psychiatric disorders.

PMID:41781648 | DOI:10.1007/s11517-026-03530-2

J Affect Disord. 2026 Mar 2:121532. doi: 10.1016/j.jad.2026.121532. Online ahead of print.

ABSTRACT

PURPOSE: To elucidate the neurobiological architecture of late-life depression (LLD) by integrating macroscopic multimodal MRI with microscopic transcriptomic and neurochemical mapping.

METHODS: We analyzed 39 first-episode, drug-naive LLD patients and 41 matched healthy controls (HC) using structural MRI, diffusion kurtosis imaging, and resting-state fMRI. Data were processed to extract gray matter volume (GMV), white matter diffusion metrics (FA, MD, AD, RD), and functional indices (fALFF, DC), with group differences evaluated under rigorous multiple-comparison corrections. To elucidate molecular underpinnings, partial least squares (PLS) regression was applied to link neuroimaging t-maps with Allen Human Brain Atlas gene expression profiles. Spatial correlations between neuroimaging statistical maps and PET-derived neurotransmitter receptor density maps were further examined to identify neurochemical substrates.

RESULTS: LLD patients exhibited localized gray matter volume (GMV) reductions in the right fusiform gyrus and limbic structures, widespread microstructural white matter degradation across fronto-limbic tracts, and increased functional amplitude and network centrality centered on the medial prefrontal cortex (mPFC). Transcriptomic decoding linked these macroscopic anomalies to gene sets enriched for mitochondrial metabolism, calcium signaling, and synaptic organization. Furthermore, spatial correlation analyses uniquely revealed that structural atrophy was predominantly associated with the glutamatergic system, whereas functional and network alterations aligned most robustly with the opioid system.

CONCLUSIONS: Our findings support a multi-scale neurobiological model of LLD, wherein distinct molecular deficits in bioenergetics, glutamatergic excitotoxicity, and opioid neurotransmission underlie macroscopic structural and functional network failures. This integrative framework identifies specific neurochemical systems as prime mechanistic substrates and potential therapeutic targets for the cognitive-emotional dysregulation characteristic of LLD.

PMID:41780696 | DOI:10.1016/j.jad.2026.121532

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