Most recent paper

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

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