Most recent paper

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

bioRxiv [Preprint]. 2026 Feb 19:2026.02.18.706630. doi: 10.64898/2026.02.18.706630.

ABSTRACT

The early diagnosis of Alzheimer's disease (AD), a cause of progressive cognitive decline, remains challenging. Recent information-theoretic advances allow brain dynamics to be quantified in terms of how regions share and combine information. Integrated Information Decomposition (ΦID) separates redundant (the same content present in multiple regions) from synergistic information (new content that emerges only when regions are considered together). Such information-dynamic measures may provide biomarkers relevant to AD risk and progression. Here we applied integrated information decomposition (ΦID) to resting-state fMRI from the Alzheimer's Disease Neuroimaging Initiative (ADNI), to test whether ΦID measures are diagnostically sensitive and track cognition along the AD spectrum. For each region, we computed total synergy and redundancy and compared values across cognitively normal (CN), mild cognitive impairment (MCI), and AD groups. Compared to CN, AD patients showed a striking synergy reduction across the entire brain, in concert with widespread redundancy increases, particularly in the executive and default mode networks. Transitions from CN to AD included an intermediate MCI decrease in redundancy, possibly reflecting early disease compensation strategies. This AD informational shift from complex higher level information processing to more robust inefficient forms likely reflects a cognitive shift to simpler, less integrative cognitive processes. Indeed, when re-analysing the data according to a standard cognitive clinical test (the Montreal Cognitive Assessment), we found a synergy-redundancy shift in high versus low performers broadly very similar to the CN to AD shift. AD shows a clear information-processing signature: reduced global synergy and increased redundancy, especially in the executive control network. These striking results provide powerful insights into the widespread information processing reconfiguration that occurs in AD, with clear changes already emerging at the earlier MCI stage. Further, these results provide a novel route to support early diagnosis and stratification.

PMID:41757079 | PMC:PMC12934565 | DOI:10.64898/2026.02.18.706630

Front Psychiatry. 2026 Feb 11;17:1765885. doi: 10.3389/fpsyt.2026.1765885. eCollection 2026.

ABSTRACT

BACKGROUND: Chronic insomnia disorder (CID) and depression exhibit high comorbidity, yet the underlying neurobiological mechanisms remain poorly understood. Neuroimaging meta-analyses suggest the subgenual anterior cingulate cortex (sgACC) is a key node, but the characteristics of its network connectivity in CID patients with depressive symptoms (CID-D) are unclear.

METHODS: This study enrolled 197 participants: 66 CID patients without depression (CID-nD), 67 CID-D patients, and 64 good sleep controls (GSC). Using resting-state functional magnetic resonance imaging (fMRI), we compared sgACC-based functional connectivity (FC) across groups. We also examined correlations between altered FC and clinical symptoms, and investigated whether altered sgACC FC mediated the relationship between insomnia severity and depressive symptoms.

RESULTS: Significant group differences in sgACC FC were found in the left inferior temporal gyrus (ITG), inferior frontal gyrus (IFGtri), right supplementary motor area (SMA), postcentral gyrus (POCG), and medial superior frontal gyrus (SFGmed). Specifically, compared to CID-nD, CID-D patients showed increased FC with ITG.L and IFGtri.L, and decreased FC with SMA.R and POCG.R. FC between sgACC and ITG.L or IFGtri.L was positively correlated with depressive symptoms, while sgACC-POCG.R FC was negatively correlated. Mediation analysis revealed that sgACC-ITG.L FC partially mediated the link between insomnia and depressive symptoms.

CONCLUSION: Our findings identify specific alterations in sgACC functional network in CID patients with comorbid depression. The mediating role of sgACC-ITG.L connectivity highlights a potential neural pathway through which insomnia contributes to depressive symptoms, identifying a putative target for neuromodulation therapies.

PMID:41756572 | PMC:PMC12932571 | DOI:10.3389/fpsyt.2026.1765885

Entropy (Basel). 2026 Feb 9;28(2):191. doi: 10.3390/e28020191.

ABSTRACT

Major depressive disorder (MDD) represents a heterogeneous condition lacking reliable neurobiological biomarkers and a mechanistic understanding. Time-resolved characterization of brain dynamics reveals that mental health is associated with a characteristic dynamical regime, exhibiting spontaneous switching between a repertoire of ghost attractor states forming resting-state networks. Analysing resting-state fMRI data from 848 patients with MDD and 794 healthy controls across 17 sites in China (REST-meta-MDD) using Leading Eigenvector Dynamics Analysis (LEiDA), we found patients with MDD exhibited significantly reduced default mode network (DMN) occupancy (p < 0.001; Hedges' g = -0.51) and increased occipito-parieto-temporal state occupancy (p < 0.001; Hedges' g = 0.42), suggesting compensatory dynamical rebalancing. These findings extend prior observations of DMN disruption in MDD, aligning with the emerging dynamical systems framework for mental health to advance the mechanistic understanding of MDD pathophysiology.

PMID:41751694 | PMC:PMC12939193 | DOI:10.3390/e28020191

Biomedicines. 2026 Jan 31;14(2):333. doi: 10.3390/biomedicines14020333.

ABSTRACT

Functional magnetic resonance imaging (fMRI) is a valuable tool for presurgical brain mapping, traditionally implemented with task-based paradigms (tb-fMRI) that measure blood oxygenation level-dependent (BOLD) signal changes during controlled motor or cognitive tasks. Tb-fMRI is a well-established tool for non-invasive localization of cortical eloquent areas, yet its dependence on patient cooperation and intact cognition limits use in individuals with aphasia, cognitive impairment, or in pediatric and other vulnerable populations. Resting-state fMRI (rs-fMRI) provides a task-free alternative by leveraging spontaneous low-frequency BOLD fluctuations to delineate intrinsic functional networks, including motor and language systems that show good spatial concordance with tb-fMRI and with direct cortical stimulation. This narrative review outlines the methodological foundations of tb-fMRI and rs-fMRI, comparing acquisition protocols, preprocessing and denoising pipelines, analytic approaches, and validation strategies relevant to presurgical planning. Particular emphasis is given to the technical and physiological foundations of BOLD imaging, statistical modeling, and the influence of motion, noise, and standardization on data reliability. Emerging evidence indicates that rs-fMRI can reliably expand mapping to patients with limited task compliance and may serve as a robust complementary modality in complex clinical contexts, though its methodological heterogeneity and absence of unified practice guidelines currently constrain widespread adoption. Future advances in harmonized preprocessing, multicenter validation, and integration with connectomics and machine learning frameworks are likely to be critical for translating rs-fMRI into routine, reliable presurgical workflows.

PMID:41751232 | DOI:10.3390/biomedicines14020333

Brain Sci. 2026 Feb 10;16(2):208. doi: 10.3390/brainsci16020208.

ABSTRACT

Background: Resting-state functional MRI (rs-fMRI) studies typically rely on linear Pearson correlation to characterize brain connectivity, potentially overlooking the distributional characteristics of functional networks. This study introduces a kernel-transformed functional connectivity (FC) entropy framework to quantify network dedifferentiation in bipolar disorder (BD). Methods: We utilized a Gaussian kernel function to execute a nonlinear similarity transformation (referred to as reweighting) on standard linear correlation matrices. This approach acts as a functional filter to amplify the contrast between strong and weak connections. Multiscale entropy (global, modular, and nodal) was subsequently calculated to characterize the uniformity of connectivity weight distributions. Results: Compared to Normal Controls (NCs), patients with BD exhibited significantly higher entropy at the global level and within the Default Mode, Salience, and Somatosensory-Motor networks, indicating widespread network dedifferentiation (distributional flattening). These alterations were robust across different kernel widths and remained significant after rigorously controlling for head motion (Mean FD). Furthermore, manic symptom severity (YMRS) was negatively correlated with global entropy, suggesting a pathological "locking-in" or rigidity of specific neural circuits during manic states. Conclusions: The kernel-transformed FC entropy serves as a distribution-sensitive complement to conventional linear metrics. Our findings highlight network dedifferentiation as a key pathophysiological feature of BD and suggest this framework as a promising candidate metric for characterizing network dysregulation.

PMID:41750208 | DOI:10.3390/brainsci16020208