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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

Brain Sci. 2026 Jan 31;16(2):181. doi: 10.3390/brainsci16020181.

ABSTRACT

Background/Objectives: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by differences in social communications and restricted, repetitive patterns of behaviors and interests, affecting approximately 1% of children globally. While functional magnetic resonance imaging (fMRI) has provided insights into altered brain connectivity patterns in ASD, classification based on neuroimaging remains a challenging due to the heterogeneity of the disorder and variability in imaging data across sites. This study employs a network-based approach using large-scale, multi-site rs-fMRI dataset from the Autism Brain Imaging Data Exchange (ABIDE I and II) to classify ASD and healthy controls using machine learning. Methods: A semi-blind Independent Component Analysis method, specifically the spatial constraint reference ICA, is applied to identify functional brain networks, and the ComBat harmonization technique is used to address site-specific variability across 11 independent datasets, ensuring consistency in feature representation. Support Vector Machines (SVMs) are employed for classification, focusing on three key networks: the Default Mode Network (DMN), Sensorimotor Network (SMN), and Visual Sensory Network (VSN). Results: The results demonstrate high classification accuracy, with the VSN achieving the highest performance (83.23% accuracy, 87.90% AUC), followed by the DMN (81.43% accuracy, 84.53% AUC) and the SMN (80.52% accuracy, 84.96% AUC), positioned with their recognized roles in social cognition and sensory-motor processing, respectively. Conclusions: The integration of ICA-based feature extraction with ComBat harmonization significantly improved classification accuracy compared to previous studies. These findings point out the potential of network-based approaches in ASD classification and point out the importance of integrating multi-site neuroimaging data for identifying reproduceable network-level features.

PMID:41750182 | DOI:10.3390/brainsci16020181

Hum Brain Mapp. 2026 Mar;47(4):e70485. doi: 10.1002/hbm.70485.

ABSTRACT

The brain's rich club is a network of particularly densely interconnected regions, metabolically costly to maintain but central to the balance between functional segregation and integration. We assessed whether the rich club can accordingly be described as a control center of the brain, and present a systematic analysis of its involvement in maintenance of and traversal between various cognitively relevant functional states. Brain states were defined based on fMRI task-evoked and resting-state patterns of activity as provided by the Human Connectome Project (HCP). Using tools from network control theory (NCT), we computed the necessary effort needed for control of dynamics when the rich club, versus a size-matched set of low-degree peripheral regions, was prohibited from exerting control over dynamics. Control energy needed to traverse functional states was significantly higher, and stability of states significantly lower, when the set of peripheral regions was prohibited from control. Findings were stable across various rich-club and null model definitions and across different parameter settings. A region's contribution to optimal control processes was instead associated with its affiliation with certain intrinsic connectivity networks and its position on the visual-sensorimotor, but not sensory-transmodal cortical gradient. We accordingly report that the rich club was systematically less involved in control of dynamics than the size-matched set of peripheral regions. These results do not negate an integratory role of the rich club, but question its proposed role as a driver of control. Indeed, if it would inhabit such a role, we would have expected opposite results. Our findings fit with a position describing the rich club as a passive "data-highway" which, by means of its high connectivity, can be easily controlled by peripheral regions and thus facilitate relevant communication channels between them. We call for methodological expansions of the control theoretical toolbox allowing for elaborations on the temporal dynamics of control processes.

PMID:41749476 | DOI:10.1002/hbm.70485

Mol Autism. 2026 Feb 26;17(1):14. doi: 10.1186/s13229-026-00710-7.

ABSTRACT

BACKGROUND: Social camouflaging refers to strategies to hide or compensate for social difficulties, often at significant mental health costs, and is particularly prevalent in autism. The large-scale neural network associated with this adaptation remains poorly understood. This study aimed to identify these neural network patterns and their link to potential mental health issues.

METHODS: Using a dimensional approach, we recruited 110 healthy young adults who completed self-report questionnaires measuring autistic traits and camouflaging as well as depression and anxiety, and underwent resting-state fMRI scans. The interaction between camouflaging and autistic traits on brain network connectivity was examined using the 300-node Seitzman atlas, encompassing 13 functional networks.

RESULTS: Among individuals with higher autistic traits, greater camouflaging was associated with increased connectivity between the Default Mode Network (DMN) and the Cingulo-Opercular Network (CON), as well as within the CON. Crucially, DMN-CON hyperconnectivity statistically mediated the relationship between camouflaging and potential mental health costs (i.e., depression and anxiety scores) but only in individuals with higher autistic traits. Limitations: Our study was limited by its predominantly non-clinical sample, the cross-sectional design, and the use of resting-state rather than task-based fMRI.

CONCLUSIONS: These findings reveal specific compensatory neural network patterns associated with camouflaging in those high in autistic traits, involving interoception, self-referential, and executive control systems, and provide a neurobiological explanation for its potential mental health burden, highlighting the need for societal changes that reduce the pressure for such adaptations.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13229-026-00710-7.

PMID:41742299 | PMC:PMC12950243 | DOI:10.1186/s13229-026-00710-7

Sci Rep. 2026 Feb 26. doi: 10.1038/s41598-026-40813-y. Online ahead of print.

ABSTRACT

Brain functional development from birth to adolescence follows the cortical gradient from primary sensorimotor to higher-order association regions. Precuneus (PCun) is crucial in spatial cognition, visual-motor integration, and social cognition. However, functional connectivity changes of PCun subregions in this dynamic developmental period are not known. Multimodal cross-sectional diffusion MRI and resting-state fMRI of subjects from birth to early adolescence were acquired to obtain structural and functional connectivity. PCun in neonates, 1-year-olds, 2-year-olds, and early adolescent subjects were consistently parcellated into four subregions based on structural connectivity of PCun. Significant developmental changes were found in functional connectivity between the parcellated PCun subregions and default mode network (DMN), and between the parcellated PCun subregions and cerebellum network. To understand altered development of PCun in brain disorders, connectivity-based parcellation was performed in the subjects with autism spectrum disorder (ASD). Similar parcellation pattern of PCun was found, but the relative volume of the dorsal-posterior subregion significantly decreased in the subjects with ASD compared to typically developmental subjects. These findings revealed functional developmental patterns of PCun subregions in their connected networks in typical developing brains and revealed PCun subregion alteration in ASD, shedding light onto functional changes of PCun architecture during development.

PMID:41748807 | DOI:10.1038/s41598-026-40813-y

NPJ Parkinsons Dis. 2026 Feb 26. doi: 10.1038/s41531-026-01294-y. Online ahead of print.

ABSTRACT

Impulse control disorders (ICDs) affect up to 45% of Parkinson's disease (PD) patients, yet their neural mechanisms remain unclear. Using multimodal PET and resting-state fMRI in 23 PD patients (11 PDICD + , 12 PD-ICD-) and 14 healthy controls, we identified specific brain pathways underlying ICDs. PDICD+ patients showed steeper delay discounting and altered functional connectivity, including enhanced posterior parietal coupling within executive networks and disrupted salience-executive interactions. Critically, aberrant right supplementary motor area-amygdala connectivity was linked to ICD severity and decisional impulsivity. Path analysis revealed that increased SMA 5-HT₂ₐ receptor availability was associated with enhanced SMA-amygdala coupling, which in turn was positively associated with ICD symptoms. By linking serotonergic dysfunction to disrupted motor-limbic networks and impulsive behavior, this study identifies targetable pathways for managing a common non-motor complication of PD.

PMID:41748653 | DOI:10.1038/s41531-026-01294-y

Brain Res Bull. 2026 Feb 24:111787. doi: 10.1016/j.brainresbull.2026.111787. Online ahead of print.

ABSTRACT

BACKGROUND: Post-stroke cognitive impairment (PSCI) is associated with abnormal dynamic functional connectivity, yet the temporal dynamic of brain activity and their underlying molecular mechanisms remain unclear.

METHODS: Participants were classified into two groups based on neuropsychological assessments: PSCI group (N=67) and post-stroke with no cognitive impairment (NPSCI) group (N=65), alongside 47 healthy controls (HCs). Dynamic brain states were analyzed using a Hidden Markov Model (HMM) with the Brainnetome Atlas, yielding metrics like fractional occupancy (FO), mean dwell time (MDT), switching rate (SR) and transition probabilities (TP) based on resting-state functional magnetic resonance imaging (rs-fMRI). Finally, we further assessed the spatial correlations between the mean activation of HMM state and neurotransmitter receptors/transporters distribution, cognitive relative term, and gene expression profiles.

RESULTS: Five HMM states were identified. Compared with HCs and NPSCI group, patients with PSCI group exhibited different dynamics, including FO, MDT, SR, and TP. Additionally, we found that the mean activation maps of HMM state were associated with the neurotransmitter receptors/transporters distribution and cognitive relative term. Furthermore, our results demonstrated a spatial correlation between the mean activation maps of state 5 and gene expression patterns. Finally, enrichment analysis indicated that PLS-positive genes were enriched in pathways related to DNA/RNA metabolism, signal transduction and regulation, and immune-disease associations, whereas, PLS-negative genes were mainly enriched in lipid metabolism and insulin response, virus-cytokine interactions, and influenza response pathways.

CONCLUSIONS: This study provides new insights into characterizing dynamic neural activity in PSCI. The brain network dynamics defined by HMM analysis may deepen our understanding of the neurobiological underpinnings of PSCI, indicating a linkage between neural configuration and gene expression in PSCI.

PMID:41747873 | DOI:10.1016/j.brainresbull.2026.111787

Prog Neuropsychopharmacol Biol Psychiatry. 2026 Feb 24:111653. doi: 10.1016/j.pnpbp.2026.111653. Online ahead of print.

ABSTRACT

BACKGROUND: Food addiction (FA) affects a significant proportion of the general population and could contribute to excess weight and its related complications. This phenomenon has been well described in terms of behavior, but little is known about its neurological determinants. The primary aim of this systematic review is to identify the neuroimaging characteristics associated with FA, using the Yale Food Addiction Scale (YFAS) as a validated assessment tool.

METHODS: A systematic search was conducted in PubMed and ScienceDirect databases from 2009 to July 2024 in accordance with the PRISMA 2020 guidelines. Studies were included if they investigated associations between the YFAS and neuroimaging outcomes. A descriptive analysis was conducted due to the methodological heterogeneity between the included articles.

RESULTS: Of the 528 records identified, 25 studies were included in the review, representing 3081 participants in total. Functional magnetic resonance imaging (fMRI, n = 18) and structural MRI (n = 9), were the most commonly used imaging techniques. Studies reported associations between YFAS scores and altered resting-state functional connectivity or brain responses to cognitive tasks, especially in caudate, putamen, amygdala, insula, nucleus accumbens, orbitofrontal cortex, thalamus and precuneus. Yet, numerous neuroimaging findings related to FA presented discrepancies across studies.

DISCUSSION: There is some evidence of altered activation and functional connectivity in brain areas involved in reward and cognitive control among individuals with FA. However, neuroimaging outcomes related to FA remain highly inconsistent across studies, partly due to heterogenous methodologies. Methodological recommendations are provided to improve consistency of future neuroimaging research in the context of FA.

PMID:41747855 | DOI:10.1016/j.pnpbp.2026.111653

Front Comput Neurosci. 2026 Feb 10;20:1731868. doi: 10.3389/fncom.2026.1731868. eCollection 2026.

ABSTRACT

Understanding the neural mechanisms underlying the transitions between different states of consciousness is a fundamental challenge in neuroscience. Thus, we investigate the underlying drivers of changes during the resting-state dynamics of the human brain, as captured by functional magnetic resonance imaging (fMRI) across varying levels of consciousness (awake, light sedation, deep sedation, and recovery). We deploy a model-based approach relying on linear time-invariant (LTI) dynamical systems under unknown inputs (UI). Our findings reveal distinct changes in the spectral profile of brain dynamics-particularly regarding the stability and frequency of the system's oscillatory modes during transitions between consciousness states. These models further enable us to identify external drivers influencing large-scale brain activity during naturalistic auditory stimulation. Our findings suggest that these identified inputs delineate how stimulus-induced co-activity propagation differs across consciousness states. Notably, our approach showcases the effectiveness of LTI models under UI in capturing large-scale brain dynamic changes and drivers in complex paradigms, such as naturalistic stimulation, which are not conducive to conventional general linear model analysis. Importantly, our findings shed light on how brain-wide dynamics and drivers evolve as the brain transitions toward conscious states, holding promise for developing more accurate biomarkers of consciousness recovery in disorders of consciousness.

PMID:41743844 | PMC:PMC12929524 | DOI:10.3389/fncom.2026.1731868

Front Pediatr. 2026 Feb 10;14:1711271. doi: 10.3389/fped.2026.1711271. eCollection 2026.

ABSTRACT

BACKGROUND: Motor coordination impairment among children with autism spectrum disorder (ASD) has recently gained increasing attention. However, the relationship between functional connectivity (FC) alterations and motor coordination impairment among ASD remains inconclusive.

METHODS: We evaluated motor coordination function using the Developmental Coordination Disorder Questionnaire (DCDQ) and acquired resting-state functional magnetic resonance imaging (rs-fMRI) scans from 23 autistic individuals and 25 typically developing (TD) controls (6-10 years old). Within- and between-network FC was estimated using group independent component analysis (ICA) and group comparison was addressed using two-sample t-tests. Relationships between abnormal FC and motor coordination among ASD were investigated with multiple linear regression, with age, gender, and intelligence quotient (IQ) considered as covariates.

RESULTS: In the ASD group, 1) FC within the right cerebellar crus II was negatively correlated to the score of general coordination (β = -.566, p = 0.035) and control during movement (β = -0.529, p = 0.026); 2) FC between the cerebellar network and frontal-temporal-parietal network was negatively correlated to the score of general coordination (β = -2.610, p = 0.006); 3) Increased FC between the cerebellar network and insular network was associated with a higher score of fine motor/handwriting (β = -0.529, p = 0.026).

CONCLUSIONS: We confirmed the role of the insular network in interoception and motor processing among ASD, which was related to impaired information integrating, relaying, and visual feedback during movement. A significant relationship between the cerebellar network and frontal-temporal-parietal network in motor coordination indicated that a deficit in the planning of movements may contribute to atypical motor skills. The study gained an understanding of neuroimaging traits among ASD children and may provide evidence for the design of the motor-related intervention.

PMID:41743223 | PMC:PMC12930269 | DOI:10.3389/fped.2026.1711271

Neuropsychopharmacology. 2026 Feb 25. doi: 10.1038/s41386-026-02375-5. Online ahead of print.

ABSTRACT

Predicting selective serotonin reuptake inhibitor (SSRI) response in obsessive-compulsive disorder (OCD) remains a clinical challenge. Converging evidence implicated that the sensorimotor circuit is linked to OCD-related sensory phenomena and repetitive motor rituals, and it is densely innervated by serotonergic projections, making it a plausible substrate of SSRI effects. We therefore hypothesized that baseline functional connectivity (FC) of this circuit could serve as a candidate neural marker of SSRI treatment response. In this exploratory single-site resting-state fMRI study, 54 drug-naïve patients with OCD and 39 matched healthy controls (HCs) underwent scanning. Patients received sertraline for 12 weeks and were classified as responders (rOCD, n = 33) or non-responders (nOCD, n = 21) based on Yale-Brown Obsessive Compulsive Scale score reductions. Seed-based FC analysis of the sensorimotor circuit was conducted across the three groups. We observed that OCD patients exhibited abnormal FC primarily within the sensorimotor circuit and in its connections with the cerebellum. The rOCD group showed generally higher FC within the sensorimotor circuit than HCs, whereas the nOCD group showed lower FC values. Cerebellar regions with altered connectivity included areas involved in sensorimotor processing and higher-level functions. In prediction analyses, the connectivity between right thalamus and cerebellar Crus I region achieved an AUC of 0.854 for distinguishing responders from non-responders under leave-one-out cross-validation. Moreover, FC-based models showed better predictive performance than clinical models. These findings suggest that baseline sensorimotor-network FC may serve as a candidate biomarker of sertraline response in OCD, pending validation in large, independent samples.

PMID:41741690 | DOI:10.1038/s41386-026-02375-5