Most recent paper

Neuroimage. 2026 Apr 7:121908. doi: 10.1016/j.neuroimage.2026.121908. Online ahead of print.

ABSTRACT

Autism spectrum disorder (ASD) is an increasingly diagnosed neurodevelopmental condition characterized by persistent difficulties in social communication and restricted, repetitive patterns of behavior and sensory processing that leads to functional impairment. The diagnosis of ASD relies on behavioral and clinical assessment as there are no currently available biomarkers. Recent brain imaging studies have suggested abnormalities in the brain fluid flow in individuals with ASD. Cardiorespiratory and vasomotion-induced very low frequency (VLF ≤ 0.1 Hz) brain pulsations are now considered to facilitate the cerebrospinal- and interstitial fluid exchange in the brain, thus contributing to maintaining cerebral homeostasis and fluid clearance. In this study, we utilized ultrafast resting-state functional magnetic resonance imaging (fMRI) to capture and compare the powers of each physiological pulsation in groups of 18 young adults diagnosed with ASD and 19 neurotypical controls (NTC). We further probed the clinical significance of findings by undertaking regression analyses examining the associations of both Autism Spectrum Quotient (AQ) and Autism Diagnostic Observation Schedule (ADOS) scores with pulsation powers, and by receiver operating characteristics (ROC) analysis. Compared to the NTC group, the ASD group showed significantly higher VLF pulsation power, which was located predominantly in subcortical grey matter nuclei and the white matter, indicating increased vasomotor power in ASD. In addition, the individual VLF power enabled good accuracy (ROC area under curve = 0.75-0.93) for discriminating ASD subjects from NTCs. In conclusion, present findings of increased VLF power are postulated as possible indication of altered driving force of cerebral neurofluid dynamics and could potentially serve as a useful clinical classifier.

PMID:41956431 | DOI:10.1016/j.neuroimage.2026.121908

J Affect Disord. 2026 Apr 7:121751. doi: 10.1016/j.jad.2026.121751. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) is a prevalent and debilitating psychiatric condition defined by complex genetic and neurobiological underpinnings. The current study investigated the genetic variants associated with the disease and impact of significant variants on neurotransmitter pathways and their association with inherent brain connectivity patterns in MDD.

METHODS: A total of 69 patients diagnosed with MDD were recruited. Whole-exome sequencing (WES) was carried out in 30 patients to identify relevant genetic variants. This was followed by the genotyping of two frequently observed variants in TPH1 (rs1799913) and DAOA (rs2391191) genes in additional 39 patients using Sanger Sequencing. All subjects participated in resting-state functional MRI (rs-fMRI), and genotype-connectivity associations were analysed using the CONN toolbox. Functional connectivity was evaluated within the Default Mode Network (DMN), and its associations with HAM-D scores and the incidence of depressive episodes were also examined.

RESULTS: Whole exome sequencing revealed variants in 21 genes involve in neurotransmission, synaptic plasticity, and intracellular signaling pathways. Individuals possessing altered TPH1 (rs1799913) and DAOA (rs2391191) genotypes demonstrated significantly increased connectivity within the DMN, particularly involving the posterior cingulate cortex, precuneus, dorsomedial prefrontal cortex, and subcalcarine gyrus. The heightened synchrony of the default mode network exhibited a positive correlation with the severity of the Hamilton Depression Rating Scale and the occurrence of depressive episodes, suggesting a relationship between genotype, connectivity, and symptoms.

CONCLUSION: This study demonstrates that variations in TPH1 (rs rs1799913) and DAOA (rs2391191) genes are associated with atypical reinforcement of DMN connectivity in MDD. The findings support the role of serotonergic and glutamatergic pathways in maladaptive neural coupling and suggest that genotype-stratified DMN metrics may serve as intermediate neural phenotypes. Their status as disease-specific endophenotypes cannot be established in the absence of healthy or familial comparison groups. Further comprehensive, longitudinal research is essential to validate these results and evaluate their relevance for tailored interventions.

PMID:41956213 | DOI:10.1016/j.jad.2026.121751

Nat Commun. 2026 Apr 9. doi: 10.1038/s41467-026-71415-x. Online ahead of print.

ABSTRACT

Blood Oxygen Level Dependent (BOLD) variability reflects meaningful brain activity, yet its structural and biological correlates during early development remain unknown. Using longitudinal resting-state fMRI and multi-shell diffusion imaging acquired longitudinally in 54 very preterm infants (at 33-weeks' gestational age and term-equivalent-age) and 24 full-term newborns, we investigated how BOLD variability evolves in very preterm infants, its relationship with cortical microstructure and gene expression, using the BrainSpan dataset, and how it differs from full-term newborns at term-equivalent age. During preterm development, BOLD variability increased in primary sensory-sensorimotor and proto-Default-Mode-Network regions, accompanied by decreases in cortical diffusivity. Gene expression analysis revealed concurrent upregulation of genes mediating gliogenesis and neuronal ensheathment. At term-equivalent age, very preterm infants showed decreased BOLD variability and increased cortical diffusivity, compared to full-term newborns. In this work, we show that BOLD variability reflects cortical microstructural maturation, mediated by upregulation of gliogenesis and neuronal ensheathment. Interruption of these processes by preterm birth identifies putative mechanisms of preterm brain injury.

PMID:41957008 | DOI:10.1038/s41467-026-71415-x

IEEE Trans Neural Syst Rehabil Eng. 2026 Apr 9;PP. doi: 10.1109/TNSRE.2026.3682582. Online ahead of print.

ABSTRACT

Despite the remarkable success of deep brain stimulation (DBS) in alleviating Parkinson's disease (PD) symptoms, complexities arising from inherent inter-individual variability and the vast array of available methodologies for functional brain imaging data processing and interpretation have resulted in substantial heterogeneity across published reports. Within this context, advanced modelling approaches offer a promising conceptual framework. However, the optimal criteria and methodological strategies yielding reliable outputs remain to be established. Leveraging a substantial dataset of 104 PD patients managed with subthalamic nucleus DBS, the present study applied nine machine learning algorithms to distinguish between DBS ON and OFF states. The input features were derived from global and local connectivity metrics and BOLD fluctuation amplitudes obtained from resting-state functional magnetic resonance imaging (fMRI) data. Model performance was evaluated using a 5-fold cross-validation with hyperparameter optimization, and the efficacy of various feature maps was systematically compared. The generalizability of classification models was further tested through validation in an independently acquired cohort of 34 additional PD patients. Global connectivity measures when combined with linear modelling approaches - namely logistic regression and linear discriminant analysis - or with support vector classifiers employing nonlinear kernels demonstrated superior classification performance. These models achieved area under receiver operating characteristic curve values of up to 0.82, with comparable performances observed within the validation cohort. Overall, this investigation not only identifies the most promising fMRI metrics and machine learning algorithms for future DBS-fMRI research but also reinforces the prevailing view of network-wide modulation standing at the core of DBS effects.

PMID:41955135 | DOI:10.1109/TNSRE.2026.3682582

Geroscience. 2026 Apr 9. doi: 10.1007/s11357-026-02224-9. Online ahead of print.

ABSTRACT

Normal aging is accompanied by declines in executive function, and regular physical exercise has been proposed as a protective factor. However, the neural correlates linking long-term habitual exercise to executive efficiency in older adults remain unclear. This study combined resting-state functional magnetic resonance imaging (rs-fMRI) with behavioral assessments to examine whether long-term habitual exercise is associated with executive performance and resting-state neural organization in older adults. A total of 105 older adults (52 long-term habitual exercisers and 53 non-habitual exercisers) completed task-switching, Stroop and N-back paradigms and underwent rs-fMRI scanning. Behavioral outcomes included accuracy, reaction time, task cost and executive efficiency index. Neural measures included amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo) and degree centrality (DC). Older adults with long-term habitual exercise showed higher accuracy and faster responses across tasks, with no group differences in task cost but higher executive efficiency, compared with non-habitual exercisers. They also exhibited higher ALFF, ReHo and DC in frontoparietal, motor and striatal regions, alongside lower resting-state metrics in occipito-cerebellar networks. Mediation models indicated that ALFF in the pallidum, DC in prefrontal and cingulate cortices, and ReHo in frontoparietal regions statistically accounted for the association between exercise status and executive efficiency. Long-term habitual exercise was associated with better executive performance and distinct resting-state functional organization in older adults. Frontoparietal and striatal systems emerged as candidate intrinsic correlates of executive efficiency in physically active older adults.

PMID:41954831 | DOI:10.1007/s11357-026-02224-9

Imaging Neurosci (Camb). 2026 Apr 6;4:IMAG.a.1198. doi: 10.1162/IMAG.a.1198. eCollection 2026.

ABSTRACT

Multi-echo fMRI has emerged as a powerful strategy to mitigate head motion-related noise and minimize susceptibility-related signal loss in BOLD data. Multi-echo independent component analysis (ME-ICA) effectively distinguishes between BOLD-related (TE-dependent) signals and non-BOLD (TE-independent) noise, yielding substantial enhancements in performance compared to traditional echo-combination methods. We introduce a novel ICA-based denoising step, preICA, applied to raw multi-echo data before optimal T2*-weighted echo combination. This approach, combined with ME-ICA, yields substantial gains in data denoising. Our results show that preICA significantly enhances the efficacy of optimal echo combination and ME-ICA to reduce noise. To facilitate the reliable processing of multi-echo fMRI data, we integrated preICA and ME-ICA into fMRIPrep, resulting in the creation of a robust multi-echo processing pipeline, called MEPrep, offering flexibility in preprocessing options (with or without preICA and/or ME-ICA) beyond the echo combination approach offered by fMRIPrep. We validated MEPrep on an open resting-state multi-echo fMRI dataset, demonstrating that incorporating the preICA step leads to statistically significant improvements in denoising efficacy, as evidenced by (1) enhanced T2* exponential model fitting accuracy; (2) reduced motion-related BOLD fluctuations; (3) increased temporal signal-to-noise ratio; (4) improved spatial and temporal reliability of functional connectivity; and (5) increased Shannon entropy. MEPrep outperforms existing pipelines by synergistically integrating preICA and ME-ICA, achieving superior noise suppression while preserving the neurobiological complexity of denoised BOLD signals. By automating multi-echo preprocessing within a robust pipeline, MEPrep provides a scalable solution for high-quality multi-echo fMRI data preprocessing. The pipeline is openly available, ensuring reproducibility and accessibility for the neuroimaging community.

PMID:41952841 | PMC:PMC13055012 | DOI:10.1162/IMAG.a.1198

Neuropsychopharmacology. 2026 Apr 8. doi: 10.1038/s41386-026-02396-0. Online ahead of print.

ABSTRACT

Posttraumatic stress disorder (PTSD) is a highly heterogeneous psychiatric disorder, complicating efforts to identify consistent biological markers and develop targeted treatments for individuals exposed to trauma. Recent research has identified a distinct intrusive-hypervigilant (IH) phenotype, which is characterized by heightened intrusive reexperiencing and hypervigilance symptoms along with elevated levels of pituitary adenylate cyclase-activating polypeptide (PACAP), a neuropeptide involved in stress response via amygdala signaling. In an independent sample of 172 symptomatic trauma-exposed adults, we replicated this IH phenotype using latent profile analysis of Clinician-Administered PTSD Scale for DSM-5 symptom severity ratings and expanded its biological characterization using resting-state functional magnetic resonance imaging (rs-fMRI). Consistent with prior work, the identified IH group demonstrated more severe intrusive reexperiencing (Cohen's d's = 0.61-6.93) and hypervigilance symptoms (d's = 0.57-0.88) and higher PACAP levels compared to groups with generally High (d = 0.35) or Low (d = 0.44) symptom severity. Additionally, the IH phenotype exhibited stronger functional connectivity of the centromedial, but not basolateral, amygdala with regions in the occipital cortex (d's = 0.78-0.95), precuneus (d's = 1.20-1.21), and medial prefrontal cortex (d's = 0.81-1.18)-areas primarily within the Default Mode and Visual Networks. Meta-analytic decoding linked these regions to mental imagery, memory processing, fear, and threat perception. These findings support the existence of an IH phenotype of posttraumatic stress that may exhibit a distinct biological profile, characterized by exaggerated interactions between memory, threat, and arousal systems that may be mediated by PACAP and its effects on amygdala connectivity. This phenotype may serve as a promising target for precision psychiatry approaches, including pharmacological and neurotherapeutic interventions that modulate PACAP signaling and amygdala connectivity.

PMID:41951830 | DOI:10.1038/s41386-026-02396-0

Brain Res. 2026 Apr 6:150304. doi: 10.1016/j.brainres.2026.150304. Online ahead of print.

ABSTRACT

BACKGROUND: Concomitant esotropia (CE) is a prevalent strabismic disorder characterized by inward ocular deviation and impaired binocular vision. While structural and functional brain abnormalities have been reported in CE, the temporal dynamics of intrinsic neural activity remain largely unexplored.

METHODS: This study employed a multimodal framework combining resting-state functional MRI (rs-fMRI), transcriptomic data from the Allen Human Brain Atlas (AHBA), and neurotransmitter receptor density maps to investigate alterations in intrinsic neural timescales (INT) in CE. A total of 87 participants (43 CE patients, 44 matched controls) underwent rs-fMRI scanning. Voxel-wise and network-level INT were computed, followed by partial least squares (PLS) regression linking INT alterations with regional gene expression. Functional enrichment, cell-type specificity, and spatial correlations with PET-based receptor maps were also analyzed.

RESULTS: Compared to controls, CE patients exhibited significantly reduced INT in the right middle frontal gyrus and basal ganglia network, indicating impaired temporal integration in oculomotor and executive control circuits. Transcriptomic analyses revealed that INT-related genes were enriched for immune-inflammatory and neurodevelopmental pathways. Excitatory and inhibitory neurons were the dominant contributors to the altered transcriptional profiles, implicating excitation-inhibition imbalance as a core mechanism. Furthermore, INT alterations showed significant negative correlations with glutamatergic, GABAergic, and opioid receptor distributions, suggesting neuromodulatory dysregulation in CE.

CONCLUSIONS: This study provides the first evidence of altered INT in CE and uncovers their molecular and neurochemical substrates. The findings highlight INT as a sensitive biomarker for temporal dysfunction in CE and emphasize the utility of integrative imaging-genomic approaches in elucidating its pathophysiology.

PMID:41951091 | DOI:10.1016/j.brainres.2026.150304

Psychiatry Res Neuroimaging. 2026 Mar 23;360:112202. doi: 10.1016/j.pscychresns.2026.112202. Online ahead of print.

ABSTRACT

BACKGROUND: Smoking trajectories in young adults vary, with some light smokers escalating to dependence while others reduce or quit. Depressive and anxious traits relate to altered large-scale network connectivity, including the salience network (SN). Dopaminergic (DRD2 Taq1A) and serotonergic (5-HTTLPR) variants may further shape these trajectories, but trait-gene links to neural circuits and nicotine sensitivity remain unclear.

METHODS: Sixty-eight young light smokers (18-24) completed nicotine and placebo sessions. Resting-state fMRI assessed functional connectivity; reward sensitivity was measured with the Probabilistic Reward Task. Depressive/anxious traits and DRD2/5-HTTLPR genotypes were obtained, and smoking progression was tracked.

RESULTS: Depressive traits predicted weaker SN connectivity (insula-ACC; insula-dlPFC) but stronger insula-sgACC coupling. Anxious traits predicted weaker precentral-insula/dlPFC connectivity and stronger precentral-temporal-sgACC connectivity. Higher depressive traits combined with nicotine-enhanced reward sensitivity (NERS) predicted reduced prefrontal-limbic connectivity, whereas depression with smoking progression predicted increased insula-striatal-hippocampal connectivity. Gene × trait interactions suggested distinct endophenotypes: Depression × DRD2 predicted sgACC-insula and hippocampus-ACC connectivity; Anxiety × 5-HTTLPR predicted ACC-PCC and hippocampus-dlPFC connectivity.

CONCLUSIONS: The sgACC within the SN may act as a convergence hub linking affective traits, genetic risk, and nicotine sensitivity: depression-related risk reflects hypo-salience/reward deficiency, whereas anxiety-related risk reflects hyper-salience/vigilance.

PMID:41950829 | DOI:10.1016/j.pscychresns.2026.112202

Allergol Int. 2026 Apr 7:S1323-8930(26)00040-7. doi: 10.1016/j.alit.2026.03.004. Online ahead of print.

ABSTRACT

BACKGROUND: Atopic dermatitis (AD) is a chronic inflammatory skin disorder characterized by persistent itching. Growing neuroimaging evidence suggests that chronic itching involves altered brain connectivity within sensorimotor networks. This study aimed to investigate alterations in intrinsic brain connectivity in patients with AD compared to healthy controls, and to assess their association with symptom severity using resting-state functional magnetic resonance imaging (fMRI).

METHODS: We defined several regions in sensorimotor and other relevant networks as seeds and compared seed-to-whole-brain resting-state functional connectivity (FC) between 41 patients and 40 healthy controls. Correlations between symptom severity and patients' FC were examined.

RESULTS: Patients with AD exhibited decreased FC between the right primary somatosensory cortex (S1) and regions within the default mode network (DMN), and increased FC between the right primary motor cortex (M1) and regions associated with motor execution, reward processing, and emotional regulation. Significant correlations with symptom severity were observed in the FC of the right S1 and supplementary motor areas. Furthermore, differential association patterns were observed in the right S1 and right M1 regarding FC with regions in the DMN.

CONCLUSIONS: Our findings revealed altered connectivity in sensory and motor-related regions in patients with AD, reflecting disrupted neural integration of persistent chronic itch. These findings highlight the central neural mechanisms contributing to the chronic itch-scratch cycle and suggest potential clinical applications of neural markers for evaluating disease severity.

PMID:41951443 | DOI:10.1016/j.alit.2026.03.004

Behav Brain Res. 2026 Apr 6:116188. doi: 10.1016/j.bbr.2026.116188. Online ahead of print.

ABSTRACT

Carpal tunnel syndrome (CTS), a median nerve compression disorder, offers a model of chronic afferent deprivation, yet its systems-level impact on intrinsic brain networks remains unclear. We tested a tri-network hypothesis that dysregulated interactions among the salience (SN), sensorimotor (SMN), and default mode (DMN) networks underlie persistent functional impairment in CTS. Resting-state fMRI data were acquired from 52 CTS patients and 30 matched healthy controls. We examined static and dynamic functional connectivity among SN, SMN, and DMN, directional effective connectivity using network-level models, and a state-resolved cross-network interaction index quantifying the relative engagement of SN with SMN versus DMN. CTS patients showed reduced static SMN-DMN coupling and enhanced SMN-SN coupling, and effective connectivity analyses revealed diminished excitatory influence from SMN to SN. Dynamic analyses identified four recurrent connectivity states, with CTS patients spending more time in a state marked by strong SN-SMN coupling and DMN suppression. The cross-network interaction index consistently demonstrated a shift toward preferential SN-SMN engagement across dynamic states. Together, these findings indicate a reproducible pattern of SN-centered reorganization in CTS, characterized by strengthened SN-SMN coupling and diminished integration with the DMN. This tri-network profile suggests that chronic afferent deprivation leads to dysregulated salience-mediated allocation of attentional and sensorimotor resources, contributing to persistent functional impairment, and that such large-scale network alterations may serve as mechanistic markers for patient stratification and potential therapeutic modulation.

PMID:41951155 | DOI:10.1016/j.bbr.2026.116188

Front Neurosci. 2026 Mar 23;20:1787670. doi: 10.3389/fnins.2026.1787670. eCollection 2026.

ABSTRACT

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by deficits in social communication and restricted, repetitive behaviors. Increasing evidence suggests that neuroinflammatory processes are closely associated with the pathophysiology of ASD, linking immune dysregulation with altered brain development and function. This review synthesizes current findings on the relationships between neuroinflammatory mechanisms, biochemical and metabolic alterations, and functional brain connectivity, as revealed by neuroimaging-particularly functional magnetic resonance imaging (fMRI). Across clinical, postmortem, and imaging studies, individuals with ASD show consistent evidence of microglial and astroglial activation, altered cytokine profiles (including IL-1β, IL-6, and TNF-α), and markers of oxidative stress such as glutathione imbalance and lipid peroxidation. These immune and metabolic alterations are associated with changes in synaptic plasticity, neurotransmission, and large-scale neuronal network organization, including altered functional connectivity within the default mode, salience, and executive control networks. Complementary imaging modalities further support links between glial activity, excitatory-inhibitory imbalance, and aberrant connectivity patterns. Emerging evidence also highlights interactions between inflammation, lipid metabolism, neurotransmitter systems (notably serotonin and dopamine), and genetic and epigenetic factors that modulate immune responses in ASD. Integrating inflammatory and metabolic biomarkers with fMRI and spectroscopic measures provides a promising framework for characterizing biologically informed ASD subtypes and advancing precision diagnostic and therapeutic strategies. Overall, current evidence supports a multilevel neuroimmune framework in which chronic inflammation and oxidative stress are associated with atypical functional brain connectivity in ASD. Future longitudinal and multimodal studies are required to validate candidate biomarkers, clarify mechanistic pathways, and evaluate interventions targeting neuroinflammatory processes.

PMID:41947852 | PMC:PMC13050867 | DOI:10.3389/fnins.2026.1787670

Neural Plast. 2026;2026(1):e9968808. doi: 10.1155/np/9968808.

ABSTRACT

Persistent postural-perceptual dizziness (PPPD) is a disabling functional vestibular disorder characterized by chronic dizziness and visually and motion-induced unsteadiness that markedly impairs daily activities, yet it lacks objective neurobiological markers. We acquired resting-state functional MRI (rs-fMRI) in 52 patients with PPPD and 50 age- and sex-matched healthy controls (HCs) and analyzed the data using a three-tier approach: (i) functional network connectivity (FNC) of independent component analysis (ICA-FNC), (ii) voxel-wise measures of spontaneous amplitude of low-frequency fluctuations (ALFF) and fractional ALFF (fALFF), and (iii) seed-based connectivity using a priori vestibular and subcortical regions of interest (ROIs; e.g., cerebellar (CB) nodulus, parafascicular thalamus, and caudate). Integrating these analytic tiers, we observed a coherent pattern: broadly increased connectivity of CB and primary visual (VIS) networks together with selective hypoconnectivity between a brainstem-cerebellar (BSC) component and the multimodal vestibular cortex (MVC), oculomotor (frontal eye field [FEF]), and default-mode networks (DMN). Voxel metrics revealed decreased ALFF in parietal and frontal opercular cortices-key vestibular integration regions-contrasting with increased fALFF in mid-cingulate, lateral occipital, and premotor areas. Seed-based mapping identified strengthened thalamo-VIS, striato-limbic, and nodulus-hippocampal connectivity. Importantly, increased BSC-to-VIS coupling correlated positively with depressive symptom severity and state anxiety, but negatively with balance confidence and psychological resilience, linking network imbalance to the biopsychosocial phenotype of PPPD. These findings support a multiscale signature of vestibular cortical disengagement accompanied by maladaptive VIS-CB reinforcement and motivate multicenter validation of network-level markers as adjuncts to symptom-based diagnosis.

PMID:41947631 | DOI:10.1155/np/9968808

Hum Brain Mapp. 2026 Apr 1;47(5):e70526. doi: 10.1002/hbm.70526.

ABSTRACT

Network topology measures characterise brain networks' organisation. Graph theoretical approaches have shown fMRI topology metrics' association with cognitive performance. Because arbitrary connectivity threshold selection biases such metrics, alternatives including the minimum spanning tree (MST) and novel measures following principles of persistent homology were proposed. The present study compared alternative and graph theoretical metrics in association with cognition for resting-state and task-fMRI. Functional connectivity matrices were computed from Human Connectome Project (Young Adult) fMRI scans during resting-state, working memory (WM), gambling, language, motor, relational processing, social cognition, and movie-watching conditions. Global efficiency, clustering coefficient (at three thresholds), diameter, leaf fraction (LF), backbone strength (BS), and cycle strength were measured. Each was tested in association with cognitive test scores. ResultsBS significantly predicted general cognitive performance, specifically progressive matrices score, composite fluid and crystallised cognition, vocabulary, spatial orientation, and WM. Diameter significantly predicted WM. WM task BS outperformed the predictive performance of graph theory measures, but not at rest, where MST LF outperformed other measures. Stronger associations were observed between cognitive test scores and topology measures derived from task-based fMRI, especially the N-Back task, as opposed to resting-state fMRI. Among task-based topology measures, BS was the most strongly related to cognition.

PMID:41947425 | DOI:10.1002/hbm.70526

Brain Imaging Behav. 2026 Apr 7;20(2):71. doi: 10.1007/s11682-026-01135-9.

ABSTRACT

Irritable bowel syndrome (IBS) affects 9.3%–35.5% of the population, with women at greater risk. It is often comorbid with anxiety, depression, and cognitive dysfunction. Understanding the neural mechanisms underlying these comorbidities is crucial for identifying IBS pathology and developing targeted treatments. Functional magnetic resonance imaging (fMRI) offers valuable insights into brain connectivity and psychopathology. This review evaluates research on the link between functional connectivity, mood, and cognition in IBS. We systematically searched Embase, PubMed, and PsycINFO for studies published between 2010 and 2024, identifying 49 studies using both resting-state and task-based fMRI. Of these, 12 studies meeting inclusion criteria were reviewed. Across studies, IBS was associated with altered connectivity in the salience (SN), sensorimotor (SMN), default mode (DMN), and executive control (ECN) networks. Reported cognitive findings largely reflected executive and attentional control processes occurring in the context of pain anticipation, salience detection, and interoceptive awareness rather than domain-general cognitive impairment. Alterations in the SN, particularly involving the pregenual anterior cingulate cortex and anterior midcingulate cortex, were linked to increased visceral sensitivity and affective symptoms. Disrupted DMN connectivity was associated with altered self-referential processing and emotional regulation, while changes in the SMN and ECN suggested differences in sensory integration and top-down control. Notably, several studies showed that group differences in functional connectivity were reduced or no longer significant after accounting for anxiety and depression, suggesting that mood symptoms may play a mediating role in brain network alterations in IBS. While the literature is limited by small samples and sex imbalance, this review highlights a multi-network model of IBS that emphasizes emotional–cognitive–visceral interactions and points to important directions for future longitudinal research.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11682-026-01135-9.

PMID:41945210 | PMC:PMC13056754 | DOI:10.1007/s11682-026-01135-9

PLoS One. 2026 Apr 7;21(4):e0339921. doi: 10.1371/journal.pone.0339921. eCollection 2026.

ABSTRACT

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that is primarily characterized by deficits in social communication and restricted or repetitive behavioral patterns. Although psychologists contribute significantly to the understanding of ASD, offering insights into its cognitive, emotional, and behavioral dimensions through assessments, diagnoses, therapeutic approaches, and family support, the diagnostic process remains complex. This complexity arises from the diverse manifestations of the disorder and the challenges associated with data sharing. In addition, conventional machine learning approaches for ASD detection may struggle with high-dimensional neuroimaging data and may require careful feature engineering. Consequently, this motivated us to enhance ASD diagnosis by incorporating deep learning (DL) techniques for feature extraction alongside a modified exponential-trigonometric optimization (ETO) algorithm as a feature selection (FS) technique. The modified ETO integrates the Arithmetic Optimization Algorithm (AOA) and the Guided Learning Strategy (GLS) to improve diagnostic performance. To evaluate the effectiveness of the proposed model, we utilized resting-state functional MRI (rs-fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE I). Furthermore, the performance of the proposed model was compared with that of established models. The results indicate that the proposed model achieves competitive and, in most cases, superior performance compared with the benchmark methods, demonstrating superior accuracy, sensitivity, and AUC in diagnosing ASD. On average across the three atlas-based feature sets, the proposed model has an accuracy, sensitivity, and AUC of 73%, 78%, and 79%, respectively.

PMID:41945612 | DOI:10.1371/journal.pone.0339921

Dig Liver Dis. 2026 Apr 6:S1590-8658(26)00323-3. doi: 10.1016/j.dld.2026.03.012. Online ahead of print.

ABSTRACT

BACKGROUND: Minimal hepatic encephalopathy (MHE) is a neuropsychiatric syndrome that significantly affects quality of life. It is associated with neural activity changes, reflected by the amplitude of low-frequency fluctuation (ALFF) in resting-state functional magnetic resonance imaging (rs-fMRI). Zinc deficiency compromises cognition; however, its relationship with the neural activity changes in MHE remains unclear.

AIMS: To investigate blood zinc variations in MHE and their associations with ALFF.

METHODS: Blood zinc levels were compared in 150 patients with cirrhosis. Among them, 49 underwent rs-fMRI to evaluate associations between zinc levels and ALFF, after adjusting for confounders.

RESULTS: Blood zinc levels were significantly lower in patients with MHE than in those with no hepatic encephalopathy (4.63 vs. 5.06 mg/L; P = 0.022). MHE prevalence was higher in females than in males (35.2% vs. 19.0%; P = 0.025). ALFFs in the bilateral angular gyrus and precuneus were positively correlated with blood zinc levels, whereas those in the bilateral fusiform gyrus were negatively correlated (P < 0.05 and P < 0.05, respectively).

CONCLUSIONS: Low blood zinc levels in patients with MHE are associated with altered neural activity in the bilateral angular gyrus, precuneus, and bilateral fusiform gyrus, representing a neural basis for cognitive impairment in MHE.

PMID:41945038 | DOI:10.1016/j.dld.2026.03.012

Psychol Med. 2026 Apr 7;56:e93. doi: 10.1017/S0033291726103869.

ABSTRACT

BACKGROUND: Working memory (WM) impairment is a core cognitive deficit in schizophrenia, associated with dysfunction of large-scale brain networks, particularly the triple-network system comprising the default mode, frontoparietal, and salience networks. Given the role of environmental risks like childhood trauma (CT) in cognitive deficits, we investigated whether trauma relates to altered triple-network flexibility and WM in schizophrenia.

METHODS: We enrolled 190 patients with schizophrenia (SZ) and 117 healthy controls (HCs). Among them, 162 SZ and 99 HCs underwent n-back task-based functional magnetic resonance imaging. We computed temporal variability (TV) in the triple-network connectivity, defining ΔTV as the change between 0-back and 2-back conditions. Subgroup comparisons of ΔTV were conducted within each group based on trauma status. Associations of ΔTV with WM performance and clinical symptoms were examined in SZ, followed by mediation analyses testing whether ΔTV mediates the relationship between trauma and WM.

RESULTS: Among HCs, individuals with childhood trauma showed reduced ΔTV across triple-network connections, whereas no such differences appeared in SZ. In SZ, greater ΔTV within the frontoparietal network (FPN) was correlated with lower positive symptom severity (r = -0.211, p-fdr = 0.046) and better n-back target accuracy (r = 0.303, p-fdr = 0.002). Furthermore, ΔTV within the FPN partially mediated the association between trauma and n-back accuracy.

CONCLUSIONS: Our findings highlight the central role of FPN flexibility in mediating childhood trauma's effect on working memory in schizophrenia. This outlines a key pathway through which an early environmental risk (trauma) translates into cognitive and clinical manifestations in schizophrenia.

PMID:41943939 | DOI:10.1017/S0033291726103869

Alzheimers Res Ther. 2026 Apr 6. doi: 10.1186/s13195-026-02003-w. Online ahead of print.

NO ABSTRACT

PMID:41943113 | DOI:10.1186/s13195-026-02003-w

Transl Psychiatry. 2026 Apr 6. doi: 10.1038/s41398-026-03985-9. Online ahead of print.

ABSTRACT

Mild cognitive impairment (MCI) is widely recognized as an early stage of dementia. Epidemiological studies suggest that MCI is more prevalent in females than in males. Notably, there are sex differences in MCI-related brain changes. Resting-state functional magnetic resonance imaging (rs-fMRI) offers a valuable method for assessing brain activity during rest. This study aims to explore sex-specific regional brain activity in participants with MCI during resting states. 86 MCI participants (21 males and 65 females) and 107 normal controls (NCs) (38 males and 69 females) were included in the present study. Regional homogeneity (ReHo), degree centrality (DC), amplitude of low frequency fluctuations (ALFF), and fractional ALFF (fALFF) were used to assess brain activity. MCI females showed increased ReHo values in the right cerebellum inferior compared to NC females and MCI males. However, MCI males exhibited increased ReHo values in the left hippocampus compared to NC males and MCI females. ReHo values in the right cerebellum inferior were associated with visuospatial skills in MCI males, and language function in MCI females. Additionally, ReHo values in the left hippocampus were associated with attention function in MCI females but not in MCI males. In MCI participants, sex moderated the relationship between ReHo values in the right cerebellum inferior and cognitive function (visuospatial skills and language function), as well as the association between ReHo values in the left hippocampus and attention function. In conclusions, this study revealed sex differences in ReHo of right inferior cerebellum and left hippocampus in MCI, and the association between ReHo and cognitive impairment in MCI differs by sex. These sex-specific patterns of regional brain activity can aid in the development of sex-specific precision medicine.

PMID:41942447 | DOI:10.1038/s41398-026-03985-9