Most recent paper

Commun Biol. 2026 Apr 10;9(1):520. doi: 10.1038/s42003-025-09444-3.

ABSTRACT

The brain's functional activity is shaped by the complex architecture of its fibers. Yet, the lack of a direct one-to-one mapping between functional and structural connections makes this relationship elusive. To date, most studies on structure-function coupling (SFC) have conceptualized function in terms of resting-state functional Magnetic Resonance Imaging (fMRI) connectivity. Here, we extend this framework to neurophysiological data by examining how magnetoencephalography (MEG) activity relates to the structural connectome, leveraging its rich spectral content and direct sensitivity to neuronal population dynamics. We show that the decoupling of MEG activity from structure is strongly associated with the expression levels of synaptic plasticity markers, pointing to a link between flexible functional reconfiguration and the molecular mechanisms of plasticity. Moreover, regions with greater decoupling exhibit higher neurotransmitter receptor diversity, underscoring neuromodulatory heterogeneity as a substrate for functional flexibility. This association is especially pronounced for slow-acting metabotropic receptors, whose diffuse and prolonged signaling may facilitate functional reorganization atop the structural connectome.

PMID:41963461 | DOI:10.1038/s42003-025-09444-3

Biomed Phys Eng Express. 2026 Apr 10. doi: 10.1088/2057-1976/ae5dd3. Online ahead of print.

ABSTRACT

Early and precise identification of Parkinson's disease (PD) is crucial for clinical intervention. Resting-state functional magnetic resonance imaging (rs-fMRI) provides a valuable approach for revealing PD-related differences in brain functional connectivity (FC). However, existing methods often focus solely on characterizing the spatial topology of FC while neglecting its time-varying dynamic fluctuations. Furthermore, they frequently exhibit limited generalization capability when dealing with small sample sizes, and their decision-making mechanisms lack interpretability. To address these limitations, this study proposes an interpretable Graph Convolutional Network (GCN) framework. This framework integrates both static and dynamic functional connectivity information to capture both the stable topological structure and the dynamic temporal characteristics of brain networks. Simultaneously, it models population relationships by constructing an inter-subject similarity graph to enhance the model's representational capacity. Additionally, this study incorporates interpretability analysis techniques to deeply dissect the model's decision-making mechanism and identify key brain regions critical for classification. Results demonstrate that the proposed model achieves superior performance in PD classification tasks and exhibits good generalization ability. More importantly, by interpreting the model's decisions, key brain regions associated with PD discrimination were successfully identified. This study provides an effective computational framework for PD identification and offers new insights into understanding its pathological mechanisms.

PMID:41962553 | DOI:10.1088/2057-1976/ae5dd3

Psychiatry Res Neuroimaging. 2026 Apr 3;360:112208. doi: 10.1016/j.pscychresns.2026.112208. Online ahead of print.

ABSTRACT

Intrusion symptoms, a core dimension of PTSD, have recently been categorized into internally-cued intrusions (I-Int; comprising re-experiencing symptoms) and externally-cued intrusions (E-Int; comprising reactivity to external reminders), but it remains unclear whether these two symptom clusters have different neural underpinnings. We utilized the triple brain network model (comprising the default mode, central executive, and salience networks) to investigate this issue. We initially recruited 50 COVID-19 survivors from Wuhan (final sample N = 46), who underwent resting-state functional magnetic resonance imaging scans and completed self-report assessments. Based on intrusion symptom scores, participants were stratified into E-Int-positive and E-Int-negative subgroups, as well as I-Int-positive and I-Int-negative subgroups. Key findings revealed that within the I-Int subgroup classification, static triple-network analysis demonstrated significantly attenuated anti-correlation FC between the DMN and CEN in the I-Int positive group compared to the I-Int negative group. These differences were consistently replicated in dynamic states 2 (the 'Segregated State') and 5 (the 'Globally Hyper-connected State'). Within the E-Int subgroup classification, the E-Int positive group exhibited higher FC between the DMN-SN specifically in dynamic state 4 (the 'Transitional State'). Correlation analyses further indicated that I-Int scores within the I-Int positive subgroup were positively associated with DMNCEN FC in both static model and state 2 of dynamic model. These findings suggest that the two types of intrusions may have different neural underpinnings, which enhances our understanding of post-traumatic stress symptoms and offers potential directions for future targeted therapies. However, given the relatively small sample size, these findings are preliminary and require replication in larger cohorts with greater symptom severity.

PMID:41962348 | DOI:10.1016/j.pscychresns.2026.112208

CNS Neurosci Ther. 2026 Apr;32(4):e70871. doi: 10.1002/cns.70871.

ABSTRACT

OBJECTIVE: To investigate whether dynamic changes in resting-state functional MRI (rs-fMRI) metrics can serve as sensitive biomarkers for distinguishing acute basal ganglia cerebral infarction (BGCI) patients with post-stroke cognitive impairment (PSCI) from those without (non-PSCI).

MATERIALS AND METHODS: Data on various rs-fMRI metrics dynamic functional connectivity (dFC), dynamic amplitude of low-frequency fluctuation (dALFF), and percent amplitude of fluctuation (PerAF) were acquired using a Siemens Prisma 3.0T scanner from 38 PSCI and 36 non-PSCI patients, with follow-up assessments. Functional segregation and integration were analyzed using PerAF, dALFF, and dFC. Feature extraction and selection were performed using support vector machine (SVM), followed by classifier construction and evaluation.

RESULTS: Patients with PSCI showed decreased PerAF in the left cerebellar Crus I (lCbeCru1) and increased dALFF in the right cerebellar Crus I and left lingual gyrus compared to non-PSCI patients. Altered dFC was observed between cerebellar cognitive-related seed regions and widespread cortical areas, with increased dFC in the right cerebellar Crus II and left cuneus, and decreased dFC primarily in the inferior frontal gyrus and superior temporal gyrus. Among single-feature models, dFC achieved the best classification performance (AUC = 0.98, accuracy = 94.52%, sensitivity = 97.14%, specificity = 92.11%, precision = 91.89%). A combined feature model yielded the highest precision (94.12%).

CONCLUSION: SVM-based integration of PerAF, dALFF, and dFC features holds promise as a neuroimaging biomarker for PSCI in patients with BGCI. This approach may support more precise early rehabilitation strategies in clinical practice.

PMID:41961546 | DOI:10.1002/cns.70871

Front Neurol. 2026 Mar 25;17:1743723. doi: 10.3389/fneur.2026.1743723. eCollection 2026.

ABSTRACT

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is increasingly recognized as a network-level neurodegenerative disease involving distributed disruptions across structural and functional systems. While previous studies have often examined white matter integrity or functional connectivity in isolation, the nature of structure-function coupling and its reorganization in ALS remains poorly understood.

METHODS: We conducted a multimodal connectomic analysis in ALS patients and matched controls, integrating cortical thickness-based structural covariance networks, diffusion MRI tractography, and resting-state and task-based functional MRI. Graph-theoretical metrics were derived, and cross-modal structure-function correspondence was quantified using ROI-wise correlation analyses. A comprehensive 104-node parcellation scheme based on the Desikan-Killiany atlas was employed.

RESULTS: ALS participants showed preserved global network topology (p > 0.05 for efficiency and small-worldness) but evidence of selective reorganization, particularly within motor and interhemispheric pathways. Cortical covariance networks exhibited minimal association with functional dynamics, whereas diffusion-derived white matter connectivity remained closely aligned with functional organization. This structure-function coupling was maintained or even enhanced during task performance (p = 0.005), suggesting adaptive reconfiguration rather than uniform disconnection.

CONCLUSIONS: Structure-function coupling in ALS is not globally diminished but reorganized, with robust white matter-functional relationships coexisting alongside weak cortical covariance-functional associations. These findings refine the traditional disconnection model and highlight the utility of multimodal metrics for understanding disease mechanisms and developing biomarkers for progression and therapeutic response.

PMID:41959630 | PMC:PMC13056628 | DOI:10.3389/fneur.2026.1743723

bioRxiv [Preprint]. 2026 Mar 13:2026.03.12.710902. doi: 10.64898/2026.03.12.710902.

ABSTRACT

BACKGROUND: Schizophrenia is a severe neuropsychiatric disorder. Efforts to describe the underlying biology and establish diagnostic markers through non-invasive neuroimaging methods are ongoing, resulting in a range of theoretical brain-based frameworks. Prominent frameworks for aberrant schizophrenia-associated functional connectivity in resting-state functional magnetic resonance imaging (rsfMRI) include the dysconnectivity hypothesis, theory of cognitive dysmetria, and triple network theory. Although informative, prior work can be improved by increasing sample size, avoiding confirmation bias, and accounting for individual variability and the effects of medication and chronicity.

METHODS: With these recommendations in mind, we employed a data-driven, whole-brain approach using a large multi-site rsfMRI dataset ( N = 2,656; schizophrenia = 1,248). We used reference-guided independent component analysis (ICA) to generate subject-specific whole-brain functional network connectivity (FNC) and extract imaging markers of similarity to schizophrenia patterns. We modeled the relationship between medication dosage, age of onset, chronicity, symptom severity, and cognitive performance and FNC.

RESULTS: Our analysis identified a reliable schizophrenia-FNC signature characterized by aberrantly stronger negative cerebellothalamic and positive thalamocortical connectivity, implicating sensory, motor, and associative cortical circuits. While medication and chronicity were significantly associated with these signatures, the core cerebellothalamic disruptions remained a robust marker of schizophrenia.

CONCLUSIONS: This work represents the largest schizophrenia-specific rsfMRI study to date, refines existing theoretical frameworks with a more nuanced map of how clinical variables interact with brain connectivity, and provides a high-fidelity template of schizophrenia-related connectivity. We have released this template as an open-source resource to facilitate reproducibility and accelerate the development of reliable rsfMRI-based schizophrenia biomarkers.

PMID:41959363 | PMC:PMC13061033 | DOI:10.64898/2026.03.12.710902

bioRxiv [Preprint]. 2026 Mar 31:2026.03.27.713751. doi: 10.64898/2026.03.27.713751.

ABSTRACT

Linking structural connectivity (SC) to functional connectivity (FC) through mechanistic models remains challenging in network neuroscience. In this study, empirical data of diffusion magnetic resonance imaging (dMRI) and resting-state functional MRI (rs-fMRI) were used to reconstruct SC and FC of a swine connectome. We evaluated a structurally constrained Kuramoto phase-oscillator framework to reproduce resting-state FC and then assessed the model's sensitivity to traumatic brain injury (TBI) and its longitudinal progression post-TBI. A joint tuning procedure was implemented to calibrate data-informed natural frequencies and global coupling strength. The tuned Kuramoto model was then used to evolve oscillator phases constrained by the SC, followed by a Balloon-Windkessel hemodynamic model. The optimized model produced significant edge-wise correspondence between averaged simulated FC and the empirical FC (r = 0.61, p < 0.001). Graph-theoretical analysis across network densities (30-50%) showed strong agreement for global efficiency, characteristic path length, and clustering coefficient, while modularity and small-worldness exhibited deviations. Longitudinal analysis of the swine TBI dataset revealed modest reductions in structure-function coupling over time but no significant differences across injury severities. These results demonstrate that optimized Kuramoto models can reproduce key functional network features while preserving inter-subject variability.

PMID:41959043 | PMC:PMC13060334 | DOI:10.64898/2026.03.27.713751

Stroke. 2026 Apr 10. doi: 10.1161/STROKEAHA.125.054877. Online ahead of print.

ABSTRACT

BACKGROUND: Reading impairments are common in stroke-induced aphasia and limit participation in functional and leisure activities. Traditional rehabilitation strategies show limited generalization, underscoring the need for novel interventions targeting residual neural networks.

METHODS: This feasibility randomized controlled trial evaluated real-time functional magnetic resonance imaging (fMRI) neurofeedback intervention for poststroke reading deficits. Subacute left-hemisphere stroke survivors and healthy controls completed 3 weekly fMRI neurofeedback and 10 out-of-scanner practice sessions. Stroke participants were randomized to contingent neurofeedback (based on left supramarginal gyrus activity; N=4) or noncontingent neurofeedback (shuffled feedback from another participant; N=3). Healthy controls (N=4) received contingent neurofeedback and served as a normative reference. Primary outcomes were changes from baseline to postintervention (≈3 weeks) in task-based brain activity (motor imagery/word/nonword reading>baseline), resting-state connectivity, and reading aloud. Reading comprehension was a secondary outcome. Group×session effects were tested using repeated-measures analyses and planned contrasts.

RESULTS: Task fMRI revealed training-related activation increases in the left supramarginal gyrus (z=4.7; cluster-corrected P=0.05) and broader reading network in the contingent neurofeedback group, particularly during nonword reading. Activation increases in the noncontingent stroke group and healthy controls were more widespread and less reading-specific. Resting-state fMRI revealed greater integration among motor, auditory, and language networks in the contingent groups, with more disorganized patterns in the noncontingent group (permutation P=0.01; Δr=-0.1 to 0.1). No changes were observed in reading aloud. A significant group×session interaction was found for Reading Comprehension Battery for Aphasia, second edition (F[2, 8]=8.00; P<0.05; η2=0.67). The contingent neurofeedback stroke group improved more than healthy controls (mean difference in Reading Comprehension Battery for Aphasia, second edition, change=9.75 [95% CI, 1.99-17.51]; t[6]=3.07; P<0.05) and noncontingent neurofeedback stroke group (Reading Comprehension Battery for Aphasia, second edition, change=11.42 [95% CI, 1.12-21.71]; t[5]=2.85; P<0.05).

CONCLUSIONS: These findings support the feasibility of targeting the residual reading network during early recovery using fMRI neurofeedback. Confirmation of these preliminary effects awaits completion of the ongoing randomized controlled trial.

REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04875936.

PMID:41958417 | DOI:10.1161/STROKEAHA.125.054877

Ann Clin Transl Neurol. 2026 Apr 10. doi: 10.1002/acn3.70396. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVES: Transient global amnesia (TGA) is a striking model of isolated amnesia. While hippocampal lesions are well described, the network-level mechanisms and the precise neuropsychological profile remain debated. Our objective was thus to characterize functional and neuropsychological correlates of acute TGA and their longitudinal evolution.

METHODS: Prospective, single-center case-control study of 20 patients with acute TGA and 20 age- and sex-matched healthy controls. All participants completed neuropsychological testing and underwent structural and functional MRI at three time points: acute phase (< 24 h from onset), day 3, and 3 months. Primary outcomes were neuropsychological performance across episodic, semantic, and metamemory domains and resting-state fMRI connectivity within the episodic memory network. Secondary outcomes were functional connectivity within the Default Mode (DMN), Executive (ECN), and Salience (SN) networks.

RESULTS: A total of 40 participants were included (20 patients with TGA, mean age 65.5 years, 45% women; 20 controls, mean age 64.3 years, 45% women). In patients, median delay from symptoms' onset to MRI was 6.67 h. Neuropsychologically, patients showed profound multimodal anterograde amnesia during the acute phase, resolving by 3 months. This deficit was largely isolated, sparing semantic memory and metamemory. Structurally, small bilateral lesions were present in most patients. Functionally, acute hypoconnectivity was observed within the extended hippocampal system, particularly between parahippocampal and cingulate cortices, normalizing by 3 months. No consistent disruption was found in large-scale networks (default mode, executive control, salience).

INTERPRETATION: TGA is associated with transient, selective hypoconnectivity within the mesiotemporal-cingulate episodic memory network, aligning with previous reports and further precising the functional anatomy. The finding of a profound anterograde amnesia was replicated and its recovery timecourse was elucidated. Semantic memory and metamemory remain preserved, clarifying inconsistencies in prior reports. These findings suggest that TGA reflects a transient limbic dysconnectivity syndrome rather than a diffuse network disorder, reconciling structural lesions with clinical and functional data.

PMID:41958247 | DOI:10.1002/acn3.70396

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