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Imaging Neurosci (Camb). 2026 Apr 10;4:IMAG.a.1202. doi: 10.1162/IMAG.a.1202. eCollection 2026.

ABSTRACT

It has long been established that human brains remain functionally active at rest, as demonstrated with the discovery of resting-state networks (RSNs) underlying spontaneous neural activity. Recent studies suggest that classical RSNs estimated from functional magnetic resonance imaging (fMRI) data using time-domain functional connectivity measures might be driven by recurring point-process events. Due to the slow hemodynamic response, fMRI cannot reveal such point-processes at the timescale of neuronal events while electroencephalography (EEG) holds the promise due to its millisecond temporal resolution and successful reconstruction of fMRI-like RSNs. The present study reported a set of recurring transient (<100 ms) cortical co-activation patterns (CAPs) derived from resting-state EEG using a clustering algorithm with spatial-domain measures (i.e., k-means). Our results indicate that this set of CAPs exhibit strong spatial correspondence with known RSNs, not only those derived from the same EEG data using time-domain measures (i.e., independence), but also those from fMRI literature, covering visual, auditory, motor, limbic, high-order, and default mode networks. CAPs exhibit the properties of hemispheric symmetry, spatially separatable sub-systems, and intersubject variability gradient across functional systems, which have all been observed in classical RSNs. These findings suggest that classical RSNs might be driven by recurring transient neuronal activations captured in CAPs. More importantly, CAPs can reveal the fast dynamics of such brain-wide networked neuronal activations (e.g., different CAPs exhibit significantly different occurrences and lifetimes) and benefit from their intersubject reproducibility, thus underscoring their potential to advance our understanding on neuronal mechanisms of spontaneous large-scale brain activation phenomena.

PMID:41982886 | PMC:PMC13075568 | DOI:10.1162/IMAG.a.1202

BMC Psychol. 2026 Apr 14. doi: 10.1186/s40359-026-04399-4. Online ahead of print.

NO ABSTRACT

PMID:41981693 | DOI:10.1186/s40359-026-04399-4

Complement Ther Med. 2026 Apr 12:103382. doi: 10.1016/j.ctim.2026.103382. Online ahead of print.

ABSTRACT

INTRODUCTION: Tai Chi (TC) combined with transcranial direct current stimulation (tDCS) improves memory function in patients with mild cognitive impairment (MCI), but underlying neurophysiological mechanisms remain unclear. This study aims to explore whether TC and tDCS can independently or interactively regulate spontaneous neural activity in different brain regions and enhance memory function.

METHODS: In a randomized 2×2 factorial trial, 128 MCI patients were assigned to TC, tDCS, TC combined with tDCS, or health education for 12 weeks. Memory performance was assessed using the Chinese Wechsler Memory Scale-Revised (WMS-RC), Auditory Verbal Learning Test (AVLT), and Rey-Osterrieth Complex Figure Test (ROCF). Resting-state functional MRI was performed at baseline and post-intervention.

RESULTS: TC significantly improved WMS-RC memory quotient (P<0.001), AVLT-cued recall (P=0.042), recognition (P=0.005), and increased activity in the middle/inferior temporal gyrus (P<0.05). tDCS significantly enhanced memory quotient (P<0.001), ROCF-recall (P=0.030), AVLT-recognition (P=0.013), and modulated activity in the left postcentral gyrus, lingual gyrus, calcarine fissure, and bilateral frontal regions (P<0.05). TC combined with tDCS significantly interacted with immediate recall (P=0.016) and altered activity across multiple cortical regions (P<0.05), and changes in immediate recall were negatively correlated with the ALFF value of the right orbital part of the middle frontal gyrus (r=-0.263, P=0.011).

CONCLUSIONS: TC and tDCS have distinct yet complementary neural and cognitive effects in MCI, supporting their integration as a promising multimodal strategy to delay cognitive decline.

TRIAL REGISTRATION: Chinese Clinical Trial Registry, ChiCTR2200059316.

PMID:41980631 | DOI:10.1016/j.ctim.2026.103382

J Clin Med. 2026 Mar 31;15(7):2644. doi: 10.3390/jcm15072644.

ABSTRACT

Background: Navigated repetitive TMS (nrTMS) is widely used for non-invasive mapping of cortical functions. Methodological improvement might be achieved by optimizing coil positioning based on electric-field modeling and augmented reality (AR)-guided neuronavigation to enhance spatial targeting accuracy and stimulation-induced language errors. Therefore, we compared electric-field-optimized, AR-guided nrTMS with conventional nrTMS using manually planned coil positioning. Methods: Twenty-eight healthy subjects underwent two MRI-guided left hemispheric nrTMS language mapping sessions. Each session used 10 Hz stimulation at a 100% resting motor threshold applied for 1.5 s per region of interest (ROI) during a synchronized object naming task. ROIs were defined according to the Corina cortical parcellation system. Manually defined and electric-field-optimized coil placements obtained using SimNIBS (v4.1.0) were applied; the optimized session was assisted by AR goggles. The primary outcome was the quantitative and categorical differences in cortical regions mapped as language-eloquent. Resting-state fMRI was acquired to provide a reference for comparing nrTMS-derived language maps. Outcomes: Electric-field-optimized nrTMS did not result in an increase in positively mapped ROIs. A different distribution of language errors was observed between sessions. Manual mapping roughly followed the extracted resting-state language and motor networks, whereas electric-field-optimized mapping might correspond less. Optimized coil positions were not always practically feasible. AR guidance improved target location accuracy. Conclusions: While AR was a useful addition to the TMS experiment, electric-field optimization did not translate into significant behavioral differences. However, altered distribution of language errors can give insight into underlying neurophysiological processes of rTMS.

PMID:41976945 | DOI:10.3390/jcm15072644

PLoS Biol. 2026 Apr 13;24(4):e3003738. doi: 10.1371/journal.pbio.3003738. Online ahead of print.

ABSTRACT

Cognitive flexibility supports efficient switching between mental sets and contributes to the preservation of general cognition in aging. It relies on the integration between brain functional dynamics and structural architecture. However, how this structure-function integration changes with age and contributes to cognitive flexibility decline in older adults remains unclear. In this study, we investigated longitudinal aging-related changes in multimodal structure-function integration, quantified as functional signal alignment (i.e., coupling) versus liberality (i.e., decoupling) relative to individual structural connectomes, which represent distinct spectral components, and tested their longitudinal associations with cognitive flexibility. Resting-state fMRI signals were decomposed based on diffusion MRI-derived structural networks using a graph signal processing framework. We focused on subnetworks within three core large-scale cognitive systems: the executive control network (ECN), default mode network (DMN), and salience network (SN). Across two independent datasets, the task-positive SN-A subnetwork, which includes core SN regions such as the anterior insula and dorsal anterior cingulate cortex, exhibited decreased coupling and increased decoupling with aging. Importantly, these changes were associated with a greater decline in cognitive flexibility (measured by the Trail Making Test and Color Trails Test) over time. In contrast, task-negative DMN-A (centered in the medial prefrontal and posterior cingulate cortex) showed aging-related changes in the opposite direction, with increased coupling and decreased decoupling over time. Together, these findings reveal network-specific trajectories of intrinsic structure-function integration in normal aging and indicate that preserved structure-function integration within the SN may be particularly important for maintaining cognitive flexibility in older adults.

PMID:41973732 | DOI:10.1371/journal.pbio.3003738

Pain. 2026 Apr 7. doi: 10.1097/j.pain.0000000000003984. Online ahead of print.

ABSTRACT

Chronic pain is associated with maladaptive reorganization of brain networks, particularly in the anterior cingulate cortex (ACC), contributing to the affective dimension of pain. Although peripheral capsaicin administration relieves neuropathic pain in clinics, its effects on central pain networks remain unclear. In this study, we determined the resting-state functional connectivity of ACC (ACC FC) rearrangement after infraorbital nerve chronic constriction injury (ION-CCI) and subsequent peripheral administration of capsaicin through longitudinal resting-state functional magnetic resonance imaging (fMRI) in male rats. We also conducted functional silencing of the ACC using inhibitory chemogenetic receptors to determine ACC networks commonly reversed by peripheral capsaicin and chemogenetic silencing. Infraorbital nerve chronic constriction injury produced orofacial mechanical allodynia accompanied by ACC FC changes compared to sham. A single injection of capsaicin into the maxillary skin decreased mechanical allodynia. Five days after capsaicin injection, CCI-enhanced ACC FC was significantly reduced compared to the time point before the injection in the same rats or to the rats with vehicle injection. Subsequent chemogenetic silencing of ACC in the previously vehicle-treated CCI rats reduced mechanical allodynia and suppressed CCI-enhanced ACC FC. Peripheral capsaicin and chemogenetic inhibition of ACC commonly reversed approximately one-third of the CCI-enhanced ACC FC. Affected regions included the bilateral cingulate areas, primary and secondary somatosensory cortex, primary and secondary auditory areas, hippocampus, and temporal association cortex. We conclude that peripheral capsaicin administration reverses maladaptive ACC networks in male rats with nerve injury and that peripheral nociceptors contribute to the maintenance chronic pain and peripherally targeted treatment can produce long-lasting analgesia.

PMID:41973718 | DOI:10.1097/j.pain.0000000000003984

Quant Imaging Med Surg. 2026 Apr 1;16(4):309. doi: 10.21037/qims-2025-1771. Epub 2026 Feb 25.

ABSTRACT

BACKGROUND: Cognitive impairment (CI) is an under-recognized yet clinically important complication in patients with non-dialysis chronic kidney disease (CKD). Despite its significance, predictive and evaluative frameworks remain underdeveloped, limiting opportunities for timely management. We investigated the utility of multimodal functional magnetic resonance imaging (MRI) in predicting CI in patients with non-dialysis CKD.

METHODS: A prospective study of 60 patients with non-dialysis CKD was conducted using conventional MRI sequences and three-dimensional T1-weighted scans. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) scale, and patients were stratified into a CI group (MoCA score <26) and a non-cognitive impairment (NCI) group (MoCA score ≥26). Group differences in brain structure and function were examined using voxel-based morphometry (VBM) and blood oxygenation level-dependent (BOLD) analyses. The associations between brain structural metrics [gray matter volume (GMV); gray matter volume fraction (GMVF)] and CI were further evaluated with binary logistic regression and receiver operating characteristic (ROC) analysis.

RESULTS: Compared to the NCI group, patients with CKD with CI showed substantially reduced brain GMV (572.56±39.70 vs. 621.30±62.12 cm3, P=0.001). VBM analysis indicated substantially reduced GMV in the right amygdala (t=5.0291), left insula (t=5.3287), and right middle temporal gyrus (t=4.4031) in the cognitively impaired group. BOLD analysis indicated reduced amplitude of low-frequency fluctuations in the left posterior central gyrus and right supplementary motor area, and reduced regional homogeneity in the bilateral postcentral gyri (all P<0.001). After adjustment for age, education, and estimated glomerular filtration rate (eGFR), GMV remained independently associated with visuospatial/executive impairment [odds ratio per 1-cm3 decrease =0.970, 95% confidence interval (95% CI): 0.975-0.997, P=0.028]. GMV demonstrated predictive value for CI, with an area under the ROC curve of 0.729 (95% CI: 0.561-0.879, P=0.01), yielding a sensitivity of 94.7% and specificity of 53.3% at a cut-off of 619.9 cm3.

CONCLUSIONS: Patients with non-dialysis CKD and CI exhibited reduced GMV and impaired functional connectivity in specific brain regions. These structural and functional alterations were strongly associated with CI. GMV demonstrated high sensitivity in differentiating between patients with and without CI. These findings indicate the potential of multimodal MRI techniques in the early diagnosis and intervention planning in cognitive decline associated with non-dialysis CKD.

PMID:41972076 | PMC:PMC13066838 | DOI:10.21037/qims-2025-1771

Neurobiol Lang (Camb). 2026 Mar 26;7:NOL.a.225. doi: 10.1162/NOL.a.225. eCollection 2026.

ABSTRACT

The visual word form area (VWFA) has been consistently identified as a crucial structure in word reading, and its function differs across subregions. Nevertheless, the functional roles of its subregions and their functional origins remain controversial. Here, we adopted multimodal neuroimaging techniques (i.e., task-state fMRI, resting-state fMRI, and diffusion MRI) combined with representational similarity analysis to investigate the functional role of VWFA subregions and the brain circuitry supporting their function in two experiments. Results revealed respective roles of the posterior and anterior VWFA subregions in visual and semantic processing, which is consistent with their respective connectivity to orthographic and semantic networks. In addition, processing demands modulated the neural representations of high-level linguistic information in the VWFAs. These convergent findings elucidated the local neural computations in the VWFAs and their cooperative mechanism with distant brain regions related to language processing, jointly providing multimodal neuroimaging evidence for the connectivity-biased hypothesis.

PMID:41971749 | PMC:PMC13065096 | DOI:10.1162/NOL.a.225

Front Public Health. 2026 Mar 27;14:1762620. doi: 10.3389/fpubh.2026.1762620. eCollection 2026.

ABSTRACT

BACKGROUND: Chronic smoking has well-documented impacts on brain structure. Voxel-based morphometry (VBM) investigations have revealed diverse regional gray matter (GM) changes in chronic smokers, hindering a unified understanding of smoking-induced neuropathology. To reconcile these findings, this study aimed to identify common intrinsic functional networks underlying these structural alterations using a functional connectivity network mapping (FCNM) approach. We further explored potential exposure-dependent variations to characterize how brain network architecture relates to cumulative smoking dose.

METHODS: We utilized coordinate-based FCNM to quantitatively integrate heterogeneous findings from previous VBM studies. We systematically reviewed VBM studies reporting GM differences between chronic smokers and non-smokers. We identified peak coordinates from 27 studies, encompassing 36 contrasts with 1,336 smokers and 1803 non-smokers. Resting-state fMRI from 1,093 healthy participants (Human Connectome Project) were utilized to create individual functional connectivity maps based on seed coordinates. Maps were combined to identify a shared alteration network and evaluated for spatial overlap with established canonical brain networks. Sensitivity analysis were conducted with different seed radii. Crucially, subgroup analysis stratified studies into higher-exposure and lower-exposure groups to investigate exposure-dependent mechanisms.

RESULTS: Functional connectivity network mapping identified a widespread network linked to smoking-induced GM changes. Key nodes included the supramarginal gyrus, insula, anterior cingulate cortex, caudate nucleus, putamen, and superior temporal gyrus. Spatial overlap analysis revealed predominant involvement of the posterior Salience Network (51.59%), anterior Salience Network (32.15%), basal ganglia network (31.52%), and auditory network (24.19%). Sensitivity analysis confirmed the robustness of these findings. Subgroup analysis revealed exposure-dependent patterns: while the Salience and basal ganglia networks were consistently affected in both groups, the auditory network and ventral Default Mode Network showed markedly greater involvement in the higher-exposure group, largely spared in the lower-exposure group.

CONCLUSION: This FCNM approach identified consistent brain networks, predominantly the Salience, basal ganglia, and auditory networks, associated with chronic smoking-related GM alterations. These findings offer network-level insight into the structural effects of smoking, helping to resolve discrepancies and potentially guiding tailored interventions. Furthermore, the findings suggest a progressive neuropathological expansion, characterized by the concurrent recruitment of sensory (auditory) and high-order cognitive systems (ventral Default Mode Network) with cumulative smoking exposure.

PMID:41971270 | PMC:PMC13066286 | DOI:10.3389/fpubh.2026.1762620

Neuroimage. 2026 Apr 10:121919. doi: 10.1016/j.neuroimage.2026.121919. Online ahead of print.

ABSTRACT

Brain entropy (BEN) quantifies the irregularity of regional brain activity and serves as an index of neural complexity, yet how BEN co-varies with large-scale brain connectivity remains unclear. Given the brain's dynamic nature, this study examined how whole-brain connectivity patterns co-vary with recurring BEN states. Using a large resting-state fMRI data dataset (N = 812), we applied a sliding-window approach and k-means clustering to derive dynamic BEN states and their corresponding connectivity patterns. Four distinct BEN states were identified, each showing unique functional and cognitive relevance. A low-BEN state (State 1) was associated with a strongly segregated, weakly integrated organization and negative cognitive relevance, while a high-BEN state (State 4) showed a highly integrated but weakly segregated organization and neutral cognitive relevance. Two intermediate-BEN states differed in regional entropy and connectivity: State 2, with low entropy in the default mode (DMN), executive control (ECN), and salience (SAN) networks, showed positive cognitive relevance and balanced integration-segregation; State 3, with low entropy in sensorimotor (SMN) and visual networks (VN), showed no significant cognitive relevance. Moreover, BEN-connectivity correlations were significantly negative and varied across states, being strongest in the cognitively relevant states. These findings demonstrate that the relationship between BEN and brain connectivity is dynamic and state-dependent, advancing BEN as a marker of the brain's complex, state-dependent functional organization.

PMID:41967787 | DOI:10.1016/j.neuroimage.2026.121919

J Affect Disord. 2026 Apr 9:121770. doi: 10.1016/j.jad.2026.121770. Online ahead of print.

ABSTRACT

BACKGROUND: Facial emotion recognition (FER), an essential component of emotion processing that influences social functioning and interpersonal relationship satisfaction, plays a key role in major depressive disorder (MDD). This study aimed to investigate the heterogeneity of FER performance within individuals with MDD and its relationship with whole-brain functional connectivity (FC) using multivariate methods.

METHODS: A total of 202 patients with MDD and 202 healthy controls (HCs) were included in the study and completed FER assessments. Among them, 158 patients with MDD and 128 HCs underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. Data-driven clustering was employed to identify FER-based clusters within the MDD group based on recognition performance. Group differences in FC were explored at both the edge and large-scale network levels. Partial least squares (PLS) correlation analysis was then applied to investigate the association between whole-brain FC patterns and FER performance.

RESULTS: Clustering analysis identified three MDD subgroups characterized by progressively decreasing FER performance, accompanied by a corresponding stepwise reduction in overall network connectivity strength. Comparisons between subgroups highlighted the crucial involvement of hippocampal and prefrontal regions, as well as subcortical and visual systems. The PLS results revealed a distinctive FC pattern associated with FER performance.

CONCLUSIONS: Our findings suggest that multilevel neural alterations, including disrupted connectivity within the hippocampal-prefrontal-limbic circuitry and abnormal coupling between large-scale information integration and sensory-motor networks, may collectively impair affective information processing and contribute to individual differences in FER observed among individuals with MDD.

PMID:41966228 | DOI:10.1016/j.jad.2026.121770

Neuroimage Clin. 2026 Apr 7;50:103990. doi: 10.1016/j.nicl.2026.103990. Online ahead of print.

ABSTRACT

PURPOSE: The exact mechanisms underlying the distinct neurological outcomes between Traumatic Brain Injury (TBI) and Hemorrhagic Stroke (HS) remain unclear. Our objective is to assess distinct features of neurologic state between comatose patients with TBI and HS during the stage of acute disorder of consciousness (aDoC) and to identify the correlation of neurologic features with prognosis.

METHODS: Data were analyzed from TBI and HS patients examined by positron emission tomography (PET) and resting-state functional magnetic resonance imaging (rs-fMRI) simultaneously. Primary clinical outcomes consisted of the state of consciousness and neurological prognosis. The regional neural activity was assessed by the amplitude of fractional low-frequency fluctuation (fALFF) and regional homogeneity (ReHo) on rs-fMRI scans. The standardized uptake value (SUV) on PET scans quantified neural metabolism. Functional connectivity (FC) and graph theoretic approach (GTA) were employed to compare the FC patterns between TBI and HS. Correlations of PET/rs-fMRI indicators with the prognosis of HS and TBI were identified.

RESULTS: Muti-modal PET/rs-fMRI analysis showed more active local neurological state in TBI patients than HS patients, specifically in the right precentral gyrus (PreCG.R), right postcentral gyrus (PoCG.R), right superior temporal gyrus (STG.R) and right middle temporal gyrus (MTG.R). TBI patients demonstrated significantly higher clustering coefficient and nodal efficiency of the sensorimotor network (SMN) along with lower connectivity and network efficiency in the default network (DMN) compared to HS patients. PET/rs-fMRI indicators significantly correlated with the neurological prognosis of TBI and HS.

CONCLUSIONS: This study elucidated the underlying mechanisms contributing to the distinct neurologic prognosis between comatose TBI and HS patients, and may contribute to the development of early targeted intervention strategies for specific diseases.

PMID:41965151 | DOI:10.1016/j.nicl.2026.103990

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