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Front Neurol. 2026 Feb 9;17:1699988. doi: 10.3389/fneur.2026.1699988. eCollection 2026.

ABSTRACT

OBJECTIVE: To investigate the local brain functional changes after heat-sensitive moxibustion at the left ST35 (Dubi) acupoint in patients with knee osteoarthritis (KOA) based on resting-state functional magnetic resonance imaging (rs-fMRI), and to explore the possible neuroregulatory mechanisms of heat-sensitive moxibustion for pain relief using the fractional amplitude of low-frequency fluctuation (fALFF) analysis.

METHODS: A total of 30 KOA patients who were found to be insensitive to the heat of moxibustion in the non-heat-sensitive moxibustion (NHSM) group, and enrolled another 30 KOA patients with moxibustion sensation in the heat-sensitive moxibustion (HSM) group. Both groups received moxibustion at the left ST35 acupoint for 10 min (once daily for 10 consecutive days) at a distance of about 3 cm from the skin. Before the first treatment and after the tenth treatment, we assessed knee pain using visual analog scale (VAS) and performed rs-fMRI scans on the patients. The fALFF data of both groups were processed using the SPM 12 module of MATLAB software.

RESULTS: Compared with pre-moxibustion, the fALFF value of the HSM group in the frontal lobe, white matter, and left temporal lobe was significantly higher, while the occipital lobe and the right hemisphere was significantly lower. The region with the highest increase was the left temporal lobe, followed by white matter, and the region with the strongest decrease was the occipital lobe, followed by the frontal lobe and the right hemisphere. In the NHSM group, the fALFF value in the left occipital lobe, left medial frontal gyrus, left middle frontal gyrus, right superior frontal gyrus, right superior temporal gyrus, and right cerebellar posterior lobe was significantly lower, with the strongest decrease in the right cerebellar posterior lobe, followed by the right superior temporal gyrus. Compared with the NHSM group after treatment, the fALFF value of the HSM group in the external nucleus, white matter, right hemisphere, left cerebellum, and left hemisphere was significantly higher, and the frontal lobe, occipital lobe, and precentral gyrus was significantly lower. Additionally, a positive correlation was found between the fALFF changes of the left temporal lobe and the VAS score changes for each patient (pre- vs. post-treatment) in the HSM group (r = 0.764, p < 0.01), whereas a negative correlation was observed for the occipital lobe (r = -0.595, p < 0.01).

CONCLUSION: This study reveals that the superior pain relief from heat-sensitive moxibustion is underpinned by a sensation-specific, bidirectional modulation of the brain's pain-processing network. Unlike the generalized suppression observed in the NHSM group, the heat-sensitive state is characterized by a concerted increase in temporal lobe activity and decrease in occipital lobe activity, both changes being strongly predictive of individual clinical improvement. These results offer compelling neuroimaging evidence that the subjective heat-sensitive sensation reflects a more efficient and integrated brain state for analgesia.

CLINICAL TRIAL REGISTRATION: https://www.chictr.org.cn/, ChiCTR2000033075.

PMID:41738006 | PMC:PMC12926134 | DOI:10.3389/fneur.2026.1699988

Brain Behav Immun Health. 2026 Feb 9;53:101196. doi: 10.1016/j.bbih.2026.101196. eCollection 2026 May.

ABSTRACT

BACKGROUND: Persistent fatigue is one of the most common and disabling sequelae of COVID-19, yet its neurobiological mechanisms remain poorly understood. Emerging evidence implicates systemic inflammation and fronto-striatal dysfunction in fatigue across diverse clinical conditions. However, the links between early inflammatory responses, brain connectivity, and the acute-to-chronic trajectory of post-COVID fatigue are unclear.

METHODS: In a multi-center longitudinal cohort of 193 young-to-middle-aged adults with mild COVID-19, we assessed acute-phase C-reactive protein (CRP), fatigue severity (FAS) at <1 month (acute, FAS-1) and 3 months (chronic, FAS-2) post-infection, and resting-state fMRI at 3 months. Functional connectivity (FC) differences between participants with persistent (n = 48) and non-persistent fatigue (n = 145) were examined, and mediation analyses were performed to evaluate pathways linking CRP, FC alterations, and fatigue progression.

RESULTS: Acute-phase CRP levels were elevated in the persistent fatigue group and positively correlated with fatigue severity at both time points. Compared with the non-persistent group, individuals with persistent fatigue showed reduced functional connectivity (FC) between the left superior frontal gyrus (SFG L) and striatal regions (caudate L and putamen L). This SFG L-striatal FC was negatively correlated with fatigue severity. Crucially, a chain mediation model suggested that the association between CRP on chronic fatigue was statistically mediated through two sequential pathways: (1) via acute fatigue alone, and (2) via acute fatigue followed by reduced SFG L-striatal FC.

CONCLUSION: In this cohort of mild COVID-19 survivors, this study identifies acute inflammation (elevated CRP) as a significant predictor of post-COVID fatigue and suggests that reduced fronto-striatal connectivity may mediate the transition from acute to chronic fatigue. These findings highlight the fronto-striatal circuit as a potential imaging biomarker and point to the acute phase as a critical window for anti-inflammatory or neuromodulatory interventions. Further longitudinal and interventional studies are needed to validate these mechanisms and therapeutic strategies.

PMID:41737723 | PMC:PMC12926603 | DOI:10.1016/j.bbih.2026.101196

Cogn Affect Behav Neurosci. 2026 Feb 25. doi: 10.3758/s13415-025-01397-8. Online ahead of print.

ABSTRACT

Evaluating others' actions requires integrating their intentions with the outcomes they produce. Several studies have investigated the neural processes supporting this aspect of moral judgment, but findings remain heterogeneous. We conducted a pooled Activation Likelihood Estimation (ALE) meta-analysis of fMRI studies comparing evaluations of intentional and accidental harm, which is preregistered at https://doi.org/10.17605/OSF.IO/2HTFU . Following a systematic search on PubMed, Scopus, and Web of Science (last search: October 2024), eight studies met our inclusion criteria, yielding a total of 18 contrasts. Eligible studies reported whole-brain group analyses with stereotactic coordinates for direct contrasts between intentional and accidental harm. Studies were excluded if they focused on patient populations or lacked such contrasts. The meta-analysis identified two regions of consistent activation: the right amygdala and the left hippocampus. To better characterize their functional roles, we performed meta-analytic connectivity modeling and resting-state connectivity analyses. The amygdala showed reliable associations with regions involved in salience detection and affective regulation, supporting its established role in encoding harm-related signals. The hippocampus exhibited a broad connectivity profile, suggesting possible roles in interpersonal harm evaluation, such as episodic simulation, contextual reconstruction, and schema-based reasoning. These results confirm key aspects of existing models of moral judgment and offer novel insights by highlighting the involvement of the hippocampus, a region not typically emphasized in intent-based moral evaluation.

PMID:41735754 | DOI:10.3758/s13415-025-01397-8

Yonsei Med J. 2026 Mar;67(3):238-250. doi: 10.3349/ymj.2025.0012.

ABSTRACT

PURPOSE: Recently, mental health interventions through mobile applications have been increasing. This study sought to explore what changes occurred in psychometric properties and brain functional connectivity (FC) among people who practiced mindfulness meditation through a mobile application.

MATERIALS AND METHODS: Subjects underwent mindfulness-based intervention (MBI) for about 24 minutes every day for 8 weeks through a mobile application. Before and after MBI, a total of 21 adult men and women completed self-report questionnaires and functional magnetic resonance imaging (fMRI) tests. The fMRI data were acquired during an attention network test and during the resting state.

RESULTS: In self-report questionnaires, participants reported increased levels of mindfulness and decreased emotion regulation difficulties after MBI. In task-based fMRI, the time-by-intervention effect was not significant. In resting-state fMRI, FC between the right posterior insula and the left ventromedial prefrontal cortex (VMPFC) increased after MBI. FC between the default mode network-related regions and the occipital regions decreased after MBI. The decrease in FC between the VMPFC and the cuneus showed a significant correlation with the improvement in emotional awareness after MBI.

CONCLUSION: In a pre- and post-MBI comparison of a single group, subjects who underwent mobile-based MBI showed FC changes including the VMPFC. In particular, some of these FC changes were correlated with changes in emotional awareness. The results of this study suggest that further research is needed to verify whether mobile-based MBI affects improvement in emotion regulation through neural changes in functional brain networks.

PMID:41734985 | DOI:10.3349/ymj.2025.0012

Alzheimers Res Ther. 2026 Feb 24. doi: 10.1186/s13195-026-01991-z. Online ahead of print.

NO ABSTRACT

PMID:41736154 | DOI:10.1186/s13195-026-01991-z

BMC Res Notes. 2026 Feb 24. doi: 10.1186/s13104-026-07745-2. Online ahead of print.

NO ABSTRACT

PMID:41736146 | DOI:10.1186/s13104-026-07745-2

Hum Brain Mapp. 2026 Feb 15;47(3):e70482. doi: 10.1002/hbm.70482.

ABSTRACT

There is an urgent need to advance understanding of the neural underpinnings of depression, especially early in the life span. Examination of neural dynamics using resting-state functional magnetic resonance imaging (fMRI) data can provide indices of neural flexibility, which may provide important new insights for the neurobiology of pediatric depression. Here we applied Hidden Markov Modeling (HMM) to resting-state fMRI data to investigate neural flexibility in relation to depression and suicidal thinking in children. We utilized data from the Adolescent Brain Cognitive Development℠ Study (ABCD Study), and included data from 10,763 children (9-10 years) who completed two 5-min resting state fMRI scans at the baseline visit. After applying the NeuroMark framework to the data, HMM was applied with a varying number of states; a six-state model was selected from candidate models based on between-scan reliability. We applied linear mixed-effect modeling to test the relationship between two clinical predictors: current major depressive disorder (MDD) diagnosis and presence of suicidal ideation (SI) with our primary outcome for neural flexibility: the frequency of transitions between HMM-derived states ("state-switching"), while including sex, age, and other socio-demographic variables as covariates. Analyses were conducted both with and without correction for head motion. We also explored relationships with total time and dwell time in each state of the six states. Lower state-switching during rest was associated with both MDD and SI, although these findings were no longer significant after correcting for head motion. Notably, state-switching was inversely related to head motion and was higher in females than males. Exploratory analysis showed that MDD was associated with shorter dwell time in one state and longer dwell time in another, suggesting altered temporal persistence of specific neural configurations. Tentative evidence supported our hypothesis that lower state-switching in children with MDD and SI may reflect a reduction in brain flexibility, potentially contributing to a tendency to become "stuck" in negative patterns of thinking and feeling. However, the relatively low frequency of these problems in late childhood reduced statistical power after correcting for motion. Future research is needed to assess these relationships at later adolescent time points, when higher prevalence of depression and SI and lower prevalence of head motion will allow more powerful tests of these associations.

PMID:41733392 | DOI:10.1002/hbm.70482

Alzheimers Res Ther. 2026 Feb 24. doi: 10.1186/s13195-026-01993-x. Online ahead of print.

ABSTRACT

BACKGROUND: Digital speech-based assessments provide scalable tools for detecting subtle cognitive decline. Here, we investigated whether digitally derived speech-based composite score of cognition and individual speech features were associated with alterations in functional connectivity (FC) within task-related brain networks in the Alzheimer's disease spectrum, which are known to reflect cognitive performance and disease-related changes.

METHODS: Data were analyzed from 129 participants of the German PROSPECT-AD study, ranging from cognitively healthy individuals to those with mild cognitive impairment. Speech-based cognitive scores and speech features were derived from automated phone-administered semantic verbal fluency (SVF) and verbal learning tasks (VLT). Resting-state fMRI assessed FC, with intrinsic connectivity networks identified via independent component analysis and dual regression. Associations were examined using permutation-based voxel-wise regression, controlling for demographic and clinical covariates. Seed-to-voxel analyses were conducted to support network identification and complement findings.

RESULTS: Greater language network connectivity in the left middle temporal gyrus was associated with increased SVF temporal cluster switching (FWE < .05, cluster size = 12 voxels, mean T = 3.86). Exploratory analyses (uncorrected p < .01) demonstrated no significant associations between cognitive composite scores and FC. However, individual SVF and VLT speech features exhibited network-specific associations across executive, language, and default mode networks, indicating exploratory yet spatially distinct connectivity patterns.

CONCLUSION: Digital speech-based assessments may have limited current utility for detecting FC alterations in at-risk individuals. Further validation using complementary methodological approaches, shorter intervals between fMRI and speech assessments, and testing in independent cohorts, are essential to establish their reliability and clinical relevance for monitoring brain network changes.

PMID:41731608 | DOI:10.1186/s13195-026-01993-x

Dev Psychopathol. 2026 Feb 24:1-14. doi: 10.1017/S0954579426101266. Online ahead of print.

ABSTRACT

Adolescence represents a pivotal neurodevelopmental period marked by escalating anxiety symptoms and heightened approach motivations. Although anxiety is typically linked to avoidance, concurrent shifts in motivational systems and neurocircuitry may alter its behavioral and neural expression, shaping developmental trajectories and treatment response. This study investigated how approach motivations (Behavioral Activation System; BAS) interact with anxiety to influence behavior and brain function in N = 121 adolescents (ages 9-13; 44% girls; 33.1% White, 22.3% Latino, 19.8% Asian, 14.9% Black, 9.9% Mixed Race). Participants completed a decision-making task and resting-state fMRI. Dimensional analyses examined joint effects of anxiety and BAS on risk-taking behaviors, task-evoked neural activity and connectivity, and intrinsic connectivity at rest. Higher anxiety was associated with risk aversion and inhibition when BAS was low, but with risk-taking and impulsivity when BAS was high (risk-taking: β = 0.25, p = .012; inhibitory control: β = 0.13, p < .001). During risk-taking, anxiety and BAS showed interactive effects on striatal (β = -0.10, p = .006) and amygdala (β = 0.10, p = .005) activity alongside distinct effects on prefrontal-subcortical connectivity (β = -0.30, p = .014; β = 0.17, p = .01). Higher BAS was associated with stronger intrinsic prefrontal-striatal connectivity (β = 0.23, p = .012), while anxiety showed no significant resting-state effects. Findings underscore the role of reward-related systems in adolescent anxiety and support developmentally informed, personalized intervention strategies.

PMID:41731343 | DOI:10.1017/S0954579426101266

Cogn Affect Behav Neurosci. 2026 Feb 24. doi: 10.3758/s13415-025-01391-0. Online ahead of print.

ABSTRACT

Schizophrenia is a complex psychiatric disorder that affects approximately 20 million people worldwide. Patients show face-processing deficits that significantly affect their social interactions and social cognitive abilities (e.g., recognizing human faces). Although face processing has been extensively studied by using functional magnetic resonance imaging (fMRI), there have been very few systematic reviews investigating links between social-cognitive dysfunction, face processing networks, and clinical symptoms associated with key large-scale brain networks, such as the triple networks. We review brain networks, their dysconnectivity across patient subtypes, and relationships to clinical symptoms. Reviewed studies from 2020-2025 were 1) written in English, 2) focused on face and/or emotion processing in schizophrenia (not limited to first episode psychosis [FEP]), and 3) were resting or task-based fMRI studies investigating neural networks subserving face/emotion processing. PubMed, PsycINFO, Web of Science, and Google Scholar were utilized. Nine articles were reviewed. Resting-state studies and task-based fMRI studies showed elevated Positive and Negative Syndrome Scale (PANSS) positive scores in FEP patients coupled with social cognition deficits. Dysconnectivity was most consistently observed in the executive function network, the ToM /mentalizing network, the default mode network, limbic regions, and the visual-perceptual systems. Subtype dysconnectivity patterns included broad deficits in social cognition, empathy, emotion processing and face/emotion recognition. Social-cognitive deficits broadly stem from challenges in recognizing and processing negative emotional faces. Factors, such as trauma, suicidality, and inflammation, should be further examined, along with subtype presentations.

PMID:41731284 | DOI:10.3758/s13415-025-01391-0

Transl Psychiatry. 2026 Feb 24. doi: 10.1038/s41398-026-03883-0. Online ahead of print.

ABSTRACT

Conventional functional connectivity of blood oxygenation level-dependent (BOLD) signals varies with Alzheimer's disease (AD) progression. However, it is unable to describe how white matter (WM) is engaged in brain networks. In this study, we introduced a novel communication connectivity metric, which was defined as the triple-wise correlation coefficient between BOLD signals from pairs of gray matter volume and white matter bundles, to investigate the change of signal transition through WM bundles. A total of 169 participants with longitudinal resting-state fMRI data from the ADNI dataset were included, which consisted of 44 cognitively normal (CN), 58 early mild cognitive impairment (EMCI), 45 late MCI (LMCI), and 22 AD. Cross-sectional analyses at baseline and longitudinal within-group comparisons were conducted to examine changes in pattern correlation coefficients (CC) between 2D graphs across the AD continuum. In the cross-sectional study, the 2D graph of the CN group showed moderate correlation with those of the EMCI and LMCI groups, whereas these associations generally declined in the AD dementia group. Bootstrapping test showed that the pattern CC for the right retrolenticular part of internal capsule (RLIC.R) and posterior corona radiata (PCR.R) significantly declined in the EMCI, LMCI, and AD groups for both cross-sectional and longitudinal studies. These results demonstrated that signal transmission in RLIC.R and PCR.R has great potential to be markers in the early diagnosis of AD and tracking the progression of AD. Communication connectivity based on rs-fMRI is a promising tool for investigating WM signal transmission alterations in AD.

PMID:41730850 | DOI:10.1038/s41398-026-03883-0

J Neural Eng. 2026 Feb 23. doi: 10.1088/1741-2552/ae4926. Online ahead of print.

ABSTRACT

This study investigates whether dynamic functional connectivity (dFC) dwell-time patterns derived from resting-state fMRI (rs-fMRI) can distinguish Alzheimer's disease (AD) genetic risk profiles, specifically the APOE-ε4 (A+) and PICALM rs3851179 (P+) variants, in cognitively healthy, middle-aged adults.&#xD;&#xD;Approach. We estimated recurring dFC clusters from rs-fMRI data and quantified the dwell-time (total duration spent in specific connectivity states) for three cohorts: not-at-risk, A+P-, and A+P+. To evaluate the utility of these temporal features, group differences in dwell-time profiles were assessed, and logistic regression with permutation testing was employed to classify genotypes based on dFC patterns.&#xD;&#xD;Main results. Individuals in at-risk groups (A+P- and A+P+) exhibited significantly reduced dwell-time in left-hemisphere hubs compared to the not-at-risk group, aligning with known left-hemisphere vulnerability in early AD progression. The logistic regression models achieved above-chance discrimination of genotypes, with permutation tests confirming a significant trend when distinguishing not-at-risk individuals from the combined at-risk cohorts.&#xD;&#xD;Significance. These findings suggest that the temporal dFC features are sensitive to subtle functional brain alterations linked to AD genetic risk before clinical symptoms appear. Dwell-time features represent a promising physiological marker for early risk stratification and warrant further validation in larger longitudinal datasets. Our code is available at https://github.com/Shyamal-Dharia/APOE-PICALM-dFC-dwell-time.git.&#xD;&#xD;&#xD;&#xD.

PMID:41730245 | DOI:10.1088/1741-2552/ae4926

Proc IEEE Int Symp Biomed Imaging. 2025 Apr;2025. doi: 10.1109/isbi60581.2025.10981094. Epub 2025 May 12.

ABSTRACT

Functional MRI measuring BOLD signal is an increasingly important imaging modality in studying brain functions and neurological disorders. It can be acquired in either a resting-state or a task-based paradigm. Compared to resting-state fMRI, task-based fMRI is acquired while the subject is performing a specific task designed to enhance study-related brain activities. Consequently, it generally has more informative task-dependent signals. However, due to the variety of task designs, it is much more difficult than in resting state to aggregate task-based fMRI acquired in different tasks to train a generalizable model. To resolve this complication, we propose a supervised task-aware network TA-GAT that jointly learns a general-purpose encoder and task-specific contextual information. The encoder-generated embedding and the learned contextual information are then combined as input to multiple modules for performing downstream tasks. We believe that the proposed task-aware architecture can plug-and-play in any neural network architecture to incorporate the prior knowledge of fMRI tasks into capturing functional brain patterns.

PMID:41728050 | PMC:PMC12922581 | DOI:10.1109/isbi60581.2025.10981094

bioRxiv [Preprint]. 2026 Feb 13:2026.02.12.702420. doi: 10.64898/2026.02.12.702420.

ABSTRACT

Brain maps (e.g. retinotopy, somatotopy) vary across individuals. This is thought to reflect underlying computational differences. However, artificial neural networks (ANNs) show that similar performance and internal representations can coexist with diverse circuit layouts. Consequently, we tested the presumption that spatial diversity reflects representational diversity in the brain, but found this presumption often breaks down. Using task and resting-state fMRI data we compared regional functional topographies and representational geometries-the within-individual dissimilarities among activity patterns. Across individuals ( n = 414), representations converged in higher-order cortex despite substantial topographic diversity, indicating that similar information was encoded by different, individual-specific activity patterns. Topography only tracked representational differences in sensory-motor cortices and regions under strong architectural constraints, such as myelination or laminar differentiation. We show this parallels ANNs: architectural permissiveness allows idiosyncratic layouts to arise from random initializations rather than learned representations. To test whether topographies and representations show analogous developmental origins, we examined twins ( n = 394), and found topographies were more heritable than representations. This shows that representational convergence occurs across idiosyncratic layouts in both artificial and biological systems, but is moderated by architectural constraints on implementation flexibility. Accordingly, the relevance of localization- and representation-based paradigms of brain function depends on neural architecture.

PMID:41727092 | PMC:PMC12918918 | DOI:10.64898/2026.02.12.702420

bioRxiv [Preprint]. 2026 Feb 9:2025.09.12.675913. doi: 10.1101/2025.09.12.675913.

ABSTRACT

Functional brain networks support human cognition, yet how individualized network architecture emerges in early childhood remains poorly understood. Averaging across participants can obscure age-specific organization and person-to-person differences, particularly in slowly developing association cortices. We developed an age-appropriate functional reference that captured common structure across toddlers without averaging away individual variability, enabling estimation of each child's networks from resting-state fMRI. Across cohorts of 8-60-month-old children, we found individualized network organization-including finer-scale subdivisions and emerging language lateralization-well before age five. Network layouts showed longitudinal stability, with greater consistency in sensory than association regions. Within-network connectivity was stronger and explained age-related variance when networks were defined using individualized rather than group-consensus topography. Left-lateralization of language networks tracked age-normalized verbal ability, linking early functional architecture to emerging cognition. These findings show that behaviorally relevant brain networks arise far earlier than previously recognized, providing a foundation for studying typical development and early biomarkers.

PMID:41727068 | PMC:PMC12919052 | DOI:10.1101/2025.09.12.675913

Front Neurosci. 2026 Feb 5;20:1751746. doi: 10.3389/fnins.2026.1751746. eCollection 2026.

ABSTRACT

INTRODUCTION: The study investigated glioma patients after surgical resection of tumor tissue using postoperative functional magnetic resonance imaging (fMRI) to assess cavity-adjacent (perilesional) functional connectivity as a predictor of overall survival and functional recovery.

METHODS: We developed an analytic method to quantify the postoperative whole-brain functional connectivity. Resting-state whole-brain fMRI scans acquired from 12 glioma patients following surgical resection were analyzed as part of a proof-of-concept study. In particular, connectivity of the resected perilesional area was compared to that of the corresponding contralateral homologue region, and the difference between perilesional and contralateral connectivity was calculated. To test whether the functional connectivity metric could predict recovery of neurological outcomes, we compared patients' connectivity metrics from postoperative scans with changes in Karnofsky Performance Status (KPS) score between preoperative assessment and 6-month follow-up. Additionally, we examined whether the connectivity metric could predict overall survival by dividing the patients into subgroups based on their median survival time and comparing connectivity metrics.

RESULTS: Our analysis showed altered functional connectivity between perilesional and corresponding contralateral regions following surgical resection of glioma. The connectivity metric from postoperative scans was significantly correlated with recovery of neurological outcomes, as reflected by changes in KPS from preoperative to 6 months postoperative period (ρ = 0.97, p < 0.001). Moreover, individuals with survival times greater than 15 months showed significantly higher connectivity than those with shorter survival times (p = 0.0016 and Cohen's d = 2.74 in all subjects, p = 0.02 and Cohen's d = 1.90 in the subset of subjects with Grade IV gliomas). Furthermore, we developed machine learning models based on functional connectivity features, and they were able to predict the survival time with an accuracy of 92% and predict the KPS changes with an absolute error of 5.84 ± 6.08.

DISCUSSION: Overall, our study showed that resting-state fMRI from patients after glioma resection is relevant to their long-term neurological outcomes: decreased connectivity in the perilesional regions compared to the contralateral regions indicates less survival time and worsened functional outcomes. The reported analytics from postsurgical fMRI scans, combined with the machine learning model, could provide important prognostic information for postsurgical recovery management.

PMID:41725846 | PMC:PMC12916621 | DOI:10.3389/fnins.2026.1751746

Front Neurol. 2026 Jan 27;17:1703554. doi: 10.3389/fneur.2026.1703554. eCollection 2026.

ABSTRACT

BACKGROUND: Recent functional magnetic resonance imaging (fMRI) evidence suggests that pre-supplementary motor area (pre-SMA) activity supports language recovery in post-stroke aphasia (PSA). As a key hub within domain-general cognitive networks, the pre-SMA represents a promising target for individualized neuromodulation. While intermittent theta burst stimulation (iTBS) can enhance language recovery, its efficacy may be limited by generalized targeting strategies.

OBJECTIVE: This study aims to investigate the efficacy of fMRI-guided, neuronavigated iTBS targeting the individualized pre-SMA for promoting language recovery in subacute PSA and to elucidate its underlying neural mechanisms via functional connectivity (FC) analysis.

METHODS: In this single-center, randomized, double-blind, sham-controlled trial, 40 participants with early subacute PSA (1-3 months post-stroke) are allocated to receive either active or sham iTBS targeting the left or right pre-SMA, localized via individualized MRI mapping. Participants will undergo a 2-week intervention, with language and neuroimaging assessments conducted at baseline, immediately post-intervention, and at a 1-month follow-up. Primary outcome measures are the Western Aphasia Battery (WAB). Second outcomes measures will be including the Boston Naming Test (BNT), the Boston Diagnostic Aphasia Examination (BDAE), non-language cognitive assessment (NLCA), alongside functional connectivity analysis from resting-state fMRI.

EXPECTED OUTCOMES: We anticipate that this trial demonstrates the efficacy of individualized pre-SMA iTBS in improving language recovery in PSA. Furthermore, we expect to identify treatment-induced neuroplastic changes in functional and structural brain connectivity. The findings could establish a novel precision neuromodulation approach for aphasia rehabilitation by identifying patient-specific biomarkers of treatment response.

CLINICAL TRIAL REGISTRATION: https://www.chictr.org.cn/, ChiCTR2500108996.

PMID:41725716 | PMC:PMC12917774 | DOI:10.3389/fneur.2026.1703554

Sci Data. 2026 Feb 21. doi: 10.1038/s41597-026-06879-z. Online ahead of print.

ABSTRACT

Dense longitudinal neuroimaging usually requires substantial institutional resources, yet can also be achieved by an individual using standard clinical MRI infrastructure. This work presents a multimodal single-subject dataset comprising 85 hours of resting-state fMRI acquired over 11 months, including 51.6 hours under a standardized protocol (paired eyes-open/-closed runs, 128 sessions over 7.5 months). Additional data include 195 T1-weighted structural scans, 54 diffusion MRI sessions, physiological recordings, pre-session behavioral assessments, and detailed medication and lifestyle logs. Scans were collected primarily via self-administered acquisition on a clinical 3 T system, with sub-3 mm between-session positioning reproducibility observed in later sessions. Quality control identified 58 hours of low-motion data (mean framewise displacement <0.2 mm), with higher-motion runs occurring predominantly during sleep. The acquisition period included antidepressant dose changes and seasonal variation, forming a single-subject naturalistic context with collinear factors that preclude causal inference. The dataset follows the BIDS standard and is intended for methodological development, reliability analyses, preprocessing benchmarking, and educational use.

PMID:41723198 | DOI:10.1038/s41597-026-06879-z

Brain Res Bull. 2026 Feb 19:111786. doi: 10.1016/j.brainresbull.2026.111786. Online ahead of print.

ABSTRACT

BACKGROUND: Complex neuronal network interactions underlie cognitive processes, enabling the brain to adapt effectively to the external environment. Advanced neuroimaging techniques have facilitated the identification of potential targets and relevant endophenotypes for diagnosis and rehabilitation purposes. This study aims to explore the neuroanatomical correlation of various cognitive tasks using a combination of functional, structural, and diffusion MRI data to to characterize how brain regions across multiple modalities covary with cognitive performance.

METHODS: Three hundred healthy adults from the IBID cohort completed a 15-test neuropsychological battery spanning memory, visuospatial ability, executive control, decision-making and processing speed. Structural MRI, diffusion MRI and resting-state fMRI were processed to derive gray-matter VBM maps, fractional anisotropy and intrinsic connectivity in MNI space; voxelwise regressions with cognitive scores were followed by total/combined maps and multimodal fusion using non-parametric combination and joint ICA, with atlas-based, FDR-corrected ROI correlations quantifying and localizing multimodal brain-cognition associations.

RESULTS: Single-modality analyses of gray matter, white matter and resting-state fMRI showed the largest voxel involvement in the left thalamus, left cerebellum, left superior temporal gyrus, right middle frontal gyrus and bilateral cingulate cortex. Multimodal fusion and FDR-corrected ROI analyses further indicated that middle frontal gyri, cingulate cortex, insula and superior/inferior parietal lobules were most strongly related to executive and speeded tasks (TMT-A/B, Stroop, SDMT, N-back, verbal fluency), whereas hippocampus, parahippocampal gyrus, posterior cingulate cortex and precuneus were selectively associated with episodic memory performance (RAVLT, Benson).

CONCLUSION: Taken together, these findings suggest that integrating structural, diffusion, and resting-state fMRI provides a nuanced but strictly descriptive view of how gray-matter morphology, white-matter microstructure, and intrinsic functional connectivity covary with performance across multiple cognitive domains in healthy adults. The resulting multimodal patterns are best regarded as a normative scaffold for future longitudinal and clinical studies of brain-cognition coupling, rather than as direct evidence for diagnostic utility or specific therapeutic interventions.

PMID:41722786 | DOI:10.1016/j.brainresbull.2026.111786

J Psychiatr Res. 2026 Feb 18;196:306-313. doi: 10.1016/j.jpsychires.2026.02.033. Online ahead of print.

ABSTRACT

OBJECTIVE: Estimating brain age, a promising biomarker for evaluating brain health, continues to present significant challenges in terms of accuracy. This study investigates the potential of multi-time scale dynamic effective brain networks (MTS-DEBN) to enhance the prediction of brain age and to identify atypical aging patterns associated with major depressive disorder (MDD) using resting-state functional magnetic resonance imaging (rs-fMRI).

METHODS: Rs-fMRI data were collected from 80 healthy controls (HC) and 80 MDD patients, including subgroups in current phases (n = 46) and remitted phases (n = 34). Time-series signals were extracted from 116 brain regions to construct dynamic effective networks across four temporal scales, utilizing a coarse-graining algorithm, with an integrated feature set (ALL) created. A support vector regression model was trained using data from the HC group to estimate brain age. The optimal model identified was applied to predict brain age in the MDD groups. Model performance was assessed through mean absolute error (MAE). The brain age gap (BAG) was compared between groups.

RESULTS: The features ALL achieved the highest prediction accuracy in HCs (MAE = 3.64 years). The mean BAG was 1.96 years for HCs, 4.56 years for current MDD, and 3.16 years for remitted MDD. Post hoc tests with Bonferroni correction showed significantly higher BAG in current MDD compared to HC (t = 4.85, p < 0.001) and in remitted MDD compared to HC (t = 2.72, p = 0.009), but no significant difference between current and remitted MDD groups. No significant correlations were found between BAG and depression duration or HAMD scores.

CONCLUSION: MTS-DEBN significantly improves brain age prediction accuracy and reveals accelerated brain aging in both current and remitted MDD patients. These findings support the use of MTS-DEBN as a sensitive biomarker for tracking brain aging dynamics and treatment effects in neuropsychiatric disorders.

PMID:41722426 | DOI:10.1016/j.jpsychires.2026.02.033