Most recent paper

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

bioRxiv [Preprint]. 2026 Feb 10:2026.02.08.704721. doi: 10.64898/2026.02.08.704721.

ABSTRACT

The brain is never truly at rest. Even in the absence of external stimuli or cognitive tasks, the central nervous system continuously receives peripheral signals from visceral organs, such as the heart, lungs, and stomach, and sends motor commands to regulate organ physiology. This bidirectional brain-body interaction, also known as interoception, engages neural pathways via a hierarchical set of brain regions, including the nucleus of the solitary tract, hypothalamus, paraventricular nuclei of the thalamus, insular cortex, and anterior cingulate cortex, among others. However, it is largely unclear to what extent interoceptive signaling shapes ongoing fluctuations and correlations of brain activity. To address this question, we recorded resting state functional magnetic resonance imaging data from 34 anesthetized rats, examined intrinsic correlations (or functional connectivity), and tested their dependence on the body's digestive state and peripheral nerve integrity. We observed reciprocal functional connectivity among brain regions situated along established interoceptive pathways, revealing a cohesive network, which we refer to as the interoception network. This network showed stronger functional connectivity in the fed condition (digestive phase) compared to the fasted condition (inter-digestive phase), suggesting its dependence on distinct states of gastrointestinal interoception without apparent cardiac or respiratory confounds. Importantly, we found that the interoception network relied on the integrity of the vagus nerve, a primary component of the peripheral nervous system for visceral sensation and parasympathetic control. When vagal signaling was surgically severed by bilateral cervical vagotomy, functional connectivity within the interoception network was notably reduced. Given these findings, we conclude that resting state functional connectivity is not sustained by the central nervous system alone, but relies on interoceptive signaling mediated through peripheral nerves that connect the brain and viscera.

PMID:41726989 | PMC:PMC12919019 | DOI:10.64898/2026.02.08.704721

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

J Psychiatr Res. 2026 Feb 17;196:291-305. doi: 10.1016/j.jpsychires.2026.01.058. Online ahead of print.

ABSTRACT

In recent years, the use of functional Magnetic Resonance Imaging (fMRI) methods to predict treatment response in schizophrenia (SCZ) through statistical and machine learning (ML) algorithms has increased. We conducted a comprehensive literature review to assess the role of various fMRI measures in predicting pharmacological treatment response in psychosis. Literature available on PubMed from January 1990 to December 2023 was reviewed, and 21 fMRI studies were included. The results suggest that many studies have employed ML techniques, which may enhance the accuracy of treatment outcome predictions. Additionally, several studies utilizing resting-state fMRI have identified potential associations of functional connectivity patterns across multiple large-scale networks, including the default mode network (DMN), the salience network (SN), the central executive network (CEN), and sensory-motor circuits. These findings suggest that altered connectivity within and between these networks may be relevant for personalized treatment strategies in patients with psychosis, although further investigation is needed to confirm their predictive value. Future research should focus on developing robust and generalizable models to more reliably optimize treatment outcomes in psychosis.

PMID:41722425 | DOI:10.1016/j.jpsychires.2026.01.058

J Neuroimaging. 2026 Jan-Feb;36(1):e70129. doi: 10.1111/jon.70129.

ABSTRACT

BACKGROUND AND PURPOSE: Resting state functional connectivity can be measured using resting state functional MRI (fMRI), but also task-dependent fMRI in blocked designs. The latter has been demonstrated in healthy participants but not yet validated in clinical cohorts. Since functional connectivity of resting state networks (e.g., default mode network [DMN] and somatomotor network [SMN]) is altered in people with epilepsy, and the impact of the disease on the quality of the intermittent resting state data is unclear, we aimed to validate the method using a clinical fMRI in people with epilepsy.

METHODS: We compared functional connectivity derived from a standard resting state with rest periods of a clinical language fMRI (intermittent resting state) of 92 people with focal epilepsy. Both methods were analyzed across different aspects of functional connectivity: topography, within-network connectivity, and group-level comparisons. Therefore, we conducted independent component analyses (ICAs), similarity-, regions of interest (ROI)-to-ROI-, and second-level seed-based analyses.

RESULTS: Results indicated similar ICA-derived topography of DMN and SMN from both methods. Within-network connectivity also yielded comparable results. Seed-based analyses of left and right hippocampal connectivity in people with left and right temporal lobe epilepsy also revealed analogous results, with minor restrictions in right hippocampal connectivity.

CONCLUSION: The intermittent resting state method produces highly similar results to a standard resting state method in people with epilepsy across different aspects of functional connectivity. It is, therefore, an efficient approach to gain insights into functional connectivity networks in a clinical cohort without performing an additional resting state fMRI.

PMID:41721540 | DOI:10.1111/jon.70129

PLoS Comput Biol. 2026 Feb 20;22(2):e1013995. doi: 10.1371/journal.pcbi.1013995. Online ahead of print.

ABSTRACT

Resting state fMRI signals in mammals exhibit rich dynamics on a fast, frame-by-frame timescale of seconds, including the robust emergence of recurring fMRI co-activation patterns (CAPs). To understand how such dynamics emerges from the underlying anatomical cortico-cortical connectivity, we developed a whole-cortex model of resting-state fMRI signals in the mouse. Our model implemented neural input-output nonlinearities and excitatory-inhibitory interactions within cortical regions, as well as directed anatomical connectivity between regions inferred from the Allen mouse brain atlas. We found that, even if the model parameters were fitted to explain static properties of fMRI signals on the timescale of minutes, the model generated rich frame-by-frame attractor dynamics, with multiple stationary and oscillatory attractors. Guided by these theoretical predictions, we found that empirical mouse fMRI time series exhibited analogous signatures of attractor dynamics, and that model attractors recapitulated the topographical organization of empirical fMRI CAPs. The model established key relationships between attractor dynamics, CAPs and features of the directed cortico-cortical intra- and inter-hemispheric anatomical connectivity. Specifically, we found that neglecting fiber directionality severely affected the number of model's attractors and their ability to explain CAPs. Furthermore, modifying inter-hemispheric anatomical connectivity strength by decreasing or increasing it from the value of real mouse anatomical data, resulted in fewer attractors generated by cortico-cortical interactions and reduced non-homotopic features of the attractors generated by the model, which were important for better predicting empirical CAPs. These results offer novel theoretical insight into the dynamic organization of resting state fMRI in the mouse brain and suggest that the frame-wise BOLD activity captured by CAPs reflects an emerging property of cortical dynamics resulting from directed cortico-cortical interactions.

PMID:41719380 | DOI:10.1371/journal.pcbi.1013995

Aging Male. 2026 Dec 31;29(1):2632959. doi: 10.1080/13685538.2026.2632959. Epub 2026 Feb 19.

ABSTRACT

BACKGROUND: Integrating brain multimodal features (e.g. structural, functional, and cerebrospinal fluid metabolomic data) offers a promising approach to elucidate the neural mechanisms underlying erectile dysfunction (ED).

METHODS: Using two-sample Mendelian randomization, we assessed causal effects of 83 whole-brain morphological, 191 resting-state fMRI, and 338 cerebrospinal fluid metabolite phenotypes on ED. A p-value < 0.05 indicated statistical significance.

RESULTS: Reduced volumes in the left and right accumbens and enlarged volumes in the left pars opercularis and right putamen were associated with increased ED risk. Increased connectivity between occipital/precuneus and superior frontal gyrus (default/executive networks) elevated ED risk, while connectivity between postcentral/precentral and subcortical regions (motor/subcortical-cerebellum networks) reduced risk. Several metabolites were identified: elevated 4-Methylcatechol sulfate, adenine, gulonate, pyroglutamine, and thioproline increased ED risk, while higher cortisone, malate, phosphate, and X-21733 decreased risk.

CONCLUSION: We identified four morphological, two functional connectivity, and nine metabolic causal relationships with ED, enhancing understanding and suggesting novel therapeutic targets.

PMID:41715886 | DOI:10.1080/13685538.2026.2632959

Brain Stimul. 2026 Feb 17:103055. doi: 10.1016/j.brs.2026.103055. Online ahead of print.

ABSTRACT

BACKGROUND: This study aims to investigate whether intermittent theta-burst stimulation (iTBS) over the cerebellar vermis enhances balance recovery in subacute stroke, and examine the underlying neural mechanisms.

METHODS: Fifty-two patients with subacute stroke and balance impairment were randomized to receive either three weeks of iTBS (n = 26) or sham stimulation (n = 26). The primary outcome was the Berg Balance Scale (BBS). Secondary outcomes included additional motor function measures and surface electromyography (sEMG). Clinical assessments were conducted at baseline and at weeks 1, 2, 3, and 6 after treatment onset. sEMG and resting-state functional MRI were acquired before and after the intervention. Seed-based functional connectivity (FC) of the cerebellar vermis was analyzed using a 2 × 2 mixed-effects ANOVA. Associations between changes in BBS (ΔBBS) and FC alterations were examined using Pearson correlation analyses. Patients were further stratified into subgroups based on the direction of FC change (increase vs. decrease) to characterize distinct clinical and neural response patterns.

RESULTS: Compared with the sham group, patients receiving iTBS showed significantly greater improvements in balance, accompanied by increased sEMG activation of trunk and proximal lower-limb muscles, with effects sustained at follow-up. FC analyses revealed enhanced connectivity between Vermis X and bilateral occipitotemporal cortices, which was positively correlated with balance improvement (ΔBBS). Subgroup analyses identified distinct clinical and neural profiles: the FC-increase subgroup demonstrated sustained functional gains and enhanced cerebello-frontal connectivity, whereas the FC-decrease subgroup exhibited short-term improvement and reduced intracerebellar connectivity.

DISCUSSION: These findings indicate that cerebellar vermis-targeted iTBS facilitates balance recovery after subacute stroke through reorganization of cerebello-visual networks. Subgroup-specific patterns further highlight heterogeneous intracerebellar and cerebello-frontal plasticity, supporting the notion of patient-specific network pathways underlying the therapeutic effects of cerebellar stimulation.

PMID:41713679 | DOI:10.1016/j.brs.2026.103055

J Psychiatr Res. 2026 Feb 10;196:234-243. doi: 10.1016/j.jpsychires.2026.02.006. Online ahead of print.

ABSTRACT

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects normal brain development and results in impaired brain function. Most studies have focused on connectivity changes within the traditional low-frequency range, whereas the frequency-dependent nature of brain dynamics remains largely unexplored. This study employed whole-brain co-activation pattern (CAP) analysis to investigate the frequency-dependent spatiotemporal dynamics of spontaneous brain activity in ASD across three frequency bands: LFO (0.01-0.1 Hz), slow-5 (0.01-0.027 Hz), and slow-4 (0.027-0.073 Hz). Resting-state fMRI data were obtained from the NYU site of the ABIDE I database, comprising 52 individuals with ASD and 52 typical controls (TCs). Six CAPs were identified within each frequency band using k-means clustering. We then calculated and compared CAP dynamics, including the appearance frequency, duration, entry rate, and transition probability. Our results revealed that (1) CAPs across different frequency bands exhibited overall similar spatial patterns but showed significant differences in temporal evolution, with the slow-5 band demonstrating lower dynamic variability; (2) compared to the TC group, individuals with ASD exhibited abnormal brain dynamics in both the LFO and slow-4 bands, whereas no significant differences were observed in the slow-5 band; and (3) significant correlations were found between the dynamic metrics of CAPs in the LFO and slow-5 bands and the severity of restricted and repetitive behaviors (RRB) in individuals with ASD. These findings reveal frequency-specific abnormalities in brain dynamics in ASD, providing new insights into its time-varying neural mechanisms.

PMID:41713174 | DOI:10.1016/j.jpsychires.2026.02.006