Most recent paper

BMC Med Imaging. 2026 Feb 14. doi: 10.1186/s12880-026-02224-3. Online ahead of print.

ABSTRACT

BACKGROUND: HIV-associated neurocognitive disorders (HAND) are common complications in HIV-infected individuals, and working memory impairment is one of the core features. Although combination antiretroviral therapy (cART) has reduced the incidence of severe HAND, mild HAND remains prevalent. This study aims to explore the functional brain characteristics related to working memory in HIV-infected individuals with different cognitive states using resting-state and task-based functional magnetic resonance imaging (fMRI), and to identify candidate imaging markers for early diagnosis and inform future intervention targeting.

METHODS: Fifty-nine HIV-infected individuals (30 with cognitive integrity [CI], 29 with asymptomatic neurocognitive impairment [ANI]) and 37 healthy controls (HC) were enrolled. Resting-state and task-based fMRI were acquired. Task-fMRI was performed using a spatial working memory task to analyze brain activation, functional connectivity (FC), and reconfiguration efficiency of FC from rest to task. FC networks were constructed as ROI-ROI Pearson correlation matrices (Fisher z-transformed) and significant group differences were identified using network-based statistics. Pearson and Spearman correlation analyses were used to explore the relationships between reconfiguration efficiency and clinical/cognitive variables.

RESULTS: HC showed better task performance than both HIV groups, and ANI exhibited the poorest accuracy. Compared with CI, ANI had significantly lower neurocognitive domain T-scores in memory, attention/working memory, and abstraction/executive function. In task-fMRI analyses, ANI showed decreased activation in the bilateral orbital middle frontal gyri and the left middle temporal gyrus, alongside increased activation in the left cerebellum crus I relative to CI. Whole-brain analyses demonstrated widespread FC increases in both HIV groups at rest and during the task compared with HC. Reconfiguration efficiency differed across groups and showed stage-related associations with immune and cognitive measures.

CONCLUSIONS: Cognitive impairment in virally suppressed HIV is accompanied by altered working-memory network engagement, with greater cortico-cerebellar involvement in ANI. While static whole-brain FC showed widespread increases but limited CI-ANI separation under stringent correction, altered rest-to-task FC reconfiguration efficiency was associated with immune indices and neurocognitive/behavioral performance, suggesting that this cross-state metric may serve as a candidate marker for HAND phenotyping and risk stratification.

PMID:41691142 | DOI:10.1186/s12880-026-02224-3

J Pain. 2026 Feb 12:106213. doi: 10.1016/j.jpain.2026.106213. Online ahead of print.

ABSTRACT

Most people experience acute pain as a temporary condition, while a small subset develops chronic pain. The role of pain-related circuits driving this transition remains unclear. Using UK Biobank data and an independent dataset (OpenPain), we analyzed MRI scans of participants with acute musculoskeletal pain (n=160), categorizing them based on those who recovered (AMPR) and those who developed chronic pain (CMPO) later. A machine learning model was applied to predict follow-up outcomes in two independent validation cohorts. AMPR participants showed increased functional connectivity (FC) between the ventral posterolateral thalamus (VPL-Thal) and left dorsolateral prefrontal cortex (DLPFC) compared to CMPO. Increased right NAc-mPFC FC was found in CMPO participants. These FC changes predicted pain chronification with AUCs of [0.74 - 0.83] across validation cohorts. Our results suggest that multiple circuits-particularly a newly observed VPL-Thal -left DLPFC pathway, alongside a previously established right NAc-mPFC pathway are involved in CMP development. These findings may inform the development of more innovative prevention strategies. PERSPECTIVE: This study identifies distinct brain connectivity patterns that differentiate acute pain outcomes (recovery vs. chronic pain development). The VPL-Thal-DLPFC and NAc-mPFC circuits underlie pain chronification, which enables early prediction and may guide targeted interventions to prevent transition from acute to chronic musculoskeletal pain.

PMID:41690397 | DOI:10.1016/j.jpain.2026.106213

Int J Radiat Oncol Biol Phys. 2026 Feb 11:S0360-3016(26)00385-8. doi: 10.1016/j.ijrobp.2026.02.200. Online ahead of print.

ABSTRACT

BACKGROUND: In this prospective observational study, we evaluated dose-response relationships between radiation dose to the brain's resting-state networks (RSNs) and neurocognitive function (NCF) changes following radiation therapy (RT) in adult patients with diffuse glioma.

METHODS: Adult patients with IDH-wild-type and IDH-mutant gliomas underwent NCF testing using the NIH Toolbox Cognition Battery and resting-state functional magnetic resonance imaging (rs-fMRI) before (baseline) and six months after RT. The battery assessed five cognitive domains and generated a fluid cognition composite score. NCF change (ΔNCF) was defined as the percent change in age-adjusted scores from baseline to follow-up. Radiation dosimetric parameters were extracted for 13 RSNs and 3 sub-cortical regions, defined by 300 rs-fMRI-derived regions of interests. Correlations between ΔNCF and dosimetric parameters were assessed using Spearman's rank correlation test (ρ). Linear regression models were compared using nested analysis of variance (ANOVA), Akaike Information Criterion (AIC), and adjusted R2.

RESULTS: Among 48 patients enrolled, 36 patients were evaluable with paired rs-fMRI and NCF data. Moderate negative correlations were observed between change in episodic memory (ΔNCFEM) and mean dose to the medial temporal lobe network (MTL: ρ=-0.41, 95% CI=(-0.66, -0.08), P=0.01), visual network (VN: ρ=-0.42, 95% CI=(-0.67, -0.09), P=0.01), and parietal memory network (PMN: ρ=-0.40, 95% CI=(-0.65, -0.07), P=0.01). No significant correlations were found for other RSNs or NCF domains. A linear regression model incorporating MTL dose and its interaction with age outperformed the age-alone model in explaining variance in ΔNCFEM (P=0.046; ΔAIC= -2.95; adjusted R2=0.313).

CONCLUSIONS: Focal dose-response relationships were observed between radiation dose to specific RSNs and episodic memory changes following RT, highlighting the prognostic and therapeutic potential of rs-fMRI for identifying targets for cognitive preservation in patients undergoing RT.

PMID:41688029 | DOI:10.1016/j.ijrobp.2026.02.200

Neuroimage. 2026 Feb 11:121799. doi: 10.1016/j.neuroimage.2026.121799. Online ahead of print.

ABSTRACT

The human brain depends on dynamic interactions among modular networks, where connector and provincial hubs facilitate efficient information integration. Most previous studies have relied on single metrics or qualitative labels to identify hubs, overlooking multi-metric integration and the quantitative contributions of nodes. Here, we introduce the Multi-Indicator Entropy Hub Score (MIEHS), which integrates six graph-theoretical metrics to quantify hub properties. Validated on benchmark and simulated networks as well as resting-state fMRI data from the Midnight Scan Club dataset, MIEHS reliably identifies hubs. High-scoring connector hubs were localized in the attention network, whereas high-scoring provincial hubs were concentrated in the default mode network. Gradient mapping further revealed that connector hubs bridge unimodal and transmodal regions, supporting information transfer from primary sensory areas to higher-order cognitive regions, while provincial hubs primarily sustain intra-network communication. Null model analyses highlighted the stability of hubs within the default mode and limbic networks. Although hubs are widely studied, they have not yet been established as robust clinical biomarkers. Using Partial Least Squares analysis in the UCLA dataset (HC = 110, ADHD = 37, BD = 40, SCHZ = 37), we observed significant associations between hub alterations in the DMN, SMN, limbic, DAN, and control networks and measures of cognitive flexibility, abstract reasoning, and verbal expression. Together, these findings demonstrate that MIEHS provides a robust and versatile framework for mapping brain network organization and characterizing functional reconfiguration.

PMID:41687694 | DOI:10.1016/j.neuroimage.2026.121799

Neural Netw. 2026 Feb 7;199:108689. doi: 10.1016/j.neunet.2026.108689. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is a currently incurable neurodegenerative disease, with early detection representing a high research priority. AD is characterized by progressive cognitive decline accompanied by alterations in brain functional connectivity. Based on its data structure similar to the graph, graph neural networks (GNNs) have emerged as important methods for brain function analysis and disease prediction in recent years. However, most GNN methods are limited by information loss caused by traditional functional connectivity calculation as well as common noise issues in functional magnetic resonance imaging (fMRI) data. This paper proposes a graph generation based AD classification model using resting state fMRI to address this issue. The connectome generation network with large kernels for GNN (CGLK-GNN) based AD Analysis contains a graph generation block and a GNN prediction block. The graph generation block employs decoupled convolutional networks with large kernels to extract comprehensive temporal features while preserving sequential dependencies, contrasting with previous generative GNN approaches. This module constructs the connectome graph by encoding both edge-wise correlations and node-embedded temporal features, thereby utilizing the generated graph more effectively. The subsequent GNN prediction block adopts an efficient architecture to learn these enhanced representations and perform final AD stage classification. Through independent cohort validations, CGLK-GNN outperforms state-of-the-art GNN and rsfMRI-based AD classifiers in differentiating AD status. Furthermore, CGLK-GNN demonstrates high clinical value by learning clinically relevant connectome node and connectivity features from two independent datasets.

PMID:41687240 | DOI:10.1016/j.neunet.2026.108689

Brain Imaging Behav. 2026 Feb 13;20(1):14. doi: 10.1007/s11682-026-01079-0.

ABSTRACT

The re-organization of brain networks induced by task performance plays a pivotal role for understanding brain mechanisms of function. Studies have demonstrated that functional magnetic resonance imaging (fMRI) data collected during task performance reflects both stimulus-based responses and ongoing intrinsic brain activity that persists even during task performance. However, the state-of-the-art statistical methods for analyzing fMRI signals are not able to extract pure task-evoked brain network activity that is distinguished from ongoing intrinsic brain activity. In order to fill this gap, we propose to use Tensor Component Analysis (TCA) to estimate stimulus evoked brain network responses disentangled from ongoing activity of intrinsic brain networks (ICNs). We conducted numerical simulations and used in-vivo task and resting state fMRI data collected by the Human Connectome Project to evaluate the performance of TCA for this purpose. We also used a subset of the HCP data to demonstrate the ability of TCA for evaluating Theory of Mind related brain networks in individuals with cannabis use disorder. Our findings show that TCA is a promising tool to extract task-evoked dynamic brain networks distinct from intrinsic brain network activity. Compared with dynamic connectivity analyses, task-evoked dynamic brain network estimated with TCA provides a more accurate way to study the brain's response to external stimuli and sheds new light on brain and behavior relationships.

PMID:41686283 | DOI:10.1007/s11682-026-01079-0

Front Neurosci. 2026 Jan 28;20:1766192. doi: 10.3389/fnins.2026.1766192. eCollection 2026.

ABSTRACT

INTRODUCTION: Disruptions in functional connectivity (FC) within the default mode network (DMN) are well established as a key neuropathology underlying cognitive impairment in type 2 diabetes mellitus (T2DM). Subcortical nuclei, including the basal forebrain (BF) and mediodorsal thalamus, play critical roles in regulating DMN-associated cognitive processes and are particularly vulnerable to hyperglycemia and brain insulin resistance. However, the specific FC patterns between these subcortical nuclei and DMN cortical regions in patients with T2DM, as well as their potential associations with cognitive impairment, remain incompletely elucidated.

METHODS: Eighty-two patients with T2DM and 79 healthy controls (HCs) were enrolled in this study. Clinical data, neuropsychological assessments, and resting-state functional magnetic resonance imaging were collected from all participants. Resting-state (rs-FNC) and dynamic (dFNC) functional network connectivity analyses were performed to characterize connectivity between subcortical nuclei and DMN cortical regions. Correlation analyses explored associations between FNC metrics showing significant intergroup differences and participants' clinical and cognitive parameters.

RESULTS: rs-FNC analysis revealed decreased FC between the BF and the dorsomedial prefrontal cortex (dMPFC), the BF and the temporal pole, and the dMPFC and the anteromedial prefrontal cortex in patients with T2DM (network-based statistic correction; edge p < 0.001, component p < 0.05). dFNC analyses indicated increased frequency and prolonged mean dwell time (MDT) of State 1 (high-frequency low-connectivity), as well as decreased frequency and shortened MDT of State 2 (high-frequency high-connectivity) compared with HCs (all p < 0.05). Reduced FC between the dMPFC and BF was positively correlated with Montreal Cognitive Assessment scores (r = 0.353, p = 0.001), whereas frequency (r = -0.434, p < 0.001) and MDT (r = -0.376, p = 0.001) of State 2 were negatively correlated with T2DM disease duration after Bonferroni correction.

CONCLUSION: These findings indicate that T2DM duration correlates with reduced highly efficient DMN connectivity, and that the BF may regulate cognitive function via the dMPFC subsystem. The results reveal temporal and functional specificity in abnormal DMN connectivity in patients with T2DM and enrich the neural atlas of DMN dysfunction in this population.

PMID:41685355 | PMC:PMC12891212 | DOI:10.3389/fnins.2026.1766192

Psychoradiology. 2025 Dec 1;6:kkaf029. doi: 10.1093/psyrad/kkaf029. eCollection 2026.

ABSTRACT

BACKGROUND: There are notable sex differences in the symptoms and treatment response of late-life depression (LLD); however, the underlying static and dynamic abnormalities in brain function that may drive these disparities remain unclear. This study was to investigate sex-specific aberrant brain activity in LLD.

METHODS: We recruited 75 LLD patients and 164 healthy controls (HCs). Static and dynamic metrics of amplitude of low-frequency fluctuation (ALFF), regional homogeneity (ReHo), and functional connectivity (FC) were compared across four groups (LLD-female, LLD-male, HC-female, and HC-male). Correlation and moderation analyses were then used to examine whether sex moderated the associations between brain activity, cognitive impairment, and depressive symptoms.

RESULTS: First, significant interaction effects between diagnosis (LLD vs. HCs) and sex were found for ALFF in the left paracentral lobule, ReHo in the right superior temporal gyrus, and static FC (sFC) between the right superior temporal gyrus and left middle frontal gyrus. Second, in LLD-female, ReHo (right superior temporal gyrus) and sFC (right superior temporal gyrus-left middle frontal gyrus) correlated with weight, and ALFF (left paracentral lobule) correlated with visuospatial skills. Third, sex significantly moderated the relationships between ReHo (right superior temporal gyrus) and cognition, ALFF (left paracentral lobule) and depressive symptoms, and sFC (right superior temporal gyrus-left middle frontal gyrus) and depressive symptoms in the LLD group.

CONCLUSION: Our study highlights sex differences in static brain activity related to cognitive impairment and depressive symptoms in LLD, indicating sex-specific neurobiological underpinnings for this disorder.

PMID:41684635 | PMC:PMC12892001 | DOI:10.1093/psyrad/kkaf029

Sci Rep. 2026 Feb 13. doi: 10.1038/s41598-026-37599-4. Online ahead of print.

NO ABSTRACT

PMID:41680306 | DOI:10.1038/s41598-026-37599-4

Magn Reson Imaging. 2026 Feb 10:110640. doi: 10.1016/j.mri.2026.110640. Online ahead of print.

ABSTRACT

Spinal cord functional magnetic resonance imaging (fMRI) has advanced significantly in recent years, revealing insights into the function of somatosensory and motor systems. However, the complex environment of the spinal cord induces unique sources of noise, limiting the quality of spinal fMRI recordings. Various hardware and software solutions have been proposed to address these challenges. Among them, susceptibility-matched padding has gained popularity due its low cost, ease of use, and effectiveness in reducing static B0 field inhomogeneities, which are a major source of artefacts in spinal fMRI. Despite anecdotal evidence, the impact of susceptibility-matched padding on the quality of spinal cord fMRI has not been assessed systematically. We investigated the effects of non-protonated perfluorocarbon liquid-filled padding (SatPad) on B0 field homogeneity and functional echo-planar imaging (EPI) in cervical and lumbar spinal cord in 10 healthy volunteers. Participants underwent two resting-state fMRI scanning sessions, one per cord section. Within each session they were scanned with and without SatPad in a pseudo-randomised order. The use of SatPad increased B0 field homogeneity and improved functional image quality metrics, including temporal signal-to-noise ratio and ghosting artefacts. While both cervical and lumbar cord data benefited from the use of SatPad, greater effects were observed in the cervical cord. These findings provide a compelling basis for integrating susceptibility-matched padding into routine spinal fMRI protocols.

PMID:41679399 | DOI:10.1016/j.mri.2026.110640

PLoS Biol. 2026 Feb 12;24(2):e3003577. doi: 10.1371/journal.pbio.3003577. eCollection 2026 Feb.

ABSTRACT

Systematic identification of global epileptic reorganization and critical seizure-controlling circuits is essential for comprehending epilepsy pathophysiology and for developing network-guided targeted therapies. The piriform cortex (PC) is a recognized epileptogenic region, but how its hyperactivity reshapes whole-brain dynamics and which specific circuits mediate seizures remains unclear. Through multimodal integration of optogenetics, fMRI, electrophysiology, Ca2+ imaging, neural tracing, and circuit-specific manipulation, we mapped the whole-brain dynamics following optogenetic stimulation of PC and identified the fundamental circuit governing piriform seizures. We observed pronounced generalized seizures in mice via repeated optogenetic stimulation of PC Vglut1+ neurons. Optogenetic kindling of PCVglut1 induced widespread blood-oxygen-level-dependent (BOLD) signal hyperactivation and resting-state functional connectivity (rsFC) alterations, notably sustained hyperactivation in the lateral entorhinal cortex (Lent) and enhanced PC-Lent rsFC. Chronic elimination of Lent neurons receiving PC projections significantly decreased the Lent-dentate gyrus (DG) rsFC. Disruption of the PC-Lent or Lent-DG circuit effectively suppressed PC-stimulation-triggered seizures and brain-wide hyperactivation. Our findings demonstrate the dominant role of the PCVglut1-Lentglut-DG circuit in mediating piriform seizures and driving their resulting brain-wide functional reorganization, offering new insights for targeted epilepsy treatments.

PMID:41678438 | DOI:10.1371/journal.pbio.3003577

Eur Arch Psychiatry Clin Neurosci. 2026 Feb 12. doi: 10.1007/s00406-026-02204-2. Online ahead of print.

NO ABSTRACT

PMID:41677825 | DOI:10.1007/s00406-026-02204-2

J Parkinsons Dis. 2026 Feb 12:1877718X261420080. doi: 10.1177/1877718X261420080. Online ahead of print.

ABSTRACT

Parkinson's disease (PD) involves degeneration of dopaminergic neurons and dysfunction across multiple neurotransmitter systems, contributing to both motor and cognitive impairments. Aerobic exercise improves clinical outcomes; however, its underlying neural mechanisms remain unclear. Using conventional resting-state fMRI combined with Receptor-Enriched Analysis of functional Connectivity by Targets (REACT), we examined molecular-enriched motor network changes following six months of supervised aerobic training in PD. Exercise-related connectivity changes were inversely correlated with baseline PD-healthy control differences, reflecting a partial normalization of PD-altered motor networks. Molecular-enriched analyses revealed selective effects on dopaminergic (FDOPA-enriched) and cholinergic (VAChT-enriched) related networks, with no changes observed in networks associated with serotonergic or noradrenergic systems. These findings provide supporting evidence for potential mechanistic links between aerobic exercise and network reorganization in PD, highlight multisystem effects, and illustrate the utility of molecular-enriched fMRI for probing neurotransmitter-specific interventions.

PMID:41677133 | DOI:10.1177/1877718X261420080

bioRxiv [Preprint]. 2026 Feb 7:2026.02.04.703882. doi: 10.64898/2026.02.04.703882.

ABSTRACT

The integration of electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) can be used to characterize temporal and spatial components of neural activity during unfolding mental experience. Here we introduce a multi-session simultaneous EEG-fMRI dataset with measures of continuous behavior and spontaneous mental experience. Data components, organized in Brain Imaging Dataset Structure (BIDS) format, include fMRI, EEG with carbon wire loop sensors for artifact removal, continuous performance task responses, experience sampling ratings, and mental health surveys, from 24 healthy adults. Tasks included the gradual-onset continuous performance task and resting state with intermittent experience sampling of 13 unique thought dimensions (36 repetitions, including 468 total ratings, per participant). The same protocol was completed on two different days, yielding approximately 1.33 hours of simultaneous EEG-fMRI data per individual. The dataset may be used to explore the behavioral and experiential relevance of brain activity during the wakeful resting state. The dataset also provides a means to study the reliability of relationships between fMRI and EEG features across sessions within individuals.

PMID:41676676 | PMC:PMC12889718 | DOI:10.64898/2026.02.04.703882

bioRxiv [Preprint]. 2026 Feb 2:2026.01.30.702883. doi: 10.64898/2026.01.30.702883.

ABSTRACT

The functional network architecture of the aging brain undergoes significant systematic and idiosyncratic changes. Emergent individualized network mapping approaches may yield better or more sensitive explanatory insight about age-related neural and behavioral variability, although most applications have focused on young adults. In the current study, we tested the validity and impact of mapping individual-specific topography in two fMRI datasets comprising 112 young (18-35 years) and 176 older adults (60-92 years). Older adults had more idiosyncratic network topography than young adults. Individualized maps from resting-state fMRI improved network homogeneity and fidelity to task fMRI activations, while also exhibiting intra-individual reliability and inter-individual discriminability over a 2-year interval. Last, traditional group-averaged ( vs . individualized) network mapping had a moderate-to-large impact on individual-level estimates of network segregation, a widely-studied measure of functional brain aging. Therefore, individualized network mapping captures important heterogeneity in older adulthood and may yield more precise characterization of neurocognitive aging.

PMID:41676650 | PMC:PMC12889444 | DOI:10.64898/2026.01.30.702883

bioRxiv [Preprint]. 2026 Feb 6:2026.02.03.703581. doi: 10.64898/2026.02.03.703581.

ABSTRACT

Cerebrovascular reactivity (CVR), the ability of cerebral blood vessels to dilate or constrict in response to a vasoactive stimulus, is a clinically meaningful measure of cerebrovascular health. Head motion and other noise sources substantially impact CVR quality, particularly in clinical populations. In this study, we evaluated multi-echo fMRI techniques, including optimal combination of echoes (ME-OC) and multi-echo independent component analysis (ME-ICA), for improving CVR quality relative to single-echo fMRI in participants with stroke. In a breath-hold fMRI dataset, ME-OC significantly improved CVR quality metrics and reduced the percentage of negative CVR values in normal-appearing gray and white matter ( p <0.05). ME-ICA reduced the dependence of BOLD signals on head motion but did not improve CVR quality metrics. In a separate resting-state dataset, ME-OC effects were largely consistent with the breath-hold dataset, but ME-ICA also significantly improved CVR quality metrics and reduced negative CVR values in normal-appearing gray and white matter relative to ME-OC ( p <0.05). These findings demonstrate that multi-echo fMRI can improve CVR estimation in clinical populations, particularly in low signal-to-noise datasets, enhancing the feasibility of CVR analyses in stroke studies and allowing for better visualization of stroke-related CVR deficits.

PMID:41676635 | PMC:PMC12889588 | DOI:10.64898/2026.02.03.703581

bioRxiv [Preprint]. 2026 Feb 3:2026.01.31.703060. doi: 10.64898/2026.01.31.703060.

ABSTRACT

Simultaneous electroencephalography (EEG) and functional MRI (fMRI) offers complementary sensitivity to fast electrophysiological dynamics of EEG and spatially resolved hemodynamics of fMRI, yet previous joint-analysis approaches are confined to fixed task paradigms and struggle with continuous or naturalistic brain states. We FSINC (Fusing Source Imaging based on a Neurovascular Coupling) model, a unified EEG-fMRI source imaging framework that reconstructs cortical activity to simultaneously explain both modalities. FSINC integrates frequency-resolved EEG source activity with fMRI via a data-driven neurovascular coupling model that estimates band-specific coupling coefficients (β) and accommodates a tunable spatial-temporal trade-off through hyperparameters ( λ 2 , λ 3 ). In realistic simulations, FSINC outperformed conventional methods (wMNE, LORETA) in both spatial and temporal accuracy across EEG SNRs (-10 to 10dB) and numbers of concurrent sources (up to five), with optimal performance at λ 2 = 10 2 and λ 3 =1 (e.g., LE: 0.51±0.24mm; SDI: 0.03±0.37mm; temporal accuracy: 0.95 ± 0.05). Applied to simultaneous EEG-fMRI during contrast-reversing visual stimulation (=5.95Hz), FSINC revealed stimulus-locked responses localized to early visual cortex and stimulus-induced modulation of intrinsic alpha oscillations extending into visual and attention networks, patterns that conventional methods failed to capture. Estimated β-weights were broadly consistent with prior reports of negative (theta/alpha) and positive (gamma) BOLD-electrophysiology associations. These findings demonstrate that FSINC enables high-spatiotemporal-resolution source imaging from EEG-fMRI recordings via data-driven hemodynamic modelling, and is expected to be well-suited for continuous and naturalistic brain states (e.g., resting state, natural moving-watching, and narrative listening) that are difficult to interrogate with either modality alone.

PMID:41676576 | PMC:PMC12889465 | DOI:10.64898/2026.01.31.703060

bioRxiv [Preprint]. 2026 Feb 3:2026.02.01.703149. doi: 10.64898/2026.02.01.703149.

ABSTRACT

Resting-state functional MRI (rs-fMRI) is a cornerstone of human brain research, yet its interpretation is complicated by its sensitivity to the slow hemodynamic response that obscures the organization of neural activity across faster time scales. Here we use simultaneous wide-field optical imaging (WOI) and rs-fMRI to directly examine the relationship between neural and hemodynamic functional connectomes across time scales. We show that much of the large-scale spatial structure is preserved across modalities, across time scales, and across frequencies. Although rs-fMRI robustly captures time-averaged neural activity, time-resolved rs-fMRI estimates of functional connectivity exhibit significantly greater variability, which partially reflects sensitivity limitations. Hemodynamic WOI signals maintain greater similarity to neural activity than rs-fMRI, although their fidelity is reduced at high frequencies. Together, our findings demonstrate that the time-averaged spatial structure of neural activity is faithfully represented in hemodynamics and rs-fMRI; provide insight into the reliability of time-resolved rs-fMRI across temporal scales; and establish a multimodal framework for validating features of dynamic brain activity.

PMID:41676555 | PMC:PMC12889463 | DOI:10.64898/2026.02.01.703149

bioRxiv [Preprint]. 2026 Feb 7:2026.02.04.703913. doi: 10.64898/2026.02.04.703913.

ABSTRACT

White matter (WM) has traditionally been considered structurally important but functionally inert in fMRI research. However, growing evidence indicates that WM exhibits meaningful BOLD fluctuations and participates in functional connectivity. Here, we investigate alterations in WM functional network connectivity (FNC) across the Alzheimers disease (AD) spectrum using resting-state fMRI data from the Alzheimers Disease Neuroimaging Initiative (ADNI 415 cognitively normal (CN), 283 mild cognitive impairment (MCI), 91 AD). We applied a guided independent component analysis (ICA) approach based on a combined multiscale template including 202 intrinsic connectivity networks (ICNs; 97 WM, 105 gray matter (GM)) to estimate subject-specific timecourses and compute static FNC (sFNC). Group differences in WMWM, GMGM, and WMGM connectivity (ADCN, ADMCI, MCICN) were assessed using two-sample t-tests with covariates for age, sex, and motion, with false discovery rate correction. Results showed robust alterations in WMWM and WMGM connectivity in AD, particularly involving WM subcortical, frontal, sensorimotor, and occipitotemporal networks. Several WMGM interactions with cerebellar and hippocampal GM networks were also disrupted, including reduced GMcerebellar:WMfrontal coupling and increased GMhippocampal to WMfrontal connectivity. Notably, MCI already showed WMGM dysconnectivity relative to CN, suggesting that functional disruption of WM circuits emerges prior to overt dementia. These findings provide converging evidence that WM functional connectivity is both measurable and selectively altered across the AD continuum. Our findings support WM sFNC as a complementary candidate biomarker to GM-based measures for staging and monitoring AD. This is, to our knowledge, the first large-scale ADNI study to jointly model WM and GM intrinsic connectivity networks and quantify WMGM dysconnectivity across CN, MCI, and AD.

PMID:41676490 | PMC:PMC12889614 | DOI:10.64898/2026.02.04.703913

Brain Dev. 2026 Feb 10;48(2):104513. doi: 10.1016/j.braindev.2026.104513. Online ahead of print.

ABSTRACT

OBJECTIVE: Preterm birth is associated with an increased risk of functional brain network alterations, which may contribute to long-term motor and neurocognitive deficits. However, the underlying neural mechanisms remain incompletely understood. This study aimed to investigate functional brain activity changes in preterm infants and their correlation with early neurobehavioral development.

METHODS: Fifteen preterm infants and 15 full-term infants underwent scanning using a 3.0T Philips MRI scanner. Three resting-state functional magnetic resonance imaging (rs-fMRI) data-driven approaches, amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and seed-based functional connectivity (FC) were used to comprehensively evaluate functional brain alterations in preterm infants at term-equivalent age (TEA). Correlations between Neonatal Behavioral Neurological Assessment (NBNA) scores and FC values of abnormally connected brain regions were further analyzed in preterm infants at TEA.

RESULTS: Compared with full-term infants, preterm infants exhibited significantly higher ALFF and ReHo values in the left precuneus. Using the left precuneus as a seed region for FC analysis, preterm infants showed reduced FC with the left Rolandic operculum, right putamen, and left hippocampus. Additionally, FC values between the left precuneus and left Rolandic operculum, as well as between the left precuneus and right putamen, were positively correlated with NBNA scores in preterm infants.

CONCLUSIONS: Preterm infants may present early functional connectivity impairments of the left precuneus, which may be a potential neural correlate of neurobehavioral abnormalities. These findings provide insights into the neurodevelopmental mechanisms underlying preterm birth-related deficits and may inform early clinical assessment strategies.

PMID:41671832 | DOI:10.1016/j.braindev.2026.104513