Most recent paper

Neuroimage Clin. 2026 Feb 4;49:103958. doi: 10.1016/j.nicl.2026.103958. Online ahead of print.

ABSTRACT

BACKGROUND: Schizophrenia is associated with widespread functional dysconnectivity, but the spatial scale and structural correlates of these alterations remain unclear. While relevant to local dysfunction, short-range connectivity is not well captured by standard approaches due to methodological constraints.

METHODS: We applied a vertex-wise, distance-dependent analysis of functional connectivity strength (FCS) to resting-state fMRI data from 86 schizophrenia patients and 99 healthy controls across two datasets. FCS was partitioned by geodesic distance on the cortical surface and analyzed by cortical hierarchy. We also assessed two proxies of intracortical microstructure: T1/T2 ratio and a novel signal-detection-based measure of individualized data-driven functional connectivity density (idFCD).

RESULTS: Schizophrenia patients exhibited reductions in short-range FCS within the dorsal primary somatosensory cortex. These functional alterations colocalized with abnormalities in both microstructural proxies and were not evident in global FCS analysis. In contrast, longer-range FCS was increased in transmodal regions, particularly the precuneus, without associated microstructural differences. Hierarchical analysis confirmed this dissociation, with structure-function disruption in primary networks and increased relative FCS in transmodal regions without microstructural association.

CONCLUSIONS: Our findings support two distinct patterns of cortical dysconnectivity in schizophrenia: short-range reductions in primary sensory areas that colocalize with microstructural abnormalities, and longer-range increases in transmodal regions that appear structurally decoupled at the local level. By integrating distance-dependent functional measures with independent proxies of intracortical microstructure, this study highlights the role of short-range connectivity disruptions in primary areas and provides a complementary framework to conventional approaches based on regional or global analyses and diffusion-weighted imaging.

PMID:41671792 | DOI:10.1016/j.nicl.2026.103958

Psychiatry Res Neuroimaging. 2026 Feb 3;358:112165. doi: 10.1016/j.pscychresns.2026.112165. Online ahead of print.

ABSTRACT

BACKGROUND: Delusions are a core symptom of schizophrenia and schizoaffective disorder (SCZ/SZA), yet their neural mechanisms remain incompletely understood. Contemporary models emphasize dysfunctional network-level interactions, particularly between subcortical and cortical regions.

OBJECTIVE: To characterize resting-state functional connectivity (rsFC) alterations specifically associated with prominent delusions in SCZ/SZA, with emphasis on cortico-subcortical and cerebellar networks.

METHODS: High-resolution ROI-to-ROI rsFC analyses were conducted in 20 SCZ/SZA patients with prominent delusions and 20 matched healthy controls. Functional connectivity was calculated across 164 regions using the Harvard-Oxford atlas. Statistical significance was assessed with threshold-free cluster enhancement (TFCE) and family-wise error (FWE) correction at p < 0.05.

RESULTS: Twenty significant connectivity clusters were identified, encompassing both hyper- and hypoconnectivity. Increased connectivity was observed between basal ganglia structures (putamen, pallidum) and cortical regions of the default mode network (DMN), frontal executive networks, and limbic areas, consistent with aberrant salience attribution and disrupted integration of internal and external signals.

CONCLUSION: Delusions in SCZ/SZA may stem from widespread dysconnectivity anchored in evolutionarily older subcortical and cerebellar regions, impairing sensorimotor, emotional, and cognitive integration. These findings support a network-based model of delusion formation and may inform potential targets for neuromodulatory intervention.

PMID:41671697 | DOI:10.1016/j.pscychresns.2026.112165

Auton Neurosci. 2026 Feb 5;264:103389. doi: 10.1016/j.autneu.2026.103389. Online ahead of print.

ABSTRACT

The vagus nerve connects the brain and the heart, allowing communication between the body and the mind. Studies have strengthened the meaning of the brain to control heart rate variability (HRV), however, brain research has largely overlooked the effects of age on the association between phasic changes in HRV and resting state functional brain connectivity. To close this gap, we studied a large open data set of 69 old and 134 young participants with two consecutive fMRI resting state scans in combination with the corresponding physiological HRV data assessed via photoplethysmography (PPG). We quantified spontaneous HRV changes from one resting state to the other and studied the unique information about the relationship between changes in functional coupling between brain areas and spontaneous HRV changes. Using a fc-MVPA, we identified functional brain coupling patterns associated with changes in HRV within brain networks, including the anterior cingulate cortex (ACC), the cerebellum, the brainstem, and the temporal lobe. These patterns were not significantly different between the two age groups - indicating age invariance of brain heart communication. Post hoc seed-to-voxel analyses indicated a stronger functional coupling of these identified clusters with brain regions such as the insula, the opercular cortex, the superior frontal gyrus, and the cerebellum when HRV increased. This pattern of findings is in accordance with prominent theories and provides further insights into the neural mechanisms underlying brain-heart communication.

PMID:41671693 | DOI:10.1016/j.autneu.2026.103389

J Neurol. 2026 Feb 11;273(2):139. doi: 10.1007/s00415-026-13665-9.

ABSTRACT

BACKGROUND: Abnormalities in large-scale brain functional networks are implicated in multiple sclerosis (MS) and linked to clinical impairments, but reported findings remain heterogeneous due to methodological and sample variations.

METHODS: Based on 25 resting-state functional magnetic resonance (rsfMRI) imaging studies (1,524 MS patients and 886 healthy controls (HCs)), in which the independent component analysis (ICA) method was used to evaluate the resting-state functional connectivity (rsFC) changes in default mode (DMN), sensorimotor (SMN), salience (SN), and visual (VN) networks, a meta-analysis was performed using the anisotropic effect size seed mapping (AES-SDM) software to quantify the differences between MS patients and HCs. The heterogeneity was assessed, and a meta-regression analysis was conducted on age, disease duration, Expanded Disability Status Scale scores, and T2-hyperintense lesion volume.

RESULTS: Compared with HCs, MS exhibited increased rsFC in left anterior cingulate/paracingulate gyri and right superior frontal gyrus (DMN), bilateral precentral gyrus and right postcentral gyrus (SMN), right angular gyrus (SN), whereas hypoconnectivity was observed in left posterior cingulate gyrus (DMN), right supplementary motor region and left precentral gyrus (SMN), right insula (SN), and left superior parietal gyrus (VN). The Meta-regression analysis revealed no significant correlation between these altered rsFC and clinical variables.

DATA CONCLUSIONS: This study demonstrates widespread and complex rsFC abnormalities in several neural networks in MS, particularly within core regions of the DMN, SMN, VN, and SN. Together with a descriptive synthesis of additional RSNs, these rsFC disruptions and reorganizations may represent intrinsic neuropathological alterations in MS, which further elucidate the pathophysiological mechanisms of functional abnormalities in MS at a large-scale level.

PMID:41670734 | DOI:10.1007/s00415-026-13665-9

Quant Imaging Med Surg. 2026 Feb 1;16(2):163. doi: 10.21037/qims-24-2442. Epub 2026 Jan 14.

ABSTRACT

BACKGROUND: Static functional networks of the brain are disrupted in minimal hepatic encephalopathy (MHE), but their dynamic alterations are unknown. This observational study utilized multilayer network analysis to investigate dynamic network characteristics in hepatitis B cirrhosis (HBC) with or without MHE and assess their association with neurocognitive function (registry: https://ctms.xyeyy.com/iit/project/index; trial registration number: LYF20240134; date: 2024-07-31).

METHODS: A total of 33 HBC patients [15 non-MHE (NMHE; HBC patients without MHE) and 18 MHE individuals], as well as 36 matched healthy controls (HCs), underwent neurocognitive assessments, resting-state functional magnetic resonance imaging (rs-fMRI), and clinical examinations. Dynamic network variations were quantified using network switching rates, and their relationships with clinical and neurocognitive parameters were evaluated.

RESULTS: Both HBC patients with and without MHE status exhibited a range of altered network switching rates compared to HCs. Specifically, differences were observed in subnetworks including somatomotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), frontoparietal network (FPN), and subcortical network (SUB), as well as in nodal regions such as the right precentral gyrus (rPrG), left fusiform gyrus (lFuG), left inferior parietal lobule (lIPL), and right hippocampus (rHipp). Furthermore, altered global level and lFuG switching rates positively correlated with Psychometric Hepatic Encephalopathy Score (PHES) (r=0.341, 0.339; P=0.004, 0.004, respectively, Bonferroni corrected).

CONCLUSIONS: This study firstly revealed that HBC patients exhibited imbalanced functional dynamics in subnetworks and nodes, suggesting a potential mechanism underlying cerebral dysfunction in MHE.

PMID:41669485 | PMC:PMC12883493 | DOI:10.21037/qims-24-2442

Quant Imaging Med Surg. 2026 Feb 1;16(2):153. doi: 10.21037/qims-2025-177. Epub 2026 Jan 13.

ABSTRACT

BACKGROUND: After vestibular impairment, the body undergoes static and dynamic compensation. Static compensation is characterized by the resolution of spontaneous nystagmus, indicating rebalanced bilateral vestibular nuclei activity. It remains unclear whether ascending vestibular projections remain abnormal thereafter. This study investigated abnormal functional activity along vestibular nucleus-thalamus-cortex projection pathways in chronic unilateral vestibulopathy (CUVP) after static compensation using degree centrality (DC) and functional connectivity (FC) analyses.

METHODS: In total, 25 CUVP patients and 25 age- and sex-matched healthy controls underwent resting-state functional magnetic resonance imaging (fMRI). The DC analysis identified abnormal functional activity in vestibular nucleus-thalamus-cortex pathways, particularly in the subcortical structures. The seed-based FC analyses used eight seeds (the bilateral vestibular nuclei, and the bilateral pulvinar, mediodorsal, and ventrolateral ventral thalamic regions) with 10,000 non-parametric permutations and a cluster-level family-wise error rate (FWER)-corrected threshold of P<0.05. The region-of-interest (ROI)-based FC analyses examined connections among the vestibular nucleus, thalamic subregions (the pulvinar and ventrolateral ventral thalamus), and multisensory vestibular/sensorimotor/visual cortices with a false discovery rate (FDR)-corrected threshold of P<0.05 to confirm the pathway abnormalities. A regression analysis assessed the relationships between the altered brain metrics and Dizziness Handicap Inventory (DHI) scores.

RESULTS: Compared with the healthy controls, the CUVP patients showed reduced DC in the bilateral thalamus (ventrolateral ventral thalamus, and pulvinar), cerebellum, precuneus, postcentral gyrus, and premotor areas (all FDR-corrected P<0.05). Using the vestibular nucleus, pulvinar, and ventrolateral ventral thalamus as the seed regions, similar bilateral patterns of FC change were observed, with notably reduced FC between the pulvinar and visual cortex, as well as between the ventrolateral ventral thalamus and sensorimotor cortex (all FWER-corrected P<0.05). The ROI-based FC analyses confirmed abnormalities along the vestibular nucleus-ventrolateral ventral thalamus/pulvinar-multisensory vestibular/sensorimotor/visual cortices pathways (all FDR-corrected P<0.05). The regression analysis revealed negative associations between thalamic DC/FC changes and DHI scores (all FDR-corrected P<0.05).

CONCLUSIONS: Patients with CUVP exhibit persistent abnormalities in the vestibular nucleus-thalamus-cortex pathways even after the establishment of static compensation. The ventrolateral ventral thalamus and pulvinar may serve as key nodes in these abnormalities.

PMID:41669411 | PMC:PMC12883438 | DOI:10.21037/qims-2025-177

Front Neurosci. 2026 Jan 26;19:1730402. doi: 10.3389/fnins.2025.1730402. eCollection 2025.

ABSTRACT

INTRODUCTION: Resting-state functional magnetic resonance imaging (rs-fMRI) is a widely used non-invasive technique for investigating brain function and identifying potential disease biomarkers. Compared with task-based fMRI, rs-fMRI is easier to acquire because it does not require explicit task paradigms. However, functional connectivity measures derived from rs-fMRI often exhibit poor reliability, which substantially limits their clinical applicability.

METHODS: To address this limitation, we propose a novel method termed time-enhanced functional connectivity, which improves reliability by identifying and removing poor-quality time points from rs-fMRI time series. This approach aims to enhance the quality of functional connectivity estimation without extending scan duration or relying on dataset-specific constraints.

RESULTS: Experimental results demonstrate that the proposed method significantly improves performance in downstream machine learning tasks, such as sex classification. In addition, time-enhanced functional connectivity yields higher test-retest reliability and reveals more pronounced statistical differences between groups compared with conventional functional connectivity measures.

DISCUSSION: These findings suggest that selectively removing low-quality time points provides a practical and effective strategy for improving the reliability and sensitivity of functional connectivity measurements in rs-fMRI, thereby enhancing their potential utility in both neuroscience research and clinical applications.

PMID:41668725 | PMC:PMC12883793 | DOI:10.3389/fnins.2025.1730402

Sci Rep. 2026 Feb 10. doi: 10.1038/s41598-026-38116-3. Online ahead of print.

ABSTRACT

This study aimed to explore abnormal patterns of functional connectivity density (FCD) and functional connectivity (FC) in patients with vestibular migraine (VM) and their associations with clinical symptoms. Resting-state functional magnetic resonance imaging (rs-fMRI) data from 49 VM patients and 61 healthy controls (HCs) were analyzed using Global FCD (GFCD), long-range FCD (LRFCD), and seed-based FC. Compared with HCs, VM patients demonstrated decreased GFCD and LRFCD in the bilateral medial prefrontal cortex (mPFC), along with increased GFCD in the right lingual gyrus (LING), right middle occipital cortex (MOC), left precuneus (preCUN), and elevated LRFCD in the middle cingulate cortex (MCC) and bilateral MOC. Seed-based FC analysis revealed significantly reduced connectivity between the mPFC and multiple regions, including the right cuneus/precuneus (CUN/preCUN), bilateral posterior cingulate cortex (PCC), bilateral hippocampus/parahippocampus (HIPP/ParaHIPP), and left calcarine cortex (CAL) in VM patients. Correlation analysis identified a positive association between GFCD in the left preCUN and Dizziness Handicap Inventory (DHI) scores (r = 0.370, p = 0.011). These findings highlight disrupted prefrontal-limbic-visual network integration in VM, with precuneus dysfunction potentially linked to dizziness severity. This study provides novel insights into the neural mechanisms underlying VM, highlighting the role of altered functional integration in symptom manifestation.

PMID:41667547 | DOI:10.1038/s41598-026-38116-3

Geroscience. 2026 Feb 10. doi: 10.1007/s11357-026-02141-x. Online ahead of print.

ABSTRACT

Exercise improves cognition, mental wellbeing, and protects against neurodegeneration. However, most prior neuroscience studies have focused on localized brain changes without quantifying their impact on brain ageing. To quantify the effect of resistance training on brain health using longitudinal assessments. Using resting-state functional magnetic resonance imaging (rs-fMRI) data from 2,433 healthy adults, we trained models to predict brain age and applied them to 309 participants from the Live Active Successful Aging (LISA) randomized trial. Participants in this trial were assigned to one of three groups: heavy-resistance training, moderate-intensity training, or a non-exercise control group. They underwent repeated rs-fMRI and physical fitness assessments at baseline, with follow-up assessments at 1 and 2 years. First, we examined changes in local connectivity between groups. Second, we assessed the impact of resistance training on brain ageing using brain clock models trained on the independent dataset of 2,433 adults. Local analyses revealed increased prefrontal functional connectivity following heavy training, while moderate- and heavy-resistance training significantly reduced brain age (-1.4 to -2.3 years, pFDR < 0.05). These effects emerged at the whole-brain level, rather than within isolated networks such as the default mode, motor, or cerebellar systems. These findings suggest a hierarchical organization of brain aging, driven by distributed network-level changes and expressed through focal regional patterns. Resistance exercise training decelerates brain ageing, as indexed by brain clocks, reinforcing its role as a preventive strategy for brain health.

PMID:41665740 | DOI:10.1007/s11357-026-02141-x

Commun Biol. 2026 Feb 9. doi: 10.1038/s42003-026-09688-7. Online ahead of print.

ABSTRACT

Human working memory is supported by a broadly distributed set of brain networks. Content-specific networks communicate with a domain-general, supramodal network that is recruited regardless of the type of content. Here, we contrasted visual and auditory working memory tasks to examine interactions between the supramodal network and two content-specific networks. Functional connectivity among visual-biased, auditory-biased, and supramodal working memory networks was assayed by collecting task and resting-state fMRI data from 24 human participants (age 18-43; 11 men and 13 women). At rest, as found previously, the supramodal network exhibited stronger functional connectivity with the visual-biased network than with the auditory-biased network. This asymmetry raises questions about how networks communicate to support robust performance across modalities. However, during auditory task performance, dynamic changes increased auditory network connectivity with supramodal and visual-biased frontal regions, while decreasing connectivity from posterior visual areas to supramodal and frontal visual regions. In contrast, the visual task produced weak changes. Across individuals, auditory working memory precision correlated with the strength of auditory network connectivity changes, while no such brain-behavior link was observed for visual working memory. These results demonstrate an asymmetry in working memory network organization and reveal that dynamic reorganization accompanies performance of working memory tasks.

PMID:41663792 | DOI:10.1038/s42003-026-09688-7

AJNR Am J Neuroradiol. 2026 Feb 9:ajnr.A9212. doi: 10.3174/ajnr.A9212. Online ahead of print.

ABSTRACT

BACKGROUND: Radiologically Isolated Syndrome (RIS) entails incidental Multiple Sclerosis (MS)-like MRI lesions. Longitudinal fMRI could clarify brain-symptom links; however, no longitudinal resting-state fMRI studies in RIS existed until now.

OBJECTIVES: Compare 14-month clinical, neuropsychological, and resting-state functional connectivity (FC) trajectories in RIS, MS, and healthy controls (HC), and relate FC change to fatigue.

METHODS: Nineteen RIS, 20 MS, and 22 HC completed baseline and 14-month assessments (fatigue, neuropsychology) and 3T MRI (rs-fMRI, 3D T1, FLAIR). FC within canonical networks and the ventral attention network (VAN) seed-to-voxel (CONN) connections were tested with a repeated-measures ANOVA (FWE-corrected). Regression analysis related to FC to fatigue; ROC curves evaluated discrimination.

RESULTS: Fatigue rose in MS but was stable in RIS. VAN connectivity showed opposing trajectories (group × time, p < 0.001): RIS increased within-VAN (and within-DAN vs. HC), whereas MS decreased within-VAN. In MS, VAN connectivity increased with orbitofrontal and striatal regions and decreased with thalamus/caudate (FWE p<0.05). Greater increases in within-VAN and VAN-thalamus/caudate connectivity were predicted to lead to fatigue reduction. A composite VAN metric differentiated RIS from MS (AUC=0.919). Lesion volumes were unchanged.

CONCLUSIONS: RIS and MS exhibit divergent, VAN-centered FC trajectories paralleling fatigue evolution. VAN-based longitudinal FC metrics may provide sensitive, noninvasive biomarkers that complement lesion measures in early MS.

PMID:41663204 | DOI:10.3174/ajnr.A9212

PLOS Ment Health. 2024 Jun 4;1(1):e0000022. doi: 10.1371/journal.pmen.0000022. eCollection 2024.

ABSTRACT

Internet usage has seen a stark global rise over the last few decades, particularly among adolescents and young people, who have also been diagnosed increasingly with internet addiction (IA). IA impacts several neural networks that influence an adolescent's behaviour and development. This article issued a literature review on the resting-state and task-based functional magnetic resonance imaging (fMRI) studies to inspect the consequences of IA on the functional connectivity (FC) in the adolescent brain and its subsequent effects on their behaviour and development. A systematic search was conducted from two databases, PubMed and PsycINFO, to select eligible articles according to the inclusion and exclusion criteria. Eligibility criteria was especially stringent regarding the adolescent age range (10-19) and formal diagnosis of IA. Bias and quality of individual studies were evaluated. The fMRI results from 12 articles demonstrated that the effects of IA were seen throughout multiple neural networks: a mix of increases/decreases in FC in the default mode network; an overall decrease in FC in the executive control network; and no clear increase or decrease in FC within the salience network and reward pathway. The FC changes led to addictive behaviour and tendencies in adolescents. The subsequent behavioural changes are associated with the mechanisms relating to the areas of cognitive control, reward valuation, motor coordination, and the developing adolescent brain. Our results presented the FC alterations in numerous brain regions of adolescents with IA leading to the behavioural and developmental changes. Research on this topic had a low frequency with adolescent samples and were primarily produced in Asian countries. Future research studies of comparing results from Western adolescent samples provide more insight on therapeutic intervention.

PMID:41661825 | DOI:10.1371/journal.pmen.0000022

Neurophotonics. 2026 Jan;13(1):015010. doi: 10.1117/1.NPh.13.1.015010. Epub 2026 Feb 6.

ABSTRACT

SIGNIFICANCE: Adapting optical imaging technology to avian models can overcome many limitations imposed by functional magnetic resonance imaging (fMRI), which currently restricts the number of species used to study functional connectivity. Developing advanced technology to expand the diversity of species that can be effectively imaged is crucial for addressing significant questions that are currently unreachable, such as understanding the evolution of cognition from a comparative perspective.

AIM: We assessed the potential of optical imaging technology to measure functional connectivity in birds, utilizing pigeons as an avian model. We evaluated whether we could partition the dorsal surface of the pigeon brain into units that correspond to known anatomical regions. Finally, we compared our results with those obtained from a separate dataset acquired using fMRI.

APPROACH: Using optical intrinsic signal imaging, a widefield optical imaging method, we imaged resting state functional connectivity in scalp-retracted anesthetized pigeons. We then used iterative parcellation and hierarchical clustering to create functional connectivity maps of correlation between parcels at two spatial scales. We recorded a second independent dataset of ten pigeons using a single-shot multi-slice gradient echo EPI sequence fMRI and applied the same parcellation method to compare functional connectivity patterns between the two methodologies.

RESULTS: We successfully partitioned signal activity into clusters of parcels that exhibit left-right symmetry between hemispheres and which align well with known anatomical regions of the dorsal surface of the pigeon brain. Moreover, functional connectivity matrices reveal positive correlations between homotopic regions. These cluster partitions and functional connectivity maps display similar patterns across and within individuals. Finally, WOI imaging results were comparable to resting state data acquired using fMRI.

CONCLUSIONS: Taken together, these results demonstrate the potential of optical imaging technology for the reliable and cost-effective characterization of functional connectivity in birds. In addition, they position optical imaging methods as a valuable tool for large-scale comparative and network-level studies in this taxon.

PMID:41660356 | PMC:PMC12879446 | DOI:10.1117/1.NPh.13.1.015010

bioRxiv [Preprint]. 2026 Jan 27:2026.01.25.701594. doi: 10.64898/2026.01.25.701594.

ABSTRACT

People differ remarkably from one another, yet isolating individual differences in their brain activity remains challenging. Non-invasive whole-brain recordings of human brain activity, such as those from resting state fMRI (rs-fMRI), are complex and noisy, making it difficult to isolate stable dimensions of individual differences. Ideally, we want to find a few core dimensions that vary across people but have high test-retest reliability, giving the same value each time they are measured in the same person. However, it is still unknown whether any such reliable dimensions exist, and if they do, what could drive this reliability. Here, we show that there is a low-dimensional linear subspace of highly-reliable rs-fMRI activity. These dimensions form personal fingerprints, allowing participants to be identified with high accuracy despite fingerprints explaining only a fraction of the total variance. Many of these dimensions inherit their reliability from a single morphological, demographic, or behavioral property, and most dimensions can be predicted from the anatomical layout of cortical regions. These dimensions were identified using reliability component analysis (RCA), a new dimensionality reduction technique similar to principal component analysis (PCA) but which maximizes reliability instead of explained variance. Together, our findings suggest that stable individual signatures can be isolated from rs-fMRI. These signatures reflect persistent anatomical and physiological differences, and provide a principled low-dimensional basis for biomarker discovery.

PMID:41659684 | PMC:PMC12873822 | DOI:10.64898/2026.01.25.701594

Front Neurosci. 2026 Jan 22;20:1745862. doi: 10.3389/fnins.2026.1745862. eCollection 2026.

ABSTRACT

BACKGROUND: Migraine is a prevalent neurological disorder that is frequently observed in clinical practice and is commonly comorbid with insomnia. Insomnia can exacerbate and precipitate migraine attacks, with both conditions exerting a reciprocal influence on one another. The cerebellar crus is significantly associated with the pathophysiology of migraine and insomnia. The relationship between cerebellar crus functional alterations and migraine-associated insomnia remains unclear. This study utilizes resting-state functional magnetic resonance imaging (rs-fMRI) to examine functional alterations in the cerebellar crus of patients with migraine and concurrent insomnia.

METHODS: Participants underwent resting-state functional magnetic resonance imaging. Subsequently, the disparity in amplitude of low-frequency fluctuations (ALFF) values among groups was analyzed, followed by functional connectivity (FC) investigations employing the cerebellum crus as seed regions.

RESULTS: Migraine patients frequently experience neuropsychological disorders and insomnia, which are interconnected. Both migraine with insomnia (MwI) and migraine without insomnia (MwoI) groups demonstrated elevated amplitude of low-frequency fluctuations (ALFF) in the left Crus I and II compared to the healthy controls (HC) group, with the MwI group exhibiting more pronounced alterations. Additionally, both patient groups showed decreased FC between the left Crus I and the right middle temporal gyrus (MTG) and inferior temporal gyrus (ITG) relative to the HC group. The MwoI group showed significantly lower FC compared to both the HC and MwI groups. A significant negative correlation was observed between ALFF in the left Crus I/II and Pittsburgh Sleep Quality Index (PSQI) scores in the MwoI group. Conversely, in the combined migraine cohort, FC between the left Crus I and the right MTG/ITG showed a positive correlation with PSQI scores.

CONCLUSION: This study identified a correlation between aberrant functional activity in the left Crus I/II and migraine comorbidity with insomnia. These findings provide fresh perspectives on the neural mechanisms underlying the migraine-insomnia relationship, thereby facilitating the identification of potential neuroimaging biomarkers and the exploration of targeted interventions for this patient subgroup.

PMID:41658941 | PMC:PMC12872798 | DOI:10.3389/fnins.2026.1745862

Imaging Neurosci (Camb). 2026 Feb 5;4:IMAG.a.1126. doi: 10.1162/IMAG.a.1126. eCollection 2026.

ABSTRACT

Resting-state fMRI time series are punctuated by spontaneous moments of high-amplitude activity lasting mere seconds. Previous research has demonstrated that such moments may contain a disproportionate amount of information and can be used to recapitulate maps of distributed brain activity or to recreate spatial functional connectivity patterns. Ultimately, this body of work has established that modeling neurovascular activity as a succession of spontaneous, punctuated moments is an effective approach for understanding cortex-wide brain activity. Here, we expand on this line of work by focusing our attention on the spatiotemporal properties of such punctuated moments, particularly on their duration. For this, we turn to an edge time series approach to resolve the dynamics of functional connectivity, identify moments of prominent synchrony, and record their duration. This procedure allows us to differentiate such punctuated moments by the time scales at which they unfold. By mapping moment duration to the cortex, we find that connectivity emanating from brain's primary sensory areas transpires with the longest durations. We further construct spatial patterns of connectivity unfolding over distinct durations, demonstrating how time scales differentially relate to traditionally constructed functional connectivity. Finally, we show how the longest connectivity moments could convey information about fluctuations in subjects' vigilance. Overall, the information that we have gleaned about prominent connectivity moments and their duration would otherwise be largely obscured when using other prevalent methods. Here we highlight an additional feature of functional connectivity to further our characterization of the brain's spatiotemporal organization.

PMID:41658341 | PMC:PMC12878659 | DOI:10.1162/IMAG.a.1126

Front Neurol. 2026 Jan 22;17:1683526. doi: 10.3389/fneur.2026.1683526. eCollection 2026.

ABSTRACT

The intrinsic brain activity measured by resting-state fMRI (rs-fMRI) consists of synchronized neural oscillations across a broad range of low frequencies. Although previous studies have linked frequency-specific changes to cognitive function and impairment, the alterations of these frequency-specific spatiotemporal patterns in chronic occlusive cerebrovascular disease remain unclear. In this study, we investigated the cross-frequency structure underlying cognitive impairment in patients with severe asymptomatic carotid stenosis (SACS) using wavelet-transformed amplitude of low-frequency fluctuation (wavelet-ALFF) of rs-fMRI. We found that, in healthy controls, frequency-specific wavelet-ALFF exhibited a spatial distribution from lower to higher frequencies, aligned with the functional hierarchy extending from the default mode network (DMN) to primary somatomotor and subcortical regions. In contrast, SACS patients exhibited frequency-dependent changes, including significantly decreased wavelet-ALFF in the anteromedial DMN at lower frequencies and the posteromedial DMN at higher frequencies. Further spatiotemporal decomposition analysis revealed that SACS patients exhibited abnormal cross-frequency coupling in the DMN. Our findings suggest that frequency-specific changes underlying cognitive impairment in SACS arise from spatiotemporally abnormal cross-frequency interplay within the DMN. These insights may contribute to a better understanding of other major brain diseases.

PMID:41657414 | PMC:PMC12872527 | DOI:10.3389/fneur.2026.1683526

Neurosci Lett. 2026 Feb 6:138537. doi: 10.1016/j.neulet.2026.138537. Online ahead of print.

ABSTRACT

BACKGROUND: Schizotypal personality (SP) is characterized by cognitive-perceptual disturbances, interpersonal difficulties, and disorganized behavior. We examined associations between SP traits and affect, and insula-centered neural mechanisms underlying this link.

METHODS: One hundred sixty-one university students completed the Schizotypal Personality Questionnaire-Brief and the Positive and Negative Affect Schedule and underwent resting-state fMRI. Seed-based whole-brain functional connectivity (FC) analyses used bilateral ventral anterior, dorsal anterior, and posterior insula seeds. Pearson correlations and mediation analyses tested associations among SP traits, Negative Affect, and FC.

RESULTS: Cognitive-Perceptual traits correlated positively with Negative Affect (r = 0.36, p < 0.001). FC between the right inferior parietal lobule (IPL.R) and the left ventral anterior insula (vAI.L) was positively correlated with Cognitive-Perceptual traits (r = 0.33, p < 0.001), whereas FC between the right cerebellar Crus I and the vAI.L was negatively correlated (r = -0.37, p < 0.001). FC between the right ventral anterior insula (vAI.R) and the Left Calcarine Gyrus (CAL.L) was also negative (r = -0.30, p < 0.001). vAI.L-IPL.R FC partially mediated the Cognitive-Perceptual traits-Negative Affect association (indirect effect = 0.1883, 95% bootstrap CI [0.0246, 0.4022]).

CONCLUSION: vAI.L-IPL.R FC partially accounts for the link between Cognitive-Perceptual traits and Negative Affect, highlighting a potential neural pathway underlying affective vulnerability in SP.

PMID:41655807 | DOI:10.1016/j.neulet.2026.138537

Comput Biol Med. 2026 Feb 7;204:111533. doi: 10.1016/j.compbiomed.2026.111533. Online ahead of print.

ABSTRACT

Brain networks display hierarchical organization, a complexity that is challenging for deep learning models that are often flat classifiers and lack interpretability. To address this, we propose a novel architecture called the Emergent Language Symbolic Autoencoder (ELSA), a hierarchical symbolic autoencoder informed by weak supervision and an Emergent Language framework that learns to represent brain networks as interpretable symbolic sentences while simultaneously reconstructing the original data. Our framework's primary innovations are a set of hierarchically-aware loss functions and their application to modeling resting-state fMRI networks. By combining weak supervision from Independent Component Analysis (ICA) order with novel Progressive, Strict, and Containing Bias losses, we explicitly enforce a coarse-to-fine structure on the emergent language without requiring extensive manual labeling. We evaluated ELSA on data from the publicly available 1000 Functional Connectomes Project. The model generated sentences with clear hierarchical organization, where early symbols corresponded to broad parent networks and later symbols specified finer sub-networks. With the use of our proposed Progressive Strict loss function and containing bias penalty, the model's hierarchical consistency drastically improves compared to baseline, achieving near-perfect consistency at higher ICA orders and 43.5% at the challenging lowest order. The model also produces qualitatively superior visual progressions of the network reconstructions. By replacing opaque feature vectors with an interpretable symbolic language, ELSA provides a transparent, multi-level description of functional brain organization and offers a general framework for studying other hierarchically structured biomedical data.

PMID:41655479 | DOI:10.1016/j.compbiomed.2026.111533

J Prev Alzheimers Dis. 2026 Feb 6;13(4):100501. doi: 10.1016/j.tjpad.2026.100501. Online ahead of print.

ABSTRACT

BACKGROUND: Brain age gap (BAG)-the difference between predicted and chronological age-captures neurobiological aging, but MRI-only models insufficiently reflect Alzheimer's disease (AD) pathology. Whether incorporating regional amyloid-β (Aβ) positron emission tomography (PET) improves sensitivity to early AD processes remains unknown.

OBJECTIVES: To develop an amyloid-informed multimodal BAG model and examine its associations with cognition, plasma biomarkers, and functional connectivity across the AD continuum.

DESIGN: Cross-sectional analysis using integrated machine-learning models.

SETTING: Chinese Preclinical Alzheimer's Disease Study (CPAS), a cohort recruited from community settings and memory clinics.

PARTICIPANTS: Nine hundred ninety community-dwelling adults spanning normal cognition, subjective cognitive decline (SCD), mild cognitive impairment (MCI), and dementia.

MEASUREMENTS: Regional Aβ-PET and structural MRI informed BAG estimation. Cognitive tests, plasma biomarkers (p-tau217, p-tau181, neurofilament light [NfL], glial fibrillary acidic protein [GFAP], Aβ42/40), and hippocampus-default mode network (DMN) connectivity from resting-state fMRI were assessed.

RESULTS: Higher BAG was associated with greater odds of SCD, MCI, or dementia across the cohort, with stronger effects in Aβ-positive individuals. BAG explained more cognitive variance than global Aβ burden and was linked to multidomain cognitive deficits. Elevated BAG corresponded to higher p-tau217, p-tau181, NfL, and GFAP and lower Aβ42/40, indicating early biomarker alterations. BAG was also associated with reduced hippocampus-DMN connectivity.

CONCLUSIONS: An amyloid-informed multimodal BAG model captures convergent AD-related pathology, biomarker alterations, and cognitive vulnerability beyond amyloid burden alone, supporting its value for individualized risk s2tratification and prevention-focused assessment.

PMID:41653882 | DOI:10.1016/j.tjpad.2026.100501