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bioRxiv [Preprint]. 2025 Oct 11:2025.10.10.681678. doi: 10.1101/2025.10.10.681678.

ABSTRACT

Understanding how dynamic brain networks support language perception and production is central to cognitive neuroscience. A vast network based literature has employed functional connectivity (FC), primarily using resting-state and task-based fMRI. However, methodological limitations have hindered this approach in language processing, particularly during speech production. Here, we address this gap by employing a large cohort of electrocorticographic (ECoG) patients (N=42) to investigate the networks driving speech perception and production. We acquired data while patients were engaged in a controlled battery of speech production tasks focusing on five cognitive states (auditory perception, picture perception, reading perception, speech production, and baseline). Using linear classifiers we were able to robustly decode cognitive states from single-trial FC (i.e. Pearson correlations) of the neural activity patterns, achieving a mean accuracy of 64.4%. These classifiers revealed distinct network signatures underlying auditory and visual perception as well as speech production via stable network connectivity. Importantly, the network signatures included both regions with robust local neural activity and those with minimal or no detectable activation. Such signatures indicate that even low-activity regions contribute critically to differentiating cognitive states. Our findings underscore the significance of functional connectivity analysis as a complementary dimension to investigating local neural activity, and suggest that the functional networks supporting speech extend beyond the most metabolically active regions.

PMID:41279744 | PMC:PMC12632296 | DOI:10.1101/2025.10.10.681678

bioRxiv [Preprint]. 2025 Oct 14:2025.10.10.681622. doi: 10.1101/2025.10.10.681622.

ABSTRACT

Menopause affects the aging process in women through significant ovarian hormone production decline in midlife. Women who experience early menopause face an accelerated physiological aging rate, along with impaired memory and increased risks of neurodegenerative diseases. However, it remains elusive how the timing of menopause affects brain activity, which could be crucial for understanding menopause-related acceleration of aging and increased risk of dementia. Recent studies have revealed a highly structured infra-slow (< 0.1 Hz) global brain activity across species and linked it to arousal and memory functions, as well as waste clearance in Alzheimer's diseases (AD). In this study, we examined how this global brain activity relates to age of menopause using resting-state fMRI data from the Human Connectome Project-Aging dataset. We found that women who experienced earlier menopause (mean menopausal age 45±3.5 yr) exhibited weaker global brain activity ( p = 5.0 × 10 -4 ) with reduced coupling to cerebrospinal fluid (CSF) flow ( p = 0.017) compared to age-matched later-menopausal women (mean menopausal age 54±1.2 yr). Differences appeared mainly in higher-order brain regions, where activation levels correlated with memory performance in earlier but not in intermediate or later menopausal women. These findings highlight brain activity changes linked to early menopause, suggesting a potential mechanism underlying memory decline and the increased risk of AD and dementias in early-onset menopausal women.

PMID:41279691 | PMC:PMC12632297 | DOI:10.1101/2025.10.10.681622

bioRxiv [Preprint]. 2025 Oct 16:2025.10.16.682939. doi: 10.1101/2025.10.16.682939.

ABSTRACT

Brain-age prediction from neuroimaging data provides a proxy of biological aging, yet most models rely on structural magnetic resonance imaging (MRI), a modality that captures macroanatomy but offers limited biological specificity. We tested whether integrating molecular-enriched functional connectivity (FC), from resting-state functional MRI (rs-fMRI) data, improves brain-age prediction and biological explainability. We analyzed MRI data of 2,120 healthy adults (1,243/877 F/M; 18-90 years) from three public datasets. Molecular-enriched connectivity maps were derived with Receptor-Enriched Analysis of functional Connectivity by Targets (REACT) using receptor-density templates for the dopamine (DAT), norepinephrine (NET), and serotonin (SERT) transporter systems. Support vector regression models were applied to predict chronological age from molecular-enriched FC, structural morphometry, or both combined. The effect of multi-site variability was mitigated via ComBat harmonization with and without Empirical Bayes pooling. We additionally conducted a common-parcellation analysis to assess the impact of differing parcellations between modalities. Single-transporter molecular-enriched FC explained up to 51% of age variance. The most predictive transporter varied by dataset, with DAT dominating in the harmonized and common-parcellation settings. Combining the three molecular-enriched maps consistently improved prediction over any single map and increased explained variance up to 64%. In the merged multi-site cohort using a common parcellation, augmenting structural information with transporter-enriched FC reduced mean absolute error (MAE) from 6.02 to 5.81 years, supporting complementarity of the two modalities. In contrast, when different parcellations were applied, incorporating molecular-enriched FC into brain age prediction resulted in a 2% higher MAE compared to structural morphometry alone, suggesting that parcellation mismatch may obscure the functional contributions. In conclusion, molecular-enriched FC is a feasible and biologically informative extension to brain-age modeling, enhancing prediction and interpretability with respect to neurotransmitter systems.

PMID:41279273 | PMC:PMC12632960 | DOI:10.1101/2025.10.16.682939

bioRxiv [Preprint]. 2025 Oct 22:2025.03.18.643969. doi: 10.1101/2025.03.18.643969.

ABSTRACT

While numerous researchers claim that the minimization of prediction error (PE) is a general force underlying most brain functions, others argue instead that PE minimization drives low-level, sensory-related neuronal computations but not high-order, abstract cognitive operations. We investigated this issue using behavioral, fMRI, and EEG data. Studies 1A/1B examined semantic- and reward-processing PE using task-fMRI, yielding converging evidence of PE's global effects on large-scale connectivity: high-PE states broadly upregulated ventral-dorsal connectivity, and low-PE states upregulated posterior-anterior connectivity. Investigating whether these global patterns characterize cognition generally, Studies 2A/2B used resting-state fMRI and showed that individuals continuously fluctuate between ventral-dorsal (high-PE) and posterior-anterior (low-PE) dynamic connectivity states. Additionally, individual differences in PE task responses track differences in resting-state fluctuations, further endorsing that these fluctuations represent PE minimization at rest. Finally, Study 3 combined fMRI and EEG data, and the study found that the fMRI fluctuation amplitude correlates most strongly with EEG power at 3-6 Hz, consistent with the PE network fluctuations occurring at Delta/Theta oscillation speeds. This whole-brain layout and timeline together are consistent with high/low-PE fluctuations playing a role in integrative and general sub-second cognitive operations.

PMID:41278838 | PMC:PMC12633279 | DOI:10.1101/2025.03.18.643969

Imaging Neurosci (Camb). 2025 Nov 19;3:IMAG.a.1020. doi: 10.1162/IMAG.a.1020. eCollection 2025.

ABSTRACT

Apparent diffusion coefficient (ADC)-fMRI offers a promising functional contrast, capable of mapping neuronal activity directly in both grey and white matter. However, previous studies have shown that diffusion-weighted fMRI (dfMRI), from which ADC-fMRI derives, is influenced by blood-oxygen level dependent (BOLD) effects, leading to a concern that the dfMRI contrast is still rooted in neurovascular rather than neuromorphological coupling. Mitigation strategies have been proposed to remove vascular contributions while retaining neuromorphological coupling, by: i) analysing ADC timecourses calculated from two interleaved diffusion-weightings, known as ADC-fMRI; ii) using b-values of at least 200 s mm - 2 ; and iii) using a sequence compensated for cross-terms with fluctuating background field gradients associated with blood oxygenation. Respiration-induced haemodynamic fluctuations, which are dissociated from neural activity, are an excellent test-bed for the robustness of ADC-fMRI to vascular contributions. In this study, we investigate the association between end-tidal CO 2 and ADC-fMRI, in comparison with dfMRI and BOLD, in both breath-hold and resting-state paradigms in the human brain. We confirm a strong dependence of the BOLD signal on respiration, and a pattern of delayed haemodynamic response in white matter. While dfMRI mitigates much of the vascular contribution, it retains some association with respiration, as expected. Conversely, ADC-fMRI is mostly unaffected by vascular contribution, exhibiting minimal correlation between expired CO 2 and ADC timeseries, as well as low inter- and intra-subject reproducibility in correlation maps. These findings validate ADC-fMRI as a predominantly non-vascular contrast sensitive to microstructural dynamics, enabling whole-brain functional imaging unconstrained by vascular confounds.

PMID:41278581 | PMC:PMC12630321 | DOI:10.1162/IMAG.a.1020

Cureus. 2025 Nov 20;17(11):e97329. doi: 10.7759/cureus.97329. eCollection 2025 Nov.

ABSTRACT

Resting-state functional magnetic resonance imaging (rs-fMRI) is a noninvasive imaging technique that measures spontaneous brain activity to map functional connectivity within and between brain networks characterized as the triple network model (TNM). In Alzheimer's disease (AD), rs-fMRI has been used to detect early network disruptions, track disease progression, and evaluate therapeutic interventions. While craniosacral therapy (CST) has shown clinical benefits for conditions like chronic pain and migraine, its impact on TNM connectivity in AD, as evidenced by rs-fMRI, has not been explored. This case report involves a 79-year-old man with early-stage AD who presented with mild delusions, anxiety, irritability, and nighttime behaviors and a Mini-Mental State Examination (MMSE) score of 24 and a Clinical Dementia Rating (CRD) of 0.5, indicating a mild neurocognitive disorder. Preliminary rs-fMRI data revealed changes in the default mode network (DMN), salience network (SN), and central executive network (CEN) following CST. These changes suggest greater connectivity within the CEN and SN, alongside reduced variability in the DMN following CST. These observations suggest potential reorganization of TNM dynamics. The clinical relevance of these findings remains under evaluation. The observations from this single case report limit the ability to draw definitive conclusions about the impact of CST on TNM connectivity in early-stage AD. A further study is needed to determine if the TNM changes observed by rs-fMRI can be replicated in additional participants and if the changes are correlated with clinical outcomes. Further studies with larger cohorts, extended treatment durations, and longer follow-up periods are needed to explore the potential clinical benefits of CST in this population.

PMID:41278048 | PMC:PMC12640224 | DOI:10.7759/cureus.97329

Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Nov 21:S2451-9022(25)00360-X. doi: 10.1016/j.bpsc.2025.11.003. Online ahead of print.

ABSTRACT

BACKGROUND: Major depressive disorder (MDD) involves subtle, distributed alterations across multiple large-scale resting-state brain networks (RSNs), highlighting the need for integrative approaches to uncover synergistic network patterns driving clinical symptoms.

METHODS: In this study, we employed a dynamical systems approach to investigate patterns of simultaneous RSN activation - i.e. co-activation - in 867 participants, including 487 healthy controls (HC), 175 patients with current MDD (cMDD), and 205 with remitted MDD (rMDD) from the Marburg-Münster Affective Disorders Cohort Study. Using a pairwise Maximum Entropy Model, we estimated RSN co-activation probabilities based on resting state fMRI data of seven RSNs-default mode network (DMN), frontoparietal network (FPN), sensorimotor network (SMN), visual network (VIS), salience network, dorsal attention network (DAN), and language network (LAN)-capturing 128 possible states of co-activation.

RESULTS: General linear models revealed elevated co-activation probabilities in cMDD, particularly for states involving DMN, FPN, and VIS, with the co-activation state involving DMN, VIS, DAN, FPN, and LAN showing the strongest association with MDD diagnosis, clinical status, and symptom severity. Canonical Correlation Analysis (CCA) on the full sample further identified two distinct network-symptom profiles: Canonical variate (CV) 1 linked high DMN and DAN co-activation probabilities to cognitive, insomnia, and mood/anhedonia symptoms, while CV2 tied SMN and VIS to cognitive and somatic symptom domains.

CONCLUSIONS: These results demonstrate that MDD, especially during acute episodes, is marked by a dominance of DMN, FPN, and VIS co-activation, pointing to altered dynamic network organization. They highlight how changes in brain state dynamics are linked to MDD symptoms.

PMID:41275967 | DOI:10.1016/j.bpsc.2025.11.003

Neuroimage. 2025 Nov 21:121607. doi: 10.1016/j.neuroimage.2025.121607. Online ahead of print.

ABSTRACT

Task-based functional MRI (tb-fMRI) has gained prominence for investigating brain connectivity by engaging specific functional networks during cognitive or behavioral tasks. Compared to resting-state fMRI (rs-fMRI), tb-fMRI provides greater specificity and interpretability, making it a valuable tool for examining task-relevant networks and individual differences in brain function. In this study, we evaluated the utility of the BOLD-filter-a method originally developed to extract reliable BOLD (blood oxygenation level-dependent) components from rs-fMRI-by applying it to tb-fMRI data as a preprocessing step for functional connectivity (FC) analysis. The goal was to enhance the sensitivity and specificity of detecting task-induced functional activity. Compared to the conventional preprocessing method, the BOLD-filter substantially improved the isolation of task-evoked BOLD signals. It identified over eleven times more activation voxels at a high statistical threshold and more than twice as many at a lower threshold. Moreover, FC networks derived from BOLD-filtered signals revealed clearer task-related patterns, including gender-specific differences in brain regions linked to everyday behaviors. These patterns were not detectable using conventional preprocessing approaches. Our findings demonstrate that the BOLD-filter enhances the robustness and interpretability of FC analysis in tb-fMRI. By effectively isolating meaningful functional networks, this approach offers advantages over conventional preprocessing methods. Overall, the BOLD-filter provides a useful improvement for enhancing the characterization of task-induced brain activity in tb-fMRI analysis.

PMID:41275945 | DOI:10.1016/j.neuroimage.2025.121607

Neuroimage Clin. 2025 Nov 12;48:103908. doi: 10.1016/j.nicl.2025.103908. Online ahead of print.

ABSTRACT

Mesial temporal lobe epilepsy (MTLE) seizures are known to alter neural architecture, yet imaging studies report conflicting findings of their effects on the brain. This study aimed to identify consistent regions exhibiting structural or functional changes in MTLE and compare the regional distributions of pathology detected by different neuroimaging modalities. To that end, thirty-six coordinate-based meta-analyses were performed by applying Alteration Likelihood Estimation to voxel-based morphometry (VBM) and voxel-based physiology (VBP) studies. The meta-analyses revealed convergent MTLE pathology in the epileptogenic hippocampus, bilateral thalamus (medial dorsal nucleus and pulvinar), and striatum (caudate and putamen); significant findings were partially colocalized between VBM-atrophy and VBP analyses, with VBP effects driven primarily by reports of cerebral hypometabolism. Subgroup meta-analyses of blood-oxygen-level-dependent (BOLD) signal-derived metrics revealed additional regions of functional disturbance but were underpowered, requiring further investigation to establish their potential for revealing novel aspects of MTLE pathophysiology via functional magnetic resonance imaging (fMRI). These findings support the current understanding of MTLE as a network-based pathology with progressive neurodegeneration in the hippocampus and connected regions. This study also highlights promising neuroimaging targets for investigating disease-related alterations and recommends incorporating these regions into functional network models of MTLE. Finally, the present work encourages further exploration of BOLD-derived metrics and specifically urges the epilepsy imaging research community to report amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) measures for resting-state fMRI studies in standard space coordinates, to advance neuroimaging approaches for improving diagnosis, prognosis, and treatment strategies in MTLE.

PMID:41275547 | DOI:10.1016/j.nicl.2025.103908

Mol Psychiatry. 2025 Nov 22. doi: 10.1038/s41380-025-03362-w. Online ahead of print.

ABSTRACT

Memory deficits observed in various neurological and psychiatric disorders may, in part, arise from dysregulated adult-born immature neurons (ABNs) in the dentate gyrus (DG). However, the mechanisms by which these aberrant neurons contribute to brain-wide network dysfunction and memory impairment remain poorly understood. Using a well-established mouse model with aberrantly integrated ABNs and associated memory deficits, we employed resting-state functional magnetic resonance imaging (rs-fMRI) and found that a few hundred dysregulated ABNs (<0.1% of total DG granule neurons) were sufficient to disrupt functional connectivity between the DG and the insular cortex, two regions lacking direct anatomical connections. Further investigation using rabies-based retrograde tracing and fiber photometry recording revealed that dysregulated ABNs impaired calcium dynamics, inter-regional synchrony, and temporal coordination across both local hippocampal circuits and distal regions, including the mediodorsal thalamus and insular cortex, during a spatial memory task. Together, these findings reveal how a small population of aberrantly integrated ABNs can disrupt brain-wide network dynamics and ultimately impair spatial memory processing.

PMID:41275018 | DOI:10.1038/s41380-025-03362-w

Neuropsychologia. 2025 Nov 20:109322. doi: 10.1016/j.neuropsychologia.2025.109322. Online ahead of print.

ABSTRACT

Aphantasia is the inability to experience mental imagery during full wakefulness without any prominent perceptual deficits. Visual aphantasia is associated with differences in distributed brain networks, but its neurobiological underpinnings remain a mystery. In particular, aphantasia may arise due to impairments in the top-down control over visual imagination. We predicted that this in turn would prevent the brains of aphantasic participants from differentiating neural activity encoding the contents of imagination from the background noise of resting activity, particularly within the ventral temporal lobes. To test this hypothesis, we re-analysed functional magnetic resonance imaging (fMRI) data collected from aphantasics (n = 21), hyperphantasics (those with "photographic imagery"; n = 20), and controls (n = 17) during a simple perception and imagery task. We used two measures of informational complexity to quantify the complexity of the spatial pattern of thresholded BOLD signals in the participants' temporal lobes during visual perception and imagery. Both measures of spatial complexity showed significant correlations with imagery vividness. We then performed dynamic functional connectivity analyses on the same data revealing that the higher-order networks of aphantasics were abnormally coupled with the temporal lobes during imagery (p < 0.05). These results provide a novel perspective, reframing aphantasia as an inability of the visual system to selectively activate regions encoding object-specific visual categories above background levels of noise.

PMID:41274634 | DOI:10.1016/j.neuropsychologia.2025.109322

Med Image Anal. 2025 Nov 2;108:103860. doi: 10.1016/j.media.2025.103860. Online ahead of print.

ABSTRACT

Brain functional connectivity (FC) constructed from resting-state functional MRI (rs-fMRI) is the predominant method for studying brain functional organization of infants. Predicting the full dynamic developmental trajectory of infant FC from existing incomplete longitudinal data can enrich our understanding of brain function developmental patterns and mechanisms and help identify neurodevelopmental disorders. However, the scarcity of longitudinal infant functional MRI scans with frequent irregular missing data poses significant challenges in accurately predicting and delineating the dynamic trajectory of early normal and abnormal brain development. Moreover, existing deep learning methods typically predict FC at a single target timepoint from each available FC independently, overlooking longitudinal dependencies and yielding temporally inconsistent and inaccurate predictions during infancy. To this end, we propose a novel Triplet Longitudinal Masked Autoencoder (TL-MAE) for the prediction of the full dynamic developmental trajectory of infant FC. Specifically, we adopt the following novel strategies: 1) Creating a longitudinally consistent prediction strategy to ensure the temporal consistency and robustness in the FC generation process; 2) Introducing the FC-specified Masked Autoencoder to capture FC domain features and pre-training this model by leveraging large-scale high-quality data; 3) Developing a dual triplet network alongside an identity conditional module to disentangle entangled identity and age information, enabling individualized predictions at any given age. Experiments on 696 longitudinal infant fMRI scans from two datasets demonstrate that our method not only yields more accurate and temporally consistent predictions of FC developmental trajectories, but also excels at capturing individualized features compared to state-of-the-art techniques.

PMID:41274084 | DOI:10.1016/j.media.2025.103860

Hum Brain Mapp. 2025 Dec 1;46(17):e70380. doi: 10.1002/hbm.70380.

ABSTRACT

The estimation of brain networks is instrumental in quantifying and evaluating brain function. Nevertheless, achieving precise estimations of subject-level networks has proven to be a formidable task. In response to this challenge, researchers have developed group-inference frameworks that leverage robust group-level estimations as a common reference point to infer corresponding subject-level networks. Generally, existing approaches either leverage the common reference as a strict, voxel-wise spatial constraint (i.e., strong constraints at the voxel level) or impose no constraints. Here, we propose a targeted approach that harnesses the topological information of group-level networks to encode a high-level representation of spatial properties to be used as constraints, which we refer to as Topologically Optimized Intrinsic Brain Networks (TOIBN). Consequently, our method inherits the significant advantages of constraint-based approaches, such as enhancing estimation efficacy in noisy data or small sample sizes. On the other hand, our method provides a softer constraint than voxel-wise penalties, which can result in the loss of individual variation, increased susceptibility to model biases, and potentially missing important subject-specific information. Our analyses show that the subject maps from our method are less noisy and true to the group networks while promoting subject variability that can be lost from strict constraints. We also find that the topological properties resulting from the TOIBN maps are more expressive of differences between individuals with schizophrenia and controls in the default mode, subcortical, and visual networks.

PMID:41272954 | DOI:10.1002/hbm.70380

BMC Psychiatry. 2025 Nov 21. doi: 10.1186/s12888-025-07586-6. Online ahead of print.

NO ABSTRACT

PMID:41272514 | DOI:10.1186/s12888-025-07586-6

J Headache Pain. 2025 Nov 21;26(1):268. doi: 10.1186/s10194-025-02207-9.

NO ABSTRACT

PMID:41272442 | DOI:10.1186/s10194-025-02207-9

Sci Rep. 2025 Nov 21;15(1):41448. doi: 10.1038/s41598-025-19860-4.

ABSTRACT

Stroke impairs limb motor function, which affects patients' quality of life and imposes economic burdens. Early prediction of motor recovery is essential for guiding treatment and rehabilitation. While the corticospinal tract is a known biomarker, the role of non-motor brain regions remains under explored. Fifty-five stroke patients with unilateral subcortical lesions and 49 healthy controls underwent resting-state functional MRI scans at 1 week, 4 weeks, and 12 weeks after stroke. Focusing on two motor and 15 non-motor networks defined by the Schaefer atlas, machine learning models were used to predict changes in motor function measured by the Fugl-Meyer assessment using functional connectivity (FC) data. The network-based statistic (NBS) method was used to identify significant FC differences between patients and controls. Among 90 predictive models tested, only the model based on FC within the Somatomotor A (SomMotA) and Control A (ContA) networks at 1 week after stroke significantly predicted motor recovery from the acute to subacute phases (p = 0.00040 after Bonferroni correction). The ContA network contributed more to the prediction than the SomMotA network did. NBS analysis revealed significant FC alterations within the SomMotA network in patients versus controls but no direct correlation between predictive FC and group differences. This study revealed acute-phase FC between the non-motor ContA and motor SomMotA networks can be used to effectively predict motor recovery in stroke patients. These findings highlight the significant role of non-motor networks in motor recovery and suggest that rehabilitation strategies incorporating non-motor interventions may improve patient outcomes.

PMID:41271861 | DOI:10.1038/s41598-025-19860-4

Transl Psychiatry. 2025 Nov 21. doi: 10.1038/s41398-025-03760-2. Online ahead of print.

ABSTRACT

A central challenge in psychiatry is the need for improved diagnostic accuracy and treatment efficacy. Recent dimensional frameworks like the Research Domain Criteria (RDoC) initiative address this by promoting a transdiagnostic approach to identify shared neural mechanisms across psychiatric disorders. Here, we conducted a transdiagnostic meta-analysis of resting-state fMRI studies that employed amplitude-based measures of spontaneous brain activity-the amplitude of low-frequency fluctuations/fractional ALFF (ALFF/fALFF) and regional homogeneity (ReHo). Our results revealed that patients, compared to healthy controls, exhibited significantly elevated ALFF/fALFF in the lateral orbitofrontal cortex, anterior insula, and caudate, as well as increased ReHo in the ventrolateral prefrontal cortex but reduced ReHo in the middle occipital gyrus. These regions were then subjected to resting-state functional connectivity and functional decoding analyses based on a dataset of 110 healthy participants, allowing for a data-driven inference on psychophysiological functions. These regions and their networks are mapped onto systems implicated in cognitive control, social functioning, emotional processing, and sensory perception. Collectively, our findings delineate a suite of transdiagnostic neural aberrations reflected in resting-state activity, thereby advancing the neurobiological validation of the dimensional frameworks and highlighting potential common targets for therapeutic intervention.

PMID:41271623 | DOI:10.1038/s41398-025-03760-2

Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Nov 19:S2451-9022(25)00359-3. doi: 10.1016/j.bpsc.2025.11.002. Online ahead of print.

ABSTRACT

BACKGROUND: Neuroimaging methods rely on models of neurovascular coupling that assume hemodynamic responses are canonical; evolving seconds after changes in neural activity. However, emerging evidence reveals noncanonical blood oxygen level dependent (BOLD) responses that are delayed under stress and aberrant in neuropsychiatric conditions.

METHODS: We simultaneously recorded EEG and fMRI in people with schizophrenia (n=57) and psychiatrically unaffected participants (n=46) during a resting-state paradigm. We focused on alpha band power to examine correlations with voxelwise, time-lagged BOLD signals as a dynamic measure of EEG-BOLD coupling.

RESULTS: We found pronounced diversity in the temporal profile of alpha-BOLD coupling across the brain. This included early coupling (0-2 seconds BOLD lag) for more posterior regions of the default mode network (DMN), thalamus and brainstem. Anterior regions of the DMN showed coupling at more canonical lags (4-6 seconds), although some participants showed greater than expected lags associated with self-reported measures of stress as well as greater lag scores in participants with schizophrenia. Overall, noncanonical alpha-BOLD coupling is widespread across the DMN and other non-cortical regions, and is delayed in people with schizophrenia.

CONCLUSIONS: These findings suggest that hemodynamic signals are dynamically coupled to ongoing neural activity across distributed networks. And further, that the hemo-neural lag may be associated with subjective arousal or stress. Our work highlights the need for more studies of neurovascular coupling in psychiatric conditions.

PMID:41271013 | DOI:10.1016/j.bpsc.2025.11.002

Phys Rev Lett. 2025 Nov 7;135(19):198401. doi: 10.1103/lvwj-hjr3.

ABSTRACT

We apply the phenomenological renormalization group to resting-state fMRI time series of brain activity in a large population. By recursively coarse graining the data, we compute scaling exponents for the series variance, log probability of silence, and largest covariance eigenvalue. The scaling exponents clearly exhibit linear interdependencies in the form of scaling relations and inherent variability of values closely related to the structure of correlations of brain activity. The scaling relations between the exponents are derived analytically. We find a significant correlation of exponents with clinical (gray matter volume) and behavioral (cognitive performance) traits. Akin to scaling relations near critical points in thermodynamics, our results suggest that this interdependency is intrinsic to brain organization, and may also exist in other complex systems.

PMID:41269946 | DOI:10.1103/lvwj-hjr3

Front Hum Neurosci. 2025 Nov 5;19:1682584. doi: 10.3389/fnhum.2025.1682584. eCollection 2025.

ABSTRACT

Minimal hepatic encephalopathy (MHE) is the initial stage of hepatic encephalopathy (HE), MHE patients have associated with widespread neuro-psychological impairment. Liver transplantation (LT) can restore metabolic abnormalities but the mechanisms are unclear. This study aimed to longitudinally evaluate brain function alteration in MHE patients one month after LT and their correlation with cognitive changes by using resting-state functional magnetic resonance imaging (rs-fMRI). Rs-fMRI data was collected from 32 healthy controls and 27 MHE before and 1 month after LT. Between-group comparisons of demographic data and neuropsychological scores were analyzed using SPSS 25.0. Functional imaging data were analyzed using RESTplus and SPM12 software based on MATLAB 2017b. Gender, age, and years of education were used as covariates to obtain low-frequency fluctuationd (ALFF) and dynamic low-frequency fluctuation (dALFF) dindices. Correlation analyses were performed to explore the relationship between the change of ALFF and dALFF with the change of clinical indexes pre- and post-LT. Compared to controls, ALFF values increased in the Left Cerebelum 8, right orbital part of the inferior frontal gyrus (ORBinf), right superior occipital gyrus (SOG) and decreased in right PreCG and left middle frontal gyrus (MFG) in patients post-LT; dALFF values increased in the right temporal pole and middle temporal gyrus (TPOmid), right ORBinf, left caudate nucleus (CAU), right SOG and decreased in left PreCG, left PCUN, left ANG, left SMA and left MFG in patients post-LT. Compared to pre-LT, ALFF values of post-LT patients increased in the right calcarine fissure and surrounding cortex (CAL), right MOG and decreased in right cerebelum 8, left PCUN; dALFF values of post-LT patients decreased in right thalamus (THA), left posterior cingulate gyrus (PCG) and left MFG. The changes of ALFF in the left PCUN, right CAL and right MOG were correlated with change of digit symbol test (DST) scores (P < 0.05). In summary, this study not only showcases the potential of ALFF/dALFF algorithms for assessing alterations in spontaneous neural activity in MHE, but also provides new insights into the altered brain functions in MHE patients 1 month after LT, which may facilitate the elucidation of elucidation of mechanisms underlying cognitive restoration post-LT in MHE patients.

PMID:41268147 | PMC:PMC12626923 | DOI:10.3389/fnhum.2025.1682584