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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

Sci Rep. 2025 Nov 20;15(1):41056. doi: 10.1038/s41598-025-24944-2.

ABSTRACT

Identifying brain mechanisms implicated in the intergenerational transmission of major depressive disorder (MDD) is crucial for early detection and developing novel interventions. One promising mechanism involves altered intrinsic connectivity patterns in brain networks supporting emotion processing, including within the cognitive control network (CCN). The current preliminary study used resting state functional magnetic resonance imaging (fMRI) to examine whether altered CCN connectivity patterns are a brain-based mechanism of intergenerational risk for depression. We tested whether CCN connectivity patterns (1) differentiated mothers with and without recurrent MDD, (2) differentiated their high-risk (HR) and low-risk (LR) daughters, and (3) served as prospective predictors of daughters' depressive symptoms over a multi-wave follow-up. Participants were 56 mother-daughter pairs who completed a resting state fMRI scan. Mothers with, versus without, a history of MDD exhibited reduced connectivity between the CCN and other regions within the CCN, such as the middle frontal gyrus and dorsal anterior cingulate cortex (ACC). Reduced connectivity between the CCN and dorsal ACC was also observed in HR, relative to LR, daughters, correlated significantly among mothers and daughters, and associated with higher depression symptoms in daughters across 18 months. Reduced connectivity within the CCN may constitute one brain-based marker to further investigate as a target for prevention to attenuate the intergenerational transmission of depression.

PMID:41266636 | DOI:10.1038/s41598-025-24944-2

NMR Biomed. 2026 Jan;39(1):e70189. doi: 10.1002/nbm.70189.

ABSTRACT

Cerebrovascular reactivity (CVR) is a crucial physiological marker of vascular health and has been linked to aging-related cerebrovascular decline. Resting-state BOLD MRI-based relative CVR mapping (RS-rCVR) offers a noninvasive and compliance-friendly alternative to gas-challenge methods, making it suitable for lifespan studies. This study aimed to examine age-related differences in RS-rCVR among healthy adults using voxel-wise and region-of-interest (ROI) analyses. We prospectively recruited 54 healthy adults, including 27 younger (20-28 years, mean = 23.3 ± 3.4; 16 females) and 27 older (57-75 years, mean = 66.5 ± 5.3; 16 females) participants. Resting-state fMRI data were acquired using a T2*-weighted gradient-echo EPI sequence at 3T. RS-rCVR maps were generated by linear regression of the voxel-wise BOLD time series against the global BOLD signal and normalized to cerebellar gray matter. Group-level analyses included voxel-wise comparisons, histogram analyses, and ROI-based statistical tests. The distribution of RS-rCVR values significantly differed between age groups (Kolmogorov-Smirnov test: KS = 0.039, p < 0.001). Voxel-wise comparisons revealed age-related reductions in RS-rCVR in the medial frontal cortex, precuneus, and cuneus in older adults. In contrast, ROI-averaged RS-rCVR values showed no statistically significant group differences across frontal, temporal, and occipital cortices (p > 0.05). Effect size analysis indicated small to moderate differences in specific regions (e.g., occipital cortex: d = 0.441; parietal cortex: d = 0.430; frontal middle gyrus: d = 0.275), but negligible effects in others (e.g., cingulate cortex: d = 0.006). While voxel-wise RS-rCVR mapping detects spatially localized age-related reductions in cerebrovascular reactivity, ROI-based analysis may obscure these effects due to anatomical averaging. These findings underscore the spatial heterogeneity of cerebrovascular aging and support the utility of voxel-level RS-rCVR approaches in lifespan research.

PMID:41265868 | DOI:10.1002/nbm.70189

Neuroimage Clin. 2025 Nov 8;48:103906. doi: 10.1016/j.nicl.2025.103906. Online ahead of print.

ABSTRACT

Very preterm birth significantly increases the risk of lifelong cognitive and motor deficits by disrupting early brain development. We characterised functional brain organisation at six years of age in children born very preterm (VPT) compared to term-born controls (TC). Functional MRI comprised i) visually cued ∼1 Hz right- and left-hand tapping analysed with a general linear model and permutation testing, and ii) eyes-closed resting-state acquisitions assessed with ROI-to-ROI connectivity for cortical/cerebellar and subcortical networks. Seventy-one children born <31 weeks gestation were scanned; high-quality task data were available from 58 (43 VPT, 15 TC) for right-hand and 48 (36 VPT, 12 TC) for left-hand tapping, and resting-state data from 40 (28 VPT, 12 TC). Both groups activated expected motor regions, but VPT children showed greater activation and stronger left-temporal lateralisation during right-hand tapping (p < 0.032). Resting-state analyses revealed weaker connectivity in VPT children within striatal circuits and between salience, dorsal-attention, and visual networks, alongside reduced anticorrelation between default-mode and frontoparietal networks. Our findings indicate that children born very preterm exhibit a persistent reorganisation of both task-evoked motor activation and large-scale cortical and subcortical resting-state network connectivity at six years of age. These differences in functional brain organisation provide insights into long-lasting neurobiological changes associated with very preterm birth during a critical period of child development.

PMID:41265076 | DOI:10.1016/j.nicl.2025.103906

Commun Biol. 2025 Nov 20;8(1):1619. doi: 10.1038/s42003-025-08972-2.

ABSTRACT

The hippocampus and cerebellum are traditionally considered to support distinct memory systems, yet evidence from nonhuman species indicates a close relationship during spatial-mnemonic behaviour, with hippocampal projections to and from several cerebellar regions. However, little is known about this relationship in humans. To address this, we applied seed-based functional connectivity analysis to resting-state fMRI data from 479 cognitively normal participants aged 18-88 years. We identified significant functional correlations between the hippocampus and widespread areas of cerebellar cortex, particularly lobules HIV, HV, HVI, HVIIA (Crus I and II), HIX, and HX. Moreover, anterior hippocampus showed stronger connectivity with right Crus II, whereas posterior hippocampus was strongly connected to vermal lobule V. Finally, we observed age-related reductions in functional connectivity between the hippocampus and lobules HVI and HV. These findings provide insight into the topography of hippocampal-cerebellar functional organisation in humans and the influence of ageing on this system.

PMID:41266803 | DOI:10.1038/s42003-025-08972-2

Imaging Neurosci (Camb). 2025 Nov 17;3:IMAG.a.1022. doi: 10.1162/IMAG.a.1022. eCollection 2025.

ABSTRACT

Many studies utilize resting-state functional magnetic resonance imaging (rs-fMRI) metrics, such as functional connectivity (FC), to investigate the neuronal underpinnings of autism and identify functional brain networks related to autistic behaviors. However, fMRI indirectly measures neuronal activity by imaging local fluctuations in the blood oxygen level dependent (BOLD) signal, which, in turn, rely on the cerebrovascular system to efficiently direct oxygenated blood flow. Most rs-fMRI studies of autism interpret group differences in FC as autism-related changes in neuronal activity, without considering the underlying vascular function. Yet, atypical cerebrovasculature has been identified in preclinical and post-mortem studies of autism, strongly underscoring the need to characterize cerebrovascular differences to enhance our neurobiological understanding of autism. We evaluated relative cerebrovascular reactivity (rCVR) in autistic and non-autistic children using a novel measure of local brain vasodilatory capacity based on rs-fMRI. We leveraged the cross-sectional Autism Brain Imaging Data Exchange repository to quantify rCVR in 199 non-autistic (74 female) and 95 autistic (16 female) children, 9-12 years old. We identified sex-specific differences in rCVR in autism, particularly in right-frontal brain regions, where rCVR was increased in autistic females compared to non-autistic females. Then, within the same rs-fMRI scans, we demonstrated that rCVR in the right inferior frontal gyrus was positively associated with its FC to regions associated with attentional control in girls, suggesting that cerebrovascular differences may differentially affect FC findings between regions and sexes in children. Our study highlights potential sex differences in cerebrovascular function in autism that enhance our neurobiological understanding of autism and improve interpretations of rs-fMRI findings in children.

PMID:41262555 | PMC:PMC12624364 | DOI:10.1162/IMAG.a.1022