Most recent paper

Cereb Cortex. 2025 Nov 1;35(11):bhaf314. doi: 10.1093/cercor/bhaf314.

ABSTRACT

Insomnia disorder is the most common sleep disorder affecting millions of people. Brain research has linked insomnia to dysfunction in large-scale brain networks, not only during sleep but also in wakeful rest. Yet, the underlying brain dynamics remain little understood. In the present study, we directly addressed this using a data-driven framework for evaluating time-varying large-scale brain activity. We used functional magnetic imaging to compare participants with insomnia disorder to matched controls with no sleep complaints. Using Hidden Markov modeling (HMM) for a completely data-driven characterization of the brain dynamics of whole-brain activity, we found that insomnia disorder is characterized by significantly reduced switching rates between large-scale brain states. In particular, HMM was used to compare insomnia patients to controls, which showed that their brains spent significantly less time in two whole-brain states-the default mode network and a fronto-parietal network-complemented by increased time spent in a global activation state. Overall, the findings reveal the brain dynamics of insomnia to show that insomnia disorder is characterized by less flexible transitions between brain states at wakeful rest. This highlights the importance of evaluating the spatiotemporal dynamics of brain activity to advance the understanding of the neural underpinnings of insomnia disorder.

PMID:41283931 | DOI:10.1093/cercor/bhaf314

medRxiv [Preprint]. 2025 Oct 31:2025.10.29.25339059. doi: 10.1101/2025.10.29.25339059.

ABSTRACT

Migraine is a prevalent and disabling neurological disorder, characterized by difficulties in regulating headache activity, sensory processing, and cognitive-emotional states. Brain entropy quantifies the complexity of neural dynamics, where reduced entropy may reflect diminished neural adaptability, but its assessment with fMRI in migraine remains limited. Here, we examined alterations in brain entropy and their associations with clinical burden, attack timing, and symptomatology. Resting-state fMRI data were acquired from adults with episodic migraine, chronic migraine, and healthy controls. Following standard preprocessing, voxel-wise sample entropy was computed, and group differences were assessed using ANCOVA with age and sex as covariates. Associations with clinical burden and symptom measures were examined within affected regions. In chronic migraine, attack timing-related changes in entropy were further explored, and the largest Lyapunov exponent (LLE) was estimated to characterize chaotic dynamics underlying attack-related complexity changes. Migraine patients showed reduced entropy in sensory, attentional, and default mode regions compared to controls, most pronounced in chronic migraine. Lower entropy correlated with greater headache frequency and longer illness duration. In chronic migraine, entropy relatively increased during attacks in multisensory integration regions and was associated with positive and elevated LLEs, indicating partially restored complexity with weakly chaotic dynamics. Patients experiencing phonophobia and nausea also exhibited increased entropy in multisensory and default mode regions. Our findings demonstrate widespread reductions in brain entropy in migraine, reflecting impaired neural adaptability, whereas attacks may transiently restore complexity partially through chaotic dynamics. These results advance understanding of migraine pathophysiology and highlight potential targets for therapeutic intervention.

HIGHLIGHTS: Migraine is marked by reduced brain entropy across sensory, attentional and default mode regions, which correlates with disease burden.Reduced entropy reflects constrained neural adaptability within affected regions.Migraine attacks transiently restore entropy, suggesting partial recovery of neural adaptability.Positive and elevated Lyapunov exponents indicate a shift toward weakly chaotic dynamics during attacks.Symptoms such as phonophobia and nausea are linked to increased entropy in multisensory integration and default mode regions.

PMID:41282924 | PMC:PMC12636680 | DOI:10.1101/2025.10.29.25339059

medRxiv [Preprint]. 2025 Oct 23:2025.10.22.25338400. doi: 10.1101/2025.10.22.25338400.

ABSTRACT

Children with neurodevelopmental disorders have a high rate of listening difficulties, but despite decades of research, the relation between these conditions remains unclear. Using resting state fMRI to image the child's brain noninvasively, we investigate the distribution of speech, non-speech 'sound', and visual processing networks in the forebrain, and examine a cross-section of age differences within these networks in children (6-14 years old) with normal hearing, including typically developing children, children with listening difficulties (LiD), and children with attention-deficit/hyperactivity disorder (ADHD). Relative to typically developing children, a reduction in functional connectivity of the speech network was found in children with LiD. No reduction was found in connections processing non-speech sounds or visual stimuli in the children with LiD, suggesting a specific deficit in speech processing. A second group of children diagnosed with ADHD showed reduced connectivity in both speech and sound networks, but not in the visual network, suggesting a common underlying cause for auditory and speech difficulties in the auditory system of children with ADHD. We conclude that listening difficulties in children are mediated by speech-specific neural mechanisms. The findings strengthen research calls for obligatory speech intelligibility testing under challenging listening conditions (noise, reverberation) as a component of clinical pediatric audiological assessment.

PMID:41282742 | PMC:PMC12633567 | DOI:10.1101/2025.10.22.25338400

medRxiv [Preprint]. 2025 Oct 7:2025.10.03.25337252. doi: 10.1101/2025.10.03.25337252.

ABSTRACT

A neurobiologically-based diagnosis with superior reliability in place of clinical interview-based diagnosis is a primary goal in psychiatry. Dynamic functional connectomes (dFCs) identified using change-point detection applied to functional magnetic resonance imaging (fMRI) data was used to train graph convolutional network (GCN) models to classify persons with psychiatric diagnoses from healthy controls. We examined four samples, adolescent-onset schizophrenia (AOS), adult schizophrenia, major depressive disorder, bipolar disorder, each with healthy controls (HC) for resting state fMRI (rs-fMRI) and working memory task for AOS. Classification accuracy was as high as 89.2% (sensitivity=0.90; specificity=0.88) for adult schizophrenia. The GCNs were further examined to understand which nodes and edges contributed highly to the classification using Class Activation Mapping (CAM) and Integrated Gradients (IG), respectively. CAM and IG analysis were convergent between adult schizophrenia and AOS which included default mode network regions, cerebellum, and sensory regions for rs-fMRI. For working memory, Brodmann area 10 and dorsolateral prefrontal cortex contributed the most towards AOS classification. Applied in a clinical context, post-test probability of accurate classification was 93% for adult-onset schizophrenia using rs-fMRI with a positive test suggesting clinical usefulness of our model. Our results suggest that a combination of deep-learning models and explanatory algorithms can markedly improve diagnostic reliability, offer approaches to objective diagnostic approach, and provide a neurobiological basis for the diagnosis by identifying regions and edges in the networks.

PMID:41282733 | PMC:PMC12632661 | DOI:10.1101/2025.10.03.25337252

medRxiv [Preprint]. 2025 Nov 4:2025.11.02.25339341. doi: 10.1101/2025.11.02.25339341.

ABSTRACT

Brain aging - with and without pre-clinical Alzheimer's disease (AD) pathology - are associated with deterioration in the brain networks' coherence and/or co-activation/deactivation as well as with decline in most cognitive abilities, paving the road for a network-based conceptualization of the brain normal versus pathological aging. However, certain cognitive abilities, like crystallized memory, improve with age, which complicates the explanation of these changes solely through age-related decline in the brain networks. Using a cross-sectional cohort of 259 participants (62 young, and 197 older), which underwent two task-based (one declining and another improving with age), and one resting-state fMRI scans, plus a positron emission tomography scan (to determine preclinical amyloid accumulation), we found that the brain networks' co-activation/deactivation, but not coherence, significantly attenuate with age and/or AD pathology even in the task for which performance improves by age. Interestingly, we also found that an increase in the networks' co-activation/deactivation, but not coherence, was associated with an improvement in task performance. Finally, we provided preliminary evidence that the brain networks lose their task-evoked deactivations with age before their coherence. These findings shed light on the process of functional aging in the brain networks, differentiate functional aging of the brain networks' coherence at rest versus their task-evoked co-activation/deactivation, and emphasize the more dominant role of the task-evoked brain activity in understanding aging brain function and distinguishing it from preclinical AD.

PMID:41282727 | PMC:PMC12637759 | DOI:10.1101/2025.11.02.25339341

medRxiv [Preprint]. 2025 Oct 13:2025.10.10.25337774. doi: 10.1101/2025.10.10.25337774.

ABSTRACT

Trajectories of age-related neurocognitive decline are not uniform, and are impacted by numerous environmental and physiological factors. Earlier life phases set the stage for later life neurocognitive function, with midlife marking a critical transition characterized by increasing variability in cognitive, affective, and physiological functioning. Despite its importance, this turbulent period remains underrepresented in open neuroimaging and phenotypic data resources. To address this gap, the Nathan Kline Institute - Rockland Sample (NKI-RS) initiative created the 'Mapping Interindividual Variation in the Aging Connectome' (MIVAC) substudy-an openly shared, multimodal dataset designed to map brain aging trajectories beginning in midlife and assess the influence of modifiable factors such as cardiorespiratory fitness. This longitudinal investigation includes 348 community-ascertained participants aged 38 to 71 years at baseline. Data collection incorporated deep phenotyping across cognitive, behavioral, medical, and cardiorespiratory fitness domains, along with multimodal neuroimaging (resting-state fMRI, diffusion MRI, morphometric MRI, and arterial spin labeling) and biospecimen collection. The protocol harmonizes with prior NKI-RS substudies while incorporating age-specific considerations for cognitive and neural aging. The full dataset is openly available.

PMID:41282656 | PMC:PMC12633132 | DOI:10.1101/2025.10.10.25337774

Front Pharmacol. 2025 Nov 7;16:1705937. doi: 10.3389/fphar.2025.1705937. eCollection 2025.

ABSTRACT

INTRODUCTION: According to the original model of the Temperament and Character Inventory (TCI), personality dimensions would be related to different neurotransmitters' systems such as the dopaminergic and the serotoninergic ones.

METHODS: Our objective was to study associations between functional connectivity and personality in Parkinson's disease (PD). The data of 29 PD patients were collected (NCT04705207). It included personality evaluation using the TCI, functional connectivity from resting-state functional MRI, and anxio-depressive state from the Hospital Anxiety and Depression scale (HAD). Seed-to-voxels and ROI-to-ROI analyses were done in the CONN toolbox.

RESULTS: Significant association was found between Novelty Seeking scores and functional connectivity within the nucleus accumbens and one cluster formed of the orbitofrontal cortex. Significant associations were also found between Harm Avoidance scores and functional connectivity within the temporal pole and seven clusters (mainly formed of the post- and pre-central gyri, thalami, parietal lobule, putamen and temporal gyrus). These functional connectivities also correlated with HAD scores.

CONCLUSION: In accordance with the TCI model, Novelty Seeking seems to be related to the dopaminergic system within the nucleus accumbens and orbitofrontal cortex connectivity, implicated in impulsivity. Moreover, Harm Avoidance would be related to the serotoninergic system within the temporal and fronto-thalamo-parietal network connectivity, involved in depressive disorders.

CLINICAL TRIAL REGISTRATION: clinicaltrials.gov: NCT04705207 (https://clinicaltrials.gov/study/NCT04705207).

PMID:41282613 | PMC:PMC12634589 | DOI:10.3389/fphar.2025.1705937

Res Sq [Preprint]. 2025 Oct 10:rs.3.rs-7336363. doi: 10.21203/rs.3.rs-7336363/v1.

ABSTRACT

Schizophrenia is characterized by deficits in attention and working memory. The brain age gap (BAG), the difference between brain-predicted and chronological age, has emerged as a biomarker of brain dysfunction, but its association with dynamic brain function remains unclear. We developed brain age models using static (sFNC) and dynamic (dFNC) functional network connectivity from a large resting-state fMRI dataset ( N = 22,569; UK Biobank, HCP-Young Adult, HCP-Aging) and validated them in an independent schizophrenia cohort (FBIRN; N = 153). Higher BAGs were significantly associated with lower attention and working memory performance ( FDR p < 0.01 ), with dFNC-based models showing more potent effects than sFNC. Network-specific BAGs, particularly within cognitive control, default mode, and subcortical networks, were robust predictors of cognitive impairment. These findings establish dFNC-based BAG as a sensitive biomarker of cognitive dysfunction in schizophrenia and highlight the value of dynamic connectivity measures for advancing precision diagnostics and stratification.

PMID:41282222 | PMC:PMC12632720 | DOI:10.21203/rs.3.rs-7336363/v1

Neural Plast. 2025 Nov 14;2025:7885279. doi: 10.1155/np/7885279. eCollection 2025.

ABSTRACT

BACKGROUND: Resting-state functional magnetic resonance imaging (rs-fMRI) reveals diverse neural activity patterns in adolescent major depressive disorder (MDD) with nonsuicidal self-injury (NSSI; nsMDD). However, the reported results are inconsistent. The aim of this study was to conduct a meta-analysis to identify consistent patterns of brain activity alterations in adolescent nsMDD.

METHODS: A systematic search was conducted across PubMed, Web of Science, Embase, Google Scholar, Wanfang, and CNKI for rs-fMRI studies that compared nsMDD patients with healthy controls (HCs), up to June 30, 2025. Significant cluster coordinates were extracted for comprehensive analysis. We utilized regional homogeneity (ReHo) and amplitude of low-frequency fluctuations (ALFFs) analyses. Activation likelihood estimation (ALE) was used to identify regions of aberrant spontaneous neural activity in adolescent nsMDD compared to HCs.

RESULTS: Eight studies (249 adolescent nsMDD and 278 HCs) were included. The ALE meta-analysis revealed increased activity in the left lingual gyrus (LING; Brodmann area [BA] 18) in adolescent nsMDD compared to HCs (voxel size = 200 mm3; p < 0.05). Decreased activity was observed in the right posterior cingulate cortex (PCC; BA 29) in adolescent nsMDD compared to HCs (voxel size = 360 mm3; p < 0.05). Jackknife sensitivity analyses demonstrated robust reproducibility in five of eight tests for the left LING and in six of eight tests for the right PCC.

CONCLUSIONS: This meta-analysis confirms consistent alterations in specific brain regions in adolescent nsMDD, highlighting the potential of rs-fMRI to refine diagnostic and therapeutic strategies.

PMID:41281193 | PMC:PMC12638161 | DOI:10.1155/np/7885279

bioRxiv [Preprint]. 2025 Oct 27:2025.10.26.684597. doi: 10.1101/2025.10.26.684597.

ABSTRACT

This study proposes a novel method based on variational graph contrastive learning to quantify structure-function coupling at the regional level of the brain. We obtained whole-cortex scale structural connectivity matrices from publicly available studies and constructed matched functional connectivity matrices using resting-state functional MRI (rs-fMRI) from the WU-Minn Human Connectome Project (HCP). The core of the model is a dual-branch variational graph convolutional network, which aligns the latent representations of the same brain region across structural and functional modalities via contrastive learning, augmented with distance constraints and regularization. We define the Gaussian kernel output of the latent representations of a brain region in structural and functional connectivity as its structure-function coupling (SFC). The results indicate that the SFC is strongest in the Visual network and weakest in the Orbito-affective network, with its distribution pattern aligning with known cognitive hierarchy principles. Further ablation experiments and single run experiments validate the effectiveness of the model components and the robustness of the SFC metric. This study provides a new computational framework for extracting stable multimodal coupling features from complex brain network data.

PMID:41279894 | PMC:PMC12636471 | DOI:10.1101/2025.10.26.684597

bioRxiv [Preprint]. 2025 Nov 9:2025.11.08.687387. doi: 10.1101/2025.11.08.687387.

ABSTRACT

The neural mechanisms underlying the emergence of functional connectivity in resting-state fMRI remain poorly understood. Recent studies suggest that resting-state activity consists of brief periods of strong co-fluctuations among brain regions, which reflect overall functional connectivity. Others report a continuum in co-fluctuations over time, with varying degree of correlation to functional connectivity. These findings raise the critical question: what neural processes underlie the temporal structure of resting-state activity? To address this, we used a biophysically realistic whole-brain computational model in which resting-state activity emerged from temporal variations in the ion concentrations of potassium (K+) and sodium (Na+), intracellular chloride (Cl-), and the activity of the Na+/K+ ATPase. The model reproduced transient periods of high co-fluctuations, and the functional connectivity at different co-fluctuation levels correlated to varying degrees with the connectivity measured over the entire simulation, in line with experimental observations. The periods of high co-fluctuations were aligned with large changes in extracellular ion concentrations. Furthermore, critical parameters governing ion dynamics strongly affected both the timing of these transient events and the spatial structure of the resulting functional connectivity. The balance of excitatory and inhibitory activity further modulated their frequency and amplitude. Together, these results suggest that intrinsic fluctuations in ion dynamics could serve as a plausible neural mechanism for the temporal organization of co-fluctuations and resting-state functional connectivity.

PMID:41279802 | PMC:PMC12637618 | DOI:10.1101/2025.11.08.687387

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