Most recent paper

Transl Psychiatry. 2025 Oct 6;15(1):356. doi: 10.1038/s41398-025-03584-0.

ABSTRACT

The multidimensional nature of schizophrenia requires a comprehensive exploration of the functional and structural brain networks. While prior research has provided valuable insights into these aspects, our study goes a step further to investigate the reconfiguration of the hierarchy of brain dynamics, which can help understand how brain regions interact and coordinate in schizophrenia. We applied an innovative thermodynamic framework, which allows for a quantification of the degree of functional hierarchical organisation by analysing resting state fMRI-data. Our findings reveal increased hierarchical organisation at the whole-brain level and within specific resting-state networks in individuals with schizophrenia, which correlated with negative symptoms, positive formal thought disorder and apathy. Moreover, using a machine learning approach, we showed that hierarchy measures allow a robust diagnostic separation between healthy controls and schizophrenia patients. Thus, our findings provide new insights into the nature of functional connectivity anomalies in schizophrenia, suggesting that they could be caused by the breakdown of the functional orchestration of brain dynamics.

PMID:41053029 | PMC:PMC12501247 | DOI:10.1038/s41398-025-03584-0

J Affect Disord. 2025 Oct 4;393(Pt A):120357. doi: 10.1016/j.jad.2025.120357. Online ahead of print.

ABSTRACT

BACKGROUND: Childhood trauma is a recognized risk factor for affective dysregulation and aggressive behavior in adolescents with Internet Gaming Disorder (IGD). However, the neurobiological pathways linking trauma to aggression in IGD remain poorly understood. This study aims to investigate how childhood trauma affects aggressive behavior in adolescents with IGD through specific neurobiological mechanisms.

METHOD: We recruited 111 adolescents categorized into IGD with childhood trauma (IGD-CT, n = 43), IGD without trauma (IGDN, n = 31), and healthy controls (HC, n = 37). Using resting-state fMRI, we computed amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) to compare spontaneous brain activity. Then we correlated the regional ALFF/fALFF with levels of childhood trauma (CTQ-SF score) and aggression. Finally, we conducted a mediation analysis to examine whether brain activity modulates the relationship between childhood trauma and aggression in youths with IGD.

RESULTS: The IGD-N group showed significantly lower fALFF compared with the IGD-CT group，they also had significant differences compared to the HC group. Furthermore, the mean fALFF in the left precuneus was positively correlated with CTQ-SF (r = 0.321, p = 0.008) and reactive aggression (RA; r = 0.396, p = 0.001) scores. Importantly, the mean fALFF in the left precuneus partially mediated the correlation between CTQ-SF and RA scores (effect proportion of 28.18 %).

CONCLUSION: Our study identifies the left precuneus as a critical neural region where childhood trauma uniquely impacts aggression in adolescents with IGD. The findings highlight the necessity for targeted interventions in IGD with trauma histories, providing potential avenues to reduce aggression in at-risk adolescents affected by both trauma and gaming addiction.

PMID:41052680 | DOI:10.1016/j.jad.2025.120357

Neuroimage Rep. 2025 Jul 8;5(3):100275. doi: 10.1016/j.ynirp.2025.100275. eCollection 2025 Sep.

ABSTRACT

BACKGROUND: Obsessive-compulsive disorder (OCD) is commonly treated with cognitive-behavioral therapy (CBT), yet many patients fail to achieve remission. Neuroimaging markers, such as pre-treatment functional and structural connectivity, may help elucidate OCD pathology and CBT mechanisms, and predict treatment outcomes. This study investigates the relationship between pre-treatment functional and structural connectivity and remission status in OCD patients following CBT.

METHODS: Thirty-three OCD patients underwent multimodal MRI, including resting-state fMRI to assess pre-treatment functional connectivity and diffusion tensor imaging (DTI) to evaluate white matter integrity. Functional connectivity multivariate pattern analysis (fc-MVPA) identified patterns linked to treatment outcomes. TRACULA, a probabilistic tractography technique, analyzed white matter tracts, focusing on diffusion metrics such as fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). Analysis of covariance (ANCOVA) was used to examine group differences.

RESULTS: Remission was associated with significantly higher pre-treatment resting-state functional connectivity between the occipital pole and lateral occipital cortex (height threshold: p < 0.001 uncorrected and cluster threshold: p < 0.05 cluster-size FDR corrected for multiple comparisons), suggesting a role in visual processing. Differences in white matter integrity were found in the corpus callosum rostrum, left acoustic radiation, right dorsal cingulum bundle, and right superior longitudinal fasciculus II, though these results were not corrected for multiple comparisons.

CONCLUSION: Enhanced pre-treatment functional connectivity in visual processing regions and specific white matter tracts may serve as biomarkers for remission in OCD following CBT. These findings could improve understanding of CBT's neural effects and guide personalized treatment strategies.

PMID:41050951 | PMC:PMC12489777 | DOI:10.1016/j.ynirp.2025.100275

Neuroimage Rep. 2025 Jun 28;5(3):100278. doi: 10.1016/j.ynirp.2025.100278. eCollection 2025 Sep.

ABSTRACT

Frontal lobe epilepsy (FLE), marked by recurrent seizures arising from the frontal lobes, can significantly impair cognitive and motor function, reducing quality of life. Recent studies suggest that epilepsies can involve functional networks throughout the brain that can be identified using resting-state functional magnetic resonance imaging (fMRI). In this study, we aimed to determine whether FLE is associated with a distinct functional network brain states. Using dynamic functional connectivity analysis in combination with k-means clustering, we investigated dynamic connectivity patterns of the somatomotor network (SMN) and default mode network (DMN) of ten right-hemisphere and six left-hemisphere FLE patients, as well as nine healthy controls. We found two distinct states of rest for both the SMN and DMN: a high connectivity state and a lower, more variable connectivity state that was often specific to individual patients. Both FLE groups showed reduced overall connectivity compared to controls, with the greatest differences emerging during the low connectivity state. Right FLE patients and controls exhibited relatively uniform reductions, whereas left FLE patients showed spatially specific disruptions, including reduced lateral-to-medial SMN connectivity and decreased connectivity in posterior and left-lateralized DMN regions. Our findings suggest that dynamic connectivity analysis can uncover the temporal complexity and patient-specific nature of brain network disruption in FLE, supporting the development of personalized diagnostic and treatment strategies. Further research with larger cohorts is necessary to validate these results and explore additional factors affecting brain functional connectivity.

PMID:41050945 | PMC:PMC12489773 | DOI:10.1016/j.ynirp.2025.100278

Neuroimage Rep. 2025 Jun 25;5(3):100276. doi: 10.1016/j.ynirp.2025.100276. eCollection 2025 Sep.

ABSTRACT

Functional Near-Infrared Spectroscopy (fNIRS) is an emerging neurotechnology that has several advantages over fMRI, but questions remain about factors that affect data quality and activity in stroke survivors. We examined the effect of protocol factors (Aim 1) and participant characteristics (Aim 2) on raw fNIRS signal quality and tested associations between quality control metrics and brain activity and connectivity (Aim 3) in a sample of 107 individuals with a history of left or right hemisphere stroke. Participants completed tasks that varied by cognitive and motor speech demands (from low to high): Resting State, Discourse Comprehension, and Picture Naming. Scalp-coupling indices, peak spectral power values, and number of bad channels from each task were extracted from the Quality Testing of Near Infrared Scans (QT-NIRS) toolbox (Montero-Hernandez and Pollonini, 2020) and used to index raw data quality. Data quality did not vary by session location or protocol experience, but all data quality metrics from Picture Naming were significantly lower than those from the other tasks. fNIRS signals were generally worse for Black women compared to Black men and White individuals regardless of gender. No significant associations between the raw fNIRS signal quality and Resting State functional connectivity were found. However, relative changes in Picture Naming hemoglobin concentrations were associated with scalp-coupling indices for certain channels. These results highlight the need for careful data preprocessing of already collected data and a systematic approach in future studies to mitigate inherent biases of optical instruments, thereby enhancing the inclusion of underrepresented groups in neuroscience research.

PMID:41050942 | PMC:PMC12489784 | DOI:10.1016/j.ynirp.2025.100276

Proc IEEE Int Symp Biomed Imaging. 2025 Apr;2025:10.1109/isbi60581.2025.10980810. doi: 10.1109/isbi60581.2025.10980810. Epub 2025 May 12.

ABSTRACT

Predicting the development of functional connectivity (FC) derived from resting-state functional MRI is pivotal for elucidating the intrinsic brain functional organization and modeling its dynamic development during infancy. Existing deep learning methods typically predict FC at a target timepoint from each available FC independently, yielding inconsistent predictions and overlooking longitudinal dependencies, which introduce ambiguity in practical applications. Furthermore, the scarcity and irregular distribution of longitudinal rs-fMRI data pose significant challenges in accurately predicting and delineating the trajectories of early brain functional development. To address these issues, we propose a novel Triplet Cycle-Consistent Masked Autoencoder (TC-MAE) for the trajectory prediction of the development of infant FC. Our TC-MAE has the capability to traverse FC over an extended period, extract unique individual characteristics, and predict target FC at any given age in infancy with longitudinal consistency. Extensive experiments on 368 longitudinal infant rs-fMRI scans demonstrate the superior performance of the proposed method in longitudinal FC prediction compared with state-of-the-art approaches.

PMID:41050556 | PMC:PMC12490125 | DOI:10.1109/isbi60581.2025.10980810

Proc IEEE Int Symp Biomed Imaging. 2025 Apr;2025:10.1109/isbi60581.2025.10981047. doi: 10.1109/isbi60581.2025.10981047. Epub 2025 May 12.

ABSTRACT

How to harmonize site effects is a fundamental challenge in modern multi-site neuroimaging studies. Although many statistical models and deep learning methods have been proposed to mitigate site effects while preserving biological characteristics, harmonization schemes for multi-site resting-state functional magnetic resonance imaging (rs-fMRI), particularly for functional connectivity (FC), remain undeveloped. Moreover, statistical models, though effective for region-level data, are inherently unsuitable for capturing complex, nonlinear mappings required for FC harmonization. To address these issues, we develop a novel, flexible deep learning method, Mamba-based Residual Generative adversarial network (MR-GAN), to harmonize multi-site functional connectivities. Our method leverages the Mamba Block, which has been proven effective in traditional visual tasks, to define FC-specified sequential patterns and integrate them with a multi-task residual GAN to harmonize multi-site FC data. Experiments on 939 infant rs-fMRI scans from four sites demonstrate the superior performance of the proposed method in harmonization compared to other approaches.

PMID:41050555 | PMC:PMC12490067 | DOI:10.1109/isbi60581.2025.10981047

Clin Transl Sci. 2025 Oct;18(10):e70372. doi: 10.1111/cts.70372.

ABSTRACT

Although extensive research has linked education to the Stroop effect, the neural mechanisms by which higher education influences Stroop performance in non-demented older adults remain unclear. This study investigated this relationship in 126 older adults from Qingdao, stratified into higher education (> 12 years) and non-higher education (≤ 12 years) groups. Demographic data and Stroop performance were collected using a 50-item Stroop Color-Word Test (SCWT), yielding measures of completion time, correct responses, score-time ratio (efficiency), and time interference score (TI). Resting-state fMRI (rs-fMRI) was performed, and neural activity was assessed via amplitude of low-frequency fluctuations (ALFF) to identify regions of interest (ROIs). Multivariable regression models examined associations between education and Stroop outcomes, followed by correlation analyses between ROIs and performance. Bootstrap mediation analysis (5000 resamples) tested whether ROIs mediated the education-Stroop relationship. Results showed that higher education was significantly associated with better Stroop performance-shorter completion time, higher efficiency, and lower TI-after full adjustment (all p < 0.05). Rs-fMRI revealed greater ALFF in the right frontal eye field (FEF), right dorsolateral prefrontal cortex (DLPFC), and left dorsal anterior cingulate cortex (dACC) in the higher education group. These regions correlated negatively with completion time and TI, and positively with efficiency. Mediation analyses confirmed that right FEF, right DLPFC, and the combined ROIs significantly mediated the effects of higher education on Stroop performance. In conclusion, higher education may enhance Stroop performance in non-demented older adults by modulating resting-state neural activity in key cognitive control regions.

PMID:41046460 | PMC:PMC12497356 | DOI:10.1111/cts.70372

Radiol Med. 2025 Oct 4. doi: 10.1007/s11547-025-02079-8. Online ahead of print.

ABSTRACT

PURPOSE: Rotator cuff injury (RCI) is a prevalent cause of shoulder disability, with emerging evidence implicating localized inflammatory cascades as key mediators of nociceptive signaling. Recent studies suggest that preoperative central sensitization induced by exposure to inflammation serves as a predictor of persistent pain following surgery at one-year follow-up. However, the underlying mechanism between peripheral inflammation, central pain processing, and postsurgical pain remains poorly characterized in RCI. Therefore, we aim to characterize pain-elicited brain responses and identify brain mediators of pain hypersensitivity in RCI patients.

MATERIALS AND METHODS: Utilizing a case-control design, twenty-eight patients with right/bilateral RCI and twenty healthy controls underwent functional MRI during pressure noxious stimuli, with pain intensity quantified via the visual analog scale. Comprehensive analyses of preoperative resting-state fMRI, serum cytokine profiles, and postoperative neuroimaging were conducted in RCI patients.

RESULTS: We found significantly higher level of pain sensitivity and IL-6 concentrations in RCI patients compared to controls. RCI patients exhibited higher activation within the left primary somatosensory cortex (S1), which mediated the relationship between IL-6 levels and pain sensitivity. Notably, preoperative S1 amplitude of low-frequency fluctuations (ALFF) exhibited a strong positive correlation with IL-6 concentrations (r = 0.62) and served as a robust predictor of postoperative pain reduction. These findings establish left S1 hyperactivation as a neuroplastic hub integrating peripheral inflammatory signaling (IL-6 elevation) and central pain sensitization in RCI.

CONCLUSION: The observed preoperative associations between S1 function, cytokine profiles, and postoperative pain resolution provide translational evidence for S1 as a predictive biomarker of pain chronification risk.

PMID:41045353 | DOI:10.1007/s11547-025-02079-8

Eur J Neurosci. 2025 Oct;62(7):e70271. doi: 10.1111/ejn.70271.

ABSTRACT

Depressive symptoms are commonly observed in stress-related psychiatric disorders, such as major depressive disorder (MDD) and posttraumatic stress disorder (PTSD). To date, emerging evidence from behavior and psychology suggests the possibility of underlying neurobiological mechanisms in transdiagnostic depression. This study aims to identify predictive signatures of depression severity across MDD and PTSD using a whole-brain connectivity machine learning analysis based on resting-state functional magnetic resonance imaging (rs-fMRI). Patients with MDD (n = 84) and PTSD (n = 65), all medication-free at the time of enrollment, underwent rs-fMRI scans along with a battery of clinical assessments. Using a multivariate machine learning approach, we applied sparse connectome predictive modeling to identify a functional connectivity signature that predicts individual depression severity, as assessed by Hamilton Depression Rating Scale-17 items. The cross-validated model explained 42% of the variance in depression severity across MDD and PTSD. The identified connectome signature predominantly involved regions in the fronto-limbic circuit (e.g., middle frontal gyrus and temporal pole), subcortical areas (e.g., hippocampal, caudate, and brainstem), and the cerebellum. Our findings highlight diffuse whole-brain dysfunction patterns associated with depressive symptom severity, emphasizing the importance of transdiagnostic research in understanding the neurobiological mechanisms underlying key clinical features across disorders.

PMID:41045096 | DOI:10.1111/ejn.70271

J Neurochem. 2025 Oct;169(10):e70217. doi: 10.1111/jnc.70217.

ABSTRACT

Brain's high energy demands require abundant production of ATP from glucose oxidation, mandating coupling between neural activity and nutrient supply. Understanding how neural activity augments blood flow (CBF) to support metabolism of glucose (CMRglc) and oxygen (CMRO2) can help unravel mysteries of neurovascular and neurometabolic couplings underlying functional MRI (fMRI) with blood oxygenation level-dependent (BOLD) contrast. Key to this enigma is oxygen extraction fraction (OEF). Fundamentally, OEF is defined by flow-metabolism (i.e., CBF-CMRO2) coupling generating mitochondrial ATP to signify limits of hypoxia and ischemia. However, to fully account for observed CBF-CMRO2 coupling, the OEF must include a term for oxygen diffusivity (DO2) that is regulated by rheological properties of blood. BOLD contrast depends on intravoxel spin dephasing of tissue water protons due to paramagnetic fields generated by deoxyhemoglobin. During augmented neural activity, if CBF increases more than CMRO2, then deoxyhemoglobin (paramagnetic) is replaced by perfusing oxyhemoglobin (diamagnetic) to increase BOLD signal. Calibrated fMRI converts BOLD contrast into OEF according to the deoxyhemoglobin dilution model. Agreement across these OEF models (i.e., OEF trifecta) authenticates calibrated fMRI, both gas-based and gas-free methods. CMRO2 by gas-free calibrated fMRI easily and reproducibly tracks neural activity, while combining it with CMRglc can also reveal aerobic glycolysis. In summary, there is translational potential of gas-free calibrated fMRI for metabolic imaging in the resting and stimulated brain, from neurodegeneration to neurological disorders.

PMID:41044817 | DOI:10.1111/jnc.70217

Nat Commun. 2025 Oct 3;16(1):8817. doi: 10.1038/s41467-025-63633-6.

ABSTRACT

Changes in task-evoked activity in the entorhinal cortex (EC) and hippocampus have been shown to track changes in temporal context at short and long timescales. However, whether spontaneous changes in EC and hippocampal neural signals-in the absence of task demands-likewise reflect the passage of time remains unknown. Here, we leveraged a dense-sampling study in which two individuals underwent daily resting-state fMRI for 30 days. Similarity in EC- and anterior hippocampal-whole-brain resting connectivity patterns was negatively correlated with the time interval between sessions, suggesting a spontaneous, slow-drifting neural signature of time. These changes could not be explained by other time-varying factors (including session-wise changes in mood, hormones, or motion). Hippocampal connectivity temporal drifts followed an anterior-to-posterior gradient, and anterolateral EC showed stronger temporal drift than posteromedial EC. Finally, posterior networks (including visual and default mode) primarily drove drifts in EC- and hippocampal-whole-brain connectivity over time. Collectively, these findings reveal a resting-state connectivity signature that reflects the passage of time in the absence of task demands and follows a functional gradient along the longitudinal axis of the hippocampus.

PMID:41044064 | PMC:PMC12494713 | DOI:10.1038/s41467-025-63633-6

Neuroimage. 2025 Oct 1;321:121502. doi: 10.1016/j.neuroimage.2025.121502. Online ahead of print.

ABSTRACT

Resting-state functional MRI (fMRI) signals capture physiological processes, including systemic low-frequency oscillations (LFOs), respiration, and cardiac pulsations. These physiological oscillations-often treated as noise in functional connectivity analysis-reflect fundamental aspects of brain physiology and have recently been recognized as key drivers of brain waste clearance. However, these critical physiological signals are obscured in fMRI data due to slow sampling rates (typical repetition time (TR) > 0.8 s), which cause cardiac signal to alias into lower frequencies. To resolve physiological signals in fMRI datasets, we leveraged fast cross-slice sampling within each TR to hypersample the fMRI signal. A key novelty of this study is the development of a region-specific hypersampling approach, called HyPER (Hypersampling for Physiological signal Extraction in a Region-specific manner). HyPER enhances temporal resolution within coherently pulsating vascular and tissue compartments, including the major cerebral arteries, the superior sagittal sinus (SSS), gray matter (GM), and white matter (WM). This study is structured in three parts: (1) We developed and validated the HyPER approach using fast fMRI from a local dataset in four regions of interest: the major cerebral arteries, SSS, GM, and WM. (2) We applied this approach to the publicly available Human Connectome Project-Aging (HCP-A) dataset (ages 36-90 years), increasing the resolvable frequency by ninefold-from 0.625 Hz to 5.625 Hz-enabling clear separation of cardiac, respiration, and LFO oscillations. (3) We investigated how brain physiological pulsations change with age. Our findings revealed an age-related increase in cardiac and respiratory pulsations across all brain regions, likely reflecting an increased vessel stiffness and reduced dampening of high-frequency pulsations along the vascular network. In contrast, LFO pulsations generally declined with age, suggesting reduced vasomotion in the older brain. In summary, we demonstrated the feasibility and reliability of a region-specific hypersampling technique to resolve physiological pulsations in fMRI. This method can be broadly applied to existing fMRI datasets to uncover hidden physiological pulsations and advance our understanding of brain physiology and disease-related alterations.

PMID:41043798 | DOI:10.1016/j.neuroimage.2025.121502

Brain Res Bull. 2025 Oct 1:111563. doi: 10.1016/j.brainresbull.2025.111563. Online ahead of print.

ABSTRACT

This study explored the effects of 36-hour acute sleep deprivation on the functional connectivity of auditory-related brain regions in healthy young males, examining its associations with alertness and emotional states. Sixty participants were assessed before and after sleep deprivation using psychomotor vigilance tasks, sleepiness scales, mood scales, and resting-state fMRI. The findings indicated significant changes in the functional connectivity of auditory-related brain regions, involving multiple cognitive, emotional, and motor areas. Further correlation analysis revealed a complex relationship between auditory-related brain regions and alertness, sleepiness, and mood. This study provides new evidence on how sleep deprivation influences auditory-related brain function.

PMID:41043695 | DOI:10.1016/j.brainresbull.2025.111563

Biol Psychiatry. 2025 Oct 1:S0006-3223(25)01487-8. doi: 10.1016/j.biopsych.2025.09.012. Online ahead of print.

ABSTRACT

Functional magnetic resonance imaging (fMRI) data are often used to inform individual differences in cognitive, behavioral, and psychiatric phenotypes. These so-called "brain-behavior" association studies come in many flavors and are increasingly the focus of investigations utilizing large population neuroscience datasets. Still, many open questions surrounding the utility of task and resting state fMRI for modelling brain-behavior relationships remain, including the feasibility of conducting these investigations in developmental cohorts. With the growing availability of large neurodevelopmental datasets such as that provided by the Adolescent Brain Cognitive Development (ABCD) Study, we are now able to conduct well-powered analyses using large samples of longitudinal neuroimaging data collected from diverse populations of youth. Here we provide a high-level review of current controversies and challenges in this growing subfield of neuroscience, highlighting examples where task fMRI data and resting state fMRI data - either in isolation or combined - have yielded significant insights into brain-behavior associations. Challenges include issues related to measurement noise, appropriate estimation of effect sizes, and limits to generalizability due to insufficient diversity of samples. Innovative solutions involving advanced MRI data acquisition protocols, application of multivariate analysis methods, and more robust consideration of phenotypic complexity are reviewed. We propose that additional future directions for developmental cognitive neuroscience should include more reliable behavioral measures, multimodal neuroimaging brain-behavior studies, and greater consideration of environmental and other contextual influences on brain-behavior associations.

PMID:41043534 | DOI:10.1016/j.biopsych.2025.09.012

medRxiv [Preprint]. 2025 Sep 25:2025.09.23.25336175. doi: 10.1101/2025.09.23.25336175.

ABSTRACT

Identifying robust neuroimaging biomarkers for Alzheimer's disease (AD) and mild cognitive impairment (MCI) is essential for early diagnosis and intervention. In this study, we introduce a novel, fully automated, guided dynamic functional connectivity (dFNC) framework to extract multiple dynamic measures for distinguishing MCI/AD from cognitively normal (CN) individuals. Resting-state fMRI data were used to extract subject-specific brain networks via spatially constrained independent component analysis (scICA), using a multi-objective optimization framework to ensure alignment with known functional networks while preserving individual variability. Using these components, dFNC was computed through a sliding-window approach. ICA was then applied to the concatenated dFNC matrices from the UK Biobank (UKBB) dataset to identify five canonical brain states, each representing a replicable, independent pattern of connectivity. These states served as biologically informed priors in a state-constrained ICA (St-cICA), which was applied to each subject in the combined OASIS-3 and ADNI datasets to guide individual-level decomposition and ensure interpretable connectivity states guided by state priors derived from the normative UKBB sample. St-cICA extracted subject-specific dFNC features and associated weighted timecourses. To characterize dFNC patterns, we computed metrics from the most strongly expressed (primary) state and introduce estimation of the second-most expressed (secondary) state at each timepoint, including dwell time, occupancy rate, and transition probabilities. Group comparisons using two-sample t-tests revealed widespread and significant alterations in AD/MCI compared to CN individuals. AD/MCI participants exhibited higher dwell times and increased self-transitions, indicating reduced neural flexibility and a tendency to remain in specific connectivity states. In contrast, CN individuals showed more diverse and recurrent transitions, reflecting greater adaptability. Secondary transitions revealed widespread selective switching in CN, whereas AD/MCI showed reduced cross-state engagement. A classification model trained on 6,960 dynamic features achieved strong performance in distinguishing AD/MCI from CN (mean AUC ≈ 0.85). These findings highlight the potential of guided dFNC as a biomarker framework for early-stage AD detection using resting-state fMRI.

PMID:41040719 | PMC:PMC12485992 | DOI:10.1101/2025.09.23.25336175

bioRxiv [Preprint]. 2025 Sep 25:2025.09.23.678044. doi: 10.1101/2025.09.23.678044.

ABSTRACT

Mouse models are an essential tool for understanding behavior and disease states in neuroscience research. While genetic and sex-specific effects have been reported in many neurodegenerative and psychiatric illnesses, these factors may also alter baseline neuroanatomical features of mice. This raises the question of whether the observed changes are related to the disease being studied (i.e., pathological differences) or if there are baseline strain or sex differences that may potentially predispose animals to different responses. Over the past decade, tremendous effort has been made in mapping neural architecture at various scales; however, the complex relationships including identifying genetic and sex-specific differences in brain structure and function remain understudied. To bridge this gap, we used C57BL/6J and DBA/2J mice, two of the most widely used inbred mouse strains in neuroscience research, to investigate strain and sex-specific features of the brain connectome in awake animals using magnetic resonance imaging (MRI). By combining resting-state fMRI and diffusion MRI, we found that the motor, sensory, limbic, and salience networks exhibit significant differences in both functional and structural domains between C57BL/6J and DBA/2J mice. Further, functional and structural properties of the brain were significantly correlated in both strains. Our results underscore the importance of considering these baseline differences when interpreting the brain-behavior interactions in mouse models of human disorders.

PMID:41040314 | PMC:PMC12485708 | DOI:10.1101/2025.09.23.678044

bioRxiv [Preprint]. 2025 Sep 25:2025.09.24.678309. doi: 10.1101/2025.09.24.678309.

ABSTRACT

Dynamic coordination of metabolic demand across brain networks supports emerging cognitive abilities and may drive overall cognitive development, yet how these dynamics vary by sex and relate to cognition in late childhood remains unclear. Using resting-state fMRI from 2,000 healthy 9- to 11-year-olds in the ABCD study, we applied time-resolved dynamic time warping to quantify amplitude mismatches, a proxy of relative energy demand across brain intrinsic networks. Clustering revealed three recurring states: convergent (globally balanced), divergent (imbalanced), and mixed (intermediate). Females spent engaged more with the flexible mixed state, whereas males lingered longer in convergent and divergent states. Across the cohort, better performance on cognitive flexibility, processing speed, and long-term memory tasks correlated with greater overall time in the mixed state and with higher transition rates, but with shorter dwell in any single state. These findings indicate that neural flexibility, rather than prolonged stability, supports cognition during late childhood and that sex differences in dynamic energy coordination emerge well before adolescence.

PMID:41040180 | PMC:PMC12485794 | DOI:10.1101/2025.09.24.678309

BMC Psychiatry. 2025 Oct 2;25(1):924. doi: 10.1186/s12888-025-07351-9.

ABSTRACT

BACKGROUND: Thalamic structural and functional abnormalities in major depressive disorder (MDD) are linked to impairments in diverse cognitive and emotional functions via the thalamo-cortical circuit. Given the constraints of temporal and spatial factors on information exchange, investigating frequency-specific effective connectivity (EC) is essential for elucidating the abnormal mechanisms of spatiotemporal information communication in patients with MDD.

METHOD: We employed a large-scale, multicenter resting-state functional magnetic resonance imaging (fMRI) dataset comprising individuals with MDD and matched healthy controls. Frequency-specific EC between the thalamic subregions and cortical/subcortical regions was assessed using spectral Granger causality in four frequency bands: slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.185 Hz), and a classic frequency range (0.01–0.08 Hz). Support vector regression (SVR) models were employed to evaluate the predictive value of altered EC for clinical symptom scores.

RESULTS: Individuals with MDD exhibited significant and frequency-dependent abnormalities in thalamocortical and thalamo-subcortical EC, with the most pronounced disruptions observed in the slow-5 band. These abnormalities originate from the specific thalamic subregions and extend to cortical and subcortical regions. Among the frequency bands analyzed, EC alterations in the slow-5 band showed the strongest association with clinical severity and yielded the highest predictive performance in SVR models.

CONCLUSIONS: Frequency-specific EC disruptions, particularly within the slow-5 band, may reflect fundamental spatiotemporal communication deficits in MDD. These findings highlight the slow-5 thalamocortical and thalamo-subcortical EC as a potential neurobiological marker for diagnosis and a target for treatment strategies in MDD.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12888-025-07351-9.

PMID:41039494 | PMC:PMC12490111 | DOI:10.1186/s12888-025-07351-9

Sci Rep. 2025 Oct 2;15(1):34415. doi: 10.1038/s41598-025-17431-1.

ABSTRACT

Executive function plays an important role throughout an individual's life, and current research has shown that physical activity is an effective way to promote the development of executive function. Further research into the mechanisms in the brain that promote executive function has focused on populations with diseases, and no consistent conclusions have been drawn for healthy populations. Moreover, the differential effects of different exercise doses and sample characteristics on executive function brain activation remain unclear. In this study, we used an activation likelihood estimation (ALE) meta-analysis integrating 20 task-based and resting-state functional magnetic resonance imaging (fMRI) studies to investigate the mechanisms in the brain underlying the effects of different exercise interventions on executive functions in healthy populations. The results showed that exercise interventions significantly altered brain activation patterns during cognitive tasks, particularly in the frontal, precuneus, thalamus and cingulate regions. We examined exercise interventions in various sub-groups, showing patterns of effects in different age groups, exercise types and exercise durations.

PMID:41039062 | PMC:PMC12491555 | DOI:10.1038/s41598-025-17431-1