Most recent paper

Prog Neuropsychopharmacol Biol Psychiatry. 2025 Sep 8:111486. doi: 10.1016/j.pnpbp.2025.111486. Online ahead of print.

ABSTRACT

BACKGROUND: Sexual dimorphism in human brain has garnered significant attention in neuroscience research. Although multiple investigations have examined sexual dimorphism in gray matter (GM) functional connectivity (FC), the research of white matter (WM) FC remains relatively limited.

METHODS: Utilizing resting-state fMRI data from 569 healthy young adults, we investigated sexual dimorphism in the WM functional connectome. We constructed both WM-WM and GM-WM FC networks and subsequently analyzed their FC strength, functional connectivity density, and network topological properties. Based on identified dimorphic features, a radial basis function support vector machine model was employed for sex prediction and classification. Validation analyses confirmed the reproducibility of our findings.

RESULTS: Our analyses revealed significant sexual dimorphism in FC within both the WM-WM and GM-WM networks. Notably, females generally exhibited stronger connection strengths across numerous pathways compared to males. Topologically, females displayed greater global system aggregation (higher clustering coefficient) in the WM-WM network. Similarly, within the GM-WM connectome, females showed enhanced network integration, specifically higher global and local efficiency in the frontoparietal network and increased clustering coefficient in the attention network. Critically, these dimorphic WM features proved effective for sex classification using machine learning; an integrated model combining WM-WM and GM-WM FCs achieved superior predictive performance over models using individual feature sets, highlighting the unique information encoded in WM functional dynamics.

CONCLUSION: This finding extends our understanding of brain sex differences beyond gray matter and provides novel insights into the neurological mechanisms potentially underlying sex-specific patterns in cognition, behavior, and susceptibility to brain disorders.

PMID:40930485 | DOI:10.1016/j.pnpbp.2025.111486

Neurobiol Dis. 2025 Sep 8:107089. doi: 10.1016/j.nbd.2025.107089. Online ahead of print.

ABSTRACT

Temporal lobe epilepsy (TLE) patients experience shifts between non-seizing and seizing brain states, but the structural networks underlying these transitions remain undefined and poorly characterized. We detected dynamic brain states in resting-state fMRI and constructed linked structural networks utilizing multi-shell diffusion-weighted MR data. Leveraging network control theory, we interrogated the structural data for all possible brain state transitions, identifying those requiring abnormal levels of transition energy (low or high) in TLE compared to matched healthy participants (n's = 25). Results revealed three transitions requiring significantly higher energy in TLE; no abnormally low-energy transitions were observed. In HPs, transitions relied on mediator regions that did not belong to the initial source or final target brain areas. TLE transitions involved a more restricted set of source/target regions, predominantly outside the epileptogenic temporal lobe. Our findings highlight the abnormal and inefficient network mechanisms that accrue from the network entrainment inherent to TLE seizure activity. We argue these findings clarify the pathologic effects and help explain the well-known cognitive inefficiencies and other deficits found in the TLE disorder.

PMID:40930431 | DOI:10.1016/j.nbd.2025.107089

Diabetes Obes Metab. 2025 Sep 10. doi: 10.1111/dom.70109. Online ahead of print.

ABSTRACT

AIMS: Obesity is associated with increased insulin-stimulated brain glucose uptake (BGU) which is opposite to decreased GU observed in peripheral tissues. Increased BGU was shown to be reversed by weight loss and exercise training, but the mechanisms remain unknown. We investigated whether neuroinflammation (TSPO availability) and brain activity drive the obesity-associated increase in BGU and whether this increase is reversed by exercise training.

MATERIALS AND METHODS: Twelve monozygotic twin pairs mean age 40.4 (SD) years discordant for BMI (leaner mean 29.1 (SD) 6.3, heavier 36.7 (SD) 7.0 kg·m-2) performed 6-month long exercise intervention. Insulin-stimulated BGU during euglycaemic-hyperinsulinaemic clamp, brain inflammation (translocator protein (TSPO) availability) and brain resting state activity were studied by [18F]FDG-PET, [11C]PK11195-PET, and fMRI, respectively. Cognitive function was assessed by an online survey.

RESULTS: Exercise training had no effect on insulin-stimulated BGU, brain neuroinflammation (TSPO availability), or BMI. Exercise improved VO2peak, whole-body insulin sensitivity, and cognitive function similarly in both groups (all, p <0.05) as well as decreased resting state brain activity in heavier co-twins (p <0.05). At baseline, heavier co-twins had worse whole-body insulin sensitivity (p <0.01), increased BGU in the parietal cortex and caudatus, as well as increased resting state brain activity (both, p <0.05) and no difference in cognitive function. Leaner co-twins had higher TSPO availability in white matter and the hippocampus (p <0.05).

CONCLUSIONS: Exercise training had no effect on insulin-stimulated BGU or neuroinflammation (TSPO availability) but it reversed increased resting state brain activity in heavier co-twins. At baseline, obesity was associated with increased insulin-stimulated BGU and resting state brain activity, independent of genetics.

PMID:40926735 | DOI:10.1111/dom.70109

Comput Med Imaging Graph. 2025 Aug 28;125:102632. doi: 10.1016/j.compmedimag.2025.102632. Online ahead of print.

ABSTRACT

Bipolar disorder (BD) is a debilitating mental illness characterized by significant mood swings, posing a substantial challenge for accurate diagnosis due to its clinical complexity. This paper presents CS2former, a novel approach leveraging a dual channel-spatial feature extraction module within a Transformer model to diagnose BD from resting-state functional MRI (Rs-fMRI) and T1-weighted MRI (T1w-MRI) data. CS2former employs a Channel-2D Spatial Feature Aggregation Module to decouple channel and spatial information from Rs-fMRI, while a Channel-3D Spatial Attention Module with Synchronized Attention Module (SAM) concurrently computes attention for T1w-MRI feature maps. This dual extraction strategy is coupled with a Transformer, enhancing feature integration across modalities. Our experimental results on two datasets, including the OpenfMRI and our collected datasets, demonstrate CS2former's superior performance. Notably, the model achieves a 10.8% higher Balanced Accuracy on our dataset and a 5.7% improvement on the OpenfMRI dataset compared to the baseline models. These results underscore CS2former's innovation in multimodal feature fusion and its potential to elevate the efficiency and accuracy of BD diagnosis.

PMID:40926451 | DOI:10.1016/j.compmedimag.2025.102632

Am J Audiol. 2025 Sep 9:1-17. doi: 10.1044/2025_AJA-25-00025. Online ahead of print.

ABSTRACT

PURPOSE: This study investigated the effects of age-related hearing decline on functional networks using resting-state functional magnetic resonance imaging (rs-fMRI). The main objective of the present study was to examine resting-state functional connectivity (RSFC) and graph theory-based network efficiency metrics in 49 adults categorized by age and hearing thresholds to identify the neural mechanisms of age-related hearing decline.

METHOD: Forty-nine adults with self-reported normal hearing underwent pure-tone audiometry and rs-fMRI. RSFC within key brain networks and auditory-related brain regions, including the default mode network, salience network, dorsal attention network, and primary auditory cortices, was assessed using region-of-interest-based and graph theory approaches. Functional metrics, such as RSFC strength and global and local efficiency, were compared across age groups (middle age vs. older age) and hearing profile groups (negative screening vs. positive screening and negative high-frequency [HF] screening vs. positive HF screening).

RESULTS: Older adults demonstrated significantly weaker RSFC between the left primary auditory cortex and the left rostrolateral prefrontal cortex/anterior cingulate cortex within the salience network than middle-aged adults. Participants without age-related hearing decline exhibited weaker internetwork connectivity within the dorsal attention network and bilateral auditory regions, highlighting the impact of hearing sensitivity on network functionality. Graph theory metrics indicated greater local efficiency in nodes within the salience network among individuals without age-related hearing decline, suggesting preserved cognitive control and attentional processing.

CONCLUSIONS: Age and hearing thresholds significantly affected the functional connectivity and network efficiency of the brain. These results emphasize the importance of neuroimaging techniques like rs-fMRI in studying neural mechanisms of age-related hearing loss.

SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.29945021.

PMID:40924510 | DOI:10.1044/2025_AJA-25-00025

JAACAP Open. 2024 Sep 26;3(3):725-735. doi: 10.1016/j.jaacop.2024.09.003. eCollection 2025 Sep.

ABSTRACT

OBJECTIVE: Despite rapid advancements in understanding of cognitive disengagement syndrome (CDS) in children, less is known about the neural correlates of CDS. The aim of this study was to examine associations between CDS symptom severity and connectivity within and between specific brain networks.

METHOD: The study recruited 65 right-handed children (ages 8-13 years; 36 boys) with the full continuum of CDS symptom severity from the community. As part of a cross-sectional descriptive study investigating CDS, children underwent 10-minute resting-state functional magnetic resonance imaging. Connectivity metrics were extracted from the default mode network and ventral and dorsal attention networks. Parents and teachers completed measures of CDS and attention-deficit/hyperactivity disorder inattention symptoms. Multivariate parametric cluster analyses were performed on within- and between-network connections of the specified networks, with age and sex included as covariates. Separate models were conducted with and without controlling for attention-deficit/hyperactivity disorder inattention symptom severity.

RESULTS: Parent-rated CDS symptom severity was not significantly associated with any between- or within-network associations of interest. When attention-deficit/hyperactivity disorder inattention symptom severity was included in the model, teacher-rated CDS symptom severity was associated with greater functional connectivity between several regions of the default mode network and ventral attention network.

CONCLUSION: This study builds on theoretical and empirical evidence suggesting atypical connectivity between task-positive and task-negative networks as potentially key for understanding the neural correlates of CDS. These findings are important for building a neuroscience-based understanding of CDS and support emerging theoretical models linking CDS to mind wandering as well as DMN-related dysfunction.

PMID:40922795 | PMC:PMC12414302 | DOI:10.1016/j.jaacop.2024.09.003

JAACAP Open. 2024 Sep 27;3(3):758-767. doi: 10.1016/j.jaacop.2024.09.008. eCollection 2025 Sep.

ABSTRACT

OBJECTIVE: Psychological distress (eg, anxiety and depression) during pregnancy can disrupt fetal brain development and negatively affect infant behavior. Prenatal distress rose substantially during the COVID-19 pandemic according to most, but not all, studies, raising concerns about its potential effects on brain connectivity and behavior in infants.

METHOD: We investigated 63 mother-infant pairs as part of the Pregnancy during the COVID-19 Pandemic study. Mothers reported depression and anxiety symptoms prospectively during pregnancy; these were combined into one measure of prenatal maternal distress. Infant brain resting state functional magnetic resonance imaging (rs-fMRI) scans were obtained at 3 months of age, and mothers assessed infant behavior at 6 and 12 months using the Infant Behavior Questionnaire-Revised (IBQ-R) and the Ages and Stages Questionnaire (ASQ-3), respectively. The rs-fMRI was processed to measure functional connectivity within auditory, left frontoparietal, and default mode networks, and connectivity was tested for relationships to prenatal maternal distress. Prenatal distress and brain connectivity were also tested for relationships with infant behavior.

RESULTS: Higher prenatal maternal distress was related to stronger functional connectivity in the infant auditory network (T = 2.5, p = 0.01, q = 0.04, df = 59) and higher infant ASQ-3 personal-social scores (T = 2.9, p = 0.006, q = 0.03, df = 48). No significant associations were found between brain connectivity and infant behavior.

CONCLUSION: The impact of exposure to maternal prenatal distress on infant brain networks may be more apparent in networks that develop early, such as the auditory network, compared to later-developing networks, the effects of which may emerge later in childhood. The link between prenatal maternal distress and higher infant behavior scores may suggest compensatory changes, although further study is needed to determine how behavior manifests in the longer term.

DIVERSITY & INCLUSION STATEMENT: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented sexual and/or gender groups in science. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. We actively worked to promote sex and gender balance in our author group.

PMID:40922793 | PMC:PMC12414312 | DOI:10.1016/j.jaacop.2024.09.008

J Affect Disord. 2025 Sep 6:120230. doi: 10.1016/j.jad.2025.120230. Online ahead of print.

ABSTRACT

Major Depressive Disorder (MDD) poses a significant global health threat, impairing individual functioning and increasing socioeconomic burden. Accurate diagnosis is crucial for improving treatment outcomes. This study proposes Time-Frequency Text-Attributed DeepWalk (TF-TADW), a framework for MDD classification using resting-state functional MRI data. TF-TADW integrates time-frequency dynamics and brain network topology. A key aspect is the adaptive weighting of time-frequency features via an attention mechanism, enabling personalized representation learning to address MDD heterogeneity and mitigate site-specific biases in multi-site datasets. Matrix factorization simultaneously learns network topology and node attributes, creating a comprehensive brain network embedding. Evaluated on REST-meta-MDD and SRPBS-MDD, TF-TADW achieved accuracies of 80.13% and 91.97%, respectively. The attention mechanism also identified key MDD-related brain regions, enhancing interpretability. These results demonstrate TF-TADW's effectiveness and potential for clinical application.

PMID:40921213 | DOI:10.1016/j.jad.2025.120230

IEEE Trans Med Imaging. 2025 Sep 8;PP. doi: 10.1109/TMI.2025.3607113. Online ahead of print.

ABSTRACT

Analyzing the spontaneous activity of the human brain using dynamic approaches can reveal functional organizations. The co-activation pattern (CAP) analysis of signals from different brain regions is used to characterize brain neural networks that may serve specialized functions. However, CAP is based on spatial information but ignores temporal reproducible transition patterns, and lacks robustness to low signal-to-noise rate (SNR) data. To address these issues, this study proposes a new CAP framework based on hidden semi-Markov model (HSMM) called HSMM-CAP analysis, which can be performed to investigate spatiotemporal CAPs (stCAPs) of the brain. HSMM-CAP uses empirical spatial distributions of stCAPs as emission models, and assumes that the state sequence of stCAPs follows a semi-Markov process. Based on the assumptions of sparsity, heterogeneity, and semi-Markov property of stCAPs, the HSMM-CAP-K-means method is constructed to infer the state sequence and transition parameters of stCAPs. In addition, HSMM-CAP provides the inverse relationship between the number of states and sparsity. Simulation studies verify the performance of HSMM-CAP at different levels of SNR. The spatiotemporal dynamics of stCAPs are also revealed by the proposed method on real-world resting-state fMRI data. Our method provides a new data-driven computational framework for revealing the brain spatiotemporal dynamics of resting-state fMRI data.

PMID:40920526 | DOI:10.1109/TMI.2025.3607113

Front Hum Neurosci. 2025 Aug 21;19:1603888. doi: 10.3389/fnhum.2025.1603888. eCollection 2025.

ABSTRACT

Cocaine use disorder (CUD) is characterized by cortico-striatal circuit dysregulation and high relapse rates, with repetitive transcranial magnetic stimulation (rTMS) emerging as a potential neuromodulatory intervention. This study investigates rTMS-induced dynamic brain network reconfigurations in 30 CUD patients using longitudinal resting-state fMRI from the SUDMEX-TMS cohort. Applying Leading Eigenvector Dynamics Analysis (LEiDA) to phase-locking states, we identified four metastable network configurations mapped to canonical resting-state networks. Post-rTMS analyses revealed selective modulation of visual network (VIS)-dominant states, showing increased duration and occupancy, alongside reduced self-transition probabilities in frontoparietal control network (FPCN) states after rTMS therapy. Temporal dynamics of these states correlated with subjective craving intensity: increased duration of the VIS-dominant state was associated with lower craving severity (CCQ-N) post-treatment. These findings suggest that increased VIS metastability strengthened bottom-up sensory gating that attenuates drug-cue salience through perceptual desensitization. Although FPCN-state self-transition decreased significantly following stimulation, it was not directly linked to craving improvement, indicating a potentially supportive but nonspecific role in perceptual recalibration. Together, these dynamic markers highlight the relevance of network-level flexibility in mediating rTMS treatment efficacy for cocaine addiction. By establishing dynamic network state reconfiguration as a mechanism linking rTMS to symptom evolution, this work provides a framework for optimizing neuromodulation protocols and developing neurodynamics-dependent biomarkers in addiction therapeutics.

PMID:40919411 | PMC:PMC12408571 | DOI:10.3389/fnhum.2025.1603888

Magn Reson Lett. 2022 Dec 28;3(1):1-13. doi: 10.1016/j.mrl.2022.12.001. eCollection 2023 Feb.

ABSTRACT

As one of the most widely used languages in the world, Chinese language is distinct from most western languages in many properties, thus providing a unique opportunity for understanding the brain basis of human language and cognition. In recent years, non-invasive neuroimaging techniques such as magnetic resonance imaging (MRI) blaze a new trail to comprehensively study specific neural correlates of Chinese language processing and Chinese speakers. We reviewed the application of functional MRI (fMRI) in such studies and some essential findings on brain systems in processing Chinese. Specifically, for example, the application of task fMRI and resting-state fMRI in observing the process of reading and writing the logographic characters and producing or listening to the tonal speech. Elementary cognitive neuroscience and several potential research directions around brain and Chinese language were discussed, which may be informative for future research.

PMID:40919277 | PMC:PMC12406588 | DOI:10.1016/j.mrl.2022.12.001

Front Neurosci. 2025 Aug 21;19:1616957. doi: 10.3389/fnins.2025.1616957. eCollection 2025.

ABSTRACT

OBJECTIVE: Construct a predictive model for rehabilitation outcomes in ischemic stroke patients 3 months post-stroke using resting state functional magnetic resonance imaging (fMRI) images, as well as synchronized electroencephalography (EEG) and electromyography (EMG) time series data.

METHODS: A total of 102 hemiplegic patients with ischemic stroke were recruited. Resting - state functional magnetic resonance imaging (fMRI) scans were carried out on all patients and 86 of them underwent simultaneous electroencephalogram (EEG) and electromyogram (EMG) examinations. After data preprocessing, we established prediction models based on time-series data and fMRI images separately. The predictions of the time - series model and the fMRI model were integrated using ensemble learning methods to create a multimodal fusion prediction model. The accuracy, recall, precision, F1 - score, and the area under the ROC curve (AUC) were calculated to evaluate the performance of the model.

RESULTS: Compared to unimodal prediction models, multimodal fusion models demonstrated superior predictive performance. The ShuffleNet-LSTM model outperformed other multimodal fusion approaches. The area under the ROC curve was 0.8665, accuracy was 0.8031, F1-score was 0.7829, recall was 0.774, and precision was 0.833.

CONCLUSION: A deep learning-based rehabilitation prediction model utilizing multimodal signals was successfully developed. The ShuffleNet-LSTM model exhibited excellent performance among multimodal fusion models, effectively enhancing the accuracy of predicting lower-limb motor function recovery in stroke patients.

PMID:40918983 | PMC:PMC12408489 | DOI:10.3389/fnins.2025.1616957

Imaging Neurosci (Camb). 2025 Sep 3;3:IMAG.a.129. doi: 10.1162/IMAG.a.129. eCollection 2025.

ABSTRACT

Spatial similarity of functional connectivity profiles across matching anatomical locations in individuals is often calculated to delineate individual differences in functional networks. Likewise, spatial similarity is assessed across average functional connectivity profiles of groups to evaluate the maturity of functional networks during development. Despite its widespread use, spatial similarity is limited to comparing two samples at a time. In this study, we employed a variational autoencoder to embed functional connectivity profiles from various anatomical locations, individuals, and group averages for simultaneous comparison. We demonstrate that our variational autoencoder, with pre-trained weights, can project new functional connectivity profiles from the vertex space to a latent space with as few as two dimensions, yet still retain meaningful global and local structures in the data. Functional connectivity profiles from various functional networks occupy distinct compartments of the latent space. Moreover, the variability of functional connectivity profiles from the same anatomical location is readily captured in the latent space. We believe that this approach could be useful for visualization and exploratory analyses in precision functional mapping.

PMID:40918268 | PMC:PMC12409741 | DOI:10.1162/IMAG.a.129

Neuroimage Rep. 2025 Aug 26;5(3):100283. doi: 10.1016/j.ynirp.2025.100283. eCollection 2025 Sep.

ABSTRACT

Non-intrusive neuroimaging technology offers fast and robust diagnostic tools for neuro-disorder disease diagnosis, such as Attention-Deficit/Hyperactivity Disorder (ADHD). Resting-state functional magnetic imaging (rs-fMRI) has been demonstrated to have great potential for such applications due to its unique capability and convenience in providing spatial-temporal brain imaging. One critical challenge of using rs-fMRI data is the high dimensionality for both spatial and temporal domains. Thus, direct use of rs-fMRI data for the diagnosis will usually perform poorly due to the "curse of dimensionality." This paper proposes a novel nonlinear dimension reduction technique for rs-fMRI data for easy downstream analysis, such as diagnostics, regression, and visualization. The proposed method integrates the Curvature Augmented Manifold Embedding and Learning (CAMEL) algorithm with key rs-fMRI features, such as Amplitude of Low-Frequency Fluctuations (ALFF), Regional Homogeneity (ReHo), and Functional Connectivity (FC). The ADHD diagnosis problem is formulated as a classification problem in the reduced latent space and is validated with 551 data points from an open fMRI database. Compared to available literature models and results, 13 %-26 % improvement in diagnostic accuracy is observed. Additionally, the proposed methodology also supports individualized ADHA severity assessment by regression analysis in the latent space and provides a potential tool for personalized treatment. Finally, an ADHD sensitivity map is developed, highlighting brain regions associated with ADHD scores and providing interpretable insights into ADHD's neural underpinnings.

PMID:40917870 | PMC:PMC12409445 | DOI:10.1016/j.ynirp.2025.100283

Front Public Health. 2025 Aug 21;13:1630294. doi: 10.3389/fpubh.2025.1630294. eCollection 2025.

ABSTRACT

OBJECTIVE: To investigate the neural and molecular correlates of occupational burnout in nurses by integrating resting-state fMRI (rs-fMRI), clinical assessments, brain-wide gene expression, and neurotransmitter atlases.

METHODS: Fifty-one female nurses meeting burnout criteria and 51 matched healthy controls underwent 3 T rs-fMRI. We analyzed fractional amplitude of low-frequency fluctuations (fALFF) and seed-based functional connectivity (FC), correlating findings with burnout (emotional exhaustion [EE], depersonalization [DP], and personal accomplishment [PA]). The fALFF t-map was spatially correlated with Allen Human Brain Atlas gene expression (followed by gene ontology enrichment) and neurotransmitter system maps.

RESULTS: Nurses with burnout exhibited significantly decreased precuneus fALFF and reduced precuneus-right dorsolateral prefrontal cortex (DLPFC) FC compared to controls. The fALFF in the precuneus negatively correlated with EE and DP, and positively correlated with PA, while reduced precuneus-DLPFC FC negatively correlated with EE. Genes spatially associated with fALFF alterations were enriched in pathways involving neuronal excitability, synaptic organization, stress response, and immune modulation. The fALFF alteration pattern also spatially correlated with serotonin, norepinephrine, γ-aminobutyric acid, glutamate, and endocannabinoid system distributions.

CONCLUSION: Nurse burnout features precuneus hypoactivity and precuneus-DLPFC hypoconnectivity, linked to EE and DP severity. Associated molecular signatures implicate altered neuronal excitability, stress/immune pathways, and multiple neurotransmitter systems. The precuneus-DLPFC circuit and identified molecular pathways represent potential targets for interventions against burnout.

PMID:40917413 | PMC:PMC12408554 | DOI:10.3389/fpubh.2025.1630294

Hum Brain Mapp. 2025 Sep;46(13):e70342. doi: 10.1002/hbm.70342.

ABSTRACT

Perinatal stroke is a vascular injury occurring early in life, often resulting in motor deficits (hemiplegic cerebral palsy/HCP). Comorbidities may also include poor neuropsychological outcomes, such as deficits in memory. Previous studies have used resting state functional MRI (fMRI) to demonstrate that functional connectivity (FC) within hippocampal circuits is associated with memory function in typically developing controls (TDC) and in adults after stroke, but this is unexplored in perinatal stroke. Investigating links with visual memory function has the potential to inform prognosis and personalized cognitive rehabilitation strategies. This study aimed to quantify FC within hippocampal circuits of children and adolescents with perinatal stroke and associations with visual memory. We hypothesized that FC would differ between participant groups (AIS, PVI, TDC) and hemispheres (left vs. right stroke), and would correlate with visual memory function. Participants aged 6-19 years with HCP and MRI-confirmed unilateral perinatal stroke (n = 30) arterial ischemic stroke (AIS), n = 38 periventricular venous infarction (PVI) were recruited through the Alberta Perinatal Stroke Project and compared to n = 43 TDC. Resting fMRI volumes (150 volumes, TR/TE = 2000/30 ms, voxels 3.6 mm isotropic, 36 axial slices) were processed to compute FC values between memory-related seeds (including bilateral hippocampi) using a standard pipeline in the CONN toolbox. Seed-to-voxel and seed-to-seed analyses computed FC with each hippocampus. Hemispheric and group differences in FC were examined. A subset of stroke participants (n = 46) completed visual memory testing via CNS Vital Signs (CNSVS), a computerized neurocognitive test battery. Partial correlations assessed associations between FC and visual memory function, factoring out age. We found hemispheric differences in FC within each group. Participants with left perinatal stroke showed greater FC between the hippocampus and lateral prefrontal cortex in the lesioned compared to non-lesioned hemisphere. TDCs had higher hippocampal FC when compared to the lesioned hemisphere of stroke groups. For participants with right hemisphere stroke, associations were observed between hippocampal FC and visual memory function. We describe differences in bilateral hippocampal functional connectivity in children and adolescents with perinatal stroke that are associated with visual memory function. Our findings suggest that developmental plasticity may occur in the non-lesioned hippocampus after perinatal stroke. These findings may inform our understanding of how visual memory function is affected after early unilateral brain injury and facilitate the development of novel therapies for individuals affected by perinatal stroke.

PMID:40916901 | DOI:10.1002/hbm.70342

J Neuroimaging. 2025 Sep-Oct;35(5):e70087. doi: 10.1111/jon.70087.

ABSTRACT

BACKGROUND AND PURPOSE: Socioeconomic determinants of health impact childhood development and adult health outcomes. One key aspect is the physical environment and neighborhood where children live and grow. Emerging evidence suggests that neighborhood deprivation, often measured by the Area Deprivation Index (ADI), may influence neurodevelopment, but longitudinal and multimodal neuroimaging analyses remain limited.

METHODS: We examined the association between childhood neighborhoods and brain white matter (WM) microstructural integrity using a large, demographically representative cohort from the Adolescent Brain Cognitive Development Study. We analyzed the relationship between ADI and MRI metrics of WM microstructural integrity and resting-state funtional magnetic resonance imaging (rs-fMRI) connectivity in children with data at baseline (mean age of 9.9 years) and follow-up (mean age 12.0 years), with a sample size of n = 2615.

RESULTS: Children living in poorer neighborhoods (higher ADI) showed lower brain WM microstructural integrity at baseline and follow-up, even after adjusting for age, sex, race/ethnicity, head size, body mass index, parental education, and income levels. This reduced microstructure was seen in critical tracts, such as the superior longitudinal fasciculus, corpus callosum, and the uncinate. Additionally, baseline and follow-up rs-fMRI analysis revealed that children living in poorer neighborhoods had decreased connectivity within the retrosplenial-temporal network and between higher-order networks, such as the cingulo-opercular network.

CONCLUSIONS: These findings highlight the influence of neighborhood socioeconomic disadvantage on both WM microstructural integrity and functional brain connectivity in the preadolescent brain. Children from more deprived neighborhoods showed reduced integrity in key WM tracts and disrupted connectivity within and between higher-order networks.

PMID:40916058 | DOI:10.1111/jon.70087

Neuroimage. 2025 Sep 5:121443. doi: 10.1016/j.neuroimage.2025.121443. Online ahead of print.

ABSTRACT

Functional magnetic resonance imaging (fMRI) opens a window on observing spontaneous activities of the human brain in vivo. However, the high complexity of fMRI signals makes brain functional representations intractable. Here, we introduce a state decomposition method to reduce this complexity and decipher individual brain functions at multiple levels. Briefly, brain dynamics are captured by temporal first-order derivatives and spatially divided into 'state sets' at each time point based on the velocity and direction of change. This approach transforms the original signals into discrete series consisting of four fundamental states, which efficiently encode individual-specific information. Subsequently, we designed a suite of state-based metrics to quantify regional activities and network interactions. Compared with conventional representations such as resting-state fluctuation amplitude and Pearson's functional connectivity, the state-based representations serve as more discriminative 'brain fingerprints' for individuals and produce reproducible spatial patterns across heterogeneous cohorts (n = 1,015). Regarding functional organization, our proposed profiles extend previous representations into nonlinear domains, revealing not only the canonical default-mode dominant pattern but also patterns dominated by the attention network and basal ganglia. Moreover, we demonstrate that personal phenotypes (such as age and gender) can be decoded from regional representations with high accuracy. The equivalence between state series outperforms other existing network representations in predicting individual fluid intelligence. Overall, this framework establishes a foundation for enriching the repertoire of brain functional representations and enhancing the power of brain-phenotype modeling.

PMID:40915552 | DOI:10.1016/j.neuroimage.2025.121443

Biol Psychiatry Cogn Neurosci Neuroimaging. 2025 Sep 5:S2451-9022(25)00261-7. doi: 10.1016/j.bpsc.2025.08.013. Online ahead of print.

ABSTRACT

BACKGROUND: Late-life depression (LLD) is associated with negative outcomes including high rates of recurrence and cognitive decline. However, the neurobiological changes influencing such outcomes in LLD are not well understood. Disequilibrium in large-scale brain networks may contribute to LLD-related cognitive decline.

METHODS: Never-depressed older adults and participants in early remission from LLD were recruited as part of the REMBRANDT study. At study entry, participants completed a resting-state fMRI scan and neuropsychological testing and were subsequently monitored over two years for depression recurrence. Using a previously described algorithm, recurring whole-brain states of spatial co-activation were identified by k-means consensus clustering. Co-occurring network state properties from never-depressed participants (n = 40) were then compared to LLD participants who remained in remission (n = 50) or experienced depression recurrence (n = 33).

RESULTS: A three-network solution overlapping anatomically with the Default Mode Network, Cognitive Control Network, and Anterior Salience Network best explained recurring network states. Compared with never-depressed older adults, participants who remitted from LLD exhibited decreased network resilience and altered transitions between networks. Stability of specific networks were associated with baseline clinical and neuropsychological markers in never-depressed and sustained remission participants but were blunted for participants who experienced depression recurrence.

CONCLUSIONS: Collectively, these data suggest that LLD alters dynamic network stability lasting into remission. Furthermore, stability of specific networks states is associated with clinical and neuropsychological markers which may predict the likelihood of a recurrent episode of LLD.

PMID:40915537 | DOI:10.1016/j.bpsc.2025.08.013

Sleep Med. 2025 Aug 21;136:106741. doi: 10.1016/j.sleep.2025.106741. Online ahead of print.

ABSTRACT

OBJECTIVE: This multicenter study aimed to investigate resting-state brain functional alterations in patients with type 2 diabetes mellitus (T2DM) comorbid with obstructive sleep apnea (OSA), and to elucidate the underlying neural mechanisms.

METHODS: A total of 139 participants were enrolled from two centers, including 48 healthy controls (HCs), 46 T2DM patients, and 45 T2DM with OSA patients. Resting-state functional magnetic resonance imaging (rs-fMRI) was used to assess brain function using degree centrality (DC), amplitude of low-frequency fluctuation (ALFF), and seed-based functional connectivity (FC). Group comparisons were conducted, and partial correlation analyses were performed with clinical and neuropsychological measures.

RESULTS: Compared with HCs group, the T2DM group showed significantly lower ALFF values in the right parahippocampal gyrus, which were negatively correlated with fasting glucose levels. Compared with the T2DM group, the T2DM with OSA group exhibited significantly lower DC values in the left medial superior frontal gyrus, and lower ALFF values in the bilateral medial superior frontal gyrus and the left middle frontal gyrus. Additionally, FC between the left medial superior frontal gyrus and the left middle frontal gyrus was higher, whereas FC between the left middle frontal gyrus and the right middle temporal gyrus, as well as the right rectus gyrus, was lower. These functional alterations were closely associated with the Montreal Cognitive Assessment (MoCA) scores and oxygen desaturation index (ODI).

CONCLUSION: The findings reveal widespread brain functional abnormalities in patients with T2DM and in those with comorbid T2DM and OSA, particularly within the frontal and temporal networks. These alterations are strongly associated with hyperglycemia, intermittent hypoxia, and cognitive impairment. These findings offer novel imaging-based insights into the neural mechanisms underlying brain dysfunction in patients with coexisting T2DM and OSA, and underscore the clinical importance of implementing integrated interventions in this population to mitigate the risk of cognitive decline.

PMID:40913917 | DOI:10.1016/j.sleep.2025.106741