Most recent paper

Sci Data. 2025 Dec 9. doi: 10.1038/s41597-025-06398-3. Online ahead of print.

ABSTRACT

This dataset supports research on neuroplasticity and motor adaptation in motor learning and rehabilitation. It includes multimodal longitudinal data from 46 healthy adults performing motor imagery and physical training of a backward glide shot put task. Participants received one of three interventions: function-specific repetitive transcranial magnetic stimulation (rTMS) guided by task-based functional magnetic resonance imaging (fMRI), rTMS targeting hand motor hotspots, or motor training alone. The dataset contains resting-state and task-based fMRI, individualized stimulation coordinates, and daily kinematic parameters collected before and after intervention. These data enable analysis of brain network plasticity, motor performance changes, and the effects of targeted neuromodulation, providing a reproducible resource for advancing studies on precise brain stimulation and motor rehabilitation.

PMID:41365922 | DOI:10.1038/s41597-025-06398-3

Neuroimage. 2025 Dec 7:121636. doi: 10.1016/j.neuroimage.2025.121636. Online ahead of print.

ABSTRACT

BACKGROUND: Disruptions in synaptic plasticity and alterations in effective connectivity (EC) involving the hippocampus and amygdala are hallmarks of early Alzheimer's disease (AD). However, the interplay between these neurophysiological changes and their relationships with cognitive functions in subjective cognitive decline (SCD) and mild cognitive impairment (MCI) remains poorly understood.

METHODS: Transcranial magnetic stimulation (TMS) and resting-state functional magnetic resonance imaging (rs-fMRI) were used to assess long-term potentiation (LTP)-like plasticity and EC involving the amygdala and hippocampus in 34 individuals with SCD, 27 with MCI, and 35 healthy controls (HC). Between-group differences in cognitive performance, EC alterations, and LTP-like plasticity were examined and their relationships were assessed via correlation and mediation analyses.

RESULTS: Both SCD and MCI groups exhibited disrupted EC between the amygdala/hippocampus and the inferior occipital gyrus, inferior parietal lobule (IPL), medial frontal lobe (MFL), and precuneus. Also, both LTP-5min and LTP-10min were significantly reduced in MCI group compared to SCD and HC groups. Importantly, EC from the left hippocampus to the IPL and from the IPL, MFL, and precuneus to the hippocampus was correlated with memory and executive functions. Moreover, precuneus-to-hippocampus EC was positively correlated with LTP-10min and mediated the relationship between LTP-like plasticity and cognitive performance.

CONCLUSIONS: This study provides novel evidence that precuneus-to-hippocampus EC mediates the link between synaptic plasticity and cognitive function in SCD and MCI, suggesting the precuneus-hippocampus pathway as a promising target for early diagnosis and intervention.

PMID:41365452 | DOI:10.1016/j.neuroimage.2025.121636

Schizophr Res. 2025 Dec 8;287:113-121. doi: 10.1016/j.schres.2025.12.002. Online ahead of print.

ABSTRACT

BACKGROUND: This study examined topological features and network resilience in schizophrenia spectrum disorders (SSDs), other specified schizophrenia spectrum and other psychotic disorder (OSSO), and healthy controls (HC) with resting-state functional MRI (rs-fMRI) and graph theoretical analysis. Associations between topological metrics, resilience, and symptom severity were also explored.

METHODS: rs-fMRI data from SSDs (n = 77), OSSO (n = 86), and HC (n = 83) were analyzed for global efficiency (Eg), characteristic path length (Lp), nodal local efficiency (NLe), nodal clustering coefficient (NCp), and resilience derived from k-shell decomposition and targeted-attack simulations. Symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS).

RESULTS: Both patient groups showed reduced Eg and increased Lp compared with HC, indicating disrupted global integration. At the nodal level, the fusiform gyrus exhibited decreased NLe and NCp in both groups. In OSSO, these nodal metrics correlated with PANSS general and total scores. SSDs displayed pronounced reductions in k-core and maximum-core resilience, whereas OSSO largely retained network stability. k-Shell resilience was most impaired in SSDs, with OSSO showing intermediate deficits. Notably, k-shell resilience in the right superior occipital gyrus significantly differed between OSSO and SSDs.

CONCLUSION: This study presents the first investigation of OSSO-specific neuroimaging signatures using network resilience analysis. OSSO showed partial preservation of k-core resilience and intermediate k-shell resilience between SSDs and HC, suggesting distinct neurobiological organization within the psychosis spectrum. k-Shell resilience in the superior occipital gyrus may serve as a potential neuroimaging marker distinguishing OSSO from SSDs.

PMID:41365234 | DOI:10.1016/j.schres.2025.12.002

Psychiatry Res Neuroimaging. 2025 Nov 20;356:112093. doi: 10.1016/j.pscychresns.2025.112093. Online ahead of print.

ABSTRACT

Schizophrenia spectrum disorders (SSD) are marked by working memory impairments associated with abnormal functional brain connectivity. Although transcranial magnetic stimulation (TMS) shows promise in modulating dysconnectivity patterns and improving cognitive symptoms, current protocols often lack target personalization, overlooking significant variability in functional network topography between individuals. Twenty-two individuals with SSD and cognitive deficits underwent 20Hz repetitive TMS to the left lateral prefrontal cortex. Personalized TMS targeted regions with the strongest central executive-default mode network (CEN-DMN) antagonism, while standardized TMS focused on the EEG F3 site. Resting-state fMRI scans were conducted pre- and post-TMS sessions to evaluate changes in CEN-DMN connectivity, and working memory performance was assessed after the post-TMS fMRI scan. Both TMS protocols failed to significantly alter CEN-DMN connectivity or improve cognitive function, which may be due to the low reliability of the biomarker used for personalized targeting. However, stronger DMN intra-network connectivity at the stimulation site was positively correlated with a reduction in CEN-DMN connectivity and improved working memory performance. These findings highlight the need for more extensive fMRI data for better target determination, and suggest that targeting left prefrontal areas with higher DMN connectivity could more effectively modulate functional connectivity and improve working memory performance through TMS.

PMID:41364985 | DOI:10.1016/j.pscychresns.2025.112093

Addict Behav. 2025 Nov 27;174:108575. doi: 10.1016/j.addbeh.2025.108575. Online ahead of print.

ABSTRACT

Problematic smartphone use (PSU) has been associated with withdrawal-like symptoms and altered intrinsic neural activity (INA). While previous studies suggest that PSU affects brain function, little is known about how INA is modulated by smartphone restriction. This longitudinal fMRI study investigated group- and time-dependent changes in resting-state INA following short-term smartphone deprivation. 36 participants (aged 18-29; 22 female) were categorized into PSU (n = 19) and non-PSU (n = 17) groups using the Smartphone Addiction Scale-Short Version (SAS-SV). Resting-state fMRI scans were obtained before and after a 72-hour period of smartphone restriction. Psychometric measures included the Mannheim Craving Scale (MaCS) and the Smartphone Addiction Inventory (SPAI). A significant group-by-time interaction revealed INA changes in the left inferior frontal gyrus, bilateral posterior cingulate cortex, right middle frontal and precentral gyri, and left calcarine cortex. INA increased over time in the non-PSU group but decreased in the PSU group in prefrontal and cingulate areas. In contrast, sensorimotor and occipital regions showed increased INA over time in PSU individuals. Associations between neural activity and MaCS scores indicated that greater craving was linked to reduced INA in the posterior cingulate cortex. Within the PSU group, higher smartphone-use severity, as measured by the SPAI, was associated with altered INA in occipital, parietal, and cerebellar regions. These findings suggest PSU is linked to distinct and state-dependent neurofunctional alterations that may reflect withdrawal-related processes and maladaptive reward and cognitive control mechanisms.

PMID:41364954 | DOI:10.1016/j.addbeh.2025.108575

Cereb Cortex. 2025 Nov 27;35(12):bhaf330. doi: 10.1093/cercor/bhaf330.

ABSTRACT

Adequately responding towards a threat is a crucial mechanism for survival. Adapting this response when a threat-associated stimulus or situation has become safe requires extinction learning and formation of an extinction memory. Functional magnetic resonance imaging (fMRI) affords to longitudinally monitor network activity, yet, in the rodent, still suffers from significant variability of results and practical restrictions, mainly related to the different approaches of subject immobilization. Physical restraint of awake animals permits only short scanning times, while anesthesia can induce uncontrolled brain states with limited stimulus responsiveness and processing. Here, we implement a paradigm where light medetomidine sedation permits long scanning times in a stable brain state with functional characteristics comparable to the human resting state. We observe responsiveness of the brain to visual stimulation and large-scale resting-state network activity with small-world connectivity features. After visual fear conditioning outside the MRI scanner, rats exposed to the unreinforced visual conditioned stimulus in this stable persistent activity state inside the scanner (extinction) exhibit a significantly lower conditioned fear response when re-exposed to the conditioned stimulus days after scanning (test). We present a brain state-informed paradigm easily adaptable for future studies involving invasive neural manipulations to causally investigate extinction and its memory consolidation.

PMID:41364669 | DOI:10.1093/cercor/bhaf330

Cereb Cortex. 2025 Nov 27;35(12):bhaf327. doi: 10.1093/cercor/bhaf327.

ABSTRACT

The hippocampus, in both children and adults, has shown functional specialization along its long axis, with the anterior region associated with emotional processing and the posterior region with spatial memory and navigation. This specialization is also reflected in separate patterns of functional connectivity, but it is unclear whether it is present before birth. Here, we collected resting-state fMRI data in 51 healthy third-trimester fetuses to examine long-axis functional specialization in utero. Using structural regions of interest in the anterior and posterior hippocampus, a seed-based connectivity analysis was performed. We identified distinct networks of functional organization for the anterior and posterior hippocampus. These patterns showed spatial organization and anticorrelation consistent with long-axis specialization. While less mature than those observed in postnatal human and preclinical models, the fetal patterns suggest that the foundation for hippocampal functional differentiation supporting early affective and cognitive processing is already present before birth. Key points We used resting-state fMRI in the third trimester fetal brain to examine the functional projections of the anterior and posterior hippocampus. We identified distinct networks of functional organization that were independently related to the anterior and posterior hippocampus. The groundwork for the specificity of the hippocampus is being laid in utero, with functional anticorrelation contributing to the separation between long-axis segments.

PMID:41364666 | DOI:10.1093/cercor/bhaf327

Cereb Cortex. 2025 Nov 27;35(12):bhaf322. doi: 10.1093/cercor/bhaf322.

ABSTRACT

Choice consistency denotes the capacity to maintain stable, coherent preferences across diverse contexts-a cornerstone of rational decision-making. However, real-world decisions frequently diverge from normative models, marked by inconsistencies and irrationalities. Memory processes may underlie this variability, influencing the formation and maintenance of choice consistency. Yet, the interplay between memory and choice consistency, particularly their shared neural substrates, remains poorly understood. To address these gaps, we developed a novel behavioral paradigm integrating memory retrieval and food-based decision tasks. Resting-state and task functional magnetic resonance imaging data were acquired from 44 healthy young adults (age range: 18 to 27 years). Behaviorally, remembered food items exhibited significantly faster choice reaction times compared to forgotten items. Leveraging data-driven connectome-based predictive modeling of resting-state functional connectivity, we identified distinct neural predictors: intra-default mode network connectivity and default mode network-memory network connectivity positively predicted memory accuracy, whereas default mode network-frontoparietal control network connectivity negatively predicted memory accuracy. Furthermore, intra-default mode network connectivity and default mode network-frontoparietal control network connectivity positively predicted choice consistency. These findings advance our understanding of memory-decision interactions, highlighting the default mode network and frontoparietal control network as critical neural substrates that bridge mnemonically modulated value signals and choice consistency.

PMID:41364664 | DOI:10.1093/cercor/bhaf322

Neuroimage Rep. 2025 Nov 20;5(4):100300. doi: 10.1016/j.ynirp.2025.100300. eCollection 2025 Dec.

ABSTRACT

The ability to complete activities of daily living (ADLs) is an important part of daily life and can promote well-being and independence. There is currently limited knowledge of brain activity during ADLs (e.g. dressing tasks). Previous studies explored brain activity during dressing using functional magnetic resonance imaging (fMRI); however, the supine position during fMRI is not a natural dressing posture and may impact findings. Functional near-infrared spectroscopy (fNIRS) is a promising method of data collection as it can investigate brain activity in a natural state (sitting) during dressing. In this study, to understand brain activity during buttoning in unimpaired adults, twenty participants (25-65 years) completed an upper extremity task of buttoning in three 20 s repetitions with 15 s rest in between each activity block. Brain activation patterns were recorded using fNIRS over the prefrontal, premotor, supplementary motor, sensorimotor, and posterior parietal cortices. Compared to the resting period, significantly higher activation during the activity block was observed in all recorded regions but the posterior parietal cortex. Understanding brain activity in unimpaired adults during the performance of activities of daily living is a critical first-step for investigating brain activation in different clinical populations.

PMID:41362878 | PMC:PMC12681552 | DOI:10.1016/j.ynirp.2025.100300

Depress Anxiety. 2025 Nov 30;2025:4530547. doi: 10.1155/da/4530547. eCollection 2025.

ABSTRACT

OBJECTIVE: This study aimed to investigate the relationship between alterations in lymphocyte subsets and resting-state functional magnetic resonance imaging (rs-fMRI) patterns in patients with comorbid major depressive disorder (MDD) and insomnia disorder (ID).

METHODS: A total of 114 patients with MDD, 108 with ID, 126 with comorbid MDD and ID, and 168 healthy controls (HCs) were recruited, all experiencing their first episode. Emotional and sleep quality were assessed using the 17-item Hamilton Depression Rating Scale (HAMD-17), self-rating depression scale (SDS), Hamilton Anxiety Scale, self-rating anxiety scale (SAS), Pittsburgh Sleep Quality Index (PSQI), and Insomnia Severity Index (ISI). rs-fMRI data and lymphocyte subsets were analyzed. Multivariate prediction models were constructed using correlation analysis, least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation, and logistic regression. Model performance was evaluated with calibration curves and receiver operating characteristic (ROC) analysis.

RESULTS: No significant differences were observed in age (p=0.552), sex distribution (p=0.248), education level, or anxiety scores among the four groups, whereas depression and insomnia scores differed significantly (all p < 0.0001). The MDD with comorbid insomnia (iMDD) group exhibited lower fractional amplitude of low-frequency fluctuations (fALFFs) in the right lingual gyrus and fusiform gyrus compared to the MDD, ID, and HC groups. Additionally, compared with HCs, CD3+ and CD4+ T cell percentages were elevated, while natural killer (NK) cell percentage was reduced, with the most pronounced alterations in the iMDD group. fALFF values were negatively correlated with CD3+ and CD4+ T cell percentages, but positively correlated with NK cell percentage. The fALFF in the right lingual gyrus, CD4+ T and NK cell percentage, SDS score, and ISI score were identified as key risk predictors. Multivariable prediction models for ID, MDD, and iMDD demonstrated robust calibration (e.g., calibration degree = 0.502), high discrimination (AUC for iMDD vs. HC = 0.991; MDD vs. ID = 0.821), and good clinical applicability.

CONCLUSIONS: The identified risk predictors might facilitate individualized clinical decision-making for iMDD patients. While the multivariable prediction model demonstrated strong internal diagnostic accuracy, further external validation using independent cohorts is needed to confirm its generalizability.

PMID:41362845 | PMC:PMC12682451 | DOI:10.1155/da/4530547

Complement Med Res. 2025 Dec 8:1-19. doi: 10.1159/000549822. Online ahead of print.

ABSTRACT

Background Cognitive impairment is one of the long COVID symptoms that many people experience after Coronavirus Disease 2019 (COVID-19). Many individuals report a decline in cognitive functions, such as reduced memory and brain fog. These symptoms not only directly affect the cognitive functions of the brain but also hinder daily living activities, thereby reducing the quality of life. Moreover, these symptoms are significant risk factors for long-term cognitive decline in the elderly and can have both short-term and long-term effects on brain function.Clinically, acupuncture is widely used to improve cognitive impairment in the elderly. Elucidating the brain network mechanisms underlying acupuncture therapy for Long COVID-related cognitive impairment represents an urgently needed research focus. In this study, we employed acupuncture as an intervention to mitigate cognitive decline in Long COVID patients and investigate the potential mechanisms by which acupuncture alleviates cognitive impairment. Methods In this randomized controlled trial, 60 eligible participants are planned to be recruited and randomly assigned in a 1:1 ratio to the acupuncture group and the health education group, which will then receive acupuncture treatment and health education.The acupuncture group will participate in treatment three times per week for a total of eight weeks. The health education group will receive health education once per week for a total of eight weeks.The primary assessment index was the Montreal Cognitive Assessment Scale (MoCA), and the secondary assessment indexes included Clinical Dementia Rating (CDR), Mini-Mental State Examination (MMSE), Activity of Daily Living Scale (ADL), Auditory Verbal Learning Test - Huashan Version (AVLT-H) and resting-state functional magnetic resonance imaging (rs-fMRI) data. These assessment indicators were all tested in one week each before and after the intervention was implemented. Discussion This trial aims to investigate the therapeutic effects of acupuncture on cognitive impairment in patients with long COVID and to further explore the imaging mechanisms by which acupuncture alleviates cognitive dysfunction in these patients. Trial registration: Chinese Clinical Trial Registry (http://www,chictr,org.cn), Registration number: ChiCTR2400092961,Date of Reqistration: 2024-11-26.

PMID:41362171 | DOI:10.1159/000549822

Sci Rep. 2025 Dec 8. doi: 10.1038/s41598-025-30847-z. Online ahead of print.

ABSTRACT

Type 2 diabetes mellitus (T2DM) is a metabolic disorder characterized by chronic hyperglycemia resulting from insulin secretion and/or resistance. This study investigated intrinsic brain activity alterations using static and dynamic resting-state fMRI metrics in 65 T2DM patients versus 60 healthy controls. We analyzed fractional amplitude of low-frequency fluctuations (fALFF), dynamic fALFF (dfALFF) and dynamic functional stability(DFS). The T2DM group exhibited increased fALFF in the left inferior temporal gyrus and left fusiform gyrus and decreased fALFF in the bilateral precuneus, medial superior frontal gyrus, left inferior parietal lobule, and right supramarginal gyrus when compared with health controls. The T2DM group also showed increased dfALFF in the bilateral precuneus, left inferior parietal lobule, and right middle frontal gyrus. Moreover, the T2DM group exhibited decreased DFS in the bilateral precuneus, supramarginal gyrus, and left middle frontal gyrus, while the left cuneus showed increased dynamic stability. In the T2DM group, montreal cognitive assessment (MoCA) scores correlated negatively with glycated hemoglobin A1c (HbA1c) and fasting blood glucose (FBG), and positively with right supramarginal gyrus acticity in both fALFF and DFS difference regions, Multiple brain regions exhibiting fALFF and DFS alterations showed negative correlations with fasting blood glucose and total cholesterol. These findings indicate that T2DM brain activity demonstrates a distinctive "low-intensity, highly-fluctuating, and destabilized" pattern, suggesting complex neural network dysfunction beyond simple functional suppression.

PMID:41361357 | DOI:10.1038/s41598-025-30847-z

J Neurosci. 2025 Dec 8:e0109252025. doi: 10.1523/JNEUROSCI.0109-25.2025. Online ahead of print.

ABSTRACT

Perioperative abstinent smokers experience heightened pain sensitivity and increased postoperative analgesic requirements, likely due to nicotine withdrawal-induced hyperalgesia. However, the underlying neural mechanisms in humans remain unclear. To address this issue, this study enrolled 60 male patients (30 abstinent smokers and 30 nonsmokers) undergoing partial hepatectomy, collecting clinical data, smoking history, pain-related measures, and resting-state functional magnetic resonance imaging (rs-fMRI). Compared to nonsmokers, abstinent smokers showed lower pain threshold and higher postoperative analgesic requirements. Neuroimaging revealed altered brain function in abstinent smokers, including reduced fractional amplitude of low-frequency fluctuations (fALFF, 0.01 - 0.1 Hz) in the ventromedial prefrontal cortex (vmPFC), increased regional homogeneity (ReHo) in the left middle occipital gyrus, and decreased functional connectivity (FC) between the vmPFC to both the bilateral middle temporal gyrus and precuneus. Preoperative pain threshold was positively correlated with abstinence duration and specific regional brain activities and connectivity. Further, the observed association between abstinent time and pain threshold was mediated by the calcarine and posterior cingulate cortex activity. The dysfunction in vmPFC and left anterior cingulate cortex was totally mediated by the association between withdrawal symptoms and postoperative analgesic requirements. These findings suggest that nicotine withdrawal might alter brain functional activity and contribute to hyperalgesia for the abstinent smokers. This study provided novel insights into the supraspinal neurobiological mechanisms underlying nicotine withdrawal-induced hyperalgesia and potential therapeutic targets for postoperative pain in abstinent smokers.Significance statement Abstinent smokers experienced heightened pain and require more analgesics after surgery, yet the underlying neural mechanisms remain poorly understood. This prospective cohort study identified altered regional brain activity associated with reduced pain thresholds and increased postoperative analgesic requirements in abstinent smokers. We found specific brain regions that were functionally altered and correlated with pain-related outcomes, which mediated the relationship between abstinence and pain-related behaviors. These findings provided novel insights into the supraspinal mechanisms of nicotine withdrawal-induced hyperalgesia and point to potential therapeutic targets for improving postoperative pain management in abstinent smokers.

PMID:41360674 | DOI:10.1523/JNEUROSCI.0109-25.2025

Behav Brain Res. 2025 Dec 6:115985. doi: 10.1016/j.bbr.2025.115985. Online ahead of print.

ABSTRACT

Oxytocin (OT), a neuropeptide known for its role in social behavior, has unclear neural mechanisms when administered intranasally, especially across different ages. Brain entropy (BEN), a metric of neural irregularity, shows promise for revealing OT's neurophysiological effects. This study examined whether BEN could detect neural changes induced by intranasal OT and how these effects are modulated by age. In a randomized, double-blind, placebo-controlled trial, young adults (YA) and older adults (OA) were assigned to receive intranasal OT or placebo (PL). Using fMRI-based BEN mapping, we identified a significant age-dependent effect in the left temporoparietal junction (TPJ), where OT increased BEN in YA but decreased it in OA. Further analyses showed OT also elevated the fractional amplitude of low-frequency fluctuations (fALFF) in the same region, particularly in YA. Additionally, OT enhanced functional connectivity within the left TPJ and between the left and right TPJ in both age groups. These results establish BEN as a sensitive biomarker capable of capturing age-specific OT effects, providing information beyond traditional measures of oscillatory power and temporal synchronization. The findings suggest that the timing of post-administration brain state changes under OT may vary with age, potentially due to differences in OT receptor density.

PMID:41360155 | DOI:10.1016/j.bbr.2025.115985

IEEE J Biomed Health Inform. 2025 Dec;29(12):8719-8726. doi: 10.1109/JBHI.2025.3613793.

ABSTRACT

Understanding brain dynamics is important for neuroscience and mental health. Functional magnetic resonance imaging (fMRI) enables the measurement of neural activities through blood-oxygen-level-dependent (BOLD) signals, which represent brain states. In this study, we aim to predict future human resting brain states with fMRI. Due to the 3D voxel-wise spatial organization and temporal dependencies of the fMRI data, we propose a novel architecture which employs a 4D Shifted Window (Swin) Transformer as encoder to efficiently learn spatio-temporal information and a convolutional decoder to enable brain state prediction at the same spatial and temporal resolution as the input fMRI data. We used 100 unrelated subjects from the Human Connectome Project (HCP) for model training and testing. Our novel model has shown high accuracy when predicting 7.2s resting-state brain activities based on the prior 23.04s fMRI time series. The predicted brain states highly resemble BOLD contrast and dynamics. This work shows promising evidence that the spatiotemporal organization of the human brain can be learned by a Swin Transformer model, at high resolution, which provides a potential for reducing the fMRI scan time and the development of brain-computer interfaces in the future.

PMID:41359725 | DOI:10.1109/JBHI.2025.3613793

Sleep. 2025 Dec 8:zsaf392. doi: 10.1093/sleep/zsaf392. Online ahead of print.

ABSTRACT

STUDY OBJECTIVES: To investigate associations between social jet lag and the developing adolescent brain.

METHODS: N = 3507 youth (median (IQR) age = 12.0 (1.1) years; 50.9% females) from the Adolescent Brain Cognitive Development (ABCD) cohort were studied. Social jet lag (adjusted for sleep debt (SJLSC) versus non-adjusted (SJL)), topological properties and intrinsic dynamics of resting-state networks, and morphometric brain characteristics were analyzed.

RESULTS: Over 35% of participants had SJLSC ≥2.0 h. Boys, Hispanic and Black non-Hispanic youth, and/or those at later pubertal stages had longer SJLSC (β=0.06 to 0.68, CI=[0.02, 0.83], p≤0.02), which was also associated with higher BMI (β=0.13, CI=[0.08, 0.18], p<0.01). SJLSC and SJL were associated with lower strength of thalamic connections (β=-0.22, CI=[-0.39, -0.05], p=0.03). Longer SJLSC was also associated with lower topological resilience and lower connectivity of the salience network (β=-0.04, CI=[-0.08, -0.01], p=0.04), and lower thickness and/or volume of structures overlapping with this and other networks supporting emotional and reward processing and social function (β=-0.08 to -0.05, CI=[-0.12, -0.01], p<0.05). Longer SJL was associated with lower connectivity and efficiency of the dorsal attention network ( β=-0.05, CI=[-0.10, -0.01], p<0.05). Finally, SJLSC and SJL were associated with alterations in spontaneously coordinated brain activity, and. lower information transfer between regions supporting sensorimotor integration, social function and emotion regulation (β=-0.07 to -0.05, CI=[-0.12, -0.01], p<0.04).

CONCLUSIONS: Misaligned sleep is associated with widespread alterations in adolescent brain structures, circuit organization and dynamics of regions that play critical roles in cognitive (including social) function, and emotion and reward regulation.

PMID:41358909 | DOI:10.1093/sleep/zsaf392

medRxiv [Preprint]. 2025 Nov 25:2025.11.21.25340716. doi: 10.1101/2025.11.21.25340716.

ABSTRACT

IMPORTANCE: Identifying language dominance is a crucial step in epilepsy surgery planning. We applied a precision functional brain mapping approach to estimate individual-specific cortical resting-state networks in drug-resistant epilepsy and predict language dominance.

OBJECTIVE: To determine whether individual-specific cortical network topography can predict task-based language dominance in drug-resistant epilepsy.

DESIGN: Retrospective case-control study conducted between January 2024 and August 2025.

SETTING: Multicentre population-based study including healthy participants from the Human Connectome Project, and participants with drug-resistant epilepsy from the National Institutes of Health (NIH) and the University of Iowa.

PARTICIPANTS: Eligible participants had drug-resistant epilepsy defined by International League Against Epilepsy criteria and were undergoing pre-surgical evaluation. All participants underwent neuroimaging, with a subset receiving concurrent intracranial electrical stimulation during fMRI.

MAIN OUTCOMES AND MEASURES: Individual-specific cortical network topography and prediction of task functional magnetic resonance imaging language dominance.

RESULTS: Ninety-one participants with drug-resistant epilepsy were included: 61 (67.0%) temporal lobe epilepsy, 29 (31.9%) extra-temporal lobe epilepsy, and 1 (1.1%) undetermined seizure onset zone. The mean age was 33.0 ± 11.4 years and 50 (54.9%) were male. There were 40 healthy participants with a mean age of 29.0 ± 4.0 years, and 16 (40.0%) were male. We developed a multi-session hierarchical Bayesian model (MS-HBM) trained on NIH data to estimate individual-specific networks in drug-resistant epilepsy. MS-HBM trained on epilepsy data outperformed group-average networks or MS-HBM trained on healthy participants and generalized well to an independent dataset. During concurrent intracranial electrical stimulation, cortical activation and deactivation aligned more closely to individual-specific networks than group-average networks. Individual-specific language network topography significantly differed across left (mean lateralization index (LI) = 0.165 ± 0.106; area-under-the-curve (AUC) = 0.82), bilateral (LI = 0.056 ± 0.074; AUC = 0.72), and right (LI = 0.023 ± 0.055; AUC = 0.83) language dominance groups (p = 0.002).

CONCLUSIONS AND RELEVANCE: Our model is publicly available (github link), which may be used to predict language dominance from approximately 10 minutes of resting-state fMRI. This provides a practical, non-invasive tool for presurgical evaluation of drug-resistant epilepsy.

KEY POINTS: Question: Can individual-specific network topography from resting-state functional magnetic resonance imaging (fMRI) predict task-based language dominance in drug-resistant epilepsy?Findings: In this multi-centre case-control study of 91 participants with drug-resistant epilepsy and 40 healthy controls, individual-specific networks outperformed group-average networks and generalized well to an independent cohort. Language network topography differed significantly across left (mean lateralization index (LI) = 0.165 ± 0.106), bilateral (LI = 0.056 ± 0.074), and right (LI = 0.023 ± 0.055) dominance groups (p = 0.002).Meaning: Resting-state fMRI can estimate high-quality individual-specific cortical networks that predict language dominance, providing a non-invasive tool for presurgical evaluation.

PMID:41358301 | PMC:PMC12676542 | DOI:10.1101/2025.11.21.25340716

Front Med (Lausanne). 2025 Nov 20;12:1585799. doi: 10.3389/fmed.2025.1585799. eCollection 2025.

ABSTRACT

BACKGROUND: Spinal diseases related pain represents a critical clinical issue that demands urgent resolution. Current treatment and assessment strategies predominantly focus on peripheral mechanisms. The application of functional magnetic resonance imaging (fMRI) offers a promising approach to identifying potential central targets for intervention.

METHODS: We retrospectively included 31 patients with spinal diseases related pain and 32 controls with non-spinal, orthopedic complaints (no chronic neurological or psychiatric disorders). All participants underwent resting-state brain fMRI (eyes closed, awake). We quantified amplitude of low-frequency fluctuations (ALFF) with mean normalization (mALFF) and z-transformation (zALFF), regional homogeneity (ReHo; 27-voxel neighborhood), seed-based functional connectivity (FC; pre/postcentral seeds), and degree centrality (DC; binary and weighted). Between group tests used voxel-wise two-sample t_tests with Gaussian random field (GRF) correction.

RESULTS: Patient group was associated with increased m/zALFF in right cerebellar lobule IX and right Superior Frontal Gyrus, medial part, and lower activity in bilateral postcentral gyri and the cuneus, decreased m/zALFF in bilateral postcentral gyri. ReHo analysis confirmed reduced local synchrony in postcentral regions, spatially overlapping with ALFF findings. FC analyses revealed enhanced cerebellar-thalamic connectivity (Crus1/2, thalamus) but reduced connectivity in sensorimotor and higher-order cortical networks. DC showed hyperconnectivity in left cerebellar Crus I with reduced Superior Frontal Orbital (Frontal_Sup_Orb). All findings survived GRF correction at the pre_specified thresholds.

CONCLUSION: Resting-state brain fMRI indicates a cerebello-thalamo-cortical alteration pattern in spinal diseases related pain featuring cerebellar involvement, prefrontal subspecialization, and multilevel sensorimotor disruption. These cross-sectional associations may inform hypothesis-generation for future neuromodulation studies and provide candidate biomarkers for monitoring, pending prospective validation.

PMID:41357497 | PMC:PMC12677010 | DOI:10.3389/fmed.2025.1585799

J Chin Med Assoc. 2025 Dec 8. doi: 10.1097/JCMA.0000000000001325. Online ahead of print.

ABSTRACT

Treatment-resistant depression (TRD) and alcohol use disorder (AUD) frequently coexist, complicating clinical management and contributing to poor outcomes. Despite their distinct clinical presentations, converging neuroimaging evidence indicates shared neural circuit dysfunctions. This review synthesizes resting-state functional magnetic resonance imaging (fMRI) findings, highlighting disruptions within and between core intrinsic brain networks-the default mode network (DMN), salience network (SN), and central executive network (CEN)-as well as subcortical-limbic circuitry. Both TRD and AUD feature reduced anterior-posterior DMN connectivity (mPFC-PCC), impaired CEN function (particularly within the DLPFC), and aberrant SN connectivity (anterior insula, ACC). Altered limbic interactions involving the amygdala, hippocampus, and striatum further reflect common mechanisms of heightened reward sensitivity and emotional dysregulation. Conventional pharmacotherapies demonstrate limited efficacy, underscoring the need for novel approaches. Neuromodulation, particularly deep transcranial magnetic stimulation (dTMS), has emerged as a promising intervention targeting these shared circuit abnormalities. While current evidence remains preliminary, integrating neuroimaging biomarkers, multimodal methods, and longitudinal designs will be crucial for refining treatment precision. This review highlights the translational potential of circuit-based interventions, offering a framework for personalized neuromodulation strategies to improve outcomes in patients with TRD, AUD, and their frequent comorbidity.

PMID:41355453 | DOI:10.1097/JCMA.0000000000001325

Brain Behav. 2025 Dec;15(12):e71039. doi: 10.1002/brb3.71039.

ABSTRACT

BACKGROUND: Early detection of subjective cognitive decline (SCD), a preclinical stage of Alzheimer's disease (AD), remains a clinical challenge due to its subtle manifestations. This study aims to address these challenges by introducing a novel approach to enhance the detection and analysis of SCD.

METHODS: A Frequency Self-Adaptive Wavelet Transform (FSAWT) model was developed and optimized for functional brain network (FBN) construction using resting-state functional MRI (rs-fMRI) data. The model dynamically selected "golden frequencies" to improve the accuracy and interpretability of brain connectivity patterns. FBNs from 240 participants (106 SCD, 134 controls) were analyzed and compared using traditional methods, pearson correlation (PC) and sparse representation (SR). Receiver operating characteristic-area under the curve (ROC-AUC) analysis validated the classification results.

RESULTS: Our findings demonstrate that individuals with SCD exhibit distinct functional connectivity alterations, including reversed parahippocampal gyrus-superior parietal gyrus connectivity-suggesting early DMN disintegration, weakened temporoparietal pathways linked to memory deficits, and enhanced fusiform gyrus-orbitofrontal connectivity. The frequency-optimized SRWT method achieved superior diagnostic performance (83.71% accuracy, AUC = 0.84) with 82.11% sensitivity and 85.71% specificity, significantly outperforming traditional approaches (61.93% accuracy for PC), highlighting its potential for early SCD detection through these network-based biomarkers.

CONCLUSIONS: The FSAWT model offers a robust framework for early SCD detection by integrating frequency-specific and cross-frequency dynamics. While these findings highlight potential contributions to precision diagnostics and personalized interventions for neurodegenerative disorders, such applications remain to be established in future studies. Future applications may also explore multimodal neuroimaging and broader cognitive impairments.

PMID:41355337 | DOI:10.1002/brb3.71039