Most recent paper

Prog Neurobiol. 2026 Mar 17:102906. doi: 10.1016/j.pneurobio.2026.102906. Online ahead of print.

ABSTRACT

Many brain diseases and disorders lack objective measures of brain function as indicators of pathology. The search for brain function biomarkers is complicated by the fact that these conditions are often heterogeneous and described as a spectrum from normal to abnormal rather than a sick-healthy dichotomy. Normative modeling addresses these challenges by characterizing the normal variation of brain function given sex and age and identifying abnormalities as deviations from this norm. Focusing on functional connectivity (FC) as a way to capture the network properties of the brain's activity, we here argue that the pathological effects of neurological or psychiatric disease lie at the systemic level, and that whole-brain normative models are more suitable to capture individual variations associated to these complex conditions than existing localized approaches that analyze one connection at a time. To be able to capture the whole-brain effects of disease, we thus propose Functional Connectivity Integrative Normative Modeling (FUNCOIN) as a novel whole-brain approach to normative modeling of FC. Using FUNCOIN and UK Biobank resting-state fMRI data from 46,000 healthy subjects across training and testing sets, we found that subjects with bipolar disorder and Parkinson's disease were significantly, and substantially, more likely than healthy subjects to exhibit abnormal FC patterns, which was not seen in localized models. Subjects with bipolar disorder divided into two distinct subgroups characterized by different brain function deviations. In Parkinson's disease subjects, abnormal FC patterns were significant even on scans up to 8 years before diagnosis.

PMID:41856310 | DOI:10.1016/j.pneurobio.2026.102906

Adv Sci (Weinh). 2026 Mar 19:e17234. doi: 10.1002/advs.202517234. Online ahead of print.

ABSTRACT

Exploring the dynamics of complex systems such as the human brain is challenging due to inherent uncertainties and the limited availability of high-quality data. Here, we develop a mathematical theory for noisy linear recurrent neural networks (lRNNs) within the reservoir computing framework and demonstrate their effectiveness in constructing autonomous in silico replicas - digital-twins - of brain activity. We show that the Laplace-transform poles of high-dimensional inferred lRNNs directly encode the spectral properties of observed systems and are linked to the kernels of auto-regressive models. Notably, our approach enables accurate recovery of the system's linear spectrum even when observations undergo conventional preprocessing, including band-pass filtering pipelines commonly used in neural recordings and resting-state fMRI. In these regimes, established techniques such as dynamic mode decomposition often produce spurious spectral estimates. Applying our framework to resting-state fMRI, we successfully predict and decompose BOLD activity into spatiotemporal modes in a low-dimensional latent state space confined around a single equilibrium point. The inferred lRNNs provide interpretable signatures that differentiate subjects and brain areas, supporting biologically meaningful clustering. This flexible digital-twin framework opens the door to virtual experiments and computationally efficient real-time adaptive learning, offering a promising avenue for personalized medicine and intervention strategies.

PMID:41855584 | DOI:10.1002/advs.202517234

J Craniofac Surg. 2026 Mar 19. doi: 10.1097/SCS.0000000000012627. Online ahead of print.

ABSTRACT

OBJECTIVE: Training activities in alpine environments, characterized by low temperatures, hypoxia, and high physical demand, predispose individuals to craniomaxillofacial (CMF) complex injuries with potential involvement of the central nervous system in the event of accidents. However, the imaging manifestations and multimodal features of brain injury within this specific environmental context remain insufficiently characterized. This study aimed to retrospectively analyze the brain injury patterns in patients with CMF injuries sustained during alpine training, utilizing multimodal magnetic resonance imaging (MRI) data from the acute phase. It further sought to explore the interrelationships among structural damage, white matter microstructural alterations, and functional brain network abnormalities, thereby providing an imaging foundation for clinical assessment and risk stratification.

METHODS: This retrospective observational imaging study enrolled patients who sustained CMF injuries during alpine training and subsequently underwent multimodal MRI. All imaging data were derived from prior clinical examinations. MRI evaluations were performed during the acute post-injury period, with a median interval from injury to MRI of 2.6 days (interquartile range: 1.8-3.4 d). Short-term clinical follow-up data during the acute hospitalization phase were available for a subset of patients, with the endpoint defined as hospital discharge or completion of acute-phase treatment. No standardized longitudinal imaging follow-up was conducted. The MRI protocol encompassed conventional T1-weighted and T2-weighted imaging, diffusion-weighted imaging (DWI), susceptibility-weighted imaging (SWI), diffusion tensor imaging (DTI), and resting-state functional MRI (rs-fMRI). Primary analysis metrics included cerebral contusion/laceration and structural injury burden, the quantity and spatial distribution of cerebral microbleeds detected by SWI, DTI-derived white matter microstructural parameters, and topological indices of structural and functional brain networks constructed through connectomics methods. Multimodal integrative analysis was used to assess the associative characteristics between structural injury, white matter microstructural changes, and brain network dysfunction.

RESULTS: The findings revealed that patients with CMF injuries from alpine training exhibited multilevel brain injury features during the acute phase, including cerebral contusions, microbleeds, and diminished white matter microstructural integrity. Microbleeds were predominantly distributed in the corpus callosum and subcortical deep white matter regions. Their burden was closely associated with reduced white matter fractional anisotropy (FA) and weakened functional network connectivity. Structural and functional connectivity analyses demonstrated a widespread reduction in global network efficiency and clustering coefficient among the injured individuals, alongside a relative enhancement of connectivity in certain frontal lobe-related networks, suggesting the presence of network reorganization and compensation during the acute phase. Multimodal analysis further indicated that, within the alpine training context, structural lesions, white matter injury, and brain network dysfunction exhibited significant coupling. The overall injury phenotype seemed more severe compared with general trauma backgrounds.

CONCLUSIONS: Craniomaxillofacial complex injuries sustained during training in alpine environments can induce environmentally sensitive, multiscale brain damage in the acute phase, manifesting as coordinated alterations in structural injury, white matter microstructural abnormalities, and brain network functional disruption. Combined multimodal MRI analysis facilitates a comprehensive delineation of the imaging phenotype associated with such injuries, enhances the detection rate of occult brain damage, and provides critical reference for the clinical assessment, risk stratification, and intervention decision-making related to alpine environment-associated brain injury.

PMID:41855108 | DOI:10.1097/SCS.0000000000012627

Front Neurosci. 2026 Mar 3;20:1797621. doi: 10.3389/fnins.2026.1797621. eCollection 2026.

ABSTRACT

[This corrects the article DOI: 10.3389/fnins.2025.1484954.].

PMID:41853676 | PMC:PMC12993821 | DOI:10.3389/fnins.2026.1797621

Biol Psychiatry Glob Open Sci. 2026 Jan 10;6(3):100689. doi: 10.1016/j.bpsgos.2026.100689. eCollection 2026 May.

ABSTRACT

BACKGROUND: Childhood disruptive behavior problems are linked to aberrant integrity within large-scale cognitive control networks. However, it is unclear whether transitory or dynamic variation in the functional brain architecture is a marker of disruptive behavior problems. In this study, we tested whether functional connectivity across dynamic networks is distinctly associated with the transdiagnostic symptom domain of disruptive behavior problems in children.

METHODS: Participants were 9 to 10-year-olds from the Adolescent Brain Cognitive Development Study who completed resting-state functional magnetic resonance imaging (fMRI) (N = 877). We used a dynamic connectivity approach leveraging a hidden semi-Markov model to identify transient properties of brain networks and states. Models estimated the time spent in each state (occupancy time) and the number of consecutive time points in a state (dwell time) for each participant. Linear regression models were utilized to identify distinct associations between dynamic properties (occupancy and sojourn times) and severity of disruptive behavior problems, while accounting for other commonly co-occurring symptoms.

RESULTS: Dynamic network markers of disruptive behavior problems included increased time in network states characterized by globally aberrant connectivity patterns in circuitry involved in cognitive control including frontoparietal and dorsal attention networks. Reliability of findings was found in a held-out sample of resting-state fMRI runs in which greater severity of disruptive behavior problems was uniquely linked to greater occupancy time in similarly characterized brain states.

CONCLUSIONS: Transdiagnostic, dynamic resting-state markers of disruptive behavior problems in youth may assist in the development of brain-based biomarkers for monitoring treatment outcomes, assessing circuit target engagement, and informing clinical decisions.

PMID:41852604 | PMC:PMC12994038 | DOI:10.1016/j.bpsgos.2026.100689

Sci Rep. 2026 Mar 18. doi: 10.1038/s41598-026-42844-x. Online ahead of print.

ABSTRACT

A cascade of biological responses to spinal cord injury (SCI), including neuroinflammation, plays a pivotal role in determining long-term outcomes and has become a primary therapeutic target. Riluzole, a neuroprotective agent, has demonstrated efficacy in preserving tissue integrity and improving motor function following SCI. The study aims to use this established treatment to verify that resting-state fMRI (rsfMRI) functional connectivity (rsFC) and TSPO PET metrics are reliable biomarkers of SCI severity, progression, and treatment response. 16 male rats with a moderate lumbar contusion injury were administered Riluzole or HBC vehicle. rsfMRI and TSPO PET scans were collected post-SCI alongside motor-sensory behavioral tests. After SCI, significantly stronger rsFC between dorsal-to-dorsal gray matter horns rostral to the SCI was observed in the riluzole group, compared to the vehicle group. A majority of horn pairs rostral and caudal to injury exhibited significant decrease in rsFC over time for both groups and correlated with post-injury behavioral deficits and recovery. TSPO-PET detected increased SCI neuroinflammatory activity. Our results demonstrate reductions in rsFC disruption, validating the role of rsFC as biomarkers of SCI severity and progression. The imaging biomarkers can be used to evaluate the responsiveness to treatment and efficacy of novel therapies in preclinical studies.

PMID:41851217 | DOI:10.1038/s41598-026-42844-x

Behav Brain Res. 2026 Mar 16:116166. doi: 10.1016/j.bbr.2026.116166. Online ahead of print.

ABSTRACT

BACKGROUND: The neurobiological mechanisms underlying individual differences in susceptibility to depression remain unclear. This study combined behavioral tests and resting-state functional magnetic resonance imaging (r-fMRI) to investigate how chronic unpredictable mild stress (CUMS) affects brain function and behavior in rats, and to identify neural markers that distinguish depression-susceptible (SUS) from resilient (RES) individuals.

METHODS: Thirty-one rats (CTRL group, n = 9; CUMS group, n = 22) underwent baseline r-fMRI scans before CUMS exposure. After 5 weeks of CUMS, behavioral tests, including sucrose preference test (SPT), forced swim test (FST), open field test (OFT), elevated plus maze (EPM), and novel object recognition test (NORT) were conducted, followed by post-stress r-fMRI. Rats were classified into SUS and RES groups based primarily on SPT and FST performance.

RESULTS: CUMS induced depression-like behaviors in SUS rats, such as reduced sucrose preference, while RES rats remained comparable to controls. RsFC analysis revealed that SUS rats exhibited enhanced functional connectivity between the hippocampal subiculum and right hypothalamus/left hypothalamus after CUMS. Critically, at baseline, SUS rats already showed stronger left subiculum-left hypothalamic connectivity than RES rats, a difference not observed on the right side.

CONCLUSION: These findings reveal that individual susceptibility to depression is associated with distinct patterns of functional connectivity involving the hippocampal subiculum, hypothalamus, and amygdala. Critically, pre-existing hyperconnectivity between the left subiculum and left medial hypothalamus may distinguish SUS from RES rats before stress exposure. This specific neural signature may represent a potential vulnerability factor and could inform the development of biomarkers for early risk identification.

PMID:41850407 | DOI:10.1016/j.bbr.2026.116166

bioRxiv [Preprint]. 2026 Mar 6:2026.03.04.709586. doi: 10.64898/2026.03.04.709586.

ABSTRACT

Obsessive-compulsive disorder (OCD) is a disabling condition that is characterized by disruptions in distributed brain circuit dynamics. However, current network studies predominantly evaluate these circuits by measuring functional synchrony (connectivity) between pairs of regions of interest, potentially overlooking complex higher-order interactions. In this study, we applied a Hodge Laplacian topological framework to investigate these higher-order interactions in OCD. Using a large-scale resting-state fMRI dataset from the ENIGMA-OCD consortium (1,024 OCD patients and 1,028 healthy controls across 28 sites worldwide), we identified significant disruptions in topological loops spanning frontoparietal, default mode, and sensorimotor networks. Crucially, the edges constituting these abnormal loops largely lacked significant pairwise differences, highlighting higher-order multi-nodal disturbances. Subgroup analyses revealed that these disruptions were most pronounced in adult, medicated, and high-severity OCD patients. Our findings suggest that OCD pathology involves abnormal recurrent higher-order multi-region interactions, providing new insights into the brain's functional organization and offering potential biomarkers for clinical application.

PMID:41847031 | PMC:PMC12991123 | DOI:10.64898/2026.03.04.709586

bioRxiv [Preprint]. 2026 Mar 6:2026.03.04.709400. doi: 10.64898/2026.03.04.709400.

ABSTRACT

Relapse remains a major challenge in opioid addiction treatment, underscoring the need for innovative therapies. Progress in neuromodulation therapies has been limited by insufficient mechanistic understanding of stimulation engagement and disease-related changes in the brain. We used a novel, focal transcranial magnetic stimulation (TMS) system to deliver high-density theta burst stimulation (hdTBS) combined with resting-state fMRI to test whether anterior cingulate cortex (ACC) stimulation reduces relapse-like behavior and alters functional circuitry in a rodent model of opiate dependence. The coil focality and stimulation parameters approximate human TMS protocols, and the targeted region represents a functional homolog of the human ACC. We trained rats to self-administer oxycodone intravenously for 14 days. We then introduced an electric barrier for 13 days, which caused cessation of drug self-administration. We assessed relapse to oxycodone seeking immediately after training (early abstinence) and after electric-barrier exposure (late abstinence). We administered daily hdTBS or sham stimulation for 7 days before the late-abstinence test. Sham-treated rats showed a time-dependent increase in oxycodone seeking during abstinence (incubation of oxycodone craving) and reduced ACC functional connectivity. In contrast, hdTBS prevented the incubation of oxycodone craving and restored ACC connectivity with the dorsal and ventral striatum. Tracer-based axonal-projection data further showed that stimulation-induced effects aligned with regions receiving dense projections from the stimulation site, suggesting that the projection architecture is critical to the propagation of focal stimulation across distributed networks. These findings identify ACC-centered circuits as mechanistically informed targets for TMS-based interventions that aim to reduce opioid relapse during abstinence.

ONE SENTENCE SUMMARY: Prefrontal TMS stimulation reduced relapse-like behavior and restored corticostriatal circuits, highlighting translational targets for addiction treatment.

PMID:41847016 | PMC:PMC12991172 | DOI:10.64898/2026.03.04.709400

J Pain Res. 2026 Mar 12;19:592723. doi: 10.2147/JPR.S592723. eCollection 2026.

ABSTRACT

PURPOSE: This study conducted a randomized controlled trial by analyzing resting-state functional magnetic resonance imaging (rs-fMRI) data to determine the mechanisms by which Tuina alleviates pain and modulates multiple brain networks in lumbar disc herniation (LDH) patients.

PATIENTS AND METHODS: This study included 38 healthy subjects and 76 LDH patients. LDH patients were randomly assigned into the test group (TG; n = 38) and control group (CG; n = 38). TG patients received 14 days of Tuina therapy, whereas CG patients received a combination of transcutaneous electrical nerve stimulation (TENS) and lumbar traction therapy. The primary outcome measure, simplified McGill Pain Questionnaire (SF-MPQ), was used to assess pain. Pain pressure threshold (PPT), Oswestry Disability Index (ODI), Beck Depression Inventory II (BDI-II), and Beck Anxiety Inventory (BAI) were evaluated as secondary outcomes. Fractional amplitude of low-frequency fluctuation (fALFF) and functional connectivity (FC) values were evaluated from the rs-fMRI data before and after treatment.

RESULTS: The SF-MPQ score significantly decreased in both TG subjects [-13.00 (-19.00, -9.00); P <0.001] and CG subjects [-11.00 (-14.00, -7.00); P <0.001]. SF-MPQ scores were significantly different between the two groups (P <0.05). In TG subjects, Tuina inhibited spontaneous neural activity in the bilateral inferior frontal gyrus triangular part (IFGtri) and suppressed the interaction between IFGtri and other brain regions. Changes in FC between IFGtri.R and STG.pole.R positively correlated with improvements in SF-MPQ scores (r = 0.511, P = 0.005). Changes in FC between IFGtri.L and IFGtri.R negatively correlated with reduced PPT of the bilateral gluteus maximus (r = -0.518, P = 0.004).

CONCLUSION: Tuina effectively alleviates pain, lumbar dysfunction, and negative emotions in LDH patients by regulating the interactions between multiple neural networks in the brain, especially through the inferior frontal gyrus triangle area.

PMID:41846592 | PMC:PMC12990911 | DOI:10.2147/JPR.S592723

BMC Med. 2026 Mar 17. doi: 10.1186/s12916-026-04760-9. Online ahead of print.

ABSTRACT

BACKGROUND: Pain in knee osteoarthritis (KOA) involves maladaptive neuroplastic adaptations within the pain matrix. Tai Chi and transcranial direct current stimulation (tDCS) each alleviate KOA pain, potentially by modulating the dorsolateral prefrontal cortex (DLPFC). Whether combining them yields superior analgesic and neuromodulatory effects remains to be established.

METHODS: In this four-arm, parallel-group randomized controlled trial, 152 participants with KOA were allocated to a 12-week intervention: (1) Tai Chi combined with tDCS, (2) Tai Chi, (3) tDCS, or (4) a Health Education Control group. The primary outcome was pain intensity assessed using the Pain subscale of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Secondary outcomes included the WOMAC Stiffness and Physical Function subscales, Visual Analogue Scale (VAS), Knee Injury and Osteoarthritis Outcome Score (KOOS), Timed Up and Go Test (TUGT), and the 36-Item Short Form Health Survey (SF-36). Resting-state functional connectivity (rsFC) between the right DLPFC and key regions of the pain matrix was analyzed using functional magnetic resonance imaging (fMRI). Assessments were conducted at baseline and post-intervention.

RESULTS: The Tai Chi combined with tDCS group demonstrated a significantly greater reduction in WOMAC Pain subscores compared to the Health Education Control group (P < 0.001), the tDCS group (P = 0.003), and the Tai Chi group (P = 0.048). However, the combined intervention did not show statistically superior improvement over Tai Chi group in secondary outcomes. Neuroimaging results indicated that all active interventions decreased rsFC between the right DLPFC and several pain-matrix regions, including the left posterior cingulate cortex, bilateral thalamus, left precuneus, and left superior frontal gyrus. Furthermore, the extent of pain reduction was positively correlated with decreased connectivity between the right DLPFC and both the left posterior cingulate cortex and the left precuneus.

CONCLUSIONS: This exploratory trial suggests that combining Tai Chi with tDCS provides superior pain relief compared to either monotherapy in individuals with KOA. Post hoc exploratory neuroimaging analyses further indicate that this analgesic effect may be associated with changes in rsFC between the right DLPFC and regions of the pain matrix.

TRIAL REGISTRATION: This study was registered with the Chinese Clinical Trial Registry (ChiCTR2100047287).

PMID:41845339 | DOI:10.1186/s12916-026-04760-9

Transl Psychiatry. 2026 Mar 17. doi: 10.1038/s41398-026-03957-z. Online ahead of print.

ABSTRACT

Identifying reliable biomarkers of treatment response is central to advancing personalized psychiatry. While whole-brain functional connectivity models have shown promise in predicting clinical outcomes, especially for broad-spectrum interventions like cognitive-behavioral therapy, targeted treatments may benefit from more specific neuromarkers. In social anxiety disorder (SAD), Gaze-Contingent Music Reward Therapy (GC-MRT) is a novel attention bias modification (ABM) intervention designed to reduce preferential attentional allocation to socially threatening stimuli. Given the dorsal attention network's (DAN) key role in top-down attentional control, we tested whether resting-state intra-network DAN connectivity could serve as a neural predictor of response to GC-MRT. Participants with SAD were randomized to either receive GC-MRT (n = 22) or to a waitlist control condition (n = 24). Resting-state fMRI data were collected before and after the intervention. Intra-DAN connectivity at baseline and post-treatment were associated with post-treatment symptom severity in the GC-MRT group. Post-treatment intra-DAN connectivity significantly differed in the GC-MRT group relative to controls. These findings suggest that intra-network connectivity within the DAN may have the potential to function both as a predictive biomarker and as a neural marker of successful intervention. Our findings highlight the role of the DAN in attention-based clinical interventions and show that network-specific connectivity metrics may offer a more precise understanding of how targeted neuromodulation affects symptom change in SAD.

PMID:41844594 | DOI:10.1038/s41398-026-03957-z

Eur J Psychotraumatol. 2026 Dec;17(1):2636453. doi: 10.1080/20008066.2026.2636453. Epub 2026 Mar 17.

ABSTRACT

Background: Frontline healthcare workers (HCWs) experience unique patterns of repeated, chronic, and unpredictable traumatic event exposure, coupled with physiologic stress in the setting of shift-work circadian rhythm disruption, contributing to high rates of post-traumatic stress syndromes (PTSS) and substantial workforce and economic burden. The neurobiology underlying HCW-specific risk remains incompletely understood.Objective: To synthesise epidemiological, neuroimaging, physiological, and interventional evidence into a mechanistic model of HCW PTSS and to identify priorities for biomarker-guided prevention and care.Method: Literature for this narrative review was identified through a comprehensive search of peer-reviewed articles in PubMed, PsycINFO, and Google Scholar up to May 2025. Studies were included if they addressed (1) the epidemiology of PTSD in healthcare settings, (2) risk and protective factors specific to occupational trauma exposure, (3) neural, physiological, or molecular mechanisms associated with stress-related disorders in trauma-exposed personnel, or (4) interventions targeting PTSD/PTSS in HCWs and first responder populations.Results: Across studies, PTSS prevalence among HCWs is variable (≈15-74%). Repeated, chronic, and unpredictable occupational trauma, exacerbated by circadian disruption, appears to destabilise frontal-limbic circuits and systemic stress pathways, culminating in allostatic overload. Converging data suggest that multimodal biomarkers, including resting-state and task-evoked fMRI metrics, MR spectroscopy, heart rate variability, sleep architecture, cortisol and inflammatory indices can identify prodromal dysregulation and define risk stratification.Conclusions: Longitudinal, multimodal cohort designs are critically needed to track trajectories and evaluate neuroscientifically-informed treatment modalities for PTSS in this population. Framing HCW PTSS as an occupational neurobiological injury highlights the need to identify and prevent functional decline. A biomarker-guided strategy that links brain-circuit measures with autonomic, sleep, and molecular indices may offer a path to earlier identification, precision interventions, and improved outcomes for a critically at-need population that is essential to our workforce.

PMID:41841360 | DOI:10.1080/20008066.2026.2636453

BMC Complement Med Ther. 2026 Mar 16. doi: 10.1186/s12906-026-05343-0. Online ahead of print.

NO ABSTRACT

PMID:41840560 | DOI:10.1186/s12906-026-05343-0

Mol Neurobiol. 2026 Mar 16;63(1):504. doi: 10.1007/s12035-026-05771-6.

ABSTRACT

Alzheimer's disease (AD) is a dementia disease that causes loss of cognitive functions. Also, it is a noncurable disease. However, early diagnosis and proper medication reduce AD's progression time. Yet, the prevailing AD diagnosis models did not concentrate on the claustrum in the brain, reducing the efficiency of the AD diagnosis. Thus, this framework proposes an effective AD identification model with claustrum segmentation based on Collapsing Linear Unit-Convolutional Neural Network (CoLU-CNN). Primarily, the resting state-functional Magnetic Resonance Imaging (rs-fMRI) is preprocessed. Then, Gray Matter (GM), White Matter (WM), Cerebrospinal Fluid (CSF), and the hippocampus are segmented. By using Rogers and Tanimoto-based K-means (RoT-Kmeans), the putamen edge is detected from the segmented GM to segment the claustrum. Likewise, the time-series extraction is performed from rs-fMRI, and network connectivity is generated by using Seed-Based Functional Connectivity (SBFC). Then, the network connectivity and segmented claustrum are mapped. Next, the features are extracted from the segmented and mapped images, and optimal features are selected by using Kent Map-based Wild Geese Optimization (KM-WGO). Lastly, the AD is classified by using CoLU-CNN. The experimental investigation stated that the proposed methodology attained 99% AD classification accuracy.

PMID:41840320 | DOI:10.1007/s12035-026-05771-6

Med Sci Sports Exerc. 2026 Mar 12. doi: 10.1249/MSS.0000000000003961. Online ahead of print.

ABSTRACT

BACKGROUND: Brain lateralization, the hemispheric specialization of functional networks, is essential for motor and cognitive functions. However, how intensive athletic training shapes hemispheric organization remains poorly understood. This study investigated the formation of functional lateralization in elite athletes through long-term intensive training and its potential alteration due to external factors, specifically sport-related concussions.

METHODS: Resting-state fMRI data were collected from 13 world class gymnasts (WCGs) and 14 non-athletic controls. Longitudinal data were collected from 18 soccer players and 8 golfers before and after one season, with concussions monitored in the soccer players. Hemispheric integration and segregation laterality indices (LIint and LIseg) were calculated to quantify hemispheric differences in information processing for each brain region, and the standard laterality index (LI) was employed to measure hemispheric asymmetry. Associations between laterality indices and neurotransmitter receptor/transporter densities were examined. Post-season changes in these indices were assessed to evaluate concussion effects on brain lateralization.

RESULTS: The WCGs showed significantly increased LIint in several left-hemispheric regions, including the precentral gyrus, cingulate gyrus, thalamus, superior parietal lobule, and lateral occipital cortex compared with the HCs, though no results were found in LIseg. Furthermore, the LI analysis revealed that the WCGs demonstrated higher hemispheric asymmetry in the left precentral gyrus, cingulate gyrus, and thalamus. These laterality indices also showed positive correlations with certain neurotransmitters. Similar patterns of enhanced lateralization and neurotransmitter associations were observed in soccer players and golfers. However, no significant changes in laterality indices were observed as a result of concussions sustained during the season.

CONCLUSIONS: Long-term intensive training enhances functional integration in the left hemisphere, leading to stable brain lateralization patterns resilient to sport-related concussions.

PMID:41839190 | DOI:10.1249/MSS.0000000000003961

Brain Res Bull. 2026 Mar 13:111826. doi: 10.1016/j.brainresbull.2026.111826. Online ahead of print.

ABSTRACT

OBJECTIVE: Noise-induced hearing loss impacts brain health and cognition, with dynamic functional connectivity analysis offering a promising but underexplored method for studying whole-brain activity. Therefore, this study aimed to utilise dynamic functional connectivity analysis to investigate abnormal temporal variability in whole-brain functional connectivity in patients with noise-induced hearing loss.

METHODS: In this observational study, 58 patients with noise-induced hearing loss and 42 healthy male controls, matched for age and education, underwent resting-state functional magnetic resonance imaging. The sliding window approach was employed to evaluate dynamic functional connectivity between region pairs, and k-means clustering was used to identify dynamic functional connectivity states. A two-sample t-test was used to compare differences in dynamic functional connectivity variability and state metrics between patients with noise-induced hearing loss and healthy male controls (P < 0.05). Abnormal brain dynamic functional connectivity features were identified using false discovery rate correction and least absolute shrinkage and selection operator classifier. These features were used to construct support vector machine classifiers.

RESULTS: Compared with healthy male controls, patients with noise-induced hearing loss demonstrated decreased dynamic functional connectivity between the right supplementary motor area and bilateral cuneus and increased dynamic functional connectivity between the supplementary motor area and left inferior parietal gyrus. The support vector machine classifier based on abnormal dynamic functional connectivity features selected by false discovery rate correction successfully distinguished between patients with noise-induced hearing loss and healthy male controls with an accuracy of 82.5%. The accuracy of the support vector machine classifier based on least absolute shrinkage and selection operator-selected abnormal dynamic functional connectivity features reached 96.8%.

CONCLUSION: This study revealed abnormal dynamic functional connectivity patterns in patients with noise-induced hearing loss, offering insights into the complex neuropathological mechanisms underlying long-term brain network changes associated with this disease.

PMID:41833699 | DOI:10.1016/j.brainresbull.2026.111826

Brain Res Bull. 2026 Mar 13:111825. doi: 10.1016/j.brainresbull.2026.111825. Online ahead of print.

ABSTRACT

BACKGROUND: Although resting-state functional magnetic resonance imaging (rs-fMRI)-derived functional gradients have been widely used to describe cortical hierarchical organization, their application has rarely targeted acute sleep deprivation (ASD)-related cognitive vulnerability. In particular, whether ASD induces systematic reorganization of gradient architecture and whether this reorganization contributes to spatial working memory (SWM) impairment have not yet been systematically examined.

METHODS: Fifty healthy young adult males were recruited. A 1-back task was administered to assess SWM performance before and after ASD. T1-weighted and rs-fMRI data were acquired. Functional gradient-based metrics, including standard deviation of gradient values, range of gradient values, network gradient values, and inter-network relative distances, were computed to characterize cortical hierarchical organization and were subsequently correlated with SWM behavioral performance.

RESULTS: Compared with the rested wakefulness condition, ASD significantly impaired SWM performance. Functional gradient analysis revealed significant alterations in both global (standard deviation and range) and local (gradient values of specific subnetworks) features of the top three principal gradients. Notably, the standard deviation of Gradient 2 was significantly negatively correlated with omission rate. In addition, relative distances between multiple networks within Gradient 2 and 3 were also closely associated with SWM performance.

CONCLUSION: From the perspective of functional gradients, the present study highlights the global and local gradient reorganization following ASD, as well as the importance of maintaining a balance between functional segregation and integration across subnetworks in sustaining SWM performance.

PMID:41833697 | DOI:10.1016/j.brainresbull.2026.111825

Commun Biol. 2026 Mar 14. doi: 10.1038/s42003-026-09839-w. Online ahead of print.

ABSTRACT

At dawn, we experience a shift from slumber to sentience following neurophysiological transitions, termed as sleep inertia (SI). Although resting-state fMRI studies have discovered brain reorganizations during SI, the neural basis underlying impaired alertness in SI remains unclear. We conduct simultaneous EEG-fMRI recordings on 26 adults with repeated measures of pre-sleep, nocturnal sleep and consecutive post-sleep awakenings. Using the psychomotor vigilance task (PVT) to probe tonic alertness, we discover that cingulo-opercular network (CON) activation, inclusive of thalamus, troughs upon awakening, and increments along with the post-sleep awake duration. The dynamic recovery of thalamus activity during SI depends on prior sleep architecture and the awake duration, mediating the PVT performances on awakening. Although CON connectivity remains stable, the connectivity changes between thalamus and frontoparietal network (FPN) are associated with changes of thalamic activation and PVT performances during SI. Collectively, thalamic activity and its coupling with the FPN support the restoration of tonic alertness during SI, providing a concise framework for the neural mechanisms underlying cognitive recovery upon awakening.

PMID:41832350 | DOI:10.1038/s42003-026-09839-w

Biol Psychiatry. 2026 Mar 12:S0006-3223(26)00099-5. doi: 10.1016/j.biopsych.2026.03.002. Online ahead of print.

ABSTRACT

BACKGROUND: Adolescence is a vulnerable period for the onset of anxiety and depression, yet their neurodevelopmental origins remain unclear.

METHODS: In this 7-year prospective longitudinal study, we recorded resting-state electroencephalogram (EEG) data at ages 7, 9, and 11, followed by fMRI scanning and symptom assessments at age 13. Using connectome-based predictive modeling, we examined whether childhood EEG patterns could predict adolescent symptoms, with rigorous control analyses and external validation in the Healthy Brain Network dataset (HBN, n = 384). We further characterized the developmental trajectories of these predictive networks. To mechanistically ground these electrophysiological markers, we conducted mediation analyses to test whether the amygdala-seeded circuits mediate the link between childhood EEG dynamics and adolescent symptom severity.

RESULTS: We identified dissociable EEG indicators emerging at age 9 that predicted adolescent anxiety (alpha, 8-12 Hz) and depression (beta1, 12-18 Hz). Importantly, the dynamic maturation of these EEG networks highlighted distinct neurodevelopmental susceptibilities, in which longitudinal EEG shifts between ages 9 and 11 predicted symptom severity in adolescence. The divergent developmental trajectories of EEG-based networks were characterized by opposing hemispheric lateralization: leftward for anxiety and rightward for depression. Mechanistically, these predictive associations were mediated by lateralized amygdala-ventrolateral prefrontal cortex (vlPFC) circuits, with the right and left vlPFC pathways selectively mediating anxiety risk and depression risk, respectively. These models generalized robustly to the independent HBN cohort.

CONCLUSIONS: Our findings highlight early neurobiological indicators of distinct developmental trajectories and susceptibilities for anxiety and depression, providing a foundation for early risk stratification and targeted precision prevention.

PMID:41831747 | DOI:10.1016/j.biopsych.2026.03.002