In light of recent advances in sports medicine and neurorehabilitation, non-invasive brain stimulation (NIBS)-including techniques such as transcranial magnetic stimulation (TMS) and transcranial electrical stimulation (tES)-show potential for enhancing sports performance and supporting rehabilitation. For this narrative critical review, we synthesized evidence from studies in which trained athletes and healthy individuals performed sports-related tasks. These studies were identified through a structured search of PubMed, Web of Science, Scopus, and Google Scholar. The findings indicate that NIBS can modulate neural activity to improve motor learning, skill acquisition, muscle strength, endurance, attention, decision-making, emotional regulation, and brain network plasticity, with a notable potential to accelerate the mastery of sport-specific skills essential for competitive success. Nevertheless, heterogeneity in study designs and stimulation protocols, alongside unresolved ethical concerns regarding athlete autonomy and fairness, continue to limit widespread adoption. While NIBS holds considerable promise for both athletic performance optimization and rehabilitation, further research is needed to clarify its mechanisms of action, standardize protocols, and establish robust ethical guidelines to safeguard athlete safety and integrity and fair competition.

BackgroundThe deposition of amyloid-β (Aβ) in the human brain is a hallmark of Alzheimer’s disease and is associated with cognitive decline. Aβ pathology is traditionally assessed at the whole-brain level across neocortical regions using positron emission tomography (PET). However, these measures often show weak associations with future cognitive impairment. A more sensitive pathology metric is needed to quantify early Aβ burden and better predict cognitive decline. Here, we aim to develop a network-based metric of Aβ burden to improve early prediction of cognitive decline in aging populations.MethodsWe integrated subject-specific brain connectome information with Aβ-PET measures to construct a network-based metric of Aβ burden. Cross-validated predictive modeling was used to evaluate the performance of this metric in predicting longitudinal cognitive decline. Furthermore, we identified a neuropathological signature pattern linked to future cognitive decline, and we validated this pattern in an independent cohort.ResultsOur results demonstrate that incorporating individualized structural connectome, but not functional connectome, information into Aβ measures enhances predictive performance for prospective cognitive decline. The identified neuropathological signature pattern is reproducible across cohorts.ConclusionThese findings advance our understanding of the spatial patterns of Aβ pathology and its relationship to brain networks, highlighting the potential of connectome-informed network-based metrics for Aβ-PET imaging in identifying individuals at higher risk of cognitive decline.

Loneliness is an unpleasant subjective experience associated with significant psychological and physical health problems. With increasing urbanization and aging populations, loneliness is becoming a global public health concern. Thus, understanding the neural correlates of loneliness is crucial for developing targeted intervention approaches. In the current study, we collected resting-state fMRI data from 238 young adults (ages 17-26; 59 males, 179 females) and used fractional amplitude of low-frequency fluctuations (fALFF) and functional connectivity (FC) analyses to investigate the neural correlates of loneliness. Results revealed that loneliness was negatively correlated with fALFF in the right posterior precuneus. Functional connectivity analyses showed that loneliness was positively correlated with connectivity between the right posterior precuneus and right superior frontal gyrus, and negatively correlated with connectivity between the right ventromedial prefrontal cortex and a network including the right cerebellum, left fusiform gyrus, and right superior occipital gyrus. These findings reveal neural correlates of loneliness, including distinct patterns of intrinsic activity in the posterior precuneus and specific functional connectivity patterns involving regions associated with social cognition and emotional regulation. The results provide neural evidence for understanding individual differences in loneliness and could potentially inform future research on neurostimulation and cognitive-behavioral interventions targeting these specific brain networks.

Children and adolescents, diagnosed with cancer, frequently develop physical and cognitive impairments. Hence, cancer and its treatments contribute to reduced physical activity (PA) and cognitive impairments, particularly in executive functions (EFs). Research indicates that PA in school children improves EFs, with cognitively challenging PA offering potential additional benefits. The aim of this study is to investigate the effects of cognitively challenging PA during acute cancer care on cognitive and physical performance, as well as mental health. This prospective, two-arm, non-randomized, multicenter controlled study will take place at four pediatric oncology centers in Switzerland. In the intervention group the effect of cognitively challenging PA (n = 35) will be compared with standard care plus PA recommendations in the control group (n = 35) in newly diagnosed pediatric patients with cancer aged 6-18-years. The twelve-week cognitively challenging PA intervention consists of three-weekly 45-minute individualized and supervised sessions incorporating cognitive elements. Assessments of EFs, motor abilities, cardiovascular health, health-related quality of life, fatigue, and physical and psychosocial functioning will be conducted at baseline, six weeks, twelve weeks, and a six month follow-up. All participants in the intervention and control group will receive PA recommendations during the intervention period and an offer for post-therapy PA counselling. Childhood is a crucial period for brain and motor development, rendering young cancer patients especially vulnerable to cognitive and physical impairments from the disease and its treatment. This study is the first to implement cognitively challenging PA tailored to pediatric cancer patients with the aim to enhance EFs by activating brain networks responsible for higher-order processes, physical performance and mental health. The findings will provide insights into the role of cognitively challenging PA and explore its integration into standard care to improve quality of life for childhood cancer survivors. ClinicalTrials.gov (NCT06839794) German Clinical Trial Register (DRKS00036573).

Introduction Growing evidence shows that voluntary breathing maneuvers modulate cortical oscillations, yet the precise frequency-specific signatures of functional connectivity (FC) remain unclear. Methods This study investigated the impact of different respiratory conditions on brain FC using EEG recordings. Three respiratory conditions were collected and analyzed: self-paced breathing (SB), breath-holding (BH), and computer-paced breathing (PB). The power spectral density (PSD), phase-locking value (PLV), and brain network characteristics were analyzed for these different conditions. Results The results all showed significant differences. The PSD analysis revealed increased low-frequency ( δ and θ ) activity during SB and higher high-frequency ( α and β ) activity during BH conditions. The PLV analysis demonstrated significant differences in FC between conditions, indicating specific modulation of brain networks by respiratory state. The brain network properties analysis uncovered frequency-specific changes in clustering coefficient (CC), global efficiency (GE), local efficiency (LE), and degree centrality (DC), reflecting alterations in brain network organization. The three-class classifier showed superior performance in the α band, suggesting its potential as a biomarker for distinguishing respiratory conditions. Correlation analysis with forced vital capacity (FVC) revealed significant associations between brain connectivity and FVC metrics. Discussion These findings highlight the complex interplay between respiratory conditions and brain FC. These findings suggest that controlled and uncontrolled breathing patterns can influence brain network organization, a mechanistic observation that may inform future respiratory-based interventions aimed at enhancing cognitive function, although behavioural or affective outcomes were not assessed here.

ObjectiveNeuropsychiatric disorders are characterized by high complexity and comorbidity, imposing a substantial burden on both patients and society. However, their elusive pathogenic mechanisms impede accurate clinical diagnosis and effective interventions. To overcome this challenge, the present study proposes a novel framework to quantify and characterize these disorders.MethodsRoutine electroencephalogram (EEG) recordings are acquired from 236 subjects, including patients with Alzheimer’s disease (AD), mild cognitive impairment (MCI), major depressive disorder (MDD), schizophrenia, and healthy controls (HCs). Time-varying functional brain networks are constructed by phase locking value (PLV) analysis on band-pass filtered EEG signals. Subsequently, the nodal behavior characteristics within these dynamic brain networks are quantified by integrating robust dynamic community detection algorithms and network reconfiguration metrics.ResultsSignificant intergroup differences in network reconfiguration metrics are identified based on the dynamic community structures (FDR-corrected p < 0.001). Lower cohesion strength is observed across all neuropsychiatric disorders compared to healthy controls, consistent across all frequency bands and recording sites. When six machine learning classifiers are trained on these metrics, the maximum classification accuracies exceeded 80%. Since lower cohesion strength is a prominent potential biomarker for neuropsychiatric disorders, it was then selected as the independent input feature for random forest classifier, and the classification accuracy achieved 0.85 for schizophrenia group, 0.88 for both the MCI and MDD group, and 0.82 for the AD group.ConclusionsOur findings indicate that the framework based on dynamic network reconfiguration metrics effectively captures both the shared and disorder-specific alterations in brain network dynamics among neuropsychiatric disorders.SignificanceDynamic community structure advances our understanding of the pathological mechanisms underlying neuropsychiatric disorders. This study provides novel insights that may inform the development of more targeted and effective therapeutic strategies.

Introduction Understanding the role of various brain regions of interest (ROIs) in various cognitive functions or tasks, across healthy or neurodegenerative conditions and multiple degrees of separation, remains a key challenge in neuroscience. Conventional network measures can only capture localized or quasi-localized features of brain ROIs. Topological data analysis (TDA), particularly persistent homology, provides a threshold-free, mathematically rigorous framework for identifying topologically salient features in complex networks. In this paper, we introduce a new metric, the Homological Vertex Importance Profile (H-VIP), designed to assess the relevance of vertices that participate in persistent topological structures (e.g., connected components, cycles or cavities) in brain networks. The H-VIP quantifies the topological features of the network at the ROI (node) level by compressing its higher-order connectivity profile using homological constructs. Methods Leveraging homological constructs of brain connectomes, we extend two of our previously defined network-level measures—average persistence and persistence entropy—to an ROI-level measure, i.e., the H-VIP. We then applied the H-VIP to two independent datasets: structural connectomes from the Human Connectome Project and functional connectomes from the Alzheimer’s Disease Neuroimaging Initiative. Persistent homology was computed for each network, and H-VIP scores were derived to evaluate vertex-level contributions. Finally, H-VIP scores were used for the prediction of multiple cognitive measures. Results In both anatomical and functional brain networks, H-VIP values demonstrate predictive power for various cognitive measures. Notably, the connectivity of the frontal lobe exhibited stronger correlations with cognitive performance than the whole-brain network. Discussion H-VIP offers a robust and interpretable means to locate, quantify, and visualize region-specific contributions to network’s topological, higher-order landscape. Its ability to detect potentially impaired connectivity at the individual level suggests possible applications in personalized medicine for neurological diseases and disorders. Beyond brain connectomics, the H-VIP can be used for other types of complex networks where topological features are of importance, such as financial, social, or ecological networks.

Resting-state functional connectivity (rsFC) is a highly dynamic process that varies across different times of the day within each individual. Although this variability was long considered to be noise, recent evidence suggests it may allow for an optimal adaptation to changes in the environment. However, the way rsFC is shaped on a circadian scale and its association with cognition are still unclear. We analyzed data from 90 late middle-aged participants from the Cognitive Fitness in Aging study (61 women; 50-69 years). Participants completed five electroencephalographic (EEG) recordings of spontaneous resting-state activity spread over 20h of prolonged wakefulness. Using a temporal multilayer network approach, we characterized the diurnal variations of the dynamic recruitment and integration of resting-state brain networks. We focused on the theta and gamma frequency bands within the default mode network (DMN), central executive network (CEN), and salience network (SN). Additionally, we investigated the relationship between the recruitment and integration of these networks with baseline cognitive performance and at a 7-year longitudinal follow-up, as well as with positron emission tomography (PET) early neuropathological markers of Alzheimer's disease such as β-amyloid and tau/neuroinflammation. Diurnal changes in theta and gamma dynamics were associated with distinct cognitive aspects. Specifically, higher baseline memory performance was associated with higher theta dynamic integration of the SN and the CEN, as well as higher theta dynamic recruitment of the DMN. Moreover, lower longitudinal memory decline at 7 years was associated with higher theta dynamic integration of the SN, CEN, and DMN. In contrast, higher gamma diurnal dynamic integration of the SN and the CEN was associated with lower executive and attentional performance, as well as higher early β-amyloid accumulation, at baseline. These findings suggest that maintaining a balance between network flexibility and stability throughout the diurnal phase of the circadian cycle may play a crucial role in cognitive aging, with stable theta-band connectivity supporting memory, whereas excessive gamma-band stability in the SN and CEN may contribute to executive decline and early amyloid accumulation. These insights highlight the importance of considering time-of-day in brain rsFC studies, calling for a temporal multilayer approach to capture these dynamic patterns more effectively.

The diagnostic categorization in psychiatry, largely based on clinically observed symptom clusters, has proven insufficient for capturing the vast biological and phenomenological heterogeneity within and across psychiatric disorders. This heterogeneity is a primary obstacle to developing targeted, effective treatments. We propose a novel computational framework designed to map the complex, non-linear relationships between distributed neural circuit dysfunctions and the multidimensional space of psychopathology. This framework, which we term the Integrative Neuroclinical Mapping Framework (INMF), moves beyond case-control comparisons to model psychopathology as a system of continuous, overlapping dimensions. The INMF processes high-dimensional data from functional and structural magnetic resonance imaging (fMRI, sMRI), integrating them with fine-grained, dynamic symptom data acquired through digital phenotyping (e.g., ecological momentary assessment, smartphone sensors). Core to the INMF is a multi-stage analytical pipeline featuring (1) data harmonization and feature extraction using automated preprocessing and source-based morphometry/independent component analysis, (2) manifold learning to uncover low-dimensional latent neural-symptom structures, and (3) graph-based network analysis to model the dynamic interplay between brain networks and symptom domains. We present a proof-of-concept application using a simulated dataset of individuals with schizophrenia, major depressive disorder, and healthy controls, demonstrating the framework's ability to identify transdiagnostic biotypes that are more homogenous than traditional diagnoses. Our results illustrate how the INMF can delineate distinct neural pathways leading to similar symptomatic expressions (equifinality) and common neural risk factors manifesting as different disorders (multifinality). This framework offers a powerful, data-driven approach for parsing the nosological chaos of psychiatry, with the potential to revolutionize diagnosis, prognostication, and the development of personalized neurotherapeutics.

Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, yet it remains under-recognised and misdiagnosed, which delays treatment, causes inaccurate prognosis and limits research opportunities. Imaging with 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) is a supportive DLB biomarker. We evaluated a multivariate, quantifiable metabolic network biomarker, termed DLB-related pattern (DLBRP), for its further clinical translation across centres and disease stages. We analysed demographic, clinical and FDG PET imaging data of 1180 participants from 14 tertiary centres and two multicentre datasets. We included 379 DLB, 28 mild cognitive impairment-LB (MCI-LB), 195 dementia due to Alzheimer's disease (ADD), 172 MCI-AD without α-synuclein co-pathology (MCI-AD-S-), and 73 MCI-AD with α-synuclein co-pathology (S+) patients, along with a comparative group of 333 normal controls (NCs). From the scans, we calculated the expression of DLBRP, AD-related pattern (ADRP) and Parkinson's disease-related pattern (PDRP) and compared them across groups. DLBRP scores were correlated with clinical measurements. Across independent cohorts, DLBRP robustly distinguished DLB from NCs (sensitivity >89%, specificity >90%), and scores correlated with Unified Parkinson's Disease Rating Scale Part III and independently predicted Mini-Mental State Examination. DLBRP was elevated already in MCI-LB. In a small longitudinal dataset, we observed steady increases in DLBRP expression with scores exceeding the diagnostic threshold prior to dementia onset. DLBRP and PDRP discriminated DLB from ADD (sensitivity, 74%-90%; specificity, 80%). In MCI-AD groups, ADRP was expressed, whereas DLBRP and PDRP were increased only in MCI-AD-S+, although comparatively less than in MCI-LB. This study demonstrates the value of DLBRP in diagnosing prodromal and manifest DLB and distinguishing them from their AD counterparts. While overlap between patterns may reflect actual co-pathology, this possibility cannot be accepted without thorough pathological confirmation. The current findings support the use of DLBRP in patient evaluation and in future trial design.

This perspective article discusses the complex bidirectional relationship between tinnitus and insomnia, a prevalent and burdensome comorbidity. It synthesizes evidence suggesting the conditions mutually exacerbate one another through shared pathophysiological mechanisms, including central hyperarousal, emotional dysregulation, and aberrant neural network connectivity. Our study then explores acupuncture as a potential integrated therapy. Preliminary clinical studies indicate acupuncture can concurrently alleviate both tinnitus and insomnia symptoms. Its therapeutic potential is attributed to multi-target effects, such as regulating the autonomic nervous system and HPA axis, modulating neurotransmitters and neuroinflammation, and normalizing functional brain networks. Our study concludes by highlighting the critical need for rigorous research focusing specifically on the comorbidity to validate these mechanisms and establish clinical efficacy.

Abstract Recent advancements in connectome analyses have enabled more precise measurements of brain network integrity. Identifying neural measures that can operate as mechanisms of cognitive reserve is integral for the study of individual variability in age-related cognitive changes. In the present study, we tested the hypothesis that network resilience, or the network's ability to maintain functionality when facing internal or external perturbations that cause damage or error, can function as a cognitive reserve (CR) candidate, modifying the relationship between cognitive and brain changes in a lifespan cohort of cognitively healthy adults. One hundred cognitively healthy older adults from the Reference Ability Neural Network (RANN) longitudinal lifespan cohort (50-80 years) underwent resting-state fMRI and neuropsychological testing at baseline and five-year follow-up. Using undirected weighted adjacency matrices created from the Schaefer et al. (2018) 400-parcellation atlas and 19 additional subcortical regions (419 nodes in total), whole-brain network resilience was assessed through a targeted attack approach, where nodes were sequentially removed by nodal strength and resilience defined as the iteration of the steepest slope in the largest connected component (LCC) decay. We observed that network resilience moderated the effect of cortical thickness (CT) changes on longitudinal changes in Fluid Reasoning performance, even after adjusting for baseline differences, demographic factors, and the initial LCC of the unlesioned matrix, indicating that individuals with greater resilience were less sensitive to the effect of cortical thickness changes on changes in cognition. These findings support the use of targeted attack as a measure of cognitive reserve, suggesting that higher network resilience may allow individuals with reduced brain integrity to better cope with structural loss and maintain cognitive function.

Bulimia nervosa (BN) is a severe psychiatric disorder characterized by dysregulated eating behaviors and impaired cognitive-emotional control. Despite increasing recognition of brain network dysfunction in BN, the interplay between structural connectivity (SC) and functional connectivity (FC), termed SC-FC coupling, remains poorly understood. This study aimed to comprehensively characterize SC-FC coupling alterations in BN using multimodal neuroimaging and to evaluate the predictive value for disordered eating behaviors. This study enrolled 79 patients with BN and 69 healthy controls who underwent high-resolution structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and resting-state functional MRI (rs-fMRI). Functional and structural connectomes were constructed using the Schaefer-400 atlas. SC-FC coupling was quantified using eight biologically grounded similarity and communication metrics. A multivariate linear modeling framework was applied to estimate region-specific coupling profiles. Group comparisons and ridge regression-based leave-one-out cross-validation were used to identify altered coupling and predict symptom severity. The global topological properties of the SC and FC networks were preserved in BN. However, patients exhibited significantly reduced degree centrality and nodal efficiency in the inferior frontal gyrus within the FC network. SC-FC coupling, quantified using the matching index (MI), showed widespread regional alterations in BN, particularly within the default mode, control, and attention networks. Seventeen brain parcels demonstrated significant group differences in MI-based coupling (false discovery rate (FDR)-corrected, p < 0.05), with both hypercoupling and hypocoupling observed. Findings were parcellation-robust (Glasser-360 replication; Dice = 0.93 vs. Schaefer-400). Moreover, coupling features moderately predicted binge-eating frequency (r = 0.24, p < 0.001), but not questionnaire-based emotional or behavioral scores. In BN, macroscale white-matter organization is preserved, yet focal prefrontal functional decentralization and widespread, parcellation-robust SC-FC coupling changes invisible to single-modality analyses were observed. Multidimensional SC-FC coupling provides a sensitive neurobiological marker that explains clinically relevant variance in binge-eating behavior, highlighting its potential as a target for personalized diagnosis and intervention in BN.

Altered task-related brain network dynamics and performance consistency in a non-clinical group burdened with childhood trauma.

Millions of adults suffer from Major Depressive Disorder (MDD) globally. Applying network theory to study functional brain dynamics often uses the fMRI modality to identify the perturbed connectivity in depressed individuals. However, the weak temporal resolution of fMRI limits its ability to access the fast dynamics of functional connectivity (FC). Therefore, electroencephalography (EEG), which can track functional brain dynamics every millisecond, may serve as a diagnostic marker for utilizing the dynamics of intrinsic brain networks at the sensor level. This research proposes a unique neural marker for depression detection by analyzing long-range functional neurodynamics between the default mode network (DMN) and visual network (VN) via optimal EEG nodes. While DMN abnormalities in depression are well documented, the interactions between the DMN and VN, which reflect visual imagery at rest, remain unclear. Subsequently, a novel differential graph centrality index is applied to reduce a high-dimensional feature space representing EEG temporal neurodynamics, which produced an optimized brain network for MDD detection.&#xD;The proposed method achieves an exceptional classification performance with an average accuracy, f1 score, and MCC of 99.76\%, 0.998, and 0.9995 for the MODMA and 99.99\%, 0.999, and 0.9998 for the HUSM datasets, respectively.&#xD;The findings of this study suggest that a significant decrease in connection density within the beta band (15-30 Hz) in depressed individuals exhibits disrupted long-range inter-network topology, which could serve as a reliable neural marker for depression detection and monitoring. Furthermore, weak FC links between the DMN and VN indicate disengagement between the DMN and VN, which signifies progressive cognitive decline, weak memory, and disrupted thinking at rest, often accompanied by MDD.

Anorexia nervosa (AN) is a severe eating disorder associated with drastic reductions in gray and white matter (WM) volume and structural connectivity alterations. However, the hypotheses regarding underlying mechanisms are inconclusive. The current study investigated the relationships of WM volume as well as WM network architecture with neurofilament light (NF-L), a marker of axonal damage. Blood samples and magnetic resonance imaging scans from 77 predominantly adolescent female participants with acute AN were used. Associations of WM volume with NF-L were tested using linear models. The relationship between NF-L and alterations in brain networks was evaluated using network-based statistic (NBS) models, which predicted connectivity associated with NF-L levels. Additionally, associations with clinical variables and leptin were tested. To test the specificity of the results, control analyses were conducted on 77 female healthy participants (HC). We found negative associations between NF-L concentrations and WM volume. NBS analyses identified seven components, where fractional anisotropy was positively associated with NF-L. In some components, mean connectivity was negatively associated with leptin concentrations. Mediation analyses suggested that the negative correlation of leptin and NF-L might be partially mediated by changes in WM microstructure. These effects were not observed in HC. The results suggest that WM volume reductions in acute AN might be related to axonal damage. The NBS results indicate, that the elevated fractional anisotropy previously found in AN might be related to damage processes leading to axonal swelling. All in all, the present study supports NF-L as a global blood marker for brain damage processes in acute AN.

Brain-wide communication supporting flexible behavior requires coordination between sensory and associative regions but how brain networks route sensory information at fast timescales to guide action remains unclear. Using human intracranial electrophysiology and spiking neural networks during spatial attention tasks, where participants detected targets at cued locations, we show that high-frequency activity bursts (HFAbs) mark temporal windows of elevated population firing that enable fast, long-range communications. HFAbs were evoked by sensory cues and targets, dynamically coupled to low-frequency rhythms. Notably, both the strength of cue-evoked HFAbs and their decoupling from slow rhythms predicted behavioral accuracy. HFAbs synchronized across the brain, revealing distinct cue- and target-activated subnetworks. These subnetworks exhibited lead-lag dynamics following target onset, with cue-activated subnetworks preceding target-activated subnetworks when cues were informative. Computational modeling suggested that HFAbs reflect transitions to population spiking, denoting temporal windows for network communications supporting attentional performance. These findings establish HFAbs as signatures of population state transitions, supporting information routing across distributed brain networks.

Objective&#xD;Sleep is hypothesized to restore near-critical dynamics in large-scale brain networks, whereas insomnia may disrupt this self-organizing process. This study aimed to determine whether insomnia alters neural avalanche dynamics and criticality-based EEG metrics, and whether these metrics enhance prediction of sleep fragmentation compared with conventional spectral measures.&#xD;Approach&#xD;Overnight high-density electroencephalography was recorded from 50 participants aged 16-69 years, including healthy sleepers and individuals with insomnia. Neural avalanches were detected as clusters of significant amplitude excursions. The branching parameter (σ) quantified temporal propagation within avalanches, while the deviation-from-criticality coefficient (DCC) indexed the system's distance from the critical state. These criticality features were contrasted with spectral power measures in predictive models of NREM sleep fragmentation.&#xD;Main Results&#xD;Participants with insomnia exhibited reduced avalanche density and diminished slow-wave activity, accompanied by significant deviations of σ from the critical value and elevated DCC across the night. Criticality-based metrics captured fragmentation dynamics more sensitively than spectral features. In predictive modeling, criticality measures significantly outperformed spectral power in forecasting NREM fragmentation (F1-score = 0.69 vs. 0.62), with the strongest gains in mild and severe insomnia subgroups.&#xD;Significance&#xD;Insomnia is characterized by a persistent deviation from near-critical neural dynamics, reflecting compromised stability and recovery during sleep. Criticality-based EEG features provide a more mechanistic and predictive framework for identifying sleep fragmentation and may offer novel biomarkers for quantifying disrupted sleep physiology in clinical insomnia.&#xD.

Age-related decline underlies cognitive functions such as sensorimotor control, executive functioning, memory, and language production (LP), whereas language comprehension (LC) tends to remain intact or improve across healthy adult lifespan. This preservation likely stems from structural and functional integrity within core language network (cLAN) regions. To investigate this hypothesis, we analyzed the relationships among brain's resting-state functional connectivity (FC), structural connectivity (SC), and language behaviour (LC and LP) using a cross-sectional cohort of healthy adults (N = 652; M/F = 322/330; aged 18-88) from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) dataset. Six cognitive tasks assessing LC and LP were employed, with neuroimaging measures focused on region-specific connections within the cLAN. Using generalized additive mixed models (GAMMs), complex brain-behaviour interactions were identified. Behavioral analyses revealed established age-related dichotomy, LC abilities in vocabulary and proverb comprehension improved and in syntactic and semantic comprehension remained stable, whereas LP tasks, e.g., verbal fluency, picture priming, and tip of tongue exhibited significant decline across the lifespan. SC exhibited decline in both intra- and inter-hemispheric fronto-temporal and frontal lobe connections, contrasted by preserved or enhanced temporal lobe connectivity, supporting a pattern of frontal vulnerability concomitant with temporal resilience. Age-related FC patterns demonstrated overall preservation, reflecting compensatory mechanisms to sustain functional integrity despite structural degradation. GAMM analyses revealed complex relationships between brain connectivity and language performance across age. Thus, integrating knowledge of brain structure, function, and language abilities, we identified the brain network mechanisms associated with dichotomous language behavior along lifespan.Significance Statement Understanding age-related changes in language abilities is vital for early detection of cognitive decline and intervention development. This study reveals heterogeneous language aging trajectories: language production often declines, while comprehension remains stable or improves. Notably, comprehension tasks with high executive and working memory demands also show resilience, suggesting compensatory mechanisms beyond crystallized knowledge. Using multimodal neuroimaging in a large adult lifespan cohort, we link behavioral patterns to distinct network dynamics within the core language system. We uncover relative vulnerability in frontal brain regions, with pronounced structural decline in fronto-temporal pathways, contrasted by resilience in temporal connectivity, which is preserved or strengthened. These findings highlight regionally specific vulnerabilities and compensatory functional adaptations, refining current models and providing a framework to guide research promoting healthy cognitive aging.

Combining static and dynamic brain network analysis with machine learning for enhanced diagnosis of major depressive disorder.