Search Results (283)

Mild behavioral impairment (MBI) is a clinical syndrome characterized by the late-life onset and persistence of neuropsychiatric symptoms (NPSs), representing a change from longstanding behavior or personality and considered a potential prodrome of neurodegenerative disease. MBI is classified into five domains: decreased motivation, affective dysregulation, impulse dyscontrol, social inappropriateness, and psychotic symptoms. In this narrative review, we synthesize clinical, neuroanatomical, and molecular evidence linking MBI to the spectrum of tauopathies, including Alzheimer’s disease (AD), frontotemporal spectrum disorders (FTSDs), and primary four-repeat tauopathies such as progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). Emerging evidence suggests that early behavioral symptoms associated with MBI may reflect the selective vulnerability of frontolimbic, salience, default mode, and frontostriatal networks to tau-mediated neurodegeneration. Mechanistically, converging findings support roles for tau-related synaptic dysfunction, including synaptotoxic soluble tau species, cytoskeletal and axonal transport disruption, monoaminergic neurotransmitter imbalance in brainstem systems, and neuroinflammatory and glial pathways. We also highlight genotype-related behavioral profiles in genetic frontotemporal lobar degeneration and discuss how scalable blood-based biomarkers, including neurofilament light chain, glial fibrillary acidic protein, and plasma phospho-tau species, may complement MBI-based phenotyping for differential diagnosis and prognostic stratification in clinical research. Full article

Background: Major Depressive Disorder (MDD) is one of the leading causes of disability worldwide. Deep learning methods have been widely used for MDD detection, with research suggesting that deep models outperform traditional machine learning techniques. However, detecting MDD remains challenging due to data heterogeneity, model complexities and the requirement for discriminative feature representations. Objective: This review outlines recent progress in deep learning methods for MDD detection from Resting-state fMRI (rs-fMRI), with a focus on the model’s generalisability and features that most effectively represent the function/anatomy of the brain to contribute to biomarker identifications and interpretability. Further, the review assesses the applicability of current models to real-world challenges. Methods: This systematic review followed the PRISMA guidelines. Studies involved clinically diagnosed MDD subjects, a control group, and deep learning methods for classification tasks. Results: The cerebellum, thalamus, amygdala, insula, and default mode network are the most frequently reported brain regions associated with depression. Although deep learning has shown impressive results, it has limitations in terms of reliance on labelled data, heterogeneity of data from various hospitals, and model interpretability. A majority of the studies lacked external validation and had a single-site dataset or regionally homogeneous datasets, and did not consider the temporal and dynamic nature of rs-fMRI data. Conclusion: Deep learning offers considerable potential in advancing MDD diagnosis and understanding its mechanisms. Multi-regional data collection, harmonisation techniques, and rigorous testing in real-world workflows should be the primary focus of future research. Full article

Cognitive impairment persists in people with HIV (PWH) despite effective combination antiretroviral therapy, possibly as a result of persistent alterations in white matter microstructural abnormalities in the brain. Noninvasive tensor-valued diffusion MRI (dMRI) is sensitive to microstructural integrity; thus, it may contribute to the understanding of HIV-associated cognitive impairment. In this exploratory cross-sectional study, 31 healthy controls (HCs) and 24 PWH underwent 3T MRI and neurocognitive assessment. Tensor-valued dMRI metrics, including microscopic fractional anisotropy (µFA) and isotropic, anisotropic, and total mean kurtosis (MKi, MKa, MKt), and conventional DTI and DKI metrics (FA, MD, and MK) were evaluated across six functionally defined brain networks. Compared with HCs, PWH exhibited reduced FA, µFA, and MKa in the dorsal default mode and anterior salience networks, along with increased MKi in the salience network and decreased MKi in the executive control network, with moderate effect sizes. Compared with HCs, PWH performed significantly worse on measures of learning, memory, and language, but showed no differences in executive function, attention, or processing speed. Additionally, significant associations and interactions between dMRI metrics and HIV status were observed, particularly for MKi and attention, executive function, and processing speed across the default mode, salience, and executive control networks. These preliminary findings underscore tensor-valued dMRI as a sensitive biomarker of network-specific neurocognitive vulnerability in HIV. Full article

Aging is a significant risk factor for several neurodegenerative diseases. Understanding brain aging processes is a fundamental step in identifying the early signs of pathological dysfunction. Nonetheless, regional functional changes are still poorly characterized. In this study, we employed Detrended Fluctuation Analysis (DFA) to investigate age-related changes in the scale-free temporal dynamics of blood oxygen level-dependent (BOLD) signal fluctuations derived from resting-state networks. We compared DFA to fractional amplitude of low-frequency fluctuations (fALFF) to assess their ability to discriminate between young and old adults. Significant decreases (p < 0.01) in fALFF in the visuospatial and dorsal default mode networks and in DFA in the salience network, were identified as key predictors of functional brain aging. Using machine learning, we showed that DFA and fALFF provide complementary information for predicting aging, with an accuracy of approximately 80% achieved only through their combined use. Overall, DFA captures alterations in scale-free temporal organization that complement conventional spectral measures, providing additional insight into network-specific functional aging. Full article

Background and Objectives: Mild cognitive impairment (MCI) is heterogeneous within the Alzheimer’s disease (AD) continuum, and categorical labels may not reflect biological variability. We evaluated whether multimodal machine learning using routine clinical data and neuroimaging could support biologically informed enrichment across MCI and AD in a real-world memory clinic cohort. Methods: We analyzed 474 patients (1547 visits) with clinical and cognitive measures, laboratory parameters, MRI regional volumes, and FDG-PET regional uptake. Elastic Net and gradient boosting models were trained using nested cross-validation with strict patient-level separation. Results: Model discrimination improved as additional data modalities were added, and FDG-PET contributed the largest performance improvement. Hypometabolism in posterior default mode network regions consistently emerged as the most influential predictor. In the MCI subgroup, AD-like scores showed a continuous distribution consistent with biological enrichment. Conclusions: Multimodal models may provide an interpretable enrichment framework in heterogeneous memory clinic populations. Full article

Background: Previous research has found that mindfulness-based techniques are beneficial for reducing stress in heavy-drinking individuals. However, the underlying neurobiology of these stress-reducing effects are unclear. Moreover, much of the research examining neurobiological correlates of mindfulness has used static functional connectivity, suggesting that brain activity goes unchanged for the entire length of an MRI scan. Methods: In the current study, we used a state-based dynamic functional connectivity model to examine brain states during either a 10 min mindfulness session or resting control that followed an individually tailored stress imagery task. Using a hidden semi-Markov model (HSMM), six brain states and the associated dynamics of state traversal were estimated for a population of moderate-to-heavy drinkers (N = 32). We modeled the 36 Schaefer atlas regions spanning the salience and default mode networks, and the HSMM characterized each state by its distinct multivariate pattern of activity and covariance structure. Group differences in dwell times, transition behavior, and overall state dynamics were evaluated using permutation tests and mixed-effects models. Results: Participants that experienced the mindfulness session had more transitions and longer time spent in states in which the salience network was more active. Participants assigned to the control group had more transitions and increased time spent in states in which nodes of the default mode network were more active. Moreover, for control participants, increased occupancy time to SN-dominant states was associated with lower perceived stress. Conclusions: Using HSMM provided a unique insight into network connectivity during mindful states; we believe it offers a novel approach to testing and optimizing mindful-based therapies. Full article

Background: Schizophrenia is a heterogeneous disorder, and treatment-resistant schizophrenia (TRS) affects 20–30% of patients, yet objective biomarkers for its identification remain limited. Routine electroencephalography (EEG) offers a non-invasive window into cortical network dynamics, with previous studies reporting paroxysmal epileptiform activity and background slowing in a subset of patients. However, the biological significance of these findings—whether purely pathological or potentially compensatory—remains unclear. This study aimed to compare EEG abnormalities between TRS patients and those in clinical remission and to propose an integrative neurobiological interpretation. Methods: In a cross-sectional design, 89 patients with schizophrenia (39 TRS, 50 in remission) underwent routine EEG recordings using the international 10–20 system. TRS was defined according to TRRIP consensus criteria, requiring <20% symptom reduction after adequate antipsychotic trials. EEG analysis focused on the prevalence of interictal epileptiform discharges (IEDs) and the severity of background slowing, assessed on a 4-point ordinal scale. Results: IEDs were more than twice as prevalent in TRS patients compared to those in remission. Background slowing was significantly more severe in the TRS group, with the majority showing moderate-to-severe abnormalities versus predominantly normal-to-mild patterns in remission patients. Focal EEG abnormalities also followed this pattern. Multivariate analysis confirmed that both IEDs and background severity were independent predictors of TRS. Conclusions: EEG abnormalities, particularly IEDs and background slowing, are potential neurophysiological signatures associated with treatment resistance. We propose an integrative hypothesis suggesting that IEDs may originate as a failed compensatory mechanism—the brain’s attempt to restore network homeostasis. In chronic TRS these discharges become maladaptive, contributing to excitotoxicity and network dysfunction. This framework opens avenues for EEG-based stratification and novel therapeutic strategies targeting cortical excitability. Full article

Hypnosis has traditionally been conceptualized as a clinical technique for reducing physiological symptoms (e.g., pain, nausea) and psychological symptoms (e.g., anxiety, intrusive thoughts), yet emerging neuroscientific evidence suggests it operates through the fundamental mechanisms of emotional regulation and self-integration. This integrative review synthesizes research on clinical hypnosis from cognitive neuroscience, affective science, and clinical practice to examine how hypnotic phenomena modulate large-scale brain networks—particularly the default mode network (DMN), executive control network (ECN), and salience network (SaN)—to reorganize emotional experience and self-referential processing. We propose a formal mechanistic model in which hypnotic induction produces heightened experiential plasticity through coordinated network reconfiguration, enabling adaptive emotion regulation and reduced dissociative fragmentation. Central to this framework is the construct of Fundamental Peace (FP), operationalized as a dynamic neuro-experiential state characterized by: (1) flexible attentional control without effortful suppression; (2) emotional coherence across self-states; (3) reduced self-referential rigidity; (4) compassionate self-awareness. Unlike equanimity (affective neutrality) or well-being (positive evaluation), Fundamental Peace represents integrated regulatory capacity under changing conditions. Key findings from neuroimaging studies demonstrate that hypnotic states consistently reduce DMN activity, enhance ECN-SaN coupling, and modulate connectivity patterns associated with self-referential processing. Meta-analytic evidence from 85 controlled experimental trials shows robust pain reduction effects, while clinical studies document improvements in trauma-related dissociation and emotional dysregulation. We critically evaluate this framework against alternative theories (dissociated control, cold control, predictive processing, social-cognitive models), specify testable predictions, and assess evidence quality across neuroimaging and clinical domains. Implications for trauma treatment, clinical implementation, and future research integrating causal inference methods are discussed, alongside ethical and cultural considerations. Full article

Objective: The purpose of this research is to examine how motivational music immediately impacts the brain’s functional connectivity patterns in male athletes following a single session of intense endurance exercise, utilizing resting-state electroencephalography (EEG) and brain network analysis methods. Methods: The study involved 34 healthy male athletes who were tasked with performing incremental cycling exercises until exhaustion, both with and without music. Their resting-state EEG was recorded before and after the exercise. Brain functional networks were analyzed in the theta, alpha, and beta frequency bands based on changes in phase locking value (PLV). Specifically, the study examined the central executive network (CEN), default mode network (DMN), salience network (SN), sensorimotor network (SMN), and dorsal attention network (DAN), assessing their topological properties using graph theory methods. Results: Music significantly prolonged the time to exhaustion. Across frequency bands, the music condition exhibited higher global and local efficiency compared with the no-music condition. Following exhaustion without music, beta-band connectivity significantly increased, suggesting compensatory hyper-synchronization under fatigue. In contrast, music led to reduced alpha- and beta-band global connectivity post-exercise, accompanied by selective strengthening of functionally relevant couplings, particularly between SMN and CEN, and enhanced DAN–DMN coordination. Additionally, music prevented maladaptive connectivity shifts observed under fatigue, including excessive SN–CEN coupling. Conclusions: Exhaustive exercise without music induces widespread beta-band hyper-connectivity, reflecting increased neural cost under central fatigue. Music, however, promotes a more efficient and selectively integrated network configuration, supporting the neural efficiency hypothesis. These findings provide neurophysiological evidence that music optimizes large-scale brain network organization under physical stress, thereby contributing to enhanced endurance performance. Full article

Traumatic experiences can disrupt one’s sense of safety, self-efficacy, and relationships. Prolonged stress may lead to anxiety, depression, and diminished agency. The embodied, subjective manifestations of trauma call for personalized therapeutic approaches that address symptoms and foster resilience. Group Creative Arts Therapies (CATs) offer relational aesthetic interventions that promote resilience and trauma recovery. Incorporating body-based methods, movement, materials and visual expression, CATs support interoceptive awareness, multisensory integration, embodiment, and emotional–cognitive processing. This article presents a review and conceptual framework of group CAT interventions during wartime, focusing on challenges related to body awareness, self-efficacy, and autobiographical memory. It examines how creative aesthetic approaches help process trauma and strengthen resilience. Drawing on predictive processing accounts of brain function, the article explores the neuropsychological impact of trauma and how creative group work may modulate related brain mechanisms. Creative techniques can foster bodily anchored self-awareness, self-efficacy and processes of traumatic memory reconsolidation. Aesthetic experiences are associated with changes in brain activation and connectivity through processes of embodiment, externalization, and meaning making. On an intrapersonal level, converging evidence highlights the role of sensory and sensorimotor processing, along with the dynamic interplay between Default Mode, Executive Control, and Salience networks, as conceptualized in the Triple Network Model. On an interpersonal level, the literature points to the dynamics of brain and body synchronization, as emerging phenomena during shared creative engagement. These neurodynamics provide a coherent framework for understanding how creative arts-based psychotherapeutic group work can support trauma processing and the cultivation of resilience. Full article

Background: Resting-state functional MRI (rs-fMRI) studies typically rely on linear Pearson correlation to characterize brain connectivity, potentially overlooking the distributional characteristics of functional networks. This study introduces a kernel-transformed functional connectivity (FC) entropy framework to quantify network dedifferentiation in bipolar disorder (BD). Methods: We utilized a Gaussian kernel function to execute a nonlinear similarity transformation (referred to as reweighting) on standard linear correlation matrices. This approach acts as a functional filter to amplify the contrast between strong and weak connections. Multiscale entropy (global, modular, and nodal) was subsequently calculated to characterize the uniformity of connectivity weight distributions. Results: Compared to Normal Controls (NCs), patients with BD exhibited significantly higher entropy at the global level and within the Default Mode, Salience, and Somatosensory-Motor networks, indicating widespread network dedifferentiation (distributional flattening). These alterations were robust across different kernel widths and remained significant after rigorously controlling for head motion (Mean FD). Furthermore, manic symptom severity (YMRS) was negatively correlated with global entropy, suggesting a pathological “locking-in” or rigidity of specific neural circuits during manic states. Conclusions: The kernel-transformed FC entropy serves as a distribution-sensitive complement to conventional linear metrics. Our findings highlight network dedifferentiation as a key pathophysiological feature of BD and suggest this framework as a promising candidate metric for characterizing network dysregulation. Full article

Major depressive disorder (MDD) represents a heterogeneous condition lacking reliable neurobiological biomarkers and a mechanistic understanding. Time-resolved characterization of brain dynamics reveals that mental health is associated with a characteristic dynamical regime, exhibiting spontaneous switching between a repertoire of ghost attractor states forming resting-state networks. Analysing resting-state fMRI data from 848 patients with MDD and 794 healthy controls across 17 sites in China (REST-meta-MDD) using Leading Eigenvector Dynamics Analysis (LEiDA), we found patients with MDD exhibited significantly reduced default mode network (DMN) occupancy (p < 0.001; Hedges’ g = −0.51) and increased occipito–parieto–temporal state occupancy (p < 0.001; Hedges’ g = 0.42), suggesting compensatory dynamical rebalancing. These findings extend prior observations of DMN disruption in MDD, aligning with the emerging dynamical systems framework for mental health to advance the mechanistic understanding of MDD pathophysiology. Full article

Background and Objective: Adolescent bipolar disorder (BD) has substantial symptom overlaps with other psychiatric disorders. Identifying its distinctive candidate neuroimaging markers may be helpful for exploratory early differentiation and to inform future translational studies after independent validation. Methods: This cross-sectional study enrolled adolescents with BD and age- and sex-matched healthy controls. Assessments included clinical/behavioral scales and an emotional Go/NoGo task-based fMRI (Go trials require a response; NoGo trials require response inhibition) acquired across three mood states (depression, mania, and remission) and matched controls. We applied several conventional machine learning classifiers to task-fMRI data to classify BD versus healthy controls and to identify the most relevant neuroimaging predictors. Results: A total of 43 adolescents with BD (15 in remission, 11 with depression, and 17 with mania) and 43 matched healthy controls were included. Under the Go − NoGo condition, activation-derived features in the remission state showed the strongest discrimination, with RF achieving the best performance (accuracy = 94.29%, AUC = 98.57%). These findings suggest that task-evoked functional alterations may remain detectable during remission. In addition, activation patterns in regions within the limbic system, prefrontal cortex, and default mode network were significantly correlated with clinical scales and behavioral measures implicating these regions in emotion regulation and cognitive functioning in adolescents with BD. Conclusions: This study showed that adolescents with BD during remission without manic and depressive symptoms may still have aberrant neural activity in the limbic system, prefrontal cortex, and default mode network, which may serve as a potential candidate neuroimaging signature of adolescent BD. Full article

Background/Objectives: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by differences in social communications and restricted, repetitive patterns of behaviors and interests, affecting approximately 1% of children globally. While functional magnetic resonance imaging (fMRI) has provided insights into altered brain connectivity patterns in ASD, classification based on neuroimaging remains a challenging due to the heterogeneity of the disorder and variability in imaging data across sites. This study employs a network-based approach using large-scale, multi-site rs-fMRI dataset from the Autism Brain Imaging Data Exchange (ABIDE I and II) to classify ASD and healthy controls using machine learning. Methods: A semi-blind Independent Component Analysis method, specifically the spatial constraint reference ICA, is applied to identify functional brain networks, and the ComBat harmonization technique is used to address site-specific variability across 11 independent datasets, ensuring consistency in feature representation. Support Vector Machines (SVMs) are employed for classification, focusing on three key networks: the Default Mode Network (DMN), Sensorimotor Network (SMN), and Visual Sensory Network (VSN). Results: The results demonstrate high classification accuracy, with the VSN achieving the highest performance (83.23% accuracy, 87.90% AUC), followed by the DMN (81.43% accuracy, 84.53% AUC) and the SMN (80.52% accuracy, 84.96% AUC), positioned with their recognized roles in social cognition and sensory–motor processing, respectively. Conclusions: The integration of ICA-based feature extraction with ComBat harmonization significantly improved classification accuracy compared to previous studies. These findings point out the potential of network-based approaches in ASD classification and point out the importance of integrating multi-site neuroimaging data for identifying reproduceable network-level features. Full article

Introduction: Massage therapy delivers structured mechanosensory input that can influence brain function, yet the central mechanisms and potential for neuroplastic change have not been synthesized across neuroimaging modalities. This mechanistic review integrates evidence from electroencephalography (EEG), functional MRI (fMRI), and functional near-infrared spectroscopy (fNIRS) to map how massage alters human brain activity acutely and over time and to identify signals of longitudinal adaptation. Materials and Methods: We conducted a scoping, mechanistic review informed by PRISMA/PRISMA-ScR principles. PubMed/MEDLINE, Cochrane Library, Google Scholar, and ResearchGate were queried for English-language human trials (January 1990–July 2025) that (1) delivered a practitioner-applied manual massage (e.g., Swedish, Thai, shiatsu, tuina, reflexology, myofascial techniques) and (2) measured brain activity with EEG, fMRI, or fNIRS pre/post or between groups. Non-manual stimulation, structural-only imaging, protocols, and non-English reports were excluded. Two reviewers independently screened and extracted study, intervention, and neuroimaging details; heterogeneity precluded meta-analysis, so results were narratively synthesized by modality and linked to putative mechanisms and longitudinal effects. Results: Forty-seven studies met the criteria: 30 EEG, 12 fMRI, and 5 fNIRS. Results: Regarding EEG, massage commonly increased alpha across single sessions with reductions in beta/gamma, alongside pressure-dependent autonomic shifts; moderate pressure favored a parasympathetic/relaxation profile. Connectivity effects were state- and modality-specific (e.g., reduced inter-occipital alpha coherence after facial massage, preserved or reorganized coupling with hands-on vs. mechanical delivery). Frontal alpha asymmetry frequently shifted leftward (approach/positive affect). Pain cohorts showed decreased cortical entropy and a shift toward slower rhythms, which tracked analgesia. Somatotopy emerged during unilateral treatments (contralateral central beta suppression). Adjuncts (e.g., binaural beats) enhanced anti-fatigue indices. Longitudinally, repeated programs showed attenuation of acute EEG/cortisol responses yet improvements in stress and performance; in one program, BDNF increased across weeks. In preterm infants, twice-daily massage accelerated EEG maturation (higher alpha/beta, lower delta) in a dose-responsive fashion; the EEG background was more continuous. In fMRI studies, in-scanner touch and reflexology engaged the insula, anterior cingulate, striatum, and periaqueductal gray; somatotopic specificity was observed for mapped foot areas. Resting-state studies in chronic pain reported normalization of regional homogeneity and/or connectivity within default-mode and salience/interoceptive networks after multi-session tuina or osteopathic interventions, paralleling symptom improvement; some task-based effects persisted at delayed follow-up. fNIRS studies generally showed increased prefrontal oxygenation during/after massage; in motor-impaired cohorts, acupressure/massage enhanced lateralized sensorimotor activation, consistent with use-dependent plasticity. Some reports paired hemodynamic changes with oxytocin and autonomic markers. Conclusions: Across modalities, massage reliably modulates central activity acutely and shows convergent signals of neuroplastic adaptation with repeated dosing and in developmental windows. Evidence supports (i) rapid induction of relaxed/analgesic states (alpha increases, network rebalancing) and (ii) longer-horizon changes—network normalization in chronic pain, EEG maturation in preterm infants, and neurotrophic up-shifts—consistent with trait-level recalibration of stress, interoception, and pain circuits. These findings justify integrating massage into rehabilitation, pain management, mental health, and neonatal care and motivate larger, standardized, multimodal longitudinal trials to define dose–response relationships, durability, and mechanistic mediators (e.g., connectivity targets, neuropeptides). Full article