Search Results (419)

Objectives: Autism spectrum disorder (ASD) is increasingly conceptualized as a neurodevelopmental condition with prenatal origins. Advances in fetal magnetic resonance imaging (MRI), including high-resolution structural imaging and resting-state functional connectivity analysis, now enable in vivo characterization of the developing human brain before birth. This review examines whether fetal MRI biomarkers are associated with later ASD diagnosis or autistic traits. Methods: We conducted a PRISMA-informed narrative review of human studies identified through MEDLINE, EMBASE, SCOPUS, and Web of Science. Eligible studies included original human investigations using fetal MRI to assess brain structure and/or function, with postnatal ASD diagnosis or standardized autistic-trait outcomes. Results: Eight eligible studies provide converging evidence that neurodevelopmental divergence associated with ASD may be detectable in utero. Structural analyses consistently report prenatal volumetric alterations, particularly enlargement of the insular cortex between the second and third trimesters. Additional findings of regional overgrowth and hemispheric asymmetries suggest distributed deviations in cortical maturation. Functional fetal MRI studies further demonstrate atypical large-scale network organization prior to birth. Altered connectivity within cingulate, prefrontal, temporal, and cerebellar circuits has been prospectively associated with later autistic traits, indicating that network-level integration may diverge before behavioral symptoms emerge. Evidence from high-risk conditions, including isolated ventriculomegaly and tuberous sclerosis complex, reinforces the association between prenatal structural abnormalities and increased ASD risk. Conclusions: Current evidence suggests that structural and functional brain alterations identifiable by fetal MRI may precede the clinical manifestation of ASD. These findings support a model of ASD as a condition potentially rooted in prenatal neurodevelopmental divergence. However, larger, standardized, multicenter studies are required before fetal MRI biomarkers can be translated into predictive or clinical applications. Full article

Background: Multi-center resting-state functional magnetic resonance imaging (rs-fMRI) is important for neurodevelopmental disorder diagnosis, but cross-site differences in repetition time (TR) can cause temporal feature misalignment. In addition, blood-oxygen-level-dependent (BOLD) signals are non-stationary, so disease-related information may be distributed across multiple time scales. Existing methods usually do not explicitly model physical sampling intervals or coordinate temporal and spectral information across scales, which may limit cross-site generalization in heterogeneous multi-center settings. Methods: We propose Spec-RWKV, a spectrum-guided linear recurrent framework for multi-site rs-fMRI diagnosis. It includes three components: PrismTimeMix, which models temporal dynamics using decay rates derived from physical half-lives and converts them adaptively across TRs; a TR-adaptive continuous wavelet transform, which aligns spectral representations across sites by adjusting frequency coverage; and spectrum-guided adaptive temporal aggregation, which uses spectral context to weight temporal features. Results: On ABIDE-I and ADHD-200, Spec-RWKV achieved AUCs of 75.86% and 76.31%, respectively. Under leave-one-site-out validation, it achieved the best mean AUC on ABIDE-I and the best mean accuracy and AUC on ADHD-200. Conclusions: Spec-RWKV explicitly models sampling-rate differences and multi-scale spectral structure, with results supporting strong cross-site generalizability. Full article

Background: Amnestic mild cognitive impairment (aMCI) is considered a transitional state between normal aging and dementia, often without visible abnormalities on standard brain magnetic resonance (MR) images. The aim of the study was to analyze both microstructural and functional brain abnormalities using advanced MR techniques. Methods: The study included 27 patients with aMCI and an age-matched control group (CG) of 25 healthy subjects. All MR studies were performed on a 3T MR scanner (Philips, Ingenia) with a 32-channel head and neck coil using volumetric 3D T1 sequences, followed by a resting-state functional MRI (rs-fMRI) sequence. Volumetric analysis was performed using the Destrieux atlas to assess potential structural differences between groups. Seed-to-voxel functional connectivity analyses were conducted using the bilateral hippocampi and both anterior and posterior divisions of the parahippocampal gyri as seed regions. Results: Compared to healthy controls, reduced cortical thickness was observed in aMCI subjects in the temporal regions, frontal and orbitofrontal areas, limbic areas, parietal and sensorimotor cortices, as well as occipito-temporal regions. Additionally, significantly increased functional connectivity was observed between bilateral medial temporal lobe (MTL) regions and the right thalamus. Conclusions: Cortical thinning in various brain regions along with the increased functional connectivity between the MTL regions and the right thalamus may reflect potential compensatory mechanisms in response to initial subtle degenerative changes, emphasizing the importance of using both functional and structural imaging to detect early changes in aMCI patients. Full article

Parkinson’s disease (PD) may benefit from non-pharmacological motor–cognitive rehabilitation, but sensitive neuroimaging markers of training-related brain changes remain limited. This study investigated whether 4 weeks of daily Quadrato Motor Training (QMT) modulate resting-state functional connectivity (FC) in PD and secondarily explored whether whole-brain radiomic features derived from T1-weighted and fractional anisotropy (FA) images could detect pre–post differences over this short intervention interval. Fifty patients with idiopathic PD were randomized to QMT or a SHAM repetitive stepping condition, and 48 completed the protocol (25 SHAM, 23 QMT). MRI was acquired at baseline and after 4 weeks and included resting-state fMRI, 3D T1-weighted imaging, and diffusion-derived FA maps. Resting-state fMRI was analyzed using independent component analysis and dual regression, whereas an IBSI-compliant radiomics workflow and machine-learning models were used for exploratory scan-level classification. Compared with baseline, the SHAM group showed reduced synchronization across several resting-state networks, whereas the QMT group showed increased synchronization in the right sensorimotor and frontoparietal networks and no significant reductions. Between-group analyses showed lower delta-FC in SHAM than QMT in the cerebellar and sensorimotor networks. In contrast, radiomics showed limited discrimination between pre- and post-QMT scans; the best model achieved a ROC-AUC of 0.65 with near-chance accuracy, and no selected predictor remained significant after multiple-comparison correction. These findings suggest that QMT may support short-term functional network stability or task-relevant reorganization in PD relative to the SHAM condition, whereas whole-brain structural radiomics appears less sensitive for detecting early training-related effects in this setting. Full article

Background/Objectives: Alzheimer’s disease (AD) is an irreversible neurodegenerative disorder that imposes a profound burden on global public health. While resting-state functional magnetic resonance imaging (rs-fMRI)-based dynamic functional connectivity (dFC) analysis has demonstrated promise in capturing time-varying brain network abnormalities, existing deep learning methods suffer from three fundamental limitations: (1) an inability to model temporal dependencies across dynamic connectivity windows, (2) reliance on post hoc black-box explainability tools, and (3) misalignment between feature learning and classification objectives. Methods: To address these challenges, we propose MambaKAN, an end-to-end interpretable framework integrating a Variational Autoencoder (VAE), a Selective State Space Model (Mamba), and a Kolmogorov–Arnold Network (KAN). The VAE encodes each dFC snapshot into a compact latent representation, preserving nonlinear connectivity patterns. The Mamba encoder captures long-range temporal dynamics across the sequence of latent representations via input-selective state transitions. The KAN classifier provides intrinsic interpretability through learnable B-spline activation functions, enabling direct visualization of how latent features influence diagnostic decisions without post-hoc approximation. The entire pipeline is trained end-to-end with a joint loss function that aligns feature learning with classification. Results: Evaluated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset across five classification tasks (CN vs. AD, CN vs. EMCI, EMCI vs. LMCI, LMCI vs. AD, and four-class), MambaKAN achieves accuracies of 95.1%, 89.8%, 84.0%, 86.7%, and 70.5%, respectively, outperforming strong baselines including LSTM, Transformer, and MLP-based variants. Conclusions: Comprehensive ablation studies confirm the indispensable contribution of each module, and the three-layer interpretability analysis reveals key temporal patterns and brain regions associated with AD progression. Full article

Background: Frequent nocturnal arousals are a core feature of comorbid insomnia and obstructive sleep apnea (COMISA), yet the underlying central mechanisms remain unclear. Identifying brain functional correlates of nocturnal awakenings may help clarify arousal-related mechanisms and inform potential interventional targets. Methods: A total of 99 participants (COMISA, insomnia alone, OSA alone, and healthy controls) underwent clinical assessments, polysomnography, and brain magnetic resonance imaging (MRI). MRI metrics were compared across groups, followed by correlation and regression analyses with the arousal index, adjusting for respiratory events and insomnia-related factors. Results: Patients with COMISA exhibited more severe insomnia symptoms, greater daytime dysfunction, and more frequent nocturnal awakenings than those with insomnia alone, although their arousal index did not differ from that of the OSA group. Patients with COMISA exhibited altered activity in the right cerebellar lobule VIII (Cerebelum_8_R), left middle temporal gyrus, and right inferior frontal gyrus, opercular part. Lower fractional amplitude of low-frequency fluctuations (fALFF) in the Cerebelum_8_R was associated with a higher arousal index. This association remained significant after controlling for insomnia severity and sleep efficiency but was attenuated after adjustment for AHI. Conclusions: Reduced functional activity in the Cerebelum_8_R was independently associated with sleep fragmentation in COMISA, independent of insomnia severity but potentially mediated by respiratory events. These findings suggest this region may be involved in arousal-related neural regulation and could represent a therapeutic target for the complex symptoms of COMISA. Trial Registration: Chinese Clinical Trial Registry, ChiCTR2500095809. Full article

Understanding Alzheimer’s Disease (AD) progression using resting-state functional Magnetic Resonance Imaging (rs-fMRI) remains an open challenge. Variational Autoencoders (VAEs) provide compact representations of high-dimensional neuroimaging data but lack mechanisms to highlight disease-relevant features. We propose KS-VAE, a novel framework that integrates the Kolmogorov–Smirnov test into the latent space of VAEs to identify statistically significant variables discriminating healthy from pathological brain states. This integration enables measurement of latent space shifts associated with cognitive decline, offering a quantitative approach to neurodegenerative processes. By modifying the most relevant variables, KS-VAE generates synthetic samples that simulate transitions between clinical conditions while preserving anatomical plausibility. The method enhances the modeling of temporal and distributional dynamics underlying disease progression and provides interpretable analysis of class-relevant features. Applied to rs-fMRI scans of 220 subjects from the ADNI cohort, KS-VAE demonstrated robust class separation between cognitively normal and Alzheimer’s disease subjects, achieving a classification accuracy of 84.5% and an F1-score of 84.5%, and clinically consistent synthetic transitions. KS-VAE thus offers a statistically grounded and clinically interpretable framework for understanding Alzheimer’s disease progression. Full article

Background/Objectives: Migraine with aura (MwA) is a heterogeneous disorder comprising pure visual aura (MwAv) and more complex phenotypes with additional somatosensory and/or dysphasic symptoms (MwAvsd). Previous structural magnetic resonance imaging (MRI) studies have demonstrated hippocampal subfield volume reductions associated with aura complexity, suggesting a role for the hippocampus in MwA pathophysiology. However, functional network mechanisms underlying these structural differences remain unclear. This study aimed to investigate hippocampal resting-state functional connectivity (FC) in MwA subtypes and healthy controls (HCs), and to determine whether hippocampal connectivity patterns differ according to aura complexity. Methods: In this comparative cross-sectional study, 27 patients with MwAvsd, 18 with MwAv, and 29 age- and sex-matched HCs underwent resting-state functional MRI on a 3T scanner. Seed-based FC analyses were performed using both hippocampi as regions of interest. Results: MwAvsd patients demonstrated significantly increased FC between the right hippocampus and the left dorsal parietal cortex and right sensory association cortex compared with MwAv patients. In contrast, MwAv patients showed increased FC between the left hippocampus and the right dorsolateral prefrontal cortex compared with MwAvsd patients. Additionally, MwAv patients exhibited stronger FC between the left hippocampus and bilateral anterior prefrontal cortices and the left angular cortex compared with HCs. No other significant hippocampal FC differences were observed. Conclusions: Hippocampal FC is altered in MwA and varies according to aura phenotype. Complex aura is characterized by enhanced hippocampal coupling with multisensory integration regions and reduced connectivity with executive control areas, whereas pure visual aura demonstrates increased hippocampal–prefrontal and hippocampal–parietal associative connectivity compared with HCs. These findings suggest that the hippocampus might serve as a target for future neuromodulatory and therapeutic investigations in MwA patients. 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

Background: Navigated repetitive TMS (nrTMS) is widely used for non-invasive mapping of cortical functions. Methodological improvement might be achieved by optimizing coil positioning based on electric-field modeling and augmented reality (AR)-guided neuronavigation to enhance spatial targeting accuracy and stimulation-induced language errors. Therefore, we compared electric-field-optimized, AR-guided nrTMS with conventional nrTMS using manually planned coil positioning. Methods: Twenty-eight healthy subjects underwent two MRI-guided left hemispheric nrTMS language mapping sessions. Each session used 10 Hz stimulation at a 100% resting motor threshold applied for 1.5 s per region of interest (ROI) during a synchronized object naming task. ROIs were defined according to the Corina cortical parcellation system. Manually defined and electric-field-optimized coil placements obtained using SimNIBS (v4.1.0) were applied; the optimized session was assisted by AR goggles. The primary outcome was the quantitative and categorical differences in cortical regions mapped as language-eloquent. Resting-state fMRI was acquired to provide a reference for comparing nrTMS-derived language maps. Outcomes: Electric-field-optimized nrTMS did not result in an increase in positively mapped ROIs. A different distribution of language errors was observed between sessions. Manual mapping roughly followed the extracted resting-state language and motor networks, whereas electric-field-optimized mapping might correspond less. Optimized coil positions were not always practically feasible. AR guidance improved target location accuracy. Conclusions: While AR was a useful addition to the TMS experiment, electric-field optimization did not translate into significant behavioral differences. However, altered distribution of language errors can give insight into underlying neurophysiological processes of rTMS. Full article

Background: The recognition of autism spectrum disorder (ASD) has been a challenge due to the heterogeneity in symptoms and complex variations in brain function. Resting-state functional magnetic resonance imaging (rs-fMRI) has become instrumental in studying these disorders by accessing underlying abnormal neural activity and connectivity. Recently, deep learning approaches have shifted the analysis of brain networks by capturing spatiotemporal information from fMRI sequences. Nonetheless, most existing studies are limited by relying on a single representational scale, typically restricting analysis to either voxel-level spatiotemporal patterns or static connectivity matrices. Additionally, the dynamic reconfiguration of functional coupling and its variations across different anatomical parcellations are often ignored, which obscures neurobiologically meaningful dynamics. Methods: In this regard, we propose a multi-atlas dynamic connectivity transformer fused with 4D spatiotemporal modeling for ASD recognition (MADCT-4D). Specifically, the framework comprises two complementary branches. The 4D spatiotemporal branch encodes raw rs-fMRI volumes to learn hierarchical representations of evolving neural activity, while the dynamic-connectivity branch models time-resolved functional connectivity sequences constructed from multiple atlases, enabling the network to capture dynamic reconfiguration at the connectome level under different parcellation granularities. Moreover, we perform late fusion by combining the branch-specific decision scores with a learnable gate, allowing the model to adaptively weight voxel-level dynamics and multi-atlas connectivity evidence for each subject. Results: Extensive experiments on the publicly available ABIDE dataset demonstrate that the proposed method achieves 90.2% accuracy for ASD recognition, outperforming multiple competitive baselines. Conclusions: The proposed framework yields interpretable biomarkers based on learned dynamic connectivity patterns that are consistent with altered functional coupling in ASD. Full article

Background/Objectives: Performing presurgical functional magnetic resonance imaging (fMRI) mapping in young patients is considered a challenge for clinicians, as fMRI maps are the sole source of information about the functional organization of cognitive functions/areas, especially when an awake craniotomy is not possible, as is often the case for pediatric populations. The literature on the fMRI tasks used in pediatric populations with brain injuries shows a certain heterogeneity in the approaches (task-based or resting states) and tasks, with a preference for motor/language mapping: tasks assessing extra-language functions are lacking. Methods: We have designed fMRI tasks focused on language and extra-language functions, which can be easily be applied when clinicians need to perform presurgical mapping. We present a retrospective case series of 17 patients. Results: Seventeen young patients (13.4 ± 2.8 years; range 7–16) were included in the study, for whom fMRI was performed. All underwent successful fMRI mapping by completing fMRI tasks selected based on their lesion site. The number of tasks performed by each patient significantly correlated with their age (r(17) = 0.561, p = 0.019). The patients tolerated the assessment and had good motion control: their movement parameters were minimal (range of rotation of −0.015–0.01 degrees; range of translation of −0.8–0.2 mm). The most administered fMRI tasks were tongue motor localizer (60%) and object naming (70%), with some patients performing extra-language function mapping involving visuo-spatial processing, selective attention, memory, and inhibition. Conclusions: This is an exploratory study given the sample size. fMRI measurements were considered feasible, as patients were able to complete the tasks under clinically realistic conditions. We discuss the clinical implication/usefulness of administering tasks for a personalized functional assessment of the young patient before surgery. 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/Objectives: Young adulthood is a critical developmental period during which lifestyle behaviors may shape intrinsic brain network dynamics that support cognition. This pilot longitudinal intervention study examined whether variability in physical activity and sedentary behavior during an 8-week exercise and/or cognitive intervention protocol was associated with changes in intrinsic brain dynamics and cognitive and mood outcomes in undergraduate young adults. Methods: Participants (n = 32) completed resting-state functional magnetic resonance imaging (rs-fMRI) at baseline (T1) and post-intervention (T2). Dynamic functional network connectivity (dFNC) was estimated from 53 intrinsic connectivity networks derived using spatially constrained independent component analysis (ICA). Ten recurring dynamic connectivity states were identified and individualized using constrained dynamic double functional independent primitives (c-ddFIPs). State occupancy and dynamic convergence and divergence metrics were computed to characterize network flexibility. Results: Greater moderate-to-vigorous physical activity was modestly but consistently associated with increased occupancy of integrative higher-order states, particularly States 6 and 7, and reduced occupancy of more segregated configurations. More physically active individuals also demonstrated greater divergence between integrative and low-engagement states, whereas greater sedentary time corresponded to increased similarity among segregated configurations. Working memory performance showed parallel associations with more integrative and better-differentiated dynamic patterns. Conclusions: These findings suggest that dynamic functional network reconfiguration may represent a neurobiological mechanism linking lifestyle behaviors and cognitive health in young adulthood. Furthermore, they highlight the translational promise of engagement-driven, low-burden programs for college-aged young adults, showing that even modest variability in habitual physical activity corresponds to greater engagement and differentiation of integrative connectivity states linked to executive and broader cognitive functions. 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