Search Results (129)

Functional magnetic resonance imaging (fMRI) is commonly used to investigate the neural bases of aging and psychological disorders. However, the BOLD signal captured by fMRI is affected by many factors that are non-neural in origin. We tested how vascular health risks, which often go unmeasured in neuroimaging studies, and aging interact to modify the shape and/or timing of the HRF, which then affect the differences in patterns of brain activity in a task-evoked memory encoding paradigm. Adult participants (aged 20–74) answered questions about their health and underwent two fMRI tasks: viewing a flashing checkerboard and a memory encoding task. Aging and vascular risk had the largest impacts on the maximum peak value of the HRF. Using a subject-specific HRF resulted in a dampening of brain activity in task-positive and task-negative regions. Across three vascular risk factors, using a subject-specific HRF resulted in more consistent brain regions that reached significance and larger effect sizes compared with the canonical HRF. These findings serve as a cautious tail when interpreting task-evoked fMRI activity, especially in populations experiencing alterations to brain vasculature including many older adults and people with neurocognitive disorders like Alzheimer’s disease and related dementias. Full article

Alzheimer’s disease (AD) is the most prevalent form of dementia. This disease significantly impacts cognitive functions and daily activities. Early and accurate diagnosis of AD, including the preliminary stage of mild cognitive impairment (MCI), is critical for effective patient care and treatment development. Although advancements in deep learning (DL) and machine learning (ML) models improve diagnostic precision, the lack of large datasets limits further enhancements, necessitating the use of complementary data. Existing convolutional neural networks (CNNs) effectively process visual features but struggle to fuse multimodal data effectively for AD diagnosis. To address these challenges, we propose NeuroNet-AD, a novel multimodal CNN framework designed to enhance AD classifcation accuracy. NeuroNet-AD integrates Magnetic Resonance Imaging (MRI) images with clinical text-based metadata, including psychological test scores, demographic information, and genetic biomarkers. In NeuroNet-AD, we incorporate Convolutional Block Attention Modules (CBAMs) within the ResNet-18 backbone, enabling the model to focus on the most informative spatial and channel-wise features. We introduce an attention computation and multimodal fusion module, named Meta Guided Cross Attention (MGCA), which facilitates effective cross-modal alignment between images and meta-features through a multi-head attention mechanism. Additionally, we employ an ensemble-based feature selection strategy to identify the most discriminative features from the textual data, improving model generalization and performance. We evaluate NeuroNet-AD on the Alzheimer’s Disease Neuroimaging Initiative (ADNI1) dataset using subject-level 5-fold cross-validation and a held-out test set to ensure robustness. NeuroNet-AD achieved 98.68% accuracy in multiclass classification of normal control (NC), MCI, and AD and 99.13% accuracy in the binary setting (NC vs. AD) on the ADNI dataset, outperforming state-of-the-art models. External validation on the OASIS-3 dataset further confirmed the model’s generalization ability, achieving 94.10% accuracy in the multiclass setting and 98.67% accuracy in the binary setting, despite variations in demographics and acquisition protocols. Further extensive evaluation studies demonstrate the effectiveness of each component of NeuroNet-AD in improving the performance. Full article

Background: MicroRNA-129 (miR-129) is a tumor suppressor involved in regulating oncogenic pathways, but its role in gastric adenocarcinoma and its potential connections to vascular and neurological dysfunction remain insufficiently defined. Objectives: To assess gastric juice-derived miR-129 as a diagnostic and prognostic biomarker for gastric cancer and to explore its associations with systemic inflammation, vascular impairment, and neurodegenerative changes. Methods: A prospective study was conducted in 38 patients undergoing upper gastrointestinal endoscopy (22 with histologically confirmed gastric adenocarcinoma, 16 controls). Gastric juice was aspirated prior to biopsy, and miR-129-2-3p expression was quantified by means of RT-qPCR normalized to U6 RNA. Tumor stage, serum biomarkers (CEA, CA 19-9, LDH, and CRP), carotid index (Doppler ultrasound), and neuroimaging (MRI) were recorded. Statistical analyses included ANOVA, Mann–Whitney U, ROC curve analysis, and correlation testing. Results: miR-129 expression was significantly reduced in gastric cancer compared with controls (ANOVA: F(3,34) = 3.70, p = 0.021, η² = 0.25). ΔCt values increased progressively from controls to T2–T4 tumors, indicating stage-dependent downregulation. ROC analysis demonstrated moderate diagnostic performance (AUC = 0.75, 95% CI 0.54–0.92). Lower miR-129 levels correlated inversely with serum tumor markers (CEA, CA 19-9), LDH, and CRP. Patients with elevated carotid index (>1.3) and abnormal brain imaging findings exhibited significantly lower miR-129 expression (both p < 0.05). Conclusion: Gastric juice-derived miR-129 is downregulated in gastric adenocarcinoma, with progressive decline across tumor stages. Its inverse association with systemic tumor and inflammatory markers, as well as vascular and neurological impairment, suggests that miR-129 may function as a minimally invasive, multi-system biomarker for integrated cancer and vascular–neurological risk assessment. Full article

Background: Autosomal dominant intellectual developmental disorder 51 (MIM #617788) is caused by pathogenic variants in KMT5B, a histone methyltransferase essential for transcriptional repression and central nervous system development. The disorder manifests as a complex neurodevelopmental syndrome with variable neurological and systemic features. Methods: Two adolescents with nonsense KMT5B variants underwent detailed clinical, neuropsychological, and neuroimaging evaluations, including MRI and ¹⁸FDG PET/CT, analyzed with Statistical Parametric Mapping against matched controls. RNA sequencing was performed, and the literature was reviewed to assess genotype–phenotype correlations. Results: Both patients showed global developmental delay, progressing to autism spectrum disorder (ASD) and developmental coordination disorder (DCD), without intellectual disability (ID). The MRI was normal, but neuropsychological testing revealed executive function impairment, expressive language deficits, and behavioral disturbances. PET/CT consistently demonstrated cerebellar and temporal lobe hypometabolism, correlating with symptom severity. RNA sequencing identified shared dysregulated pathways, notably DDIT4 upregulation, linked to synaptic dysfunction and neuronal atrophy in animal models. Conclusions: The findings highlight cerebellar involvement in DCD and ASD, medial temporal lobe contribution to ASD and executive dysfunction, and DDIT4 as a possible molecular signature of KMT5B loss-of-function. An integrative multimodal approach refined genotype–phenotype correlations and revealed novel brain regions and pathways implicated in KMT5B-related disorders. Full article

Idiopathic normal-pressure hydrocephalus (iNPH) is clinically characterized by the Hakim–Adams triad: gait disturbance, cognitive decline, and urinary incontinence. Cognitive impairment in iNPH predominates as a subcortical syndrome, with deficits in executive functions, psychomotor slowing, and memory inefficiency. However, the cognitive profile is heterogeneous and often overlaps with other neurodegenerative conditions, complicating differential diagnosis. This study investigated the cognitive features of iNPH using the Rey Auditory Verbal Learning Test (RAVLT), comparing 29 iNPH patients to 28 healthy controls. Demographic and neuroimaging parameters—such as Evans Index and Callosal Angle—were assessed. Results indicated significantly lower Montreal Cognitive Assessment (MoCA) and RAVLT scores in iNPH patients compared to controls. Analysis of serial position effects revealed that, whereas healthy individuals exhibited typical primacy and recency effects in verbal memory, iNPH patients demonstrated a selective impairment of the primacy effect, likely reflecting hippocampal dysfunction. These findings underline the importance of detailed neuropsychological evaluation in differentiating iNPH from other dementias and suggest that damage to medial temporal lobe structures plays a prominent role in the verbal memory deficit observed in iNPH. Full article

Complex Regional Pain Syndrome (CRPS) is a chronic pain disorder with several sensory, autonomic, motor, and trophic symptoms. Diagnosis is based on clinical criteria like the Budapest Criteria, but there are limitations to those criteria, especially for pediatric cases and different clinical presentations. Technical testing—including laboratory tests, electrophysiological studies, sensory and autonomic function tests, and more advanced imaging—provides supportive, but not definitive, evidence. Biomarkers such as certain microRNAs, inflammatory mediators, and autoantibodies may offer the potential for improved diagnostic accuracy, although they have not yet been adequately validated. New imaging techniques, including ultrasound elastography and neuroimaging, have identified both peripheral and central pathophysiological changes in CRPS. We can improve our diagnosis of CRPS by integrating standardized clinical criteria with technical evaluations and biomarker improvements; this should serve to make diagnosis earlier, reduce diagnostic delay, and promote individualized treatment. Full article

As an essential medium for the cultural narrative of architectural space, studying the cognitive transformation mechanisms of traditional ethnic decorative patterns is critical for their effective preservation and innovative application. This research focuses on typical decorative motifs found in She ethnic architectural heritage, systematically classifying them into five categories—animal, plant, human figure, totem, and geometric—based on symbolic themes, formal structure, and cultural function. Correspondingly, 20 sets of standardized black-and-white line drawing stimuli were developed for experimental use. Methodologically, this study utilized the EyeLink 1000 eye-tracking system to acquire real-time gaze metrics, including fixation duration and saccadic amplitude, as well as pupil dilation responses from participants engaged in a controlled pattern observation task. Immediately after observation, participants completed a semantic differential assessment using a five-point Likert scale. Data analysis employed descriptive statistics, analysis of variance (ANOVA), Kruskal–Wallis tests, and Bonferroni-adjusted post hoc comparisons (α = 0.05). Attention allocation was further examined through heatmaps and gaze trajectory visualizations to provide comprehensive insight into visual engagement. Two principal findings were identified: first, male participants showed a predominant focus on holistic structural composition and cultural symbol representation, whereas female participants exhibited a processing bias towards fine details; second, concrete symbols imbued with historical significance elicited more pronounced emotional responses, while abstract geometric patterns necessitated formal reconstruction to enhance cognitive accessibility. These findings offer empirical support for gender-inclusive architectural design strategies and inform practical approaches for safeguarding cultural heritage within contemporary architectural environments. Consequently, modern reinterpretation of traditional decorative patterns should balance cultural narrative fidelity with functional adaptation, achieving inclusive expression through contextual reconstruction and interactive design strategies. Future research directions include expanding participant demographics to encompass cross-cultural cohorts and incorporating multimodal neuroimaging techniques to elucidate the underlying cognitive and affective mechanisms, thereby advancing the sustainable transmission and innovation of ethnic cultural heritage. Full article

Objective: This study aims to develop and validate an interpretable deep learning framework that leverages self-supervised time reversal (TR) pretraining to identify consistent, biologically plausible functional network biomarkers across multiple neurological and psychiatric disorders. Methods: We pretrained a hierarchical LSTM model using a TR pretext task on the Human Connectome Project (HCP) dataset. The pretrained weights were transferred to downstream classification tasks on five clinical datasets (FBIRN, BSNIP, ADNI, OASIS, and ABIDE) spanning schizophrenia, Alzheimer’s disease, and autism spectrum disorder. After fine-tuning, we extracted latent features and employed a logistic regression probing analysis to decode class-specific functional network contributions. Models trained from scratch without pretraining served as a baseline. Statistical tests (one-sample and two-sample t-tests) were performed on the latent features to assess their discriminative power and consistency. Results: TR pretraining consistently improved classification performance in four out of five datasets, with AUC gains of up to 5.3%, particularly in data-scarce settings. Probing analyses revealed biologically meaningful and consistent patterns: schizophrenia was associated with reduced auditory network activity, Alzheimer’s with disrupted default mode and cerebellar networks, and autism with sensorimotor anomalies. TR-pretrained models produced more statistically significant latent features and demonstrated higher consistency across datasets (e.g., Pearson correlation = 0.9003 for schizophrenia probing vs. −0.67 for non-pretrained). In contrast, non-pretrained models showed unstable performance and inconsistent feature importance. Conclusions: Time Reversal pretraining enhances both the performance and interpretability of deep learning models for fMRI classification. By enabling more stable and biologically plausible representations, TR pretraining supports clinically relevant insights into disorder-specific network disruptions. This study demonstrates the utility of interpretable self-supervised models in neuroimaging, offering a promising step toward transparent and trustworthy AI applications in psychiatry. Full article

Background/Objectives: Psychotic disorders with childhood or adolescent onset pose major therapeutic challenges due to their complex etiology and variable treatment response. While pharmacogenetics and neuroimaging biomarkers have independently shown potential for guiding therapy, their combined utility remains underexplored. This study aimed to investigate whether integrating CYP2D6 pharmacogenetic profiles with structural neuroimaging findings can enhance personalized treatment and predict clinical outcomes in pediatric psychotic disorders. Methods: This prospective observational study included 63 children and adolescents (10–18 years) with DSM-5 diagnosed psychotic disorders. All patients underwent baseline MRI and standardized clinical assessments (PANSS, CGI, GAF). CYP2D6 genotyping was performed in 31 patients (49.2%), categorizing them as extensive (EMs) or intermediate metabolizers (IMs). Patients were treated with atypical antipsychotics and followed for 18 months. Outcomes included symptom severity, global functioning, and side-effect profiles. Results: EM patients demonstrated superior clinical improvement, as evidenced by a reduction in PANSS scores (from 118 to 40) and a corresponding increase in GAF scores (from 39 to 76), compared to the IM and non-tested groups. IM patients exhibited a higher prevalence of MRI abnormalities and slower response. Significant correlations emerged between CYP2D6 genotype, MRI findings, and treatment outcomes (p < 0.001). Combined biomarker profiles enhanced the prediction of therapeutic response and tolerability. Conclusions: Integrating CYP2D6 pharmacogenetic data with neuroimaging biomarkers provides valuable guidance for personalizing antipsychotic treatment in pediatric psychosis. This approach improves clinical outcomes and reduces adverse effects. Future research should further explore the integration of multimodal biomarkers in larger, longitudinal cohorts to optimize individualized psychiatric care for this vulnerable population. Full article

Background: Post-concussion syndrome (PCS) and Functional Neurological Disorder (FND), including Functional Cognitive Disorder (FCD), are two frequently encountered but diagnostically complex conditions. While PCS is conceptualized as a sequela of mild traumatic brain injury (mTBI), FND/FCD encompasses symptoms incompatible with recognized neurological disease, often arising in the absence of structural brain damage. Yet, both conditions exhibit considerable clinical overlap—particularly in the domains of cognitive dysfunction, emotional dysregulation, and symptom persistence despite negative investigations. Objective: This review critically examines the shared and divergent features of PCS and FND/FCD. We explore their respective epidemiology, diagnostic criteria, and risk factors—including personality traits and trauma exposure—as well as emerging insights from neuroimaging and biomarkers. We propose the “Functional Overlay Model” as a clinical tool for navigating diagnostic ambiguity in patients with persistent post-injury symptoms. Results: PCS and FND/FCD frequently share features such as subjective cognitive complaints, fatigue, anxiety, and heightened somatic vigilance. High neuroticism, maladaptive coping, prior psychiatric history, and trauma exposure emerge as common risk factors. Neuroimaging studies show persistent network dysfunction in both PCS and FND, with overlapping disruption in fronto-limbic and default mode systems. The Functional Overlay Model helps to identify cases where functional symptomatology coexists with or replaces an initial organic insult—particularly in patients with incongruent symptoms and normal objective testing. Conclusions: PCS and FND/FCD should be conceptualized along a continuum of brain dysfunction, shaped by injury, psychology, and contextual factors. Early recognition of functional overlays and stratified psychological interventions may improve outcomes for patients with persistent, medically unexplained symptoms after head trauma. This review introduces the Functional Overlay Model as a novel framework to enhance diagnostic clarity and therapeutic planning in patients presenting with persistent post-injury symptoms. Full article

Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder with chronic inattention, hyperactivity, and impulsivity and is linked with significant functional impairment. Despite being highly prevalent, diagnosis of ADHD continues to rely on subjective assessment reports of behavior and is often delayed or inaccurate. This review summarizes current advances in biomarkers and neuropsychological tests for the improvement of ADHD diagnosis and treatment. Key biomarkers are neuroimaging methods (e.g., structural and functional MRI), electrophysiological measures (e.g., EEG, ERP), and biochemical measures (e.g., cortisol, vitamin D). Additionally, novel experimental measures, e.g., eye-tracking, pupillometry, and microbiome analysis, hold the promise to be objective and dynamic measures of ADHD symptoms. The review also comments on the impact of the burden of ADHD on quality of life, e.g., emotional well-being, academic achievement, and social functioning. Additionally, differences between individuals, such as age, sex, comorbidities, and the impact of social and family support, are also addressed in relation to ADHD outcomes. In summary, we highlight the potential of these emerging biomarkers and tools to revolutionize ADHD diagnosis and guide personalized treatment strategies. These insights have significant implications for improving patient outcomes. Full article

Prognostication of neurodevelopmental outcomes for neonates with hypoxic–ischemic encephalopathy (HIE) is primarily reliant on structural assessment using conventional brain magnetic resonance imaging in the clinical setting. Diffuse optical tomography (DOT) can provide complementary information on brain function at the bedside, further enhancing prognostic accuracy. The predictive accuracy and generalizability of DOT-based neuroimaging markers are unknown. This study aims to test the feasibility of prospectively recruiting and retaining neonates for 12 months in a larger study that investigates the prognostic utility of DOT-based biomarkers of HIE. The study will recruit 25 neonates with HIE over one year and follow them beyond NICU discharge at 6 and 12 months of age. Study subjects will undergo resting-state DOT measurement within 7 days of life for a 30–45-min period without sedation. A customized neonatal cap with 10 sources and eight detectors per side will be used to quantify cortical functional connectivity and to generate brain networks using MATLAB-based software (version 24.2). The Ages and Stages Questionnaires—3rd edition will be used for standardized developmental assessments at follow-up. This feasibility study will help refine the design and sample-size calculation for an adequately powered larger study that determines the clinical utility of DOT-based neuroimaging in perinatal brain injury. Full article

Background: Musculoskeletal (MSK) pain is a leading contributor to global disability and healthcare burdens. While self-reported pain scales remain the clinical standard, they are limited by subjectivity and inter-individual variability. Therefore, objective assessment tools are increasingly sought to enhance diagnostic precision, guide treatment, and enable reproducible research outcomes. Methods: This comprehensive narrative review synthesizes evidence from physiological, behavioral, and neuroimaging approaches used to evaluate MSK pain objectively. Emphasis is placed on autonomic biomarkers (e.g., heart rate variability, skin conductance), facial expression analysis, electromyographic methods, and functional neuroimaging modalities such as fMRI and PET. Emerging applications of artificial intelligence and multimodal diagnostic strategies are also discussed. Results: Physiological signals provide quantifiable correlations of pain-related autonomic activity but are influenced by psychological and contextual factors. Behavioral analyses, including facial action coding systems and reflex testing, offer complementary, though complex, indicators. Neuroimaging techniques have identified pain-related brain patterns, yet clinical translation is limited by variability and standardization issues. Integrative approaches show promise for improving diagnostic validity. Conclusions: Objective assessment of MSK pain remains methodologically challenging but holds substantial potential for enhancing clinical diagnostics and personalized management. Future research should focus on multimodal integration, standardization, and translational feasibility to bridge the gap between experimental tools and clinical practice. Full article

Background/Objectives: Neuroimaging techniques have been increasingly utilized to explore neuroplasticity induced by various training regimens. Magnetic resonance imaging (MRI) enables to study these changes non-invasively. While visual and motor training have been widely studied, less is known about how auditory training affects brain activity. Our objective was to investigate the effects of sound localization training on brain activity and identify brain regions exhibiting significant changes in activation pre- and post-training to understand how sound localization training induces plasticity in the brain. Method: Six blindfolded participants each underwent 30-minute sound localization training sessions twice a week for three weeks. All participants completed functional MRI (fMRI) testing before and after the training. Results: fMRI scans revealed that sound localization training led to increased activation in several cortical areas, including the superior frontal gyrus, superior temporal gyrus, middle temporal gyrus, parietal lobule, precentral gyrus, and postcentral gyrus. These regions are associated with cognitive processes such as auditory processing, spatial working memory, planning, decision-making, error detection, and motor control. Conversely, a decrease in activation was observed in the left middle temporal gyrus, a region linked to language comprehension and semantic memory. Conclusions: These findings suggest that sound localization training enhances neural activity in areas involved in higher-order cognitive functions, spatial attention, and motor execution, while potentially reducing reliance on regions involved in basic sensory processing. This study provides evidence of training-induced neuroplasticity, highlighting the brain’s capacity to adapt through targeted auditory training intervention. Full article

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that impairs cognitive function, making early detection crucial for timely intervention. This study proposes a novel AD detection framework integrating gaze and head movement analysis via a dual-pathway convolutional neural network (CNN). Unlike conventional methods relying on linguistic, speech, or neuroimaging data, our approach leverages non-invasive video-based tracking, offering a more accessible and cost-effective solution to early AD detection. To enhance feature representation, we introduce GazeMap, a novel transformation converting 1D gaze and head pose time-series data into 2D spatial representations, effectively capturing both short- and long-term temporal interactions while mitigating missing or noisy data. The dual-pathway CNN processes gaze and head movement features separately before fusing them to improve diagnostic accuracy. We validated our framework using a clinical dataset (112 participants) from Konkuk University Hospital and an out-of-distribution dataset from senior centers and nursing homes. Our method achieved 91.09% accuracy on in-distribution data collected under controlled clinical settings, and 83.33% on out-of-distribution data from real-world scenarios, outperforming several time-series baseline models. Model performance was validated through cross-validation on in-distribution data and tested on an independent out-of-distribution dataset. Additionally, our gaze-saliency maps provide interpretable visualizations, revealing distinct AD-related gaze patterns. Full article