Search Results (958)

Background/Objectives: Mild Traumatic Brain Injury (mTBI) is a prevalent form of cranial trauma that can elicit a range of acute and chronic neuropsychiatric symptoms, and may increase the risk of neurodegenerative diseases. Its accurate identification remains a significant challenge in the field of forensic medicine. This study aimed to identify differential gut microbiota as potential biomarkers following mTBI and to preliminarily explore the association between alterations in gut microbiota and brain metabolites. Methods: An animal model was used to induce mTBI in male Sprague-Dawley (SD) rats. Dynamic changes in the gut microbiota and brain metabolites were analyzed via 16S rRNA sequencing and untargeted metabolomics. Results: Key discriminative taxa included Staphylococcus, Streptococcus, and Aeromonadaceae. Concurrently, brain metabolites, such as C24:1 Sphingomyelin and Thioetheramide PC, exhibited significant alterations. Multi-omics integration revealed that these changes were strongly correlated; in addition, a pathway analysis implicated disruptions in short-chain fatty acid and glycerophospholipid metabolism, which were linked to the regulation of inflammatory factors. Conclusions: This study demonstrates that mTBI induces distinct, time-dependent alterations in both the gut microbiota and brain metabolome, thereby providing a novel direction for research into the forensic diagnosis and mechanistic investigation of mTBI. Future studies are warranted to validate these potential biomarkers in human cohorts and to further elucidate the causal mechanisms underlying gut–brain axis interactions. Full article

Parkinson’s Disease (PD) is a progressive neurodegenerative disorder for which early and reliable diagnosis remains challenging. To address this challenge, the key innovation of this work is a confidence-gated fusion mechanism that dynamically weights classifier outputs based on per-sample prediction certainty, overcoming the limitations of static ensemble strategies. Building on this idea, we propose a Confidence-Gated Hybrid CNN Ensemble that integrates CNN-based acoustic feature extraction with heterogeneous classifiers, including XGBoost, Support Vector Machines, and Random Forest. By adaptively modulating the contribution of each classifier at the sample level, the proposed framework enhances robustness against data imbalance, inter-speaker variability, and feature complexity. The method is evaluated on two benchmark PD speech datasets, where it consistently outperforms conventional machine learning and ensemble approaches, achieving a best classification accuracy of up to 97.9% while maintaining computational efficiency compatible with real-time deployment. These results highlight the effectiveness and clinical potential of confidence-aware ensemble learning for non-invasive PD detection. Full article

Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that utilizes magnetic fields to induce cortical electric currents, enabling both the measurement and modulation of neuronal activity. Initially developed as a diagnostic tool, TMS now serves dual roles in clinical neurology, offering insight into neurophysiological dysfunctions and the therapeutic modulation of abnormal cortical excitability. This review examines key TMS outcome measures, including motor thresholds (MT), input–output (I/O) curves, cortical silent periods (CSP), and paired-pulse paradigms such as short-interval intracortical inhibition (SICI), short-interval intracortical facilitation (SICF), intracortical facilitation (ICF), long interval cortical inhibition (LICI), interhemispheric inhibition (IHI), and short-latency afferent inhibition (SAI). These biomarkers reflect underlying neurotransmitter systems and can aid in differentiating neurological conditions. Diagnostic applications of TMS are explored in Parkinson’s disease (PD), dystonia, essential tremor (ET), Alzheimer’s disease (AD), and mild cognitive impairment (MCI). Each condition displays characteristic neurophysiological profiles, highlighting the potential for TMS-derived biomarkers in early or differential diagnosis. Therapeutically, repetitive TMS (rTMS) has shown promise in modulating cortical circuits and improving motor and cognitive symptoms. High- and low-frequency stimulation protocols have demonstrated efficacy in PD, dystonia, ET, AD, and MCI, targeting the specific cortical regions implicated in each disorder. Moreover, the successful application of TMS in differentiating and treating AD and MCI underscores its clinical utility and translational potential across all neurodegenerative conditions. As research advances, increased attention and investment in TMS could facilitate similar diagnostic and therapeutic breakthroughs for other neurological disorders that currently lack robust tools for early detection and effective intervention. Moreover, this review also aims to underscore the importance of maintaining standardized TMS protocols. By highlighting inconsistencies and variability in outcomes across studies, we emphasize that careful methodological design is critical for ensuring the reproducibility, comparability, and reliable interpretation of TMS findings. In summary, this review emphasizes the value of TMS as a distinctive, non-invasive approach to probing brain function and highlights its considerable promise as both a diagnostic and therapeutic modality in neurology—roles that are often considered separately. Full article

Objective: Alzheimer’s disease (AD) and other forms of dementia are a heterogeneous group of neurodegenerative diseases characterized by progressive cognitive decline. Differential diagnosis between AD and other dementias is crucial for choosing the optimal treatment strategy. Currently, cerebrospinal fluid (CSF) analysis remains the most accurate diagnostic method, but its invasiveness limits its use. In this regard, the search for reliable biomarkers in the blood is an urgent task. Methods: The study included 31 dementia patients (23 women and 8 men) diagnosed via interdisciplinary consultations and neuropsychological testing (MMSE ≤ 24). CSF and blood plasma samples were collected and analyzed using Luminex technology. Biomarker concentrations were measured, and statistical analyses (ANOVA, Kruskal–Wallis, and Pearson correlation) were performed to compare groups and assess correlations. Results: Levels of Aβ40 and Aβ42 in CSF were significantly lower in patients with AD compared with non-AD dementia (p = 0.02 and p < 0.001, respectively). The Aβ42/40 ratio in CSF was higher in patients with non-AD dementia (p = 0.048). The concentration of Aβ42 in blood plasma was increased in patients with AD (p = 0.001). Positive correlations were found between Aβ42 in CSF and TDP-43 in plasma in non-AD dementia (r = 0.97, p < 0.001), as well as between neurogranin and TDP-43 in plasma in AD (r = 0.845, p < 0.001). Conclusions: The study demonstrates the potential of blood biomarkers, in particular Aβ42, for the differential diagnosis of AD and other forms of dementia. The discovered correlations between CSF and plasma biomarkers deepen the understanding of neurodegenerative processes and contribute to the development of noninvasive diagnostic methods. Full article

Parkinson’s disease is a neurodegenerative disorder with progressive impairment in patients worldwide, featuring manifestations of both motor dysfunction and various/list-specific non-motor symptoms. Early diagnosis and personalized treatment thus remain the biggest challenges in managing the disease. Artificial intelligence (AI), especially machine learning techniques, has shown immense potential for countering such challenges during the past years. This short review aims to summarize recent innovations in applying Machine Learning (ML) and Deep Learning (DL) to Parkinson’s disease, explicitly directed toward developing diagnostic tools, the prediction of progression, and personalized treatment strategies. We discuss several ML and DL approaches, including supervised and unsupervised learning models that have been applied to classify symptoms and identify biomarkers. In addition, integrating clinical and imaging data into disease models continues to advance. This indicates the emerging role of DL in bypassing the limitations of standard methods. This review of the future of AI in Parkinson’s disease research outlines its possible directions for enhancing patient care and clinical outcomes. Full article

Creutzfeldt–Jakob disease (CJD) is a rare but devastating neurodegenerative disorder characterized by the pathological misfolding of the cellular prion protein (PrP^C) into the pathogenic isoform-scrapie prion protein (PrP^Sc), ultimately leading to fatal outcomes. Cerebrospinal fluid (CSF) biomarkers play a pivotal role in early diagnosis, longitudinal monitoring, and prognostic assessment, thereby enhancing the clinical management of this challenging disease. This review summarizes the established CSF biomarkers, 14-3-3 protein, tau protein (total tau), phosphorylated tau isoforms, α-synuclein, neurofilament light chain (Nfl), S100B, neuron-specific enolase (NSE), and phosphorylated neurofilament heavy chain (pNFH), highlighting typical sensitivity ranges (14-3-3 ~70–85%; RT-QuIC > 90%) and subtype-dependent performance variation. We further dissect limitations related to assay variability, inter-laboratory cut-off inconsistencies, and reduced specificity in non-prion dementias. Looking ahead, we discuss emerging multi-omics discovery, integration of CSF with blood-based biomarkers and imaging signatures, and AI-enabled diagnostic modeling. We propose a three-tier biomarker framework combining Real-Time Quaking-Induced Conversion (RT-QuIC) as a confirmatory assay, tau/NfL/pNFH as injury-severity indicators, and multi-omics-derived signatures for early detection and prognosis stratification. Full article

Non-coding RNAs have emerged as central regulators of gene expression in neurodegenerative diseases, offering new opportunities for diagnosis and therapy. This review synthesizes current knowledge on microRNAs, long non-coding RNAs, and circular RNAs in Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis, emphasizing their roles in synaptic function, proteostasis, mitochondrial biology, and neuroinflammation. We evaluate evidence supporting non-coding RNAs as circulating and tissue-based biomarkers for early detection, disease monitoring, and patient stratification, and we compare analytical platforms and biofluid sources. Mechanistic insights reveal how non-coding RNAs modulate pathogenic protein aggregation, neuronal excitability, immune cell crosstalk, and blood–brain barrier integrity. Translational efforts toward RNA-targeted interventions are reviewed, including antisense oligonucleotides, small interfering RNAs, miRNA mimics and inhibitors, circular RNA decoys, and extracellular vesicle-mediated delivery systems. We discuss pharmacological modulation, delivery challenges, safety concerns, and strategies to enhance specificity and CNS penetration. Finally, we outline emerging computational and multi-omics approaches to prioritize therapeutic targets and propose a roadmap for advancing non-coding RNA research from preclinical models to clinical trials. Addressing biological heterogeneity and delivery barriers will be pivotal to realizing the diagnostic and therapeutic promise of the non-coding transcriptome in neurodegenerative disease. Collaboration across disciplines and rigorous clinical validation are urgently needed. Full article

Brain age has been widely recognized as an important biomarker for monitoring adolescent brain development and assessing dementia risk. However, existing model visualization methods primarily highlight brain regions associated with aging, making it difficult to comprehensively reveal broader brain changes. In this study, we developed a VGGNet-based brain age prediction model and proposed the Softmax-Derived Brain Age Mapping algorithm to simultaneously identify brain regions associated with both youthful and aging features. The resulting saliency maps provide explicit representations of developmental and degenerative processes across different brain regions. Brain Age Map analysis revealed that aging features in the healthy group were primarily confined to the frontal cortex, aligning with findings that the frontal lobe is the earliest region to undergo natural senescence. In contrast, the dementia group exhibited widespread aging across the frontal, temporal, parietal, and occipital lobes, as well as the ventricular regions. These results suggest that the spatial distribution of brain aging can serve as a critical biomarker for distinguishing normal aging trajectories from pathological degeneration. From an application perspective, we further explored the potential of the proposed framework in neurodegenerative diseases. The analysis reveals that dementia patients generally exhibit an advanced brain age, with cortical aging being markedly more pronounced than in age-matched healthy samples. Notably, although dementia cases were not included in the training set, the model was still able to localize abnormalities in relevant brain regions, underscoring its potential value as an assistive tool for early dementia diagnosis. Full article

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that leads to the gradual loss of motor control, typically resulting in paralysis and death within 3 to 5 years of diagnosis. ALS shares neuropathological and genetic associations with fronto-temporal dementia (FTD), a neurodegenerative condition primarily impacting cognitive functions. These two conditions are increasingly viewed as manifestations of a single molecular disease process that affects distinct brain systems, impacting motor neuronal pathways in ALS and fronto-cortical functions in FTD. However, this simple dichotomy belies the complexity of these conditions. In particular, patients with primary motor ALS can also experience significant cognitive deficits. Investigating the pathobiological and neurophysiological underpinnings of these impairments is essential for a comprehensive understanding of ALS and may open avenues for targeted therapies to alleviate these debilitating symptoms. Moreover, the biophysical correlates of cognitive deficits in ALS may serve as sensitive biomarkers for evaluating potential therapeutics. In this narrative review, we begin with an overview of the clinical features and genetics of ALS, followed by a review of the associated cognitive deficits that are adjunctive to motor decline. We then highlight neuroimaging studies from our laboratory and the broader literature, using EEG and other modalities that are beginning to uncover systems-level brain disruptions potentially underlying cognitive impairment in motor-dominant ALS. Full article

Over the years, there has been extensive research conducted on Parkinson’s Disease (PD), a neurodegenerative disorder known for causing motor impairment and behavioral changes. In more recent years, the roles of dysregulated microRNAs (miRNAs) in PD pathology have been studied in the hopes of developing new diagnostic methods or even treatments. This systematic review pinpoints and examines studies between 2010 and 2024 that have identified significant dysregulation of miRNAs in patients with PD. Upon filtering out the search results by a series of exclusion criteria, this review was conducted using 56 relevant studies. These studies revealed a vast array of significantly dysregulated miRNAs identified in the samples of patients with PD, when compared to healthy controls. A number of these miRNAs, such as miR-29c-3p, are likely biomarkers for more accurate PD diagnosis, and many, such as miR-485-3p, were found to be involved in PD pathogenesis. With further research, miRNAs could become a helpful diagnostic and prognostic tool for PD, with some of them even being candidate therapeutic targets for future treatments. Full article

The progressive nature of dementia necessitates early detection strategies capable of identifying preclinical cognitive decline. Gait disturbances, mediated by higher-order cognitive functions, have emerged as potential digital biomarkers in this context. This bibliometric review systematically maps the scientific output from 2010 to 2025 on the application of wearable sensor technologies and gait analysis in the early diagnosis of dementia. A targeted search of the Scopus database yielded 126 peer-reviewed studies, which were analyzed using VOSviewer for performance metrics, co-authorship networks, bibliographic coupling, co-citation, and keyword co-occurrence. The findings delineate a multidisciplinary research landscape, with major contributions spanning neurology, geriatrics, biomedical engineering, and computational sciences. Four principal thematic clusters were identified: (1) Cognitive and Clinical Aspects of Dementia, (2) Physical Activity and Mobility in Older Adults, (3) Technological and Analytical Approaches to Gait and Frailty and (4) Aging, Cognitive Decline, and Emerging Technologies. Despite the proliferation of research, significant gaps persist in longitudinal validation, methodological standardization, and integration into clinical workflows. This review emphasizes the potential of sensor-derived gait metrics to augment early diagnostic protocols and advocates for interdisciplinary collaboration to advance scalable, non-invasive diagnostic solutions for neurodegenerative diseases. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder whose early symptoms, especially mild tremor, are often clinically imperceptible. Early detection is crucial for initiating neuroprotective interventions to slow dopaminergic neuronal degeneration. Current PD diagnosis relies predominantly on subjective clinical assessments due to the absence of definitive biomarkers. This study proposes a novel approach for the early detection of PD through a custom-developed smart wristband equipped with an inertial measurement unit (IMU). Unlike previous paper-based or resting-tremor approaches, this study introduces a mid-air Archimedean spiral task combined with an attention-enhanced Long Short-Term Memory (LSTM) architecture, enabling substantially more sensitive detection of subtle early-stage Parkinsonian motor abnormalities. We propose LAFNet, a model based on an attention-enhanced LSTM network, which processes motion data that has been filtered using a Kalman algorithm for noise reduction, enabling rapid and accurate diagnosis. Clinical data evaluation demonstrated exceptional performance, with an accuracy of 99.02%. The proposed system shows significant potential for clinical translation as a non-invasive screening tool for early-stage Parkinson’s disease (PD). Full article

Populations with intellectual disabilities, especially individuals with genetic syndromes such as Down syndrome, are at very high risk of developing neurodegenerative diseases. This article aims to systematically review the capacities and limitations of biomarkers in the diagnosis and treatment of these diseases in these vulnerable populations. A narrative review was conducted using a systematic search of PubMed, Scopus, and Web of Science for studies published between 2000 and 2025 on biomarkers in intellectual disability and neurodegenerative diseases. Peer-reviewed articles in English or Persian were included, and the extracted data were synthesized thematically. Findings show that various biomarkers, including protein biomarkers (such as Aβ and tau), imaging (such as PET and MRI), genetic biomarkers, and fluid-based (blood and CSF) biomarkers, have significant potential in early diagnosis, monitoring disease progression, and evaluating treatment response. However, the use of these biomarkers in the population with intellectual disabilities faces unique challenges, including inherent biological heterogeneity, the presence of comorbidities, methodological barriers in assessment, and complex ethical considerations. The final conclusion indicates that achieving the maximum potential of these biomarkers requires the development of standardized and validated protocols for this specific population, conducting further longitudinal studies, and seriously considering ethical issues. This review emphasizes the importance of international collaborations and multidisciplinary approaches for transforming clinical care for these individuals. Full article

Parkinson’s disease (PD) is a multifaceted neurodegenerative disorder characterized by dopaminergic neuronal loss and widespread α-synuclein pathology. Nuclear medicine imaging offers essential in vivo tools for early diagnosis, differential assessment, and monitoring disease progression. This review summarizes key PET and SPECT radiotracers targeting dopaminergic synthesis and transport, vesicular storage, post-synaptic receptors, neuroinflammation, and protein aggregation, highlighting their roles in clinical evaluation and phenotyping. Clinically, these modalities support earlier recognition of PD, distinction from atypical parkinsonian syndromes, and assessment of non-motor involvement. Future directions include the development of selective α-synuclein tracers and multimodal imaging strategies to refine prodromal detection and guide personalized therapeutic interventions. Full article

Background: Chronic rhinosinusitis (CRS) affects nearly 9% of the global population with a rising incidence over recent decades. Neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease pose significant global burden, and emerging evidence suggests pathophysiological links through shared bioenergetic dysfunction, peripheral-to-central inflammatory signaling, and altered nasal microbiota. This review evaluates the evidence for CRS as a potentially modifiable peripheral contributor to neurodegenerative disease progression. Methods: A systematic review was conducted using PubMed, Cochrane, Web of Science, Embase, and CENTRAL from January 2000 to July 2025. Search terms included “Chronic Rhinosinusitis,” “Neurodegeneration,” “Mild Cognitive Impairment,” “Alzheimer’s Disease,” “Parkinson’s Disease,” “Bioenergetics,” and “Microbiome.” Clinical and experimental studies exploring epidemiological links, mechanistic pathways, biomarkers, and therapeutic targets were included. Results: Twenty-one studies involving over 100,000 participants met the inclusion criteria. Existing meta-analytic evidence demonstrated significant associations between CRS and cognitive impairment, with patients scoring approximately 9% lower on global cognitive measures than controls. However, other large-scale cohort studies did not pinpoint an increased dementia incidence, suggesting CRS may contribute to early, potentially reversible cognitive decline without directly driving dementia onset. Neuroimaging studies revealed altered frontoparietal connectivity and orbitofrontal hyperactivity in CRS patients. Mechanistic studies support peripheral inflammatory cytokines disrupting the blood–brain barrier, autonomic dysfunction impairing mucociliary clearance, microbiome-driven amyloid cross-seeding, and compromised cerebrospinal fluid clearance via olfactory–cribriform pathways. Discussion: Evidence supports complex, bidirectional relationships between CRS and neurodegeneration characterized by convergent inflammatory, autonomic, and bioenergetic pathways. Therapeutic strategies targeting sinonasal inflammation, microbiome dysbiosis, and mitochondrial dysfunction represent promising intervention avenues. Recognizing CRS as a treatable factor in neurodegenerative risk stratification may enable earlier diagnosis and prevention strategies. Full article