Search Results (139)

The cerebellum is traditionally considered a structure responsible for motor control, but it is also involved in auditory perception, vocalization, speech, memory, emotional control, and social cognition. Due to its high intrinsic synaptic plasticity and complex connectivity with other brain regions, it is of potential interest for monitoring adaptive responses under various physiological and pathological conditions to capture global brain dynamics. Nevertheless, the use of electrocerebellography (ECeG) to detect changes in cerebellar electrical activity is limited, and a systematic evaluation of ECeG data to inform future research directions is lacking. This review summarizes recent ECeG research to explore the contribution of this time-honored method to deciphering the cerebellum’s spatial and temporal dynamics in health and disease. ECeG studies from the past three decades examining the slow and fast adaptive responses of the cerebellum in different cerebellar layers during sleep, anesthesia, brain injury, epilepsy, neurodegenerative diseases, and neuropsychiatric disorders are compiled from the PubMed, Scopus, and Google Scholar databases and discussed accordingly. It can be concluded that, despite certain limitations, ECeG is a practical, valuable, and reliable technique for detecting and predicting the complex spatial and temporal features of cerebellar electrical activity. Full article

Background and Clinical Significance: Cerebral amyloid angiopathy (CAA) is a neurovascular disorder characterized by the deposition of amyloid beta (Aβ) peptides within the walls of small-to-medium-sized cerebral vessels, leading to vascular fragility and an increased risk of lobar intracerebral hemorrhage (ICH), cognitive decline, and recurrent stroke. CAA is an important cause of spontaneous ICH in elderly patients and may be underrecognized, particularly when presenting with acute neurologic symptoms that mimic ischemic stroke. Early identification has significant implications for management, prognosis, and secondary prevention. Case Presentation: An 80-year-old male presented to the emergency department with incoherent speech, rambling, and severe headache concerning for acute stroke. His medical history was notable for a prior cerebrovascular accident, hypertension, diabetes mellitus, benign prostatic hyperplasia, and recent evaluation for dementia-like symptoms. Initial neuroimaging revealed a 3.2 cm intraparenchymal hemorrhage in the left occipital lobe with surrounding edema. Subsequent MRI demonstrated a lobar hemorrhage pattern suggestive of CAA based on imaging findings and clinical context. The patient was admitted to the intensive care unit (ICU) for close neurologic monitoring. He remained hemodynamically stable with no new motor or sensory deficits. Over a three-day hospital course, his speech and visual deficits improved. Blood pressure was carefully controlled, and repeat imaging demonstrated stable hemorrhage without progression. He was diagnosed with probable CAA and discharged home with supportive services. Conclusions: This case highlights the importance of considering cerebral amyloid angiopathy in elderly patients presenting with spontaneous lobar intracerebral hemorrhage and cognitive symptoms. Prompt recognition and appropriate neuroimaging are critical for diagnosis, risk stratification, and guiding management. Full article

Background: Parkinson’s disease (PD) is a progressive neurodegenerative disorder that affects millions globally, particularly those in the elderly population. Several occupational exposures typical of maritime environments are recognized or suspected risk factors for PD, warranting attention within occupational health frameworks. The disease is characterized by motor symptoms such as tremor, rigidity, and bradykinesia, as well as non-motor impairments including speech abnormalities. Objective: Early diagnosis is crucial for effective disease management but remains challenging due to symptoms overlapping with normal aging and other neurological conditions. This study presents a machine learning (ML)-based approach for the early diagnosis of PD using speech signal analysis. Methods: We employed six supervised ML classifiers to differentiate between PD patients and healthy controls based on vocal features. The experimental dataset, MDVR-KCL, consists of speech recordings from both reading tasks and spontaneous dialogs, collected via mobile devices. From these recordings, we extracted Mel-Frequency Cepstral Coefficients (MFCCs), Gammatone Frequency Cepstral Coefficients (GTCCs), and acoustic features such as jitter, shimmer, and harmonic-to-noise ratio. These features capture a broad range of prosodic, spectral, and articulatory characteristics associated with PD-related speech impairments. Speaker diarization was applied in spontaneous dialog recordings to separate participant speech. Hyperparameter tuning was performed using GridSearchCV with 10-fold cross-validation, while final model evaluation was conducted using Leave-One-Subject-Out Cross-Validation (LOSOCV) to ensure subject-independent performance assessment. Results: In the read-text task, the SVM model performed exceptionally, yielding 95.45% accuracy, 94.62% sensitivity, 95.97% specificity, an F1-score of 94.12%, and an AUC of 0.98 with an MCC value of 0.90, for GTCCs with the acoustic features. In the spontaneous dialog task, the XGB model demonstrated the highest overall performance across all metrics, with a test accuracy of 83.7%, a sensitivity of 76.3.9%, a specificity of 88.9%, an F1-score of 79.5%, an AUC value of 0.88, and an MCC value of 0.66. Conclusions: Comparable results were obtained on both spontaneous dialog and reading speech subsets, demonstrating the robustness of the approach across different speaking contexts. These results demonstrate the effectiveness of integrating cepstral and acoustic features with machine learning models for non-invasive PD classification. The findings support the use of speech-based digital biomarkers in early PD detection and highlight the potential for developing scalable tools. This work highlights the potential of speech-based digital diagnostics to support clinical decision-making and improve patient outcomes. Full article

Background: Pediatric epilepsy is a clinically and genetically heterogeneous group of disorders, particularly in early-onset and syndromic presentations. Data integrating molecular findings with detailed clinical phenotyping remain limited in Kazakhstan and Central Asia. Methods: We conducted a retrospective, single-center, clinically selected, referral-based case series study of 31 pediatric patients evaluated at a tertiary center in Kazakhstan for epilepsy or epilepsy-associated neurodevelopmental disorders and found to have clinically relevant or potentially relevant genetic findings. Clinical records were reviewed for demographics, age at seizure onset, seizure semiology, developmental profile, EEG, MRI, extra-neurological features, treatment response, and family history. Variants were interpreted using ACMG-based criteria, and inheritance/segregation data were incorporated where available. Results: The cohort included 15 males and 16 females. Median seizure onset was 5.0 months (IQR 2.0–13.0), and 22/31 patients (71.0%) presented within the first year of life. Developmental delay/intellectual impairment was observed in 20/31 cases (64.5%), speech delay in 17/31 (54.8%), motor delay in 15/31 (48.4%), and hypotonia in 12/31 (38.7%). Variants were identified in 23 genes, including several variants with limited or no prior support in ClinVar or peer-reviewed reports. Ion channelopathies were the largest mechanistic group (12/31, 38.7%), followed by mitochondrial/metabolic disorders (6/31, 19.4%), mTOR pathway disorders (5/31, 16.1%), and neurodevelopmental/chromatin/transcriptional disorders (4/31, 12.9%). SCN1A was the most recurrent gene (8/31, 25.8%) and showed a broad phenotypic continuum from Dravet-compatible to non-Dravet presentations. EEG and MRI abnormalities were common and often syndromic in pattern. Treatment response was frequently partial or poor, although selected genotype-linked treatment observations were noted in PRRT2 (carbamazepine), PNPO (pyridoxine), and one SCN1A case (stiripentol). Conclusions: This study expands the clinicogenetic characterization of pediatric epilepsy cases with clinically relevant or potentially relevant genetic findings in Kazakhstan and highlights the value of integrated molecular, phenotypic, and segregation analysis in underrepresented populations. Full article

Parkinson’s disease (PD) is a progressive neurodegenerative disorder primarily caused by the degeneration of dopamine-producing neurons in the substantia nigra, leading to characteristic motor symptoms such as tremors, rigidity, and bradykinesia, as well as non-motor manifestations including depression, sleep disturbances, and speech impairments. Among these symptoms, speech abnormalities affect approximately 90% of individuals with PD, making acoustic analysis a promising non-invasive cue for early detection. However, subtle speech variations are often imperceptible to the human ear, and speech-only analysis may overlook complementary visual manifestations, such as hypomimia—reduced facial expressivity commonly observed in PD patients. To address these limitations, we propose Parkinson’s Detection via Attentional Fusion Network (PDAF-Net), a novel multimodal deep learning framework for early PD detection that jointly models acoustic and facial dynamic features in a binary classification setting. The proposed architecture consists of a Dual-Stream Feature Encoder (DSFE), with an audio branch based on a one-dimensional convolutional neural network (1D-CNN) and bidirectional long short-term memory (BiLSTM), and a visual branch built upon a two-dimensional convolutional neural network (2D-CNN) and a Transformer encoder. Multimodal integration is achieved through a Cross-Attention-guided Attentional Feature Fusion (CA-AFF) module, which explicitly models bidirectional cross-modal interactions and performs adaptive feature recalibration via an iterative attentional fusion mechanism. We conducted experiments on a self-collected Chinese multimodal dataset comprising 100 PD patients and 100 healthy controls. Although the data are balanced at the subject level, sliding-window segmentation introduces sample-level imbalance; to address this issue, a class-balanced focal loss is employed. Model performance was evaluated using subject-wise five-fold cross-validation. The results demonstrate that PDAF-Net consistently outperforms unimodal baselines across multiple evaluation metrics, achieving an accuracy of 89.3%, an F1-score of 0.884, and an AUC of 0.916. These findings highlight the effectiveness of explicit cross-modal interaction modeling and adaptive feature fusion for improving automated early PD screening in real-world clinical settings. Full article

Background: Communication, language, and speech disorders are highly prevalent in children with cerebral palsy (CP) and substantially impact social, educational, and community participation. However, few studies have systematically characterized communicative and linguistic profiles using standardized assessments. This paper outlines the work of the ‘Italian CP & Language Network’ over the last two years, focusing on identifying research priorities, developing specialized assessment protocols, and proposing a shared classification system for speech and language disorders in children with CP. Methods: A survey was sent to 11 specialized centers to investigate clinical practices and assessment tools. Based on the results and an extensive literature review, the group developed three age- and complexity-based diagnostic protocols and a shared classification system. Results: The survey highlighted high variability in test selection, especially for speech and pragmatic assessment, and a significant need for ad hoc tools for augmentative and alternative communication (AAC). Three standardized protocols were defined: (1) early language (<48 months), (2) school-age language and pragmatics (4–12 years), and (3) minimally verbal children (6–12 years). A multi-level classification system for language and speech disorders was proposed to improve diagnostic consistency. Conclusions: Standardizing assessment is a critical step toward early identification of communicative vulnerabilities to guide tailored interventions and promote participation and quality of life across developmental stages. The group provides a framework for prospective multicenter data collection to correlate linguistic and speech phenotypes with neuroradiological features and motor outcomes. Full article

Automated video-based detection of cognitive disorders can enable a scalable non-invasive health monitoring. However, existing methods focus on a single disease and provide limited interpretability, whereas real-world videos often contain co-occurring conditions. We propose a novel unified multi-task method to detect depression and Parkinson’s disease (PD) from in-the-wild video data called DEPART (DEpression and PArkinson’s Recognition Technique). It performs body region extraction, Contrastive Language-Image Pre-training (CLIP)-based visual encoding, Transformer-based temporal modeling, and prototype-aware classification with a gated fusion technique. Gradient-based attention maps are used to visualize task-specific regions that drive predictions. Experiments on the In-the-Wild Speech Medical (WSM) corpus demonstrate competitive performance: the multi-task model achieves Recall of 82.39% for depression and 78.20% for PD, compared with 87.76% and 78.20%, for the best single-task models. The multi-task learning initially increases false positives for healthy persons in the PD subset, mainly due to annotation–modality mismatches, static visual content misinterpreted as motor impairments, and occasional body detection failures. After cleaning the test data, Recall for healthy individuals becomes comparable across models; the multi-task model improves Recall for both depression (from 82.39% to 87.50%) and PD (from 78.20% to 86.14%), suggesting better robustness for real-life clinical applications. Full article

STT3A encodes the catalytic subunit of the oligosaccharyltransferase A (OST-A) complex and is classically linked to severe autosomal-recessive congenital disorder of glycosylation (CDG). To define the distinct autosomal-dominant disorder, we reviewed all published cases and integrated three previously unpublished individuals from the CDG natural history study. Across 21 individuals, abnormal transferrin glycosylation was present in nearly all individuals (20/21), and subtle facial dysmorphism was common (18/21). Neurodevelopmental involvement was frequent, including motor delay (13/21), learning difficulties (13/21), speech delay (12/21), and intellectual disability (10/21). Musculoskeletal manifestations were also common, including skeletal abnormalities (12/21), short stature (11/21), muscle cramps (8/21), and early-onset osteoarthritis in adults (6/21). Less frequent features included congenital heart defects (5/21) and coagulation factor deficiency (5/21). Importantly, the newly reported individuals expand dominant STT3A-CDG with previously unreported features, including anorectal malformation, morbid obesity, and clinically significant bleeding diathesis with von Willebrand factor and factor VIII deficiency. Biochemical signatures ranged from classic type I transferrin patterns to subtle or atypical abnormalities, emphasizing that near-normal transferrin testing does not exclude the diagnosis. Variants clustered in conserved catalytic regions, with recurrent p.Arg405 across de novo, inherited, and mosaic cases supporting a mutational hotspot and likely dominant-negative mechanism. Full article

Background/Objectives: Amyotrophic lateral sclerosis (ALS) is a clinically heterogeneous neurodegenerative disease in which bulbar involvement frequently affects speech and voice production. Although acoustic voice analysis can detect phonatory alterations in ALS, its ability to differentiate clinical phenotypes remains limited. This study investigated whether biomechanical voice parameters provide complementary information for characterizing bulbar involvement across bulbar-onset ALS (ALS-B) and spinal-onset ALS (ALS-S) and explored their association with clinical and functional measures. Methods: This cross-sectional observational study included 50 patients with ALS (20 ALS-B, 30 ALS-S) and 50 controls with non-neurological voice disorders. Sustained vowel phonation was analyzed using acoustic measures and biomechanical voice parameters derived from a standardized model of vocal fold vibration. Perceptual voice severity was assessed using the GRBAS scale, while functional status was evaluated with the ALS Functional Rating Scale–Revised (ALSFRS-R) and the Barthel Index. Associations with clinical measures were explored in secondary analyses. Results: Compared with controls, ALS patients showed significant differences in acoustic measures and several biomechanical parameters related to glottal closure and vibratory stability. Biomechanical analysis revealed significant differences between ALS-B and ALS-S, particularly in parameters reflecting vibratory asymmetry, glottal tension and cycle-to-cycle instability. Unexpectedly, ALS-B showed greater perceptual voice severity and higher Barthel Index scores than ALS-S, while no differences were observed in global ALSFRS-R total scores. Conclusions: Biomechanical voice analysis appears to capture physiologically meaningful alterations in vocal fold function in ALS and provides complementary information for characterizing bulbar motor involvement across clinical phenotypes, particularly ALS-B disease. When combined with acoustic and clinical assessments, this approach may enhance the evaluation of bulbar involvement and functional status in ALS. Full article

Dysarthria in patients post-stroke is often accompanied by central facial paralysis, which impairs facial motor control and emotional expression. Current assessments rely on acoustic modalities, overlooking facial pathological cues and their correlation with emotional expression, which hinders comprehensive disease assessment. To address this issue, we propose a multimodal severity classification framework that integrates facial and acoustic features. Firstly, a multi-level annotation algorithm based on a pre-trained model and motion amplitude was designed to overcome the problem of data scarcity. Secondly, facial topology was modeled using Delaunay triangulation, with spatial relationships captured via graph convolutional networks (GCNs), while abnormal muscle coordination is quantified using facial action units (AUs). Finally, we proposed a multimodal feature set fusion technology framework to achieve the compensation of facial visual features for acoustic modalities and the analysis of disease classification. Our experimental results using the THE-POSSD dataset demonstrate an accuracy of 92.0% and an F1 score of 91.6%, significantly outperforming single-modality baselines. This study reveals the changes in facial movements and sensitive areas of patients under different emotional states, verifies the compensatory ability of visual patterns for auditory patterns, and demonstrates the potential of this multimodal framework for objective assessment and future clinical applications in speech disorders. Full article

Background/Objectives: Speech difficulties are an early and disabling manifestation of Parkinson’s disease (PD), affecting communication and quality of life. This study aimed to examine demographic, clinical, dopaminergic imaging and cerebrospinal fluid (CSF) correlates of speech difficulties in early PD, comparing treatment-naïve and levodopa-treated patients. Methods: A cross-sectional analysis was conducted using data from the Parkinson’s Progression Markers Initiative (PPMI). The sample included 376 treatment-naïve and 133 levodopa-treated early PD participants. Speech difficulties were defined by Movement Disorder Society—Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) Part III, with Item 3.1 ≥ 1. Group comparisons and binary logistic regression identified predictors among demographic, clinical, dopaminergic and CSF biomarker variables, including [¹²³I]FP-CIT specific binding ratios (SBRs). All analyses were cross-sectional, and findings reflect associative relationships rather than treatment effects or causal mechanisms. Results: Speech difficulties were present in 44% of treatment-naïve and 57% of levodopa-treated participants. In both cohorts, higher MDS-UPDRS Part III ON scores—reflecting greater motor severity—and lower mean putamen SBR values were significant independent predictors of speech impairment. Age was an additional predictor in the treatment-naïve group. No significant differences were found in CSF biomarkers (α-synuclein, amyloid-β, tau, phosphorylated tau). These findings indicate that striatal dopaminergic loss, particularly in the putamen, and motor dysfunction relate to early PD-related speech difficulties, whereas CSF neurodegeneration markers do not differentiate affected patients. Conclusions: Speech difficulties in early PD are primarily linked to dopaminergic and motor dysfunction rather than global neurodegenerative biomarker changes. Longitudinal and multimodal studies integrating acoustic, neuroimaging, and cognitive measures are warranted to elucidate the neural basis of speech decline and inform targeted interventions. Full article

Down Syndrome Regression Disorder (DSRD) is a rare but severe neuropsychiatric condition characterized by abrupt loss of speech, autonomy, and cognitive abilities in individuals with Down syndrome, often associated with immune dysregulation and gut–brain axis dysfunction. We report the case of an 11-year-old girl with Down syndrome who developed developmental regression at age five, in temporal proximity to a family transition (the birth of a younger sibling), with loss of continence, language, and comprehension, alongside persistent behavioral agitation and gastrointestinal symptoms. Laboratory assessment revealed Giardia duodenalis infection, elevated fecal calprotectin and secretory IgA, and microbial imbalance with overgrowth of Streptococcus anginosus and S. sobrinus. The patient received a single oral dose of tinidazole (2 g), daily folinic acid (1 mg/kg), and a 90-day course of transcranial and abdominal photobiomodulation (PBM) (1064 nm, 10 min per site). Post-treatment, stool analysis showed normalized inflammation markers and restoration of beneficial bacterial genera (Bacteroides, Bifidobacterium, Lactobacillus) with absence of Enterococcus growth. Behaviorally, she exhibited marked recovery: CARS-2-QPC decreased from 106 to 91, ABC from 63 to 31, and ATEC from 62 to 57, alongside regained continence, speech, and fine-motor coordination. These outcomes suggest that abdominal and transcranial PBM, by modulating mitochondrial metabolism, mucosal immunity, and microbiota composition, may facilitate systemic and neurobehavioral recovery in DSRD. This translational case supports further investigation of PBM as a non-invasive, multimodal therapy for neuroimmune regression in genetic and developmental disorders including validation through future randomized controlled clinical trials. Full article

Dysphagia and dysarthria are common, co-occurring manifestations in neurodegenerative diseases, resulting from damage to distributed neural networks involving cortical, subcortical, cerebellar, and brainstem regions. These disorders profoundly affect patient health and quality of life through complex sensorimotor impairments. Objective: The aims was to provide a comprehensive, evidence-based review of the neuroanatomical substrates, pathophysiology, diagnostic approaches, and management strategies for dysphagia and dysarthria in neurodegenerative diseases with emphasis on their multisystem nature and integrated treatment approaches. Methods: A narrative literature review was conducted using PubMed, Scopus, and Web of Science databases (2000–2024), focusing on Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), progressive supranuclear palsy (PSP), and multiple system atrophy (MSA). Search terms included “dysphagia”, “dysarthria”, “neurodegenerative diseases”, “neural networks”, “swallowing control” and “speech production.” Studies on neuroanatomy, pathophysiology, diagnostic tools, and therapeutic interventions were included. Results: Contemporary neuroscience demonstrates that swallowing and speech control involve extensive neural networks beyond the brainstem, including bilateral sensorimotor cortex, insula, cingulate gyrus, basal ganglia, and cerebellum. Disease-specific patterns reflect multisystem involvement: PD affects basal ganglia and multiple brainstem nuclei; ALS involves cortical and brainstem motor neurons; MSA causes widespread autonomic and motor degeneration; PSP produces tau-related damage across multiple brain regions. Diagnostic approaches combining fiberoptic endoscopic evaluation, videofluoroscopy, acoustic analysis, and neuroimaging enable precise characterization. Management requires multidisciplinary Integrated teams implementing coordinated speech-swallowing therapy, pharmacological interventions, and assistive technologies. Conclusions: Dysphagia and dysarthria in neurodegenerative diseases result from multifocal brain damage affecting distributed neural networks. Understanding this multisystem pathophysiology enables more effective integrated assessment and treatment approaches, enhancing patient outcomes and quality of life. Full article

Background/Objectives: Cluttering in childhood and adolescence is characterised by unstable speech timing, excessive coarticulation, irregular rate and reduced intelligibility, yet the developmental mechanisms underpinning these behaviours remain partially understood. This review synthesises empirical and conceptual evidence to examine cluttering through the lenses of speech motor development, neurocognitive mechanisms, task demands and allied-health practice. Four research questions guided the review, focusing on motor characteristics, developmental and neurocognitive mechanisms, task dependence and clinical implications. Methods: Following the PRISMA guidelines, a comprehensive search across seven databases identified studies examining cluttering in children and adolescents. Screening and full-text review were conducted in Covidence by two reviewers, with disagreements resolved by the first author. Twelve studies met the inclusion criteria. Data were extracted into a structured evidence table, and findings were synthesised. Results: Across studies, cluttering emerged as a developmental motor–cognitive integration disorder. Speech motor systems, linguistic formulation and executive control showed difficulty aligning under real-world communicative demands, leading to timing instability, articulatory blurring and reduced intelligibility. Symptoms were strongly influenced by task complexity, with spontaneous and extended discourse eliciting the most pronounced breakdowns. Conclusions: Cluttering reflects a developmental vulnerability in coordinating speech motor, linguistic and executive processes. Understanding cluttering in this way challenges narrow rate-based definitions and supports more nuanced approaches to assessment and intervention. Significant evidence gaps remain, particularly in longitudinal, mechanistic, multilingual and ecologically valid research. This developmental motor–cognitive framework strengthens the conceptual foundations of cluttering and clarifies its relevance to children’s motor development. Full article

Background/Objectives: Down Syndrome (DS) is frequently associated with oral-motor dysmorphologies, like oral hypotonia, tongue protrusion, short palate, and malocclusion, compromising the oral functions of sucking, chewing, swallowing, and speech production. Therapeutic interventions with stimulating palatal plates (SPP) have been proposed to prevent and improve oral-motor dysmorphologies in DS. This study proposes a new digital workflow for the manufacturing and use of a modified SPP. Methods: We report the application of the new workflow to five clinical cases, all infants with DS showing oral-motor disorders, aged between 5 and 11 months. The workflow is described step-by-step, from the mouth scanning protocol and model printing to SPP manufacturing and delivering, and assessment of oral-morphological features and language abilities via video captures and parental questionnaires. Key novel features include an SPP with an acrylic extension with a pacifier terminal and, importantly, the use of an infant-friendly intraoral scanner. Results: The new workflow had good acceptability by infants and parents, offering a safe, easy-to-implement, and feasible solution for SPP design, as it avoided the high risks associated with impression materials. It also supported the use of the SPP to promote tongue stimulation, retraction, and overall oral-muscle function in oral-motor disorders in children with DS, especially in infants. Conclusions: Within the limitations of the current study, it was shown that the proposed digital workflow constitutes a viable and infant-friendly approach to the production and use of a modified SPP, and thus promises to contribute to improving oral morphology and auditory-motor language abilities. Full article