Acute disseminated encephalomyelitis (ADEM) is a rare, immune-mediated demyelinating disorder of the central nervous system that typically follows viral or bacterial infections. There are only two known case reports that describe an association with ADEM post rickettsial infection. A female in her 80s presented with progressive neurological decline, including dysarthria, dysphagia and hemiparesis, following recent treatment in Hong Kong for rickettsial infection. Initial CT brain imaging showed a right subcortical infarct. She deteriorated with encephalopathy and respiratory failure, requiring intubation. MRI revealed extensive brain and spinal demyelination. Infectious, autoimmune and paraneoplastic investigations were negative, while serology revealed markedly elevated spotted fever group antibodies. Treatment with high-dose corticosteroids and plasma exchange therapy resulted in significant neurological recovery. She was discharged after rehabilitation with a good recovery at 70%-80% of pre-morbid function. This case supports a possible post-infectious immune-mediated mechanism linking rickettsia and ADEM and underscores the importance of early recognition and treatment of ADEM.

Neurological disorders can alter motor control, cognition, and speech production in subtle ways that are difficult to detect using standard diagnostic tools. Because conventional imaging techniques such as CT and MRI often fail to reveal functional abnormalities, there is growing interest in non-invasive digital health approaches. This study investigates the potential of speech-based deep learning for detecting concussion-related dysfunction using structured speech tasks. Audio recordings from 225 high school and college athletes (approximately 200 retained after quality filtering) performing eight speech tasks were analyzed. The recordings were converted into Mel spectrograms and processed using a convolutional neural network (CNN), with data augmentation applied to improve model generalization. The results demonstrate strong classification performance, particularly for tasks requiring dynamic articulation and rhythmic control. To better interpret model behavior, the speech tasks were grouped into three functional categories and examined using gradient-weighted class activation mapping (Grad-CAM). Sentence and word reading tasks, as well as rapid syllable repetition, achieved the highest accuracy (up to 96%) and exhibited focused activation patterns associated with phonetic complexity and motor timing. In contrast, sustained vowel tasks produced lower performance and more diffuse model attention, likely due to their limited acoustic variability. These findings highlight the effectiveness of CNN-based analysis of dynamic speech features and emphasize the importance of task selection in speech-based digital diagnostics. Although the framework may inform future speech-based neurological assessment research, this study focuses on mild traumatic brain injury (concussion) as an initial use case.

A man in his 50s, with a history of cervical spondylosis, presented with acute right-sided tongue swelling, dysarthria and dysphagia. Although tongue deviation was present, unilateral tongue oedema led to initial suspicion of angio-oedema and malignancy; however, blood tests and tongue MRI did not lead to a definitive diagnosis. Tranexamic acid was prescribed for presumed angio-oedema; however, at follow-up, rightward tongue deviation persisted without any other neurological deficits. Localised tenderness was noted over the right occipital area, corresponding to the atlanto-occipital joint. Imaging revealed an osteophyte compressing the outlet of the right hypoglossal canal with joint fluid, raising suspicion of hypoglossal nerve palsy due to cervical spondylosis with possible involvement of calcium pyrophosphate deposition disease although this was not confirmed. This case highlights the importance of considering neurogenic causes of unilateral tongue swelling and recognising cervical spondylosis as a cause.

Two-stage models of grammatical encoding posit that sentence production unfolds in two sequential steps: the construction of a hierarchical syntactic structure, followed by its linearization into a sequence suitable for articulation. While widely accepted in psycho- and neurolinguistics, such models lack formal computational implementations. Here, we introduce a novel multinomial processing tree (MPT) model that operationalizes this two-stage framework to explain syntactic error patterns in individuals with aphasia. Drawing on discourse samples annotated for distinct error types, we fit an MPT model to estimate individual abilities at each processing stage. These ability estimates correlated with observed error rates and localized to distinct neural substrates: hierarchical encoding ability was linked to the posterior superior temporal sulcus and parietal cortex, linearization to posterior inferior frontal regions, and omission-related processes to more dorsal frontal regions and the underlying white matter. Our findings support a neurocomputational dissociation between hierarchical and linear stages of grammatical encoding, aligning with prior lesion-symptom mapping and theoretical accounts of expressive agrammatism and paragrammatism. This work represents the first computational instantiation of a two-stage model of syntactic production, bridging formal modeling and lesion analysis to advance our understanding of the architecture and breakdown of grammatical encoding.

This study assessed the ability of three acoustic measures of vowel space, two token-based and one trajectory-based, to quantify speech differences in people with Parkinson's disease compared to controls and correlations with speech intelligibility. Fifty-six speakers (28 people with Parkinson's and 28 controls) read a custom reading passage containing corner vowels. For token-based measures, the triangular vowel space area (tVSA) in kHz2 and the unitless ratio of vowel articulation index (VAI) were calculated. For the trajectory-based measure, the articulatory-acoustic vowel space (AAVS) in kHz2 was calculated. Speakers also read five unique sentences varying in word length, which were used to collect speech intelligibility ratings. Analyses of Variance revealed that all vowel space measures yielded group differences. Binary logistic regression revealed that only the token-based measures were able to statistically discriminate PwPD from controls, however all models performed poorly (R2 < 0.113). Linear regressions showed that token-based metrics were statistically related to intelligibility and that the VAI yielded the best model for the current sample. The results support that token-based measures are well-suited for quantifying changes in acoustic vowel space and perceived speech intelligibility in people with Parkinson's. Future work should examine the generalizability of these findings to other motor speech disorders as well as across different speech stimuli.

BackgroundDigital speech analysis affords robust markers of Parkinson's disease (PD). However, most studies target late-onset PD (LOPD), neglecting early-onset PD (EOPD) -an increasingly prevalent subtype. This proof-of-concept study tackles such gap.MethodsWe used machine learning to discriminate persons with EOPD (with symptom onset before age 50) and LOPD (with symptom onset after age 50) from healthy controls (HCs) through prosodic and articulatory features from natural speech.ResultsMaximal classification between patients and HCs was afforded by combined prosodic and articulation features in LOPD (AUC = 0.90) and by articulation alone in EOPD (AUC = 0.79), with chance-level discrimination between patient groups (AUC = 0.55). Motor severity (MDS-UPDRS-III) scores predicted by these features correlated with actual motor severity scores in both LOPD (r = 0.52, p < 0.001) and EOPD (r = 0.27, p < 0.001).ConclusionsDigital speech markers offer markers of PD irrespective of age of onset.

This study was performed to develop and validate the quality of life scale for postpalatoplasty and noncleft FAD (Functional Articulation Disorder) patients and to survey their quality of life (QoL) status. A mixed-methods scale development process yielded a 20-item instrument. Psychometric properties were evaluated in 128 children (41 postpalatoplasty FAD, 87 noncleft FAD). Speech clarity was assessed in a subset (n=38). The scale demonstrated excellent reliability (Cronbach α=0.963) and validity. Postpalatoplasty FAD patients reported significantly poorer QoL than noncleft FAD patients, with a greater impact on females. Parent-child agreement was lower on social perception items. Objective speech clarity was negatively correlated with QoL scores (P<0.05). This valid scale fills an important assessment gap. Results confirm the substantial psychosocial burden of FAD, indicating a need for targeted psychosocial support alongside speech therapy.

Speech production difficulties in autism spectrum disorder (ASD) are heterogeneous and are not uniformly characterized by articulatory impairment across the spectrum. However, some autistic subgroups, particularly minimally verbal children, children with low expressive-language ability, and those with suspected co-occurring speech-motor difficulties, may show atypical vowel-acoustic patterns. Because vowel production can be quantified through measures such as formant frequencies, vowel-space area, duration, and variability, these parameters may offer useful objective information for characterizing speech impairment and informing rehabilitation planning in selected phenotypes. This narrative review critically examined the literature on vowel-acoustic characteristics in autistic children and their possible relevance to rehabilitation-oriented systems. Structured searches of PubMed, Scopus, and Web of Science were conducted for studies published up to September 2025. Evidence suggests that some autistic subgroups may exhibit reduced vowel distinctiveness, greater acoustic variability, and atypical temporal or dynamic speech features, although findings are heterogeneous and do not support a single uniform acoustic profile across ASD. Direct intervention evidence remains limited. Most rehabilitation-related studies are small, exploratory investigations, and the available literature is concentrated largely around Auditory-Motor Mapping Training (AMMT), an indirectly relevant speech-motor intervention that reports vowel-related outcomes but does not directly target vowel-acoustic parameters in isolation.Overall, vowel-acoustic measures are better regarded as candidate quantitative tools for subgroup characterization, monitoring, and rehabilitation planning rather than as universal biomarkers across ASD. Their translational potential is promising but remains insufficiently validated, requiring stronger longitudinal, mechanistic, and intervention research before broad clinical application can be justified.