Distinguishing Parkinson's disease (PD) from Progressive supranuclear palsy (PSP) at early disease stages is important for clinical trial enrollment and clinical care/prognostication. We recruited 21 participants with PSP(n = 11) or PD(n = 10) with reliable caregivers. Standardized passage reading, counting, and sustained phonation were recorded on the BioDigit Home tablet (BioSensics LLC, Newton, MA USA), and speech features from the assessments were analyzed using the BioDigit Speech platform (BioSensics LLC, Newton, MA USA). An independent t-test was performed to compare each speech feature between PSP and PD participants. We also performed Spearman's correlations to evaluate associations between speech measures and clinical scores (e.g., PSP rating scales and MoCA). In addition, the model's performance in classifying PSP and PD was evaluated using Rainbow passage reading analysis. During Rainbow passage reading, PSP participants had a significantly slower articulation rate (2.45(0.49) vs 3.60(0.47) words/minute), lower speech-to-pause ratio (2.33(1.08) vs 3.67(1.18)), intelligibility dynamic time warping (DTW, 0.26(0.19) vs 0.53(0.26)), and similarity DTW (0.43(0.27) vs 0.67(0.13)) compared to PD participants. PSP participants also had longer pause times (17.24(5.47) vs 8.45(3.13) sec) and longer total signal times (52.44(6.67) vs (36.67(6.73) sec) when reading the passage. In terms of the phonation 'a', PSP participants showed a significant higher spectral entropy, spectral centroid, and spectral spread compared to PD participants and no differences were found for phonation 'e'. PD participants had more accurate reverse number counts than PSP participants (14.89(3.86) vs 7.36(4.67)). PSP Rating Scale (PSPRS) dysarthria (r = 0.79, p = 0.004) and bulbar item scores (r = 0.803, p = 0.005) were positively correlated with articulation rate in reverse number counts. Correct reverse number counts were positively correlated with total Montreal Cognitive Assessment scores (r = 0.703, p = 0.016). Machine learning models using passage reading-derived measures obtained an AUC of 0.93, and the sensitivity/specificity in correctly classifying PSP and PD participants were 0.95 and 0.90, respectively. Our study demonstrates the feasibility of differentiating PSP from PD using a digital health technology platform. Further multi-center studies are needed to expand and validate our initial findings.

Shared emotional experiences during musical activities among musicians can be coupled with brainwave synchronization. For non-speaking individuals with CP, verbal communication may be limited in expressing mutual empathy. Therefore, this case study explored interbrain synchronization among a non-speaking CP (female, 18 yrs), her parent, and a music therapist by measuring their brainwaves simultaneously during four music and four storytelling sessions. In only the youth-parent dyad, we observed a significantly higher level of interbrain synchronization during music rather than story-telling condition. However, in both the youth-parent and youth-therapist dyad, regardless of condition type, significant interbrain synchronization emerged in frontal and temporal lobes in the low-frequency bands, which are associated with socio-emotional responses. Although interbrain synchronization may have been induced by multiple factors (e.g., external stimuli, shared empathetic experiences, and internal physiological rhythms), the music activity setting deserves further study as a potential facilitator of neurophysiological synchrony between youth with CP and caregivers/healthcare providers.

Playing musical instruments may have positive effects on motor, emotional, and cognitive deficits in patients with Parkinson’s disease (PD). This pilot study examined the feasibility of a six-week nontraditional guitar instruction program for individuals with PD. Twenty-six participants with idiopathic PD (Age: 67.22 ± 8.07; 17 males) were randomly assigned to two groups (intervention first or 6 weeks of usual care control exposure) with stepwise exposure to the guitar intervention condition with cross-over at six weeks. Outcomes were assessed at baseline, 6, 12, and 18 weeks. Twenty-four participants completed the study. Combined analysis of the groups showed significant BDI-II improvement immediately after intervention completion (3.04 points, 95% CI [−5.2, −0.9], ). PDQ-39 total quality of life scores improved from baseline to immediately postintervention 5.19 points (95% CI [−9.4, −1.0]) at trend significance (corrected ). For Group 1 (exposed to the intervention first), MDS-UPDRS total scores improved by a mean of 8.04 points (95% CI [−12.4, −3.7], ) and remained improved at 12 weeks by 10.37 points (95% CI [−14.7, −6.0], ). This group also had significant improvements in mood and depression at weeks 6 and 12, remaining significant at week 18 (BDI-II: 3.75, 95% CI [−5.8, −1.7], ; NeuroQoL-depression: 10.6, 95% CI [−4.9. −1.4], ), and in anxiety at week 6 and week 18 (NeuroQoL; 4.42, 95% CI [−6.8, −2.1], ; 3.58, 95% CI [−5.9, −1.2], , respectively). We found clinically and statistically significant improvements in mood/anxiety after 6 weeks of group guitar classes in individuals with PD. Group guitar classes can be a feasible intervention in PD and may improve mood, anxiety, and quality of life.

Speech impairment is a prevalent symptom of neurological disorders, including Parkinson’s disease (PD), Progressive Supranuclear Palsy (PSP), Huntington’s disease (HD), and Amyotrophic Lateral Sclerosis (ALS), with mechanisms and severity varying across and within conditions. Scalable digital health tools and machine learning (ML) are essential for diagnosing and tracking neurodegenerative disease. A total of 92 individuals were included in this study (21 PSP, 21 PD, 18 HD, 15 ALS, and 16 healthy elderly controls (CTR)). The Rainbow Passage was collected on a digital device and analyzed to extract 12 speech features representing speech production. A set of Elastic Net ML models was trained on these speech features to differentiate between diagnostic classes. A specialized Support Vector Machine ML model was then developed to differentiate PSP from PD. Elastic Net models achieved a balanced accuracy of 77% over 5 diagnostic classes (group-specific sensitivities of 76% for PSP, 67% for PD, 83% for HD, 73% for ALS, and 88% for CTR) and 83% over 4 diagnostic classes (group-specific sensitivities of 83% for PSP-PD, 83% for HD, 73% for ALS, and 94% for CTR). The PSP vs. PD classification model demonstrated a balanced accuracy of 85%, with sensitivity of 88% for PSP and 82% for PD. Key speech features differentiated clinical conditions, with Total Voiced Time being the strongest positive feature for combined PSP-PD. In HD, ALS, and CTR, Ratio Extra Words, Pauses per Second, and Intelligibility were the most strongly differentiating features, respectively. Articulatory Rate emerged as the most distinguishing feature between PD and PSP. Our findings highlight the potential of digital health technology and ML in identifying and monitoring speech features in neurodegenerative diseases.

Background/Objectives: Progressive supranuclear palsy (PSP) is an atypical Parkinsonian disorder characterized by Parkinsonism with gait imbalance, vertical gaze palsy, and frontal cognitive dysfunction. Though digital health technologies (DHTs) are widely used both clinically and in research as outcome measures, there is a lack of consistency applied to these devices and their resulting metrics. This scoping review aims to identify efforts taken to validate wearable DHTs for use in PSP, identify gaps in research, and discuss the steps needed to expand their use and acceptance as primary trial endpoints. Methods: In this scoping review, we conducted a search of the MEDLINE database to examine the use of DHTs as outcome measures in Progressive Supranuclear Palsy. Results: A total of 17 publications were identified and reviewed. Included articles evaluated the use of DHT to measure lower extremity function/gait, balance, upper extremity function, and speech. Conclusions: Our scoping review highlights the importance of standardization of DHT metrics by thorough assessment of their content validity, reliability, construct validity, responsiveness, and discriminant validity. Efforts must be taken to ensure DHTs capture clinically relevant, patient-centered outcome measures that are comparable to conventional rating scales, that consistently discriminate disease progression. Incorporation of DHTs as clinical trial endpoints has the potential to encourage clinical research and to advance patient care.

Background: Motor speech disorders are early, common, and functionally limiting features of atypical parkinsonian disorders (APDs) such as progressive supranuclear palsy (PSP), corticobasal syndrome (CBS), and multiple system atrophy (MSA). These impairments are underrecognized and undertreated in neurology clinics. Objectives: This review aims to characterize speech impairment in APDs, offer practical guidance for clinical evaluation, highlight the role of Speech‐Language pathologists (SLPs) in diagnosis and management, and outline current and emerging management strategies. Methods: A narrative review was conducted by the Diagnosis and Treatment Working Group of CurePSP's Centers of Care, integrating literature and clinical experience to summarize evaluation, diagnosis, and treatment of motor speech disorders in APDs. Results: Speech changes in APDs are often mixed dysarthrias with hypokinetic, spastic, and/or ataxic components, and may include apraxia of speech; these are frequently more severe and progress quicker than in Parkinson's disease. These features can assist in differential diagnosis and should prompt early referral to SLPs. Despite the high prevalence of speech and voice changes, comprehensive assessment of motor speech disorders is uncommon in neurology clinics. Current evidence regarding the efficacy of interventions is mixed. Digital acoustic analysis and neuromodulation offer promising directions for diagnosis and treatment. Conclusions: Early, collaborative management of motor speech impairment elevates care in APDs. Neurologists and SLPs must work together to improve recognition, diagnosis, and care. Future research should focus on objective biomarkers and personalized therapies to support communication, autonomy, and quality of life for individuals living with APDs.