Citations

Purpose: This study examined whether phoneme-level posterior probabilities generated by an articulatory-informed deep learning model (Phonet) can serve as interpretable and sensitive markers of articulatory impairment in motor speech disorders (MSDs). The goal was to determine whether these features distinguish individuals with and without MSDs and reflect varying severity levels of articulatory impairment. Method: A total of 676 participants (199 with MSDs, 477 without MSDs) completed a remotely captured word repetition task. Speech recordings were phonetically aligned, and posterior probabilities were extracted using a retrained version of the Phonet model, which estimates frame-level probabilities for 23 phonological classes. Segment-level posterior values were computed and compared across groups and severity levels using linear mixed-effects regression models. Results: Posterior probabilities were significantly reduced in the MSD group across all phoneme types. Within the MSD group, posterior probabilities decreased progressively with greater clinical severity, as measured by both perceptual sound distortion ratings and a cumulative articulation error score. Segment-specific analyses revealed systematic differences across phoneme classes, indicating that posterior probabilities capture distinct patterns of articulatory degradation associated with motor speech impairment. Conclusions: Phoneme-level posterior probabilities serve as interpretable, segment-specific indicators of articulatory impairment in MSDs. These features differentiate clinical status and reflect gradations of severity, making them promising tools for augmenting traditional perceptual speech assessments. Their linguistic transparency and scalability support future clinical applications in diagnosis, monitoring, and targeted intervention.

Abstract Purpose To evaluate whether two automated measures of nasality, Nasal Cognate Distinctiveness (NCD) and nasal posterior probability (NPP), provide clinically meaningful markers of hypernasality in speakers with motor speech disorders (MSDs) and to determine whether hypernasality-related information extends beyond stop consonants, which are typically targeted in cognate-based approaches. Method Sentence-level speech recordings were obtained from 561 patients, including 374 neurologically typical controls and 187 individuals with MSDs. NCD was computed for voiceless stops (/p/, /t/, /k/) using likelihood ratios between stops and their nasal cognates. NPP was extracted using a phonological feature-based neural network model and analyzed for stops, consonants, and vowels. Hypernasality severity was rated by speech-language pathologists. Group differences and associations with hypernasality severity were evaluated using linear mixed-effects models and Spearman correlations, and receiver operating characteristic (ROC) analyses were used to evaluate discrimination of absent versus present hypernasality. An additional 20 controls with repeat recordings were used to evaluate test-retest reliability and minimal detectable change (MDC). Results NCD generally decreased and NPP increased with increasing hypernasality severity, with phoneme-specific severity effects observed for NCD. Across stops, NCD and NPP were strongly inversely correlated and showed comparable discrimination of absent versus present hypernasality (AUCs = 0.796 and 0.730, respectively). Consonant and vowel NPP measures were both positively associated with hypernasality severity and demonstrated identical classification performance (AUC = 0.853). No significant differences in ROC performance were observed between NCD and NPP or between consonant and vowel NPP measures. Test-retest change exceeded the MDC threshold in 2 of 20 controls for NCD and 1 of 20 for NPP. Conclusions Both NCD and NPP provide objective markers of hypernasality in speakers with MSDs. The measures capture related aspects of nasalization and demonstrate comparable ability to identify clinically meaningful hypernasality. NPP captures hypernasality-related acoustic information across both consonants and vowels, with similar relationships to severity and classification performance across sound classes. Test-retest findings provide preliminary MDC benchmarks for interpreting longitudinal change.

Assessing balance is crucial in neurological rehabilitation, yet while wearable sensors enable real-world monitoring, identifying reliable digital biomarkers remains challenging. This study utilized a high-fidelity instrumented walkway to determine which gait parameters best predict balance impairment, providing robust targets for future wearable applications. We analyzed 49 steady-state gait metrics from 140 individuals with diverse neurological conditions. Using statistical analysis and machine learning, we evaluated these parameters against objective force plate sway scores and clinical fall-history labels. Group analysis identified 16 parameters significantly distinguishing fallers from non-fallers, and a neural network classified fallers with an area under the curve of 0.75. Across all analytical approaches, overall gait variability, e.g., Stride Width S.D. and the Gait Variability Index, emerged as a universal predictor of balance impairment and fall risk. Furthermore, while traditional linear models emphasized spatial postural control, machine learning classification uniquely identified inter-limb asymmetry as a premier driver of fall prediction. These findings indicate that instrumented gait analysis effectively identifies digital biomarkers for balance deficits. Isolating these specific metrics provides a clear blueprint for meaningful metrics required for continuous objective monitoring and future development of personalized, adaptive rehabilitation strategies.

Abstract Motor speech disorders (MSDs) are early markers of neurological disease, but expert perceptual analysis is rarely available outside specialized centers. Automated speech analysis offers a scalable alternative, yet prior studies have not systematically compared modeling approaches or assessed clinically relevant metrics in independent datasets. This study compared static acoustic features, articulatory informed Phonet features, and self-supervised pretrained models for binary and multi label MSD classification. We trained and evaluated models on 583 speech samples using speaker level splits. Baseline models included logistic regression and Gated Recurrent Units (GRUs) trained on eGeMAPS and MFCCs. We extracted three types of Phonet derived features and evaluated pretrained HuBERT and SSAST models in frozen, partially fine-tuned, and fully fine-tuned configurations. Binary classification distinguished MSDs from controls, while multi label classification identified six MSD subtypes. Models were assessed using validation AUC, and cut points were tested on two independent datasets. Pretrained and Phonet based models substantially outperformed static acoustic features. In binary classification, HuBERT achieved the highest AUC (0.95), while compact Phonet derived GRUs achieved comparable performance (up to 0.94). These models generalized well to independent datasets, maintaining high sensitivity (0.94) and specificity (0.97). In multi label classification, Phonet models achieved the highest macro average AUC (0.86), but threshold-based subtype performance declined on unseen data. Automated MSD detection is feasible and clinically promising. Binary classification generalized well, whereas multi label classification showed limited threshold stability across datasets.

Abstract This study examined the extent to which goodness of pronunciation (GoP) scores and phonological posterior probabilities capture perceptual ratings of speech severity in individuals with motor speech disorders (MSD). Speech recordings of the word catastrophe were obtained from 489 participants, including 333 neurologically typical controls and 156 individuals with MSD. GoP scores were derived using traditional acoustic features and self-supervised speech representations, including WavLM and XLS-R, across multiple modeling approaches, while phonological posterior probabilities were extracted using Phonet. Model performance was evaluated using Kendall’s rank correlations, regression, and receiver operating characteristic analyses against speech-language pathologists’ perceptual ratings of sound distortion and intelligibility. Both GoP and phonological posterior probabilities were significantly associated with perceptual ratings. Self-supervised speech representations substantially outperformed traditional acoustic features, with WavLM-based GoP using k-nearest neighbors achieving the strongest performance. Across correlation, regression, and classification analyses, GoP consistently outperformed phonological posterior probabilities for both sound distortion and intelligibility. Age and gender had minimal influence on model-derived measures or their relationships with perceptual ratings. These findings demonstrate the value of self-supervised GoP as an objective measure of speech impairment while highlighting the complementary role of phonological posterior probabilities in characterizing articulatory aspects of motor speech disorders.

2016 Background: Bevacizumab therapy (Bev) for recurrent glioblastoma (rGBM) has not demonstrated improved overall survival (OS) in randomized clinical trials. However, single-center and preliminary multi-center studies suggest that relative cerebral blood volume (rCBV) measured with dynamic susceptibility contrast MRI (DSC-MRI) at baseline or shortly after treatment initiation may predict response to Bev. The primary aim of ECOG-ACRIN EAF151 was to evaluate whether early binary change in rCBV, assessed 2–3 weeks after initiation of Bev-containing therapy, identifies patients with an OS benefit of ≥4 mos. Pre-specified secondary aims assessed baseline rCBV; post-hoc exploratory analyses examined early post-Rx rCBV. Methods: A prospective, phase II multi-center trial without randomization enrolled subjects with rGBM receiving their first Bev-containing therapy. Progression was determined by local sites (RANO criteria; MRI within 28 days of registration, ≥42 days since end of chemoradiation). Baseline (S0) and follow-up (S1) DSC-MRI were performed before the first Bev infusion (within 3 days), and 12-25 days post-infusion but before the second infusion. Anatomic MRI and DSC-MRI (double-dose Gadavist injection, full-dose preload) complied with recommended protocols. Mean normalized (nRCBV) and standardized (sRCBV) rCBV were extracted from contrast-enhancing tumor ROIs. A 2-sided logrank test (α=0.1) was used to test the primary aim (change in mean nRCBV from S0 to S1, ≥0 vs. <0). Cox regression with restricted cubic splines was used to assess the association between continuous rCBV markers and OS. Results: 146 subjects were accrued from 33 sites. 134 completed S0 and 118 completed both S0 and S1. After exclusion of uninterpretable scans, 107 were evaluable for change in rCBV, and 124 for baseline rCBV. There was no statistically significant difference in OS between subjects with binary increase (n=40; median OS 8.0 mos [90% CI 5.5–9.2]) vs. decrease (n=67; 7.9 mos [90% CI 6.3–10.3]) in mean nRCBV (p=0.67). Based on the spline fits, we observed a strong nonlinear association between OS and continuous nRCBV and sRCBV at both S0 and S1: low marker values are associated with lower risk, but beyond a threshold, higher values are not associated with greater risk. As an exploratory analysis, we estimated optimal cut points (nRCBV: S0=1.11, S1=1.16; sRCBV: S0=1.13, S1=0.99) and found that subjects whose S1 rCBV decreased below threshold had a median OS 2.7 mos (nRCBV, HR 0.61 [90% CI 0.41–0.88]) and 5.2 mos (sRCBV, HR 0.43 [90% CI 0.28–0.63]) longer than subjects whose rCBV either increased or did not decrease below threshold. Conclusions: EAF151 prospectively evaluated whether DSC-MRI markers predict OS in patients with rGBM treated with Bev. Although the change in these markers was not predictive, both baseline and early post-Rx markers were predictive of OS. Clinical trial information: NCT03115333 .

Background/Objectives: White matter (WM) tract detection is critical in the presurgical planning of tumor resection. However, standard-of-care imaging techniques including T1-weighted, T2-weighted, and Diffusion Tensor Imaging (DTI) often fail to identify WM tracts within edematous regions. In T1/T2-weighted imaging, edema increases extracellular water and reduces tissue contrast, and in diffusion-weighted imaging, edema elevates isotropic diffusion, reducing sensitivity to anisotropic diffusion along WM tracts. Advanced biophysical diffusion modeling techniques such as Neurite Orientation Dispersion and Density Imaging (NODDI) and the Standard Model (SM) address this limitation by compartmentalizing the diffusion signal into free-water, intra-neurite, and extra-neurite contributions. Here, we test if biophysical multi-compartment models can robustly identify WM tracts and recover tractography streamlines within edematous regions. Methods: In this study, we use multi-shell diffusion-weighted MRI data obtained from patients with meningiomas—a pathology allowing for isolation of the effects of edema without the confounding effects of tumor cell invasion. We compared FA from standard and free-water-corrected DTI, the orientation dispersion index (ODI) from NODDI, and P2 (a scalar descriptor of fiber orientation coherence) from the SM fODF in edematous and unaffected contralateral WM regions. As a proof of concept, we visually evaluated the tractography performance across models. Results: Our results show that (1 − ODI) and P2 values in edema remained close to within-subject contralateral measurements, contrasting with substantial reductions in FA and FW-FA. (1 − ODI) showed a small but statistically significant increase in edema (~8%, p = 0.02), while P2 was unchanged. Conclusions: These results highlight the potential of biophysical diffusion models for preoperative mapping in edema.

Abstract Background Meningiomas exhibit clinical heterogeneity. Radiotherapy (RT) remains the only adjuvant therapy, but tumor-control is variable, and biomarkers are limited. NRG/RTOG-0539 is the first prospective phase 2 trial to stratify meningioma patients for adjuvant RT based on clinical risk. Here, we apply modern molecular tools to this cohort and identify correlates of RT response. Methods Tumor tissue from 100 RTOG-0539 patients was profiled using DNA methylation arrays, RNA sequencing, and whole-exome sequencing. Recurrence scores, Molecular Groups, gene expression, and copy number alterations were compared across clinical groups and between RT responders and non-responders; non-response was defined as progression or death within 3 years. Results Modern grading criteria, including brain invasion, TERT mutation, CDKN2A/B deletion, and 1p/1q status, would reclassify 10% of tumors and alter treatment group assignment in 7%. Non-responders to RT exhibited more frequent 1p and 14q loss, and more copy number alterations. Transcriptomic and epigenetic profiling revealed immune-related signatures in responders and cell cycle–related pathways in non-responders, several of which overlapped with targets of vorinostat, a histone deacetylase inhibitor previously validated in aggressive meningioma models. The Proliferative Molecular Group was an independent predictor of post-RT recurrence in multivariable analysis, outperforming WHO grade. Conclusion Multi-omic analysis of the NRG/RTOG-0539 cohort shows that updated WHO grading criteria, incorporating molecular and cytogenetic features, improve risk stratification. However, molecular classification, particularly the Proliferative group, remains an independent and stronger predictor of RT response. These findings support integrating molecular biomarkers alongside modern grading frameworks to guide treatment and trial design in meningioma. Clinical Trial Information NCT00895622.

Abstract M.D.-Ph.D. programs in the United States have traditionally followed a “2- n -2” curricular model, with the graduate phase occurring between the pre-clerkship and clerkship portions of medical training. While well established, this format can limit trainee autonomy in shaping their physician-scientist training trajectories. In response, some programs have introduced a “3- n -1” model, allowing students to complete clerkships before beginning Ph.D. training. Our institution implemented multiple flexible curricula in 2017. Understanding why trainees choose one pathway is important as programs consider implementing more adaptable training structures. To investigate these factors, we surveyed M.D.-Ph.D. students at our institution, which offers multiple flexible curricular alternatives, about contributions to their curricular decisions. Responses supported that trainees weigh considerations across medical education, scientific development, and integrated physician-scientist training domains. Although the traditional pathway was a popular option, most students pursued one of the flexible pathways. Our findings suggest that introducing flexibility in the timing of undergraduate medical education and graduate training can support diverse educational, logistical, and personal priorities while maintaining the rigor of physician-scientist training. Offering multiple pathways empowers trainees to design trajectories that best fit their needs. Continued longitudinal studies are needed to assess the long-term impacts of flexible curricula on physician-scientist career outcomes.