Brain volumetric correlates of dysarthria in multiple sclerosis

Rusz, Ján; Vaněčková, Manuela; Beňová, Barbora; Tykalová, Tereza; Novotný, Michal; Růžičková, Hana; Uher, Tomáš; Andělová, Michaela; Novotná, Klára; Friedová, Lucie; Motýl, Jiří; Kucerova, Karolina; Krásenský, Jan; Horáková, Dana

doi:10.1016/j.bandl.2019.04.009

Cited by 19 publications

(34 citation statements)

References 33 publications

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“…The proportion of pwMS with overt dysarthria was smaller in comparison to previous reports [1,2] (35% vs. 50%), which is likely due to differences in disability level of included participants (median EDSS of 2.5 in our study versus 6 in Hartelius et al [1]). In agreement with previous studies [3,15], pwMS presented primarily with slower speech rate, prolonged pauses and, to a minor degree, impairment of articulatory accuracy (consonants and vowels) and loss of speech intensity (monoloudness and loudness decay). Such impairments were mostly evident in the EDSS ≥ 6 subgroup.…”

Section: Discussionsupporting

confidence: 92%

“…Acoustic metrics of frequency instability and energy variability were associated with MRI whole brain volume in agreement with Rusz et al (termed frequency variability and excessive loudness variation in that study) [15]. Although we also found age, disability status, MRI acquisition and MRI feature extraction very similar between studies, we did not find an association between whole brain volumes and acoustic speech rate.…”

Section: Discussionsupporting

confidence: 91%

“…Methodologically, Rusz et al used the average of two trials for acoustic analysis, whereas we used the second of two trials. Nonetheless, it is unlikely that this difference alone could explain the differences in results between studies given the short-term reliability of these measurements [15,31], and further investigation is needed.…”

Section: Discussionmentioning

confidence: 99%

“…Through objective measurement of speech, acoustic analysis can inform on change of neurological function. The relationship between specific acoustic metrics and neurological dysfunction [17][18][19] or brain volumetrics [15,19] was recently described in MS. The primary aim of the current study was to describe the relationship between speech measurements and general neurological impairment, brain volume, brain lesion load and quality of life in MS in a single cohort.…”

Section: Introductionmentioning

confidence: 99%

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Speech metrics, general disability, brain imaging and quality of life in multiple sclerosis

Noffs

Boonstra

Perera

et al. 2020

Euro J of Neurology

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Background and purpose: Objective measurement of speech has shown promising results to monitor disease state in multiple sclerosis. In this study, we characterize the relationship between disease severity and speech metrics through perceptual (listener based) and objective acoustic analysis. We further look at deviations of acoustic metrics in people with no perceivable dysarthria. Methods: Correlations and regression were calculated between speech measurements and disability scores, brain volume, lesion load and quality of life. Speech measurements were further compared between three subgroups of increasing overall neurological disability: mild (as rated by the Expanded Disability Status Scale ≤2.5), moderate (≥3 and ≤5.5) and severe (≥6). Results: Clinical speech impairment occurred majorly in people with severe disability. An experimental acoustic composite score differentiated mild from moderate (P < 0.001) and moderate from severe subgroups (P = 0.003), and correlated with overall neurological disability (r = 0.6, P < 0.001), quality of life (r = 0.5, P < 0.001), white matter volume (r = 0.3, P = 0.007) and lesion load (r = 0.3, P = 0.008). Acoustic metrics also correlated with disability scores in people with no perceivable dysarthria. Conclusions: Acoustic analysis offers a valuable insight into the development of speech impairment in multiple sclerosis. These results highlight the potential of automated analysis of speech to assist in monitoring disease progression and treatment response.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Speech metrics, general disability, brain imaging and quality of life in multiple sclerosis

Noffs

Boonstra

Perera

et al. 2020

Euro J of Neurology

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“…Rusz et al 22 studied the correlation of dysarthria with brain volume. They found that abnormal diadochokinesis was associated with the loss of cerebellar white matter and gray matter and that a slow articulation rate was associated with bilateral white and gray matter.…”

Section: Current Developments and Future Perspectivesmentioning

confidence: 99%

Update on Recent Developments in Communication and Swallowing in Multiple Sclerosis

Biagi¹,

Heikkola²,

Nordio³

et al. 2020

International Journal of MS Care

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Swallowing and communication disorders are common in persons with multiple sclerosis (MS). Both disorders are extremely variable and can have a major effect on health status and quality of life. This is why it is important to provide health care professionals who are working with persons with MS with tools to signal, assess, and treat swallowing and communication disorders. This synthesis gives an update on relevant and recent literature on swallowing and communication disorders, supplemented with current practice-based evidence. Studies on swallowing and communication disorders in MS are scarce: more and higher-quality research is needed. It should be emphasized that therapists need to focus on the patient’s acquisition of skills to participate in daily life. This means that each patient requires an individual approach based on their own needs.

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Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set

Marzi

d’Ambrosio

Diciotti

et al. 2022

Human Brain Mapping

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Many patients with multiple sclerosis (MS) experience information processing speed (IPS) deficits, and the Symbol Digit Modalities Test (SDMT) has been recommended as a valid screening test. Magnetic resonance imaging (MRI) has markedly improved the understanding of the mechanisms associated with cognitive deficits in MS. However, which structural MRI markers are the most closely related to cognitive performance is still unclear. We used the multicenter 3T‐MRI data set of the Italian Neuroimaging Network Initiative to extract multimodal data (i.e., demographic, clinical, neuropsychological, and structural MRIs) of 540 MS patients. We aimed to assess, through machine learning techniques, the contribution of brain MRI structural volumes in the prediction of IPS deficits when combined with demographic and clinical features. We trained and tested the eXtreme Gradient Boosting (XGBoost) model following a rigorous validation scheme to obtain reliable generalization performance. We carried out a classification and a regression task based on SDMT scores feeding each model with different combinations of features. For the classification task, the model trained with thalamus, cortical gray matter, hippocampus, and lesions volumes achieved an area under the receiver operating characteristic curve of 0.74. For the regression task, the model trained with cortical gray matter and thalamus volumes, EDSS, nucleus accumbens, lesions, and putamen volumes, and age reached a mean absolute error of 0.95. In conclusion, our results confirmed that damage to cortical gray matter and relevant deep and archaic gray matter structures, such as the thalamus and hippocampus, is among the most relevant predictors of cognitive performance in MS.

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Brain volumetric correlates of dysarthria in multiple sclerosis

Cited by 19 publications

References 33 publications

Speech metrics, general disability, brain imaging and quality of life in multiple sclerosis

Speech metrics, general disability, brain imaging and quality of life in multiple sclerosis

Update on Recent Developments in Communication and Swallowing in Multiple Sclerosis

Prediction of the information processing speed performance in multiple sclerosis using a machine learning approach in a large multicenter magnetic resonance imaging data set

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