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1	Multimodal Neural and Behavioral Data Predict Response to Rehabilitation in Chronic Poststroke Aphasia
	Billot, Anne; Lai, Sha; Varkanitsa, Maria; Braun, Emily J.; Rapp, Brenda; Parrish, Todd B.; Higgins, James; Kurani, Ajay S.; Caplan, David; Thompson, Cynthia K.; Ishwar, Prakash; Betke, Margrit; Kiran, Swathi
	In: Stroke (2022)
	Abstract: Poststroke recovery depends on multiple factors and varies greatly across individuals. Using machine learning models, this study investigated the independent and complementary prognostic role of different patient-related factors in predicting response to language rehabilitation after a stroke. METHODS: Fifty-five individuals with chronic poststroke aphasia underwent a battery of standardized assessments and structural and functional magnetic resonance imaging scans, and received 12 weeks of language treatment. Support vector machine and random forest models were constructed to predict responsiveness to treatment using pretreatment behavioral, demographic, and structural and functional neuroimaging data. RESULTS: The best prediction performance was achieved by a support vector machine model trained on aphasia severity, demographics, measures of anatomic integrity and resting-state functional connectivity (F1=0.94). This model resulted in a significantly superior prediction performance compared with support vector machine models trained on all feature sets (F1=0.82, P<0.001) or a single feature set (F1 range=0.68–0.84, P<0.001). Across random forest models, training on resting-state functional magnetic resonance imaging connectivity data yielded the best F1 score (F1=0.87). CONCLUSIONS: While behavioral, multimodal neuroimaging data and demographic information carry complementary information in predicting response to rehabilitation in chronic poststroke aphasia, functional connectivity of the brain at rest after stroke is a particularly important predictor of responsiveness to treatment, both alone and combined with other patient-related factors.
	Keyword: Original Contributions
	URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9022691/ http://www.ncbi.nlm.nih.gov/pubmed/35078348 https://doi.org/10.1161/STROKEAHA.121.036749
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2	Relationships Between Anxiety Symptoms, Hopelessness and Suicidal Ideation Among Parental Caregivers of Mandarin-Speaking Children With Speech Impairment: The Mediating Effect of Depressive Symptoms
	MA, Si-Wei; LAI, Sha; YANG, Yan-Yan...
	In: ISSN: 1664-0640 ; Frontiers in Psychiatry ; https://hal.archives-ouvertes.fr/hal-03323043 ; Frontiers in Psychiatry, Frontiers, 2021, 12, pp.648885. ⟨10.3389/fpsyt.2021.648885⟩ (2021)
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3	W9-2 - An Exploration of Machine Learning Methods for Predicting Post-stroke Aphasia Recovery ...
	PErvasive Technologies Related to Assistive Environments 2021; Lai, Sha. - : Underline Science Inc., 2021
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4	Relationships Between Anxiety Symptoms, Hopelessness and Suicidal Ideation Among Parental Caregivers of Mandarin-Speaking Children With Speech Impairment: The Mediating Effect of Depressive Symptoms
	Ma, Si-Wei; Lai, Sha; Yang, Yan-Yan...
	In: Front Psychiatry (2021)
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