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Cortical microstructure in primary progressive aphasia: a multicenter study.
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In: Alzheimer's research & therapy, vol 14, iss 1 (2022)
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BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.
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BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.
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Deformation-based shape analysis of the hippocampus in the semantic variant of primary progressive aphasia and Alzheimer's disease.
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Study on structural atrophy changes and functional connectivity measures in Alzheimer’s disease
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In: J Med Imaging (Bellingham) (2020)
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Rates of Amyloid Imaging Positivity in Patients With Primary Progressive Aphasia.
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In: JAMA neurology, vol 75, iss 3 (2018)
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Longitudinal white matter change in frontotemporal dementia subtypes and sporadic late onset Alzheimer's disease.
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Longitudinal white matter change in frontotemporal dementia subtypes and sporadic late onset Alzheimer's disease.
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Effects of HIV and childhood trauma on brain morphometry and neurocognitive function.
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In: Journal of neurovirology, vol 22, iss 2 (2016)
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Multivariate pattern classification of pediatric Tourette syndrome using functional connectivity MRI.
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In: Developmental science, vol 19, iss 4 (2016)
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Abstract:
Tourette syndrome (TS) is a developmental neuropsychiatric disorder characterized by motor and vocal tics. Individuals with TS would benefit greatly from advances in prediction of symptom timecourse and treatment effectiveness. As a first step, we applied a multivariate method - support vector machine (SVM) classification - to test whether patterns in brain network activity, measured with resting state functional connectivity (RSFC) MRI, could predict diagnostic group membership for individuals. RSFC data from 42 children with TS (8-15yrs) and 42 unaffected controls (age, IQ, in-scanner movement matched) were included. While univariate tests identified no significant group differences, SVM classified group membership with ~70% accuracy (p<.001). We also report a novel adaptation of SVM binary classification that, in addition to an overall accuracy rate for the SVM, provides a confidence measure for the accurate classification of each individual. Our results support the contention that multivariate methods can better capture the complexity of some brain disorders, and hold promise for predicting prognosis and treatment outcome for individuals with TS.
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Keyword:
4.1 Discovery and preclinical testing of markers and technologies; 4.2 Evaluation of markers and technologies; Adolescent; Biomedical Imaging; Brain Disorders; Case-Control Studies; Child; Clinical Research; Cognitive Sciences; Developmental & Child Psychology; Female; Humans; Linguistics; Magnetic Resonance Imaging; Male; Models; Multivariate Analysis; Nerve Net; Neurodegenerative; Neurological; Neurosciences; Pediatric; Prognosis; Psychology; Rare Diseases; Support Vector Machine; Tourette Syndrome
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URL: https://escholarship.org/uc/item/8wr2h7pn
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Modality prediction of biomedical literature images using multimodal feature representation ... : Klassifikation von Bildern der biomedizinischen Literatur unter Anwendung multimodaler Merkmale ...
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Atrophy patterns in early clinical stages across distinct phenotypes of Alzheimer's disease.
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In: Human brain mapping, vol 36, iss 11 (2015)
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Silent Spatialized Communication Among Dispersed Forces
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In: DTIC (2015)
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High-resolution dynamic speech imaging with joint low-rank and sparsity constraints
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Parallel ICA of FDG-PET and PiB-PET in three conditions with underlying Alzheimer's pathology.
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Parallel ICA of FDG-PET and PiB-PET in three conditions with underlying Alzheimer's pathology.
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Neuroimaging correlates of handwriting quality as children learn to read and write.
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