scholarly journals Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach

2016 ◽  
Vol 37 (4) ◽  
pp. 1443-1458 ◽  
Author(s):  
Zaixu Cui ◽  
Zhichao Xia ◽  
Mengmeng Su ◽  
Hua Shu ◽  
Gaolang Gong
Keyword(s):  
Machine Learning ◽  
White Matter ◽  
Developmental Dyslexia ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
White Matter Connectivity

scholarly journals Multi-parameter machine learning approach to the neuroanatomical basis of developmental dyslexia

2016 ◽  
Vol 38 (2) ◽  
pp. 900-908 ◽  
Author(s):  
Piotr Płoński ◽  
Wojciech Gradkowski ◽  
Irene Altarelli ◽  
Karla Monzalvo ◽  
Muna van Ermingen-Marbach ◽  
...  
Keyword(s):  
Machine Learning ◽  
Developmental Dyslexia ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Associated factors of white matter hyperintensity volume: a machine-learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sergio Grosu ◽  
Susanne Rospleszcz ◽  
Felix Hartmann ◽  
Mohamad Habes ◽  
Fabian Bamberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
White Matter ◽  
Social Environment ◽  
White Matter Hyperintensity ◽  
Cerebral White Matter ◽  
Learning Approach ◽  
Male Age ◽  
Independent Population ◽  
The Social ◽  
Machine Learning Approach

AbstractTo identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we used a data-driven machine-learning approach. We analysed two independent cohorts (KORA and SHIP). WMH volumes were derived from cMRI-images (FLAIR). 90 (KORA) and 34 (SHIP) potential determinants of WMH including measures of diabetes, blood-pressure, medication-intake, sociodemographics, life-style factors, somatic/depressive-symptoms and sleep were collected. Elastic net regression was used to identify relevant predictor covariates associated with WMH volume. The ten most frequently selected variables in KORA were subsequently examined for robustness in SHIP. The final KORA sample consisted of 370 participants (58% male; age 55.7 ± 9.1 years), the SHIP sample comprised 854 participants (38% male; age 53.9 ± 9.3 years). The most often selected and highly replicable parameters associated with WMH volume were in descending order age, hypertension, components of the social environment (i.e. widowed, living alone) and prediabetes. A systematic machine-learning based analysis of two independent, population-based cohorts showed, that besides age and hypertension, prediabetes and components of the social environment might play important roles in the development of WMH. Our results enable personal risk assessment for the development of WMH and inform prevention strategies tailored to the individual patient.

scholarly journals Alterations of white matter abnormalities in Parkinson’s disease: a machine learning approach

2021 ◽  
Author(s):  
long qian ◽  
chaoyong xiao ◽  
Sidong Liu ◽  
zaixu cui ◽  
xiao hu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
White Matter ◽  
Rating Scale ◽  
Mean Diffusivity ◽  
Learning Approach ◽  
Healthy Controls ◽  
White Matter Tracts ◽  
Machine Learning Approach

Abstract The inter-tract/region dependencies of white-matter in Parkinson’s disease are usually ignored by standard statistical tests. Moreover, it remains unclear whether the disruption of white-matter tracts/regions suffices to identify Parkinson’s disease patients from healthy controls. A machine learning approach was applied to capture the interdependencies between white-matter tracts/regions and to differentiate PD patients from healthy controls. First, the mean regional white-matter profiles, including white-matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity, were extracted as features in Parkinson’s disease patients (N = 78) and in healthy controls (N = 91). Then, the feature selection and classification were performed using t-test and linear support vector machine, respectively. Last, the relationships between clinical variables and regional magnetic resonance indices were estimated. Our results showed the combined features (white-matter volume, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity) had the best performance with an accuracy of 75.15% and area under curve of 0.8171, respectively. The most discriminative white-matter features were centered on the association fibers, commissural fibers, projection fibers, and striatal fibers. The discriminative regions of right anterior limb of internal capsule had positive association trends with the Unified Parkinson Disease Rating Scale III score; while the genu of corpus callosum and right retrolenticular part of internal capsule had positively association trends with the Hamilton Depression Rating Scale score. Our finding showed the multivariate machine learning approach is a promising tool to detect abnormal white-matter tracts/regions in Parkinson’s disease, and provides us a multidimensional means for neuroimaging classification.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use
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