Machine Learning Methods for Structural Brain MRIs: Applications for Alzheimer’s Disease and Autism Spectrum Disorder
Research output: Book/Report › Doctoral thesis › Collection of Articles
|Publisher||Tampere University of Technology|
|Number of pages||56|
|Publication status||Published - 26 May 2017|
|Publication type||G5 Doctoral dissertation (article)|
|Name||Tampere University of Technology. Publication|
In the current study, we aim to construct effective biomarkers with sufficient sensitivity and speciﬁcity that can help physicians better understand the diseases and make improved diagnoses or treatment choices. The main contributions are 1) development of a novel biomarker for predicting Alzheimer’s disease in mild cognitive impairment patients by integrating structural MRI data and neuropsychological test results and 2) the development of a new computational approach for predicting disease severity in autistic patients in agglomerative data by automatically combining structural information obtained from different brain regions.
In addition, we investigate various data-driven feature selection and classiﬁcation methods for whole brain, voxel-based classiﬁcation analysis of structural MRI and the use of semi-supervised learning approaches to predict Alzheimer’s disease. We also analyze the relationship between disease-related structural changes and cognitive states of patients with Alzheimer’s disease.
The positive results of this effort provide insights into how to construct better biomarkers based on multisource data analysis of patient and healthy cohorts that may enable early diagnosis of brain disorders, detection of brain abnormalities and understanding effective processing in patient and healthy groups. Further, the methodologies and basic principles presented in this thesis are not only suited to the studied cases, but also are applicable to other similar problems.