Combined Use of MRI, fMRIand Cognitive Data for Alzheimer’s Disease: Preliminary Results

Abstract

MRI can favor clinical diagnosis providing morphological and functional information of several neurological disorders. This paper deals with the problem of exploiting both data, in a combined way, to develop a tool able to support clinicians in the study and diagnosis of Alzheimer’s Disease (AD). In this work, 69 subjects from the ADNI open database, 33 AD patients and 36 healthy controls, were analyzed. The possible existence of a relationship between brain structure modifications and altered functions between patients and healthy controls was investigated performing a correlation analysis on brain volume, calculated from the MRI image, the clustering coefficient, derived from fRMI acquisitions, and the Mini Mental Score Examination (MMSE). A statistically-significant correlation was found only in four ROIs after Bonferroni’s correction. The correlation analysis alone was still not sufficient to provide a reliable and powerful clinical tool in AD diagnosis however. Therefore, a machine learning strategy was studied by training a set of support vector machine classifiers comparing different features. The use of a unimodal approach led to unsatisfactory results, whereas the multimodal approach, i.e., the synergistic combination of MRI, fMRI, and MMSE features, resulted in an accuracy of 95.65%, a specificity of 97.22%, and a sensibility of 93.93%.

Crosstalk between Depression and Dementia with Resting-State fMRI Studies and Its Relationship with Cognitive Functioning

“…Recent studies using directed graph theory or combining multiple imaging tools have presented promising results in the field of diagnosis and prediction [ 227 , 228 ]. Therefore, future studies combining multimodal imaging tools such as PET, MRI, and fMRI in AD and LLD patients samples with special considerations such as age, sex, age of onset, treatment outcomes, the severity of illness, and cognitive impairment would help us understand the fundamental functional pathological changes in AD and LLD.…”

Artificial Intelligence in Colorectal Cancer Diagnosis Using Clinical Data: Non-Invasive Approach

“…Unsupervised learning suggests training a model on unlabeled dataset. Clustering is the best-known unsupervised learning technique, which implies the machine finding hidden patterns in a dataset and creating clusters based on these shared characteristics [ 7 , 24 , 25 , 26 , 27 ]. The use of unsupervised machine learning in medical applications is an emerging field.…”

Early Detection of Alzheimer’s Disease: Detecting Asymmetries with a Return Random Walk Link Predictor

“…Our goal is to compare normal subjects with those suffering from early mild cognitive impairment (Normal vs. EMCI). The most common strategy in the literature is to mix early and late phase (EMCI+LMCI cognitive impairment data) into a unique group (MCI) [ 39 , 40 , 41 , 42 , 43 ]. This is because the objective of this work is the early detection of the disease and determining which regions of the brain are involved in this phase.…”