Single and Combined Neuroimaging Techniques for Alzheimer’s Disease Detection

Abstract

Alzheimer’s disease (AD) consists of the gradual process of decreasing volume and quality of neuron connection in the brain, which consists of gradual synaptic integrity and loss of cognitive functions. In recent years, there has been significant attention in AD classification and early detection with machine learning algorithms. There are different neuroimaging techniques for capturing data and using it for the classification task. Input data as images will help machine learning models to detect different biomarkers for AD classification. This marker has a more critical role for AD detection than other diseases because beta-amyloid can extract complex structures with some metal ions. Most researchers have focused on using 3D and 4D convolutional neural networks for AD classification due to reasonable amounts of data. Also, combination neuroimaging techniques like functional magnetic resonance imaging and positron emission tomography for AD detection have recently gathered much attention. However, gathering a combination of data can be expensive, complex, and tedious. For time consumption reasons, most patients prefer to throw one of the neuroimaging techniques. So, in this review article, we have surveyed different research studies with various neuroimaging techniques and ML methods to see the effect of using combined data as input. The result has shown that the use of the combination method would increase the accuracy of AD detection. Also, according to the sensitivity metrics from different machine learning methods, MRI and fMRI showed promising results.

Hemispheric Cortical, Cerebellar and Caudate Atrophy Associated to Cognitive Impairment in Metropolitan Mexico City Young Adults Exposed to Fine Particulate Matter Air Pollution

“…Lifelong exposures to PM 2.5 above the current USEPA standard are associated with a significant decrease in gray matter from higher order cortical regions, commonly associated with Alzheimer's, Parkinson's and FTD in 42.7 ± 9.3 years old, healthy, college educated, Metropolitan Mexico City residents [45][46][47][48][49][50][51][52][53][54][55][56]. Significant caudate nuclei and cerebellar gray and white matter atrophy were also documented [57][58][59][60][61][62][63][64][65][66][67][68].…”

A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson’s Disease: Past Studies and Future Perspectives

“…The diagnosis of brain disorders including PD, AD, and schizophrenia (SZ) is often greatly aided by multimodality neuroimaging data [ 184 ]. Various clinical investigations have described the reliable detection of PD using a combination of neuroimaging modalities, such as EEG-fMRI [ 185 , 186 , 187 , 188 ], MRI-PET [ 189 , 190 , 191 , 192 ], fMRI-MEG [ 193 , 194 ], and fMRI-sMRI [ 195 , 196 , 197 ]. Diagnosis of PD using multimodality neuroimaging data is complicated and time-consuming for doctors, despite all the advantages.…”

Multimodal magnetic resonance imaging for Alzheimer's disease diagnosis using hybrid features extraction and ensemble support vector machines

“…Studies based on ML and CNN using multimodal have shown their ability and effectiveness in alleviating the limitations of unimodal data use and thus improving the prediction models' accuracy in the diagnosis of the disease. 20,21 Most studies concentrated on combining DTImaps and sMRI in the screening of AD. For example, Marzban et al 22 used DTI-derived measurements (FA, MD, and mode of diffusivity [MO]), and GM segmented from T1-weighted sMRI.…”