Characterization of various type of cognitive impairment using multiomics – a pilot imaging study from a local institution
Eva YW Cheung, Henry Mak, Anson CM Chau, Patrick Ka‐Chun Chiu, Yat Fung Shea, Joseph SK Kwan- Psychiatry and Mental health
- Cellular and Molecular Neuroscience
- Geriatrics and Gerontology
- Neurology (clinical)
- Developmental Neuroscience
- Health Policy
- Epidemiology
Abstract
Background
In addition to clinical diagnosis, multi‐modal imaging has been employed for early detection of various types of dementia. Image features from various types of images provided information for accurate diagnosis. This study aimed to evaluate the role of multiple imaging, including MRI T1W, T2W, resting state functional MRI (rs‐fMRI) and 18‐F Flutemetamol PET image in diagnosis of various types of cognitive impairments.
Methods
56 participants (16 Alzheimer’s Disease (AD), 10 AD type Mild Cognitive impairment (A‐MCI), 18 vascular type MCI (V‐MCI), 12 Vascular dementia (VD) and 25 Healthy Control (HC) were recruited in an local memory clinic. All types of images were re‐orientated, segmentation and normalized to the whole brain mask template. 45 brain regional volumes (Vol) were calculated from T1w using Freesurfer. Brain regional white matter hyperintensities (WMH) were obtained from T2w images. Interhemispheric functional connectivity (IFC) of brain regions were measured from rs‐fMRI. Standard uptake value (SUV) were measured and calculated from the PET images. Also, patient demographics including age, sex and HK‐MoCa score (Demo) were recorded. 70% of each group of participants were assigned to the training group, whereas the rest 30% were assigned to test group. The neural network toolbox of MATLAB was employed to build four characterization models: Model 1 based Vol and WMH; Model 2 based on Vol, WMH and IFC; Model 3 based on Vol, WMH, IFC, and demo; Model 4 based on Vol, WMH, IFC, demo and SUV.
Results
The receiver operating characteristics (ROC) curves showed that the Model 1, 2 and 3 achieved satisfactory accuracy, with 79%, 68% and 70% respectively. While Model 4 achieved excellent accuracy of 100%. The sensitivity and specificity of each model for each group was over 75% to 100%. The model with more image features attained higher accuracy in dementia diagnosis.
Conclusion
Artificial neural network model with multiomics can accurately differentiate AD, A‐MCI, V‐MCI, VD and HC. Further study is suggested with more participants for models verification.