Combining Structural Magnetic Resonance Imaging and Visuospatial Tests to Classify Mild Cognitive Impairment

• Published in: Current Alzheimer research Vol. 15; no. 3; p. 237
• Main Authors: Fasano, Fabrizio, Mitolo, Micaela, Gardini, Simona, Venneri, Annalena, Caffarra, Paolo, Pazzaglia, Francesca
• Format: Journal Article
• Language: English
• Published: United Arab Emirates 01.01.2018
• Subjects: Age Factors; Aged; Brain - diagnostic imaging; Cognitive Dysfunction - classification; Cognitive Dysfunction - diagnostic imaging; Cognitive Dysfunction - psychology; Female; Humans; Image Interpretation, Computer-Assisted; Magnetic Resonance Imaging; Male; Mental Status Schedule; Middle Aged; Neuropsychological Tests; Photic Stimulation; Recognition (Psychology); Space Perception - physiology; magnetic resonance imaging; mild cognitive impairment; support vector machine; classification; Visuospatial memory; spatial abilities
• ISSN: 1875-5828
• DOI: 10.2174/1567205014666171030112339

Recently, efforts have been made to combine complementary perspectives in the assessment of Alzheimer type dementia. Of particular interest is the definition of the fingerprints of an early stage of the disease known as Mild Cognitive Impairment or prodromal Alzheimer's Disease. Machine learning approaches have been shown to be extremely suitable for the implementation of such a combination. In the present pilot study we combined the machine learning approach with structural magnetic resonance imaging and cognitive test assessments to classify a small cohort of 11 healthy participants and 11 patients experiencing Mild Cognitive Impairment. Cognitive assessment included a battery of standardised tests and a battery of experimental visuospatial memory tests. Correct classification was achieved in 100% of the participants, suggesting that the combination of neuroimaging with more complex cognitive tests is suitable for early detection of Alzheimer Disease. In particular, the results highlighted the importance of the experimental visuospatial memory test battery in the efficiency of classification, suggesting that the high-level brain computational framework underpinning the participant's performance in these ecological tests may represent a "natural filter" in the exploration of cognitive patterns of information able to identify early signs of the disease.