Diagnostic power of resting‐state fMRI for detection of network connectivity in Alzheimer's disease and mild cognitive impairment: A systematic review

• Published in: Human brain mapping Vol. 42; no. 9; pp. 2941 - 2968
• Main Authors: Ibrahim, Buhari, Suppiah, Subapriya, Ibrahim, Normala, Mohamad, Mazlyfarina, Hassan, Hasyma Abu, Nasser, Nisha Syed, Saripan, M Iqbal
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.06.2021
• Subjects: Abnormalities; accuracy; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Blood flow; Cerebral blood flow; classifiers; Cognitive ability; Cognitive Dysfunction - diagnostic imaging; Connectome - standards; Cortex (cingulate); Cortex (parietal); default mode network; Diagnostic systems; Functional magnetic resonance imaging; functional MRI; Homogeneity; Humans; Impairment; Learning algorithms; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - standards; Neural networks; Neurodegenerative diseases; Nodes; Predictive Value of Tests; Review; Substantia grisea; Support vector machines; Systematic review; classifiers; functional MRI; accuracy; default mode network; machine learning; Alzheimer's disease
• ISSN: 1065-9471; 1097-0193
• DOI: 10.1002/hbm.25369

Resting‐state fMRI (rs‐fMRI) detects functional connectivity (FC) abnormalities that occur in the brains of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). FC of the default mode network (DMN) is commonly impaired in AD and MCI. We conducted a systematic review aimed at determining the diagnostic power of rs‐fMRI to identify FC abnormalities in the DMN of patients with AD or MCI compared with healthy controls (HCs) using machine learning (ML) methods. Multimodal support vector machine (SVM) algorithm was the commonest form of ML method utilized. Multiple kernel approach can be utilized to aid in the classification by incorporating various discriminating features, such as FC graphs based on “nodes” and “edges” together with structural MRI‐based regional cortical thickness and gray matter volume. Other multimodal features include neuropsychiatric testing scores, DTI features, and regional cerebral blood flow. Among AD patients, the posterior cingulate cortex (PCC)/Precuneus was noted to be a highly affected hub of the DMN that demonstrated overall reduced FC. Whereas reduced DMN FC between the PCC and anterior cingulate cortex (ACC) was observed in MCI patients. Evidence indicates that the nodes of the DMN can offer moderate to high diagnostic power to distinguish AD and MCI patients. Nevertheless, various concerns over the homogeneity of data based on patient selection, scanner effects, and the variable usage of classifiers and algorithms pose a challenge for ML‐based image interpretation of rs‐fMRI datasets to become a mainstream option for diagnosing AD and predicting the conversion of HC/MCI to AD. Resting state fMRI (rs‐fMRI) can aid in the early detection of Alzheimer's disease (AD) and MCI. Deep machine learning (ML) methods can provide a platform for the assessment of the diagnostic accuracy of rs‐fMRI in AD. Much work is yet to be done to achieve homogeneity of the studies before ML can become a mainstream algorithm utilized in clinical practice.