Your browser doesn't support javascript.
loading
Identification of amnestic mild cognitive impairment by structural and functional MRI using a machine-learning approach.
Hwang, Hyunyoung; Kim, Si Eun; Lee, Ho-Joon; Lee, Dong Ah; Park, Kang Min.
Affiliation
  • Hwang H; Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
  • Kim SE; Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
  • Lee HJ; Department of Radiology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
  • Lee DA; Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
  • Park KM; Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea. Electronic address: smilepkm@hanmail.net.
Clin Neurol Neurosurg ; 238: 108177, 2024 03.
Article in En | MEDLINE | ID: mdl-38402707
ABSTRACT

OBJECTIVE:

The importance of early treatment for mild cognitive impairment (MCI) has been extensively shown. However, classifying patients presenting with memory complaints in clinical practice as having MCI vs normal results is difficult. Herein, we assessed the feasibility of applying a machine learning approach based on structural volumes and functional connectomic profiles to classify the cognitive levels of cognitively unimpaired (CU) and amnestic MCI (aMCI) groups. We further applied the same method to distinguish aMCI patients with a single memory impairment from those with multiple memory impairments.

METHODS:

Fifty patients with aMCI were enrolled and classified as having either verbal or visual-aMCI (verbal or visual memory impairment), or both aMCI (verbal and visual memory impairments) based on memory test results. In addition, 26 CU patients were enrolled in the control group. All patients underwent structural T1-weighted magnetic resonance imaging (MRI) and resting-state functional MRI. We obtained structural volumes and functional connectomic profiles from structural and functional MRI, respectively, using graph theory. A support vector machine (SVM) algorithm was employed, and k-fold cross-validation was performed to discriminate between groups.

RESULTS:

The SVM classifier based on structural volumes revealed an accuracy of 88.9% at classifying the cognitive levels of patients with CU and aMCI. However, when the structural volumes and functional connectomic profiles were combined, the accuracy increased to 92.9%. In the classification of verbal or visual-aMCI (n = 22) versus both aMCI (n = 28), the SVM classifier based on structural volumes revealed a low accuracy of 36.7%. However, when the structural volumes and functional connectomic profiles were combined, the accuracy increased to 53.1%.

CONCLUSION:

Structural volumes and functional connectomic profiles obtained using a machine learning approach can be used to classify cognitive levels to distinguish between aMCI and CU patients. In addition, combining the functional connectomic profiles with structural volumes results in a better classification performance than the use of structural volumes alone for identifying both "aMCI versus CU" and "verbal- or visual-aMCI versus both aMCI" patients.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Cognitive Dysfunction Limits: Humans Language: En Journal: Clin Neurol Neurosurg Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Cognitive Dysfunction Limits: Humans Language: En Journal: Clin Neurol Neurosurg Year: 2024 Document type: Article