Identification of the Neural Circuit Underlying Episodic Memory Deficit in Amnestic Mild Cognitive Impairment via Machine Learning on Gray Matter Volume
Restricted accessBrief reportFirst published online November 23, 2021
Identification of the Neural Circuit Underlying Episodic Memory Deficit in Amnestic Mild Cognitive Impairment via Machine Learning on Gray Matter Volume
Based on whole-brain gray matter volume (GMV), we used relevance vector regression to predict the Rey’s Auditory Verbal Learning Test Delayed Recall (AVLT-DR) scores of individual amnestic mild cognitive impairment (aMCI) patient. The whole-brain GMV pattern could significantly predict the AVLT-DR scores (r = 0.54, p < 0.001). The most important GMV features mainly involved default-mode (e.g., posterior cingulate gyrus, angular gyrus, and middle temporal gyrus) and limbic systems (e.g., hippocampus and parahippocampal gyrus). Therefore, our results provide evidence supporting the idea that the episodic memory deficit in aMCI patients is associated with disruption of the default-mode and limbic systems.
WangZ, DaiZ, ShuH, LiaoX, YueC, LiuD, GuoQ, HeY, ZhangZ (2017) APOE genotype effects on intrinsic brain network connectivity in patients with amnestic mild cognitive impairment. Sci Rep7, 397.
5.
YarkoniT, WestfallJ (2017) Choosing prediction over explanation in psychology: Lessons from machine learning. Perspect Psychol Sci12, 1100–1122.
6.
GongQ, LiL, DuM, Pettersson-YeoW, CrossleyN, YangX, LiJ, HuangX, MechelliA (2014) Quantitative prediction of individual psychopathology in trauma survivors using resting-state FMRI. Neuropsychopharmacology39, 681–687.
7.
WangZ, YuanY, JiangY, YouJ, ZhangZ (2021) Identification of specific neural circuit underlying the key cognitive deficit of remitted late-onset depression: A multi-modal MRI and machine learning study. Prog Neuropsychopharmacol Biol Psychiatry108, 110192.
8.
TippingME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res1, 211–244.
9.
Tzourio-MazoyerN, LandeauB, PapathanassiouD, CrivelloF, EtardO, DelcroixN, MazoyerB, JoliotM (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage15, 273–289.
10.
Thomas YeoB, KrienenFM, SepulcreJ, SabuncuMR, LashkariD, HollinsheadM, RoffmanJL, SmollerJW, ZölleiL, PolimeniJR (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol106, 1125–1165.
11.
BaumGL, CiricR, RoalfDR, BetzelRF, MooreTM, ShinoharaRT, KahnAE, VandekarSN, RupertPE, QuarmleyM, CookPA, ElliottMA, RuparelK, GurRE, GurRC, BassettDS, SatterthwaiteTD (2017) Modular segregation of structural brain networks supports the development of executive function in youth. Curr Biol27, 1561–1572.
12.
ZhuY, LuT, XieC, WangQ, WangY, CaoX, SuY, WangZ, ZhangZ (2020) Functional disorganization of small-world brain networks in patients with ischemic leukoaraiosis. Front Aging Neurosci12, 203.
13.
ChenJ, ShuH, WangZ, LiuD, ShiY, ZhangX, ZhangZ (2015) The interaction of APOE genotype by age in amnestic mild cognitive impairment: A voxel-based morphometric study. J Alzheimers Dis43, 657–668.
14.
SperlingRA, LaviolettePS, O’KeefeK, O’BrienJ, RentzDM, PihlajamakiM, MarshallG, HymanBT, SelkoeDJ, HeddenT, BucknerRL, BeckerJA, JohnsonKA (2009) Amyloid deposition is associated with impaired default network function in older persons without dementia. Neuron63, 178–188.
15.
ShiY, LuX, ZhangL, ShuH, GuL, WangZ, GaoL, ZhuJ, ZhangH, ZhouD (2019) Potential value of plasma amyloid-β, total tau, and neurofilament light for identification of early Alzheimer’s disease. ACS Chem Neurosci10, 3479–3485.
