Dataset Information

Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data.

ABSTRACT: BACKGROUND:Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data. METHODS:Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimer's disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination. RESULTS:The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66?±?0.52% of the balanced dataset and 97.23?±?0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49?±?0.53 and 96.34?±?1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The 'time orientation' and '3-word recall' score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment. CONCLUSIONS:The machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting.

SUBMITTER: Kang MJ

PROVIDER: S-EPMC6873409 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data.

Kang Min Ju MJ Kim Sang Yun SY Na Duk L DL Kim Byeong C BC Yang Dong Won DW Kim Eun-Joo EJ Na Hae Ri HR Han Hyun Jeong HJ Lee Jae-Hong JH Kim Jong Hun JH Park Kee Hyung KH Park Kyung Won KW Han Seol-Heui SH Kim Seong Yoon SY Yoon Soo Jin SJ Yoon Bora B Seo Sang Won SW Moon So Young SY Yang YoungSoon Y Shim Yong S YS Baek Min Jae MJ Jeong Jee Hyang JH Choi Seong Hye SH Youn Young Chul YC

BMC medical informatics and decision making 20191121 1

<h4>Background</h4>Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data.<h4>Methods</h4>Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Batte ...[more]

PMID: 31752864

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Project description:ObjectiveAlzheimer's Disease (AD) is a progressive condition characterized by cognitive decline. AD is often preceded by mild cognitive impairment (MCI), though the diagnosis of both conditions remains a challenge. Early diagnosis of AD, and prediction of MCI progression require data-driven approaches to improve patient selection for treatment. We used a machine learning tool to predict performance in neuropsychological tests in AD and MCI based on functional connectivity using a whole-brain connectome, in an attempt to identify network substrates of cognitive deficits in AD.MethodsNeuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 149 MCI, and 85 AD patients; and 140 cognitively unimpaired geriatric participants. A novel machine learning tool, Hollow Tree Super (HoTS) was utilized to extract feature importance from each machine learning model to identify brain regions that were associated with deficit and absence of deficit for 11 neuropsychological tests.Results11 models attained an area under the receiver operating curve (AUC-ROC) greater than 0.65, while five models had an AUC-ROC ≥ 0.7. 20 parcels of the Human Connectome Project Multimodal Parcelation Atlas matched to poor performance in at least two neuropsychological tests, while 14 parcels were associated with good performance in at least two tests. At a network level, most parcels predictive of both presence and absence of deficit were affiliated with the Central Executive Network, Default Mode Network, and the Sensorimotor Networks. Segregating predictors by the cognitive domain associated with each test revealed areas of coherent overlap between cognitive domains, with the parcels providing possible markers to screen for cognitive impairment.ConclusionApproaches such as ours which incorporate whole-brain functional connectivity and harness feature importance in machine learning models may aid in identifying diagnostic and therapeutic targets in AD.

Dataset Information

Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data.

Publications

Prediction of cognitive impairment via deep learning trained with multi-center neuropsychological test data.

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