Evidence map›Paper›PMID 36449040›Full record

ArticleClinical neuroradiology2023

Brain Microstructural Changes in Patients with Amnestic mild Cognitive Impairment : Detected by Neurite Orientation Dispersion and Density Imaging (NODDI) Combined with Machine Learning.

Xiuwei Fu, Xiaonan Wang, Yu Zhang, Tongtong Li, Zixuan Tan, Yuanyuan Chen, Xianchang Zhang, Hongyan Ni

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Article in Clinical neuroradiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

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3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  4. [Association between age-typical structural brain changes and whole brain irradiation].Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025
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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Xiuwei FuDepartment of Radiology, Tianjin Medical University General Hospital, Tianjin, China.
Xiaonan WangDepartment of Radiology, First Central Clinical institution, Tianjin Medical University, Tianjin, China.
Yu ZhangDepartment of Radiology, First Central Clinical institution, Tianjin Medical University, Tianjin, China.
Tongtong LiDepartment of Radiology, First Central Clinical institution, Tianjin Medical University, Tianjin, China.
Zixuan TanInstitute of Medical Imaging, Tianjin Medical University, Tianjin, China.
Yuanyuan ChenInstitute of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.
Xianchang ZhangMR Collaboration, Siemens Healthineers Ltd., Beijing, China.
Hongyan NiDepartment of Radiology, Tianjin First Central Hospital, 24 Fukang Road, 300192, Nankai District, Tianjin, China. hongyni@163.com.ORCID http://orcid.org/0000-0002-8907-8300

Funding

Tianjin Municipal Health and Health Committee Project ZC20161
6 · The paper itself

Abstract

purposeThis study investigated brain microstructural changes in patients with amnestic mild cognitive impairment (aMCI) by retrospectively analyzing neurite orientation dispersion and density imaging (NODDI) data with machine learning algorithms.

methodsA total of 26 aMCI patients and 24 healthy controls (HC) underwent NODDI magnetic resonance imaging (MRI) examinations. The NODDI parameters including neurite density index (NDI), orientation dispersion index (ODI), and volume fraction of isotropic water molecules (Viso) were estimated. Machine learning algorithms such as K‑nearest neighbor (KNN), logistic regression (LR), random forest (RF), and support vector machine (SVM) were used to evaluate the diagnostic efficacy of NODDI parameters in predicting aMCI. The differences in the NODDI parameter values between the aMCI and HC groups were analyzed using the independent sample t‑test, False discovery rate (FDR) correction was used for multiple testing. After adjusting for age, sex, and educational years, partial correlation analysis was used to evaluate the relationship between NODDI parameters and clinical cognitive status of aMCI patients.

resultsThe NDI, ODI, and Viso values of white matter (WM) and gray matter (GM) structure templates combined with the KNN, LR, RF and SVM machine learning algorithms accomplished the discrimination between aMCI and HC groups. The NDI and ODI values decreased (p value range, < 0.001-0.042) and Viso values increased (p value range, < 0.001-0.043) in the aMCI group compared to the HCs. The NDI, ODI, and Viso values of the WM and GM structure templates with significant differences were significantly correlated with mini-mental state examination (MMSE) and Montreal cognitive assessment (MoCA) scores.

conclusionNODDI combined with machine learning algorithms is a promising strategy for early diagnosis of aMCI. Moreover, NODDI parameters correlated with the clinical cognitive status of aMCI patients.

Indexed as

Cognitive DysfunctionWhite MatterBrainDiffusion Magnetic Resonance ImagingDiffusion Tensor ImagingHumansNeuritesRetrospective StudiesClassification algorithmCognitive impairmentDiffusion weighted imagingNeurite densityOrientation dispersion

Identifiers

PMID36449040

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.