ArticleHuman brain mapping2021
Classification of type 2 diabetes mellitus with or without cognitive impairment from healthy controls using high-order functional connectivity.
Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Integrated cortical-cognitive signatures identified by machine learning enable early detection of MCI in type 2 diabetes.Scientific reports · 2026Article
- Impact of glycemic control on brain microstructure in type 2 diabetes mellitus: insights from diffusion tensor imaging.BMC medical imaging · 2025Article
- Cognitive Function Deficits Associated With Type 2 Diabetes and Retinopathy: Volumetric Brain MR Imaging Study.Brain and behavior · 2025Article
- Prevalence of hypertension, diabetes mellitus, and their risk factors in an informal settlement in Freetown, Sierra Leone: a cross-sectional study.BMC public health · 2024Article
- Topological disruption of low- and high-order functional networks in presbycusis.Brain communications · 2024Article
- The Key Role of Mitochondrial Function in Health and Disease.Antioxidants (Basel, Switzerland) · 2023Review
- Detecting type 2 diabetes mellitus cognitive impairment using whole-brain functional connectivity.Scientific reports · 2023Article
- Topological disruption of high-order functional networks in cognitively preserved Parkinson's disease.CNS neuroscience & therapeutics · 2023Article
- Risk factors and drug discovery for cognitive impairment in type 2 diabetes mellitus using artificial intelligence interpretation and graph neural networks.Frontiers in endocrinology · 2023Article
- Genuine high-order interactions in brain networks and neurodegeneration.Neurobiology of disease · 2022Article
- Article
- MRI-based machine learning model: A potential modality for predicting cognitive dysfunction in patients with type 2 diabetes mellitus.Frontiers in bioengineering and biotechnology · 2022Article
- Altered cortical thickness, degree centrality, and functional connectivity in middle-age type 2 diabetes mellitus.Frontiers in neurology · 2022Article
- Classification of type 2 diabetes mellitus with or without cognitive impairment from healthy controls using high-order functional connectivity.Human brain mapping · 2021Article
- Identifying Type 2 Diabetic Brains by Investigating Disease-Related Structural Changes in Magnetic Resonance Imaging.Frontiers in neuroscience · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Type 2 diabetes mellitus (T2DM) is associated with cognitive impairment and may progress to dementia. However, the brain functional mechanism of T2DM-related dementia is still less understood. Recent resting-state functional magnetic resonance imaging functional connectivity (FC) studies have proved its potential value in the study of T2DM with cognitive impairment (T2DM-CI). However, they mainly used a mass-univariate statistical analysis that was not suitable to reveal the altered FC "pattern" in T2DM-CI, due to lower sensitivity. In this study, we proposed to use high-order FC to reveal the abnormal connectomics pattern in T2DM-CI with a multivariate, machine learning-based strategy. We also investigated whether such patterns were different between T2DM-CI and T2DM without cognitive impairment (T2DM-noCI) to better understand T2DM-induced cognitive impairment, on 23 T2DM-CI and 27 T2DM-noCI patients, as well as 50 healthy controls (HCs). We first built the large-scale high-order brain networks based on temporal synchronization of the dynamic FC time series among multiple brain region pairs and then used this information to classify the T2DM-CI (as well as T2DM-noCI) from the matched HC based on support vector machine. Our model achieved an accuracy of 79.17% in T2DM-CI versus HC differentiation, but only 59.62% in T2DM-noCI versus HC classification. We found abnormal high-order FC patterns in T2DM-CI compared to HC, which was different from that in T2DM-noCI. Our study indicates that there could be widespread connectivity alterations underlying the T2DM-induced cognitive impairment. The results help to better understand the changes in the central neural system due to T2DM.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.