Decreased Spontaneous Brain Activity and Functional Connectivity in Type 1 Diabetic Patients Without Microvascular Complications

Xia, Wenqing; Chen, Yu‐Chen; Luo, Yong; Zhang, Danfeng; Chen, Huiyou; Ma, Jianhua; Yin, Xindao

doi:10.1159/000495960

Cited by 15 publications

(11 citation statements)

References 42 publications

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“…As previous reported, T1DM impaired cognitive function such as learning and memory in children (32)(33)(34). Compared with the healthy controls, T1DM patients showed significantly reduced FC between the PCC and the right middle frontal gyrus (12). In line with the previous study, our study also showed decreased FC of the PCC in children with new-onset T1DM, indicated that the functions of this brain region were diminished even at the initial stage of T1DM.…”

Section: Discussionsupporting

confidence: 92%

“…Previous rs-fMRI studies mainly focused on adult T1DM patients. Xia et al found that decreased functional connectivity (FC) was mainly in the default mode network, which was correlated with specific impaired cognition in T1DM (12). In the study done by van Duinkerken et al, T1DM patients showed changes in FC of the subgenual cingulate cortex, mainly manifested as decreased connectivity in executive control network and increased connectivity in default mode network (13).…”

Section: Introductionmentioning

confidence: 99%

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Abnormal Functional Connectivity Density in New-Onset Type 1 Diabetes Mellitus Children: A Resting-State Functional Magnetic Resonance Imaging Study

et al. 2020

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Abnormal Functional Connectivity Density in New-Onset Type 1 Diabetes Mellitus Children: A Resting-State Functional Magnetic Resonance Imaging Study

et al. 2020

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“…Notably, fALFF produces better effects in reducing the physiological noise than ALFF, and it can effectively suppress artifacts in non-specific brain regions, such as the ventricles and the vicinity of blood vessels ( Zou et al, 2008 ). ReHo is a data-driven measure for the local measurement of spontaneous neural activity ( Xia et al, 2018 ), and it can effectively evaluate resting-state brain activity based on the hypothesis that brain activity is more likely to occur in clusters rather than in a single voxel ( Zhang et al, 2012 ). Recently, these methods have wide access to explore brain diseases with potential functional alterations, such as depression ( Yu Y. et al, 2019 ), Alzheimer’s disease ( Cheng et al, 2019 ), Tourette syndrome ( Liu et al, 2017 ), and so on.…”

Section: Introductionmentioning

confidence: 99%

Altered Spontaneous Brain Activity Related to Neurologic and Sleep Dysfunction in Children With Obstructive Sleep Apnea Syndrome

et al. 2021

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Childhood obstructive sleep apnea (OSA) is a common chronic sleep-related breathing disorder in children, which leads to growth retardation, neurocognitive impairments, and serious complications. Considering the previous studies about brain structural abnormalities in OSA, in the present study, we aimed to explore the altered spontaneous brain activity among OSA patients, using amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) methods based on resting-state functional magnetic resonance imaging (MRI). Thirty-one untreated OSA children and 33 age-and gender-matched healthy children (HC) were included in this study. Compared with controls, the OSA group showed significant lower ALFF in the right lingual gyrus, decreased fALFF in the left middle frontal gyrus (MFG), but increased fALFF in the left precuneus. Decreased ReHo was found in the left inferior frontal gyrus (orbital part) and left middle frontal gyrus. Notably, the mean fALFF value of left MFG was not only significantly related to multiple sleep parameters but also demonstrated the best performance in ROC curve analysis. These findings revealed OSA children were associated with dysfunctions in the default mode network, the frontal lobe, and the lingual gyrus, which may implicate the underlying neurophysiological mechanisms of intrinsic brain activity. The correlation between the altered spontaneous neuronal activity and the clinical index provides early useful diagnostic biomarkers for OSA children as well.

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“…Patients performed worse on several cognitive tests; the researchers speculated that this impaired cognitive performance was correlated with decreased activity in the cuneus and lingual gyrus in the occipital lobe. Recent studies by Xia et al [18] indicated that T1DM patients showed significantly decreased ALFF values in the posterior cingulate cortex (PCC) and right inferior frontal gyrus compared with the healthy controls. Furthermore, they found a positive correlation between decreased ALFF values in the PCC and Rey-Osterrieth Complex Figure Test-(CFT-) delay scores in T1DM patients.…”

Section: Introductionmentioning

confidence: 99%

A Pilot Study of Diabetes Mellitus Classification from rs-fMRI Data Using Convolutional Neural Networks

Liu

Xian

Yang

et al. 2020

Mathematical Problems in Engineering

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Background. As a chronic progressive disease, diabetes mellitus (DM) has a high incidence worldwide, and it impacts on cognitive and learning abilities in the lifetime even in the early stage, may degenerate memory in middle age, and perhaps increases the risk of Alzheimer’s disease. Method. In this work, we propose a convolutional neural network (CNN) based classification method to help classify diabetes by distinguishing the brains with abnormal functions from the normal ones on resting-state functional magnetic resonance imaging (rs-fMRI). The proposed classification model is based on the Inception-v4-Residual convolutional neural network architecture. In our workflow, the original rs-fMRI data are first mapped to generate amplitude of low-frequency fluctuation (ALFF) images and then fed into the CNN model to get the classification result to indicate the potential existence of DM. Result. We validate our method on a realistic clinical rs-fMRI dataset, and the achieved average accuracy is 89.95 % in fivefold cross-validation. Our model achieves a 0.8690 AUC with 77.50% and 77.51% sensitivity and specificity using our local dataset, respectively. Conclusion. It has the potential to become a novel clinical preliminary screening tool that provides help for the classification of different categories based on functional brain alteration caused by diabetes, benefiting from its accuracy and robustness, as well as efficiency and patient friendliness.

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Decreased Spontaneous Brain Activity and Functional Connectivity in Type 1 Diabetic Patients Without Microvascular Complications

Cited by 15 publications

References 42 publications

Abnormal Functional Connectivity Density in New-Onset Type 1 Diabetes Mellitus Children: A Resting-State Functional Magnetic Resonance Imaging Study

Abnormal Functional Connectivity Density in New-Onset Type 1 Diabetes Mellitus Children: A Resting-State Functional Magnetic Resonance Imaging Study

Altered Spontaneous Brain Activity Related to Neurologic and Sleep Dysfunction in Children With Obstructive Sleep Apnea Syndrome

A Pilot Study of Diabetes Mellitus Classification from rs-fMRI Data Using Convolutional Neural Networks

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