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      The Predictive Value of Dynamic Intrinsic Local Metrics in Transient Ischemic Attack

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          Abstract

          Background

          Transient ischemic attack (TIA) is known as “small stroke.” However, the diagnosis of TIA is currently difficult due to the transient symptoms. Therefore, objective and reliable biomarkers are urgently needed in clinical practice.

          Objective

          The purpose of this study was to investigate whether dynamic alterations in resting-state local metrics could differentiate patients with TIA from healthy controls (HCs) using the support-vector machine (SVM) classification method.

          Methods

          By analyzing resting-state functional MRI (rs-fMRI) data from 48 patients with and 41 demographically matched HCs, we compared the group differences in three dynamic local metrics: dynamic amplitude of low-frequency fluctuation (d-ALFF), dynamic fractional amplitude of low-frequency fluctuation (d-fALFF), and dynamic regional homogeneity (d-ReHo). Furthermore, we selected the observed alterations in three dynamic local metrics as classification features to distinguish patients with TIA from HCs through SVM classifier.

          Results

          We found that TIA was associated with disruptions in dynamic local intrinsic brain activities. Compared with HCs, the patients with TIA exhibited increased d-fALFF, d-fALFF, and d-ReHo in vermis, right calcarine, right middle temporal gyrus, opercular part of right inferior frontal gyrus, left calcarine, left occipital, and left temporal and cerebellum. These alternations in the dynamic local metrics exhibited an accuracy of 80.90%, sensitivity of 77.08%, specificity of 85.37%, precision of 86.05%, and area under curve of 0.8501 for distinguishing the patients from HCs.

          Conclusion

          Our findings may provide important evidence for understanding the neuropathology underlying TIA and strong support for the hypothesis that these local metrics have potential value in clinical diagnosis.

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          Most cited references67

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          Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.

          An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (< 0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation (P < 10(-3)) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.
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            Power failure: why small sample size undermines the reliability of neuroscience.

            A study with low statistical power has a reduced chance of detecting a true effect, but it is less well appreciated that low power also reduces the likelihood that a statistically significant result reflects a true effect. Here, we show that the average statistical power of studies in the neurosciences is very low. The consequences of this include overestimates of effect size and low reproducibility of results. There are also ethical dimensions to this problem, as unreliable research is inefficient and wasteful. Improving reproducibility in neuroscience is a key priority and requires attention to well-established but often ignored methodological principles.
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              Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI.

              In children with attention deficit hyperactivity disorder (ADHD), functional neuroimaging studies have revealed abnormalities in various brain regions, including prefrontal-striatal circuit, cerebellum, and brainstem. In the current study, we used a new marker of functional magnetic resonance imaging (fMRI), amplitude of low-frequency (0.01-0.08Hz) fluctuation (ALFF) to investigate the baseline brain function of this disorder. Thirteen boys with ADHD (13.0+/-1.4 years) were examined by resting-state fMRI and compared with age-matched controls. As a result, we found that patients with ADHD had decreased ALFF in the right inferior frontal cortex, [corrected] and bilateral cerebellum and the vermis as well as increased ALFF in the right anterior cingulated cortex, left sensorimotor cortex, and bilateral brainstem. This resting-state fMRI study suggests that the changed spontaneous neuronal activity of these regions may be implicated in the underlying pathophysiology in children with ADHD.
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                Author and article information

                Contributors
                Journal
                Front Aging Neurosci
                Front Aging Neurosci
                Front. Aging Neurosci.
                Frontiers in Aging Neuroscience
                Frontiers Media S.A.
                1663-4365
                10 February 2022
                2021
                : 13
                : 808094
                Affiliations
                [1] 1School of Information and Electronics Technology, Jiamusi University , Jiamusi, China
                [2] 2Integrated Medical School, Jiamusi University , Jiamusi, China
                [3] 3Key Laboratory of Intelligent Education Technology and Application of Zhejiang Province, Zhejiang Normal University , Jinhua, China
                [4] 4Department of Neurology, The First Affiliated Hospital, Dalian Medical University , Dalian, China
                [5] 5Department of Neurology, Anshan Changda Hospital , Anshan, China
                [6] 6Faculty of Western Languages, Heilongjiang University , Harbin, China
                [7] 7School of Teacher Education, Zhejiang Normal University , Jinhua, China
                [8] 8Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University , Hangzhou, China
                Author notes

                Edited by: Feng Yan, Zhejiang University, China

                Reviewed by: Xin Huang, Renmin Hospital of Wuhan University, China; Xiaofen Ma, Guangdong Second Provincial General Hospital, China

                *Correspondence: Yulin Song, yulinsong1969@ 123456sina.com

                These authors have contributed equally to this work and share first authorship

                This article was submitted to Neuroinflammation and Neuropathy, a section of the journal Frontiers in Aging Neuroscience

                Article
                10.3389/fnagi.2021.808094
                8868122
                35221984
                44471c5e-cb42-4762-b33f-eeed088b2bf9
                Copyright © 2022 Ma, Huang, Li, Han, Sun, Zhan, Wang, Jia, Han, Li, Song and Lv.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 03 November 2021
                : 30 December 2021
                Page count
                Figures: 3, Tables: 2, Equations: 0, References: 67, Pages: 10, Words: 8010
                Funding
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Funded by: National Natural Science Foundation of China, doi 10.13039/501100001809;
                Categories
                Aging Neuroscience
                Original Research

                Neurosciences
                resting-state fmri,dynamic local metric,machine learning,support-vector machine,transient ischemic attack

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