scholarly journals Less Is Better: Single-Digit Brain Functional Connections Predict T2DM and T2DM-Induced Cognitive Impairment

2021 ◽  
Vol 14 ◽  
Author(s):  
Haotian Qian ◽  
Dongxue Qin ◽  
Shouliang Qi ◽  
Yueyang Teng ◽  
Chen Li ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Low Frequency ◽  
Neural Mechanism ◽  
Brain Regions ◽  
Imaging Biomarkers ◽  
Support Vector ◽  
Long Distance ◽  
Single Digit ◽  
Functional Connections ◽  
Brain Connections

Type 2 diabetes mellitus (T2DM) leads to a higher risk of brain damage and adversely affects cognition. The underlying neural mechanism of T2DM-induced cognitive impairment (T2DM-CI) remains unclear. This study proposes to identify a small number of dysfunctional brain connections as imaging biomarkers, distinguishing between T2DM-CI, T2DM with normal cognition (T2DM-NC), and healthy controls (HC). We have recruited 22 T2DM-CI patients, 31 T2DM-NC patients, and 39 HCs. The structural Magnetic Resonance Imaging (MRI) and resting state fMRI images are acquired, and neuropsychological tests are carried out. Amplitude of low frequency fluctuations (ALFF) is analyzed to identify impaired brain regions implicated with T2DM and T2DM-CI. The functional network is built and all connections connected to impaired brain regions are selected. Subsequently, L1-norm regularized sparse canonical correlation analysis and sparse logistic regression are used to identify discriminative connections and Support Vector Machine is trained to realize three two-category classifications. It is found that single-digit dysfunctional connections predict T2DM and T2DM-CI. For T2DM-CI versus HC, T2DM-NC versus HC, and T2DM-CI versus T2DM-NC, the number of connections is 6, 7, and 5 and the area under curve (AUC) can reach 0.912, 0.901, and 0.861, respectively. The dysfunctional connection is mainly related to Default Model Network (DMN) and long-distance links. The strength of identified connections is significantly different among groups and correlated with cognitive assessment score (p < 0.05). Via ALFF analysis and further feature selection algorithms, a small number of dysfunctional brain connections can be identified to predict T2DM and T2DM-CI. These connections might be the imaging biomarkers of T2DM-CI and targets of intervention.

scholarly journals Causal connectivity abnormalities of regional homogeneity in children with attention deficit hyperactivity disorder: a rest-state fMRI study

ADMET & DMPK ◽  
2017 ◽  
Vol 5 (4) ◽  
pp. 242-252 ◽  
Author(s):  
Dechun Zhao ◽  
Shuxing Zheng ◽  
Li Yang ◽  
Yin Tian
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Low Frequency ◽  
Brain Regions ◽  
Regional Homogeneity ◽  
Support Vector ◽  
Attention And Memory ◽  
Causality Analysis ◽  
Hyperactivity Disorder ◽  
Significant Difference

The present study aimed to investigate individual differences of causal connectivity between brain regions in attention deficit hyperactivity disorder (ADHD) which was a psychiatric disorder. Resting-state functional magnetic resonance imaging (R-fMRI) data of typically-developing controls (TDC) children group and combined ADHD (ADHD-C) children group were distinguished by the support vector machine (SVM) with linear kernel function, based on regional homogeneity (ReHo), amplitude of low frequency fluctuation (ALFF) and fractional ALFF (FALFF). The highest classification accuracy yielded by ReHo was 90.91 %. Furthermore, the granger causality analysis (GCA) method based on the classified weight map of regions of interesting (ROIs) showed that five causal flows existed significant difference between TDC and ADHD-C. That is, the averaged GCA values of three causal connections (i.e. left VLPFC à left CC1, right PoCG à left CC1, and right PoCG à right CC2) for ADHD-C were separately stronger than those for TDC. And the other two connections (i.e. right FEF à right SOG and right CC1 à right SOG) were weaker for ADHD-C than those for TDC. In addition, only two causality flows (i.e. left VLPFC à left CC1 and right PoCG à right CC2) presented that their GCA values were positively correlation with ADHD index scores, respectively. Our findings revealed that ADHD children represented widespread abnormalities in the causality connectivity, especially involved in the attention and memory related regions. And further provided evidence that the potential neural causality flows could play a key role in characterizing individual’s ADHD.

scholarly journals A Strategy for Identifying the Risk of Developing Alzheimer's Disease From Healthy Controls: Distance to Novelty Detection Boundary

2020 ◽  
Author(s):  
Jiangbing Mao ◽  
Qinyong Ye ◽  
Hongqing Yang ◽  
Magda Bucholc ◽  
Shuo Liu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Cognitive Decline ◽  
Early Stage ◽  
Novelty Detection ◽  
Imaging Biomarkers ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Healthy Controls

Abstract Background:Machine learning (ML) techniques are expected to tackle the problem of the high prevalence of Alzheimer’s disease (AD) we are facing worldwide. However, few studies of novelty detection (ND), a typical ML technique for safety-critical systems especially in healthcare, were engaged for identifying the risk of developing cognitive impairment from healthy controls (HC) population.Materials and Methods: Two independent datasets were used for this study, including the Australian Imaging Biomarkers and Lifestyle Study of Ageing (AIBL) and the Fujian Medical University Union Hospital (FMUUH), China datasets. Multiple feature selection methods were applied to identify the most relevant features for predicting the severity of AD. Four easily interpretable ND algorithms, including k nearest neighbor, Mixture of Gaussian (MoG), KMEANS, and support vector data description were used to construct predictive models. The models were visualized by drawing their decision boundaries tightly surrounding the HC data. A distance to boundary (DtB) strategy was proposed to differentiate individuals with mild cognitive impairment (MCI) and AD from HC. Results: The best overall MCI&AD detection performance in both AIBL and FMUUH was obtained on the cognitive and functional assessments (CFA) modality only using MoG-based ND with AUC of 0.8757 and 0.9443, respectively. The highest sensitivity of MCI was presented by using a combination of CFA and brain imaging modality. The DTB value reflects the risk of developing cognitive impairment for HC and the dementia severity of MCI/AD.Conclusions: Our findings suggest that applying some non-invasive and cost-effective features can significantly detect cognitive decline in an early stage. The visualized decision boundary and the proposed DtB strategy illustrated the severity of cognitive decline of potential MCI&AD patients in an early stage. The results would help inform future guidelines for developing a clinical decision-making support system aiming at an early diagnosis and prognosis of MCI&AD.

scholarly journals Surface-Based Falff: A Potential Novel Biomarker for Prediction of Radiation Encephalopathy in Patients With Nasopharyngeal Carcinoma

2021 ◽  
Vol 15 ◽  
Author(s):  
You-ming Zhang ◽  
Ya-fei Kang ◽  
Jun-jie Zeng ◽  
Li Li ◽  
Jian-ming Gao ◽  
...  
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Low Frequency ◽  
Superior Temporal Gyrus ◽  
Brain Regions ◽  
Imaging Biomarker ◽  
Support Vector ◽  
Radiation Encephalopathy ◽  
Radiation Induced

Radiation encephalopathy (RE) is an important potential complication in patients with nasopharyngeal carcinoma (NPC) who undergo radiotherapy (RT) that can affect the quality of life. However, a functional imaging biomarker of pre-symptomatic RE has not yet been established. This study aimed to assess radiation-induced gray matter functional alterations and explore fractional amplitude of low-frequency fluctuation (fALFF) as an imaging biomarker for predicting or diagnosing RE in patients with NPC. A total of 60 patients with NPC were examined, 21 in the pre-RT cohort and 39 in the post-RT cohort. Patients in the post-RT cohort were further divided into two subgroups according to the occurrence of RE in follow-up: post-RT non−RE (n = 21) and post-RT REprovedinfollow−up (n = 18). Surface-based and volume-based fALFF were used to detect radiation-induced functional alterations. Functional derived features were then adopted to construct a predictive model for the diagnosis of RE. We observed that surface-based fALFF could sensitively detect radiation-induced functional alterations in the intratemporal brain regions (such as the hippocampus and superior temporal gyrus), as well as the extratemporal regions (such as the insula and prefrontal lobe); however, no significant intergroup differences were observed using volume-based fALFF. No significant correlation between fALFF and radiation dose to the ipsilateral temporal lobe was observed. Support vector machine (SVM) analysis revealed that surface-based fALFF in the bilateral superior temporal gyri and left insula exhibited impressive performance (accuracy = 80.49%) in identifying patients likely to develop RE. We conclude that surface-based fALFF may serve as a sensitive imaging biomarker in the prediction of RE.

scholarly journals Different Modes of Low-Frequency Focused Ultrasound-Mediated Attenuation of Epilepsy Based on the Topological Theory

Micromachines ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1001
Author(s):  
Minjian Zhang ◽  
Bo Li ◽  
Yafei Liu ◽  
Rongyu Tang ◽  
Yiran Lang ◽  
...  
Keyword(s):  
Focused Ultrasound ◽  
Continuous Wave ◽  
Low Frequency ◽  
Brain Regions ◽  
Brain Network ◽  
Functional Brain ◽  
Functional Brain Network ◽  
Significant Difference ◽  
The Brain ◽  
Brain Connections

Epilepsy is common brain dysfunction, where abnormal synchronized activities can be observed across multiple brain regions. Low-frequency focused pulsed ultrasound has been proven to modulate the epileptic brain network. In this study, we used two modes of low-intensity focused ultrasound (pulsed-wave and continuous-wave) to sonicate the brains of KA-induced epileptic rats, analyzed the EEG functional brain connections to explore their respective effect on the epileptic brain network, and discuss the mechanism of ultrasound neuromodulation. By comparing the brain network characteristics before and after sonication, we found that two modes of ultrasound both significantly affected the functional brain network, especially in the low-frequency band below 12 Hz. After two modes of sonication, the power spectral density of the EEG signals and the connection strength of the brain network were significantly reduced, but there was no significant difference between the two modes. Our results indicated that the ultrasound neuromodulation could effectively regulate the epileptic brain connections. The ultrasound-mediated attenuation of epilepsy was independent of modes of ultrasound.

scholarly journals Functional MRI-Specific Alterations in Salience Network in Mild Cognitive Impairment: An ALE Meta-Analysis

2021 ◽  
Vol 13 ◽  
Author(s):  
Yu Song ◽  
Wenwen Xu ◽  
Shanshan Chen ◽  
Guanjie Hu ◽  
Honglin Ge ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Meta Analysis ◽  
Low Frequency ◽  
Superior Temporal Gyrus ◽  
Frequency Fluctuation ◽  
Imaging Biomarkers ◽  
Salience Network ◽  
Cingulate Gyrus ◽  
Amnestic Mci

Background Mild cognitive impairment (MCI) is an intermediate stage between normal aging and dementia. Amnestic MCI (aMCI) and non-amnestic MCI are the two subtypes of MCI with the former having a higher risk for progressing to Alzheimer's disease (AD). Compared with healthy elderly adults, individuals with MCI have specific functional alterations in the salience network (SN). However, no consistent results are documenting these changes. This meta-analysis aimed to investigate the specific functional alterations in the SN in MCI and aMCI.Methods: We systematically searched PubMed, Embase, and Web of Science for scientific neuroimaging literature based on three research methods, namely, functional connectivity (FC), regional homogeneity (ReHo), and the amplitude of low-frequency fluctuation or fractional amplitude of low-frequency fluctuation (ALFF/fALFF). Then, we conducted the coordinate-based meta-analysis by using the activation likelihood estimation algorithm.Results: In total, 30 functional neuroimaging studies were included. After extracting the data and analyzing it, we obtained specific changes in some brain regions in the SN including decreased ALFF/fALFF in the left superior temporal gyrus, the insula, the precentral gyrus, and the precuneus in MCI and aMCI; increased FC in the thalamus, the caudate, the superior temporal gyrus, the insula, and the cingulate gyrus in MCI; and decreased ReHo in the anterior cingulate gyrus in aMCI. In addition, as to FC, interactions of the SN with other networks including the default mode network and the executive control network were also observed mainly in the middle frontal gyrus and superior frontal gyrus in MCI and inferior frontal gyrus in aMCI.Conclusions: Specific functional alternations in the SN and interactions of the SN with other networks in MCI could be useful as potential imaging biomarkers for MCI or aMCI. Meanwhile, it provided a new insight in predicting the progression of health to MCI or aMCI and novel targets for proper intervention to delay the progression.Systematic Review Registration: [PROSPERO], identifier [No. CRD42020216259].

scholarly journals Strategic infarct location for post-stroke cognitive impairment: A multivariate lesion-symptom mapping study

2017 ◽  
Vol 38 (8) ◽  
pp. 1299-1311 ◽  
Author(s):  
Lei Zhao ◽  
J Matthijs Biesbroek ◽  
Lin Shi ◽  
Wenyan Liu ◽  
Hugo J Kuijf ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Basal Ganglia ◽  
Brain Regions ◽  
Angular Gyrus ◽  
Support Vector ◽  
Subcortical Structures ◽  
Cognitive Domains ◽  
Post Stroke ◽  
Lesion Symptom Mapping ◽  
Basal Ganglia Structures

Lesion location is an important determinant for post-stroke cognitive impairment. Although several ‘strategic’ brain regions have previously been identified, a comprehensive map of strategic brain regions for post-stroke cognitive impairment is lacking due to limitations in sample size and methodology. We aimed to determine strategic brain regions for post-stroke cognitive impairment by applying multivariate lesion-symptom mapping in a large cohort of 410 acute ischemic stroke patients. Montreal Cognitive Assessment at three to six months after stroke was used to assess global cognitive functioning and cognitive domains (memory, language, attention, executive and visuospatial function). The relation between infarct location and cognition was assessed in multivariate analyses at the voxel-level and the level of regions of interest using support vector regression. These two assumption-free analyses consistently identified the left angular gyrus, left basal ganglia structures and the white matter around the left basal ganglia as strategic structures for global cognitive impairment after stroke. A strategic network involving several overlapping and domain-specific cortical and subcortical structures was identified for each of the cognitive domains. Future studies should aim to develop even more comprehensive infarct location-based models for post-stroke cognitive impairment through multicenter studies including thousands of patients.

scholarly journals Identification of Alzheimer’s Disease using Functional Connectivity Measures

2019 ◽  
Vol 9 (2) ◽  
pp. 1452-1456
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Correlation Method ◽  
Brain Regions ◽  
Cognitive Disorder ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Functional Connections ◽  
Learning Techniques

Alzheimer’s disease (AD) is a gradual neuro cognitive disorder caused by the damage of brain cells over a certain period of time. One non-invasive and efficient technique to investigate AD is to use functional magnetic resonance imaging (fMRI). Functional connectivity is a change in the functional connections between brain regions when an activity takes place. The correlation value gives the strength of functional connectivity. Pearson’s correlation method was used to calculate the correlation coefficient between two time series. Mutual information which denotes the information successfully transmitted through a channel was also considered. In this paper, these two measures are compared and their performance and suitability is assessed for fMRI connectivity modelling based on the classification accuracy. Machine learning techniques such as support vector machine (SVM) is employed for connectivity analysis and classification of Alzheimer’s from control population

scholarly journals Baseline Neuroimaging Predicts Decline to Dementia From Amnestic Mild Cognitive Impairment

2021 ◽  
Vol 13 ◽  
Author(s):  
Joseph M. Gullett ◽  
Alejandro Albizu ◽  
Ruogu Fang ◽  
David A. Loewenstein ◽  
Ranjan Duara ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Limbic System ◽  
Performance Metrics ◽  
Brain Regions ◽  
Amnestic Mild Cognitive Impairment ◽  
Support Vector ◽  
Feature Weights ◽  
Precise Prediction

Background and Objectives: Prediction of decline to dementia using objective biomarkers in high-risk patients with amnestic mild cognitive impairment (aMCI) has immense utility. Our objective was to use multimodal MRI to (1) determine whether accurate and precise prediction of dementia conversion could be achieved using baseline data alone, and (2) generate a map of the brain regions implicated in longitudinal decline to dementia.Methods: Participants meeting criteria for aMCI at baseline (N = 55) were classified at follow-up as remaining stable/improved in their diagnosis (N = 41) or declined to dementia (N = 14). Baseline T1 structural MRI and resting-state fMRI (rsfMRI) were combined and a semi-supervised support vector machine (SVM) which separated stable participants from those who decline at follow-up with maximal margin. Cross-validated model performance metrics and MRI feature weights were calculated to include the strength of each brain voxel in its ability to distinguish the two groups.Results: Total model accuracy for predicting diagnostic change at follow-up was 92.7% using baseline T1 imaging alone, 83.5% using rsfMRI alone, and 94.5% when combining T1 and rsfMRI modalities. Feature weights that survived the p < 0.01 threshold for separation of the two groups revealed the strongest margin in the combined structural and functional regions underlying the medial temporal lobes in the limbic system.Discussion: An MRI-driven SVM model demonstrates accurate and precise prediction of later dementia conversion in aMCI patients. The multi-modal regions driving this prediction were the strongest in the medial temporal regions of the limbic system, consistent with literature on the progression of Alzheimer’s disease.

scholarly journals The Effects of Acupuncture at Real or Sham Acupoints on the Intrinsic Brain Activity in Mild Cognitive Impairment Patients

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Baohui Jia ◽  
Zhishun Liu ◽  
Baoquan Min ◽  
Zhenchang Wang ◽  
Aihong Zhou ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Brain Activity ◽  
Low Frequency ◽  
Resting State Fmri ◽  
Brain Regions ◽  
Brain Network ◽  
Healthy Controls ◽  
The Difference ◽  
The Brain

Accumulating neuroimaging studies in humans have shown that acupuncture can modulate a widely distributed brain network in mild cognitive impairment (MCI) and Alzheimer’s disease (AD) patients. Acupuncture at different acupoints could exert different modulatory effects on the brain network. However, whether acupuncture at real or sham acupoints can produce different effects on the brain network in MCI or AD patients remains unclear. Using resting-state fMRI, we reported that acupuncture at Taixi (KI3) induced amplitude of low-frequency fluctuation (ALFF) change of different brain regions in MCI patients from those shown in the healthy controls. In MCI patients, acupuncture at KI3 increased or decreased ALFF in the different regions from those activated by acupuncture in the healthy controls. Acupuncture at the sham acupoint in MCI patients activated the different brain regions from those in healthy controls. Therefore, we concluded that acupuncture displays more significant effect on neuronal activities of the above brain regions in MCI patients than that in healthy controls. Acupuncture at KI3 exhibits different effects on the neuronal activities of the brain regions from acupuncture at sham acupoint, although the difference is only shown at several regions due to the close distance between the above points.

scholarly journals An ALE Meta-Analysis of Specific Functional MRI Studies on Subcortical Vascular Cognitive Impairment

2021 ◽  
Vol 12 ◽  
Author(s):  
Wenwen Xu ◽  
Yu Song ◽  
Shanshan Chen ◽  
Chen Xue ◽  
Guanjie Hu ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Functional Connectivity ◽  
Meta Analysis ◽  
Vascular Cognitive Impairment ◽  
Brain Regions ◽  
Angular Gyrus ◽  
Imaging Biomarkers ◽  
Precentral Gyrus ◽  
The Brain ◽  
Subcortical Vascular Cognitive Impairment

Background: Subcortical vascular cognitive impairment (sVCI), caused by cerebral small vessel disease, accounts for the majority of vascular cognitive impairment, and is characterized by an insidious onset and impaired memory and executive function. If not recognized early, it inevitably develops into vascular dementia. Several quantitative studies have reported the consistent results of brain regions in sVCI patients that can be used to predict dementia conversion. The purpose of the study was to explore the exact abnormalities within the brain in sVCI patients by combining the coordinates reported in previous studies.Methods: The PubMed, Embase, and Web of Science databases were thoroughly searched to obtain neuroimaging articles on the amplitude of low-frequency fluctuation, regional homogeneity, and functional connectivity in sVCI patients. According to the activation likelihood estimation (ALE) algorithm, a meta-analysis based on coordinate and functional connectivity modeling was conducted.Results: The quantitative meta-analysis included 20 functional imaging studies on sVCI patients. Alterations in specific brain regions were mainly concentrated in the frontal lobes including the middle frontal gyrus, superior frontal gyrus, medial frontal gyrus, and precentral gyrus; parietal lobes including the precuneus, angular gyrus, postcentral gyrus, and inferior parietal lobule; occipital lobes including the lingual gyrus and cuneus; temporal lobes including the fusiform gyrus and middle temporal gyrus; and the limbic system including the cingulate gyrus. These specific brain regions belonged to important networks known as the default mode network, the executive control network, and the visual network.Conclusion: The present study determined specific abnormal brain regions in sVCI patients, and these brain regions with specific changes were found to belong to important brain functional networks. The findings objectively present the exact abnormalities within the brain, which help further understand the pathogenesis of sVCI and identify them as potential imaging biomarkers. The results may also provide a basis for new approaches to treatment.

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