Identifying the white matter impairments among ART-naïve HIV patients: a multivariate pattern analysis of DTI data

Tang, Zhenchao; Liu, Zhenyu; Li, Ruili; Xin, Yang; Cui, Xingwei; Wang, Shuo; Yu, Dongdong; Li, Hongjun; Dong, Enqing; Tian, Jie

doi:10.1007/s00330-017-4820-1

Cited by 43 publications

(37 citation statements)

References 57 publications

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“…Variable results from DTI studies may be due, at least in part, to timing of evaluation relative to treatment (i.e., treatment naïve, currently un-medicated, chronically medicated, or older HIV infected individuals). For example, in early, treatment naïve HIV infection, white matter impairment (Tang et al, 2017 ) correlated with days since infection (Wright et al, 2015 ). In those on ART, a number of fiber tracts, including those of the corpus callosum and corona radiate are often reported as compromised (Leite et al, 2013 ; Xuan et al, 2013 ; Su et al, 2016 ; Wang et al, 2016 ).…”

Section: In Vivo Neuroimaging Of Hiv and Comorbiditiesmentioning

confidence: 99%

The Aging Brain With HIV Infection: Effects of Alcoholism or Hepatitis C Comorbidity

Zahr

2018

Front. Aging Neurosci.

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As successfully treated individuals with Human Immunodeficiency Virus (HIV)-infected age, cognitive and health challenges of normal aging ensue, burdened by HIV, treatment side effects, and high prevalence comorbidities, notably, Alcohol Use Disorders (AUD) and Hepatitis C virus (HCV) infection. In 2013, people over 55 years old accounted for 26% of the estimated number of people living with HIV (~1.2 million). The aging brain is increasingly vulnerable to endogenous and exogenous insult which, coupled with HIV infection and comorbid risk factors, can lead to additive or synergistic effects on cognitive and motor function. This paper reviews the literature on neuropsychological and in vivo Magnetic Resonance Imaging (MRI) evaluation of the aging HIV brain, while also considering the effects of comorbidity for AUD and HCV.

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Section: In Vivo Neuroimaging Of Hiv and Comorbiditiesmentioning

confidence: 99%

The Aging Brain With HIV Infection: Effects of Alcoholism or Hepatitis C Comorbidity

Zahr

2018

Front. Aging Neurosci.

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“…Compared with previous techniques that process medical images as pictures for visual inspection, radiomics introduces a new way to mine the information contained in medical images, and thus, radiomics offers an unprecedented opportunity for clinically assisted diagnosis. 17 Radiomics analysis has been successfully applied to produce imaging biomarkers for neuropsychiatric diseases such as Alzheimer disease, 18 human immunodeficiency virus-related neurodegeneration, 19 and bipolar disorder. Therefore, such studies have demonstrated the potential of radiomics in predicting hippocampal sclerosis.…”

Section: Introductionmentioning

confidence: 99%

Automated detection of hippocampal sclerosis using clinically empirical and radiomics features

Liu

Sun

et al. 2019

Epilepsia

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Objective: Temporal lobe epilepsy is a common form of epilepsy that might be amenable to surgery. However, magnetic resonance imaging (MRI)-negative hippocampal sclerosis (HS) can hamper early diagnosis and surgical intervention for patients in clinical practice, resulting in disease progression. Our aim was to automatically detect and evaluate the structural alterations of HS. Methods: Eighty patients with pharmacoresistant epilepsy and histologically proven HS and 80 healthy controls were included in the study. Two automated classifiers relying on clinically empirical and radiomics features were developed to detect HS. Cross-validation was implemented on all participants, and specificity was assessed in the 80 controls. The performance, robustness, and clinical utility of the model were also evaluated. Structural analysis was performed to investigate the morphological abnormalities of HS. Results: The computational model based on clinical empirical features showed excellent performance, with an area under the curve (AUC) of 0.981 in the primary cohort and 0.993 in the validation cohort. One of the features, gray-white matter boundary blurring in the temporal pole, exhibited the highest weight in model performance.Another model based on radiomics features also showed satisfactory performance, with AUC of 0.997 in the primary cohort and 0.978 in the validation cohort. In particular, the model improved the detection rate of MRI-negative HS to 96.0%. The novel feature of cortical folding complexity of the temporal pole not only played a crucial role in the classifier but also had significant correlation with disease duration. |MO et al.

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“…For fMRI, the rst 10 time-point images were discarded for magnetization balance, after that, the remaining 230 time-point images were corrected and aligned to the rst time-point image to correct head movements; the resultant motion-corrected volumes were co-registered to the anatomical T1-weighted images and normalized to the Montreal Neurological Institute template, resampling to a 3 mm cube voxel resolution. For DTI, we employed the Eddy Correct tool to correct the head motion and eddy current distortions [38,39] , and used the Brain Extraction tool to remove the non-brain tissues of the B0 image and create the brain mask [40] , then we adopted the DTIFIT tool to t the diffusion tensor at each voxel and produced four parameter maps, encompassing fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity [41] . For amyloid-PET, the structural images were individually registered to the averaged PET images, then we performed segmentation of all the co-registered structural images, spatially normalized the PET images to the Montreal Neurological Institute standard space by using the forward parameters (estimated during the segmentation), smoothed the images with an 8 mm full width at half maximum Gaussian kernel.…”

Section: Imaging Acquisition and Preprocessingmentioning

confidence: 99%

Radiomics analysis of magnetic resonance imaging helps to identify preclinical Alzheimer’s disease: an exploratory study

Li¹,

Wu²,

Jiang³

et al. 2020

Preprint

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Background: Diagnosing Alzheimer’s disease (AD) in the preclinical stage offers opportunities to early intervention, however, there is a lack of convenient biomarkers currently. By using the methods of radiomics analysis, we aimed to determine whether the features extracted from multi-parameter magnetic resonance imaging (MRI) can be used as potential biomarkers. Methods: This study is part of the SILCODE project (NCT03370744). All participants were cognitively healthy at baseline. The cohort 1 (n=183) was divided into individuals with preclinical AD (n=78) and controls (n=105) by amyloid-positron emission tomography, used as the training dataset (80%) and validation dataset (the rest 20%); cohort 2 (n=51) was divided into “converters” and “non-converters” by individuals’ future cognitive status, used as a separate test dataset; cohort 3 included 37 “converters” (13 from ADNI), was used as another test set for independent longitudinal researches. We extracted radiomics features from multi-parameter MRI of each participant, used t-tests, autocorrelation tests, and three independent selection algorithms, respectively, to select features. Then we established two classification models (support vector machine (SVM) and random forest (RF)) to verify the efficiency of retained features. Five-fold cross-validation and 100 repetitions were carried out for the above process. Furthermore, the acquired stable high-frequency features were tested in cohort 3 by paired two-sample t-tests and survival analyses, in order to identify whether their levels change with cognitive decline and impact conversion time. Results: The SVM and RF models both showed excellent classification efficiency, with the average accuracy of 89.65%-95.90% and 87.07%-90.81% respectively in the validation set, 81.86%-89.10% and 83.19%-83.68% respectively in the test set. Three stable high-frequency features were identified, namely Large zone high-gray-level emphasis feature of right posterior cingulate gyrus, Variance feature of left superior parietal gyrus and Coarseness feature of left posterior cingulate gyrus, all based on structural MRI modality; their levels were correlated with amyloid-β deposition, played good roles in predicting future cognitive decline (AUCs 64.9%-76.1%). In addition, levels of the Variance feature at baseline timepoint decreased with cognitive decline, and can affect the conversion time (p<0.05). Conclusion: In this exploratory study, we show radiomics features of multi-parameter MRI can be potential biomarkers of preclinical AD.

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Identifying the white matter impairments among ART-naïve HIV patients: a multivariate pattern analysis of DTI data

Cited by 43 publications

References 57 publications

The Aging Brain With HIV Infection: Effects of Alcoholism or Hepatitis C Comorbidity

The Aging Brain With HIV Infection: Effects of Alcoholism or Hepatitis C Comorbidity

Automated detection of hippocampal sclerosis using clinically empirical and radiomics features

Radiomics analysis of magnetic resonance imaging helps to identify preclinical Alzheimer’s disease: an exploratory study

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