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Item Brain structural connectome in neonates with prenatal opioid exposure(Frontiers Media, 2022-09-16) Vishnubhotla, Ramana V.; Zhao, Yi; Wen, Qiuting; Dietrich, Jonathan; Sokol, Gregory M.; Sadhasivam, Senthilkumar; Radhakrishnan, Rupa; Radiology and Imaging Sciences, School of MedicineIntroduction: Infants with prenatal opioid exposure (POE) are shown to be at risk for poor long-term neurobehavioral and cognitive outcomes. Early detection of brain developmental alterations on neuroimaging could help in understanding the effect of opioids on the developing brain. Recent studies have shown altered brain functional network connectivity through the application of graph theoretical modeling, in infants with POE. In this study, we assess global brain structural connectivity through diffusion tensor imaging (DTI) metrics and apply graph theoretical modeling to brain structural connectivity in infants with POE. Methods: In this prospective observational study in infants with POE and control infants, brain MRI including DTI was performed before completion of 3 months corrected postmenstrual age. Tractography was performed on the whole brain using a deterministic fiber tracking algorithm. Pairwise connectivity and network measure were calculated based on fiber count and fractional anisotropy (FA) values. Graph theoretical metrics were also derived. Results: There were 11 POE and 18 unexposed infants included in the analysis. Pairwise connectivity based on fiber count showed alterations in 32 connections. Pairwise connectivity based on FA values showed alterations in 24 connections. Connections between the right superior frontal gyrus and right paracentral lobule and between the right superior occipital gyrus and right fusiform gyrus were significantly different after adjusting for multiple comparisons between POE infants and unexposed controls. Additionally, alterations in graph theoretical network metrics were identified with fiber count and FA value derived tracts. Conclusion: Comparisons show significant differences in fiber count in two structural connections. The long-term clinical outcomes related to these findings may be assessed in longitudinal follow-up studies.Item Differences in White Matter Microstructure and Connectivity in Nontreatment‐Seeking Individuals with Alcohol Use Disorder(Wiley, 2018-05) Chumin, Evgeny J.; Goñi, Joaquín; Halcomb, Meredith E.; Durazzo, Timothy C.; Džemidžić, Mario; Yoder, Karmen K.; Radiology and Imaging Sciences, School of MedicineBackground Diffusion‐weighted imaging (DWI) has been widely used to investigate the integrity of white matter (WM; indexed by fractional anisotropy [FA]) in alcohol dependence and cigarette smoking. These disorders are highly comorbid, yet cigarette use has often not been adequately controlled in neuroimaging studies of alcohol‐dependent populations. In addition, information on WM deficits in currently drinking, nontreatment‐seeking (NTS) individuals with alcohol dependence is limited. Therefore, the aim of this work was to investigate WM microstructural integrity in alcohol use disorder by comparing matched samples of cigarette smoking NTS and social drinkers (SD). Methods Thirty‐eight smoking NTS and 19 smoking SD subjects underwent DWI as well as structural magnetic resonance imaging. After an in‐house preprocessing of the DWI data, FA images were analyzed with tract‐based spatial statistics (TBSS). FA obtained from the TBSS skeleton was tested for correlation with recent alcohol consumption. Results Smoking NTS had lower FA relative to smoking SD, predominantly in the left hemisphere (p < 0.05, family‐wise error rate corrected across FA skeleton). Across the full sample, FA and number of drinks per week were negatively related (ρ = −0.348, p = 0.008). Qualitative analyses of the structural connections through compromised WM as identified by TBSS showed differential connectivity of gray matter in NTS compared to SD subjects of left frontal, temporal, and parietal regions. Conclusions NTS subjects had lower WM FA than SD, indicating compromised WM integrity in the NTS population. The inverse relationship of entire WM skeleton FA with self‐reported alcohol consumption supports previous evidence of a continuum of detrimental effects of alcohol consumption on WM. These results provide additional evidence that alcohol dependence is associated with reduced WM integrity in currently drinking NTS alcohol‐dependent individuals, after controlling for the key variable of cigarette smoking.Item Integrated Visualization of Human Brain Connectome Data(Springer, 2015-08) Li, Huang; Fang, Shiaofen; Goni, Joaquin; Contreras, Joey A.; Liang, Yanhua; Cai, Chengtao; West, John D.; Risacher, Shannon L.; Wang, Yang; Sporns, Olaf; Saykin, Andrew J.; Shen, Li; Department of Radiology and Imaging Sciences, IU School of MedicineVisualization plays a vital role in the analysis of multi-modal neuroimaging data. A major challenge in neuroimaging visualization is how to integrate structural, functional and connectivity data to form a comprehensive visual context for data exploration, quality control, and hypothesis discovery. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. New surface texture techniques are developed to map non-spatial attributes onto the brain surfaces from MRI scans. Two types of non-spatial information are represented: (1) time-series data from resting-state functional MRI measuring brain activation; (2) network properties derived from structural connectivity data for different groups of subjects, which may help guide the detection of differentiation features. Through visual exploration, this integrated solution can help identify brain regions with highly correlated functional activations as well as their activation patterns. Visual detection of differentiation features can also potentially discover image based phenotypic biomarkers for brain diseases.Item Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup(Wiley, 2021) Faria, Andreia V.; Zhao, Yi; Ye, Chenfei; Hsu, Johnny; Yang, Kun; Cifuentes, Elizabeth; Wang, Lei; Mori, Susumu; Miller, Michael; Caffo, Brian; Sawa, Akira; Biostatistics and Health Data Science, School of MedicineMulti-institutional brain imaging studies have emerged to resolve conflicting results among individual studies. However, adjusting multiple variables at the technical and cohort levels is challenging. Therefore, it is important to explore approaches that provide meaningful results from relatively small samples at institutional levels. We studied 87 first episode psychosis (FEP) patients and 62 healthy subjects by combining supervised integrated factor analysis (SIFA) with a novel pipeline for automated structure-based analysis, an efficient and comprehensive method for dimensional data reduction that our group recently established. We integrated multiple MRI features (volume, DTI indices, resting state fMRI-rsfMRI) in the whole brain of each participant in an unbiased manner. The automated structure-based analysis showed widespread DTI abnormalities in FEP and rs-fMRI differences between FEP and healthy subjects mostly centered in thalamus. The combination of multiple modalities with SIFA was more efficient than the use of single modalities to stratify a subgroup of FEP (individuals with schizophrenia or schizoaffective disorder) that had more robust deficits from the overall FEP group. The information from multiple MRI modalities and analytical methods highlighted the thalamus as significantly abnormal in FEP. This study serves as a proof-of-concept for the potential of this methodology to reveal disease underpins and to stratify populations into more homogeneous sub-groups.