Non-local diffusion-based biomarkers in patients with cocaine use disorder

Q4 Neuroscience
Alfonso Estudillo-Romero , Raffaella Migliaccio , Bénédicte Batrancourt , Pierre Jannin , John S.H. Baxter
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引用次数: 0

Abstract

Cocaine use disorder (CUD) is widely known to result in neurological reconfiguration which can be observed via local diffusivity characteristics of the brain. These changes can be highly correlated while simultaneously variable across patients with different comorbidities or histories of substance use. This implies that more complex neuroimage analysis tools may be necessary to better detect specific biomarkers that vary across these dimensions. We investigated white and gray matter integrity using voxel-based diktiometry (VBD) on whole brain diffusion tensor images (DTI) across a database of CUD patients and healthy controls using a purely data-driven approach. These VBD maps reveal significant cortical and subcortical differences that are indicative of these neural modifications including the insula, cerebellum, ventricles, thalamo-cortical radiations, and corpus callosum bundles. In order to disambiguate these results and investigate the heterogeneity of CUD, the VBD maps have been decomposed into five decorrelated biomarkers: one in the region surrounding the left insula, one implicating the corpus callosum, two concentrated in the left cerebellum, and the last concerning a proximal region of the interhemispheric fissure which serve as potential biomarkers playing a role in CUD. These decorrelated biomarkers have themselves been correlated with consumption patterns and psychiatric and borderline personality disorder scores on the CUD patient group. This preliminary approach to using machine learning techniques to both detect and disambiguate complex non-linear patterns shows promise for better understanding complex and heterogeneous disorders such as CUD.

Abstract Image

可卡因使用障碍患者的非局部扩散生物标志物
众所周知,可卡因使用障碍(CUD)会导致神经系统重构,这可以通过大脑局部扩散特性观察到。这些变化可能具有高度相关性,但同时在具有不同并发症或药物使用史的患者身上也会发生变化。这意味着可能需要更复杂的神经图像分析工具才能更好地检测出这些维度上不同的特定生物标志物。我们采用纯数据驱动的方法,在 CUD 患者和健康对照者数据库中的全脑弥散张量图像(DTI)上使用基于体素的二尖瓣测量法(VBD)研究了白质和灰质的完整性。这些 VBD 图揭示了皮层和皮层下的显著差异,这些差异表明了这些神经改变,包括岛叶、小脑、脑室、丘脑-皮层辐射和胼胝体束。为了区分这些结果并研究 CUD 的异质性,我们将 VBD 图分解为五个与装饰相关的生物标志物:一个位于左侧岛叶周围区域,一个涉及胼胝体,两个集中在左侧小脑,最后一个涉及半球间裂隙的近端区域,它们是在 CUD 中发挥作用的潜在生物标志物。这些与装饰相关的生物标志物本身与 CUD 患者群体的消费模式、精神疾病和边缘型人格障碍评分相关。这种利用机器学习技术检测和区分复杂的非线性模式的初步方法,为更好地理解 CUD 等复杂的异质性疾病带来了希望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Neuroimage. Reports
Neuroimage. Reports Neuroscience (General)
CiteScore
1.90
自引率
0.00%
发文量
0
审稿时长
87 days
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