Estudillo-Romero Alfonso, Migliaccio Raffaella, Batrancourt Bénédicte, Jannin Pierre, Baxter John S H
Laboratoire de Traitement du Signal et de l'Image (LTSI- INSERM UMR 1099), Université de Rennes, Rennes, 35000, France.
Institut du Cerveau, Paris, France.
Neuroimage Rep. 2024 Apr 26;4(2):100202. doi: 10.1016/j.ynirp.2024.100202. eCollection 2024 Jun.
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.
众所周知，可卡因使用障碍（CUD）会导致神经重构，这可以通过大脑的局部扩散特性观察到。这些变化可能高度相关，同时在患有不同合并症或有不同物质使用史的患者中存在差异。这意味着可能需要更复杂的神经影像分析工具，以更好地检测在这些维度上变化的特定生物标志物。我们使用基于体素的张量测量法（VBD），对整个大脑的扩散张量图像（DTI）进行白质和灰质完整性研究，该研究基于一个CUD患者和健康对照的数据库，采用纯数据驱动的方法。这些VBD图谱揭示了显著的皮质和皮质下差异，这些差异表明了包括脑岛、小脑、脑室、丘脑 - 皮质辐射和胼胝体束在内的这些神经改变。为了明确这些结果并研究CUD的异质性，VBD图谱已被分解为五个去相关的生物标志物：一个位于左侧脑岛周围区域，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的近端区域，这些生物标志物在CUD中可能发挥潜在作用。这些去相关的生物标志物本身已与CUD患者组的消费模式、精神疾病和边缘性人格障碍评分相关联。这种使用机器学习技术来检测和明确复杂非线性模式的初步方法，有望更好地理解诸如CUD等复杂和异质性疾病。
