Older individuals infected by Human Immunodeficiency Virus (HIV) are at risk for developing HIV-Associated Neurocognitive Disorder (HAND), i.e., from reduced cognitive functioning similar to HIV-negative individuals with Mild Cognitive Impairment (MCI) or to Alzheimer’s Disease (AD) if more severely affected. Incompletely understood is how brain structure can serve to differentiate cognitive impairment (CI) in the HIV-positive (i.e., HAND) from the HIV-negative cohort (i.e., MCI and AD). To that end, we designed a multi-label classifier that labels the structural magnetic resonance images (MRI) of individuals by their HIV and CI status via two binary variables. Proper training of such an approach traditionally requires well-curated datasets containing large number of samples for each of the corresponding four cohorts (healthy controls, CI HIV-negative adults a.k.a. CI-only, HIV-positive patients without CI a.k.a. HIV-only, and HAND). Because of the rarity of such datasets, we proposed to improve training of the multi-label classifier via a multi-domain learning scheme that also incorporates domain-specific classifiers on auxiliary single-label datasets specific to either binary label. Specifically, we complement the training dataset of MRIs of the four cohorts (Control: 156, CI-only: 335, HIV-only: 37, HAND: 145) acquired by the Memory and Aging Center at the University of California - San Francisco with a CI-specific dataset only containing MRIs of HIV-negative subjects (Controls: 229, CI-only: 397) from the Alzheimer’s Disease Neuroimaging Initiative and an HIV-specific dataset (Controls: 75, HIV-only: 75) provided by SRI International. Based on cross-validation on the UCSF dataset, the multi-domain and multi-label learning strategy leads to superior classification accuracy compared with one-domain or multi-class learning approaches, specifically for the undersampled HIV-only cohort. The ‘prediction logits’ of CI computed by the multi-label formulation also successfully stratify motor performance among the HIV-positive subjects (including HAND). Finally, brain patterns driving the subject-level predictions across all four cohorts characterize the independent and compounding effects of HIV and CI in the HAND cohort.

感染人类免疫缺陷病毒 (HIV) 的老年人有患上 HIV 相关神经认知障碍 (HAND) 的风险，即认知功能下降类似于患有轻度认知障碍 (MCI) 或阿尔茨海默病 (AD) 的 HIV 阴性个体如果影响更严重。尚未完全了解的是大脑结构如何用于区分 HIV 阳性（即 HAND）和 HIV 阴性人群（即 MCI 和 AD）中的认知障碍 (CI)。为此，我们设计了一个多标签分类器，通过两个二元变量根据 HIV 和 CI 状态对个体的结构磁共振图像 (MRI) 进行标记。这种方法的正确训练传统上需要精心策划的数据集，其中包含对应四个队列（健康对照，CI HIV 阴性成人又名仅 CI，没有 CI 的 HIV 阳性患者又名仅 HIV，和 HAND）。由于此类数据集的稀有性，我们建议通过多领域学习方案改进多标签分类器的训练，该方案还在特定于任一二进制标签的辅助单标签数据集上结合特定领域分类器。具体来说，我们补充了加州大学旧金山分校记忆与衰老中心获得的四个队列（对照组：156，仅 CI：335，仅 HIV：37，HAND：145）的 MRI 训练数据集CI 特定数据集仅包含来自阿尔茨海默病神经影像学倡议的 HIV 阴性受试者的 MRI（对照：229，仅 CI：397）和 SRI 提供的 HIV 特定数据集（对照：75，仅 HIV：75）国际的。基于对 UCSF 数据集的交叉验证，多域和多标签学习策略与单域或多类学习方法相比具有更高的分类准确性，特别是对于样本不足的 HIV 队列。由多标签公式计算的 CI 的“预测对数”也成功地对 HIV 阳性受试者（包括 HAND）的运动表现进行了分层。最后，驱动所有四个队列的受试者水平预测的大脑模式表征了 HAND 队列中 HIV 和 CI 的独立和复合效应。由多标签公式计算的 CI 的“预测对数”也成功地对 HIV 阳性受试者（包括 HAND）的运动表现进行了分层。最后，驱动所有四个队列的受试者水平预测的大脑模式表征了 HAND 队列中 HIV 和 CI 的独立和复合效应。由多标签公式计算的 CI 的“预测对数”也成功地对 HIV 阳性受试者（包括 HAND）的运动表现进行了分层。最后，驱动所有四个队列的受试者水平预测的大脑模式表征了 HAND 队列中 HIV 和 CI 的独立和复合效应。