Yu Renping, Zhang Han, Wu Xuehai, Fei Xuan, Yang Qing, Ma Zhiwei, Qi Zengxin, Zang Di, Tang Weijun, Mao Ying, Shen Dinggang
IEEE J Biomed Health Inform. 2023 Jan;27(1):469-479. doi: 10.1109/JBHI.2022.3218652. Epub 2023 Jan 4.
It is quite challenging to establish a prompt and reliable prognosis assessment for acquired brain injury (ABI) patients with persistent severe disorders of consciousness (DOC) like unconscious comatose and unresponsive wakefulness syndrome (a.k.a., vegetative state). Recent advances in brain functional imaging and functional net-work analysis have demonstrated its potential in determining the consciousness level and prognostic outcome for ABI patients with DOC. However, the diagnostic and prognostic usefulness of the whole-brain functional connectome based on advanced machine learning techniques has not been fully evaluated. The first aim of this study is to predict the outcome of individual unconscious ABI patients during a three-month follow-up. The second aim is to conduct precise individualized differentiation among different consciousness levels for exploring the neurobiological mechanisms underlying DOC. Based on resting-state fMRI, we construct large-scale functional networks by using a weighted sparse model, which ensures sparsity and interpretability by preserving strong functional connections. The functional connection strengths are exploited as features for outcome prediction and consciousness level differentiation. We achieve significantly improved consciousness level classification (accuracy: 84.78%) and recovery outcome prediction (accuracy: 89.74%) compared to other network construction methods. More importantly, we reveal the contributive connections across the entire brain in both tasks. These connections could serve as the potential biomarkers for better understanding of consciousness and further provide new insight into the development of diagnostic, prognostic, and effective therapeutic guidelines for ABI patients with DOC.
对于患有持续性严重意识障碍（DOC）的获得性脑损伤（ABI）患者，如昏迷和无反应觉醒综合征（又称植物状态），建立快速可靠的预后评估颇具挑战性。脑功能成像和功能网络分析的最新进展已证明其在确定患有DOC的ABI患者意识水平和预后结果方面的潜力。然而，基于先进机器学习技术的全脑功能连接组的诊断和预后效用尚未得到充分评估。本研究的首要目标是预测个体无意识ABI患者在三个月随访期间的结果。第二个目标是对不同意识水平进行精确的个体化区分，以探索DOC背后的神经生物学机制。基于静息态功能磁共振成像，我们使用加权稀疏模型构建大规模功能网络，该模型通过保留强功能连接来确保稀疏性和可解释性。功能连接强度被用作结果预测和意识水平区分的特征。与其他网络构建方法相比，我们在意识水平分类（准确率：84.78%）和恢复结果预测（准确率：89.74%）方面取得了显著改善。更重要的是，我们揭示了两项任务中全脑的贡献性连接。这些连接可作为潜在的生物标志物，以更好地理解意识，并进一步为患有DOC的ABI患者制定诊断、预后和有效治疗指南提供新的见解。
