Gao Mengxia, Lam Charlene L M, Lui Wai M, Lau Kui Kai, Lee Tatia M C
The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong 999077, China.
Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong, Hong Kong 999077, China.
Brain Commun. 2022 Aug 20;4(5):fcac213. doi: 10.1093/braincomms/fcac213. eCollection 2022.
Moyamoya disease is a rare cerebrovascular disorder associated with cognitive dysfunction. It is usually treated by surgical revascularization, but research on the neurocognitive outcomes of revascularization surgery is controversial. Given that neurocognitive impairment could affect the daily activities of patients with moyamoya disease, early detection of postoperative neurocognitive outcomes has the potential to improve patient management. In this study, we applied a well-established connectome-based predictive modelling approach to develop machine learning models that used preoperative resting-state functional connectivity to predict postoperative changes in processing speed in patients with moyamoya disease. Twelve adult patients with moyamoya disease (age range: 23-49 years; female/male: 9/3) were recruited prior to surgery and underwent follow-up at 1 and 6 months after surgery. Twenty healthy controls (age range: 24-54 years; female/male: 14/6) were recruited and completed the behavioural test at baseline, 1-month follow-up and 6-month follow-up. Behavioural results indicated that the behavioural changes in processing speed at 1 and 6 months after surgery compared with baseline were not significant. Importantly, we showed that preoperative resting-state functional connectivity significantly predicted postoperative changes in processing speed at 1 month after surgery (negative network: = 0.63, = 0.017) and 6 months after surgery (positive network: = 0.62, = 0.010; negative network: = 0.55, = 0.010). We also identified cerebro-cerebellar and cortico-subcortical connectivities that were consistently associated with processing speed. The brain regions identified from our predictive models are not only consistent with previous studies but also extend previous findings by revealing their potential roles in postoperative neurocognitive functions in patients with moyamoya disease. Taken together, our findings provide preliminary evidence that preoperative resting-state functional connectivity might predict the post-surgical longitudinal neurocognitive changes in patients with moyamoya disease. Given that processing speed is a crucial cognitive ability supporting higher neurocognitive functions, this study's findings offer important insight into the clinical management of patients with moyamoya disease.
烟雾病是一种与认知功能障碍相关的罕见脑血管疾病。它通常通过手术血运重建进行治疗，但关于血运重建手术的神经认知结果的研究存在争议。鉴于神经认知障碍可能影响烟雾病患者的日常活动，早期检测术后神经认知结果有可能改善患者管理。在本研究中，我们应用一种成熟的基于连接组的预测建模方法来开发机器学习模型，该模型使用术前静息态功能连接来预测烟雾病患者术后处理速度的变化。12例成年烟雾病患者（年龄范围：23 - 49岁；女性/男性：9/3）在手术前被招募，并在术后1个月和6个月进行随访。20名健康对照者（年龄范围：24 - 54岁；女性/男性：14/6）被招募，并在基线、1个月随访和6个月随访时完成行为测试。行为结果表明，与基线相比，术后1个月和6个月处理速度的行为变化不显著。重要的是，我们发现术前静息态功能连接显著预测术后1个月（负性网络：= 0.63，= 0.017）和术后6个月（正性网络：= 0.62，= 0.010；负性网络：= 0.55，= 0.010）处理速度的变化。我们还确定了与处理速度始终相关的脑 - 小脑和皮质 - 皮质下连接。从我们的预测模型中识别出的脑区不仅与先前的研究一致，而且通过揭示它们在烟雾病患者术后神经认知功能中的潜在作用扩展了先前的发现。综上所述，我们的研究结果提供了初步证据，表明术前静息态功能连接可能预测烟雾病患者术后的纵向神经认知变化。鉴于处理速度是支持更高神经认知功能的关键认知能力，本研究结果为烟雾病患者的临床管理提供了重要见解。
