Center for Visual and Neurocognitive Rehabilitation, Atlanta Veterans Affairs Medical Center, 1670 Clairmont Rd., Decatur, GA 30033, United States; Department of Neuroscience and Behavioral Biology, Emory University, 201 Dowman Dr., Atlanta, GA 30322, United States.
Center for Visual and Neurocognitive Rehabilitation, Atlanta Veterans Affairs Medical Center, 1670 Clairmont Rd., Decatur, GA 30033, United States; Joint GSU, Georgia Tech, and Emory Center for Translational Research in Neuroimaging and Data Science (TReNDS), Atlanta, GA, United States; Department of Physics and Astronomy, Georgia State University, Atlanta, GA 30302, United States; Department of Radiology and Imaging Sciences, Emory University, 201 Dowman Dr., Atlanta, GA 30322, United States.
Behav Brain Res. 2023 Aug 24;452:114575. doi: 10.1016/j.bbr.2023.114575. Epub 2023 Jul 7.
With the diversity in aphasia coupled with diminished gains at the chronic phase, it is imperative to deliver effective rehabilitation plans. Treatment outcomes have therefore been predicted using lesion-to-symptom mapping, but this method lacks holistic functional information about the language-network. This study, therefore, aims to develop whole-brain task-fMRI multivariate analysis to neurobiologically inspect lesion impacts on the language-network and predict behavioral outcomes in persons with aphasia (PWA) undergoing language therapy. In 14 chronic PWA, semantic fluency task-fMRI and behavioral measures were collected to develop prediction methodologies for post-treatment outcomes. Then, a recently developed imaging-based multivariate method to predict behavior (i.e., LESYMAP) was optimized to intake whole-brain task-fMRI data, and systematically tested for reliability with mass univariate methods. We also accounted for lesion size in both methods. Results showed that both mass univariate and multivariate methods identified unique biomarkers for semantic fluency improvements from baseline to 2-weeks post-treatment. Additionally, both methods demonstrated reliable spatial overlap in task-specific areas including the right middle frontal gyrus when identifying biomarkers of language discourse. Thus whole-brain task-fMRI multivariate analysis has the potential to identify functionally meaningful prognostic biomarkers even for relatively small sample sizes. In sum, our task-fMRI based multivariate approach holistically estimates post-treatment response for both word and sentence production and may serve as a complementary tool to mass univariate analysis in developing brain-behavior relationships for improved personalization of aphasia rehabilitation regimens.
由于失语症的多样性以及慢性期的收益减少，必须提供有效的康复计划。因此，已经使用病灶-症状映射来预测治疗结果，但这种方法缺乏语言网络的整体功能信息。因此，本研究旨在开发全脑任务 fMRI 多变量分析，从神经生物学角度检查病灶对语言网络的影响，并预测接受语言治疗的失语症患者（PWA）的行为结果。在 14 名慢性 PWA 中，收集了语义流畅性任务 fMRI 和行为测量数据，以开发针对治疗后结果的预测方法。然后，优化了一种新开发的基于成像的预测行为的多变量方法（即 LESYMAP），以摄入全脑任务 fMRI 数据，并使用大规模单变量方法对其可靠性进行了系统测试。我们还在这两种方法中都考虑了病灶大小。结果表明，无论是大规模单变量方法还是多变量方法，都可以从基线到治疗后 2 周识别出语义流畅性提高的独特生物标志物。此外，当识别语言话语的生物标志物时，这两种方法都在包括右中额回在内的特定任务区域中显示出可靠的空间重叠。因此，全脑任务 fMRI 多变量分析具有识别功能有意义的预后生物标志物的潜力，即使对于相对较小的样本量也是如此。总之，我们基于任务 fMRI 的多变量方法可以全面估计单词和句子产生的治疗后反应，并且可以作为大规模单变量分析的补充工具，用于开发大脑-行为关系，以改善失语症康复方案的个性化。
