ArticlemedRxiv : the preprint server for health sciences2024
Baseline functional connectivity predicts who will benefit from neuromodulation: evidence from primary progressive aphasia.
Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Identifying the characteristics of individuals who demonstrate response to an intervention allows us to predict who is most likely to benefit from certain interventions. Prediction is challenging in rare and heterogeneous diseases, such as primary progressive aphasia (PPA), that have varying clinical manifestations. We aimed to determine the characteristics of those who will benefit most from transcranial direct current stimulation (tDCS) of the left inferior frontal gyrus (IFG) using a novel heterogeneity and group identification analysis. Methods: We compared the predictive ability of demographic and clinical patient characteristics (e.g., PPA variant and disease progression, baseline language performance) vs. functional connectivity alone (from resting-state fMRI) in the same cohort. Results: Functional connectivity alone had the highest predictive value for outcomes, explaining 62% and 75% of tDCS effect of variance in generalization (semantic fluency) and in the trained outcome of the clinical trial (written naming), contrasted with <15% predicted by clinical characteristics, including baseline language performance. Patients with higher baseline functional connectivity between the left IFG (opercularis and triangularis), and between the middle temporal pole and posterior superior temporal gyrus, were most likely to benefit from tDCS. Conclusions: We show the importance of a baseline 7-minute functional connectivity scan in predicting tDCS outcomes, and point towards a precision medicine approach in neuromodulation studies. The study has important implications for clinical trials and practice, providing a statistical method that addresses heterogeneity in patient populations and allowing accurate prediction and enrollment of those who will most likely benefit from specific interventions.
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