2022/06/17 by Kayson Fakhar, Claus C. Hilgetag · 1 voice · 4 citations
Neuroscience · #EEG and Brain-Computer Interfaces #Functional Brain Connectivity Studies #Neural dynamics and brain function
paper · pdf · doi:10.1371/journal.pcbi.1010250
openalex publication_date 2022/06/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/06/24
Lesion inference analysis is a fundamental approach for characterizing the causal contributions of neural elements to brain function. This approach has gained new prominence through the arrival of modern perturbation techniques with unprecedented levels of spatiotemporal precision. While inferences drawn from brain perturbations are conceptually powerful, they face methodological difficulties. Particularly, they are challenged to disentangle the true causal contributions of the involved elements, since often functions arise from coalitions of distributed, interacting elements, and localized perturbations have unknown global consequences. To elucidate these limitations, we systematically and exhaustively lesioned a small artificial neural network (ANN) playing a classic arcade game. We determined the functional contributions of all nodes and links, contrasting results from sequential single-element perturbations with simultaneous perturbations of multiple elements. We found that lesioning individual elements, one at a time, produced biased results. By contrast, multi-site lesion analysis captured crucial details that were missed by single-site lesions. We conclude that even small and seemingly simple ANNs show surprising complexity that needs to be addressed by multi-lesioning for a coherent causal characterization.