Causal functional contributions and interactions in the attention network of the brain: an objective multi-perturbation analysis

作者: Melissa Zavaglia , Claus C. Hilgetag

DOI: 10.1007/S00429-015-1058-Z

关键词: Network modelPsychologyVisual attentionSuperior colliculusAttention networkPosterior parietal cortexLesionCategorical variableNeuroscienceLateralization of brain function

摘要: Spatial attention is a prime example for the distributed network functions of brain. Lesion studies in animal models have been used to investigate intact attentional mechanisms as well perspectives rehabilitation injured Here, we systematically analyzed behavioral data from cooling deactivation and permanent lesion experiments cat, where unilateral posterior parietal cortex (in vicinity middle suprasylvian cortex, pMS) or superior colliculus (SC) cause severe neglect contralateral hemifield. Counterintuitively, additional structures opposite hemisphere reverses deficit. Using such data, employed game-theoretical approach, multi-perturbation Shapley value analysis (MSA), inferring functional contributions interactions bilateral pMS SC performance visual studies. The approach provides an objective theoretical strategy inferences allows unique quantitative characterization regional on basis multi-perturbations. demonstrated that right made strongest positive left-field orienting, while left brain regions had negative contributions, implying their perturbation may reverse effects lesions improve normal function. An modulations among revealed redundant (implying overlap) between within each hemisphere, synergistic regions. To assess reliability MSA method face variable incomplete input performed sensitivity analysis, investigating how much contribution values four depended specific configurations prediction unknown performances. results suggest sensitive categorical, but insensitive gradual changes data. Finally, created basic model was based known anatomical cortical-tectal reproduced experimentally observed behavior orienting. We discuss structural organization relative causal identified by MSA, aid mechanistic understanding

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