用深卷积神经网络模拟神经退行性疾病和创伤性脑损伤后的认知缺陷
Modeling cognitive deficits following neurodegenerative diseases and traumatic brain injuries with deep convolutional neural networks
摘要：
神经退行性疾病和创伤性脑损伤（TBI）的准确诊断和评估仍然是一个挑战。由于局灶性轴突损伤（FAS），两者都会导致认知和功能缺陷，但由于我们在体内细胞水平评估受损神经元的能力有限，因此难以提供预后。我们使用卷积神经网络（CNN）作为我们的认知模型来模拟神经退行性疾病和TBI的影响。我们利用FAS的生物药理学相关统计数据，以功能相关的方式破坏CNN中的连接。我们通过修剪CNN来减少过度设计，从而将能量约束纳入大脑。定性地，我们证明了损害会导致类似人类的错误。我们的实验还提供了准确性如何受到各种类型和水平的损害的定量评估。固定数量的损伤所导致的赤字在很大程度上取决于哪些连接是随机受损的，这为为什么难以预测损伤提供了直观的解释。在解释人类大脑等复杂系统的认知缺陷时，存在很大程度的主观性。然而，我们提供了重要的见解和定量框架的疾病，其中FAS牵连。
英文摘要：
The accurate diagnosis and assessment of neurodegenerative disease and traumatic brain injuries (TBI) remain open challenges. Both cause cognitive and functional deficits due to focal axonal swellings (FAS), but it is difficult to deliver a prognosis due to our limited ability to assess damaged neurons at a cellular level in vivo. We simulate the effects of neurodegenerative disease and TBI using convolutional neural networks (CNNs) as our model of cognition. We utilize biophysically relevant statistical data on FAS to damage the connections in CNNs in a functionally relevant way. We incorporate energy constraints on the brain by pruning the CNNs to be less over-engineered. Qualitatively, we demonstrate that damage leads to human-like mistakes. Our experiments also provide quantitative assessments of how accuracy is affected by various types and levels of damage. The deficit resulting from a fixed amount of damage greatly depends on which connections are randomly injured, providing intuition for why it is difficult to predict impairments. There is a large degree of subjectivity when it comes to interpreting cognitive deficits from complex systems such as the human brain. However, we provide important insight and a quantitative framework for disorders in which FAS are implicated.
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