Adhikari Mohit H, Hacker Carl D, Siegel Josh S, Griffa Alessandra, Hagmann Patric, Deco Gustavo, Corbetta Maurizio
Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Ramon Trias Fargas, 25-27, Barcelona, 08005, Spain.
Department of Bioengineering, Washington University Saint Louis, USA.
Brain. 2017 Apr 1;140(4):1068-1085. doi: 10.1093/brain/awx021.
While several studies have shown that focal lesions affect the communication between structurally normal regions of the brain, and that these changes may correlate with behavioural deficits, their impact on brain's information processing capacity is currently unknown. Here we test the hypothesis that focal lesions decrease the brain's information processing capacity, of which changes in functional connectivity may be a measurable correlate. To measure processing capacity, we turned to whole brain computational modelling to estimate the integration and segregation of information in brain networks. First, we measured functional connectivity between different brain areas with resting state functional magnetic resonance imaging in healthy subjects (n = 26), and subjects who had suffered a cortical stroke (n = 36). We then used a whole-brain network model that coupled average excitatory activities of local regions via anatomical connectivity. Model parameters were optimized in each healthy or stroke participant to maximize correlation between model and empirical functional connectivity, so that the model's effective connectivity was a veridical representation of healthy or lesioned brain networks. Subsequently, we calculated two model-based measures: 'integration', a graph theoretical measure obtained from functional connectivity, which measures the connectedness of brain networks, and 'information capacity', an information theoretical measure that cannot be obtained empirically, representative of the segregative ability of brain networks to encode distinct stimuli. We found that both measures were decreased in stroke patients, as compared to healthy controls, particularly at the level of resting-state networks. Furthermore, we found that these measures, especially information capacity, correlate with measures of behavioural impairment and the segregation of resting-state networks empirically measured. This study shows that focal lesions affect the brain's ability to represent stimuli and task states, and that information capacity measured through whole brain models is a theory-driven measure of processing capacity that could be used as a biomarker of injury for outcome prediction or target for rehabilitation intervention.
虽然多项研究表明局灶性病变会影响大脑结构正常区域之间的通信，且这些变化可能与行为缺陷相关，但其对大脑信息处理能力的影响目前尚不清楚。在此，我们检验这样一个假设：局灶性病变会降低大脑的信息处理能力，而功能连接的变化可能是其可测量的相关指标。为了测量处理能力，我们采用全脑计算模型来估计大脑网络中信息的整合与分离。首先，我们使用静息态功能磁共振成像测量了健康受试者（n = 26）和皮质中风患者（n = 36）不同脑区之间的功能连接。然后，我们使用了一个全脑网络模型，该模型通过解剖连接耦合局部区域的平均兴奋性活动。在每个健康或中风参与者中对模型参数进行优化，以最大化模型与经验性功能连接之间的相关性，从而使模型的有效连接成为健康或受损脑网络的真实表征。随后，我们计算了两个基于模型的指标：“整合”，这是一个从功能连接中获得的图论指标，用于测量大脑网络的连通性；以及“信息容量”，这是一个无法通过经验获得的信息论指标，代表大脑网络编码不同刺激的分离能力。我们发现，与健康对照组相比，中风患者的这两个指标均降低，尤其是在静息态网络水平。此外，我们发现这些指标，特别是信息容量，与行为损伤指标以及经验测量的静息态网络分离相关。这项研究表明，局灶性病变会影响大脑表征刺激和任务状态的能力，并且通过全脑模型测量的信息容量是一种由理论驱动的处理能力指标，可作为损伤生物标志物用于结果预测或康复干预靶点。
