A major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

神经科学的一个主要挑战是确定大脑白质结构连通性和突发活动之间的定量关系。我们试图通过构建五名难治性癫痫患者在平均约 14 小时带通期间发作间期激活模式的成对最大熵模型 (MEM) 来揭示对其功能活动至关重要的大脑区域之间的内在关系每个患者的颅内脑电图 (iEEG) 记录。我们发现成对 MEM 准确地预测了 iEEG 电极的激活模式概率及其成对相关性。我们证明，估计的成对 MEM 的相互作用权重预测结构连通性及其在多个频率上的强度显着超出仅基于大多数患者的采样区域距离的预期。成对的 MEM 共同提供了一个解释 iEEG 功能连接的框架，并提供了对大脑结构连接组如何通过促进连接结构之间的共同激活而产生大规模激活模式的见解。