Tabe-Bordbar Shayan, Anastasio Thomas J
Computational Neurobiology Laboratory, Department of Molecular and Integrative Physiology, Beckman Institute, University of Illinois at Urbana-Champaign Urbana, IL, USA.
Front Comput Neurosci. 2016 Apr 26;10:27. doi: 10.3389/fncom.2016.00027. eCollection 2016.
Food-intake control is mediated by a heterogeneous network of different neural subtypes, distributed over various hypothalamic nuclei and other brain structures, in which each subtype can release more than one neurotransmitter or neurohormone. The complexity of the interactions of these subtypes poses a challenge to understanding their specific contributions to food-intake control, and apparent consistencies in the dataset can be contradicted by new findings. For example, the growing consensus that arcuate nucleus neurons expressing Agouti-related peptide (AgRP neurons) promote feeding, while those expressing pro-opiomelanocortin (POMC neurons) suppress feeding, is contradicted by findings that low AgRP neuron activity and high POMC neuron activity can be associated with high levels of food intake. Similarly, the growing consensus that GABAergic neurons in the lateral hypothalamus suppress feeding is contradicted by findings suggesting the opposite. Yet the complexity of the food-intake control network admits many different network behaviors. It is possible that anomalous associations between the responses of certain neural subtypes and feeding are actually consistent with known interactions, but their effect on feeding depends on the responses of the other neural subtypes in the network. We explored this possibility through computational analysis. We made a computer model of the interactions between the hypothalamic and other neural subtypes known to be involved in food-intake control, and optimized its parameters so that model behavior matched observed behavior over an extensive test battery. We then used specialized computational techniques to search the entire model state space, where each state represents a different configuration of the responses of the units (model neural subtypes) in the network. We found that the anomalous associations between the responses of certain hypothalamic neural subtypes and feeding are actually consistent with the known structure of the food-intake control network, and we could specify the ways in which the anomalous configurations differed from the expected ones. By analyzing the temporal relationships between different states we identified the conditions under which the anomalous associations can occur, and these stand as model predictions.
食物摄入控制由不同神经亚型组成的异质性网络介导，这些神经亚型分布于下丘脑的各个核团及其他脑结构中，其中每个亚型可释放不止一种神经递质或神经激素。这些亚型之间相互作用的复杂性给理解它们对食物摄入控制的具体贡献带来了挑战，数据集中明显的一致性可能会被新发现所推翻。例如，越来越多的人达成共识，即表达刺鼠相关肽的弓状核神经元（AgRP神经元）促进进食，而表达阿黑皮素原的神经元（POMC神经元）抑制进食，但有研究结果与之矛盾，即低AgRP神经元活性和高POMC神经元活性可能与高食物摄入量相关。同样，越来越多的人认为下丘脑外侧的γ-氨基丁酸能神经元抑制进食，但也有研究结果表明情况恰恰相反，这与上述共识相矛盾。然而，食物摄入控制网络的复杂性允许存在许多不同的网络行为。某些神经亚型的反应与进食之间的异常关联实际上可能与已知的相互作用一致，但其对进食的影响取决于网络中其他神经亚型的反应。我们通过计算分析探索了这种可能性。我们构建了一个计算机模型，模拟已知参与食物摄入控制的下丘脑及其他神经亚型之间的相互作用，并优化其参数，使模型行为在一系列广泛的测试中与观察到的行为相匹配。然后，我们使用专门的计算技术搜索整个模型状态空间，其中每个状态代表网络中单元（模型神经亚型）反应的不同配置。我们发现，某些下丘脑神经亚型的反应与进食之间的异常关联实际上与食物摄入控制网络的已知结构一致，并且我们可以明确异常配置与预期配置的不同之处。通过分析不同状态之间的时间关系，我们确定了异常关联可能发生的条件，这些条件可作为模型预测。
