Dowman Robert, Ritz Benjamin, Fowler Kathleen
Department of Psychology, Clarkson University, 8 Clarkson Ave, Potsdam, NY, 13699-5825, USA.
Department of Mathematics, Clarkson University, Potsdam, NY, USA.
Cogn Affect Behav Neurosci. 2016 Aug;16(4):689-708. doi: 10.3758/s13415-016-0424-5.
Connectionist modeling was used to investigate the brain mechanisms responsible for pain's ability to shift attention away from another stimulus modality and toward itself. Different connectionist model architectures were used to simulate the different possible brain mechanisms underlying this attentional bias, where nodes in the model simulated the brain areas thought to mediate the attentional bias, and the connections between the nodes simulated the interactions between the brain areas. Mathematical optimization techniques were used to find the model parameters, such as connection strengths, that produced the best quantitative fits of reaction time and event-related potential data obtained in our previous work. Of the several architectures tested, two produced excellent quantitative fits of the experimental data. One involved an unexpected pain stimulus activating somatic threat detectors in the dorsal posterior insula. This threat detector activity was monitored by the medial prefrontal cortex, which in turn evoked a phasic response in the locus coeruleus. The locus coeruleus phasic response resulted in a facilitation of the cortical areas involved in decision and response processes time-locked to the painful stimulus. The second architecture involved the presence of pain causing an increase in general arousal. The increase in arousal was mediated by locus coeruleus tonic activity, which facilitated responses in the cortical areas mediating the sensory, decision, and response processes involved in the task. These two neural network architectures generated competing predictions that can be tested in future studies.
采用联结主义建模来研究大脑机制，该机制负责疼痛将注意力从另一种刺激模式转移并指向自身的能力。使用不同的联结主义模型架构来模拟这种注意力偏差背后不同的可能大脑机制，其中模型中的节点模拟被认为介导注意力偏差的脑区，节点之间的连接模拟脑区之间的相互作用。运用数学优化技术来寻找模型参数，如连接强度，这些参数能对我们之前工作中获得的反应时间和事件相关电位数据产生最佳的定量拟合。在测试的几种架构中，有两种对实验数据产生了出色的定量拟合。一种涉及意外的疼痛刺激激活背侧后岛叶中的躯体威胁探测器。这种威胁探测器的活动由内侧前额叶皮层监测，内侧前额叶皮层进而在蓝斑诱发一个相位反应。蓝斑的相位反应导致对与疼痛刺激时间锁定的决策和反应过程中涉及的皮层区域的促进作用。第二种架构涉及疼痛的存在导致总体觉醒增加。觉醒的增加由蓝斑的紧张性活动介导，其促进了在任务中涉及的感觉、决策和反应过程的皮层区域中的反应。这两种神经网络架构产生了相互竞争的预测，可在未来研究中进行测试。
