In this series we are looking to build a model of the Insular Cortex in terms of emotional regulation. Along the way we have seen that a number of other brain circuits are involved in emotions as reflected in the diagram above. In this post I have taken a diversion into the Limbic System to better contextualise the role of the Insular Cortex in emotions.
We can ask the question what is the role of the Limbic System in emotions that would distinguish it from the Insular Cortex? In order to begin to answer this question we would need to better understand the Limbic System itself. In this post I’m looking at a review article about the Hippocampus, a key part of the Limbic System. The paper is titled ‘Decoding information in the Human Hippocampus: A User’s Guide‘ by Chadwick, Bonnici and Maguire.
The authors look at a neat statistical analytical technique known as multi-voxel pattern analysis (MPVA) which is increasingly being used in fMRI analysis. In fMRI analysis, the brain is divided up into voxels which are volume units. This facilitates analysis of the activity detected by the scanner. Much of the analysis has involved region of interest analysis where activity in a region of the brain is analysed in relation to the experimental variables of interest.
The problem inherent in this technique is that it makes an assumption about brain activity which then constrains the subsequent analysis. The analysis assumes that the region is involved in the activity rather than discrete areas within multiple regions or even discrete areas within brain regions. However if activity occurs within discrete networks within a single region, then a region of interest analysis will overlook this activity which is lost in the averaged data.
MVPA divides voxels into groups and assesses activity against the experimental variables of interest. In a simple example it would be state A or B. There are multiple groups which allow multiple tests of the data. The analysis uses the number of experimental states to facilitate an assessment of the accuracy of the analysis in predicting activity-state correlates.
The paper then summarises some of the research that has been done using this approach and the results are very interesting. Check the paper for more details (including a few missing steps based on frequency maps to get to some of the results below) but what some of the research is suggesting is that
1. The Hippocampus works by separating out patterns that are presented to it and turning them into unique events. A crude example would be separating out apples and oranges that are presented in sequence. Components of both apples and oranges such as red, orange, peel and shape would be utilised to discriminate one from the other. This has been suggested from previous research but some of the experimental evidence here is quite neat.
2. The Hippocampus stores information about scenes according to spatial location distributed amongst different memories. Based on research by Hassabis and colleagues there was also a suggestion that the Posterior Parahippocampal Cortex played a role in storing different aspects of environments in a way which was possibly complementary to the Hippocampus
The Parahippocampal Gyrus
3. Episodic memories were likely to be stored in the Hippocampus, Parahippocampal Cortex and Entorhinal Cortex with Hippocampal activity appearing to be better correlated with these memories.
4. That the Hippocampus may form memories based on principles utilised in neural networks. The story here however is less straightforward. Rather than simple attractor networks it looks as though the Hippocampus creates/stores simple networks and then generates modified networks based on novel but slight different patterns. To me this sounded a bit like the process of induction.
5. Activity in the Hippocampus was correlated with higher level decision making based on classifying patterns.
Perhaps the fifth point is one of the most important ones as it suggests that the Hippocampus as part of the Limbic System could be playing a central role in decision making on the basis of received sensory information/perception. This easily lends itself to a discussion of the emotions.
Related Resources on this Site
Contributors to the Model (links are to the posts in which contributions were made – these links may contain further links directly to the contributors)
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