Combining Computational Modeling and Neuroimaging to Examine Multiple Category Learning Systems in the Brain

Considerable evidence has argued in favor of multiple neural systems supporting human category learning, one based on conscious rule inference and one based on implicit information integration. However, there have been few attempts to study potential system interactions during category learning. The PINNACLE (Parallel Interactive Neural Networks Active in Category Learning) model incorporates multiple categorization systems that compete to provide categorization judgments about visual stimuli. Incorporating competing systems requires inclusion of cognitive mechanisms associated with resolving this competition and creates a potential credit assignment problem in handling feedback. The hypothesized mechanisms make predictions about internal mental states that are not always reflected in choice behavior, but may be reflected in neural activity. Two prior functional magnetic resonance imaging (fMRI) studies of category learning were re-analyzed using PINNACLE to identify neural correlates of internal cognitive states on each trial. These analyses identified additional brain regions supporting the two types of category learning, regions particularly active when the systems are hypothesized to be in maximal competition, and found evidence of covert learning activity in the “off system” (the category learning system not currently driving behavior). These results suggest that PINNACLE provides a plausible framework for how competing multiple category learning systems are organized in the brain and shows how computational modeling approaches and fMRI can be used synergistically to gain access to cognitive processes that support complex decision-making machinery

The effect of theta-burst TMS on cognitive control networks measured with resting state fMRI

It has been proposed that two relatively independent cognitive control networks exist in the brain: the cingulo-opercular network (CO) and the fronto-parietal network (FP). Past work has shown that chronic brain lesions affect these networks independently. It remains unclear, however, how these two networks are affected by acute brain disruptions. To examine this, we conducted a within-subject theta-burst transcranial magnetic stimulation (TBS) experiment in healthy individuals that targeted left anterior insula/frontal operculum (L aI/fO, a region in the CO network), left dorsolateral prefrontal cortex (L dlPFC, a region in the FP network), or left primary somatosensory cortex (L S1, an experimental control region). Functional connectivity was measured in resting state fMRI scans collected before and after continuous TBS on each day. We found that TBS was accompanied by generalized increases in network connectivity, especially FP network connectivity, after TBS to either region involved in cognitive control. Whole-brain analyses demonstrated that the L dlPFC and L aI/fO showed increased connectivity with regions in frontal, parietal, and cingulate cortex after TBS to either L dlPFC or L aI/fO, but not to L S1. These results suggest that acute disruption by TBS to cognitive control regions causes widespread changes in network connectivity not limited to the targeted networks

Relationship between TBS effects across sites.

<p>This figure shows the relationship between changes in rCBF under the coil for different TMS sites targeted on separate days, with each individual point representing a single participant. Changes in rCBF under the coil were not strongly correlated across different TBS locations (<i>dlPFC to aI/fO, top: r = </i>−<i>0.06; S1 to dlPFC, middle: r = </i>−<i>0.14; S1 to aI/fO, bottom: r = 0.26</i>). Dashed lines indicate the linear regression fit, and black diagonal lines indicate equality between the TBS effects on perfusion.</p

rCBF measures after TBS across all sites.

<p>(<b>A</b>) Changes in rCBF under the TMS site are shown for the three targeted locations. (<b>B</b>) At L S1 and L dlPFC, where consistent effects of TBS were seen under the coil, site selectivity was examined by comparing changes across the different targeted locations and regions of interest. (<i>*p<0.05, ∼p<0.10; horizontal lines in </i><b><i>A</i></b><i> indicate a significant main effect of block</i>).</p

Relationship between rCBF and connectivity.

<p>(<b>A</b>) There was no relationship at baseline between rCBF and functional connectivity for either the aI/fO (<i>top</i>) or dlPFC (<i>bottom</i>) node. (<b>B</b>) Changes in aI/fO rCBF were related to changes in connectivity of aI/fO to the CO network after TBS to aI/fO (<i>top-left</i>) but not after TBS to S1 (<i>top-right</i>). Similarly, changes in dlPFC rCBF were related to changes in connectivity of dlPFC to the FP network after TBS to dlPFC (<i>bottom-left</i>) but not after TBS to S1 (<i>bottom-right</i>).</p