Search

Many computational models assume that reinforcement learning relies on changes in synaptic efficacy between cortical regions representing stimuli and striatal regions involved in response selection, but this assumption has thus far lacked empirical support in humans. We recorded hemodynamic signals with fMRI while participants navigated a virtual maze to find hidden rewards. We fitted a reinforcement-learning algorithm to participants’ choice behavior and evaluated the neural activity and the changes in functional connectivity related to trial-by-trial learning variables. Activity in the posterior putamen during choice periods increased progressively during learning. Furthermore, the functional connections between the sensorimotor cortex and the posterior putamen strengthened progressively as participants learned the task. These changes in corticostriatal connectivity differentiated participants who learned the task from those who did not. These findings provide a direct link between changes in corticostriatal connectivity and learning, thereby supporting a central assumption common to several computational models of reinforcement learning.

Differing imaging modalities provide unique channels of information to probe differing aspects of the brain’s structural or functional organization. In combination, differing modalities provide complementary and mutually informative data about tissue organization that is more than their sum. We acquired and spatially coregistered data in four MRI modalities – anatomical MRI, functional MRI, diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (MRS) – from 20 healthy adults to understand how inter-individual variability in measures from one modality account for variability in measures from other modalities at each voxel of the brain. We detected significant correlations of local volumes with the magnitude of functional activation, suggesting that underlying variation in local volumes contributes to individual variability in functional activation. We also detected significant inverse correlations of NAA (a putative measure of neuronal density and viability) with volumes of white matter in the frontal cortex, with DTI-based measures of tissue organization within the superior longitudinal fasciculus, and with the magnitude of functional activation and default-mode activity during simple visual and motor tasks, indicating that substantial variance in local volumes, white matter organization, and functional activation derives from an underlying variability in the number or density of neurons in those regions. Many of these imaging measures correlated with measures of intellectual ability within differing brain tissues and differing neural systems, demonstrating that the neural determinants of intellectual capacity involve numerous and disparate features of brain tissue organization, a conclusion that could be made with confidence only when imaging the same individuals with multiple MRI modalities.

Studies have found abnormalities of resting EEG measures of hemispheric activity in depressive disorders. Similar EEG findings and a prominent thinning of the cortical mantle have been reported for persons at risk for depression. The correspondence between EEG alpha power and magnetic resonance imaging (MRI) measures of cortical thickness was examined in a multigenerational study of individuals at risk for depression. Seventy-five participants underwent resting EEG and approximately 5 years later underwent MRI scanning. High-risk participants (n = 37) were biological descendants of probands having major depression and low-risk participants (n = 38) were descendants of individuals without a history of depression. EEG alpha power was interpolated across the surface of a template brain and coregistered with measures of cortical thickness. Voxel-wise correlations of cortical thickness and alpha power were computed while covarying for age and gender. The high-risk group, when compared to the low-risk group, showed greater alpha asymmetry in an eyes-closed condition, with relatively less activity over right parietal cortex. Alpha power correlated inversely with cortical thickness, particularly over the right posterior region, indicating that EEG evidence of reduced cortical activity was associated with increased cortical thinning. This is the first report of widespread correlation of EEG alpha activity with MRI measures of cortical thickness. Although both EEG and MRI measures are associated with risk for depression, we did not detect evidence that cortical thickness mediated the alpha asymmetry findings. Thus, alpha asymmetry, alone or in combination with MRI, may be a marker of vulnerability for a familial form of depression.