Investigating microstructural variation in the human hippocampus using non-negative matrix factorization.

In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses.

Effective connectivity of AKT1-mediated dopaminergic working memory networks and pharmacogenetics of anti-dopaminergic treatment.

Working memory is a limited capacity system that integrates and manipulates information across brief periods of time, engaging a network of prefrontal, parietal and subcortical brain regions. Genetic control of these heritable brain processes have been suggested by functional genetic variations influencing dopamine signalling, which affect prefrontal activity during complex working memory tasks. However, less is known about genetic control over component working memory cortical-subcortical networks in humans, and the pharmacogenetic implications of dopamine-related genes on cognition in patients receiving anti-dopaminergic drugs. Here, we examined predictions from basic models of dopaminergic signalling in cortical and cortical-subcortical circuitries implicated in dissociable working memory maintenance and manipulation processes. We also examined pharmacogenetic effects on cognition in the context of anti-dopaminergic drug therapy. Using dynamic causal models of functional magnetic resonance imaging in normal subjects (n = 46), we identified differentiated effects of functional polymorphisms in COMT, DRD2 and AKT1 genes on prefrontal-parietal and prefrontal-striatal circuits engaged during maintenance and manipulation, respectively. Cortical synaptic dopamine monitored by the COMT Val158Met polymorphism influenced prefrontal control of both parietal processing in working memory maintenance and striatal processing in working memory manipulation. DRD2 and AKT1 polymorphisms implicated in DRD2 signalling influenced only the prefrontal-striatal network associated with manipulation. In the context of anti-psychotic drugs, the DRD2 and AKT1 polymorphisms altered dose-response effects of anti-psychotic drugs on cognition in schizophrenia (n = 111). Thus, we suggest that genetic modulation of DRD2-AKT1-related prefrontal-subcortical circuits could at least in part influence cognitive dysfunction in psychosis and its treatment.

A Bayesian Account of Generalist and Specialist Formation Under the Active Inference Framework.

This paper offers a formal account of policy learning, or habitual behavioral optimization, under the framework of Active Inference. In this setting, habit formation becomes an autodidactic, experience-dependent process, based upon what the agent sees itself doing. We focus on the effect of environmental volatility on habit formation by simulating artificial agents operating in a partially observable Markov decision process. Specifically, we used a "two-step" maze paradigm, in which the agent has to decide whether to go left or right to secure a reward. We observe that in volatile environments with numerous reward locations, the agents learn to adopt a generalist strategy, never forming a strong habitual behavior for any preferred maze direction. Conversely, in conservative or static environments, agents adopt a specialist strategy; forming strong preferences for policies that result in approach to a small number of previously-observed reward locations. The pros and cons of the two strategies are tested and discussed. In general, specialization offers greater benefits, but only when contingencies are conserved over time. We consider the implications of this formal (Active Inference) account of policy learning for understanding the relationship between specialization and habit formation.