Research Examples

Computational Psychiatry and Aging Research: Examples

In the following, we present four research topics to illustrate the scope and potential of the MPS-UCL initiative.

Example 1

Refining the Phenotype in Autistic Spectrum Disorder (ASD)

It is widely believed that ASD represents a disorder in representing the fact that others have minds, so-called theory of mind (ToM). The UCL group have recently developed a computational model of ToM based upon the manner in which subjects, during competitive interactions, optimize their strategies with different sophistication levels by inferring the strategy of an opponent using Bayesian inference with bounded rationality [1]. By applying this model to the analysis of brain imaging data acquired from healthy human subjects we have shown that it is possible to fractionate the role of previously identified ToM areas in prefrontal cortex. Thus, our computational model has enabled us to attribute encoding for uncertainty in belief inference to activity of medial prefrontal cortex and encoding of sophistication level to dorsolateral prefrontal cortex [2]. By extending this approach to study autistic patients (e.g., in a preliminary collaboration with the MPI for Human Development, Berlin), we have now shown considerable diagnostic heterogeneity in what has otherwise appeared to be a uniform diagnostic category. In a high-functioning group of ADS subjects we found that a selective difficulty representing the level of strategic sophistication of others, namely inferring others’ mindreading strategy, specifically predicts symptom severity. In contrast, a reduced ability in iterative planning was predicted by overall intellectual level [3]. These findings provide the first quantitative approach that can reveal the underlying computational dysfunctions that generate the autistic "spectrum." In the context of the MPS-UCL initiative, we propose to extend this approach to study the neuronal and behavioral characteristics of larger cohorts of subjects with ASD. Note that the presence of distinct computationally defined deficits also highlights the possibility of using more targeted treatment strategies in these populations.

[1] Yoshida, W., Dolan, R. J., & Friston, K. J. (2008). Game theory of mind. PLoS Computational Biology, 4. doi: 10.1371/journal.pcbi.1000254

[2] Yoshida, W., Seymour, B., Friston, K. J., & Dolan, R. J. (2010). Neural mechanisms of belief inference during cooperative games. Journal of Neuroscience, 30, 10744–10751. doi: 10.1523/jneurosci.5895-09.2010.

[3] Yoshida W., Dziobek, I., Kliemann, D., Heekeren, H.R., Friston K. J., & Dolan R. J. (2010). Cooperation and heterogeneity of the autistic mind. Journal of Neuroscience, 30, 8815–8818. doi: 10.1523/jneurosci.0400-10.2010 

Example 2

Impulsivity in ADHD and Addiction

The behavioral phenotypes of common psychiatric disorders are unlikely to represent expressions of unique psychopathologies. Indeed lack of progress in identifying relevant biological processes across a range of psychopathology is likely to reflect noise from underlying heterogeneity. One powerful approach to unravelling core underlying processes derives from use of formal models. We have recently described a model of inter-temporal choice that captures the behavior of subjects when they are forced to trade-off between immediate and delayed rewards; in this context, impulsivity can be defined as a propensity to choose smaller sooner versus later larger rewards [1]. Impulsivity at a phenomenological level can arise from a range of underlying processes, including a distortion in the curvature of a utility function or a change in a discount control parameter. We show that when we can render subjects more impulsive by a pharmacological manipulation of their dopamine neurotransmission, this reflects an effect on the discount control parameter [2]. In the context of the MPS-UCL initiative we aim to extend this approach to examine the computational basis of impulsivity in individuals with addictions to substances such as amphetamines and cocaine, enabling a deeper understanding of the core processes that are awry in these disorders.

[1] Pine, A., Seymour, B., Roiser, J. P., Bossaerts, P., Friston, K. J., Curran, H.V., & Dolan, R. J. (2009). Encoding of marginal utility across time in the human brain. Journal of Neuroscience, 29, 9575–9581. doi:10.1523/jneurosci.1126-09.2009

[2] Pine, A., Shiner, T., Seymour, B., & Dolan R. J. (2010). Dopamine, time and impulsivity in humans. Journal of Neuroscience, 30, 8888–8896. doi: 10.1523/jneurosci.6028-09.2010.

Example 3

Mechanisms and Heterogeneity of Memory Aging

Individual differences in episodic memory (EM) functioning and brain regional integrity increase from early to late adulthood. Recently, a two-component framework of EM development has been proposed to account for changes in EM from childhood to old age [1]. According to the framework, older adults’ difficulties in EM originate from impairments in both strategic and associative components, reflecting senescent changes in the prefrontal cortex and the medial temporal lobes, in particular, the hippocampus. The associative component refers to mechanisms that bind different aspects of an event into a cohesive memory episode. In contrast, the strategic component refers to cognitive control operations that support the encoding of discrete memory traces, and that initiate the subsequent strategic search, retrieval, and evaluation of stored representations. The results of several recent studies are with predictions derived from the two-component framework [e.g., 2]. The MPS-UCL initiative will aim at refining and developing neurocomputational models of aging cognition [3] that link the contributions of senescent changes in the structure and function of hippocampus subfields and of changes in dopaminergic neuromodulation to changes in associative and strategic EM components and their interaction. These models should be extended to aging changes in memory consolidation and forgetting, and may help to delineate features that discriminate normal cognitive aging from aging with dementia.

[1] Shing, Y. L., Werkle-Bergner, M., Brehmer, Y., Mueller, V., Li, S.-C., & Lindenberger, U. (2010). Episodic memory across the lifespan: The contributions of associative and strategic components. Neuroscience & Biobehavioral Reviews, 34, 1080–1091. doi: 10.1016/j.neubiorev.2009.11.002

[2] Shing, Y. L., Werkle-Bergner, M., Li, S.-C., & Lindenberger, U. (2008). Associative and strategic components of episodic memory: A life-span dissociation. Journal of Experimental Psychology: General, 137, 495–513. doi: 10.1037/0096-3445.137.3.495

[3] Li, S.-C., Lindenberger, U., & Sikström, S. (2001). Aging cognition: From neuromodulation to representation. Trends in Cognitive Sciences, 5, 479–486. doi: 10.1016/S1364-6613(00)01769-1

Example 4

Decision Making in Normal Aging

During decision making, choice dimensions are compared and integrated. Decision neuroscience has made important progress in uncovering the neurobiological and computational basis of comparison and integration processes in the adult human brain [1, 2]. In particular, variations in dopaminergic and serotonergic systems have been linked to individual differences in decision making. Extending the general framework of Bäckman et al. [3], we have proposed a triadic relation among economic decision making, dopaminergic and serotonergic neuromodulation, and normal cognitive aging [4]. To delineate this triadic relation more effectively, the prevailing focus on mean differences between groups of younger and older adults needs to be replaced by methods that capture both the degree of heterogeneity within age groups, and the degree of invariance in mechanisms promoting efficient decision-making across age groups [5]. Combining genetic variation in transmitter-related genes with neuroimaging methods will help to elucidate how differences in dopaminergic and serotonergic neuromodulation give rise to differences in brain response patterns during risk and delay processing. It will also help to explore how reward, risk, and delay of reward are integrated, and how normal aging affects mechanisms of integration. The MPS-UCL collaboration will provide a computational platform for modelling individual and age-related variations in decision making, and their implications for decision making in everyday contexts.

[1] Heekeren, H. R., Marrett, S., & Ungerleider, L. G. (2008). The neural systems that mediate human perceptual decision making. Nature Reviews Neuroscience, 9, 467-479. doi: 10.1038/nrn2374

[2] Philiastides, M. G., Biele, G., & Heekeren, H. R. (2010). A mechanistic account of value computation in the human brain. Proceedings of the National Academy of Sciences of the United States of America, 107, 9430–9435. doi: 10.1073/pnas.1001732107

[3] Bäckman, L., Nyberg, L., Lindenberger, U., Li, S.-C., & Farde, L. (2006). The correlative triad among aging, dopamine, and cognition: Current status and future prospects. Neuroscience & Biobehavioral Reviews, 30, 791-807. doi: 10.1016/j.neubiorev.2006.06.005

[4] Mohr, P. N. C., Li, S.-C., & Heekeren, H. R. (2010). Neuroeconomics and aging: Neuromodulation of economic decision making in old age. Neuroscience & Biobehavioral Reviews, 34, 678–688. doi: 10.1016/j.neubiorev.2009.05.010

[5] Nagel, I. E., Preuschhof, C., Li, S.-C., Nyberg, L., Bäckman, L., Lindenberger, U., et al. (2009). Performance level modulates adult age differences in brain activation during spatial working memory. Proceedings of the National Academy of Sciences of the United States of America, 106, 22552-22557. doi: 10.1073/pnas.0908238106