Computational psychiatry

Abstract

Psychiatric disorders such as autism and schizophrenia, arise from abnormalities in brain systems that underlie cognitive, emotional, and social functions. The brain is enormously complex and its abundant feedback loops on multiple scales preclude intuitive explication of circuit functions. In close interplay with experiments, theory and computational modeling are essential for understanding how, precisely, neural circuits generate flexible behaviors and their impairments give rise to psychiatric symptoms. This Perspective highlights recent progress in applying computational neuroscience to the study of mental disorders. We outline basic approaches, including identification of core deficits that cut across disease categories, biologically realistic modeling bridging cellular and synaptic mechanisms with behavior, and model-aided diagnosis. The need for new research strategies in psychiatry is urgent. Computational psychiatry potentially provides powerful tools for elucidating pathophysiology that may inform both diagnosis and treatment. To achieve this promise will require investment in cross-disciplinary training and research in this nascent field.

Figure 1

(A) Mechanistic understanding of brain functions must relate structure (molecules, cells and network connectivity) and dynamics with behavior. Brain measures probe spatiotemporal neural activity patterns that are correlated with specific aspects of behavior. Theory and modeling provide a powerful tool to elucidate how such a pattern is produced by its biological substrate, on one hand, and give rise to computations necessary to account for brain function, on the other hand. (B) Biologically-based neural circuit modeling is calibrated by physiology of single neurons and synapses, and constrained by quantitative network connectivity data. This approach is arguably necessary for the 3-way understanding between function, neural dynamics and computation, biological mechanism.

Figure 2

(A) Gene regions, genes, and putative endophenotypes implicated in a biological systems approach to schizophrenia research. The dynamic developmental interplay among genetic, environmental, and epigenetic factors that produce cumulative liability to developing schizophrenia. Endophenotypes as schizophrenia discriminators involve sensory motor gating, oculomotor function, working memory, and glial cell abnormalities. Many more gene loci, genes, and candidate endophenotypes remain to be discovered (represented by question marks). The figure is not to scale. (B) The impulsivity and compulsivity constructs. The diagram describes possible psychological component mechanisms underlying the two constructs. It would appear that these different measures likely do not inter-correlate well, which would argue against a unitary construct for either impulsivity or compulsivity, but this issue is still actively being researched. Both impulsivity and compulsivity involve motor/response disinhibition, but at different stages of the response process. (A) was reproduced from Gottesman and Gould (2003), (B) from Robbins et al. (2012), with permission.

Figure 3

(A) Illustration of the 4 levels of computational psychiatry. Clinical and nonclinical populations are tested on a battery of cognitive tasks. Computational models can relate raw task performance (e.g. RT and accuracy) to psychological and/or neurocognitive processes. These models can be estimated via various methods. Finally, based on resulting computational multidimensional profile, training using learning algorithms can either uncover groups and subgroups in clinical and healthy populations, or relate model parameters to clinical symptom severity. (B) Conceptual overview of model-aided clustering of fMRI data. First, separately for each subject, BOLD time series are extracted from a number of regions of interest. Second, subject-specific time series are used to estimate the parameters of a model. Third, subjects are embedded in a score space in which each dimension represents a specific model parameter. This space implies a similarity metric under which any two subjects can be compared. Fourth, a clustering algorithm is used to identify salient substructures in the data. Fifth, the resulting clusters are validated against known external (clinical) variables. Once validated, a clustering solution can, sixth, be interpreted mechanistically in the context of the underlying model. (C–D) Model-based clustering of fMRI data from schizophrenic patients in a working memory task. (C) An unsupervised clustering analysis of the patient group only, using Gaussian mixture models operating on dynamical causal model (DCM) parameter estimates, yield the average posterior parameter estimates (in terms of maximum a posteriori estimates) for each coupling and input parameter in the model. This is displayed graphically by the thickness of the respective arrows. (D) The three subgroups, which are defined on the basis of connection strengths, also differ in terms of negative clinical symptoms as operationalized by the negative symptoms (NS) subscale of the PANSS score. (A) was reproduced from Wiecki et al. (2014), (B–D) from (Brodersen et al. 2014), with permission.

Figure 4

(A–B) Spiking network model of working memory. (A) Model architecture. Excitatory pyramidal cells are labeled by their preferred cues (0° to 360°). Pyramidal cells of similar preferred cues are connected through local excitatory-to-excitatory connections. Inhibitory interneurons receive inputs from excitatory cells and send feedback inhibition by broad projections. (B) A stimulus is encoded and actively maintained by a self-sustained network persistent activity pattern (a “bump attractor”) in a simulation of the delayed oculomotor experiment. C: cue period D: delay period, R: response period. Pyramidal neurons are labeled along the y-axis according to their preferred cues. The x axis represents time. A dot in the rastergram indicates a spike of a neuron whose preferred location is at y, at time x. An elevated and localized neural activity is triggered by a transient cue stimulus and persists during the delay period. (C) The effects of iontophoretic NMDA blockade on working memory activity in a computational model of working memory. Under control conditions, a stimulus cue selectively activates a group of neurons, leading to persistent activity sustained by NMDAR-dependent recurrent excitation. NMDA conductance is reduced from control to 90%, 80%, and 70% (to bottom) of a reference level in a few pyramidal neurons in the network model. Stimulus-selective persistent activity gradually decreases with more NMDAR blockade and eventually disappears in these affected cells. (D) An example of an individual dorsolateral PFC cell recorded from behaving monkey in a delayed oculomotor response task. Upper panels: control condition, lower panels: after iontophoresis of Ro 25-6981 (25 nA), a blocker of NR2B-containing NMDA receptors. The rasters and histograms show firing patterns for the neuron’s preferred direction and the nonpreferred direction (opposite to the preferred direction). Iontophoresis of Ro 25-6981 markedly reduced mnemonic delay period firing to baseline. (B) was adapted from Compte et al. (2000), (C–D) from Wang et al. (2013), with permission.

Figure 5

Computational modeling of excitation-inhibition (E/I) balance in working memory circuits. (A) A spatial working-memory model can generate a bump-shaped stimulus-selective persistent activity pattern following stimulus withdrawal. Disinhibition, mediated by NMDAR hypofunction on interneurons, broadens working-memory representations at the neural level. (B) The parameter space of NMDAR hypofunction highlights the importance of E/I balance for working memory function. If the E/I ratio is elevated as in disinhibition, the width of the representation increases. In contrast, if the E/I ratio is reduced too much through weakened recurrent excitation between pyramidal cells, the circuit cannot support memory-related persistent activity (upper left corner). (C) Broadening of working-memory representations was tested using behavioral data from human subjects performing a spatial working-memory task combined with ketamine infusion, a pharmacological model of schizophrenia. Consistent with broadening, ketamine induced errors specifically for near distractor probes (left), as predicted by the model (right). (D) Compensations can restore E/I balance and ameliorate behavioral deficits in the model. We paired the disinhibition mechanism with either reduced excitation (purple) or increased inhibition (green), following proposed pharmacological treatments. Adapted with permission from (Murray et al. 2014).