Connectivity, pharmacology, and computation: toward a mechanistic understanding of neural system dysfunction in schizophrenia

Abstract

Neuropsychiatric diseases such as schizophrenia and bipolar illness alter the structure and function of distributed neural networks. Functional neuroimaging tools have evolved sufficiently to reliably detect system-level disturbances in neural networks. This review focuses on recent findings in schizophrenia and bipolar illness using resting-state neuroimaging, an advantageous approach for biomarker development given its ease of data collection and lack of task-based confounds. These benefits notwithstanding, neuroimaging does not yet allow the evaluation of individual neurons within local circuits, where pharmacological treatments ultimately exert their effects. This limitation constitutes an important obstacle in translating findings from animal research to humans and from healthy humans to patient populations. Integrating new neuroscientific tools may help to bridge some of these gaps. We specifically discuss two complementary approaches. The first is pharmacological manipulations in healthy volunteers, which transiently mimic some cardinal features of psychiatric conditions. We specifically focus on recent neuroimaging studies using the NMDA receptor antagonist, ketamine, to probe glutamate synaptic dysfunction associated with schizophrenia. Second, we discuss the combination of human pharmacological imaging with biophysically informed computational models developed to guide the interpretation of functional imaging studies and to inform the development of pathophysiologic hypotheses. To illustrate this approach, we review clinical investigations in addition to recent findings of how computational modeling has guided inferences drawn from our studies involving ketamine administration to healthy subjects. Thus, this review asserts that linking experimental studies in humans with computational models will advance to effort to bridge cellular, systems, and clinical neuroscience approaches to psychiatric disorders.

Keywords: NMDA receptors; computational modeling; functional connectivity; glutamate; pharmacology; schizophrenia; thalamus.

Figure 1

Understanding complex mental illness from synapses to circuits to neural systems. A major challenge facing the field of clinical neuroscience is building the links across levels of inquiry, from the level of receptors and cells, to microcircuits, and ultimately scale to the level of neural systems and behavior. At present, there is a vast explanatory gap across these levels in our understanding of psychiatric symptoms (top panel). Bridging this gap represents a major effort in explaining how alterations of specific mechanisms across neural systems may produce complex behavioral alterations seen in serious mental illness. This challenge is also exemplified by the National Institute of Mental Health Research Domain Criteria (RDoC) initiative (45) in order to map the biology across levels of inquiry onto behavior in a more systematic and data-driven way. We argue that computational modeling approaches (55) combined with additional experimental tools such as functional neuroimaging and pharmacology (47) offer one possible path toward this objective (bottom panel). We detail emerging efforts in functional connectivity work that may present a unique opportunity in this regard (59). Note: top left receptor figure was adapted with permission from Kotermanski and Johnson (60).

Figure 2

Global brain connectivity identifies globally dysconnected regions despite substantial within or between subject variability. We hypothesized that some brain regions may have global variable disruptions in functional connectivity, which may contribute to individual differences in symptom severity in brain disorders. GBC can be used as a data-driven approach to search for such regions. GBC is computed as the average connectivity of each voxel to all others. Gray circles indicate regions with altered connectivity to the depicted red voxels. (A) GBC will identify a region with consistent global dysconnectivity even when there are substantial differences in connectivity patterns within the region. Seed or ICA approaches would be unable to identify dysconnectivity in such a region because of inconsistent dysconnectivity patterns across neighboring voxels. (B) GBC, unlike seed or ICA approaches, will identify a region with consistent global dysconnectivity even when there are substantial individual differences in connectivity across subjects. This allows the identification of regions with many individual differences in connectivity, which might correlate with individual differences in symptoms. (C) GBC is also sensitive to consistent group differences in connectivity that might be identified using seed or ICA approaches, though GBC might be more sensitive due to pooling of results for both consistent and inconsistent dysconnectivity. GBC, global brain connectivity; ICA, independent component analysis. Note: figure adapted with permission from Cole and colleagues (11).

Figure 3

Neural system-level dysconnectivity in schizophrenia – emerging biomarkers. (A) Results from a recent large connectivity investigation examining thalamo-cortical connectivity alterations in 90 patients diagnosed with schizophrenia relative to 90 matched healthy controls (38). Anticevic and colleagues found robust alterations in thalamo-cortical information flow in schizophrenia, whereby sensory-motor cortical regions showed over-connectivity in schizophrenia (regions shown in yellow-red), but prefrontal-striatal-cerebellar regions showed under connectivity in schizophrenia relative to controls (regions shown in blue). Anticevic and colleagues fully replicated this pattern in an independent sample. Woodward and colleagues, using complementary approaches, found highly comparable effects (37). (B) A similar pattern of over/under connectivity was identified in patients with schizophrenia when using a DLPFC seed region identified via GBC; as with the thalamic seed, there was increased coupling with sensory (posterior regions, shown in yellow-red) but reduced connectivity with prefrontal and other higher-order temporal regions (shown in blue). This over/under pattern recapitulated qualitatively the observations found for the thalamic analysis in (A), as shown in the distribution plots on the bottom of each panel. Note: figures adapted with permission from Anticevic and colleagues (38) and Cole and colleagues (11).

Figure 4

Characterizing connectivity alterations using pharmacological neuroimaging. While functional connectivity neuroimaging alone has been a powerful tool for characterizing neural system-level alterations in schizophrenia, it is ultimately a correlational tool. That is, we are examining alterations in the associations as a function of illness presence or absence. To move toward understanding the possible role of specific neurotransmitter mechanisms in schizophrenia, however, neuroimaging studies can be combined with pharmacological manipulations (47, 175). Such causal experimental manipulations can shed light on the role of specific neurotransmitter systems in schizophrenia (15). (A) Driesen and colleagues have shown that administration of ketamine profoundly altered the global connectivity of the brain. Specifically they demonstrated that the GBC measure increased following ketamine administration, possibly reflecting a hyper-glutamatergic state or a state of cortical disinhibition (54) consistent with animal models (111, 176). Formal computational models are needed in order to provide a deeper intuition for such pharmacological effects on large-scale neural systems. (B) Anticevic and colleagues have shown that administration of an NMDAR antagonist, ketamine, alters the connectivity of large-scale anti-correlated neural systems during performance of a working memory task (18). Although this connectivity study was performed during task performance rather than rest, it illustrates a proof of concept for how a pharmacological challenge can alter connectivity in a causal way. Note: figures adapted with permission from Driesen and colleagues (54) and Anticevic and colleagues (18).

Figure 5

Computational modeling of system-level effects via biophysically realistic computational approaches. While detailed microcircuit models have made an impact on our understanding of cortical dynamics (56), the challenge remains to scale such models to incorporate dynamic interactions across large-scale neural systems, which are likely profoundly affected in schizophrenia (and other severe neuropsychiatric conditions). (A) A recently published elegant study by Deco and colleagues (59) illustrates an approach where a biophysically realistic model of cortical computations has been applied to understand the generation of slow-frequency fluctuations in the BOLD signal. The authors used diffusion-spectrum imaging to anatomically constrain the model and in turn fitted the modeling results to empirically-derived resting-state functional connectivity data. The result illustrates that coherent fluctuations in the BOLD signal (i.e., resting functional connectivity) may emerge from a system that is at the edge of chaos, allowing linear but transient departures in neuronal firing rates. (B) Anticevic and colleagues have used a functional model of working memory, a cognitive process that is profoundly affected in schizophrenia (26), to better understand the role of NMDA receptor function in the interaction of large-scale anti-correlated neural systems. Specifically they studied the functional antagonism present during a cognitive task between the task-activated (fronto-parietal module) and task-deactivated (default-mode module) networks (196). Following a complete parameter sweep (left), the authors found that a small perturbation of the NMDARs on inhibitory interneurons within each cortical microcircuit captured the firing that was observed experimentally following ketamine administration in healthy volunteers (18). Collectively, these studies offer examples for how biologically constrained modeling approaches can be applied to understand large-scale neural system physiology in both resting-state (non-functional) and task-based (functional) settings. Note: (A) of the figure was adapted with permission from Deco and colleagues (59).