Targeted neural network interventions for auditory hallucinations: Can TMS inform DBS?

Abstract

The debilitating and refractory nature of auditory hallucinations (AH) in schizophrenia and other psychiatric disorders has stimulated investigations into neuromodulatory interventions that target the aberrant neural networks associated with them. Internal or invasive forms of brain stimulation such as deep brain stimulation (DBS) are currently being explored for treatment-refractory schizophrenia. The process of developing and implementing DBS is limited by symptom clustering within psychiatric constructs as well as a scarcity of causal tools with which to predict response, refine targeting or guide clinical decisions. Transcranial magnetic stimulation (TMS), an external or non-invasive form of brain stimulation, has shown some promise as a therapeutic intervention for AH but remains relatively underutilized as an investigational probe of clinically relevant neural networks. In this editorial, we propose that TMS has the potential to inform DBS by adding individualized causal evidence to an evaluation processes otherwise devoid of it in patients. Although there are significant limitations and safety concerns regarding DBS, the combination of TMS with computational modeling of neuroimaging and neurophysiological data could provide critical insights into more robust and adaptable network modulation.

Introduction

Invasive neural network interventions such as ablative surgery or deep brain stimulation (DBS) are among the most controversial in modern psychiatry. The ethical, scientific and clinical barriers to the development and implementation of these treatments are juxtaposed with an urgent need to treat patients who remain profoundly disabled despite comprehensive treatment strategies. (Bell and Racine, 2013, Naesstrom et al., 2016, Nangunoori et al., 2013, Saleh and Fontaine, 2015). Recent proposals for the development of DBS for medication-resistant symptoms of schizophrenia highlight this juxtaposition (Mikell et al., 2009, Mikell et al., 2015, Salgado-Lopez et al., 2016).

One of the primary challenges inherent to all psychiatric DBS endeavors is extricating a target symptom from its Diagnostic and Statistical Manual of Mental Disorders (American Psychiatric Association, 2013) construct. The diagnosis of schizophrenia, for example, is a cluster of symptoms associated with a wide range of pathologies involving nearly every part of the brain. By contrast, individual symptoms within this cluster may have more objective or individualized network-specific representations (Boschloo et al., 2015, Braga and Buckner, 2017). Verbal auditory hallucinations (AH) are one example of this specificity (Jardri et al., 2011, Zmigrod et al., 2016). As others have pointed out, DSM and International Statistical Classification of Diseases and Related Health Problems (ICD) codes do not always correspond with findings from behavioral, genetic and systems neuroscience (Cuthbert and Insel, 2013). DBS selectively targets neural networks (white matter targeting) or singular nodes within such networks (gray matter targeting) (Williams et al., 2014). Thus, DBS may have a better chance at reducing a target symptom associated with a target network than it would an entire cluster of symptoms encapsulated by a clinical diagnostic construct. This idea is consistent with the Research Domain Criteria (RDoC) approach developed by the National Institute of Mental Health (Insel et al., 2010, Insel, 2014).

A second primary challenge relates to the translation of DBS strategies from animal subjects into human patients (Cuthbert and Insel, 2013). Potential DBS targets are often first identified and manipulated in animal models of psychiatric disease. It is often difficult to evaluate the validity and fidelity of these models, particularly given the heterogeneity of human psychiatric disorders as well as the clustering of symptoms into clinical diagnostic constructs. The specific biological manipulations made to render a model ‘of schizophrenia,’ for example, tend to recapitulate a suite of behaviors that are relevant to positive, negative and cognitive symptoms. There are a few examples of rodent models in which hallucinations and delusions have been modeled (Honsberger et al., 2015, McDannald and Schoenbaum, 2009). Such strategies often embrace an RDoC perspective on hallucinations as trans‑diagnostic symptoms (Forrest et al., 2014, Robbins, 2017). Even with more nuanced animal models of behaviors or symptoms, several barriers to translating animal DBS findings into humans remain; there are few causal tools with which to predict response, refine targeting or guide treatment decisions in patients considering DBS (Alhourani et al., 2015, O'Halloran et al., 2016).

Transcranial magnetic stimulation (TMS) is an external or non-invasive form of brain stimulation that may provide a means by which to partially address both challenges. There is emerging evidence that TMS may reduce AH in patients with schizophrenia and related psychotic disorders (Freitas et al., 2009, Hoffman et al., 2003, Hoffman et al., 2013, Montagne-Larmurier et al., 2011, Otani et al., 2015, Slotema et al., 2014). TMS has been used as an investigational tool for neural circuit mapping as well as a therapeutic tool for neural circuit modulation (George et al., 2013a), but few have explicitly examined its potential in the development and implementation of invasive circuit-based interventions such as DBS (Pathak et al., 2016). In this editorial, we briefly review the data on TMS for AH and propose that TMS may be a causal tool with which to carefully advance the development of DBS for individual symptoms within psychiatric constructs.