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Pushing the Boundaries of Psychiatric Neuroimaging to Ground Diagnosis in Biology

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Pushing the Boundaries of Psychiatric Neuroimaging to Ground Diagnosis in Biology Opinion | Cognition and Behavior Pushing the boundaries of psychiatric neuroimaging to ground diagnosis in biology https://doi.org/10.1523/ENEURO.0384-19.2019 Cite as: eNeuro 2019; 10.1523/ENEURO.0384-19.2019 Received: 20 September 2019 Revised: 22 October 2019 Accepted: 23 October 2019 This Early Release article has been peer-reviewed and accepted, but has not been through the composition and copyediting processes. The final version may differ slightly in style or formatting and will contain links to any extended data. Alerts: Sign up at www.eneuro.org/alerts to receive customized email alerts when the fully formatted version of this article is published. Copyright © 2019 Saggar and Uddin This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. Manuscript Title Pushing the boundaries of psychiatric neuroimaging to ground diagnosis in biology Abbreviated Title Pushing the boundaries of psychiatric neuroimaging List all Author Names and Affiliations in order as they would appear in the published article Manish Saggar1, Ph.D. and Lucina Q. Uddin2, Ph.D. 1Department of Psychiatry & Behavioral Sciences, Stanford University 2Department of Psychology, University of Miami Author Contributions: Both authors contributed to literature survey, discussion and writing of the manuscript Correspondence should be addressed to Manish Saggar 401 Quarry Rd, Stanford CA 94305 Email: [email protected] Number of figures: 2 Number of tables: 0 Number of words for Abstract: 47 Number of words for Significance Statement: 79 Number of words for Introduction: 590 Number of words for Discussion: 3,134 Acknowledgements: We thank the anonymous reviewers for their valuable feedback. Conflict of Interest: No, Authors report no conflict of interest Funding Sources: This work was supported in part by a Career Development Award (K99/R00 MH104605) and a New Innovator Award (DP2 MH119735) from the NIMH to M.S. and by an R01 Award (R01 MH107549) from the NIMH to L.U. 1 1 Abstract 2 To accurately detect, track progression of, and develop novel treatments for mental illnesses, a 3 diagnostic framework is needed that is grounded in biological features. Here we present the case 4 for utilizing personalized neuroimaging, computational modeling, standardized computing, and 5 ecologically valid neuroimaging to anchor psychiatric nosology in biology. 6 7 Significance Statement 8 There is a growing recognition that the boundaries of human neuroimaging data acquisition and 9 analysis must be pushed to ground psychiatric diagnosis in biology. For successful clinical 10 translations, we outline several proposals across the four identified domains of human 11 neuroimaging, namely, (a) reliability of findings; (b) effective clinical translation at the 12 individual subject level; (c) capturing mechanistic insights; and (d) enhancing ecological validity 13 of lab findings. Advances across these domains will be necessary for further progress in 14 psychiatric neuroimaging. 15 16 1. Introduction 17 Mental illness affects a large proportion of the global population and has a significant economic 18 impact due to treatment costs and lost productivity. In the United States alone, nearly one in five 19 adults lives with a mental illness (44.7 million in 2016; retrieved from 20 https://www.samhsa.gov/data/). Given this widespread prevalence and societal cost, there is a 21 crucial need for finding ways to prevent and treat mental illness. Despite the challenges of 22 understanding the complexity of the human brain in health and disease, researchers have made 23 large strides in developing tools and methodologies that have allowed us to get a sneak peek into 24 brain functioning over the last two decades. It is, however, shocking that even after such an 25 accelerated pace of discovery in neuroscience and bioengineering, the pace of development for 26 treatment of mental illness has not only been slow but has almost stagnated. This slow progress 27 in the development of psychiatric treatments could be largely attributed to the lack of an accurate 28 and neurobiologically-grounded diagnostic nosology (Redish and Gordon 2016). 29 30 The diagnostic nosology, or disorder classification system, most commonly used in psychiatry 31 (the Diagnostic and Statistical Manual of Mental Disorders; DSM-V) is built entirely upon 32 assessment of symptoms by clinicians. The Research Domain Criteria (RDoC) initiative put forth 33 by the National Institute of Mental Health (Cuthbert and Insel 2013) aims to address the 34 challenge of revising this diagnostic nosology by creating a framework integrating multiple 35 levels information from genomics to neural circuits and behavior to explore basic dimensions of 36 function across clinical and non-clinical populations. Grounding of a psychiatric diagnosis in 37 biological features can not only potentially provide reliable and valid diagnosis, but can also 38 reveal specific biomarkers to track the course of illness and test the efficacy of new treatments. 39 To borrow an example from (Redish and Gordon 2016), type II diabetes has a complex etiology 40 and pathophysiology; however, unlike psychiatric illnesses, the diagnosis for type II diabetes is 41 largely based on the biological feature of the amount of hemoglobin A1c in the blood. This 2 42 biological feature not only helps in precise diagnosis, but also in proper management of blood 43 glucose as well as in testing the efficacy of treatments over time. 44 45 Different disciplines of science are actively engaged to illuminate biological features that 46 contribute to major psychiatric illnesses. Specifically, several studies from genetics and 47 neuroimaging are at the frontier. Large-scale genomic investigations have linked molecular 48 genetic variations to major psychiatric illnesses. These studies not only present evidence of 49 heterogeneity and polygenicity of psychiatric disorders, but also reveal that connecting multiple 50 levels of molecular, cellular, and circuit functions to complex human behavior is immensely 51 challenging (Geschwind and Flint 2015). Further, we are just beginning to understand how 52 observed genetic variants give rise to changes in brain function and behavior (Gandal et al. 53 2018). Along the same lines, large-scale neuroimaging investigations of both brain structure and 54 function have seemingly failed to pinpoint differences across phenotypically distinct psychiatric 55 diagnosis (Etkin 2019). Interestingly, instead of finding disorder-specific differences, many 56 large-scale studies have either shown converging evidence towards common circuit dysfunction 57 in brain structure (Goodkind et al. 2015) and/or provided evidence for non-specificity in brain 58 function (Baker et al. 2019; (Uddin 2015)). 59 60 In this review article, we first outline some of the pan-disciplinary issues that broadly hamper 61 progress in the search for disorder-specific biological features in psychiatry. We then focus 62 specifically on issues unique to neuroimaging research, and lastly present novel avenues that are 63 capitalizing on recent advances in machine learning and biophysical network modeling to push 64 the boundaries of personalized neuroimaging. 65 66 2. Pan-disciplinary issues in anchoring psychiatric nosology in biology 67 One of the major challenges in finding disorder-specific biomarkers is the fact that clinical 68 symptoms, on which diagnoses are based, may not have a one-to-one mapping to the underlying 69 biological mechanisms. In other words, different biological mechanisms may have led to the 70 same cluster of clinical symptoms. This lack of one-to-one relation between clinical symptoms 71 and biology goes against the traditionally held assumption that symptoms-based stratification 72 could help us discover disorder-specific biomarkers, which in turn would largely explain the 73 observed heterogeneity and comorbidity repeatedly observed in psychiatric disorders (Bijl, 74 Ravelli, and van Zessen 1998; Uddin et al. 2017). Thus, several researchers now argue that 75 instead of performing small scale case-controlled studies, where stratification of the sample is 76 based on symptoms, large-scale transdiagnostic/dimensional studies are needed (Etkin 2019). 77 78 The second major and related challenge for biomarker studies is how to validate the observed 79 biotypes. The lack of one-to-one mapping between clinical symptoms and biology postulates that 80 clinical symptoms alone cannot be used to validate the observed biotypes. Thus, newer 3 81 approaches for validation should be explored, including treatment outcomes and performance on 82 tasks assessing different dimensions of functioning (e.g., RDoC). 83 84 The third challenge pertains to lack of group-to-individual translation of findings. Although most 85 studies are conducted at the group level, for the best translational outcomes, such group-level 86 findings need to be reliable even at the single-patient level. However, it has been previously 87 argued that due to the non-ergodicity in human social and psychological processes, the 88 inferences based on group-level data are challenging to generalize to an individual experience or 89 behavior (Fisher, Medaglia, and Jeronimus 2018). Thus, novel methods that can provide 90 statistically significant as well as behaviorally relevant information at both group and single-
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