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Published in final edited form as:

Perspect Psychol Sci. 2019 May ; 14(3): 419–436. doi:10.1177/1745691618810696.

A Hierarchical Taxonomy of Psychopathology Can Transform

Mental Health Research
Christopher C. Conway1, Miriam K. Forbes2, Kelsie T. Forbush3, Eiko I. Fried4, Michael N.
Hallquist5, Roman Kotov6, Stephanie N. Mullins-Sweatt7, Alexander J. Shackman8, Andrew
E. Skodol9, Susan C. South10, Matthew Sunderland11, Monika A. Waszczuk6, David H.
Zald12, Mohammad H. Afzali13, Marina A. Bornovalova14, Natacha Carragher15, Anna R.
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Docherty16, Katherine G. Jonas6, Robert F. Krueger17, Praveetha Patalay18, Aaron L.

Pincus5, Jennifer L. Tackett19, Ulrich Reininghaus20,21, Irwin D. Waldman22, Aidan G.C.
Wright23, Johannes Zimmermann24, Bo Bach25, R. Michael Bagby26, Michael
Chmielewski27, David C. Cicero28, Lee Anna Clark29, Tim Dalgleish30, Colin G. DeYoung17,
Christopher J. Hopwood31, Masha Y. Ivanova32, Robert D. Latzman33, Christopher J.
Patrick34, Camilo J. Ruggero35, Douglas B. Samuel10, David Watson29, and Nicholas R.
Eaton36

1Department of Psychological Sciences, College of William & Mary, Williamsburg, VA, USA;
2Centre for Emotional Health, Department of Psychology, Macquarie University, Sydney, Australia;
3University of Kansas, Department of Psychology, Lawrence, KS, USA; 4Department of

Psychology, University of Amsterdam, Amsterdam, Netherlands; 5Department of Psychology, The

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Pennsylvania State University, State College, PA, USA; 6Department of Psychiatry, State
University of New York, Stony Brook, NY, USA; 7Oklahoma State University, Department of
Psychology, Stillwater, OK, USA; 8Department of Psychology and Neuroscience and Cognitive
Science Program, University of Maryland, College Park, MD, USA; 9Department of Psychiatry,
University of Arizona, Tucson, AZ, USA; 10Purdue University, Department of Psychological
Sciences, West Lafayette, IN, USA; 11NHMRC Centre for Research Excellence in Mental Health
and Substance Use, National Drug and Alcohol Research Centre, University of New South Wales,
Sydney, Australia; 12Department of Psychology, Vanderbilt University, Nashville, TN, USA;
13Department of Psychiatry, University of Montreal, Montreal, Québec; 14Department of

Psychology, University of South Florida, Tampa, FL, USA; 15Medical Education and Student
Office, Faculty of Medicine, University of New South Wales Australia, Sydney, New South Wales,
Australia; 16Department of Psychiatry, University of Utah, Salt Lake City, UT, USA; 17Department
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of Psychology, University of Minnesota, Minneapolis, MN, USA; 18Institute of Psychology, Health

and Society, University of Liverpool, Liverpool, UK; 19Department of Psychology, Northwestern
University, Evanston, IL, USA; 20Department of Psychiatry and Psychology, School for Mental
Health and Neuroscience, Maastricht University, The Netherlands; 21Centre for Epidemiology and
Public Health, Health Service and Population Research Department, Institute of Psychiatry,
Psychology & Neuroscience, King’s College London, London, UK; 22Department of Psychology,

Corresponding author: Christopher C. Conway, Department of Psychological Sciences, College of William & Mary, 540 Landrum
Drive, Williamsburg, VA 23188, USA; conway@wm.edu.
Footnotes
 Conway et al. Page 2
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Emory University, Atlanta, GA, USA; 23Department of Psychology, University of Pittsburgh,

Pittsburgh, PA, USA; 24Department of Psychology, University of Kassel, Germany; 25Psychiatric
Research Unit, Slagelse Psychiatric Hospital, Slagelse, Denmark; 26Departments of Psychology
and Psychiatry, University of Toronto, Toronto, Canada; 27Department of Psychology, Southern
Methodist University, TX, USA; 28Department of Psychology, University of Hawaii at Manoa, HI,
USA; 29Department of Psychology, University of Notre Dame, Notre Dame, IN, USA; 30Medical
Research Council Cognition and Brain Sciences Unit, Cambridge, UK; 31Department of
Psychology, University of California, Davis, CA, USA; 32Department of Psychiatry, University of
Vermont, Burlington, VT, USA; 33Department of Psychology, Georgia State University, Atlanta,
GA, USA; 34Department of Psychology, Florida State University, Tallahassee, FL, USA;
35Department of Psychology, University of North Texas, Denton, TX, USA; 36Department of

Psychology, Stony Brook University, Stony Brook, NY, USA

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Abstract
For over a century, research on psychopathology has focused on categorical diagnoses. Although
this work has produced major discoveries, growing evidence points to the superiority of a
dimensional approach to the science of mental illness. Here we outline one such dimensional
system—the Hierarchical Taxonomy of Psychopathology (HiTOP)—that is based on empirical
patterns of psychological symptom co-occurrence. We highlight key ways in which this framework
can advance mental health research, and we provide some heuristics for using HiTOP to test
theories of psychopathology. We then review emerging evidence that supports the value of a
hierarchical, dimensional model of mental illness across diverse research areas in psychological
science. These new data suggest that the HiTOP system has the potential to accelerate and improve
research on mental health problems as well as efforts to more effectively assess, prevent, and treat
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mental illness.

Keywords
mental illness; nosology; individual differences; transdiagnostic; Hierarchical Taxonomy of
Psychopathology (HiTOP); ICD; DSM; RDoC

Dating back to Kraepelin and other early nosologists, research on psychopathology has been
framed around mental disorder categories (e.g., What biological malfunctions typify
generalized anxiety disorder? How does antisocial personality disorder disrupt close
relationships?). This paradigm has produced valuable insights into the nature and origins of
psychiatric problems. Yet there is now abundant evidence that categorical approaches to
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mental illness are hindering scientific progress. Grounded in decades of research, an

alternate framework has emerged that characterizes psychopathology using empirically
derived dimensions that cut across the boundaries of traditional diagnoses. Recent efforts by
a consortium of researchers to review and integrate findings relevant to this framework have
given rise to a proposed consensus dimensional system, the Hierarchical Taxonomy of
Psychopathology (HiTOP1; Kotov et al., 2017).

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Here, we first summarize the rationale behind dimensional rubrics for mental illness and
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briefly sketch the topography of the HiTOP system (for detailed reviews, see Kotov et al.,
2017, Krueger et al., in press). Second, we explain how HiTOP can be used to improve
research practices and theory testing. Third, we review new evidence for the utility of
HiTOP dimensions across various research contexts, from developmental psychology to
neuroscience. Finally, we offer some practical recommendations for conducting HiTOP-
informed research.

A Brief History of HiTOP

Mental illness is a leading burden on public health resources and the global economy
(DiLuca & Olesen, 2014; Vos et al., 2016). Recent decades have witnessed the development
of improved social science methodologies and powerful new tools for quantifying variation
in the genome and brain, leading to initial optimism that psychopathology might be more
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readily explained and objectively defined (e.g., Hyman, 2007). Yet, billions of dollars of
research have failed to yield much in the way of new cures, objective assays, or other major
breakthroughs (Shackman & Fox, 2018).

A growing number of clinical practitioners and researchers—including the architects of the

National Institute of Mental Health Research Domain Criteria (RDoC)—have concluded that
this past underperformance reflects problems with categorical diagnoses, rather than any
intrinsic limitation of prevailing approaches to understanding risk factors and treatment
methods (Gordon & Redish, 2016). Categorical diagnoses—such as those codified in the
Diagnostic and Statistical Manual of Mental Disorders (DSM) and the International
Classification of Diseases (ICD)—pose several well-documented barriers to discovering the
nature and origins of psychopathology, including pervasive comorbidity, low symptom
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specificity, marked diagnostic heterogeneity, and poor reliability (Clark, Cuthbert, Lewis-
Fernández, Narrow, & Reed, 2017; Helzer et al., 2009; Markon, Chmielewski, & Miller,
2011; Regier et al., 2013). Regarding reliability, for instance, DSM-5 field trials found that
approximately 40% of diagnoses examined did not reach the cutoff for acceptable inter-rater
agreement (Regier et al., 2013). Attesting to symptom profile heterogeneity in DSM, there
are over 600,000 symptom presentations that satisfy diagnostic criteria for DSM-5
posttraumatic stress disorder (Galatzer-Levy & Bryant, 2013).

Addressing these problems requires a fundamentally different approach. HiTOP—like other

dimensional proposals, such as RDoC (e.g., Brown & Barlow, 2009; Cuthbert & Insel, 2013)
—focuses on continuously distributed traits theorized to form the scaffolding for
psychopathology. In the tradition of early factor analyses of disorder signs and symptoms in
adults (e.g., Eysenck, 1944; Lorr et al., 1963; Tellegen, 1985) and children (e.g., Achenbach,
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1966; Achenbach, Howell, Quay, Conners, & Bates, 1991), more recent quantitative analysis
of psychological symptom co-occurrence has established a reproducible set of dimensions
theorized to reflect the natural structure of psychological problems (Kotov et al., 2017).

1See https://medicine.stonybrookmedicine.edu/HITOP/

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Figure 1 provides a simplified schematic depiction of HiTOP, which features broad,

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heterogeneous constructs near the top of the model and specific, homogeneous dimensions
near the bottom. HiTOP accounts for diagnostic comorbidity by positing dimensions (e.g.,
internalizing) that span multiple DSM diagnostic categories, and it also models diagnostic
heterogeneity by specifying fine-grain processes (e.g., worry, panic) that constitute the
building blocks of mental illness. Indeed, profiles of narrow symptom dimensions (e.g., low
well-being, suicidality, situational fears) explain variation on broad dimensions (e.g.,
elevated internalizing) in more detail.

HiTOP is an evolving model. An international group of researchers has assembled to

investigate this structure and update it as informed by new data (Krueger et al., in press).2
(The HiTOP consortium will publish revisions to the model, as new research findings
accumulate, on its website.) Indeed, the explicit goal of the HiTOP project is to follow the
evidence. The system is open for any type of revision that is supported by sufficient
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evidence; its core assumption is that a more valid nosology can be developed based on the
empirical pattern of clustering among psychopathology phenotypes (i.e., symptoms and
maladaptive traits).

Refining this dimensional model is a key priority, but it is only one step in the evolution of
HiTOP. Another key priority is to use HiTOP to improve and accelerate research focused on
mental health and illness. As described in detail below, HiTOP has the potential to advance
theories of psychopathology and make mental health research more efficient and
informative.

HiTOP as a Psychopathology Research Framework

A distinguishing feature of HiTOP is its hierarchical organization (Figure 1). Various
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processes—some specific, others quite broad—are potentially implicated in the origins and
consequences of psychological problems across the lifespan (Forbes, Tackett, Markon, &
Krueger, 2016). The hierarchical structure implies that any cause or outcome of mental
illness could emerge because of its effects on broad higher order dimensions, the syndromes,
or specific lower order dimensions (Figure 2). Take trauma, for example. Suppose that
research based on the HiTOP framework establishes that trauma exposure better predicts
variation in the internalizing spectrum than in its constituent syndromes (e.g., depression,
posttraumatic distress). How would this result change our conceptualization of this research
area? It would call for an expansion of our etiological models of posttraumatic distress to
focus on the broad internalizing spectrum, including psychobiological processes shared by
the mood and anxiety disorders. We might advise a moratorium on research studies that
examine only one DSM disorder in relation to trauma exposure; instead, for maximum
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efficiency, we would consider various aspects of the internalizing spectrum (e.g., worry,
rituals, insomnia, irritability) as outcomes simultaneously in research studies. Additionally,
when making policy decisions regarding prevention and intervention resources, we might

2Unlike DSM and ICD workgroups, HiTOP membership has developed organically rather than through selection. The consortium was
founded by Roman Kotov, Robert Krueger, and David Watson, who invited all scientists with significant publication records on
quantitative mental-health nosologies to join the consortium. As the consortium grew and gained greater recognition, scientists began
contacting the consortium offering to contribute their effort.

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prioritize screening trauma-exposed individuals for the full range of internalizing problems,
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not just posttraumatic stress disorder. In sum, thinking hierarchically about mental illness
can promote more efficient research practices and more nuanced theory.

To illustrate these points, we now consider a more detailed example of putting HiTOP into
practice (Figure 3). For ease of presentation, DSM diagnoses comprise the basic units of
assessment.3 A subset of HiTOP constructs are involved (listed in order of increasing
granularity): the internalizing spectrum; fear, distress, and eating pathology subfactors; and
their component syndromes (e.g., binge eating disorder, agoraphobia). These constructs
serve as the predictor variables here.

For this example, we consider a test of an autonomic stress reactivity theory of social
phobia. The outcome of interest is skin conductance level during an impromptu speech
delivered to a group of impassive confederates. The researchers’ theory—which, like many
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others in psychopathology research, pertains to one particular categorical disorder—dictates

that predictive path a in Figure 3 should eclipse the others: the social phobia diagnosis
should be specifically associated with exaggerated autonomic reactivity in this evaluative
social context. Alternatively, one could reasonably expect that excessive autonomic
reactivity is a more general characteristic of fear disorders (e.g., social phobia, panic
disorder, agoraphobia), as compared to distress or eating pathology syndromes. In that case,
path b should surpass the others in terms of variance explained. Finally, given evidence
linking the full complement of anxiety and depressive disorders to stress responsivity, it is
possible that reactivity is best captured at the spectrum level. In this last scenario, path c
should predominate.

This heuristic illustrates that examining the validity of any DSM diagnosis in isolation—a
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conventional research strategy—is unnecessarily limiting. A zero-order association between

a DSM diagnosis and some outcome could reflect one (or more) qualitatively distinct
pathways (in our example, paths a, b, or c in Figure 3). Hierarchical frameworks, like
HiTOP, provide a ready means of quantitatively comparing these alternatives. If, in our
example, the effect for path a is comparatively small, the research team will know to revise
the “autonomic arousal theory of social phobia” to encompass fear-based or internalizing
disorders more generally.

We supplement this case study with a real-world example of theory building driven by
HiTOP. The stress generation theory posits that individuals with DSM major depression
encounter more stressful life events—including ones they have had a role in creating (e.g.,
romantic relationship dissolution, school expulsion)—than non-depressed counterparts
(Hammen, 1991). Indeed, there is evidence that depression prospectively predicts stress
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exposure. But more recent work suggests that this effect is not specific to depression. In fact,
stress generation appears to be a general feature of the internalizing disorders and
dispositional negativity (Liu & Alloy, 2010). Consistent with this hypothesis, Conway and
colleagues demonstrated that the internalizing spectrum, externalizing spectrum, and DSM

3We emphasize, however, that it is optimal from a HiTOP perspective to orient data collection around more homogeneous signs and
symptoms of mental disorder (e.g., Markon, 2010; Waszczuk, Kotov, Ruggero, Gamez, & Watson, 2017).

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major depression all contributed to the prediction of future stress exposure when considered
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simultaneously (cf. Figure 3; Conway, Hammen, & Brennan, 2012). Interestingly, panic
disorder had an inverse effect on stress occurrence after adjusting for the transdiagnostic
dimensions. The authors labeled this novel association a “stress inhibition” effect.

These findings prompted a reformulation of stress generation theory. First, stress generation
processes are now hypothesized to operate across a range of internalizing and externalizing
syndromes, not just DSM major depression. Second, the HiTOP-consistent analysis pointed
to a role for depression-specific pathology in predicting stressful events above and beyond
the effects of the internalizing spectrum (i.e., incremental validity). Theorists can use that
result to consider the specific portions of DSM major depression that increase the likelihood
of encountering significant stressors. Third, this work highlights the need to understand
more fully the stress inhibiting consequences of panic symptoms, a signal that was not
detectable when analyzing DSM diagnoses only.
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Up to this point, we have considered how a hierarchical approach—that is, comparing

pathways to and from dimensions at different levels of HiTOP—can advance our
understanding of psychopathology. Although this approach has been the most common
application of HiTOP, it is not the only one. Some researchers have used HiTOP to dissect
DSM diagnoses into components and compare their criterion validity (e.g., Simms, Grös,
Watson, & O’Hara, 2008) (Figure 2b). For example, panic disorder could be decomposed
into physiological (e.g., tachycardia, choking sensations) and psychological symptoms (e.g.,
thoughts of dying or going crazy). The predictive validity of these two symptom domains
could then be compared in relation to a clinical outcome of interest (e.g., emergency room
visits). Other researchers have evaluated the joint predictive power of sets of HiTOP
dimensions above and beyond the corresponding DSM-5 diagnosis (see Waszczuk, Kotov, et
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al., 2017; Waszczuk, Zimmerman, et al., 2017). This approach explicitly compares the
explanatory potential of dimensional versus categorical approaches to psychopathology
(Figure 2c).

Investigators are beginning to use these research strategies to reevaluate existing theories and
findings through a HiTOP lens. In the sections that follow, we describe studies that have
approached etiological and clinical outcome research from a HiTOP perspective as a way of
selectively illustrating its utility.

Etiological Research from a HiTOP Perspective

Quantitative and Molecular Genetics.
Twin studies find that some HiTOP dimensions are underpinned by distinct genetic liability
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factors, suggesting that the phenotypic and genetic structures of psychopathology may be
closely aligned (e.g., Lahey, Krueger, Rathouz, Waldman, & Zald, 2017; Røysamb et al.,
2011). For example, twin research has documented an overarching genetic liability factor
that resembles a general factor of psychopathology (Pettersson, Larsson, & Lichtenstein,
2016). This general factor (see the top level of Figure 1) was first described in phenotypic
analyses (Lahey et al., 2012) and was termed the “p-factor” as a counterpart to the g-factor
in the intelligence literature (Caspi et al., 2014; Caspi & Moffitt, 2018). Consistent with the

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broad intercorrelations among higher order spectra in psychometric studies, there is growing
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evidence that common genetic vulnerabilities underlie a general (i.e., transdiagnostic) risk
for psychopathology (Selzam, Coleman, Moffitt, Caspi, & Plomin, 2018; Waszczuk et al.,
2018).

At a lower level of the hierarchy, genetic influences operating at the level of spectra have
also been identified. For example, anxiety and depressive disorders appear to substantially
share a common genetic diathesis, whereas antisocial behavior and substance use conditions
share a distinct substrate (Kendler & Myers, 2014). Also, there is a consistent, but
underdeveloped, line of twin research that provides biometric support for the genetic
coherence of the thought disorder and detachment spectra (Livesley, Jang, & Vernon, 1998;
Tarbox & Pogue-Geile, 2011). Further attesting to the hierarchical structure of genetic risk,
distinct genetic influences have been identified for the distress and fear subfactors of the
internalizing spectrum (Waszczuk, Zavos, Gregory, & Eley, 2014). Finally, twin research
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shows that narrow psychiatric syndromes—and even certain symptom components within
them—might possess unique genetic underpinnings alongside the genetic vulnerability
shared with other psychiatric conditions more broadly (e.g., Kendler, Aggen, & Neale, 2013;
Rosenström et al., 2017). Overall, although these specific genetic factors often are
comparatively small, they provide etiological support for a hierarchical conceptualization of
psychopathology. A key challenge for future research will be to evaluate more
comprehensive versions of the HiTOP model in adequately powered, genetically informative
samples (e.g., twin, GWAS).

Emerging cross-disorder molecular genetic studies also suggest that genetic influences
operate across diagnostic boundaries (Smoller et al., in press). For example, a recent meta-
analysis of genome-wide association studies (GWAS) of DSM generalized anxiety disorder,
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panic, agoraphobia, social anxiety, and specific phobia identified common variants
associated with a higher order anxiety factor, consistent with the HiTOP fear subfactor
(Otowa et al., 2016). Other work reveals moderate (38%) single nucleotide polymorphism
(SNP)-based heritability of the p-factor, indicating that common SNPs are associated with a
general psychopathology factor in childhood (Neumann et al., 2016). Beyond these broader
spectra, several molecular genetic studies have focused on constructs at the subordinate level
of the HiTOP hierarchy, partly to reduce phenotypic heterogeneity and amplify genetic
signals. For example, one GWAS investigated a narrowly defined phenotype of mood
instability, which led to a discovery of four new genetic variants implicated in mood
disorders (Ward et al., 2017). Together, these emerging results suggest that it will be possible
to identify specific genetic variants at different levels of HiTOP hierarchy, with some
influencing nonspecific psychopathology risk and others conferring risk for individual
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spectra, subfactors, or even symptoms (e.g., anhedonia). In contrast, traditional case-control

designs and even studies focused on pairs of disorders are incapable of untangling such
hierarchical effects. In short, HiTOP promises to provide a more effective framework for
discovering the genetic underpinnings of mental illness, although further empirical evidence
and replications of any specific molecular genetic findings are, of course, needed.

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Neurobiology.
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Paralleling the genetics literature, there is growing evidence that many measures of brain
structure and function do not respect the boundaries implied by traditional DSM/ICD
diagnoses. There are no clear-cut depression or schizophrenia “centers” in the brain (e.g.,
Sprooten et al., 2017). Instead, associations between the brain and mental illness often show
one-to-many or many-to-many relations (i.e., multifinality; Zald & Lahey, 2017).
Heightened amygdala reactivity, for example, has been shown to confer risk for the future
emergence of mood and anxiety symptoms, posttraumatic distress, and alcohol abuse (e.g.,
McLaughlin et al., 2014; Swartz, Knodt, Radtke, & Hariri, 2015). The internalizing and
externalizing spectra are both associated with altered maturation of subcortical structures in
late childhood (Muetzel et al., 2018). In some cases, these relations have been shown to
reflect specific symptoms that cut across DSM’s categorical diagnoses. For instance,
anhedonia is a central feature of both mood and thought disorders in DSM, and dimensional
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measures of anhedonia have been linked to aberrant ventral striatum function (i.e., activity
and functional connectivity) in several large-scale, mixed-diagnosis studies (Sharma et al.,
2017; Stringaris et al. 2015).

Evidence of one-to-many relations is not limited to the neuroimaging literature. The P3

event-related potential (ERP), for example, has been linked to a variety of externalizing
disorders and to dimensional measures of externalizing (Iacono, Malone & McGue, 2003;
Patrick et al., 2006). Cross-sectional and prospective studies have linked the error-related
negativity (ERN) to a variety of DSM anxiety disorders, to the development of internalizing
symptoms, and to dimensional measures of anxiety (Cavanagh & Shackman, 2015; Meyer,
2017).

Although the neural bases of the p-factor remain far from clear, recent neuroimaging
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research has begun to reveal some neural systems with conspicuously similar (i.e.,
transdiagnostic) features. In a recent meta-analysis, McTeague and colleagues (2017)
identified a pattern of aberrant activation shared by the major mental disorders. When
performing standard cognitive control tasks (e.g., Go/No-Go, Stroop), patients diagnosed
with DSM anxiety disorders, bipolar disorder, depression, schizophrenia, or substance abuse
all exhibited reduced activation in parts of the so-called salience network, including regions
of the cingulate, insular, and prefrontal cortices. Applying a similar approach to voxel-by-
voxel measures of brain structure, Goodkind and colleagues (2015) identified a neighboring
set of regions in the midcingulate and insular cortices showing a common pattern of cortical
atrophy across patients diagnosed with a range of DSM disorders (anxiety, bipolar disorder,
depression, obsessive-compulsive, and schizophrenia). Few disorder-specific effects were
detected in either of these large meta-analyses.
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More recent imaging research has begun to adopt the kinds of analytic tools widely used in
psychometric and genetic studies of psychopathology, enabling a direct comparison of
different levels of HiTOP (cf. Figure 2a) and new clues about the neural bases of the p-
factor. Using data acquired from the Philadelphia Neurodevelopmental Cohort, Shanmugan
and colleagues (2016) identified the p-factor and four nested sub-dimensions (antisocial
behavior, distress, fear, and psychosis; cf. Figure 1, subfactor level). Higher levels of the p-
factor were associated with reduced activation and aberrant multivoxel patterns of activity in

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the salience network (cingulate and insular cortices) during the performance of the n-back
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task, a widely used measure of working memory capacity and executive function. After
accounting for the phenotypic variance explained by the p-factor, the antisocial, distress, and
psychosis dimensions were each associated with additional subfactor-specific alterations in
task-evoked activation (e.g., psychosis was uniquely associated with hypoactivation of the
dorsolateral prefrontal cortex). Using the same sample, Kaczkurkin and colleagues (in press)
found an analogous pattern of results with measures of resting activity. These observations
converge with the meta-analytic results discussed above (Goodkind et al., 2015; McTeague
et al., 2017) and reinforce the idea that a circuit centered on the cingulate cortex underlies a
range of common psychiatric symptoms and syndromes. Still, it is implausible that this
circuit will completely explain a phenotype as broad as the p-factor. Indeed, other correlates
are rapidly emerging (Romer et al., 2018; Sato et al., 2016; Snyder, Hankin, Sandman, Head,
& Davis, 2017).
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Collectively, these results highlight the value of the HiTOP framework for organizing
neuroscience and other kinds of biological research. Adopting a hierarchical dimensional
approach makes it possible to dissect brain structure and function quantitatively, facilitating
the discovery of features that are common to many or all of the common mental disorders,
those that are particular to specific spectra and syndromes, and those that underlie key
transdiagnostic symptoms—a level of insight not afforded by RDoC or traditional diagnosis-
centered nosologies.

Environmental Risk.
Stressful environments are intimately intertwined with risk for mental illness. For decades,
researchers have proposed theories about the connections between stressors and specific
diagnoses (e.g., loss and DSM major depression). Yet it is clear that most stressors are non-
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specific and confer increased risk for diverse psychopathologies (McLaughlin, 2016).
Socioeconomic adversity, discrimination, harsh parenting, bullying, and trauma all increase
the likelihood of developing psychiatric illness (Caspi & Moffitt, in press; Lehavot &
Simoni, 2011; Wiggins, Mitchell, Hyde, & Monk, 2015). This lack of specificity raises the
possibility that many stressors act on illness processes that are shared across entire
subfactors (e.g., distress, antisocial behavior), spectra (e.g., internalizing), or even super-
spectra. Investigators can use HiTOP to identify the level or levels where stressful
environments exert their effects.

Childhood maltreatment represents an instructive case because it has potent and non-specific
relations with future psychopathology (Green et al., 2010). Several studies have used a
hierarchical approach to assess the relative importance of higher order (i.e., transdiagnostic)
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versus diagnosis-specific pathways from early maltreatment to mental disorders in

adulthood. Leveraging interview-based diagnoses and retrospective reports of childhood
maltreatment collected as part of the National Epidemiological Survey on Alcohol and
Related Conditions (n > 34,000), Keyes and colleagues observed strong relations between
childhood maltreatment and the internalizing and externalizing spectra (cf. path c in Figure
3), but not specific diagnoses (cf. path a in Figure 3) (Keyes et al., 2012). In other words, the
marked impact of childhood maltreatment on adult psychopathology was fully mediated by

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the transdiagnostic spectra. Similar findings emerged in a community sample of over 2,000
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youth enriched for exposure to maltreatment (Vachon, Krueger, Rogosch, & Cicchetti, 2015;
see also Conway, Raposa, Hammen, & Brennan, 2018; Lahey et al., 2012; Meyers et al.,
2015; Sunderland et al., 2016).

The HiTOP framework has also been used to understand the influence of chronic stressors in
adulthood (Snyder, Young, & Hankin, 2017). For instance, Rodriguez-Seijas et al. (2015)
recently showed that racial discrimination has strong associations with the internalizing and
externalizing spectra (cf. path c in Figure 3) in a nationally representative sample of over
5,000 Black Americans. For most disorders, the pathway from discrimination to particular
DSM diagnoses (e.g., ADHD, social phobia) was largely explained by its impact on higher
order spectra. In a few cases, discrimination was directly associated with specific diagnoses
(e.g., alcohol use disorder). These effects make it clear that multiple pathways from
environmental adversity to psychopathology are possible—some centered on transdiagnostic
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spectra, others on more specific syndromes—with important implications for efforts to

develop more effective and efficient strategies for preventing and treating mental illness.

Clinical Outcome Research from a HiTOP Perspective

Like etiological factors, clinical outcomes often reflect a mixture of specific and
transdiagnostic effects and, as a result, are better aligned with HiTOP than traditional
diagnostic systems, like DSM or ICD.

Prognosis.
Clinicians and researchers often seek to forecast the onset or recurrence of psychological
problems based on diagnostic and symptom data (e.g., Morey et al., 2012). The HiTOP
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system has the potential to streamline this prognostic decision-making. For instance, using
data gleaned from the World Mental Health Surveys (N > 20,000), Kessler et al. (2011)
examined the prognostic value of 18 DSM-IV disorders in predicting new onsets of
subsequent diagnoses. They found that the vast majority of the development of categorical
diagnoses arising at later time points was attributable to variation on internalizing and
externalizing dimensions earlier in life (for similar results, see Eaton et al., 2013). This
result suggests that higher order dimensions can often provide a more efficient means of
predicting the natural course of mental illness (see also Kotov, Perlman, Gamez, & Watson,
2015; Olino et al., in press).

Suicide.
The HiTOP framework has also proven useful for optimizing suicide prediction. Tools for
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forecasting suicide are often based on the presence or absence of specific diagnoses (e.g.,
bipolar disorder, borderline personality disorder). But recent large-scale studies have
consistently shown that the predictive power of DSM diagnoses pales in comparison to that
of higher order dimensions. For instance, in the NESARC sample described earlier, the
distress subfactor (Figure 1) explained ~34% of the variance in suicide attempt history. In
contrast, the top-performing DSM diagnoses only accounted for ~1% (Eaton et al., 2013; see
also Naragon-Gainey & Watson, 2011; Sunderland & Slade, 2015). These kinds of

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observations indicate that suicide risk is better conceptualized at the level of spectra, not
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syndromes, contrary to standard research and clinical practices.

Impairment.
Psychosocial impairment is typically a core feature of contemporary definitions of
psychopathology, and it often persists long after acute symptoms have abated.
Understanding impairment is important for prioritizing scarce resources. But is impairment
better explained and, more importantly, predicted by DSM/ICD diagnoses or transdiagnostic
dimensions? Using data from the Collaborative Longitudinal Personality Disorders Study (N
= 668), Morey et al. (2012) found that maladaptive personality traits were twice as effective
at predicting patients’ functional impairment across a decade-long follow-up, when
compared to traditional diagnoses (cf. Figure 2c). Likewise, Forbush and colleagues
demonstrated that higher order dimensions explain 68% of the variance in impairment in a
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sample of eating disorder patients (Forbush et al., 2017). In contrast, DSM anxiety,
depression, and eating disorder diagnoses collectively explained only 11%. In the area of
psychosis, van Os and colleagues (1999) compared the predictive power of five dimensions
versus eight DSM diagnoses in a large longitudinal sample across 20 distinct psychosocial
outcomes (e.g., disability, unemployment, cognitive impairment, and suicide). For every
outcome with a clear difference in predictive validity, dimensions outperformed diagnoses.

Waszczuk, Kotov, et al. (2017) reported similar results in two samples evaluated with the
Interview for Mood and Anxiety Symptoms (IMAS), which assesses the lower order
components of emotional pathology (e.g., lassitude, obsessions). These dimensions
explained nearly two times more variance in functional impairment compared to DSM
diagnoses. Moreover, DSM diagnoses did not show any incremental power over the
dimensional scores—a particularly striking result given that impairment is part of the DSM
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diagnostic criteria but not directly captured by IMAS scores (cf. Figure 2c). In sum, this line
of research suggests that transdiagnostic dimensions of the kinds embodied in HiTOP have
superior prognostic value—both concurrently and prospectively—for psychosocial
impairment (see also Jonas & Markon, 2013; Markon, 2010; South, Krueger, & Iacono,
2011).

Summary
Traditionally, theoretical models of the causes and consequences of psychiatric problems
have been framed around diagnoses. New research highlights the importance of extending
this focus to encompass transdiagnostic dimensions, including both narrowly defined
symptoms and traits (e.g., anhedonia) and broader clusters of psychological conditions (e.g.,
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internalizing spectrum). In contrast to other classification systems (e.g., DSM) and unlike
RDoC, HiTOP provides a convenient framework for directly testing the relative importance
of symptom components, syndromes, spectra, and super-spectra (e.g., p-factor) for the
emergence and treatment of psychopathology (Figure 1). The evidence reviewed here
suggests that in many cases mental illness is better conceptualized in terms of
transdiagnostic dimensions.

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 Conway et al. Page 12

HiTOP: A Practical Guide

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A primary objective of this review is to provide investigators with some practical

recommendations for incorporating HiTOP into their research. Here we outline design,
assessment, and analytic strategies that follow from the theory and available data
underpinning HiTOP.

Design.
Historically, the lion’s share of clinical research has been conducted using traditional case-
control designs, in which participants meeting criteria for a particular diagnosis are
compared to a group free of that disorder or perhaps any mental illness. This approach is
generally inconsistent with a dimensional perspective on psychopathology. There is
compelling evidence that mental illness is continuously distributed in the population,
without the gaps or “zones of discontinuity” expected of categorical illnesses (Krueger et al.,
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in press; although for a different perspective see Borsboom et al., 2016). These observations
indicate that artificially separating cases from non-cases leads to an appreciable loss of
information (Markon et al., 2011), consistent with more general recommendations to avoid
post hoc dichotomization (e.g., median splits) of continuous constructs (Preacher, Rucker,
MacCallum, & Nicewander, 2005).

The case-control strategy also ignores the issue of diagnostic comorbidity. The ubiquitous
co-occurrence of disorders makes it extremely difficult to establish discriminant validity for
most categorical syndromes. In practical terms, any distinction between, say, DSM panic
disorder patients and healthy controls in a particular study may not be a unique characteristic
of panic disorder. It could instead reflect the influence of a higher order dimension, such as
the HiTOP fear subfactor, that permeates multiple diagnoses (e.g., panic disorder,
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agoraphobia, social anxiety disorder, and specific phobia). By disregarding the symptom
overlap among clusters of related conditions, the case-control design is bound to
underestimate the breadth of psychopathology associated with a given clinical outcome.

From an efficiency standpoint, recruiting on the basis of particular diagnoses creates a

fragmented scientific record. The traditional approach of studying one disorder in relation to
one outcome has spawned many insular journals, societies, and scholarly sub-communities
(“silos”). This convention belies the commonalities among disorders and has led to
piecemeal progress. For example, the initial phases of psychiatric genetic research were
oriented around specific diagnoses. There were separate studies focused on the molecular
genetic origins of obsessive-compulsive disorder, generalized anxiety disorder, posttraumatic
stress disorder, and so on. Analogously, there are voluminous literatures on childhood
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maltreatment in relation to various individual syndromes. These lines of research have

consumed considerable resources, but they have revealed few (if any) replicable one-to-one
associations between risks and disorders. A more parsimonious and efficient approach is to
recruit participants on the basis of a particular psychopathological dimension (e.g., antisocial
behavior, excitement seeking), either sampling to ensure adequate representation of all
ranges of this dimension, or recruiting at random from the population of interest (e.g.,
community, students, or outpatients) to provide a representative sample.4 Then, as was the
case for our fictional study of autonomic disruptions in social phobia, the effects of both

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general and more specific dimensions of psychopathology can be compared empirically.

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Thinking broadly, such a strategy promises to facilitate more cumulative, rapid progress in
developing etiological models for a wide range of psychological conditions.

It merits comment that some of these recommendations can be addressed after the fact.
Many of the analyses that we have reviewed were carried out using datasets that were not
assembled with HiTOP in mind. However, these projects have generally included a thorough
assessment of psychopathology outcomes, which can serve as building blocks for
quantitative investigations of symptom or syndrome co-occurrence via factor analysis or
related techniques. For example, there have been several studies of the correlates (e.g.,
demographic features, racial discrimination, childhood maltreatment) of higher order
dimensions versus syndromes in epidemiological studies, such as the National Comorbidity
Survey-Replication and NESARC (e.g., Eaton et al., 2013; Keyes et al., 2012; Slade, 2007).
Investigators have also taken advantage of comprehensive psychopathology assessments in
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longitudinal cohort studies—such as the Dunedin Multidisciplinary Health and Development

Study and the Pittsburgh Girls Study—to examine the temporal course and longitudinal
correlates of HiTOP dimensions (e.g., Krueger et al., 1998; Lahey et al., 2015; McElroy,
Belsky, Carragher, Fearon, & Patalay, in press). These cohort studies are particularly
valuable for theory building because they tend to have rich assessments of validators
(etiological factors, clinical outcomes; e.g., Caspi et al., 2014).

Studies need not have especially large samples or wide-ranging assessment batteries (e.g.,
“big data”) to take advantage of the HiTOP framework. Often, dimensional measures of
psychopathology can be integrated into typical (in terms of resources and sample size) study
designs. Take, for example, the fictional study of autonomic reactivity described earlier. We
described a scenario in which diagnoses were the basic units of mental illness and were used
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to infer standing on the next higher-level dimensions (i.e., the subfactor and spectrum
levels). However, analogous tests could be carried out if researchers administered a self-
report questionnaire assessing both the broad and specific features of the internalizing
domain, such as the Inventory of Depression and Anxiety Symptoms (Watson et al., 2012).
For instance, the effect of lower order symptom components (e.g., lassitude, obsessions; cf.
Figure 3 path c) on autonomic reactivity could be compared to the effect of a higher-level
(e.g., spectrum) dimension (e.g., dysphoria; cf. Figure 3 path a). We expect that, in most
research situations, moderately sized samples would suffice to precisely gauge these effects.
More generally, we expect that empirically derived, dimensional measures of mental illness
can be integrated effectively into most standard research designs. Along those lines, we plan
to publish a series of “worked examples” on the HiTOP consortium website that illustrate
the methodological and data analytic steps—including relevant materials, data, and code—in
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typical studies that apply the HiTOP framework.

Assessment.
Although assessing multiple syndromic or symptom constructs in the same study represents
an improvement over “one disorder, one outcome” designs, there are limitations to this

4Incidentally, this is roughly the same recruitment strategy recommended under the RDoC framework.

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approach. DSM/ICD diagnoses and many symptom measures are notoriously heterogeneous,
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meaning they are composed of multiple lower order dimensions of psychopathology. For
instance, many common depressive symptom scales include not only cognitive and
vegetative symptoms, which arguably have separate etiologies and correlates, but also
include anxiety symptoms (e.g., Fried, 2017). Thus, a more optimal approach is to forego
traditional diagnostic constructs in favor of assessing lower order dimensions of pathology
(e.g., the symptom component level of Figure 1). This strategy maximizes the precision of
the dimensions that can be examined, improving our ability to “carve nature at its joints.”

Consequently, we recommend using assessment instruments that measure both higher and
lower order dimensions of psychopathology. A number of such measures are reviewed in
Kotov et al. (2017). No omnibus inventory yet exists that covers the entirety of the HiTOP
framework, although our consortium is currently developing one. Instead, there are many
existing measures that assess specific aspects (e.g., component/trait, syndrome, and
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subfactor levels) of the HiTOP model (see https://psychology.unt.edu/hitop). Researchers

can use these measures to perform a complete assessment of one spectrum (e.g., antagonistic
externalizing) or several (e.g., antagonistic externalizing, disinhibited externalizing, thought
disorder). The list of measures is expected to continue evolving, and researchers can refer to
the HiTOP website to access the latest inventories, including a forthcoming comprehensive
measure of the full HiTOP model, as currently constituted. At present, most facets of the
HiTOP structure can be assessed economically with questionnaire measures that are
available in self- and informant-report versions. Structured and semi-structured interview
approaches can also be used, assuming they allow for dimensional scoring. Of course, for
such assessments to be compatible with HiTOP, they may need to be modified to eliminate
“skip rules” (e.g., if neither significant depressed mood or anhedonia is endorsed, some
interview procedures automatically exit the major depression section) and hierarchical
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decision rules (e.g., DSM-IV stipulated that generalized anxiety disorder could not be
diagnosed if it presented only in the context of a co-occurring depressive disorder) in order
to collect all symptom data. Overriding these rules permits assessment of the full clinical
picture.

Analysis.
There are several different ways for investigators to test the association of dimensional
constructs with outcomes of interest. Expertise with latent variable modeling is not a
prerequisite. Many popular measurement tools (e.g., the Child Behavior Checklist;
Achenbach, 1991) include a combination of broad (e.g., externalizing) and narrow (e.g.,
aggression) dimensions. Connections of these scales with background characteristics or
clinical outcomes could then be contrasted using standard regression approaches.
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In the case of large samples, it is possible to use latent variable modeling to empirically
extract the relevant dimensions. Exploratory factor analysis (EFA) is an atheoretical
approach to determining the appropriate number and nature of latent dimensions
undergirding psychological problems. In many common statistical packages, it is possible to
perform an EFA and then extract factor scores—values that represent a person’s standing on
a latent dimension—that can be used as variables in standard regression or analysis of

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variance procedures (although this procedure has some drawbacks; e.g., Devlieger, Mayer, &
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Rosseel, 2016). Confirmatory factor analysis, a hypothesis-driven approach in which the

researcher specifies the relations of symptom or diagnostic constructs to latent dimensions,
is another common approach. Finally, Goldberg’s (2006) approach of using a series of factor
analyses to explicate a hierarchical factor structure, by proceeding from higher (broader) to
lower (narrower) levels of specificity (termed the “bass-ackwards” method), can be useful in
extracting HiTOP dimensions from symptom- or diagnostic-level data.

Future Challenges
There are clear and compelling scientific reasons to adopt HiTOP-style approaches to
understanding psychopathology. But it is equally clear that additional work will be required
to refine this framework and determine its optimal role in mental illness research.
Uncertainties remain about several architectural elements of HiTOP. Additional research is
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needed to incorporate psychiatric problems not currently included in HiTOP (e.g., autism
spectrum disorder and other neurodevelopmental conditions) and to validate the placement
of domains of psychopathology that have received limited attention in structural studies
(e.g., lower order dimensions of mania as components of internalizing versus thought
disorder). At the spectrum level, data are particularly limited for HiTOP’s somatoform and
detachment dimensions. Further, continued research is needed on possible latent taxa, as
opposed to dimensions, involved in mental illness. Taxometric research has favored
dimensions over categories for every HiTOP construct that has been examined to date;
however, in theory, “zones of discontinuity” could emerge and would therefore merit
inclusion in the HiTOP model. For example, deviation on multiple dimensions may yield
discontinuous cutpoints (cf. Kim & Eaton, 2017). In short, the HiTOP framework is a work
in progress and researchers are encouraged to consult the consortium website for updates or
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to apply for membership in the consortium and contribute to improving the model.

Moving forward, we also need to examine carefully the use and interpretation of factor
analysis with respect to HiTOP. There are questions about whether the theoretical constructs
outlined in HiTOP satisfy assumptions of the common factor model (e.g., van Bork,
Epskamp, Rhemtulla, Borsboom, & van der Maas, 2017; see also Borsboom, Mellenbergh,
& van Heerden, 2003). For instance, are the factors (e.g., fear, detachment) naturally
occurring phenomena that directly cause variation in their indicators (e.g., panic, social
phobia)? Or are the HiTOP factors simply useful—and, to some extent, artificial—
summaries of symptom covariation (see Jonas & Markon, 2016)? We note that although
factor analysis has proved to be a useful tool in this area of research, HiTOP outcomes need
not be represented by latent variables; it is possible to operationalize them directly using
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questionnaire and interview measures of the types mentioned earlier, although every specific
measure has strengths, weakness, and a particular range of applicability, so it will be
important not to equate measures with constructs.

Additional work will also be required to better understand the degree to which HiTOP is
compatible with network models and the RDoC framework (e.g., Clark et al., 2017; Fried &
Cramer, 2017). Network models conventionally assume that psychopathology does not
reflect latent traits; psychological syndromes instead arise from a chain reaction of

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symptoms activating one another (e.g., Cramer, Waldorp, van der Maas, & Borsboom,
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2010). A common example is that a constellation of depression symptoms might coalesce

not because of the guiding influence of an unobserved, unitary depression dimension, but
rather due to a “snowballing” sequence of symptom development (e.g., insomnia → fatigue,
fatigue → concentration problems, and so on). The purpose of the network model is to
discern these hypothesized causal pathways among symptoms. In contrast, HiTOP aims to
identify replicable clusters of symptoms that have shared risk factors and outcomes. Both
perspectives can be useful for understanding the nature of psychopathology and are not
necessarily mutually exclusive (e.g., Fried & Cramer, 2017).

Like HiTOP, the National Institute of Mental Health’s RDoC initiative deconstructs
psychopathology into more basic units that cut across traditional diagnoses (Table 1).
However, its primary focus is on fundamental biobehavioral processes (e.g., reward,
anxiety), especially those conserved across species, that are disrupted in mental illness
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(Clark et al., 2017). This approach has gained traction in biological psychology and
psychiatry as an alternative to DSM diagnoses, but its utility for other areas of research may
be more limited because RDoC does not specifically model the observable signs and
symptoms of mental illness that are the subject of most theories of psychopathology. That is,
it does not include detailed representations of clinical phenotypes (e.g., aggression,
narcissism, emotional lability) that are common targets in research on organizations, close
relationships, social groups, aging, psychotherapy, and many other fields wherein the
prevailing theoretical models have little (or no) biological emphasis.

A complementary nosological framework is needed to link the basic science discoveries

spurred by RDoC—and similar NIH initiatives, such as the National Institute of Alcohol
Abuse and Alcoholism’s Addictions Neuroclinical Assessment and the National Institute of
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Drug Abuse’s Phenotyping Assessments Battery—to the signs and symptoms that lead
people to seek treatment. HiTOP, which provides a clear and comprehensive system of
clinical phenotypes, offers such a bridge. Research that integrates these dimensional
frameworks has the potential to make RDoC clinically relevant and to provide important
insights into the biological bases of the dimensions embodied in the HiTOP framework.

Whereas RDoC proponents acknowledge that it is unlikely to have much applied clinical
value in the near-term, HiTOP is poised for clinical implementation. HiTOP encapsulates
clinical problems that practitioners are familiar with and routinely encounter. Existing
questionnaire and interview measures that capture HiTOP dimensions can be administered
to patients or other informants (Kotov et al., 2017). Normative data are available for many
measures and will continue to accrue (e.g., Stasik-O’Brien et al., in press). Clinicians can
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use dimensional scores to compare patients’ scores to clinical cutoffs or other norms to
inform decisions about prognosis and treatment (see Ruggero et al., 2018). Moreover,
dimensional measures are more useful for monitoring treatment progress than are categorical
diagnoses because they tend to be more sensitive to change while also yielding more reliable
change indices (e.g., Kraemer, Noda, & O’Hara, 2004). One of the most important
challenges for the future will be to gather appropriate normative data for more instruments
and refine their use in clinical assessment and treatment planning.

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The hierarchical structure of HiTOP implies that targeting higher order dimensions, like the
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internalizing spectrum, may cause therapeutic effects to percolate across multiple DSM
conditions, augmenting the efficiency of psychological treatment. For example, the Unified
Protocol for Transdiagnostic Treatment of Emotional Disorders (Barlow et al., 2014) was
developed to act on common temperamental processes theorized to lie at the core of
internalizing problems. Rather than using separate protocols to treat individual diagnoses,
such as major depression and generalized anxiety disorder, the Unified Protocol uses
cognitive-behavioral strategies to reduce negative emotionality and increase positive
emotionality, traits thought to maintain anxiety and depression over time, and there is
emerging evidence that such transdiagnostic psychotherapies can be as effective as
traditional (i.e., diagnosis-specific) treatments (Barlow et al., 2017). Practitioners can apply
the Unified Protocol to a diverse set of anxiety and depressive conditions, streamlining the
training process and minimizing barriers to dissemination, as compared to standard training
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models that involve learning a separate treatment framework for each disorder (Steele et al.,
2018). The policy of using one psychological treatment for various conditions is analogous
to standard prescription practices for psychiatric medications, which often work across—and
in many cases have regulatory approval for treatment of—multiple diagnoses.

The most important avenue for future empirical work, in our view, is continued validation
research into the utility (for research and theory building) of the dimensions that make up
the HiTOP model. In particular, validation studies to date have been mostly limited to the
spectrum level (e.g., correlates of internalizing, disinhibited externalizing), and criterion-
validity research is needed at other levels of the hierarchy. Also, existing research has largely
relied on snapshots of symptoms and syndromes without modeling illness course.
Longitudinal studies that are designed to examine the correlates and structure of HiTOP
dimensions in diverse samples across the lifespan are a pressing priority (cf. Lahey et al.,
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2015; Wright, Hopwood, Skodol, & Morey, 2016), as is research on the short-term dynamics
of psychopathology symptoms (e.g., Wright & Simms, 2016). Although research has
supported the invariance of the internalizing and externalizing spectra across gender,
developmental stages, and various racial, ethnic, and cultural groups (see Rodriguez-Seijas
et al., 2015), investigation of other HiTOP dimensions with regard to aging, culture, context,
and so forth will be important.

Conclusion
There is compelling evidence that the nature of psychopathology is dimensional and
hierarchical, with many studies indicating that genes, neurobiology, and clinical outcomes
align with this new conceptualization. We recommend a shift in mental health research
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practices to match the HiTOP model. This emerging system has the potential to (i) expand
existing theories and generate new hypotheses; (ii) unify unnecessarily fragmented empirical
literatures; (iii) increase the utility of classification systems for both basic and applied
research; and (iv) establish novel phenotypes that explain the etiology of psychological
problems and serve as more efficient assessment and treatment targets. Although many
important challenges remain, HiTOP has the potential to transform research practices for the
better and accelerate theory development across diverse areas of psychological science.

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Acknowledgments:
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This paper was organized by members of the HiTOP Consortium, a grassroots organization open to all qualified
investigators (https://medicine.stonybrookmedicine.edu/HITOP/).

References
Achenbach TM (1966). The classification of children’s psychiatric symptoms: A factor-analytic study.
Psychological Monographs, 80, 1–37.
Achenbach TM (1991). Manual for the child behavior checklist/4–18 and 1991 profile. Burlington:
University of Vermont, Department of Psychiatry.
Achenbach TM, Howell CT, Quay HC, Conners CK, & Bates JE (1991). National Survey of problems
and competencies among four- to sixteen-year-olds: Parents’ reports for normative and clinical
samples. Monographs of the Society for Research in Child Development, 56, 1–131.
Barlow DH, Farchione TJ, Bullis JR, Gallagher MW, …, & Cassiello-Robbins C (2017). The Unified
Protocol for Transdiagnostic Treatment of Emotional Disorders compared with diagnosis-specific
Author Manuscript

protocols for anxiety disorders: A randomized clinical trial. JAMA Psychiatry, 74, 875–884.
[PubMed: 28768327]
Barlow DH, Sauer-Zavala S, Carl JR, Bullis JR, & Ellard KK (2014). The nature, diagnosis, and
treatment of neuroticism: Back to the future. Clinical Psychological Science, 2, 344–365.
Borsboom D, Mellenbergh GJ, & Van Heerden J (2003). The theoretical status of latent variables.
Psychological Review, 110, 203–219. [PubMed: 12747522]
Borsboom D, Rhemtulla M, Cramer AOJ, van der Maas HLJ, Scheffer M, & Dolan CV (2016). Kinds
versus continua: a review of psychometric approaches to uncover the structure of psychiatric
constructs. Psychological Medicine, 1–13.
Brown TA, & Barlow DH (2009). A proposal for a dimensional classification system based on the
shared features of the DSM-IV anxiety and mood disorders: Implications for assessment and
treatment. Psychological Assessment, 21, 256–271. [PubMed: 19719339]
Caspi A, Houts RM, Belsky DW, Goldman-Mellor SJ, Harrington H, Israel S, … Moffitt TE (2014).
The p factor: One general psychopathology factor in the structure of psychiatric disorders? Clinical
Psychological Science, 2, 119–137. [PubMed: 25360393]
Author Manuscript

Caspi A, & Moffitt TE (in press). All for one and one for all: Mental disorders in one dimension
American Journal of Psychiatry.
Cavanagh JF, & Shackman AJ (2015). Frontal midline theta reflects anxiety and cognitive control:
Meta-analytic evidence. Journal of Physiology, 109, 3–15. [PubMed: 24787485]
Clark LA, Cuthbert B, Lewis-Fernández R, Narrow WE, & Reed GM (2017). Three Approaches to
Understanding and Classifying Mental Disorder: ICD-11, DSM-5, and the National Institute of
Mental Health’s Research Domain Criteria (RDoC). Psychological Science in the Public Interest,
18, 72–145. [PubMed: 29211974]
Cramer AO, Waldorp LJ, van der Maas HL, & Borsboom D (2010). Complex realities require complex
theories: Refining and extending the network approach to mental disorders. Behavioral and Brain
Sciences, 33, 178–193.
Conway CC, Hammen C, & Brennan PA (2012). Expanding stress generation theory: Test of a
transdiagnostic model. Journal of Abnormal Psychology, 121, 754–766. [PubMed: 22428789]
Conway CC, Raposa EB, Hammen C, & Brennan PA (2018). Transdiagnostic pathways from early
Author Manuscript

social stress to psychopathology: a 20-year prospective study. Journal of Child Psychology and
Psychiatry.
Cuthbert BN, & Insel TR (2013). Toward the future of psychiatric diagnosis: The seven pillars of
RDoC. BMC Medicine, 11, 126–134. [PubMed: 23672542]
Devlieger I, Mayer A, & Rosseel Y (2016). Hypothesis testing using factor score regression: A
comparison of four methods. Educational and Psychological Measurement, 76, 741–770.
[PubMed: 29795886]
DiLuca M, & Olesen J (2014). The cost of brain diseases: a burden or a challenge?. Neuron, 82, 1205–
1208. [PubMed: 24945765]

Perspect Psychol Sci. Author manuscript; available in PMC 2020 May 01.
 Conway et al. Page 19

Eaton NR, Krueger RF, Markon KE, Keyes KM, Skodol AE, Wall M, … Grant BF (2013). The
structure and predictive validity of the internalizing disorders. Journal of Abnormal Psychology,
Author Manuscript

122, 86–92. [PubMed: 22905862]

Eysenck HJ (1944). Types of personality: a factorial study of seven hundred neurotics. The British
Journal of Psychiatry, 90, 851–861.
Forbes MK, Tackett JL, Markon KE, & Krueger RF (2016). Beyond Comorbidity: Toward a
dimensional and hierarchal approach to understanding psychopathology across the lifespan.
Development and Psychopathology, 28, 971–986. [PubMed: 27739384]
Forbush KT, Hagan KE, Kite BA, Chapa DA, Bohrer BK, & Gould SR (2017). Understanding eating
disorders within internalizing psychopathology: A novel transdiagnostic, hierarchical-dimensional
model. Comprehensive Psychiatry, 79, 40–52. [PubMed: 28755757]
Fried EI (2017). The 52 symptoms of major depression: Lack of content overlap among seven common
depression scales. Journal of Affective Disorders, 208, 191–197. [PubMed: 27792962]
Fried EI, & Cramer AOJ (2017). Moving forward: Challenges and directions for psychopathological
network theory and methodology. Perspectives on Psychological Science, 12, 999–1020. [PubMed:
28873325]
Author Manuscript

Goldberg LR (2006). Doing it all bass-ackwards: The development of hierarchical factor structures
from the top down. Journal of Research in Personality, 40, 347–358.
Goodkind M, Eickhoff SB, Oathes DJ, Jiang Y, Chang A, Jones-Hagata LB, …, Etkin A (2015).
Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry, 72,
305–315. [PubMed: 25651064]
Gordon JA, & Redish AD (2016). On the cusp. Current challenges and promises in psychiatry In
Redish AD & Gordon JA (Eds.), Computational psychiatry: New perspectives on mental illness
(pp. 3–14). Cambridge, MA: MIT Press.
Green JG, McLaughlin KA, Berglund PA, Gruber MJ, Sampson NA, Zaslavsky AM, & Kessler RC
(2010). Childhood adversities and adult psychiatric disorders in the national comorbidity survey
replication I: Associations with first onset of DSM-IV disorders. Archives of General Psychiatry,
67, 113–123. [PubMed: 20124111]
Hammen C (1991). Generation of stress in the course of unipolar depression. Journal of Abnormal
Psychology, 100, 555–561. [PubMed: 1757669]
Author Manuscript

Iacono WG, Malone SM, & McGue M (2003). Substance use disorders, externalizing
psychopathology, and P300 event-related potential amplitude. International Journal of
Psychophysiology, 48, 147–178. [PubMed: 12763572]
Hyman SE (2007). Can neuroscience be integrated into the DSM-V?. Nature Reviews Neuroscience, 8,
725–732. [PubMed: 17704814]
Jonas KG, & Markon KE (2013). A model of psychosis and its relationship with impairment. Social
Psychiatry and Psychiatric Epidemiology, 48, 1367–1375. [PubMed: 23306423]
Jonas KG, & Markon KE (2016). A descriptivist approach to trait conceptualization and inference.
Psychological Review, 123, 90–96. [PubMed: 26214685]
Kaczkurkin AN, Moore TM, Calkins ME, Ciric R, Detre JA, Elliott MA, … & Ruparel K (in press).
Common and dissociable regional cerebral blood flow differences associate with dimensions of
psychopathology across categorical diagnoses Molecular Psychiatry.
Kendler KS, Aggen SH, & Neale MC (2013). Evidence for multiple genetic factors underlying DSM-
IV criteria for major depression. JAMA Psychiatry, 70, 599–607. [PubMed: 23740048]
Kendler KS, & Myers J (2014). The boundaries of the internalizing and externalizing genetic spectra in
Author Manuscript

men and women. Psychological Medicine, 44, 647–655. [PubMed: 23574685]

Kessler RC, Petukhova M, & Zaslavsky AM (2011). The role of latent internalizing and externalizing
predispositions in accounting for the development of comorbidity among common mental
disorders. Current Opinion in Psychiatry, 24, 307–312. [PubMed: 21602684]
Kim H, & Eaton NR (2017). A hierarchical integration of person-centered comorbidity models:
structure, stability, and transition over time. Clinical Psychological Science, 5, 595–612.
Keyes KM, Eaton NR, Krueger RF, McLaughlin KA, Wall MM, Grant BF, & Hasin DS (2012).
Childhood maltreatment and the structure of common psychiatric disorders. The British Journal of
Psychiatry, 200, 107–115. [PubMed: 22157798]

Perspect Psychol Sci. Author manuscript; available in PMC 2020 May 01.
 Conway et al. Page 20

Kotov R, Krueger RF, Watson D, Achenbach TM, Althoff RR, Bagby RM, … & Zimmerman M
(2017). The Hierarchical Taxonomy of Psychopathology (HiTOP): A dimensional alternative to
Author Manuscript

traditional nosologies. Journal of Abnormal Psychology, 126, 454–477. [PubMed: 28333488]

Kotov R, Perlman G, Gámez W, & Watson D (2015). The structure and short-term stability of the
emotional disorders: a dimensional approach. Psychological Medicine, 45, 1687–1698. [PubMed:
25499142]
Kraemer HC, Noda A, & O’Hara R (2004). Categorical versus dimensional approaches to diagnosis:
methodological challenges. Journal of Psychiatric Research, 38, 17–25. [PubMed: 14690767]
Krueger RF, Caspi A, Moffitt TE, & Silva PA (1998). The structure and stability of common mental
disorders (DSM-III-R): A longitudinal-epidemiological study. Journal of Abnormal Psychology,
107, 216–227. [PubMed: 9604551]
Krueger RF, Kotov R, Watson D, Forbes MK, Eaton NR, Ruggero CJ, … Zimmermann J (in press).
Progress in achieving quantitative classification of psychopathology World Psychiatry.
Lahey BB, Applegate B, Hakes JK, Zald DH, Hariri AR, & Rathouz PJ (2012). Is there a general
factor of prevalent psychopathology during adulthood? Journal of Abnormal Psychology, 121,
971–977. [PubMed: 22845652]
Author Manuscript

Lahey BB, Krueger RF, Rathouz PJ, Waldman ID, & Zald DH (2017). A hierarchical causal taxonomy
of psychopathology across the life span. Psychological Bulletin, 143, 142–186. [PubMed:
28004947]
Lahey BB, Rathouz PJ, Keenan K, Stepp SD, Loeber R, & Hipwell AE (2015). Criterion validity of
the general factor of psychopathology in a prospective study of girls. Journal of Child Psychology
and Psychiatry, 4, 415–422.
Lehavot K, & Simoni JM (2011). The impact of minority stress on mental health and substance use
among sexual minority women. Journal of Consulting and Clinical Psychology, 79, 159. [PubMed:
21341888]
Livesley WJ, Jang KL, & Vernon PA (1998). Phenotypic and genetic structure of traits delineating
personality disorder. Archives of General Psychiatry, 55, 941–948. [PubMed: 9783566]
Liu RT, & Alloy LB (2010). Stress generation in depression: A systematic review of the empirical
literature and recommendations for future study. Clinical Psychology Review, 30, 582–593.
[PubMed: 20478648]
Author Manuscript

Lorr M, Klett CJ, & McNair DM (1963). Syndromes of psychosis. New York, NY: Pergamon Press.
Markon KE (2010). Modeling psychopathology structure: A symptom-level analysis of Axis I and II
disorders. Psychological Medicine, 40, 273–288. [PubMed: 19515267]
Markon KE, Chmielewski M, & Miller CJ (2011). The reliability and validity of discrete and
continuous measures of psychopathology: a quantitative review. Psychological Bulletin, 137, 856.
[PubMed: 21574681]
McElroy E, Belsky J, Carragher N, Fearon RMP, & Patalay P (in press). Developmental stability of
general and specific factors of psychopathology from early childhood through adolescence:
Dynamic mutualism or p-differentiation?. Journal of Child Psychology and Psychiatry.
McGue M, Zhang Y, Miller MB, Basu S, Vrieze S, Hicks B, … & Iacono WG (2013). A genome-wide
association study of behavioral disinhibition. Behavior Genetics, 43, 363–373. [PubMed:
23942779]
McLaughlin KA (2016). Future directions in childhood adversity and youth psychopathology. Journal
of Clinical Child & Adolescent Psychology, 45, 361–382. [PubMed: 26849071]
McLaughlin KA, Busso DS, Duys A, Green JG, Alves S, Way M, & Sheridan MA (2014). Amygdala
Author Manuscript

response to negative stimuli predicts PTSD symptom onset following a terrorist attack. Depression
and Anxiety, 31, 834–842. [PubMed: 24995938]
McTeague LM, Huemer J, Carreon DM, Jiang Y, Eickhoff SB, & Etkin A (2017). Identification of
common neural circuit disruptions in cognitive control across psychiatric disorders. American
Journal of Psychiatry, 174, 676–685. [PubMed: 28320224]
Meyer A (2017). A biomarker of anxiety in children and adolescents: A review focusing on the error-
related negativity (ERN) and anxiety across development. Developmental Cognitive Neuroscience,
27, 58–68. [PubMed: 28818707]

Perspect Psychol Sci. Author manuscript; available in PMC 2020 May 01.
 Conway et al. Page 21

Meyers JL, Lowe SR, Eaton NR, Krueger R, Grant BF, & Hasin D (2015). Childhood maltreatment,
9/11 exposure, and latent dimensions of psychopathology: A test of stress sensitization. Journal of
Author Manuscript

Psychiatric Research, 68, 337–345. [PubMed: 26037889]

Moffitt TE (1993). Adolescence-limited and life-course-persistent antisocial behavior: a developmental
taxonomy. Psychological Review, 100, 674. [PubMed: 8255953]
Morey LC, Hopwood CJ, Markowitz JC, Gunderson JG, Grilo CM, McGlashan TH, … Skodol AE
(2012). Comparison of alternative models for personality disorders, II: 6-, 8- and 10-year follow-
up. Psychological Medicine, 42, 1705–1713. [PubMed: 22132840]
Muetzel RL, Blanken LM, van der Ende J, El Marroun H, Shaw P, Sudre G, … & White T (2018).
Tracking brain development and dimensional psychiatric symptoms in children: A longitudinal
population-based neuroimaging study. American Journal of Psychiatry, 1, 54–62.
Naragon-Gainey K, & Watson D (2011). The anxiety disorders and suicidal ideation: accounting for
co-morbidity via underlying personality traits. Psychological Medicine, 41, 1437–1447. [PubMed:
21054917]
Neumann A, Pappa I, Lahey BB, Verhulst FC, Medina-Gomez C, Jaddoe VW, … & Tiemeier H
(2016). Single nucleotide polymorphism heritability of a general psychopathology factor in
Author Manuscript

children. Journal of the American Academy of Child & Adolescent Psychiatry, 55, 1038–1045.
[PubMed: 27871638]
Olino TM, Bufferd SJ, Dougherty LR, Dyson MW, Carlson GA, & Klein DN (in press). The
development of latent dimensions of psychopathology across early childhood: stability of
dimensions and moderators of change. Journal of Abnormal Child Psychology.
Otowa T, Hek K, Lee M, Byrne EM, Mirza SS, Nivard MG, … & Fanous A (2016). Meta-analysis of
genome-wide association studies of anxiety disorders. Molecular psychiatry, 21, 1391–1399.
[PubMed: 26754954]
Patrick CJ, Bernat EM, Malone SM, Iacono WG, Krueger RF, & McGue M (2006). P300 amplitude as
an indicator of externalizing in adolescent males. Psychophysiology, 43, 84–92. [PubMed:
16629688]
Patrick CJ, & Hajcak G (2016). RDoC: Translating promise into progress. Psychophysiology, 53, 415–
424. [PubMed: 26877135]
Pettersson E, Larsson H, & Lichtenstein P (2016). Common psychiatric disorders share the same
genetic origin: A multivariate sibling study of the Swedish population. Molecular Psychiatry, 21,
Author Manuscript

717–721. [PubMed: 26303662]

Preacher KJ, Rucker DD, MacCallum RC, & Nicewander WA (2005). Use of the extreme groups
approach: A critical reexamination and new recommendations. Psychological Methods, 10, 178–
192. [PubMed: 15998176]
Regier DA, Narrow WE, Clarke DE, Kraemer HC, Kuramoto SJ, Kuhl EA, & Kupfer DJ (2013).
DSM-5 field trials in the United States and Canada, Part II: Test-retest reliability of selected
categorical diagnoses. The American Journal of Psychiatry, 170, 59–70. [PubMed: 23111466]
Rodriguez-Seijas C, Eaton NR, Stohl M, Mauro PM, & Hasin DS (2017). Mental disorder comorbidity
and treatment utilization. Comprehensive Psychiatry, 79, 89–97. [PubMed: 28215792]
Rodriguez-Seijas C, Stohl M, Hasin DS, & Eaton NR (2015). Transdiagnostic factors and mediation of
the relationship between perceived racial discrimination and mental disorders. Journal of the
American Medical Association Psychiatry, 72, 706–713. [PubMed: 25901783]
Romer AL, Knodt AR, Houts R, Brigidi BD, Moffitt TE, Caspi A, & Hariri AR (2018). Structural
alterations within cerebellar circuitry are associated with general liability for common mental
Author Manuscript

disorders. Molecular Psychiatry, 23, 1084–1090. [PubMed: 28397842]

Rosenström T, Ystrom E, Torvik FA, Czajkowski NO, Gillespie NA, Aggen SH, … Reichborn-
Kjennerud T (2017). Genetic and environmental structure of DSM-IV criteria for antisocial
personality disorder: A twin study. Behavior Genetics, 47, 265–277. [PubMed: 28108863]
Røysamb E, Kendler KS, Tambs K, Ørstavik RE, Neale MC, Aggen SH, … & Reichborn-Kjennerud T
(2011). The joint structure of DSM-IV Axis I and Axis II disorders. Journal of Abnormal
Psychology, 120, 198–209. [PubMed: 21319931]
Ruggero C (2018, 8 18). Integrating a Dimensional, Hierarchical Taxonomy of Psychopathology into
Clinical Practice. Retrieved from psyarxiv.com/r2jt6

Perspect Psychol Sci. Author manuscript; available in PMC 2020 May 01.
 Conway et al. Page 22

Sato JR, Salum GA, Gadelha A, Crossley N, Vieira G, Manfro GG, … Hoexter MQ (2016). Default
mode network maturation and psychopathology in children and adolescents. Journal of Child
Author Manuscript

Psychology and Psychiatry, 57, 55–64. [PubMed: 26111611]

Selzam S, Coleman JR, Moffitt TE, Caspi A, & Plomin R (2018). A polygenic p factor for major
psychiatric disorders. bioRxiv, 287987.
Shackman AJ, & Fox AS (2018). Getting serious about variation: Lessons for clinical neuroscience (A
commentary on ‘The myth of optimality in clinical neuroscience’). Trends in Cognitive Sciences,
22, 368–369. [PubMed: 29576465]
Shanmugan S, Wolf DH, Calkins ME, Moore TM, Ruparel K, Hopson RD, … & Gennatas ED (2016).
Common and dissociable mechanisms of executive system dysfunction across psychiatric
disorders in youth. American Journal of Psychiatry, 173, 517–526. [PubMed: 26806874]
Sharma A, Wolf DH, Ciric R, Kable JW, Moore TM, Vandekar SN, … & Elliott MA (2017). Common
dimensional reward deficits across mood and psychotic disorders: a connectome-wide association
study. American Journal of Psychiatry, 174, 657–666. [PubMed: 28135847]
Simms LJ, Grös DF, Watson D, & O’Hara MW (2008). Parsing the general and specific components of
depression and anxiety with bifactor modeling. Depression and Anxiety, 25, 34–46.
Author Manuscript

Slade T (2007). The descriptive epidemiology of internalizing and externalizing psychiatric

dimensions. Social Psychiatry and Psychiatric Epidemiology, 42, 554–560. [PubMed: 17516015]
Smoller JW, Andreassen OA, Edenberg HJ, Faraone SV, Glatt SJ, & Kendler KS (2018). Psychiatric
genetics and the structure of psychopathology. Molecular Psychiatry.
Snyder HR, Hankin BL, Sandman CA, Head K, & Davis EP (2017). Distinct patterns of reduced
prefrontal and limbic gray matter volume in childhood general and internalizing psychopathology.
Clinical Psychological Science, 5, 1001–1013. [PubMed: 29399423]
Snyder HR, Young JF, & Hankin BL (2017). Chronic stress exposure and generation are related to the
p-factor and externalizing specific psychopathology in youth. Journal of Clinical Child and
Adolescent Psychology, 1–10.
South SC, Krueger RF, & Iacono WG (2011). Understanding general and specific connections between
psychopathology and marital distress: A model based approach. Journal of Abnormal Psychology,
120, 935–947. [PubMed: 21942335]
Sprooten E, Rasgon A, Goodman M, Carlin A, Leibu E, Lee WH, & Frangou S (2017). Addressing
Author Manuscript

reverse inference in psychiatric neuroimaging: Meta-analyses of task-related brain activation in

common mental disorders. Human Brain Mapping, 38, 1846–1864. [PubMed: 28067006]
Stasik-O’Brien SM, Brock RL, Chmielewski M, Naragon-Gainey K, Koffel E, McDade-Montez E, …
& Watson D (in press). Clinical utility of the Inventory of Depression and Anxiety Symptoms
(IDAS). Assessment. 10.1177/1073191118790036
Steele SJ, Farchione TJ, Cassiello-Robbins C, Ametaj A, Sbi S, Sauer-Zavala S, & Barlow DH (2018).
Efficacy of the Unified Protocol for transdiagnostic treatment of comorbid psychopathology
accompanying emotional disorders compared to treatments targeting single disorders. Journal of
Psychiatric Research, 104, 211–216. [PubMed: 30103069]
Stringaris A, Vidal-Ribas Belil P, Artiges E, Lemaitre H, Gollier-Briant F, Wolke S, … & Fadai T
(2015). The brain’s response to reward anticipation and depression in adolescence: dimensionality,
specificity, and longitudinal predictions in a community-based sample. American Journal of
Psychiatry, 172, 1215–1223. [PubMed: 26085042]
Sunderland M, Carragher N, Chapman C, Mills K, Teesson M, Lockwood E, … & Slade T (2016). The
shared and specific relationships between exposure to potentially traumatic events and
Author Manuscript

transdiagnostic dimensions of psychopathology. Journal of Anxiety Disorders, 38, 102–109.

[PubMed: 26874292]
Sunderland M, & Slade T (2015). The relationship between internalizing psychopathology and
suicidality, treatment seeking, and disability in the Australian population. Journal of Affective
Disorders, 171, 6–12. [PubMed: 25282144]
Swartz JR, Knodt AR, Radtke SR, & Hariri AR (2015). A neural biomarker of psychological
vulnerability to future life stress. Neuron, 85, 505–511. [PubMed: 25654256]

Perspect Psychol Sci. Author manuscript; available in PMC 2020 May 01.
 Conway et al. Page 23

Tarbox SI, & Pogue-Geile MF (2011). A multivariate perspective on schizotypy and familial
association with schizophrenia: a review. Clinical Psychology Review, 31, 1169–1182. [PubMed:
Author Manuscript

21855827]
Tellegen A (1985). Structures of mood and personality and their relevance to assessing anxiety, with an
emphasis on self-report In Tuma AH & Maser JD (Eds.), Anxiety and the anxiety disorders (pp.
681–706). Hillsdale, NJ: Erlbaum.
Vachon DD, Krueger RF, Rogosch FA, & Cicchetti D (2015). Assessment of the harmful psychiatric
and behavioral effects of different forms of child maltreatment. JAMA Psychiatry, 72, 1135–1142.
[PubMed: 26465073]
van Bork R, Epskamp S, Rhemtulla M, Borsboom D, & van der Maas HL (2017). What is the p-factor
of psychopathology? Some risks of general factor modeling. Theory & Psychology, 27, 759–773.
van Os J, Gilvarry C, Bale R, Van Horn E, Tattan T, & White I (1999). A comparison of the utility of
dimensional and categorical representations of psychosis. Psychological Medicine, 29, 595–606.
[PubMed: 10405080]
Vos T, Allen C, Arora M, Barber RM, Bhutta ZA, Brown A, … & Coggeshall M (2016). Global,
regional, and national incidence, prevalence, and years lived with disability for 310 diseases and
Author Manuscript

injuries, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. The
Lancet, 388, 1545–1602.
Ward J, Strawbridge R, Graham N, Bailey M, Freguson A, Lyall D, … & Pell J (2017). Genome-Wide
Analysis Of 113,968 Individuals In UK Biobank Identifies Four Loci Associated With Mood
Instability. bioRxiv, 117796.
Waszczuk M (2018, 8 28). Redefining phenotypes to advance psychiatric genetics: Implications from
the hierarchical taxonomy of psychopathology. 10.31234/osf.io/sf46g
Waszczuk MA, Kotov R, Ruggero C, Gamez W, & Watson D (2017). Hierarchical structure of
emotional disorders: From individual symptoms to the spectrum. Journal of Abnormal
Psychology, 126, 613. [PubMed: 28471212]
Waszczuk MA, Zavos HMS, Gregory AM, & Eley TC (2014). The phenotypic and genetic structure of
depression and anxiety disorder symptoms in childhood, adolescence, and young adulthood.
JAMA Psychiatry, 71, 905–916. [PubMed: 24920372]
Waszczuk MA, Zimmerman M, Ruggero C, Li K, MacNamara A, Weinberg A, … & Kotov R (2017).
What do clinicians treat: Diagnoses or symptoms? The incremental validity of a symptom-based,
Author Manuscript

dimensional characterization of emotional disorders in predicting medication prescription

patterns. Comprehensive Psychiatry, 79, 80–88. [PubMed: 28495012]
Wiggins JL, Mitchell C, Hyde LW, & Monk CS (2015). Identifying early pathways of risk and
resilience: The codevelopment of internalizing and externalizing symptoms and the role of harsh
parenting. Development and Psychopathology, 27, 1295–1312. [PubMed: 26439075]
Wright AG, Hopwood CJ, Skodol AE, & Morey LC (2016). Longitudinal validation of general and
specific structural features of personality pathology. Journal of Abnormal Psychology, 125,
1120–1134. [PubMed: 27819472]
Zald DH, & Lahey BB (2017). Implications of the hierarchical structure of psychopathology for
psychiatric neuroimaging. Biological Psychiatry, 2, 310–317. [PubMed: 28713866]
Author Manuscript

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Figure 1.
Hierarchical Taxonomy of Psychopathology (HiTOP) consortium working model.
Constructs higher in the figure are broader and more general, whereas constructs lower in
the figure are narrower and more specific. Dashed lines denote provisional elements
requiring further study. At the lowest level of the hierarchy (i.e., traits and symptom
components), for heuristic purposes, conceptually related signs and symptoms (e.g., Phobia)
are indicated in bold, with specific manifestations indicated in parentheses. Abbreviations
—ADHD: attention-deficit/hyperactivity disorder; GAD: generalized anxiety disorder; IED:
intermittent explosive disorder; MDD: major depressive disorder; OCD: obsessive-
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compulsive disorder; ODD: oppositional defiant disorder; SAD: separation anxiety disorder;
PD: personality disorder; PTSD: post-traumatic stress disorder.
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Figure 2.
Conceptual diagrams of three possible HiTOP research designs. (A) Comparing the
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predictive validity across HiTOP levels. (B) Comparing predictive validity within a HiTOP
level. (C) Comparing the predictive validity of categorical diagnoses to HiTOP dimensions.
Abbreviations—BPD I: bipolar I disorder; BPD II: bipolar II disorder; MDD: major
depressive disorder; OSDD: other specified depressive disorder; PDD: persistent depressive
disorder.

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Figure 3.
Heuristic model of the internalizing domain in relation to autonomic reactivity to a
laboratory challenge. Paths a, b, and c represent regressions of the outcome on dimensions at
different levels of the hierarchical model. See the main text for details.
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Table 1.

Prominent mental illness frameworks

DSM HiTOP RDoC

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Empirical foundation Historically was based on clinical heuristics; recent Data driven; observed clustering of psychopathology Expert workgroup interpretation of research
revisions are guided by systematic review of research signs and symptoms evidence
evidence

Structure Signs and symptoms are organized into diagnoses, which Hierarchical system of broad constructs near the top Five domains of functioning (e.g., negative
are in turn grouped into chapters on the basis of shared and homogeneous symptom components near the valence) each divided into 3 to 6 constructs
phenomenology and/or presumed etiology; some bottom (e.g., acute threat); domains encompass 7 units
disorders include subtypes of analysis, from molecules to verbal report

Dimensional vs categorical Predominantly categorical, but contains optional Dimensional, but able to incorporate categories Explicitly focused on dimensional processes
dimensional elements for screening and diagnosis, such (“taxa”) if empirically warranted
as the Alternative Model for Personality Disorder

Timeframe for clinical Widely used Able to guide assessment and treatment, but currently Limited prospects for clinical applications in
implementation not disseminated widely for direct clinical application near-term (e.g., assessment, treatment,
communication)

Etiology Diagnosis generally is based on observed signs and Model structure depends on observed (phenotypic) Conceptualizes clinical problems as “brain
symptoms, not putative causes (posttraumatic stress clustering—not necessarily etiological coherence—of disorders”; neurobiological correlates of mental
disorder is an exception) clinical problems; model dimensions can be validated illness are emphasized
with respect to putative etiological factors

Note. DSM = Diagnostic and Statistical Manual of Mental Disorders; HiTOP = Hierarchical Taxonomy of Psychopathology; RDoC = Research Domain Criteria.

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