Content uploaded by Anders Lillevik Thorsen
Author content
All content in this area was uploaded by Anders Lillevik Thorsen on Feb 24, 2018
Content may be subject to copyright.
Curr Treat Options Psych
DOI 10.1007/s40501-018-0142-4
Anxiety, Obsessive Compulsive, and Related Disorders (CB Nemeroff, Section Editor)
Symptom Dimensions
in Obsessive-Compulsive
Disorder as Predictors
of Neurobiology and Treatment
Response
Anders Lillevik Thorsen, Cand.Psychol
1,2,3,*
Gerd Kvale, PhD
1,2
Bjarne Hansen, PhD
1,2
Odile A. van den Heuvel, MD, PhD,
1,3,4,5
Address
*,1
OCD-team, Haukeland University Hospital, PO 14005021, Bergen, Norway
Email: anders.lillevik.thorsen@helse-bergen.no
2
Department of Clinical Psychology, University of Bergen, Bergen, Norway
3
Department of Anatomy & Neurosciences, VU University Medical Center (VUmc),
Amsterdam, The Netherlands
4
Department of Psychiatry, VUmc, Amsterdam, The Netherlands
5
Neuroscience Amsterdam, Amsterdam, The Netherlands
*Springer International Publishing AG, part of Springer Nature 2018
This article is part of theTopical Collection on Anxiety,Obsessive Compulsive,
and Related Disorders
Keywords Symptom dimension ITreatment outcome IBrain function IBrain structure
Abstract
Purpose of review Specific symptom dimensions of obsessive-compulsive disorder (OCD)
have been suggested as an approach to reduce the heterogeneity of obsessive-compulsive
disorder, predict treatment outcome, and relate to brain structure and function. Here, we
review studies addressing these issues.
Recent findings The contamination and symmetry/ordering dimensions have not been
reliably associated with treatment outcome. Some studies found that greater severity of
sexual/aggressive/religious symptoms predicted a worse outcome after cognitive behav-
ioral therapy (CBT) and a better outcome after serotonin reuptake inhibitors (SRIs).
Contamination symptoms have been related to increased amygdala and insula activation
in a few studies, while sexual/aggressive/religious symptoms have also been related to
more pronounced alterations in the function and structure of the amygdala. Increased pre-
treatment limbic responsiveness has been related to better outcomes of CBT, but most
imaging studies show that important limitations and replication in large-scale studies is
needed. We review possible reasons for the strong limbic involvement of the amygdala in
patients with more sexual/aggressive/religious symptoms, in relation to their sensitivity
to CBT.
Summary Symptom dimensions may predict treatment outcome, and patients with sexual/
religious/aggressive symptoms are at a greater risk of not starting or delaying treatment.
This is likely partly due to more shame and perceived immorality which is also related to
stronger amygdala response. Competently delivered CBT is likely to help these patients
improve to the same degree as patients with other symptoms.
Introduction
Obsessive-compulsive disorder (OCD) is characterized
by distressing obsessive thoughts, urges, or images,
which patients try to manage or neutralize through
compulsive rituals [1]. The disorder has a significant
impact on quality of life and impairment in work, social,
and family life [2], and ranks among the ten leading
causes of disability in the developed world [3]. The
current meta-analytic evidence suggests that treatment
with cognitive behavioral therapy (CBT) involving ex-
posure and response prevention (ERP) helps an average
of approximately 50% of patients recover (95% confi-
dence interval between 44 and 56%), significantly more
than those only receiving serotonin reuptake inhibitors
(SRIs) alone, the other first-line treatment for OCD [4,
5]. A recently developed concentrated exposure treat-
ment reports considerably higher remission rates of ~
75% after treatment [6,7]. The immense personal and
societal costs of OCD shows the pressing need to better
understand the disorder and possible treatment mecha-
nisms, in order to improve clinical outcomes for those
who do not respond to current treatments [8].
OCD is highly heterogeneous in terms of symp-
tom profile, comorbidity, and brain alterations,
which presents a challenge for understanding and
treating the disorder [8]. Several studies have sug-
gested that symptom dimensions may relate to
patterns of comorbidity, where especially aggres-
sive/religious/sexual symptoms may predict a great-
er risk of comorbid mood and anxiety disorders
compared to other dimensions [9–11]. That two
patients with OCD may have little to no overlap
between what they fear and which compulsions
they perform has long been recognized [12], and
a number of clinical interviews and questionnaires
have been developed to measure specific symptom
profiles [13,14]. A prominent strategy for reducing
symptom heterogeneity has been to focus on the
content of the patient’s obsessions and compul-
sions and to elucidate specific symptoms dimen-
sions. A common measure for OC symptoms is
the Yale-Brown Obsessive Scale Symptom Checklist
(Y-BOCS-SC) [15], which contains 15 categories of
obsessions and compulsions. Mataix-Cols and col-
leagues reviewed all factor analytic studies of the Y-
BOCS-SC, and proposed a prominent multidimen-
sional model [16], which has also received support
from a factor meta-analysis [17]. This model sug-
gests that the four dimensions contamination
(washing), symmetry (ordering, counting), sexual/
religious/aggressive obsessions, and hoarding may
be the best conceptualization of symptom dimen-
sions. The factor meta-analysis further indicated
that checking compulsions were most related to
aggressive/sexual/religious/obsessions [17], while
others propose that checking can be a compulsion
to decrease uncertainty present in several dimen-
sions [13]. The multidimensional model does not
argue that most OCD patients only have symptoms
in a specific dimension, but instead that patients
typically have symptoms in multiple dimensions
but not necessarily with the same severity [16].
The dimensions appear to be somewhat stable over
time, where qualitative shifts are rare, and previ-
ously having a symptom is the best predictor of
having it in the future [18,19]. However, it should
benotedthatmostoftherelevantfactoranalytic
studies use techniques which assume that there are
only a few independent dimensions, which may
Anxiety, Obsessive Compulsive, and Related Disorders (CB Nemeroff, Section Editor)
obscure the correlation between dimensions. Re-
search into symptom dimensions in OCD has also
had clinical implications, and led to hoarding dis-
order as a separate disorder in the DSM-5 [1], and
accordingly this review will not focus on hoarding
symptoms.
Symptom dimensions could also be useful in discov-
ering genetic markers of vulnerability for developing
OCD. Current genome-wide analysis studies (including
1465 and 5061 patients) have not found a reliable
genetic marker, though glutamate and serotonin-
related polymorphisms show some promise [20–22].
These mostly negative findings could be affected by
low sample sizes and the heterogeneity in OCD, and
evidence from a British twin study (including 5022 par-
ticipants) has suggested that symptom dimensions are
all affected by common heritability for OCD, but that
washing/contamination symptoms are less affected by
specific genetic factors than the other dimensions [23].
However, there is also evidence to the contrary [24], and
replication is needed.
The present review aims to briefly describe important
findings that relate symptom dimensions of OCD to
treatment outcomes and neuroimaging findings, discuss
the limitations of current studies, and suggest some
future directions.
Symptom dimensions as predictors of treatment outcome
Symptom dimensions have been related to the treatment outcome of both CBT
and SRIs in several studies [see 25,26 for reviews]. Out of a total of nine studies
using a variant of CBT for adult OCD patients [27–35], three found that
baseline sexual/religious symptoms predicted worse outcome [28,32,33•].
This was also found in one study which first provided SRIs followed by CBT
[29]. In addition, one study found that contamination symptoms predicted
better outcome [35] and one reported null findings [34]. Williams et al. [33•]
included 87 patients from 2 randomized controlled trials using standard out-
patient ERP, and found that a greater severity of sexual/religious/aggressive
obsessions was related to a worse outcome after treatment. Follow-up tests
indicated that having religious/moral and somatic obsessions was related to
approximately 16.5% less symptom reduction after treatment. This study was
strengthened by (1) their sample size, (2) including all dimensions as predictors
in the same model, (3) providing well-described treatments, and (4) having
independent raters evaluate post-treatment symptom severity.
Out of five studies using SRI treatment [36–40], two found that sexual/
religious/aggressive symptoms predicted a better outcome [36,37], and one
found that patients with somatic obsessions were less likely to respond to
treatment [40]. One study also found contamination symptoms to be a nega-
tive predictor of treatment outcome [37]. Finally, three studies reported no
significant association between any symptom dimension and response to SRIs
[38,39,41].
There are several scenarios in which sexual/religious/aggressive symptoms
might not be adequately addressed in CBT [42,43]: for example, when clini-
cians fear disrespecting the faith of religious patients during exposure to the
thought that the patient will be sent to hell or is sinful, when patients struggle
with the shame and stigma of admitting and facing taboo thoughts and images,
or when clinicians fail to address how subtle mental compulsions, avoidance,
and reassurance maintain the disorder [33•,44]. However, evidence from a
randomized controlled trial indicatesthat these patients are likely to improve as
well as the patients suffering with symptoms from other dimensions when
Symptom Dimensions, Neurobiology, and Treatment of OCD Thorsen et al.
these symptoms are included in the functional analysis and addressed using
CBT [45,46•,47]. This shows that sexual/religious symptoms are not univer-
sally related to a worse outcome after CBT.
Both studies of CBT and SRIs in the literature often share important limita-
tions: few are randomized trials with well-controlled treatments [e.g., 32,34];
some are older studies using early models of CBT [30,31,35]; few measure
treatment credibility, adherence, or compliance; and most are small sampled
and use varying measures and definitions for symptom dimensions [e.g., 29,
30]. The studies also differ in whether they model symptoms as co-occurring
dimensions (where the effect of each symptom dimension should be controlled
for against the influence of the others) or as discrete symptom categories where
patients only fall into one category (which ignores how patients often show
symptoms in multiple dimensions).
Symptom dimensions, brain structure, and function
OCD has been related to subtle alterations in brain structure and function, with
most studies focusing on parallel cortico-striato-thalamo-cortical (CSTC) cir-
cuits, which also include (para)limbic brain regions [8,48]. Briefly, adult OCD
has in meta- and mega-analyses been related to decreased gray matter volume in
the hippocampus, inferior prefrontal cortex/insula, dorsomedial prefrontal
cortex/anterior cingulate cortex (ACC), as well as increased volumes of the
pallidum and cerebellum [49,50]. Our recent meta-analysis of 25 emotion
processing studies (including a total sample size of 571 patients and 564
healthy controls) using symptom provocation, cognitive tasks with emotional
distractors, or other ways of inducing anxiety during scanning found that OCD
patients, compared to healthy controls, showed increased activation in the
bilateral amygdala, right putamen, orbitofrontal cortex (OFC) extending
into the subgenual anterior cingulate and ventromedial prefrontal cortex,
as well as the middle temporal, and left inferior occipital cortices [51].
These meta-analyses suggest that OCD is related to alterations in diverse
areas of the brain, with functional and structural connectivity studies
suggesting several interacting circuits which also include fronto-parietal
and cerebellar regions [48,52–54]. Factors such as medication, comor-
bidity, and age often influence how pronounced the differences between
patients and healthy controls are [49–51,55•].
Symptom dimensions and brain structure
Six studies have used voxel-based morphometry (VBM) to assess how symptom
dimensions relate to regional gray matter volume [56–61]. Four studies found
that increased severity of sexual/religious/aggressive and/or checking obsessions
to be related to smaller gray matter volume in the temporal lobes [58],
extending into the amygdala [60] and insula, as well as leftOFC, putamen [56],
and right cerebellum volume [59]. Meanwhile, washing and contamination
symptoms have been related to smaller volume of bilateral caudate nucleus and
right insula [58,59], as well as smaller right thalamus volume [57]. Findings for
the ordering and symmetry dimension are less clear, including both bigger and
smaller volume of OFC, as well as bigger volume of other frontal regions such as
Anxiety, Obsessive Compulsive, and Related Disorders (CB Nemeroff, Section Editor)
the dorsal ACC and medial frontal cortex [59,61]. One study also found smaller
motor, insular, and parietal volumes and bigger bilateral temporal volumes in
relation to symmetry/ordering symptoms [58]. Notably, van den Heuvel and
colleagues [58] separately analyzed the relation between brain structure and
symptom dimensions using both the Y-BOCS-SC and the Padua Inventory-
revised in 55 unmedicated OCD patients and 50 healthy controls, which
showed some overlap but did not reveal exactly the same results. This shows
how the definition of symptom dimensions may affect findings. Two studies
have used diffusion tensor imaging (DTI) for symptom dimensions, but have
yielded inconsistent findings [62,63], highlighting the need for larger studies in
the future.
Unfortunately, the results of most single-site structural imaging studies have
not been replicated in current multi-site mega-analyses. Indeed, recent mega-
analyses of subcortical volume (including 1830 patients and 1759 controls)
and cortical thickness and surface area (including 1905 patients and 1760
healthy controls) found no significant association between symptom dimen-
sions and brain structure [50,55•]. The low rate of reproducible findings casts
doubts on if and how symptom dimensions relate to brain morphology.
Another open question in the current literature is what alterations in gray and
white matter volume really mean. Current studies of gray matter volume or
cortical thickness in OCD mostly rely on analysis of T1-weighted images, which
makes it difficult to distinguish between underlying mechanisms such as
changes in dendrites, synapses, glia, or neurogenesis [64]. Similarly, changes in
fractional anisotropy in DTI studies can be driven by several different mecha-
nisms which are not easily separated without additional imaging methods [64].
Finally, studies with a longitudinal design are needed to describe how the
pathophysiology of OCD develops over the lifespan, which morphological
characteristics relate to the vulnerability of developing OCD early in life, and
which morphological characteristics are the consequence of a chronic pattern of
pathological behavior or long-term effects of treatment [50,55•].
Symptom dimensions and brain function
Functional studies of brain activation have found increased activation in sub-
cortical structures such as the amygdala and insula during symptom provoca-
tion for contamination/washing symptoms [65–67]. These findings suggest an
increased involvement of the fronto-limbic and affective circuits [48]. Sexual/
religious/aggressive symptoms have been related to increased striatal activation
during continuous performance tasks and conflict processing [68,69], increased
activation in the hippocampus during reward-based spatial learning [70], and
increased hippocampus activation during symptom provocation [65]. Howev-
er, two studies using electroencephalography both reported null findings [71,
72]. Improving upon earlier studies with smaller samples, the largest study to
date included 67 OCD patients and 67 matched healthy controls who per-
formed an emotional face matching task [73]. Here, sexual/religious/aggressive
symptoms predicted greater amygdala activation during the matching of fearful
faces. In addition, these symptoms were associated with greater activation in the
ACC and premotor areas, and less activation in extended visual areas. The same
group also found that the severity of sexual/religious symptoms were associated
Symptom Dimensions, Neurobiology, and Treatment of OCD Thorsen et al.
with greater right amygdala, para-limbic, and ventrolateral prefrontal activation
during a moral dilemma task, where the participants were asked to choose the
lesser evil of two outcomes [74]. Finally, they also assessed resting-state func-
tional connectivity with a seed region in the ventral striatum [75]. Here, they
found that the severity of aggressive symptoms correlated negatively with
connectivity with the bilateral amygdala, and positively with medial prefrontal
connectivity. They also found that sexual/religious obsessions correlated posi-
tively with connectivity between the ventral striatum, the right inferior frontal
gyrus and insula, as well as with the left superior temporal gyrus [75]. In general,
neuroimaging studies of symptom dimensions have suffered from small sam-
ple sizes, variation in methodologies that limits generalizability, and replication
in multi-site mega-analyses is needed. Another challenge for functional neuro-
imaging is how to best elicit obsessions and anxiety related to aggressive, sexual,
and religious obsessions, as these symptoms can be very specific to situations,
physical sensations, or images that are hard to experimentally manipulate.
Future studies should develop more ecologically valid paradigms to better shed
light on the neural correlates of these symptoms.
The current findings suggest that aggressive/sexual/religious symptoms may
be related to the function and structure of the amygdala and other structures in
the limbic circuit. The amygdala has also been implicated in studies using pre-
treatment fMRI to predict treatment outcome [76], where greater amygdala
activation has been found to predict better treatment outcome after CBT [77,
78]. A recent resting-state fMRI study further suggested that decreased
amygdala-ventromedial prefrontal cortex connectivity predicted good treat-
ment outcome [79]. It should be noted that several of these studies were limited
by a low degree of symptom improvement after treatment, two had only small
samples (12–17 patients), and that other regions beside the amygdala also
predicted treatment outcome. Taking these limitations into account, these
studies seem to suggest that increased amygdala activation and decreased
fronto-limbic connectivity could predict a better treatment outcome for OCD
patients in general. An important question is therefore why patients with
sexual/aggressive/religious symptoms show both increased amygdala activation
and worse outcome after CBT? We discuss somepossible reasons for this below.
Linking the amygdala and sexual/aggressive/religious
symptoms
Sexual/aggressive/religious symptoms have been related to struggling with
feelings of being immoral or going against one’s religion, and these patients
more often appraise their obsessions as signs that they are immoral persons
[e.g., 80,81,82]. These factors, along with the shame and possible rejection of
admitting these symptoms [83], have also been suggested as important factors
in delaying seeking treatment [42].
There is evidence from both single studies and meta-analysis in healthy
controls that the process of moral reasoning is related to increased activation
in a network of structures, including the amygdala and ventromedial prefrontal
cortex [e.g., 84,85,86]. Recent evidence from intracranial field potential re-
cordings further suggests that amygdala serves as a key hub in the detection of
Anxiety, Obsessive Compulsive, and Related Disorders (CB Nemeroff, Section Editor)
potential intentional harm [87]. This fits with the results of the only functional
neuroimaging study of moral reasoning in OCD, reporting that patients with
more severe sexual/aggressive symptoms also showed more amygdala activa-
tion during moral reasoning [74]. Though more research is clearly needed, this
may suggest that the link between sexual/aggressive/religious symptoms,
amygdala activation, and connectivity could be partly explained by greater
moral disgust and shame [43].
As reviewed above, patients with sexual/aggressive/religious symptoms may
show increased amygdala activity (which has previously been related to a better
outcome after CBT), but also have worse outcome after CBT than other symp-
tom dimensions in some studies. One reason for this could be that while these
patients show intense distress (and amygdala activation) in response to their
obsessions,this distress is not useful if it is inadequately addressed in treatment.
Indeed, the current literature suggests that higher initial distress levels during
exposure are not a reliable predictor of outcome [88,89]. Instead, recovery is
likely a result of learning new ways of tolerating this distress through several
factors, including early reduction of safety behaviors and ritualization, exposing
oneself in varied contexts, and with variability in distress [88–91]. Given the
greater shame and perceived immorality in these patients [42], it is likely even
more important to ensure that both patients and therapists adhere to the
treatment principles. When this condition is met, they are also much more
likely to improve as much as patients struggling with other symptom dimen-
sions [25,43,46•,47].
As reviewed above, the development of symptom dimensions in OCD was
partly meant to reduce the heterogeneity of the disorder [16]. However, early
findings of symptom dimensions predicting treatment outcome and neurobiology
have often not been replicated in larger samples [43,46•,50,55•]. We propose
that several improvements are needed. First, the validity of symptom dimensions as
predictors for successful treatment need to be evaluated in larger patient samples
receiving well-described behavioral treatment of high quality. Second, we need to
understand the mechanism for why some symptoms are harder to treat with CBT
or SSRIs, and move beyond correlational studies on symptom dimensions solely,
and include the full spectrum of behavioral and cognitive characteristics of the
clinical phenotype in relation to reliable and reproducible neuroimaging markers.
Third, we should apply recent methodological developments, such as machine
learning, to see if symptom dimensions provide an added benefit over other
clinical and neuroimaging markers in predicting treatment outcome and pheno-
typical variation. By combining these sources of information, it could be possible
to understand why some patients improve and others do not, and how we can
better tailor treatment to the individual [92].Arecentexamplecomesfromastudy
of 118 depressed patients, which used pattern recognition to combine structural
T1-weighted images, fMRI from emotional face and Tower of London tasks, as well
as clinical characteristics to predict which patients remit and which follow a chronic
course after 2 years [93]. This study found that activation in response to emotional
faces predicted the course better than all other sources of information.
There are currently only two studies that have used machine learning in
OCD, and none of them have applied neuroimaging or biological markers. One
investigated who remains remitted in a naturalistic study [including 296 pa-
tients; 94], and one investigated who responded to internet-delivered CBT
[including 61 adolescent patients; 95]. Only one of these found that having a
Symptom Dimensions, Neurobiology, and Treatment of OCD Thorsen et al.
contamination/washing subtype was related to ever remitting from OCD [94].
However, given the naturalistic and uncontrolled nature of the study, it should
be interpreted with caution. The use of machine learning could also be used to
reevaluate the relevance of previous regression-based studies, which look at
individual voxels or regions but not how they form patterns and interact to
predict outcome [77].
Conclusion
The three symptom dimensions of aggressive/sexual/religious obsessions, con-
tamination, and symmetry/ordering partly describe the heterogeneity in OCD.
The severity of contamination and symmetry/ordering symptoms are not reli-
ably related to treatment outcome, while some studies suggests that sexual/
religious/aggressive thoughts could be harder to treat than other symptoms in
CBT, and that these respond better to SRIs than other dimensions. Neuroim-
aging studies have related sexual/religious/aggressive symptoms to more pro-
nounced alterations in the function and structure of the amygdala and related
limbic regions. However, many of the current studies are suffering from meth-
odological limitations and small sample sizes, and most findings have not yet
been replicated in larger samples. This weakens the strength of their conclu-
sions, and shows the need for larger and robust studies in the future. Patients
with sexual/religious/aggressive symptoms are at a greater risk of not starting or
delaying treatment, partly due to more shame and perceived immorality. Ade-
quate CBT that issensitive to these issues islikely to helpthese patients improve
to the same degree as other patients.
Compliance with ethical standards
Conflict of interest
Anders Lillevik Thorsen declares that he has no conflict of interest. Gerd Kvale declares that she has no conflict of
interest. Bjarne Hansen declares that he has no conflict of interest. Odile A. van den Heuvel declares that she has no
conflict of interest.
Human and animal rights and informed consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
References and Recommended Reading
Papers of particular interest, published recently, have been
highlighted as:
•Of importance
1. American Psychiatric Association. Diagnostic and sta-
tistical manual of mental disorders (5th ed.). Wash-
ington, DC: Author; 2013. https://doi.org/10.1176/
appi.books.9780890425596.
2. Huppert JD, Simpson HB, Nissenson KJ, Liebowitz MR,
Foa EB. Quality of life and functional impairment in
obsessive-compulsive disorder: a comparison of pa-
tients with and without comorbidity, patients in
Anxiety, Obsessive Compulsive, and Related Disorders (CB Nemeroff, Section Editor)
remission, and healthy controls. Depress Anxiety.
2009;26(1):39–45. https://doi.org/10.1002/da.20506.
3. Mathers C, Fat DM, Boerma JT. The global burden of
disease: 2004 update. World Health Organization; 2008.
4. Öst L-G, Havnen A, Hansen B, Kvale G. Cognitive
behavioral treatments of obsessive–compulsive disor-
der. A systematic review and meta-analysis of studies
published 1993–2014. Clin Psychol Rev.
2015;40:156–69. https://doi.org/10.1016/j.cpr.2015.
06.003.
5. Skapinakis P, Caldwell DM, Hollingworth W, Bryden
P, Fineberg NA, Salkovskis P, et al. Pharmacological
and psychotherapeutic interventions for management
of obsessive-compulsive disorder in adults: a system-
atic review and network meta-analysis. Lancet Psychi-
atry. 2016;3(8):730–9. https://doi.org/10.1016/
S2215-0366(16)30069-4.
6. Havnen A, Hansen B, Öst L,Kvale G.Concentrated ERP
delivered in a group setting: a replication study. Behav
Cogn Psychother. 2017;45(05):530–6. https://doi.org/
10.1017/S1352465817000091.
7. Havnen A, Hansen B, Öst L-G, Kvale G. Concentrated
ERP delivered in a group setting: an effectiveness study.
J Obsessive Compuls Relat Disord. 2014;3(4):319–24.
https://doi.org/10.1016/j.jocrd.2014.08.002.
8. Pauls DL, Abramovitch A, Rauch SL, Geller DA.
Obsessive-compulsive disorder: an integrative genetic
and neurobiological perspective. Nat Rev Neurosci.
2014;15(6):410–24. https://doi.org/10.1038/
nrn3746.
9. Hasler G, LaSalle-Ricci VH, Ronquillo JG, Crawley SA,
Cochran LW, Kazuba D, et al. Obsessive–compulsive
disorder symptom dimensions show specific relation-
ships to psychiatric comorbidity. Psychiatry Res.
2005;135(2):121–32. https://doi.org/10.1016/j.
psychres.2005.03.003.
10. Hasler G, Pinto A, Greenberg BD, Samuels J, Fyer AJ,
Pauls D, et al. Familiality of factor analysis-derived
YBOCS dimensions in OCD-affected sibling pairsfrom
the OCD collaborative genetics study. Biol Psychiatry.
2007;61(5):617–25. https://doi.org/10.1016/j.
biopsych.2006.05.040.
11. Torres AR, Fontenelle LF, Shavitt RG, Ferrao YA, do
Rosario MC, Storch EA, et al. Comorbidity variation in
patients with obsessive-compulsive disorder according
to symptom dimensions: results from a large
multicentre clinical sample. J Affect Disord.
2016;190:508–16. https://doi.org/10.1016/j.jad.2015.
10.051.
12. Sanavio E, Vidotto G. The components of the Maudsley
obsessional-compulsive questionnaire. Behav Res
Ther. 1985;23(6):659–62. https://doi.org/10.1016/
0005-7967(85)90061-0.
13. Rosario-Campos MC, Miguel EC, Quatrano S, Chacon
P, Ferrao Y, Findley D, et al. The dimensional Yale-
Brown obsessive-compulsive scale (DY-BOCS): an in-
strument for assessing obsessive-compulsive symptom
dimensions. Mol Psychiatry. 2006;11(5):495–504.
https://doi.org/10.1038/sj.mp.4001798.
14. Abramowitz JS, Deacon BJ, Olatunji BO, Wheaton MG,
Berman NC, Losardo D, et al. Assessment of obsessive-
compulsive symptom dimensions: development and
evaluation of the dimensional obsessive-compulsive
scale. Psychol Assess. 2010;22(1):180–98. https://doi.
org/10.1037/a0018260.
15. Goodman WK, Price LH, Rasmussen SA, Mazure C,
Fleischmann RL, Hill CL, et al. The Yale-Brown obses-
sive compulsive scale. I. Development, use, and reli-
ability. Arch Gen Psychiatry. 1989;46(11):1006–11.
https://doi.org/10.1001/archpsyc.1989.
01810110048007.
16. Mataix-Cols D, Rosario-Campos MC, Leckman JF. A
multidimensional model of obsessive-compulsive dis-
order. Am J Psychiatry. 2005;162(2):228–38. https://
doi.org/10.1176/appi.ajp.162.2.228.
17. Bloch MH, Landeros-Weisenberger A, Rosario MC,
Pittenger C, Leckman JF. Meta-analysis of the symptom
structure of obsessive-compulsive disorder. Am J Psy-
chiatry. 2008;165(12):1532–42. https://doi.org/10.
1176/appi.ajp.2008.08020320.
18. Mataix-Cols D, Rauch SL, Baer L, Eisen JL, Shera DM,
Goodman WK, et al. Symptom stability in adult
obsessive-compulsive disorder: data from a naturalistic
two-year follow-up study. Am J Psychiatry.
2002;159(2):263–8. https://doi.org/10.1176/appi.ajp.
159.2.263.
19. Fullana MA, Mataix-Cols D, Caspi A, Harrington H,
Grisham JR, Moffitt TE, et al. Obsessions and compul-
sions in the community: prevalence, interference, help-
seeking, developmental stability, and co-occurring
psychiatric conditions. Am J Psychiatry.
2009;166(3):329–36. https://doi.org/10.1176/appi.
ajp.2008.08071006.
20. Mattheisen M, Samuels JF, Wang Y, Greenberg BD, Fyer
AJ, McCracken JT, et al. Genome-wide association
study in obsessive-compulsive disorder: results from
the OCGAS. Mol Psychiatry. 2015;20(3):337–44.
https://doi.org/10.1038/mp.2014.43.
21. Taylor S. Disorder-specific genetic factors in obsessive-
compulsive disorder: a comprehensive meta-analysis.
Am J Med Genet B Neuropsychiatr Genet.
2016;171b(3):325–32. https://doi.org/10.1002/ajmg.
b.32407.
22. Stewart SE, Yu D, Scharf JM, Neale BM, Fagerness JA,
Mathews CA, et al. Genome-wide association study of
obsessive-compulsive disorder. Mol Psychiatry.
2013;18(7):788–98. https://doi.org/10.1038/mp.
2012.85.
23. Iervolino AC, Rijsdijk FV, Cherkas L, Fullana MA,
Mataix-Cols D. A multivariate twin study of obsessive-
compulsive symptom dimensions. Arch Gen Psychia-
try. 2011;68(6):637–44. https://doi.org/10.1001/
archgenpsychiatry.2011.54.
24. van Grootheest DS, Boomsma DI, Hettema JM,
Kendler KS. Heritability of obsessive-compulsive
symptom dimensions. Am J Med Genet B
Neuropsychiatr Genet. 2008;147b(4):473–8. https://
doi.org/10.1002/ajmg.b.30622.
Symptom Dimensions, Neurobiology, and Treatment of OCD Thorsen et al.
25. Williams MT, Mugno B, Franklin M, Faber S. Symptom
dimensions in obsessive-compulsive disorder: phe-
nomenology and treatment outcomes with exposure
and ritual prevention. Psychopathology.
2013;46(6):365–76. https://doi.org/10.1159/
000348582.
26. Keeley ML, Storch EA, Merlo LJ, Geffken GR. Clinical
predictors of response to cognitive-behavioral therapy
for obsessive–compulsive disorder. Clin Psychol Rev.
2008;28(1):118–30. https://doi.org/10.1016/j.cpr.
2007.04.003.
27. Abramowitz JS, Franklin ME, Schwartz SA, Furr JM.
Symptom presentation and outcome of cognitive-
behavioral therapy for obsessive-compulsive disor-
der. J Consult Clin Psychol. 2003;71(6):1049–57.
https://doi.org/10.1037/0022-006X.71.6.1049.
28. Mataix-Cols D, Marks IM, Greist JH, Kobak KA, Baer L.
Obsessive-compulsive symptom dimensions as pre-
dictors of compliance with and response to behaviour
therapy: results from a controlled trial. Psychother
Psychosom. 2002;71(5):255–62. https://doi.org/10.
1159/000064812.
29. Alonso P, Menchon JM, Pifarre J, Mataix-Cols D,
Torres L, Salgado P, et al. Long-term follow-up
and predictors of clinical outcome in obsessive-
compulsive patients treated with serotonin reup-
take inhibitors and behavioral therapy. J Clin
Psychiatry. 2001;62(7):535–40. https://doi.org/10.
4088/JCP.v62n07a06.
30. Başoğlu M, Lax T, Kasvikis Y, Marks IM. Predictors of
improvement in obsessive-compulsive disorder. J
Anxiety Disord. 1988;2(4):299–317. https://doi.org/
10.1016/0887-6185(88)90026-6.
31. Foa EB, Goldstein A. Continuous exposure and com-
plete response prevention in the treatment of
obsessive-compulsive neurosis. Behav Ther.
1978;9(5):821–9. https://doi.org/10.1016/S0005-
7894(78)80013-6.
32. Rufer M, Fricke S, Moritz S, Kloss M, Hand I. Symptom
dimensions in obsessive–compulsive disorder: predic-
tion of cognitive-behavior therapy outcome. Acta
Psychiatr Scand. 2006;113(5):440–6. https://doi.org/
10.1111/j.1600-0447.2005.00682.x.
33.•Williams MT, Farris SG, Turkheimer EN, Franklin ME,
Simpson HB, Liebowitz M, et al. The impact of symp-
tom dimensions on outcome for exposure and ritual
prevention therapy in obsessive-compulsive disorder. J
Anxiety Disord. 2014;28(6):553–8. https://doi.org/10.
1016/j.janxdis.2014.06.001.
The currently highest quality study of how symptom dimen-
sions relate to outcome after CBT, based on data from
randomzied controlled trials.
34. Chase T, Wetterneck CT, Bartsch RA, Leonard RC, Rie-
mann BC. Investigating treatment outcomes across
OCD symptom dimensions in a clinical sample of
OCD patients. Cogn Behav Ther. 2015;44(5):365–76.
https://doi.org/10.1080/16506073.2015.1015162.
35. Buchanan AW, Meng KS, Marks IM. What predicts
improvement and compliance during the behavioral
treatment of obsessive compulsive disorder? Anxiety.
1996;2(1):22–7. https://doi.org/10.1002/(SICI)1522-
7154(1996)2:1G22::AID-ANXI393.0.CO;2-F.
36. Landeros-Weisenberger A, Bloch MH, Kelmendi B,
Wegner R, Nudel J, Dombrowski P, et al. Dimensional
predictors of response to SRI pharmacotherapy in ob-
sessive–compulsive disorder. J Affect Disord.
2010;121(1-2):175–9. https://doi.org/10.1016/j.jad.
2009.06.010.
37. Stein DJ, Andersen EW, Overo KF. Response of symp-
tom dimensions in obsessive-compulsive disorder to
treatment with citalopram or placebo. Rev Bras
Psiquiatr. 2007;29(4):303–7. https://doi.org/10.1590/
S1516-44462007000400003.
38. Mataix-Cols D, Rauch SL, Manzo PA, Jenike MA, Baer L.
Use of factor-analyzed symptom dimensions to predict
outcome with serotonin reuptake inhibitors and pla-
cebo in the treatment of obsessive-compulsive disor-
der. Am J Psychiatry. 1999;156(9):1409–16. https://
doi.org/10.1176/ajp.156.9.1409.
39. Tukel R, Bozkurt O, Polat A, Genc A, Atli H. Clinical
predictors of response to pharmacotherapy with selec-
tive serotonin reuptake inhibitors in obsessive-
compulsive disorder. Psychiatry Clin Neurosci.
2006;60(4):404–9. https://doi.org/10.1111/j.1440-
1819.2006.01523.x.
40. Erzegovesi S, Cavallini MC, Cavedini P, Diaferia G,
Locatelli M, Bellodi L. Clinical predictors of drug re-
sponse in obsessive-compulsive disorder. J Clin
Psychopharmacol. 2001;21(5):488–92. https://doi.
org/10.1097/00004714-200110000-00006.
41. Stein DJ, Carey PD, Lochner C, Seedat S, Fineberg
N, Andersen EW. Escitalopram in obsessive-
compulsive disorder: response of symptom dimen-
sions to pharmacotherapy. CNS Spectr.
2008;13(06):492–8. https://doi.org/10.1017/
S1092852900016722.
42. García-Soriano G, Rufer M, Delsignore A, Weidt S.
Factors associated with non-treatment or delayed
treatment seeking in OCD sufferers: a review of the
literature. Psychiatry Res. 2014;220(1-2):1–10. https://
doi.org/10.1016/j.psychres.2014.07.009.
43. Moulding R, Aardema F, O'Connor KP. Repugnant
obsessions: a review of the phenomenology, theoreti-
cal models, and treatment of sexual and aggressive
obsessional themes in OCD. J Obsessive Compuls
Relat Disord. 2014;3(2):161–8. https://doi.org/10.
1016/j.jocrd.2013.11.006.
44. Williams MT, Farris SG, Turkheimer E, Pinto A,
Ozanick K, Franklin ME, et al. The myth of the pure
obsessional type in obsessive-compulsive disorder.
Depress Anxiety. 2011;28(6):495–500. https://doi.org/
10.1002/da.20820.
45. Freeston MH, Ladouceur R, Gagnon F, Thibodeau N,
Rhéaume J, Letarte H, et al. Cognitive-behavioral
treatment of obsessive thoughts: a controlled study. J
Consult Clin Psychol. 1997;65(3):405–13. https://doi.
org/10.1037/0022-006X.65.3.405.
Anxiety, Obsessive Compulsive, and Related Disorders (CB Nemeroff, Section Editor)
46.•Bruce SL, Ching THW, Williams MT. Pedophilia-
themed obsessive-compulsive disorder: assessment,
differential diagnosis, and treatment with exposure
and response prevention. Arch Sex Behav. 2017.
Recent and excellent overview of pedophelia-related fears in
OCD, including suggestions for addressing these in treatment.
47. Williams MT, Crozier M, Powers M. Treatment of
sexual-orientation obsessions in obsessive-compulsive
disorder using exposure and ritual prevention. Clin
Case Stud. 2011;10(1):53–66. https://doi.org/10.
1177/1534650110393732.
48. van den Heuvel OA, van Wingen G, Soriano-Mas C,
Alonso P, Chamberlain SR, Nakamae T, et al. Brain
circuitry of compulsivity. Eur Neuropsychopharmacol.
2016;26(5):810–27. https://doi.org/10.1016/j.
euroneuro.2015.12.005.
49. de Wit SJ, Alonso P, Schweren L, Mataix-Cols D,
Lochner C, Menchón JM, et al. Multicenter voxel-based
morphometry mega-analysis of structural brain scans
in obsessive-compulsive disorder. Am J Psychiatry.
2014;171(3):340–9. https://doi.org/10.1176/appi.ajp.
2013.13040574.
50. Boedhoe PSW, Schmaal L, Abe Y, Ameis SH, Arnold
PD, Batistuzzo MC, et al. Distinct subcortical volume
alterations in pediatric and adult OCD: a worldwide
meta- and mega-analysis. Am J Psychiatry.
2016;174:60–9.
51. Thorsen AL, Hagland P, Radua J, Mataix-Cols D, Kvale
G, Hansen B, et al. Emotional processing in obsessive-
compulsive disorder: a systematic review and meta-
analysis of 25 functional neuroimaging studies. Biol
Psychiatry Cogn Neurosci Neuroimaging. 2018.
https://doi.org/10.1016/j.bpsc.2018.01.009
52. Anticevic A, Hu S, Zhang S, Savic A, Billingslea E,
Wasylink S, et al. Global resting-state fMRI analysis
identifies frontal cortex, striatal, and cerebellar
dysconnectivity in obsessive-compulsive disorder. Biol
Psychiatry. 2014;75(8):595–605. https://doi.org/10.
1016/j.biopsych.2013.10.021.
53. Fan J, Zhong M, Gan J, Liu W, Niu C, Liao H, et al.
Altered connectivity within and between the default
mode, central executive, and salience networks in
obsessive-compulsive disorder. J Affect Disord.
2017;223:106–14. https://doi.org/10.1016/j.jad.2017.
07.041.
54. Reess T, Rus O, Schmidt R, De Reus M, Zaudig M,
Wagner G, et al. Connectomics-based structural net-
work alterations in obsessive-compulsive disorder.
Transl Psychiatry. 2016;6(9):e882. https://doi.org/10.
1038/tp.2016.163.
55.•BoedhoePSW, Schmaal L, Abe Y, Alonso P, Ameis SH,
Anticevic A, et al. Cortical abnormalities associated
with pediatric and adult obsessive-compulsive disor-
der: Findings from the ENIGMA Obsessive-
Compulsive Disorder working group. Am J Psychiatry.
2017;1–21.
A comprehensive mega-analysis of subcortical structural alter-
ations in OCD, which includes a large analysis of the relation
symptom dimensions and subcortical volume.
56. Alvarenga PG, do Rosario MC, Batistuzzo MC, Diniz
JB, Shavitt RG, Duran FL, et al. Obsessive-compulsive
symptom dimensions correlate to specific gray matter
volumes in treatment-naive patients. J Psychiatr Res.
2012;46(12):1635–42. https://doi.org/10.1016/j.
jpsychires.2012.09.002.
57. Hirose M, Hirano Y, Nemoto K, Sutoh C, Asano K,
Miyata H, et al. Relationship between symptom di-
mensions and brain morphology in obsessive-
compulsive disorder. Brain Imaging Behav. 2016;1–8.
58. van den Heuvel OA, Remijnse PL, Mataix-Cols D,
Vrenken H, Groenewegen HJ, Uylings HBM, et al. The
major symptom dimensions of obsessive-compulsive
disorder are mediated by partially distinct neural sys-
tems. Brain. 2009;132(Pt 4):853–68. https://doi.org/
10.1093/brain/awn267.
59. Okada K, Nakao T, Sanematsu H, Murayama K, Honda
S, Tomita M, et al. Biological heterogeneity of
obsessive-compulsive disorder: a voxel-based mor-
phometric study based on dimensional assessment.
Psychiatry Clin Neurosci. 2015;69(7):411–21.https://
doi.org/10.1111/pcn.12269.
60. Pujol J, Soriano-Mas C, Alonso P, Cardoner N,
Menchón JM, Deus J, et al. Mapping structural brain
alterations in obsessive-compulsive disorder. Arch Gen
Psychiatry. 2004;61(7):720–30. https://doi.org/10.
1001/archpsyc.61.7.720.
61. Valente AA Jr, Miguel EC, Castro CC, Amaro E Jr, Duran
FL, Buchpiguel CA, et al. Regional gray matter abnor-
malities in obsessive-compulsive disorder: a voxel-
based morphometry study. Biol Psychiatry.
2005;58(6):479–87. https://doi.org/10.1016/j.
biopsych.2005.04.021.
62. Ha TH, Kang DH, Park JS, Jang JH, Jung WH, Choi JS,
et al. White matter alterations in male patients with
obsessive-compulsive disorder. Neuroreport.
2009;20(7):735–9. https://doi.org/10.1097/WNR.
0b013e32832ad3da.
63. Koch K, Wagner G, Schachtzabel C, Schultz CC,
Straube T, Gullmar D, et al. White matter structure and
symptom dimensions in obsessive-compulsive disor-
der. J Psychiatr Res. 2012;46(2):264–70. https://doi.
org/10.1016/j.jpsychires.2011.10.016.
64. Zatorre RJ, Fields RD, Johansen-Berg H. Plasticity in
gray and white: neuroimaging changes in brain struc-
ture during learning. Nat Rev Neurosci.
2012;15(4):528–36. https://doi.org/10.1038/nn.
3045.
65. Mataix-Cols D, Wooderson S, Lawrence N, Brammer
MJ, Speckens A, Phillips ML. Distinct neural correlates
of washing, checking, and hoarding symptom dimen-
sions in obsessive-compulsive disorder. Arch Gen Psy-
chiatry. 2004;61(6):564–76. https://doi.org/10.1001/
archpsyc.61.6.564.
66. Shapira NA, Liu Y, He AG, Bradley MM, Lessig MC,
James GA, et al. Brain activation by disgust-inducing
pictures in obsessive-compulsive disorder. Biol Psy-
chiatry. 2003;54(7):751–6. https://doi.org/10.1016/
S0006-3223(03)00003-9.
Symptom Dimensions, Neurobiology, and Treatment of OCD Thorsen et al.
67. Phillips M, Marks I, Senior C, Lythgoe D, O'DWYER A-
M, Meehan O, et al. A differential neural response in
obsessive–compulsive disorder patients with washing
compared with checking symptoms to disgust. Psychol
Med. 2000;30(5):1037–50. https://doi.org/10.1017/
S0033291799002652.
68. Marsh R, Horga G, Parashar N, Wang Z, Peterson BS,
Simpson HB. Altered activation in fronto-striatal cir-
cuits during sequential processing of conflict in un-
medicated adults with obsessive-compulsive disorder.
Biol Psychiatry. 2014;75(8):615–22. https://doi.org/
10.1016/j.biopsych.2013.02.004.
69. Rauch SL, Dougherty DD, Shin LM, Alpert NM, Manzo
P, Leahy L, et al. Neural correlates of factor-analyzed
OCD symptom dimensions: a PET study. CNS Spectr.
1998;3(07):37–43. https://doi.org/10.1017/
S1092852900006167.
70. Marsh R, Tau GZ, Wang Z, Huo Y, Liu G, Hao X, et al.
Reward-based spatial learning in unmedicated adults
with obsessive-compulsive disorder. Am J Psychiatry.
2015;172(4):383–92. https://doi.org/10.1176/appi.
ajp.2014.13121700.
71. Riesel A, Kathmann N, Endrass T. Overactive perfor-
mance monitoring in obsessive-compulsive disorder is
independent of symptom expression. Eur Arch Psychi-
atry Clin Neurosci. 2014;264(8):707–17. https://doi.
org/10.1007/s00406-014-0499-3.
72. Lei H, Zhu X, Fan J, Dong J, Zhou C, Zhang X, et al. Is
impaired response inhibition independent of symp-
tom dimensions in obsessive-compulsive disorder?
Evidence from ERPs. Sci Rep. 2015;5(1):10413.
https://doi.org/10.1038/srep10413.
73. Via E, Cardoner N, Pujol J, Alonso P, Lopez-Sola M,
Real E, et al. Amygdala activation and symptom di-
mensions in obsessive-compulsive disorder. Br J Psy-
chiatry. 2014;204(01):61–8. https://doi.org/10.1192/
bjp.bp.112.123364.
74. Harrison BJ, Pujol J, Soriano-Mas C, Hernández-Ribas
R, López-Solà M, Ortiz H, et al. Neural correlates of
moral sensitivity in obsessive-compulsive disorder.
Arch Gen Psychiatry. 2012;69(7):741–9. https://doi.
org/10.1001/archgenpsychiatry.2011.2165.
75. Harrison BJ, Pujol J, Cardoner N, Deus J, Alonso P,
Lopez-Sola M, et al. Brain corticostriatal systems and
the major clinical symptom dimensions of obsessive-
compulsivedisorder. Biol Psychiatry. 2013;73(4):321–
8. https://doi.org/10.1016/j.biopsych.2012.10.006.
76. Fullana MA, Simpson HB. The potential use of neuro-
imaging biomarkers in the treatment of obsessive-
compulsive disorder. Curr Treat Options Psychiatry.
2016;3(3):246–52. https://doi.org/10.1007/s40501-
016-0087-4.
77. Olatunji BO, Ferreira-Garcia R, Caseras X, Fullana MA,
Wooderson S, Speckens A, et al. Predicting response to
cognitive behavioral therapy in contamination-based
obsessive-compulsive disorder from functional mag-
netic resonance imaging. Psychol Med. 2013:1–13.
78. Göttlich M, Krämer UM, Kordon A, Hohagen F,
Zurowski B. Resting-state connectivity of the amygdala
predicts response to cognitive behavioral therapy in
obsessive compulsive disorder. Biol Psychol.
2015;111:100–9. https://doi.org/10.1016/j.biopsycho.
2015.09.004.
79. Fullana MA, Zhu X, Alonso P, Cardoner N, Real E,
López-Solà C, et al. Basolateral amygdala–ventrome-
dial prefrontal cortex connectivity predicts cognitive
behavioural therapy outcome in adults with obsessive–
compulsive disorder. J Psychiatry Neurosci.
2017;42:160215.
80. Nelson EA, Abramowitz JS, Whiteside SP, Deacon BJ.
Scrupulosity in patients with obsessive-compulsive
disorder: relationship to clinical and cognitive phe-
nomena. J Anxiety Disord. 2006;20(8):1071–86.
https://doi.org/10.1016/j.janxdis.2006.02.001.
81. Yorulmaz O,Gencoz T,Woody S. OCD cognitions and
symptoms in different religious contexts. J Anxiety
Disord. 2009;23(3):401–6. https://doi.org/10.1016/j.
janxdis.2008.11.001.
82. Olatunji BO, Abramowitz JS, Williams NL, Connolly
KM, Lohr JM. Scrupulosity and obsessive-compulsive
symptoms: confirmatory factor analysis and validity of
the Penn inventory of scrupulosity. J Anxiety Disord.
2007;21(6):771–87. https://doi.org/10.1016/j.janxdis.
2006.12.002.
83. Cathey AJ, Wetterneck CT. Stigma and disclosure of
intrusive thoughts about sexual themes. J Obsessive
Compuls Relat Disord. 2013;2(4):439–43. https://doi.
org/10.1016/j.jocrd.2013.09.001.
84. Sevinc G, Spreng RN. Contextual and perceptual brain
processes underlying moral cognition: a quantitative
meta-analysis of moral reasoning and moral emotions.
PLoS One. 2014;9(2):e87427. https://doi.org/10.
1371/journal.pone.0087427.
85. Sevinc G, Gurvit H, Spreng RN. Salience network en-
gagement with the detection of morally laden infor-
mation. Soc Cogn Affect Neurosci. 2017;12(7):1118–
27. https://doi.org/10.1093/scan/nsx035.
86. Moll J, Zahn R, de Oliveira-Souza R, Krueger F,
Grafman J. Opinion: the neural basis of human moral
cognition. Nat Rev Neurosci. 2005;6(10):799–809.
https://doi.org/10.1038/nrn1768.
87. Hesse E, Mikulan E, Decety J, Sigman M, Garcia Mdel
C, Silva W, et al. Early detection of intentional harm in
the human amygdala. Brain. 2016;139(1):54–61.
https://doi.org/10.1093/brain/awv336.
88. Craske MG, Kircanski K, Zelikowsky M, Mystkowski J,
Chowdhury N, Baker A. Optimizing inhibitory learn-
ing during exposure therapy. Behav Res Ther.
2008;46(1):5–27. https://doi.org/10.1016/j.brat.2007.
10.003.
89. Craske MG, Treanor M, Conway CC, Zbozinek T,
Vervliet B. Maximizing exposure therapy: an inhibitory
learning approach. Behav Res Ther. 2014;58:10–23.
https://doi.org/10.1016/j.brat.2014.04.006.
90. Kircanski K, Peris TS. Exposure and response preven-
tion process predicts treatment outcome in youth with
OCD. J Abnorm Child Psychol. 2015;43(3):543–52.
https://doi.org/10.1007/s10802-014-9917-2.
Anxiety, Obsessive Compulsive, and Related Disorders (CB Nemeroff, Section Editor)
91. Kircanski K, Mortazavi A, Castriotta N, Baker AS,
MystkowskiJL, Yi R, et al. Challenges to the traditional
exposure paradigm: variability in exposure therapy for
contamination fears. J Behav Ther Exp Psychiatry.
2012;43(2):745–51. https://doi.org/10.1016/j.jbtep.
2011.10.010.
92. Stephan KE, Schlagenhauf F, Huys QJM, Raman S,
Aponte EA, Brodersen KH, et al. Computational neu-
roimaging strategies for single patient predictions.
NeuroImage. 2017;145(Pt B):180–99. https://doi.org/
10.1016/j.neuroimage.2016.06.038.
93. Schmaal L, Marquand AF, Rhebergen D, van Tol M-J,
Ruhé HG, van der Wee NJA, et al. Predicting the natu-
ralistic course of major depressive disorder using clin-
ical and multimodal neuroimaging information: a
multivariate pattern recognition study. Biol Psychiatry.
2015;78(4):278–86. https://doi.org/10.1016/j.
biopsych.2014.11.018.
94. Askland KD, Garnaat S, Sibrava NJ, Boisseau CL, Strong
D, Mancebo M, et al. Prediction of remission in ob-
sessive compulsive disorder using a novel machine
learning strategy. Int J Methods Psychiatr Res.
2015;24(2):156–69. https://doi.org/10.1002/mpr.
1463.
95. Lenhard F, Sauer S, Andersson E, Mansson KN, Mataix-
Cols D, Ruck C, et al. Prediction of outcome in
internet-delivered cognitive behaviour therapy for
paediatric obsessive-compulsive disorder: a machine
learning approach. Int J Methods Psychiatr Res. 2017;
Symptom Dimensions, Neurobiology, and Treatment of OCD Thorsen et al.
A preview of this full-text is provided by Springer Nature.
Content available from Current Treatment Options in Psychiatry
This content is subject to copyright. Terms and conditions apply.