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Department of Psychiatry, University of Cambridge, and Behavioural and Clinical Neuroscience Institute, University of Cambridge, UK;
Brain Mapping Unit, University of Cambridge, and Behavioural and Clinical Neuroscience Institute, University of Cambridge;
Department of Psychiatry, University of Cambridge, and Behavioural and Clinical Neuroscience Institute, University of Cambridge;
Brain Mapping Unit, University of Cambridge, and Behavioural and Clinical Neuroscience Institute, University of Cambridge;
Department of Psychiatry, University of Cambridge, and Behavioural and Clinical Neuroscience Institute, University of Cambridge;
Department of Psychology, University of Cambridge, and Behavioural and Clinical Neuroscience Institute, University of Cambridge;
Brain Mapping Unit, University of Cambridge, and Behavioural and Clinical Neuroscience Institute, University of Cambridge;
Department of Psychiatry, University of Cambridge, and Behavioural and Clinical Neuroscience Institute, University of Cambridge, UK
Correspondence: Samuel R. Chamberlain, Department of Psychiatry, University of Cambridge, Box 189, Addenbrooke’s Hospital, Cambridge CB2 2QQ, UK. Email: srchamb{at}gmail.com
None. Funding detailed in Acknowledgements.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMethodResultsDiscussionACKNOWLEDGMENTSREFERENCES |
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Trichotillomania (repetitive hair-pulling) is an Axis I psychiatric disorder whose neurobiological basis is incompletely understood. Whole-brain trichotillomania neuroimaging studies are lacking.
Aims
To investigate grey and white matter abnormalities over the whole brain in patients with trichotillomania.
Method
Eighteen patients with DSM–IV trichotillomania and 19 healthy controls undertook structural magnetic resonance imaging after providing written informed consent. Differences in grey and white matter were investigated using computational morphometry.
Results
Patients with trichotillomania showed increased grey matter densities in the left striatum, left amygdalo-hippocampal formation, and multiple (including cingulate, supplementary motor, and frontal) cortical regions bilaterally.
Conclusions
Trichotillomania was associated with structural grey matter changes in neural circuitry implicated in habit learning, cognition and affect regulation. These findings inform animal models of the disorder and highlight key regions of interest for future translational research.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMethodResultsDiscussionACKNOWLEDGMENTSREFERENCES |
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In comparison with other related conditions where compulsivity is also a prominent component, trichotillomania offers advantages in terms of neuroscientific investigation. Studies of obsessive–compulsive disorder and Tourette syndrome have often been confounded by current medications. The lack of established treatment guidelines for trichotillomania, and lack of public/clinician awareness, facilitates recruitment of patients who are not medicated to avoid this confound. It is difficult to model intrusive obsessional thoughts or complex vocal tics in animals. By contrast, hair-pulling is a relatively specific behaviour that occurs across species and several promising animal models exist. Mice with disruption of the HoxB8 gene, which is involved in neuronal development, exhibit pathological grooming behaviour.7 The Hoxb8 gene is expressed in multiple brain regions, including the striatum and cingulate cortex. Mice with genetic deletion of a postsynaptic scaffolding protein expressed in the striatum (SAP90/PSD95-associated protein) exhibit compulsive grooming behaviour leading to hair-loss and skin lesions.8
Although these translational models implicate developmental abnormalities of the striatum and cortex in pathological hair-pulling, and neurobiological overlap between trichotillomania and obsessive–compulsive disorder has been suggested, there have been no whole-brain studies of the disorder with which to validate these approaches. Extant trichotillomania neuroimaging studies have all used region-of-interest approaches, rather than exploring distributed abnormalities over the whole brain. Therefore, critically implicated regions may have been overlooked. This is particularly relevant when considering obsessive–compulsive-spectrum disorders, which are theoretically underpinned by abnormalities in large-scale brain systems, i.e. neurocognitive circuits, rather than lesions within a discrete region.9,10
O’Sullivan et al11 reported reduced left putamen volumes in a sample of 10 patients with trichotillomania v. 10 healthy controls. However, these results were described by the authors as preliminary owing to the relatively small sample size and lack of correction for multiple comparisons. Stein et al found no evidence for caudate volume abnormalities in 7 patients with trichotillomania compared with 12 controls using magnetic resonance imaging (MRI) (putamen volumes were not assessed).12 Keuthen et al reported reduced cerebellar volumes in a sample of 14 patients with trichotillomania v. 12 controls, using MRI parcellation techniques.13 Grachev investigated MRI abnormalities in 10 patients with trichotillomania v. 10 controls.14 No significant abnormalities were detected in the initial analysis, although a broader analysis of 48 parcellated regions (without correction for multiple comparisons) identified reduced left inferior frontal gyrus volumes and increased right cuneal cortex volumes. Thus, there exists inconsistent evidence for structural abnormalities of the striatum, frontal regions and cerebellum in trichotillomania.
The aim of the present study was to objectively investigate grey and white matter abnormalities over the whole brain in unmedicated patients with trichotillomania. We accomplished this by using cluster-based permutation analysis, which enabled automated, sensitive and unbiased analysis with correction for multiple comparisons.15,16 In light of the above discussion, it was predicted that trichotillomania would be associated with structural abnormalities of large-scale brain networks, including the striatum and cortex.
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Method |
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TOPABSTRACTINTRODUCTIONMethodResultsDiscussionACKNOWLEDGMENTSREFERENCES |
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Neuroimaging
Structural MRI scans were acquired from all participants using a 1.5 T GE
Signa system (General Electric, Milwaukee, USA) at the Department of
Radiology, Addenbrooke’s Hospital, Cambridge, UK. Axial
three-dimensional (3-D) T1-weighted images were obtained using a
spoiled gradient recall sequence with the parameters: 124 slices of thickness
2 mm, repetition time (TR)=33 ms, echo time (TE)=3 ms, flip angle 40°,
matrix size 256 x 256 and in-plane voxel dimensions 0.94 mm2.
Axial dual-echo fast spin echo images were also acquired with the parameters:
40 slices of thickness 4 mm, TR=5625 ms, TE=20 ms (proton density-weighted)
and 102 ms (T2-weighted) with 8-echo train length, matrix size 256
x 256 and in-plane voxel dimensions 0.94 mm2. Structural
scans were visually inspected by a consultant radiologist independent of the
research team for clinically significant abnormalities.
Images were preprocessed with tools from the FSL software package (www.fmrib.ox.ac.uk/fsl/). Non-brain tissues were first removed using an automated brain extraction procedure (Brain Extraction Tool).22 The resulting voxels were segmented using an automated tissue classification algorithm into probabilistic maps of grey matter, white matter, cerebrospinal fluid and ‘other’ using tissue-type segmentation and bias field correction (FSL Automated Segmentation Tool).23 For each voxel, the partial volume coefficient was calculated, which represented the probability of that voxel belonging to each of the four tissue classes. The resulting segmented grey and white partial volume maps were then registered into standard space (Montreal Neurological Institute, MNI) using affine intermodal image registration (FSL Linear Image Registration Tool).24,25 Prior to statistical inference, all segmented maps were smoothed via the Fourier domain, by a two-dimensional (2-D) Gaussian kernel with a standard deviation of 1.88 mm (2 voxels).
Statistical analyses
Between-group measurement of grey and white matter differences were
performed using permutation tests implemented in Cambridge Brain Analysis
(CAMBA) software in Linux (version 1.3.2;
www-bmu.psychiatry.cam.ac.uk/software).
An analysis of covariance (ANCOVA) model was fitted at each intracerebral
voxel in standard space, with global grey matter density, age and MADRS scores
as covariates. We tested the null hypothesis of no differences in brain
structure between the two groups by permutation at the level of spatially
contiguous 3-D voxel clusters, as described in detail
elsewhere.15,26
This non-parametric method of analysis incorporates spatial information and is
generally more powerful than other tests, such as those informed only by data
at the single voxel
level.26 For
between-group comparisons, we used probability thresholds for cluster testing
so that the average number of false-positive clusters expected per map was
less than one (equivalent P50.004), with the voxel threshold set to
P50.05. Clusters showing significant between-group differences were
then described in terms of their peak coordinates, and the automated
anatomical labelling template image regions contained
therein.27 An
additional permutation analysis was conducted to explore relationships between
grey/white matter densities and symptom scores in patients, again such that
the expected number of false-positive clusters per map was less than one, and
the voxel threshold was set to P50.05. Correlational analysis was
performed in patients between mean grey/white densities in those clusters
showing group differences and symptom scores in patients (Pearson’s
r).
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Results |
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TOPABSTRACTINTRODUCTIONMethodResultsDiscussionACKNOWLEDGMENTSREFERENCES |
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The patients did not differ significantly from controls in terms of global grey matter density (trichotillomania 9.26 arbitrary units (s.d.=0.47) v. controls 9.14 (s.d.=0.66); P=0.51). In comparison with the healthy volunteers, patients with trichotillomania showed grey matter density excesses in three clusters (see Fig. 1 and Table 2 for anatomical details and peak MNI coordinates). These comprised: (a) a mean density increase of 18% in the striatum (left putamen) and limbic system (left amygdalo-hippocampal complex); (b) a mean density increase of 23% in bilateral frontal regions (cingulate, supplemental motor, and superior cortices); and (c) a mean density increase of 21% in left occipital and parietal regions (Fig. 2). These results were not dependent on entering age as a covariate, since when the analysis was rerun without age as a covariate, the core results were still evident (i.e. increased grey matter in the left amygdalo-hippocampal formation, bilateral cingulate, and right middle/superior frontal cortices). There were no significant regions of relative grey matter reductions or changes in white matter (increases or decreases) in the patients. No clusters were found in which density covaried significantly with symptom severity in the patients. No significant correlations were found between mean grey matter densities and patient disease severity scores, in those clusters identified in the between-group analysis (P40.10).
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Discussion |
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TOPABSTRACTINTRODUCTIONMethodResultsDiscussionACKNOWLEDGMENTSREFERENCES |
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Increased grey matter has also been reported in studies of focal dystonia, Tourette syndrome and obsessive–compulsive disorder28–31 albeit not consistently.5 These novel findings support the notion that trichotillomania shares some neurobiological overlap with other putative obsessive–compulsive-spectrum disorders, with implications for upcoming diagnostic revisions. Peak onset of trichotillomania is at 12–13 years of age. Adolescence is a critical time for neurodevelopment and multiple studies have reported intense reductions of grey matter tissue in the pubescent period.32–35 These grey matter excesses detected in patients with trichotillomania could reflect deviation from normal developmental trajectories. Alternatively, they may reflect neuro-plastic changes occurring through use of brain regions involved in grooming and habit learning, there being some evidence that increases in grey matter can occur through motor-skill training.36
The striatum is thought to play a critical role in habit learning and in the chunking of automated action sequences, according to tiers of evidence from animals and humans.37 Striatal damage in rats has been shown to disrupt the ability to perform choreographed grooming sequences.38 In humans, clinical data suggest that the striatum is responsible for the gradual incremental learning of associations typical of habit learning.39 Striatal involvement in trichotillomania is reminiscent of obsessive–compulsive disorder, and supports a similar conceptualisation emphasising the role of the basal ganglia in the development of pathological habits.5,40
Cortical regions such as the cingulate and prefrontal lobes are involved in multiple high-level cognitive processes.41 The structural abnormalities detected in these regions in trichotillomania could mediate cognitive problems previously identified in patients, which likely impede quality of life. In neuropsychological studies, patients with trichotillomania showed deficits on tests of divided attention,42 response inhibition and working memory.43,44 The relationship between grey matter excesses and cognitive deficits in patients with trichotillomania merits follow-up, since these neural regions and their dependent cognitive functions represent targets for novel cognitive enhancers.45
The finding of amygdalo-hippocampal abnormalities was not predicted a priori. The amygdalo-hippocampal formation constitutes part of the limbic system, which regulates arousal and emotional learning processes.46 Several studies support a causal relationship between increased or decreased arousal and hair-pulling episodes, and between negative affect and hair-pulling episodes.47–49 Furthermore, trichotillomania has been associated with childhood trauma and post-traumatic stress disorder. In one survey, the majority of patients reported physical abuse and/or emotional neglect as a trigger in childhood.50
Positive features of this study include the use of permutation cluster analysis to maximise power while facilitating corrected whole-brain analysis, and the inclusion of patients who were untreated for at least 6 months prior to scanning. None the less, several caveats must be considered. Details of past treatments (beyond 6 months) received by patients were not available, to explore whether past treatment-seekers differed from non-treatment-seekers. It remains to be seen whether the present findings generalise to other groups of patients with trichotillomania who differ in terms of their clinical characteristics (past treatments, disease severity, comorbidities). Another potential limitation is that patients showed significantly higher MADRS (dysphoric mood) scores than controls, despite being free from DSM–IV depression. However, we controlled for this by entering MADRS scores as a covariate into the imaging analysis. Finally, voxel-based morphometry techniques carry potential limitations such as confounds due to choice of smoothing kernel size and potential misalignment of brain structures during normalisation. On the other hand, such techniques enable objective and sensitive analysis, with stringent correction for false positives over the whole brain.15,16
This study, using a cluster-level technique to investigate trichotillomania, provides an objective whole-brain-based analysis that directs researchers to areas that are abnormal in this disorder, namely cortical regions, the amygdalo-hippocampal formation and the striatum. These results suggest overlap with other putative obsessive–compulsive-spectrum disorders in terms of neurobiology.6,40 The functional significance of these grey matter abnormalities requires clarification in follow-up longitudinal studies, and in studies of unaffected first-degree relatives (asymptomatic individuals at increased genetic risk). The further application of translational neuroscientific techniques to the study of trichotillomania will extend these findings and provide new insights into its neurobiology, with implications for diagnosis and treatment of trichotillomania and related obsessive–compulsive-spectrum disorders.
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ACKNOWLEDGMENTS |
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TOPABSTRACTINTRODUCTIONMethodResultsDiscussionACKNOWLEDGMENTSREFERENCES |
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REFERENCES |
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Received for publication November 30, 2007. Revision received April 3, 2008. Accepted for publication April 30, 2008.
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
| Psychiatric Bulletin | Advances in Psychiatric Treatment | All RCPsych Journals |
