Abstract
Trichotillomania is a relatively common psychiatric condition whose neurobiological basis is unknown. Abnormalities of flexible responding have been implicated in the pathophysiology of obsessive-compulsive disorder (OCD) and thus may be relevant for trichotillomania. The goal of this study was to probe reversal learning and attentional set-shifting in trichotillomania. Twelve adults with trichotillomania and 13 matched healthy controls undertook a functional MRI task of cognitive flexibility. Group level activation maps for extradimensional and reversal switches were independently parcellated into discrete regions of interest (ROI) using a custom watershed algorithm. Activation magnitudes were extracted from each ROI and subject and compared at the group level. Reversal events evoked the expected patterns of insula and parietal regions and activity in the frontal dorsal cortex extending anterior to the frontal poles; whereas extra-dimensional shifts evoked the expected frontal dorso-lateral & parietal pattern of activity. Trichotillomania was associated with significantly increased right middle frontal and reduced right occipital cortex activation during reversal and set-shifting. This study identified elevated frontal activation coupled with reduced activation in more posterior brain regions. These pilot data suggest potentially important neural dysfunction associated with trichotillomania.
Keywords: trichotillomania, imaging, flexibility, cognition
Introduction
Trichotillomania, also known as hair pulling disorder, is a body-focused repetitive behavior characterized by compulsive pulling of hair leading to hair loss and marked functional impairment [1]. Trichotillomania is associated with a significant degree of psychosocial dysfunction, poor quality of life and medical complications and appears to be a fairly common disorder in the general population. Small prevalence studies have found point prevalence rates ranging from 0.5% to 2.0% [2]. From a phenomenological perspective, trichotillomania is characterized by repetitive and excessive maladaptive grooming habits that are difficult for individuals to suppress [3].
With only three functional neuroimaging studies published in trichotillomania, there is only scant information regarding the neurobiological mechanisms underlying this disorder. One study failed to find any significant differences in implicit learning, or in striatal or hippocampal activation, using a serial reaction time task in ten adults with trichotillomania compared to ten healthy controls [4]. Another study of 21 adults with trichotillomania found dampening of nucleus accumbens responses to reward anticipation (but relative hypersensitivity to gain and loss outcomes) compared to 14 healthy controls [5]. Finally, a study of nine children with trichotillomania, compared to ten healthy controls, found that those with trichotillomania exhibited significantly greater activation in left temporal cortex, dorsal posterior cingulate gyrus, and putamen during a visual symptom provocation, and greater activation in the precuneus and dorsal posterior cingulate gyrus during a visual and tactile provocation [6].
Trichotillomania has long been considered an obsessive–compulsive spectrum disorder given the overlapping phenomenological and clinical characteristics of trichotillomania and obsessive–compulsive disorder (OCD). Patients with OCD often show behavioral impairments in inhibitory control and flexible responding [7], and therefore the orbitofrontal cortex (OFC) is central to our understanding of OCD as it subserves reversal learning, a cognitive function wherein behavior is flexibly altered after negative feedback [8]. Adults with OCD have shown abnormally reduced activation of the lateral orbitofrontal cortex during reversal learning and these deficits extend to their first-degree relatives who are clinically asymptomatic, highlighting the centrality of this cognitive function and its implicated neural substrates in the pathophysiology of OCD [9]. Additionally, in a small study of pediatric trichotillomania (n=16), youth with hair pulling disorder demonstrated significant deficits in several areas of executive functioning performance including reversal learning [10]. Difficulties in behavior reversal may help explain pulling persistence despite negative consequences of pulling (e.g., social isolation, poor self-esteem). Furthermore, one study in skin picking disorder (a disorder with shared pathophysiology with trichotillomania) found that impaired cognitive flexibility was a potential marker of response to treatment with a glutamate modulating agent [11]. As such, functional magnetic resonance imaging (fMRI) tasks examining cognitive flexibility may be a useful means of probing frontal orbital and dorsolateral functioning in disorders related to OCD, such as trichotillomania. The objective of this study was to probe dorsolateral orbitofrontal cortex functioning in people with trichotillomania compared with matched healthy controls using an fMRI cognitive flexibility paradigm. In line with previous research [9], we hypothesized that trichotillomania would demonstrate dysfunction during reversal learning as evidenced by hypoactivation of the orbitofrontal cortices.
Methods
Study participants
Men and women aged 18–54 years with a primary diagnosis of trichotillomania based on DSM-5 criteria and a structured clinical interview with a board-certified psychiatrist with expertise in trichotillomania and body-focused repetitive behaviors were recruited by newspaper and poster advertisements. All participants were recruited and underwent neuroimaging procedures at the University of Chicago.
Inclusion criteria for trichotillomania subjects included: 1) Current primary diagnosis of trichotillomania; and 2) no contraindication to MRI. Exclusion criteria included: 1) unstable medical illness; 2) current pregnancy or inadequate contraception; thoughts of suicide; 4) history of bipolar disorder, dementia, or psychotic disorder; 5) past 12 months substance use disorder; 6) initiation of behavior therapy or psychotropic medications within the last 6 months; and 7) current use of illicit drugs based on urine toxicology.
Healthy controls were recruited via media advertisements on the basis of no history of psychiatric disorders and no known history of trichotillomania or obsessive compulsive related disorders (for example, OCD, excoriation disorder, body dysmorphic disorder) in first-degree family members.
The study procedures were carried out in accordance with the ethical standards laid out in the latest version of the Declaration of Helsinki. The Institutional Review Board of the University of Chicago approved the study and the consent statement. After complete description of the study to the subjects, written informed consent was obtained.
Assessments
All subjects first received a psychiatric, medical, and family history evaluation. Clinical instruments included the Mini International Neuropsychiatric Inventory (MINI) [12], the Massachusetts General Hospital Hair-pulling Scale (MGH-HPS) [13–14], and the Quality of Life Inventory (QoLI) [15].
Imaging
After completion of the above, participants undertook high-resolution structural imaging using a 3-Tesla (3T) scanner. 310 T2*-weighted volumes depicting blood oxygen level-dependent (BOLD) signal were acquired with the first 10 being discarded to avoid T1-equilibrium effects using the following parameters: TR=2s; TE=30ms;32 axial 3mm3 slices; FoV= 192 x 192 mm, 64 x 46 matrix.
The Intradimensional Extradimensional (IED) fMRI task has previously been validated in healthy volunteers [16] and in adults with OCD [9]. The reader is referred to these previous studies for a complete description of the task. In brief, the paradigm was derived from the principles of the Wisconsin Card Sorting Test, and was designed to decompose different aspects of learning and flexible responding. On each trial, participants view two stimuli, each made up of a super-imposed image of a face and a house. Through trial and error, the participant attempts to work out an underlying ‘rule’ about which stimulus is correct. Positive Feedback (the word ‘correct’) was presented in ~75% of trials where the subject responded correctly, no negative feedback was given. Trials such as these provide an ideal ‘cognitive baseline’ relative to other trials where the subject is searching. After the volunteer has made several correct responses, the underlying rule about the correct stimulus is changed, and/or novel stimuli are introduced. For the present study, the two key components of the task are reversal of responses, and extra-dimensional set-shifting. On reversal trials, the relevant stimulus dimension remains the same, but the correct response changes to the alternative one; for example, if the relevant dimension was the appearance of the house, then the rule changed such that the previously correct house became incorrect, and participants had to learn to select the other house. On extra-dimensional set-shifting trials, two new stimuli are introduced, and the relevant dimension changes; for example, if the appearance of the house had been the basis for making a correct decision, two new stimuli were presented, and the relevant stimulus dimension for making the correct choice became the faces rather than the houses. Across species, reversal learning is dependent on the orbitofrontal cortices, whereas extra-dimensional shifting is dependent on the lateral prefrontal cortices [8,16]. Other aspects of the task included intra-dimensional set-shifting trials, in which new stimuli were shown, but the relevant dimension for getting the rule correct remained unchanged; for example, if the appearance of the houses was important, then this remained the case following an intra-dimensional shift.
fMRI recordings were pre-processed and analyzed using SPM12. In brief, whole brain volumes were motion-corrected, slice-time acquisition corrected, co-registered to the structural scan, normalized to Montreal Neurological Institute (MNI) space and smoothed with an 8 mm full-width at half-maximum Gaussian kernel. The intradimensional, extradimensional switches, reversals, stimuli set changes and simple correct (no switching) events were convolved with the canonical hemodynamic response function (HRF) and modelled alongside the 24-parameter motion estimates [17] as nuisance regressors within the subject level General Linear Models (GLM) to compute the contribution of each condition to the BOLD signal. Subject level contrast estimates for each condition (switch events) were then calculated relative to the ‘cognitive baseline’ correct trials (no switching) and elevated to the group level.
Group-level Analysis
The subsequent analyses focused only on reversal and extradimensional switch events as these were our primary measures of interest. For both events, independent random effects analyses were conducted with one-sample t-tests using the contrast estimates from all subjects across both groups. The resulting group level activation maps for each contrast were thresholded voxelwise at p = 0.001 uncorrected and subsequently cluster corrected for multiple comparisons using a p < 0.05 False Discovery Rate (FDR). The approach applied for localizing regions active during certain tasks in the present study is well practiced and considered standard practice [18].
Cluster corrected group activation maps were segmented into independent Regions of Interest (ROI) using a custom 3D watershed algorithm. In brief, this approach segments continuous statistical spaces into discrete ‘parcels’ via an expanding voxel neighbourhood search that initializes at the local minima of an inverted statistical map and iterates until voxels from an independent local minima are met. Mean beta estimates were extracted for each ROI across subjects and examined at the group level. This being an exploratory study in a relatively small number of participants, group-level differences in regional activations were assessed with uncorrected two-tailed independent samples t-tests (α=0.05).
Results
Participant characteristics
In total, 12 participants (mean age 25.4 years, s.d. = 4.4; 12 [100%] women) with trichotillomania and 13 age-matched controls (mean age 25.4 years, s.d. = 5.0; 8 [61.5%] women) met inclusion criteria and underwent the clinical assessment and functional neuroimaging (Table 1). None of the participants had any current co-occurring psychiatric disorders (including OCD). The mean total MGH Hair-pulling disease score in the patients was 16.2 (4.7), consistent with, on average, mild-moderate disease. The two study groups did not differ significantly in terms of age, education level, state anxiety, or state depression. Compared to controls, patients had a significantly higher proportion of women and a lower quality of life. One (8.3%) participant was taking a psychotropic medication (lorazepam as needed for insomnia).
Table 1. Demographic and clinical characteristics in trichotillomania and controls.
| Measure | Trichotillomania group (N=12) | Control group (N=13) | Group comparison | |||
|---|---|---|---|---|---|---|
| Gender, female | N 12 |
% 100 |
N 10 |
% 76.9 |
Statistic LR=4.301 |
p-value P=0.04 |
| Age | Mean 25.4 |
SD 4.4 |
Mean 25.4 |
SD 5.0 |
F (1,23) < 0.001 |
p>0.90 |
| Education level | 3.7 | 1.0 | 3.6 | 0.9 | F (1,23) = 0.019 | P=0.891 |
| HAM-A | 4.4 | 3.7 | 2.0 | 2.0 | F (1,23) = 4.257; | P=0.051 |
| HAM-D | 4.8 | 4.0 | 2.2 | 2.7 | F (1,23) = 3.584 | P=0.071 |
| Quality of life t-score | 26.0 | 20.9 | 46.0 | 10.2 | F (1,21) = 8.221 | P=0.001 |
Behavioral results
Reaction times were significantly faster during extradimensional (t=-7.4381, p<0.001, d=3.32) and reversal (t=-15.3002, p<0.001, d=2.88) trials when compared to correct trials across all subjects. Groups did not differ significantly from each other on reaction times during any of the task conditions (Table 2).
Table 2. Mean Reaction Times (ms) for each trial type for the Trichotillomania and Controls during the IED task (SD=Standard Deviation).
| Trichotillomania | Controls | Two-Sample t-test | ||||
|---|---|---|---|---|---|---|
| Mean | S.D. | Mean | S.D. | t | p | |
| Extradimensional Switch | 1600.8 | 415.0 | 1545.6 | 370.9 | -0.3437 | 0.7344 |
| Intradimensional Switch | 1460.3 | 223.5 | 1572.5 | 287.6 | -1.0519 | 0.3043 |
| Correct Trials | 2188.2 | 124.0 | 2284.7 | 269.7 | -1.0909 | 0.2871 |
| Contingency Reversal | 1258.6 | 206.9 | 1265.3 | 249.3 | 0.0702 | 0.9447 |
| All stages collapsed | 1241.0 | 144.7 | 1315.9 | 264.0 | 0.8385 | 0.4108 |
Performance accuracy was assessed as the proportion of correct trials within each block that was initiated with either an intradimensional or extradimensional switch, or a contingency reversal. No differences in performance was observed between groups (Table 3).
Table 3. Mean accuracy (%) for extradimensional, intradimensional & reversal trials compared across the Trichotillomania and Control group (SD=Standard Deviation).
| Trichotillomania | Controls | Two-Sample t-test | ||||
|---|---|---|---|---|---|---|
| Mean | S.D. | Mean | S.D. | t | p | |
| Extradimensional Switch | 63.1 | 6.6 | 63.5 | 8.8 | -0.1093 | 0.9140 |
| Intradimensional Switch | 58.8 | 1.1 | 57.3 | 8.0 | 0.3899 | 0.7002 |
| Contingency Reversal | 61.1 | 4.9 | 62.0 | 3.7 | -0.5220 | 0.6069 |
Imaging results
Extradimensional switch events evoked the expected frontal dorso-lateral & parietal pattern of activity (Figure 1ai, FDR cluster corrected, k=2109, p<0.05). Within this network, trichotillomania was associated with increased activation in the right frontal middle gyrus and decreased activation in the right occipital middle gyrus during ED switches, compared to controls (right middle frontal gyrus, TTM mean activation: 0.315 [SE 0.042]; controls, CON, 0.139 [0.035]; t=2.340, p=0.028, d=1.29; right middle occipital gyrus: 0.154 [0.091]; 0.468 [0.060]; t=-2.094, p=0.048, d=1.16) (Figure 1aii, Table 4).
Figure 1. Analyses of regional activity observed at the group level.
a) Extradimensional switches showed activation at the group level across frontal dorso-lateral & parietal regions (1-Sample t-test, FDR cluster-corrected p<0.05). b) Reversal Learning events showed activation at the group level across dorsal fronto-parietal activity that extended to the frontal poles & anterior insula (1-Sample t-test, FDR cluster-corrected p<0.05). Group level activation maps were parcellated into discrete regions of interest and compared using 2-sample t-tests across the control and TTM groups (uncorrected p<0.05). ROI’s showing significant differences in activation magnitude are shaded in black for the extradimensional switches (bi – Table 4 & Reversal learning events (bii – Table 5). Anatomical labels come from the AAL atlas and were assigned by the mode voxels of an ROI.
Table 4. Extradimensional switch ROI mean parameter estimates compared across trichotillomania (TTM) and control subjects (CON) with independent samples t-test (unc.) (St.E=Standard error of the mean, AAL=Automated Anatomical Labelling atlas, BA=Brodmann Area, MNI=Montreal Neurological Institute space).
| MNI | Mean | St.E | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| AAL Label | BA | X | Y | Z | TTM | CON | TTM | CON | t | p |
| Frontal_Sup_Medial_L | 32 | 4 | 24 | 44 | 0.423 | 0.241 | 0.051 | 0.056 | 1.724 | 0.098 |
| Precuneus_R | 07 | 16 | -58 | 52 | 0.117 | 0.269 | 0.083 | 0.069 | -1.018 | 0.319 |
| Frontal_Sup_2_L | 06 | -22 | 6 | 50 | 0.252 | 0.169 | 0.041 | 0.040 | 1.047 | 0.306 |
| Frontal_Mid_2_R | 06 | 32 | 10 | 50 | 0.315 | 0.139 | 0.042 | 0.035 | 2.340 | 0.028 |
| Supp_Motor_Area_L | 06 | -2 | 10 | 56 | 0.343 | 0.163 | 0.044 | 0.057 | 1.789 | 0.087 |
| Angular_R | 07 | 32 | -60 | 44 | 0.177 | 0.333 | 0.065 | 0.059 | -1.295 | 0.208 |
| Precuneus_L | 07 | 0 | -54 | 52 | 0.061 | 0.248 | 0.086 | 0.089 | -1.088 | 0.288 |
| Precuneus_L | 07 | -12 | -60 | 54 | 0.086 | 0.075 | 0.058 | 0.048 | 0.106 | 0.917 |
| Precentral_L | 09 | -38 | 12 | 28 | 0.257 | 0.308 | 0.048 | 0.047 | -0.544 | 0.592 |
| Parietal_Sup_L | 07 | -24 | -56 | 42 | 0.131 | 0.123 | 0.059 | 0.045 | 0.079 | 0.938 |
| Lingual_R | 17 | 22 | -80 | -4 | 0.371 | 0.786 | 0.122 | 0.103 | -1.881 | 0.073 |
| Frontal_Mid_2_L | 09 | -32 | 34 | 26 | 0.198 | 0.190 | 0.060 | 0.058 | 0.073 | 0.943 |
| Occipital_Mid_R | 39 | 38 | -74 | 18 | 0.154 | 0.468 | 0.091 | 0.060 | -2.094 | 0.048 |
| Occipital_Mid_L | 31 | -26 | -76 | 18 | 0.147 | 0.307 | 0.069 | 0.059 | -1.274 | 0.215 |
| Postcentral_R | 40 | 40 | -38 | 42 | 0.013 | -0.006 | 0.042 | 0.038 | 0.239 | 0.814 |
| Lingual_L | 18 | -8 | -84 | -4 | 0.356 | 0.823 | 0.132 | 0.114 | -1.936 | 0.065 |
| Frontal_Mid_2_R | 09 | 38 | 40 | 30 | 0.189 | 0.068 | 0.052 | 0.050 | 1.217 | 0.236 |
| Frontal_Mid_2_R | 46 | 44 | 30 | 28 | 0.266 | 0.249 | 0.068 | 0.054 | 0.143 | 0.888 |
| Postcentral_L | 40 | -36 | -34 | 42 | -0.031 | -0.013 | 0.043 | 0.041 | -0.220 | 0.828 |
| Fusiform_R | 37 | 32 | -44 | -14 | 0.338 | 0.581 | 0.093 | 0.062 | -1.585 | 0.127 |
| Lingual_L | 19 | -22 | -70 | -10 | 0.302 | 0.495 | 0.093 | 0.057 | -1.293 | 0.209 |
| Frontal_Inf_Oper_R | 09 | 46 | 12 | 28 | 0.399 | 0.394 | 0.051 | 0.060 | 0.049 | 0.962 |
| Fusiform_L | 19 | -22 | -48 | -12 | 0.165 | 0.367 | 0.084 | 0.084 | -1.230 | 0.231 |
| Fusiform_R | 37 | 38 | -60 | -12 | 0.369 | 0.621 | 0.102 | 0.057 | -1.570 | 0.130 |
Reversal events evoked the expected patterns in insula and parietal regions and activity in the frontal dorsal cortex extending anterior to the frontal poles (Figure 1bi, FDR cluster corrected, k=441, p<0.05). Within this network, trichotillomania was associated with increased activation in the right frontal middle gyrus and decreased activation in the right occipital inferior gyrus and left lingual gyrus during reversal, compared to controls (right middle frontal gyrus, TTM mean activation: 0.284 [SE 0.028]; controls, CON, 0.119 [0.027]; t=2.145, p=0.042, d=1.72; right inferior occipital gyrus: 0.348 [0.077]; 0.726 [0.048]; t=-2.106, p=0.046, d=1.69; left lingual gyrus, TTM mean activation: 0.202 [SE 0.104]; controls, CON, 0.888 [SE 0.093]; t=-2.461, p=0.022, d=1.97) (Figure 1bii, Table 5).
Table 5. Reversal Learning ROI mean parameter estimates compared across trichotillomania (TTM) and control subjects (CON) with independent samples t-test (unc.) (St.E=Standard error of the mean, AAL=Automated Anatomical Labelling atlas, BA=Brodmann Area, MNI=Montreal Neurological Institute space).
| MNI | Mean | St.E | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| AAL Label | BA | X | Y | Z | TTM | CON | TTM | CON | t | p |
| Frontal_Sup_Medial_L | 32 | 4 | 22 | 42 | 0.376 | 0.2 | 0.034 | 0.039 | 1.697 | 0.103 |
| Precuneus_R | 7 | 12 | -58 | 52 | 0.083 | 0.264 | 0.057 | 0.05 | -1.199 | 0.243 |
| Parietal_Sup_R | 7 | 32 | -58 | 48 | 0.184 | 0.377 | 0.046 | 0.046 | -1.481 | 0.152 |
| Precuneus_L | 7 | -12 | -62 | 50 | 0.149 | 0.157 | 0.05 | 0.037 | -0.059 | 0.954 |
| Frontal_Mid_2_R | 8 | 34 | 14 | 46 | 0.284 | 0.119 | 0.028 | 0.027 | 2.145 | 0.043 |
| Frontal_Mid_2_R | 10 | 36 | 44 | 24 | 0.164 | 0.065 | 0.034 | 0.031 | 1.084 | 0.29 |
| Frontal_Sup_2_L | 6 | -24 | 4 | 50 | 0.268 | 0.19 | 0.03 | 0.032 | 0.885 | 0.385 |
| Frontal_Inf_Oper_L | 9 | -40 | 10 | 24 | 0.218 | 0.237 | 0.033 | 0.032 | -0.208 | 0.837 |
| Frontal_Mid_2_R | 46 | 42 | 32 | 26 | 0.237 | 0.189 | 0.046 | 0.037 | 0.409 | 0.686 |
| Occipital_Sup_R | 39 | 32 | -64 | 34 | 0.21 | 0.399 | 0.05 | 0.034 | -1.569 | 0.13 |
| Frontal_Mid_2_L | 9 | -36 | 30 | 34 | 0.253 | 0.184 | 0.042 | 0.041 | 0.596 | 0.557 |
| Frontal_Inf_Oper_R | 9 | 46 | 14 | 24 | 0.335 | 0.29 | 0.035 | 0.033 | 0.468 | 0.645 |
| Postcentral_R | 40 | 42 | -36 | 40 | 0.014 | -0.01 | 0.028 | 0.025 | 0.315 | 0.756 |
| Precuneus_L | 5 | -4 | -50 | 66 | -0.069 | -0.018 | 0.072 | 0.066 | -0.264 | 0.794 |
| Insula_R | 47 | 38 | 24 | 0 | 0.221 | 0.169 | 0.036 | 0.032 | 0.546 | 0.591 |
| Insula_L | 47 | -28 | 26 | -2 | 0.213 | 0.201 | 0.042 | 0.044 | 0.103 | 0.919 |
| Parietal_Inf_L | 7 | -24 | -54 | 38 | 0.153 | 0.198 | 0.035 | 0.029 | -0.513 | 0.613 |
| Frontal_Mid_2_L | 10 | -34 | 34 | 22 | 0.186 | 0.192 | 0.043 | 0.039 | -0.05 | 0.961 |
| Cingulate_Ant_R | 32 | 12 | 36 | 26 | 0.239 | 0.216 | 0.035 | 0.042 | 0.215 | 0.832 |
| Parietal_Inf_L | 40 | -32 | -46 | 48 | 0.008 | -0.128 | 0.051 | 0.041 | 1.046 | 0.307 |
| Frontal_Inf_Tri_L | 46 | -36 | 20 | 24 | 0.238 | 0.397 | 0.04 | 0.039 | -1.436 | 0.164 |
| Parietal_Inf_L | 40 | -38 | -34 | 40 | -0.032 | -0.022 | 0.031 | 0.028 | -0.129 | 0.898 |
| Frontal_Mid_2_R | 8 | 40 | 28 | 48 | 0.338 | 0.177 | 0.038 | 0.043 | 1.4 | 0.175 |
| Lingual_R | 18 | 18 | -80 | -2 | 0.305 | 0.771 | 0.087 | 0.076 | -2.034 | 0.054 |
| Occipital_Sup_L | 7 | -24 | -70 | 24 | 0.106 | 0.193 | 0.043 | 0.044 | -0.706 | 0.487 |
| Lingual_L | 18 | -4 | -84 | -4 | 0.202 | 0.888 | 0.104 | 0.093 | -2.461 | 0.022 |
| Parietal_Inf_R | 40 | 30 | -46 | 44 | 0.042 | 0.115 | 0.027 | 0.031 | -0.887 | 0.384 |
| Insula_L | 47 | -38 | 16 | -2 | 0.278 | 0.268 | 0.028 | 0.055 | 0.073 | 0.943 |
| Parietal_Inf_R | 40 | 40 | -44 | 50 | -0.004 | 0.033 | 0.05 | 0.046 | -0.266 | 0.793 |
| Fusiform_R | 19 | 32 | -68 | -8 | 0.222 | 0.565 | 0.074 | 0.059 | -1.814 | 0.083 |
| Occipital_Mid_R | 39 | 36 | -72 | 22 | 0.189 | 0.429 | 0.056 | 0.043 | -1.715 | 0.1 |
| Frontal_Sup_2_L | 10 | -26 | 54 | 8 | 0.176 | -0.039 | 0.05 | 0.037 | 1.731 | 0.097 |
| Occipital_Mid_L | 31 | -26 | -76 | 16 | 0.132 | 0.375 | 0.051 | 0.046 | -1.765 | 0.091 |
| Frontal_Mid_2_R | 6 | 36 | 0 | 58 | 0.331 | 0.189 | 0.05 | 0.053 | 0.976 | 0.339 |
| Lingual_L | 18 | -14 | -76 | -8 | 0.247 | 0.454 | 0.065 | 0.048 | -1.284 | 0.212 |
| Occipital_Inf_R | 19 | 40 | -74 | -10 | 0.348 | 0.726 | 0.077 | 0.048 | -2.106 | 0.046 |
| Temporal_Inf_R | 37 | 44 | -60 | -14 | 0.392 | 0.621 | 0.078 | 0.029 | -1.407 | 0.173 |
| Pallidum_R | 32 | 16 | -6 | -4 | 0.13 | 0.126 | 0.027 | 0.029 | 0.051 | 0.96 |
| Frontal_Sup_2_R | 7 | 32 | 54 | 8 | 0.044 | -0.114 | 0.034 | 0.039 | 1.514 | 0.144 |
| Pallidum_R | 7 | 20 | 4 | -2 | 0.066 | 0.046 | 0.023 | 0.025 | 0.293 | 0.772 |
| Putamen_R | 7 | 24 | 2 | 2 | 0.043 | 0.004 | 0.022 | 0.028 | 0.527 | 0.603 |
| Precentral_R | 8 | 52 | 6 | 32 | 0.296 | 0.29 | 0.031 | 0.04 | 0.059 | 0.954 |
| Thalamus_L | 10 | -8 | -12 | 4 | 0.069 | 0.12 | 0.05 | 0.045 | -0.386 | 0.703 |
| Pallidum_L | 6 | -12 | 2 | -2 | 0.058 | 0.009 | 0.028 | 0.029 | 0.61 | 0.548 |
| Frontal_Mid_2_R | 9 | 34 | 56 | -2 | 0.04 | -0.084 | 0.05 | 0.052 | 0.86 | 0.399 |
| Frontal_Mid_2_L | 46 | -24 | 46 | 24 | 0.132 | 0.092 | 0.037 | 0.042 | 0.356 | 0.725 |
| Fusiform_R | 39 | 34 | -48 | -14 | 0.425 | 0.631 | 0.075 | 0.046 | -1.184 | 0.248 |
| Fusiform_R | 9 | 34 | -54 | -8 | 0.284 | 0.432 | 0.069 | 0.051 | -0.872 | 0.392 |
| Putamen_R | 9 | 20 | 0 | 10 | 0.064 | -0.019 | 0.051 | 0.042 | 0.636 | 0.531 |
| Caudate_R | 40 | 18 | 6 | 12 | 0.1 | -0.044 | 0.045 | 0.054 | 1.016 | 0.32 |
An exploratory whole-brain analysis was conducted to test for Group (TTM, CON) by Condition (ED, Reversal) interaction effects. This contrast did not identify any surviving voxels after correction for multiple comparisons.
Supplementary Figure 1 plots the parameter estimates averaged across ROI within each control subject and broken down by condition and gender. It can be seen that means were extremely similar across genders.
Discussion
This study probed neural circuitry involved in flexible responding using a functional imaging task in patients with trichotillomania. The task activated the expected neural networks overall. Compared to controls, trichotillomania was associated with elevated activation in the right frontal gyrus coupled with reduced activation in the occipital lobe. This finding might result in improved outcomes for trichotillomania if this area proves to be a worthwhile target for treatment. Possibly due to the sample size, these group-level differences would not have withstood statistical correction for multiple comparisons, but they were with large effect size (Cohen’s d = 1.2 to 2.0). There were no significant behavioral deficits on the task in patients, as indexed by response times or accuracy.
Because right frontal neural abnormalities (specifically, relative hyperactivation) found here in trichotillomania patients were similar for both reversal and set-shifting task contrasts, this may point to more subtle abnormalities in aspects of attentional or inhibitory control, rather than to a primary deficit in flexible responding. Previous studies have identified response inhibition impairments in trichotillomania [19] with relative sparing of set-shifting [20] and reversal learning [21] in co-morbidity free cases. The right middle frontal gyrus constitutes part of a neural network involved in response inhibition, based on meta-analytic findings from functional imaging studies [22]. Interestingly, this region shows increased cortical thickness in trichotillomania compared to controls [17]. This region appears to play a greater role in adaptive online control over behavior, rather than in maintaining a task set [23]; and has strong functional connectivity with other neural regions playing a primary role in suppression of triggered motor responses [24]. Previous research in OCD using the same task found hypoactivation of the lateral orbitofrontal cortices during reversal specifically [9]. Here we were not able to explore group differences in OFC activation during reversal because this region was not well represented in the activation map; this may reflect signal drop-out, which is common for this region, and the relatively smaller total sample size.
In this study, we identified reduced activation of the right occipital lobe in trichotillomania patients compared to controls, with geographically similar but not identical regions being affected for reversal compared to set-shifting (inferior occipital lobe for the former, middle occipital lobe for the latter). The precise role of the occipital lobe in distinct cognitive functions is not well established. This brain region is involved in the processing of visual stimuli, possibly more so for facial stimuli [24] and it is noteworthy that the current fMRI paradigm included composite stimuli, with one dimension of these stimuli being faces. Right middle occipital gyrus was significantly activated across a range of face-processing tasks, including masking and inattention tasks [25]. It would be valuable in future work to use fMRI tasks of emotional face processing in trichotillomania, in view of the aberrant activation of the occipital lobe found here across task stages, coupled with the above background literature. People with trichotillomania had lower affect regulation than controls in previous work [26], and affect dysregulation has been suggested as a core feature of the disorder [27].
Several limitations should be considered in relation to the current study. The sample size is small in this study, and therefore the study was not powered to evaluate group-level differences with statistical correction for multiple comparisons, but the findings had large effects sizes. Nonetheless, they should be regarded as being in need of replication in future work. Hair pulling severity scores were very similar for the small sample and therefore precluded an analysis of the relationship between brain activity and hair pulling severity. The study was neither designed nor powered to evaluate possible effects of treatment or gender on brain activation and cognition. All patients were women while three of the controls were men, and this gender balance differed significantly between the groups. Inspection of peak activation in male and female controls (supplementary figure) indicated no tangible differences; formal covariance for gender would have been inappropriate due to the already small sample size and the fact that there were only three males.
Lastly, flexible responding is just one aspect of cognition and it would be valuable to study other domains using functional imaging in future trichotillomania research, especially response inhibition and face processing. This could be enabled by multi-site collaborative research, with a view to recruiting larger samples.
There is an ongoing search in psychiatry for models of the neurobiological circuitry implicated in given disorders. One salient aspect of trichotillomania is the seemingly uncontrollable habit of pulling hair even as the person is aware of worsening alopecia [3]. The current study found evidence for abnormal activation in the right middle frontal lobe and middle-inferior occipital cortices in patients with trichotillomania versus controls. Rather than suggesting a primary deficit in flexible responding, we suggest that this may be due to secondary deficits elsewhere because the activation patterns were abnormal across reversal learning and set-shifting.
Supplementary Material
Funding
This work was funded by a Wellcome Trust Clinical Fellowship to Dr. Chamberlain (UK; Reference 110049/Z/15/Z) and by a grant from the Trichotillomania Learning Center to Dr. Grant.
Footnotes
Potential Conflicts of Interest: Dr. Chamberlain consults for Cambridge Cognition and Shire. Dr. Grant is chair of the Scientific Advisory Board of the TLC Foundation for BFRBs and currently receives funding from its BFRB Precision Medicine Initiative. In addition, he has received research grants from NIAAA, American Foundation for Suicide Prevention, National Center for Responsible Gaming, and Takeda Pharmaceuticals. He receives yearly compensation from Springer Publishing for acting as Editor-in-Chief of the Journal of Gambling Studies and has received royalties from Oxford University Press, American Psychiatric Publishing, Inc., Norton Press, and McGraw Hill. Mr Daws and Dr. Hampshire report no potential conflicts of interest.
Contributor Information
Jon E. Grant, Department of Psychiatry & Behavioral Neuroscience, University of Chicago
Richard Daws, The Computational, Cognitive, and Clinical Neuroimaging Lab, Department of Medicine, Imperial College, London, UK.
Adam Hampshire, The Computational, Cognitive, and Clinical Neuroimaging Lab, Department of Medicine, Imperial College, London, UK.
Samuel R. Chamberlain, Department of Psychiatry, University of Cambridge, UK; & Cambridge and Peterborough NHS, Foundation Trust, UK
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