fMRI pattern recognition in obsessive–compulsive disorder

NeuroImage 60 (2012) 1186–1193

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fMRI pattern recognition in obsessive–compulsive disorder Martin Weygandt a,⁎, Carlo R. Blecker b, Axel Schäfer c, Kerstin Hackmack a, John-Dylan Haynes a, d, Dieter Vaitl b, Rudolf Stark b, e, 1, Anne Schienle f, 1 a

Bernstein Center for Computational Neuroscience Berlin, Charité–Universitätsmedizin Berlin, Germany Bender Institute of Neuroimaging, University of Giessen, Giessen, Germany Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany d Max-Planck-Institute for Human Cognitive and Brain Sciences, Leipzig, Germany e Department of Clinical and Physiological Psychology, University of Giessen, Giessen, Germany f University of Graz, Institute of Psychology, Department of Clinical Psychology, Graz, Austria b c

a r t i c l e

i n f o

Article history: Received 1 July 2011 Revised 12 December 2011 Accepted 8 January 2012 Available online 17 January 2012 Keywords: Pattern recognition Obsessive–compulsive disorder Functional magnetic resonance imaging

a b s t r a c t Patients suffering from obsessive–compulsive disorder (OCD) are characterized by dysregulated neuronal processing of disorder-specific and also unspecific affective stimuli. In the present study, we investigated whether generic fear-inducing, disgust-inducing, and neutral stimuli can be decoded from brain patterns of single fMRI time samples of individual OCD patients and healthy controls. Furthermore, we tested whether differences in the underlying encoding provide information to classify subjects into groups (OCD patients or healthy controls). Two pattern classification analyses were conducted. In analysis 1, we used a classifier to decode the category of a currently viewed picture from extended fMRI patterns of single time samples (TR = 3 s) in individual subjects for several pairs of categories. In analysis 2, we used a searchlight approach to predict subjects' diagnostic status based on local brain patterns. In analysis 1, we obtained significant accuracies for the separation of fear-eliciting from neutral pictures in OCD patients and healthy controls. Separation of disgust-inducing from neutral pictures was significant in healthy controls. In analysis 2, we identified diagnostic information for the presence of OCD in the orbitofrontal cortex, and in the caudate nucleus. Accuracy obtained in these regions was 100% (p b 10 − 6). To summarize our findings, by using multivariate pattern classification techniques we were able to identify neurobiological markers providing reliable diagnostic information about OCD. The classifier-based fMRI paradigms proposed here might be integrated in future diagnostic procedures and treatment concepts. © 2012 Elsevier Inc. All rights reserved.

Introduction Obsessive–compulsive disorder (OCD) is a psychiatric disorder that is characterized by recurrent intrusive thoughts, images and/or impulses (obsessions), which most commonly revolve around the concern of harming another person or oneself. The obsessions often trigger repetitive behaviors (compulsions) such as washing, checking or mental rituals (APA, 1994). OCD is considered an anxiety disorder because obsessions as well as the interruption of compulsions trigger fear. Furthermore, there is evidence for heightened sensitivity to generic fear-inducing stimuli in OCD patients. For example Foa et al. (1993) found that OCD patients showed longer response latencies to generic fear-eliciting words in an emotional stroop paradigm. Moreover, we could recently show that generic fear stimuli induced

⁎ Corresponding author at: Charité–Universitätsmedizin Berlin, Bernstein Center for Computational Neuroscience, Haus 6, Philippstrasse 13, 10115 Berlin, Germany. Fax: + 49 30 2093 6769. E-mail address: [email protected] (M. Weygandt). 1 These authors contributed equally to this work. 1053-8119/$ – see front matter © 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2012.01.064

stronger feelings of fear in OCD patients as compared to healthy controls (Schienle et al., 2005). These findings were supported by results of neuroimaging studies investigating the processing of generic affective stimuli in OCD (e.g., Cardoner et al., 2011; Mataix-Cols et al., 2004; Schienle et al., 2005; Shapira et al., 2003). They all detected a dysregulation of a variety of brain areas in OCD patients as compared to healthy controls, e.g. in the prefrontal cortex, mediotemporal areas (amygdala, and parahippocampal gyrus), and in the insular cortex. Correspondingly, generic fear-inducing stimuli have a special relevance for OCD patients. Recently, the investigation of diagnostic information of biological markers such as fMRI signals has become increasingly popular due to efforts to include this kind of information in diagnostic guidelines for psychiatric disorders (e.g. the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders, see Regier et al., 2009). However, diagnostic information of fMRI signals cannot be investigated properly with statistical methods used in existing OCD studies as these methods analyze fMRI response distribution differences between diagnostic groups and not the separability of individual data exemplars (i.e. subjects). Furthermore, these methods leave out a

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lot of potential sources of information because they analyze activation differences for each position of the brain independently. For optimal separation it is important to include the information encoded in conjoint activation in distributed patterns of activity in the brain (Haynes and Rees, 2006). This can be achieved through multivariate pattern recognition techniques. Such ‘classifiers’ or ‘decoders’ have a higher sensitivity compared to more traditional voxel-based analyses (Pereira et al., 2009) and are specifically designed to categorize data patterns into distinct groups (for a review see Haynes and Rees, 2006; Norman et al., 2006; Pereira et al., 2009). They allow diagnosing the clinical status of individuals based on distributed patterns of brain activity. In line with this, classifiers have been used to diagnose the presence of neurologic disease from structural MRI patterns, e.g. in multiple sclerosis (Weygandt et al., 2011a); stroke (Schmah et al., 2010); dementia (Kloeppel et al., 2008; McEvoy et al., 2009) or psychiatric disorders from fMRI patterns, e.g. in eating disorders (Weygandt et al., 2011b); depression (Fu et al., 2008; Marquand et al., 2008); substance abuse (Zhang et al., 2005); schizophrenia (Meyer-Lindenberg et al., 2001), however not yet in OCD. Besides applications in diagnostic settings, pattern classifiers can also be used to detect specific mental states from brain activity of individuals in a dynamic fashion across the course of an experiment. This approach (‘brain-reading’) has been used extensively to decode various perceptive and cognitive processes from fMRI activation patterns in healthy subjects, e.g. in vision (Haxby et al., 2001; Haynes and Rees, 2005; Haynes et al., 2005); language (Chen et al., 2006; Formisano et al., 2008); motor tasks (Strother et al., 2004); emotion (Mourão-Miranda et al., 2007); decision making (Soon et al., 2008); and attention (Mourão-Miranda et al., 2005). However, comparably little effort has yet been made to decode disorder-related brain states from fMRI activity patterns in patients suffering from psychiatric disorders. We showed recently that it is possible to decode eating disorder-related states in patients based on activity patterns in the brain reward system (Weygandt et al., 2011b). Several authors (LaConte, 2011; LaConte et al., 2007; Sitaram et al., 2010) have suggested that such an approach might lead to helpful fMRI real-time paradigms for the treatment of psychiatric disorders employing classifier-based neurofeedback. In the present study, we conducted two pattern recognition analyses based on data originally collected by Schienle et al. (2005). In the first analysis, we wanted to clarify whether it is possible to detect brain states from fMRI patterns of individual subjects (OCD patients and healthy controls [HC]) that are elicited by disorderunspecific affective stimuli. To achieve this goal, we decoded categories of currently viewed pictures (fear-inducing, disgust-inducing, and affectively neutral) from extended fMRI patterns of single time samples (TR = 3 s) of ten patients and ten controls. We chose this approach, as it allows to assess whether requirements are met to conduct a future fMRI real-time paradigm for the treatment of OCD comprising classifier-based neurofeedback. In the second analysis, and using the discriminating volumes derived from analysis 1, we investigated whether it is possible to identify neurobiological diagnostic markers for OCD by using affective stimuli that are not specific for OCD. To achieve this goal, we tried to diagnose the clinical status of individuals (OCD patient or HC) based on functional patterns of small brain areas with a searchlight decoding strategy. By using both approaches—identification of neurobiological markers for OCD based on presentation of disorder-unspecific affective stimuli and classification of single fMR images acquired during presentation of these stimuli—it might be possible to change diagnostic and treatment strategies in the future. At the moment, the most effective psychotherapeutic treatment of OCD consists of cognitive behavior therapy (Franklin and Foa, 2011). Following this approach, behavior therapists expose patients to disorder-specific stimuli in order to diagnose OCD as well as in the course of therapy. This strategy is very demanding for the patient and often leads to discontinuation

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of treatment. In contrast, the confrontation with generic affective stimuli is less stressful and might therefore be associated with greater compliance, e.g. during diagnostic procedures as well as when using classifier-based neurofeedback to diminish disorder-related dysregulation of brain activity. Methods and materials Participants Ten right-handed outpatients suffering from OCD according to DSMIV (American Psychiatric Association, 1994) participated in the experiment (6 female, 4 male; mean age = 40.7 years, range= 29–60 years. A board-certified psychotherapist (AS) obtained the OCD diagnoses. AS performed the Yale–Brown Obsessive Compulsive Scale (Y-BOCS; Goodman et al., 1989) and the diagnostic interview for DSM-IV (Mini-DIPS; Margraf, 1994) with each patient. The Y-BOCS symptom list had indicated that all OCD patients suffered from a mixture of symptoms (e.g. washing, checking, counting, ordering, obsessions about disease, accidents, symmetry). They could not be labeled as either ‘washers’ or ‘checkers’. At the time of the study, none of them was clinically depressed. However, five of the patients had a history of at least one episode of major depression. Five of the patients were medicated (three with tricyclic antidepressants: clomipramin 10 mg, 25 mg, 75 mg per day and two with selective serotonin inhibitors: fluoxetin 20 mg, citalopram 20 mg per day); five were medication naive. The 10 mentally healthy control subjects were matched for age (M = 39.1 years, range = 27–50 years), gender, handedness, duration of education and socioeconomic status. They had been interviewed with the Mini-DIPS (Margraf, 1994) to ensure that they had no current or previous psychiatric disorder. They were medication free. All subjects provided written informed consent after the study procedure had been explained to them in detail. The ethics committee of the German Society for Psychology had approved the experiment. The patients received 50 Euros and the control subjects 25 Euros for their participation. Those patients interested in treatment were assisted with referrals. For further details, see (Schienle et al., 2005). Brain imaging Brain images were acquired using a 1.5 Tesla whole-body tomograph (Magnetom Symphony, Siemens, Erlangen, Germany) with a standard head coil. For the functional imaging a total of 486 volumes were measured using a T2*-weighted gradient echo-planar imaging (EPI) sequence with 30 slices covering the whole brain (slice thickness = 5 mm; no gap; interleaved; TR = 3000 ms; TA = 100 ms; TE = 60 ms, flip angle = 90°; field of view = 192 mm × 192 mm; matrix size = 64 × 64). The orientation of the axial slices was parallel to the AC–PC line. Stimuli and design The stimulus material consisted of a total of 80 pictures, 60 of which were chosen from the International Affective Picture System (Lang et al., 1997) to represent the three emotional categories ‘disgust’, ‘fear’, and ‘neutral’. The twenty remaining pictures corresponded to a fourth category of patient-specific OCD triggers. The 20 generally fear-inducing scenes depicted threatening situations either through attacks by animals, by humans or disasters. The 20 generally disgust-evoking scenes included a broad range of different elicitors (e.g. maggots, garbage piles, humans with mattering wounds). The 20 affectively neutral scenes consisted of e.g., household articles, geometric figures, nature scenes, and humans in affectively neutral scenes. The fourth category was formed by 20 OCDspecific pictures, which had been photographed by each patient with a digital camera to show his/her specific triggers for obsessive–

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compulsive behaviors. This patient-specific strategy of stimulus selection was chosen as the first diagnostic sessions of the original study (Schienle et al., 2005) had revealed that the majority of patients' triggers for compulsions and obsessions were located in their homes. However, due to between-group differences in familiarity with these stimuli this patient-specific category was excluded from the present analysis. The 20 pictures selected for each category were presented in a block design. Each set of category-specific pictures was shown in six blocks. To avoid unwanted effects of stimulation sequence, the order of category-specific blocks was determined in a quasirandomized fashion (i.e., with the restriction that the experiment did not start with the patient-specific OCD category). Within each of the six blocked presentations, each of the 20 pictures was shown for 3 s in a sequence that was determined randomly for each block. The particular picture sequence of a given OCD patient was also viewed by the matched control subject. In order to assess whether the picture material differed between groups in terms of emotional processing patients and controls rated the affective experience during picture perception after the viewing by means of a 9-point scale. Valence (1 = ‘very negative’, 9 = ‘very positive’), arousal (1 = ‘not at all’, 9 = ‘very strong’), disgust and fear induction (both: 1 = ‘not at all’, 9 = ‘very strong’). Pattern recognition analysis 1: intra-subject decoding of picture categories Here, we used pattern classification to decode the category of a currently viewed picture from large-scale brain activity patterns on a TR-by-TR basis. We performed a separate analysis for each subject and each pair of picture categories: fear vs. neutral, disgust vs. neutral, and finally disgust vs. fear. The processing steps included in the analysis can be divided into two parts. In the first part, we generated an individualized mask of the brain regions included in the neuroanatomic atlas proposed by Tzourio-Mazoyer et al. (2002) in the native space of each subject, i.e. their native EPI images with SPM8 (Wellcome Trust Centre for Neuroimaging, Institute of Neurology, UCL, London UK—http://www.fil.ion.ucl.ac.uk/spm). The mask was determined to exclude regions containing variance of no interest such as the eyes from the analysis. All following steps conducted in the second part were performed using in-house software. The processing steps performed by this software can also be divided into two parts: training and test stage. In the training stage, the software realigned, spatially smoothed (FWHM = 12 mm), masked, and temporally smoothed (FWHM = 4 TR) the DICOM images that were acquired in a sequential fashion. The training stage ended, when the first half of all image files of the session for a subject (i.e. 243) was acquired. Then, brain activation patterns were extracted from individual images and the patterns of a given pair of picture categories were fed into a linear support vector classifier (LIBSVM—http:// www.csie.ntu.edu.tw/~cjlin/libsvm). However, following LaConte et al. (2007), the three initial EPI images of each block of a category were excluded from the training procedure to compensate for the sluggish nature of the BOLD signal. The classifier then determined a linear classification boundary to separate between patterns of a given pair of picture categories using the classification function y(xt) = sign(xtw + b). Here, xt corresponds to an activation pattern of timepoint t, y(xt) to the classification for a given pattern xt, w to the linear classification boundary, and b to a bias. As the classification function was determined directly in the voxel space the weight vector w can be thought of as a discriminating volume (DV; e.g. MourãoMiranda et al., 2005) that contains one weight for each voxel that denotes the relevance of the voxel for the multivariate separation of categories. The DVs determined for the separation of fear-inducing vs. neutral pictures, and disgust-inducing vs. neutral pictures were also used for pattern recognition analysis 2. In the test stage each DICOM image was preprocessed as in training stage. Following

preprocessing, the classifier output xtw + b was calculated for a given test pattern. Next, we applied a high-pass filter with a cut-off frequency of 0.01 Hz to the classifier output to remove potential classifier drifts. The sign of the filtered signal was then used for classification. We calculated the percentage of correct classifications as accuracy measure. The three initial EPI-images of a block of a category were excluded from accuracy calculation in each pair-wise analysis to compensate for the delay of the BOLD response. In order to test for statistical significance of accuracies obtained within and between groups we used one-sample (sign test) and two-sample (1000 random permutations of group assignments) permutation tests for location parameters as described by Good (2005). In order to rule out that the decoding of picture categories relied on subject motion inherent to the fMRI signal, we also conducted a pattern classification analysis based on the realignment parameters obtained by the realignment algorithm. Please see Supplementary material for full technical details regarding pattern recognition analysis 1. Pattern recognition analysis 2: between-subject decoding of the clinical status In this analysis we searched for brain regions that encode information on the clinical status of subjects (ten OCD patients [nOCD = 10]; ten healthy controls [nHC = 10]) using pattern classification techniques. For that we used the DV of each subject determined in pattern recognition analysis 1 for the separation of fear-inducing pictures from neutral pictures, and disgust-inducing from neutral pictures. Prior to the analysis, the DVs were normalized to the anatomic standard space of the Montreal Neurological Institute (MNI; TzourioMazoyer et al., 2002) brain template. Normalization was performed using SPM8. The software registered the first EPI image of a series of a subject to the EPI brain template included in SPM8. Then, the software applied the transformation parameters determined during registration to the DV of the given subject and mapped it to the template space. To identify diagnostically informative areas we used a searchlight approach (Haynes et al., 2007; Kriegeskorte et al., 2006) that searches across the brain for local patterns that are informative about the clinical status. For a given ‘center’ voxel cvi in the brain the searchlight is defined as a spherical cluster with a radius of three voxels surrounding a given center coordinate. Within this cluster of voxels the spatial pattern of weights is extracted from the DV of each subject. The patterns from all but one subject of each group (1… nOCD-1 and 1…nHC-1) are used as a ‘training dataset’ to train a linear support vector pattern classifier to distinguish between patterns from the two groups. The classifier is then tested by applying it to the pattern from the remaining ‘test’ subject of each group. This crossvalidation procedure was repeated ten times by leaving out the data of one subject of each group at a time from the training set (i.e., we applied 10-fold cross-validation). The percentage of correct diagnostic classifications was then noted at the coordinate cvi as the local diagnostic information related to the clinical groups. This was repeated for each center position cvi thus yielding a 3-dimensional map of diagnostic accuracy (Fig. 4). See Fig. 1 for an illustration of the technical procedure. Probabilities for obtained accuracies were calculated using the binomial distribution. Results report significant center coordinates cvi located inside several bilaterally defined regions of interest (ROI; i.e. the anterior cingulate cortex [ACC], the amygdala, the basal ganglia, the dorsolateral prefrontal cortex [DLPFC], hippocampus, insular cortex, the orbitofrontal cortex [OFC], parahippocampal gyrus, and finally thalamus). These ROI were previously discussed in the literature on OCD (e.g. Cardoner et al., 2011; Mataix-Cols et al., 2004; Schienle et al., 2005; Shapira et al., 2003). For further details on ROI please see Supplementary material. Results report classifiers that obtained

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Fig. 1. Technical display of the ‘searchlight’ classification approach used in the decoding of the clinical status. The searchlight approach searches across all coordinates located in the ACC, basal ganglia, DLPFC, insula, mediotemporal areas, OFC, and thalamus for subregions that are informative about the clinical status of subjects (ten OCD patients [nOCD = 10]; ten healthy controls [HC; nHC = 10]). (A) For a given center voxel cvi in the brain the searchlight is defined as a spherical cluster with a radius of three voxels surrounding that center coordinate. (B) A pattern of weights is extracted from the searchlight voxel cluster of the discriminating volume of each subject. The patterns from all but one subject of each group (1…nOCD-1 and 1…nHC-1) are used as a ‘training dataset’ to train a linear support vector machine classifier (LibSVM—http://www.csie.ntu.edu.tw/~cjlin/libsvm) with a fixed regularization parameter C = 1 to distinguish between patterns from the two groups. The classifier is then tested by applying it to the data from the remaining ‘test’ subject of each group (nOCD and nHC in the depicted example). This cross validation procedure was repeated ten times by leaving out the data of one subject of each group at a time from the training data set. (C) The resulting accuracy is an estimate of the local diagnostic information for the clinical status that was noted at the center position cvi of a given searchlight classifier. This was repeated for each coordinate cvi included and finally yielded a 3-dimensional diagnostic accuracy map.

a significant accuracy according to a family-wise-error (FWE) corrected threshold αFWE = α (0.05) divided by the sum of searchlight centers/ voxels contained in all ROI (37553) = 1.33 ∗ 10 − 6. Please note, that only classifiers with perfect diagnostic accuracy reached significance following this criterion. In order to validate the parametric procedure used to assess probabilities of accuracies, we compared the results obtained with this method to results obtained with a non-parametric test. Here, we first determined the probability of searchlight accuracies using an empirical accuracy distribution obtained with a permutation test (1000 random permutations of class labels; see Mourão-Miranda et al., 2005). This was done for 1000 randomly selected searchlight classifiers. Then, we calculated the Pearson correlation coefficient for p-values determined with both methods and Cohen's d as an effect size measure (Cohen, 1988) to evaluate their similarity/dissimilarity. Please note that we focus on the approach of multiple local searchlight classifiers instead of one large-scale classifier in pattern recognition analysis 2. However, we also used a standard largescale decoding procedure in order to evaluate the performance of our searchlight method. Within this approach the DV of a subject for a given pair of pictures categories was treated as one high-dimensional pattern. Accuracies obtained in the large-scale approach were tested

for statistical significance using a permutation test (1000 random permutations of class labels). Apart from that, all steps were identical to the searchlight approach described above. Results Self-report data The analysis of self-report data indicated that patients differed significantly from HC in the perception of affective pictures. Compared to healthy controls, OCD patients reported significantly stronger feelings of fear and disgust in response to both affective picture categories. See Fig. 2. Pattern recognition analysis 1: intra-subject decoding of picture categories In pattern recognition analysis 1, we obtained a significant accuracy for the separation of fear-inducing from neutral stimuli in both groups. Specifically, we obtained a median accuracy of md= 70% in patients (min = 53%; max = 85%; p = 0.002) and md = 66% in controls (min = 52%; max = 77%; p = 0.002). For the separation of disgustinducing from neutral pictures we obtained a median accuracy of

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Fig. 2. Ratings for the stimulus material. *: p b 0.05. The ratings were assessed directly after the imaging procedure. The subjects rated the pictures of all categories by means of 9-point scales for valence (1 = ‘very negative’, 9 = ‘very positive’), arousal (1= ‘not at all’, 9 = ‘very strong’), and disgust and fear induction (both: 1 = ‘not at all’, 9 = ‘very strong’).

md= 70% (min = 42%; max = 99%; p = 0.180) in OCD patients. Only in healthy controls, accuracy was significant at the group level. In this group, median accuracy was md= 66% (min = 34%; max = 97%; p = 0.022). For the separation of fear-inducing stimuli from disgust inducing we obtained a median of md= 59% (min = 33%; max = 81%; p = 0.344) in patients and a median of md= 67% (min = 42%; max = 75%; p = 0.109) in controls. Permutation tests for two-sample location parameters showed that separation of picture categories did not differ between groups (fear-inducing vs. neutral pictures p = 0.264; disgust-inducing vs. neutral pictures p = 0.402; disgust-inducing vs. fear-inducing pictures p = 0.226). Fig. 3 summarizes the results. Please note that we compared the classification results obtained with the model validation method used here (which can be labeled as ‘train on first half, test on second half’) to results obtained with a standard 6-fold cross validation approach in the Supplementary

material. Finally, we conducted a permutation test for each group and a pair of picture categories based on the accuracies obtained for the decoding of stimuli based on motion parameters. It turned out that head motion did not play a role, as we did not obtain significant results in any of the permutation tests. Pattern recognition analysis 2: between-subject decoding of the clinical status For the decoding of the clinical status (OCD patient vs. healthy control) based on DVs calculated for the separation of fear-inducing vs. neutral pictures we obtained significant results in two coordinates of the right OFC [MNI: 6, 56, − 10; 6, 62, −10] and one coordinate located in the left caudate nucleus [MNI: −14, 18, 2]. The diagnostic accuracy obtained in these regions was 100% (p b 10 − 6). For the diagnostic classification based on DVs calculated for the separation of disgust-inducing vs. neutral pictures no significant results were obtained as no classifier reached the criterion of perfect diagnostic separation. Please see Fig. 4 for a more comprehensive overview on diagnostic accuracy obtained in analysis 2. Finally, Pearson correlation coefficients and Cohen's d computed for probabilities of accuracies obtained with the parametric and the non-parametric tests revealed that both methods nearly lead to identical results (analysis based on fear from neutral picture separation: r = 0.983, d = 0.026; analysis based on disgust from neutral picture separation: r = 0.980, d = 0.002). To assess the suitability of our local searchlight approach, we also conducted a large-scale classification procedure using the full DVs of individual subjects as patterns to derive a benchmark. The results obtained by the latter approach were non-significant for both analyses (fear vs. neutral: accuracy = 45%, p = 0.647, 40% sensitivity, 50% specificity; disgust vs. neutral: accuracy = 50%, p = 0.566, 50% sensitivity, 50% specificity). Discussion

Fig. 3. Accuracy obtained for the decoding of picture categories separated by participant group and pair of picture categories. We calculated the percentage of correct classifications as accuracy measure. The central mark on each box is the median, the lower edge is the first quartile the upper edge the third quartile. The whiskers are defined as the third quartile plus 1.5 times the interquartile range for values larger than the median and the first quartile minus 1.5 times the interquartile range for values smaller than the median.

In the present study, we showed that even very brief fMRI time samples (i.e. one TR) encode information about generic fear-inducing stimuli enabling accurate classification of corresponding fMRI activity patterns in OCD patients and healthy controls. Differences in encoding of these stimuli contained diagnostic information about the clinical status of the participants.

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Fig. 4. Diagnostic accuracy maps obtained for the decoding of the clinical status. (A) depicts the local searchlight classifier accuracy obtained for the separation of OCD patients from controls based on discriminative volumes calculated for the separation of fear-inducing vs. neutral pictures in analysis 1. Indices in the lower right of each transversal slice report the z-coordinate of this slice in the standard space of the Montreal Neurological Institute brain template (Tzourio-Mazoyer et al., 2002). For better comprehensibility only each second slice of the accuracy map is displayed. (B) shows the regions of interest investigated. ACC, anterior cingulate cortex; AMY, amygdala; DLPFC, dorsolateral prefrontal cortex; GP, globus pallidus; HIP, hippocampus; INS, insular cortex; NCD, caudate nucleus; OFC, orbitofrontal cortex; PHI parahippocampal gyrus; PUT, putamen; THA, thalamus.

We conducted two pattern recognition analyses. In pattern recognition analysis 1, we investigated whether we can decode information on the presence of a currently viewed picture category based on activity patterns of single fMRI time samples. This was done using a large-scale decoding approach based on patterns extracted from extended brain areas. The results showed that separation of fear-inducing vs. neutral stimuli was successful in the OCD group. This finding is in accordance with results from our recent study on eating-disorders (Weygandt et al., 2011b) and underlines that it is possible to decode brain states that have a special relevance for disordered subjects even when the decoding is only based on single fMRI scans. In pattern recognition analysis 2, we searched for brain regions that encode diagnostic information for the separation of OCD patients and healthy controls. This was done with a searchlight decoding approach based on DVs calculated for the separation of affective and neutral pictures in analysis 1. Significant and perfect diagnostic accuracy was obtained in searchlight classifiers centered in the OFC and the caudate nucleus for the separation based on DVs calculated for fear-inducing vs. neutral pictures (see Fig. 4). The identification of the OFC is in accordance with the prevailing model for OCD proposed in the literature (e.g. Baxter et al., 1992; Saxena et al., 1998) and results from studies demonstrating an important role of this region in the modulation of responses to fear-inducing stimuli. Fox et al. (2010) showed that lesions of the OFC lead to a decrease of the freezing reaction in a fear-conditioning paradigm in the macaque. Thus, separability of OCD patients and healthy controls based on OFC patterns might suggest that OCD patients show an inadequate response to fear-inducing stimuli. Identification of the caudate nucleus again fits nicely to the best-established model for OCD and to results from neuroimaging studies investigating processing of disorder-relevant stimuli in OCD patients. Within the central model for OCD (e.g. Baxter et al., 1992; Saxena et al., 1998), caudate hyperactivity in patients leads to insufficient thalamic information filtering which in

turn leads to explicit processing of this information in frontal regions in patients instead of implicit processing which then causes the intrusions observed in OCD. Results from Mataix-Cols et al. (2004) and Rauch et al. (1994) suggest that this process is even more pronounced during the presence of disorder-relevant emotional stimuli. Finally, by revealing areas of perfect diagnostic separation, the searchlight approach clearly outperformed the large-scale approach in the diagnosis of subjects based on the separation of generic fear-inducing from neutral pictures. This is consistent with findings of our recent study (Weygandt et al., 2011b), and suggests that the local approach is better suited to deal with specific obstacles coupled to between-subject decoding (e.g. image misalignment due to inter-individual differences of neuroanatomy). An interesting future application of the described techniques could be a classifier-based fMRI neurofeedback training for OCD. Traditionally, OCD patients are confronted with disorder-specific material during cognitive behavior therapy, which constitutes the most widely used psychotherapeutic intervention method for this disorder (Franklin and Foa, 2011). Yet, this strategy is very demanding for the patients and often leads to discontinuation of treatment. Our findings suggest that it might also be a promising treatment approach to expose OCD patients to generic fear stimuli in a classifier-based fMRI neurofeedback paradigm, since the results from the self-report data and also from Foa et al. (1993) showed a heightened sensitivity to generic fear-stimuli in patients. Moreover, the confrontation with generic affective stimuli is less stressful than confrontation with disorder-specific stimuli and might therefore be associated with greater compliance. In the neurofeedback paradigm, patients should learn to produce brain states during presentation of fear-inducing stimuli that are similar to states emerging during presentation of affectively neutral stimuli. This should be achieved by presenting classifier-based feedback indicating the similarity of a currently produced pattern with patterns acquired during passive perception of fear-inducing and affectively neutral pictures in a training stage.

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Target areas for the neurofeedback paradigm would be the OFC and the caudate nucleus due to the diagnostic information contained in the patterns of these areas. Such neuronal regulation training might be a first step of therapy. Considering that the targeted brain areas are key regions for the OCD symptomatology, it is conceivable that the effect generalizes to processing of disorder-specific material. The applicability of the proposed approach depends on several requirements being met. First, classification of stimuli of interest has to work accurately in an experiment without feedback. Otherwise, it could not be inferred from low accuracy in a feedback-experiment (which would follow from brain activity manipulation conforming to the instruction) that it is indicative of neurofeedback-learning. Alternatively, it could also result from e.g., a poor signal-to-noise ratio of the data, the selection of an inappropriate separation function, or dynamic changes of the mapping of mental states onto brain activity across training and test. Accuracies obtained for the separation of fear-inducing from neutral pictures suggest that this requirement is met. Second, it must be guaranteed that decoding does not rely on effects of no interest such as subject head motion. Otherwise, subjects could learn to manipulate these effects to modulate the feedback signal. The results of the present study showed that head motion did not play an important role. However, although our results show that these two requirements are met, there are further questions that could not be addressed properly by the study. First, it has to be clarified whether OCD-related brain states can be modulated by fMRI neurofeedback. Baxter et al. (1992) could show that it is basically possible to modify OCD by behavioral interventions. These authors could show that behavioral therapy is similarly efficient in reducing OCD symptoms and functional dysregulation in patients as a selective serotonin reuptake inhibitor (Fluoxetine). Yet, whether this also holds for neurofeedback interventions is unclear. However, there is evidence that fMRI neurofeedback is able to attenuate symptom severity in chronic pain patients. In a study of DeCharms et al. (2005), these patients reported decreased baseline pain perception after an fMRI neurofeedback intervention aiming to modulate the activity of the rostral anterior cingulate cortex. Second, it has to be clarified how accuracy obtained in intrasubject decoding of affective picture categories can be improved. Although successful separation of fear and neutral pictures in patients and successful diagnostic separation based on DVs calculated for these conditions is in line with the classification of OCD as an anxiety disorder (APA, 1994), the results for the separation of disgust and neutral pictures in patients are not optimal. First, the emotion disgust is considered one of six basic human emotions (Ekman, 1992). Second, there is a variety of studies that were able to identify neural correlates of disgust in OCD patients (e.g., Mataix-Cols et al., 2004; Schienle et al., 2005; Shapira et al., 2003). The rather low accuracy obtained for this comparison most likely relies on a comparably low signal-to-noise ratio (SNR) of activation patterns. The SNR of patterns is low, since they are extracted from extended brain areas and single fMR images that were acquired with an MR scanner having 1.5 Tesla. One way to increase the SNR of activation patterns and thus the accuracy obtained might be to use an MR scanner with a stronger magnetic field. In line with this assumption, studies using 3 Tesla scanners and similar paradigms and decoding approaches obtained slightly higher accuracies (LaConte et al., 2007; Sitaram et al., 2010). In order to address this aspect, we are currently integrating our software into the real-time neuroimaging package developed by Cusack et al. (2011) that will be run on a 3 Tesla scanner (Magnetom Trio, Siemens, Erlangen, Germany). The situation is somewhat different for the separation of fear- from disgust-inducing pictures, as several studies showed that stimuli corresponding to these emotions lead at least partially to activity in the same brain areas (e.g., Schienle et al., 2002; Stark et al., 2007; Winston et al., 2003). Under these conditions, separability relies only on a small fraction of activation patterns that already have a low SNR per se. Consequently, low accuracy in

separation of fear- and disgust-inducing pictures depends on a mixture of factors that can be modified (MR hardware) but also on factors that cannot be modified (regional overlap of activity). Finally, by identifying diagnostic neurobiological markers of OCD, we were able to add information to the clinical validity of this disorder category in the sense proposed by Robins and Guze (1970) demonstrating separability of patients from controls based on functional neuroimaging data. Thus, the results presented in this study might be a first step in providing biological diagnostic markers for OCD that could be integrated in future diagnostic systems, e.g. a future version of the DSM as proposed by Regier et al. (2009). Moreover, considering that the fear stimuli used in this study were not OCDspecific but nevertheless enabled an accurate patient classification, it seems possible that other anxiety disorders might be classified as well based on this method and stimulus material. In this sense, multivariate fMRI pattern recognition might provide a suitable screening tool for the presence of various anxiety disorders by using one common, disorder-unspecific set of stimuli (e.g., generic fear pictures). However, at the moment it is unclear whether such an approach would also be able to separate between different anxiety disorders or whether it would only detect ‘disordered’ versus ‘healthy’. Thus, future studies should be conducted including anxiety disorders other than OCD in order to address the question whether the approach is also able to perform differential diagnoses. Furthermore, a future study might control more strictly for antidepressant medication than the present study. Here, varying dosages of antidepressants were applied. However, we do not believe that differences in medication across patients had an important effect on the results of the present study for two reasons. First, application of antidepressant medication typically leads to a reduction of OCD symptoms as well as a normalization of dysregulated neuronal processes after treatment (e.g., Baxter et al., 1992). Correspondingly, if medication leads to erroneous signal detection it is much more likely that it leads to an underestimation of existing effects than to false positive findings. Second, the dosage of antidepressant treatment was very small (10, 25, or 75 mg Clomipramin). These dosages are considered inadequate for effectively reducing OCD symptoms. Generally, 150 mg to 250 mg are most effective. Conclusion To summarize, we have shown that even very brief fMRI time samples encode information about OCD-relevant fear stimuli facilitating accurate classification of corresponding brain patterns on a TR-by-TR basis. This implies that the requirements are met to conduct a future fMRI real-time paradigm for the treatment of OCD using classifier-based neurofeedback. Furthermore, we were able to identify neurobiological markers of high diagnostic information for OCD using multivariate pattern classification techniques. Thus, we were able to add information to the clinical validity of OCD in the sense proposed by Robins and Guze (1970), demonstrating separability of patients from controls based on functional neuroimaging data. Correspondingly, this might be a first step in providing biological diagnostic markers for OCD that could be integrated in future diagnostic systems, e.g. a future version of the DSM as proposed by Regier et al. (2009). Role of the funding source This work was funded by a clinical research group (KFO218/1) of the German Research Foundation, the Bender Institute of Neuroimaging, and the Bernstein Computational Neuroscience Program of the German Federal Ministry of Education and Research (grant number 01GQ1001C). The funding sources had no involvement in the study design, the collection, analysis, and interpretation of data, in

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