Inclusion of studies

Included articles were obtained from exhaustive searches by the investigators in the Medline, PubMed, ScienceDirect and Scopus databases using the keywords ‘obsessive–compulsive disorder’ plus ‘morphometry’, ‘voxel-based’ or ‘voxelwise’, as well as from hand searching in the reference lists of obtained articles. In addition, the authors contacted 303 worldwide OCD experts by email ^{Reference Mataix-Cols, Pertusa and Leckman7} requesting any unpublished voxel-based morphometry study in OCD that they wished to include in this meta-analysis. Studies comprising less than 10 individuals with OCD ^{Reference Cecconi, Lopes, Duran, Santos, Hoexter and Gentil8} and studies that re-analysed previously published data ^{Reference Cardoner, Soriano-Mas, Pujol, Alonso, Harrison and Deus9,Reference Soriano-Mas, Pujol, Alonso, Cardoner, Menchon and Harrison10} were not included. Twelve studies performing whole-brain voxel-based comparisons of grey matter between individuals with OCD and healthy controls and completed before 1 December 2008 were identified and included in the meta-analysis. These included 11 published papers and a previously unpublished analysis of a published paper (Soriano-Mas et al, ^{Reference Soriano-Mas, Pujol, Alonso, Cardoner, Menchon and Harrison10} new sample). The corresponding authors were contacted by email requesting any details not included in the original publications. After contacting the authors, no methodological ambiguities remained regarding the design or analysis of any of the studies. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) guidelines ^{Reference Stroup, Berlin, Morton, Olkin, Williamson and Rennie11} are followed in our study.

Global differences in grey matter volume

Meta-analytical estimates of the differences in global grey matter volumes between the participants with OCD and the controls were calculated using both fixed and random-effects models with RevMan version 5 for Linux (The Nordic Cochrane Centre, Copenhagen). A previous heterogeneity analysis was performed to test if the observed variance across studies was larger than that resulting from sampling error alone.

Regional differences in grey matter volume

To understand how voxel-based meta-analyses work, it must first be noted that neuroimaging studies report, from each cluster of significant differences, the coordinates of the ‘voxel’ (i.e. the 3-dimensional pixel) where the difference between the participants with OCD and controls is maximum. The basic idea behind voxel-based meta-analyses is ‘counting’, for each voxel, how many times it is close ‘enough’ to the reported maxima and subsequently associating a probability to test the statistical significance. In other words, are there more studies reporting coordinates near that voxel than would be expected by chance? Two main different methods are currently available to perform meta-analyses of neuroimaging data, known as activation likelihood estimate (ALE) ^{Reference Turkeltaub, Eden, Jones and Zeffiro12} and multilevel kernel density analysis (MKDA). ^{Reference Wager, Lindquist and Kaplan13} These two methods differ in a number of important ways and each has their own strengths and limitations. Here we describe a new approach, signed differential mapping (SDM), which adopts and combines the various positive features of these two methods. In addition, SDM introduces a series of improvements and novel features that are summarised in the Appendix and explained in detail below.

The first improvement introduced by SDM is a stricter selection of the reported peak coordinates to ensure that only regions that appear statistically significant at the whole-brain level are considered for inclusion in the meta-analysis. This strict criterion is intended to avoid biases towards liberally thresholded brain regions because it is not uncommon in neuroimaging studies that the statistical threshold for some regions of interest is rather more liberal than for the rest of the brain. Another improvement related to the selection of peak coordinates is the systematic preference for results that are corrected for multiple comparisons. Signed differential mapping establishes the following order of preference:

1. (a) whole-brain analyses with correction for multiple comparisons and at least one statistically significant coordinate;

2. (b) whole-brain analyses without correction for multiple comparisons and at least one statistically significant coordinate;

3. (c) whole-brain analyses with no statistically significant coordinates;

4. (d) if none of (a)–(c) apply, the study is discarded.

Inclusion of analyses without correction for multiple comparisons does not bias the probability of finding significant results because the statistical analysis controls for the number of coordinates as described below. Coordinates reported in MNI (Montreal Neurological Institute) space are converted to Talairach space using the matrix transformations proposed by Lancaster, ^{Reference Lancaster, Tordesillas-Gutierrez, Martinez, Salinas, Evans and Zilles14} which have been shown to be more exact than earlier methods. ^{Reference Brett15} Coordinates reported in Talairach space which had been converted using earlier methods, are converted back to MNI space and subsequently converted to Talairach space using the matrix transformations.

Once the coordinates are selected and converted, a map of the differences in grey matter is separately recreated for each study. This consists of assigning a value to the voxels close to each of the reported coordinates within a grey matter map (based on the Talairach Daemon, ^{Reference Lancaster, Woldorff, Parsons, Liotti, Freitas and Rainey16} voxel size 2 × 2 × 2 m ^{Reference Scarone, Colombo, Livian, Abbruzzese, Ronchi and Locatelli3} ). Signed differential mapping uses a 25 mm full-width at half maximum (FWHM) un-normalised Gaussian kernel – it must be noted that this kernel is different in nature from the smoothing kernel used to smooth raw magnetic resonance images, since it is not intended to smooth any image but to assign indicators of proximity to reported coordinates. This kernel is adapted from that of ALE and preferred to that of MKDA because it assigns a higher value to the voxels closer to the reported coordinates. Full-width at half maximum is set at 25 mm because in previous simulations we found it to have an excellent control of false positives – consistent with our simulation work, a recent study has reported that the optimal FWHM for the previous methods is about 15–30 mm. ^{Reference Salimi-Khorshidi, Smith, Keltner, Wager and Nichols17}

When a voxel can be assigned values from more than one coordinate in the same study these values are summed. An important downside of the sum of values is a bias towards studies reporting various coordinates in close proximity, as voxels can achieve rather large values. ^{Reference Wager, Lindquist and Kaplan13} Multilevel kernel density analysis elegantly overcomes this problem by limiting the values within one study to a maximum and SDM also incorporates this feature. A novelty of our method is that both positive and negative coordinates (i.e. both increases and decreases of grey matter) are reconstructed in the same map, resulting in a signed differential map. This is an important feature that prevents a particular voxel erroneously appearing to be positive (i.e. increased volume or activation) and negative (i.e. decreased volume or activation) at the same time. This problem is often seen in published studies using previous methods, e.g. Menzies et al. ^{Reference Menzies, Chamberlain, Laird, Thelen, Sahakian and Bullmore18}

Once all the studies have their signed differential map created, a meta-analytic signed differential map is calculated. It must be noted that individual signed differential maps do not account for the variability within each study, indeed this is not reported, so that usual meta-analytic calculations are not applicable. However, MKDA overcomes this issue by defining the meta-analytic value of a voxel as the proportion of studies reporting a coordinate around the voxel (weighted by the squared root of the sample size of each study so that studies with larger samples contribute more). Thus, the question to answer at each voxel is – are there more studies reporting coordinates around that voxel than would be expected by chance? In order to adjust the approach of MKDA to our method we calculate the mean instead of the proportion of studies, although the meaning does not change.

Finally, a null distribution of the meta-analytic values is created to test which voxels have more studies reporting differences of grey matter around them than expected by chance. This is performed by means of Monte Carlo randomisations of the location of the coordinates (within a mask of grey matter plus 8 mm of white matter). The null distribution is generated at the whole-brain level to maximise statistical stability with relatively reduced computation time (almost 40 million values are obtained with 500 randomisations). We focus on results with uncorrected P<0.001 because we found in the previous simulations that uncorrected P<0.001 or even 0.002 was, in our method, empirically equivalent to corrected P<0.05. However, we also formally correct for multiple comparisons by means of the false discovery rate. ^{Reference Genovese, Lazar and Nichols19}

Descriptive analysis of quartiles

The standard randomisation test described above checks if there are more studies reporting coordinates in a particular region than in the rest of the brain. However, this information is incomplete without describing the actual proportion of studies reporting coordinates in the region (e.g. it is not the same that half of the studies report changes in a particular region than if only 5% do). To overcome this issue, a descriptive analysis of quartiles is conducted, e.g. values higher than 0 in the second quartile (median) map mean that at least 50% of the studies found increases of grey matter near the voxel. Once again, the calculations are weighted by sample size to make the studies with large samples contribute more. As this analysis could be biased by the inclusion of studies that did not correct for multiple comparisons, only studies that performed such corrections are included. ^{Reference Soriano-Mas, Pujol, Alonso, Cardoner, Menchon and Harrison10,Reference Carmona, Bassas, Rovira, Gispert, Soliva and Prado20–Reference van den Heuvel, Remijnse, Mataix-Cols, Vrenken, Groenewegen and Uylings27}

Sensitivity analysis

In order to test the replicability of the results, a systematic whole-brain voxel-based jackknife sensitivity analysis is conducted. This consists of repeating the main statistical analysis 12 times but discarding one different study each time, i.e. removing one study and repeating the analyses, then putting that study back and removing another study and repeating the analysis, and so on. The rationale of this test is that if a previously significant brain region remains significant in all or most of the combinations of studies it can be concluded that this finding is highly replicable.

Analyses of subgroups

In order to control for any possible methodological differences observed between the studies, the analysis is repeated several times including only those studies which are methodologically homogenous. Therefore, the analysis is repeated for those studies which acquired the images with a slice thickness of 1.2–1.5 mm, for those using a 12 mm smoothing kernel, for those performing parametric and voxel-based statistical tests, for those performing an additional modulation step (i.e. inference of absolute grey matter volume instead of grey matter density), for those reporting coordinates corrected for multiple comparisons, and for those that included adult participants; unfortunately, paediatric studies were too few to be analysed separately. Subgroup analyses regarding the procedure used for controlling for global volumes were not possible, as there were not enough studies using the same approach. Separate analysis depending on the magnetic strength of the scanner was not required as all studies used 1.5 tesla scanners.

Meta-regression

The potential effect of several relevant sociodemographic and clinical variables is examined by means of simple linear regression, weighted by the squared root of the sample size and restricted to only predict possible SDM values (i.e. from −1 to 1) in the observed range of values of the variable. The main output for each variable is a map of the regression slope (e.g. the amount of grey matter change per unit increase in mean Yale–Brown Obsessive–Compulsive Scale (YBOCS) score). ^{Reference Goodman, Price, Rasmussen, Mazure, Fleischmann and Hill28} In order to reduce spurious results, we only report those clusters showing a significant trend across participants with OCD along with a predicted significant difference with healthy individuals in studies at one of the extremes (e.g. a predicted significant grey matter difference with healthy individuals in studies with maximum YBOCS). Because of the small number of studies included in this meta-analysis and the number of regression models tested (n = 6), a strict control of false positives is applied (Bonferroni correction: P = 0.001/6 = 0.00017). However, the meta-regression results should be taken with some caution because of the limited variability in the data.

Variables explored by regression are the mean age, the mean YBOCS, the percentage of participants with a major depressive disorder and the percentage of individuals receiving current antidepressant medication. The percentage of people receiving current antipsychotic medication was not considered because it was the same (0%) in all the studies. The following variables could not be studied because data was available for fewer than nine studies: mean years of education, mean age at onset, illness duration, percentage of participants with anxiety disorders other than OCD and percentage of participants having received past antidepressant or antipsychotic medication.

Computational aspects

Software for all the computations was developed by J.R. by means of a C language program accessing a SQL database (www.mysql.com/). The original Ubuntu GNU/Linux (www.ubuntu.com/) version, as well as other operative system adapted versions is freely available at www.sdmproject.com/software/.

Included studies

Twelve studies were included comprising 401 individuals with OCD and 376 healthy controls. Of the 12 studies, 9 consisted of adult OCD samples and 3 of paediatric samples. The demographic and clinical characteristics of the participants are shown in Table 1. Further details and methodological aspects of each of the included studies can be found at www.sdmproject.com/database/.

Table 1

Demographic and clinical characteristics of the 12 studies included in the meta-analysis

	Sociodemographic characteristics																			Clinical characteristics: OCD participants only
	n			Age, years: mean			Males, %			Right handed %			Years education %
	OCD	Controls	OCD	Controls	OCD	Controls	OCD	Controls	OCD	Controls	Age at onset Years: mean	Illness duration Years: mean	YBOCS score: mean	Major depressive disorder %	Anxiety %	Anti-depressant %
Carmona et al Reference Carmona, Bassas, Rovira, Gispert, Soliva and Prado20	18	18	12.9	13.0	72	72	83	83	N/A	N/A	N/A	N/A	21.4	0	33	56
Christian et al Reference Christian, Lencz, Robinson, Burdick, Ashtari and Malhotra21	21	21	38.0	38.9	71	71	67	76	14.6	14.9	N/A	N/A	27.0	33	N/A	81
Gilbert et al Reference Gilbert, Mataix-Cols, Almeida, Lawrence, Nutche and Diwadkar22	25	20	37.5	29.8	52	45	N/A	N/A	N/A	N/A	29.5	8.0	26.9	36	24	80
Gilbert et al Reference Gilbert, Keshavan, Diwadkar, Nutche, Macmaster and Easter23	10	10	12.9	13.4	60	60	100	100	N/A	N/A	N/A	N/A	26.5	0	N/A	0
van den Heuvel et al Reference van den Heuvel, Remijnse, Mataix-Cols, Vrenken, Groenewegen and Uylings27	55	50	33.7	31.4	29	40	89	90	N/A	N/A	N/A	N/A	22.8	18	N/A	0
Kim et al Reference Kim, Lee, Kim, Kim, Kim and Han29	25	25	27.4	27.0	68	68	96	96	14.2	15.3	19.0	8.4	24.2	16	0	0
Pujol et al Reference Pujol, Soriano-Mas, Alonso, Cardoner, Menchon and Deus24	72	72	29.8	30.1	56	56	85	85	13.2	14.0	17.0	13.0	26.7	36	19	75
Riffkin et al Reference Riffkin, Yucel, Maruff, Wood, Soulsby and Olver25	18	18	36.1	34.6	44	44	94	94	12.1	13.4	N/A	N/A	23.3	N/A	N/A	17
Soriano-Mas et al Reference Soriano-Mas, Pujol, Alonso, Cardoner, Menchon and Harrison10	30	30	31.9	31.8	70	53	93	90	12.2	13.1	19.7	11.3	21.0	13	23	87
Szeszko et al Reference Szeszko, Christian, Macmaster, Lencz, Mirza and Taormina30	37	26	13.0	13.0	38	35	57	65	N/A	N/A	9.4	3.6	24.9	0	24	0
Valente et al Reference Valente, Miguel, Castro, Amaro, Duran and Buchpiguel26	19	15	32.7	32.3	53	47	89	73	11.7	10.4	14.4	18.3	24.6	47	84	58
Yoo et al Reference Yoo, Roh, Choi, Kang, Ha and Lee31	71	71	26.6	26.7	66	66	96	100	N/A	N/A	18.6	8.0	22.8	6	4	83

OCD, obsessive—compulsive disorder; YBOCS, Yale—Brown Obsessive—Compulsive Scale; N/A, not available.

Global grey matter volumes

Global grey matter volumes were obtained from seven studies including 297 individuals with OCD and 277 healthy controls with similar characteristics to those of the overall sample. ^{Reference Carmona, Bassas, Rovira, Gispert, Soliva and Prado20,Reference Pujol, Soriano-Mas, Alonso, Cardoner, Menchon and Deus24,Reference Valente, Miguel, Castro, Amaro, Duran and Buchpiguel26,Reference van den Heuvel, Remijnse, Mataix-Cols, Vrenken, Groenewegen and Uylings27,Reference Kim, Lee, Kim, Kim, Kim and Han29–Reference Yoo, Roh, Choi, Kang, Ha and Lee31} Heterogeneity analysis revealed that variance across studies was not only as a result of sampling error alone (χ² = 13.83, d.f. = 6, P = 0.03). No differences in global grey matter volume were found between individuals with OCD and healthy controls using both fixed and random-effects models (Z = 1.56, P = 0.12 and τ² = 315.59, Z = 1.00, P = 0.32 respectively).

Regional differences in grey matter

Coordinates for the SDM analyses were obtained from all the 12 studies representing 401 individuals with OCD and 376 healthy controls (Fig.1). As shown in Table 2 and Fig. 2, individuals with OCD had significant bilateral (larger on the left) grey matter volume increases in the lenticular nucleus (mainly ventral anterior putamen) extending to the caudate nucleus, as well as in a small region in the right superior parietal lobule (Brodmann area 7). Participants with OCD also showed significant bilateral (larger on the right) grey matter volume decreases in dorsal medial frontal/anterior cingulate gyri, extending to the supplementary motor area and frontal eye fields (Brodmann area 8, 32, 6 and 9).

Table 2

Regional differences in grey matter volume between individuals with obsessive–compulsive disorder and healthy controls

	Maximum					Cluster
	Talairach coordinates	SDM value	Uncorrected P	Number of voxels	Breakdown (number of voxels) a
Clusters of increased grey matter
Left lenticular nucleus (mainly anterior putamen)	-18, 8, 0	0.248	0.000005	506	Left lenticular nucleus (464)
					Left caudate nucleus (41)
					Left subcallosal gyrus (1)
Right superior parietal lobule and precuneus	14, -60, 62	0.210	0.00009	75	Right Brodmann area 7 (75)
Right lenticular nucleus (mainly anterior putamen)	14, 10, -2	0.187	0.0003	68	Right lenticular nucleus (54)
					Right caudate nucleus (14)
Clusters of decreased grey matter
Right/left dorsal medial frontal gyri/anterior cingulate gyri	4, 28, 36	-0.278	0.00002	385	Right Brodmann area 8 (93)
					Right Brodmann area 32 (96)
					Right Brodmann area 6 (34)
					Right Brodmann area 9 (22)
					Left Brodmann area 8 (59)
					Left Brodmann area 32 (41)
					Left Brodmann area 6 (26)
					Left Brodmann area 9 (14)

SDM, signed differential mapping.

a. Brodmann area 6: supplementary motor cortex; Brodmann area 7: somatosensory association cortex; Brodmann area 8: frontal eye fields; Brodmann area 9: dorsal medial frontal gyrus; Brodmann area 32: dorsal anterior cingulate gyrus. All voxels with P < 0.001 uncorrected (SDM value thresholds of 0.163 for increases and of -0.183 for decreases). No significant voxels were found after the false discovery rate correction.

Descriptive analysis of quartiles

Decreases of grey matter in dorsal mediofrontal/anterior cingulate gyri were detected in the median analysis (maximum at Talairach (20, 32, 38), SDM −0.179), meaning that most of the studies had found some degree of decreased grey matter in the region. These findings were rather large as expected in the first quartile analysis (maximum at Talairach (2, 32, 40), SDM −0.978), including several nearby clusters.

Increases of grey matter in the left lenticular nucleus were only detected in the third quartile analysis (maximum at Talairach (–22, 16, 2), SDM 0.743), meaning that at least 25% but less than 50% of the studies had found some degree of increased grey matter in this region. In this analysis, no changes of grey matter were detected in the right lenticular nucleus.

Fig. 1

Plots of all the significant coordinates included in the meta-analysis (n = 12 studies). Reported peak coordinates of grey matter increases (obsessive–compulsive disorder (OCD)>controls; blue circles) and decreases (OCD<controls; white triangles) have been projected to the sagittal, coronal and axial planes.

Sensitivity analysis

As seen in Table 3, whole-brain jackknife sensitivity analysis showed that the grey matter increase in left lenticular nucleus and grey matter decrease in bilateral dorsal mediofrontal/anterior cingulate gyri were highly replicable, as these findings were preserved throughout all the 12 combinations of 11 studies. Grey matter increases in right lenticular nucleus and superior parietal cortex failed to emerge in two of the study combinations. No additional significant clusters were found in any of the 12 study combinations.

Table 3

Analyses of subgroups and sensitivity analyses ^a

	Increased grey matter
	L lenticular nucleus	R superior parietal lobule	R lenticular nucleus	Decreased grey matter R/L dMFG/ACG
Studies with slice thickness ≤ 1.5 mm at acquisition Reference Christian, Lencz, Robinson, Burdick, Ashtari and Malhotra21-Reference Gilbert, Keshavan, Diwadkar, Nutche, Macmaster and Easter23,Reference Riffkin, Yucel, Maruff, Wood, Soulsby and Olver25-Reference van den Heuvel, Remijnse, Mataix-Cols, Vrenken, Groenewegen and Uylings27,Reference Kim, Lee, Kim, Kim, Kim and Han29-Reference Yoo, Roh, Choi, Kang, Ha and Lee31 (n = 9)	Yes	Yes	No	No
Studies using a 12 mm smoothing kernel Reference Carmona, Bassas, Rovira, Gispert, Soliva and Prado20,Reference Gilbert, Mataix-Cols, Almeida, Lawrence, Nutche and Diwadkar22,Reference Pujol, Soriano-Mas, Alonso, Cardoner, Menchon and Deus24,Reference Valente, Miguel, Castro, Amaro, Duran and Buchpiguel26,Reference van den Heuvel, Remijnse, Mataix-Cols, Vrenken, Groenewegen and Uylings27,Reference Kim, Lee, Kim, Kim, Kim and Han29,Reference Yoo, Roh, Choi, Kang, Ha and Lee31 (n = 7)	Yes	No	No	Yes b
Studies with an additional modulation step Reference Soriano-Mas, Pujol, Alonso, Cardoner, Menchon and Harrison10,Reference Carmona, Bassas, Rovira, Gispert, Soliva and Prado20-Reference Gilbert, Mataix-Cols, Almeida, Lawrence, Nutche and Diwadkar22,Reference Pujol, Soriano-Mas, Alonso, Cardoner, Menchon and Deus24-Reference van den Heuvel, Remijnse, Mataix-Cols, Vrenken, Groenewegen and Uylings27,Reference Szeszko, Christian, Macmaster, Lencz, Mirza and Taormina30 (n = 9)	Yes c	No	Yes c	Yes
Studies performing parametric voxel-based statistical tests Reference Soriano-Mas, Pujol, Alonso, Cardoner, Menchon and Harrison10,Reference Carmona, Bassas, Rovira, Gispert, Soliva and Prado20-Reference Pujol, Soriano-Mas, Alonso, Cardoner, Menchon and Deus24,Reference Valente, Miguel, Castro, Amaro, Duran and Buchpiguel26,Reference van den Heuvel, Remijnse, Mataix-Cols, Vrenken, Groenewegen and Uylings27,Reference Kim, Lee, Kim, Kim, Kim and Han29-Reference Yoo, Roh, Choi, Kang, Ha and Lee31 (n = 11)	Yes	Yes	Yes	Yes
Studies with correction for multiple comparison Reference Soriano-Mas, Pujol, Alonso, Cardoner, Menchon and Harrison10,Reference Carmona, Bassas, Rovira, Gispert, Soliva and Prado20-Reference van den Heuvel, Remijnse, Mataix-Cols, Vrenken, Groenewegen and Uylings27 (n = 9)	Yes	No	Yes	Yes
Studies in adult individuals Reference Soriano-Mas, Pujol, Alonso, Cardoner, Menchon and Harrison10,Reference Christian, Lencz, Robinson, Burdick, Ashtari and Malhotra21,Reference Gilbert, Mataix-Cols, Almeida, Lawrence, Nutche and Diwadkar22,Reference Pujol, Soriano-Mas, Alonso, Cardoner, Menchon and Deus24-Reference van den Heuvel, Remijnse, Mataix-Cols, Vrenken, Groenewegen and Uylings27,Reference Kim, Lee, Kim, Kim, Kim and Han29,Reference Yoo, Roh, Choi, Kang, Ha and Lee31 (n = 9)	Yes	No	No	Yes
Jackknife sensitivity analysis, discarded study
Carmona et al Reference Carmona, Bassas, Rovira, Gispert, Soliva and Prado20	Yes	Yes	Yes	Yes
Christian et al Reference Christian, Lencz, Robinson, Burdick, Ashtari and Malhotra21	Yes	Yes	Yes	Yes
Gilbert et al Reference Gilbert, Mataix-Cols, Almeida, Lawrence, Nutche and Diwadkar22	Yes	Yes	Yes	Yes
Gilbert et al Reference Gilbert, Keshavan, Diwadkar, Nutche, Macmaster and Easter23	Yes	Yes	Yes	Yes
van den Heuvel et al Reference van den Heuvel, Remijnse, Mataix-Cols, Vrenken, Groenewegen and Uylings27	Yes	Yes	Yes	Yes
Kim et al Reference Kim, Lee, Kim, Kim, Kim and Han29	Yes	Yes	Yes d	Yes
Pujol et al Reference Pujol, Soriano-Mas, Alonso, Cardoner, Menchon and Deus24	Yes	Yes	No	Yes b
Riffkin et al Reference Riffkin, Yucel, Maruff, Wood, Soulsby and Olver25	Yes	Yes	Yes	Yes
Soriano-Mas et al Reference Soriano-Mas, Pujol, Alonso, Cardoner, Menchon and Harrison10	Yes	Yes	Yes	Yes b
Szeszko et al Reference Szeszko, Christian, Macmaster, Lencz, Mirza and Taormina30	Yes	No	No	Yes
Valente et al Reference Valente, Miguel, Castro, Amaro, Duran and Buchpiguel26	Yes	Yes	Yes	Yes
Yoo et al Reference Yoo, Roh, Choi, Kang, Ha and Lee31	Yes	No	Yes	Yes

R, right; L, left; dMFG, dorsal medial frontal gyri; ACG, anterior cingulate gyri;

a. Yes, brain region remains significantly increased/decreased in the subgroup analysis or after exclusion of the study in the jackknife analysis; no, brain region is no longer significantly increased/decreased in the subgroup analysis or after exclusion of the study in the jackknife analysis.

b. Maximum of the dorsal mediofrontal/anterior cingulate gyri cluster was displaced to right cingulate gyrus when only studies using a 12mm smoothing kernel were included, or when either S₇ or S₉ was not included in the analysis.

c. Increases in bilateral lenticular nuclei were significant after the false discovery rate correction in the analysis of studies performing an additional modulation step.

d. Maximum of the right lenticular nucleus cluster was displaced to right caudate nucleus when S₆ was not included in the analysis.

Analyses of subgroups

The above results remained largely unchanged when the analyses were repeated and limited to methodologically homogenous groups of studies (Table 3). Only one additional significant cluster in the left cerebellum (maximum at Talairach (–12, −46, −12), SDM 0.179, P = 0.0002) emerged in the subanalysis of studies reporting coordinates corrected for multiple comparisons.

Fig. 2

Main increased (a) and decreased (b) grey matter regions in individuals with obsessive–compulsive disorder compared with healthy controls, and usual targets of capsulotomy/deep brain stimulation (c) and cingulotomy (d). (a) Increased grey matter in lenticular nuclei and caudate, (b) decreased grey matter in dorsal mediofrontal/anterior cingulate gyri, (c) target of capsulotomy and deep brain stimulation, (d) target of cingulotomy. Images (a) and (c) are shown in the axial plane (Z = −2); images (b) and (d) are shown in the sagittal plane (X = 4). Note that the clusters of grey matter increase in bilateral lenticular nuclei include the usual targets of capsulotomy and deep brain stimulation. Similarly, the meta-analytic cluster of grey matter decrease in dorsal mediofrontal gyri/anterior cingulate gyri includes the usual target of cingulotomy. Significant clusters and surgery targets have been overlaid to an MRIcron template for Linux (www.mricro.com/mricron) for display purposes only.

Meta-regression

Regression analyses showed that mean age (available in all the studies) was not associated with OCD-related grey matter changes, at least linearly (slope smallest P = 0.0004). Symptom severity (YBOCS scores, available in all the studies) was associated with increased grey matter volumes in bilateral lenticular nuclei (left maximum slightly displaced to claustrum, Talairach (–24, 18, −6) and (20, 14, 0), SDM +0.106 and + 0.116 per 1 point increase in mean YBOCS score, P = 0.0001 and P = 0.00004), with predicted grey matter increase in studies including individuals with more severe symptoms (maxima at Talairach (–20, 14, −4) and (18, 12, 0), SDM 0.475 and 0.482, P = 0.000001 and P = 0.0000007) (Fig. 3).

The reported percentage of participants with comorbid major depressive disorder (available in all the studies but one, Riffkin et al) ^{Reference Riffkin, Yucel, Maruff, Wood, Soulsby and Olver25} was found to be negatively associated with grey matter volumes in the right superior parietal lobule (maximum at Talairach (14, −58, 62), SDM −0.146 per 10% increase in the percentage of people with comorbid major depressive disorder, P = 0.00014), with predicted grey matter increase in studies reporting no individuals with comorbid major depressive disorder (maximum at Talairach (14, −60, 62), SDM 0.502, P = 0.000004). No effect of current antidepressant medication (available in all the studies) was detected (slope smallest P = 0.003).

Limitations

It is important to highlight several limitations of this study, some of which are inherent to all meta-analytical approaches. First, voxel-based meta-analyses are based on summarised (i.e. coordinates from published studies) rather than raw data and this may result in less accurate results. ^{Reference Salimi-Khorshidi, Smith, Keltner, Wager and Nichols17} However, obtaining and analysing the raw images from these studies is logistically and technically difficult. Second, despite our attempts to contact worldwide OCD experts and include as many unpublished voxel-based morphometry studies as possible, even if their results were negative, the possibility of publication bias cannot be entirely ruled out. Third, it must be noted that normal brain regions close to abnormal brain regions may artificially appear to be abnormal. Therefore, the breakdown of a cluster should not be understood as ‘all these regions are abnormal’ but as ‘one or more of these regions are abnormal’. Fourth, most of the results were only significant before correction for multiple comparisons by the false discovery rate. However, in previous simulation work, we established that uncorrected P<0.001 or even 0.002 was, in our method, empirically equivalent to corrected P<0.05. Fifth, as mentioned above, our regression analyses should be taken cautiously because they included a small number of studies and variability in the data was limited. Similarly, we could not perform subgroup analyses by specific symptom subtypes or dimensions. This is important since OCD is likely to be aetiologically heterogeneous ^{Reference Mataix-Cols, Rosario-Campos and Leckman63} and preliminary evidence suggests that each of the major symptom dimensions of OCD may have partially distinct neural substrates. ^{Reference van den Heuvel, Remijnse, Mataix-Cols, Vrenken, Groenewegen and Uylings27,Reference Mataix-Cols, Wooderson, Lawrence, Brammer, Speckens and Phillips40,Reference Saxena, Brody, Maidment, Smith, Zohrabi and Katz64,Reference An, Mataix-Cols, Lawrence, Wooderson, Giampietro and Speckens65}

Main features of the signed differential mapping (SDM) method for voxel-based meta-analysis of neuroimaging data