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. 2011 Aug 31;33(9):2125–2134. doi: 10.1002/hbm.21341

Role of medial cortical networks for anticipatory processing in obsessive‐compulsive disorder

Kristina T Ciesielski 1,2,✉, Scott L Rauch 3, Seppo P Ahlfors 1,4, Mark E Vangel 1,4, Sabine Wilhelm 3, Bruce R Rosen 1,4, Matti S Hämäläinen 1,4
PMCID: PMC3235253  NIHMSID: NIHMS290479  PMID: 21882299

Abstract

Recurrent anticipation of ominous events is central to obsessions, the core symptom of obsessive‐compulsive disorder (OCD), yet the neural basis of intrinsic anticipatory processing in OCD is unknown. We studied nonmedicated adults with OCD and case matched healthy controls in a visual‐spatial working memory task with distractor. Magnetoencephalography was used to examine the medial cortex activity during anticipation of to‐be‐inhibited distractors and to‐be‐facilitated retrieval stimuli. In OCD anticipatory activation to distractors was abnormally reduced within the posterior cingulate and fusiform gyrus compared to prominent activation in controls. Conversely, OCD subjects displayed significantly increased activation to retrieval stimuli within the anterior cingulate and supplementary motor cortex. This previously unreported discordant pattern of medial anticipatory activation in OCD was accompanied by normal performance accuracy. While increased anterior cortex activation in OCD is commonly viewed as failure of inhibition, the current pattern of data implicates the operation of an anterior compensatory mechanism amending the posterior medial self‐regulatory networks disrupted in OCD. Hum Brain Mapp 33:2125–2134, 2012. © 2011 Wiley Periodicals, Inc.

Keywords: OCD, MEG, anticipatory top‐down control, posterior medial self‐regulatory networks, compensatory mechanism

INTRODUCTION

Anticipation provides a primary basis for mental regulation. In the visual domain, it involves top‐down processing engaging the frontal‐parietal‐extrastriate network. It serves as a memory‐driven attentional biasing for behaviorally relevant stimuli [Desimone and Duncan, 1995; Posner and Raichle, 1994]. Persistent anticipation of unwanted, disturbing events is an epitome of obsessions, the core symptom of obsessive‐compulsive disorder (OCD). Thus, understanding the neural mechanism of the anticipatory system sited in the medial brain is essential for understanding the pathophysiology of the disease. OCD is well defined by measures of obsessions, compulsions and lingering cognitive, emotional, and social deficits, but the mechanism of anticipatory networks has not been studied. We investigate the properties of intrinsic anticipatory dynamics in the medial cortex of nonmedicated subjects with OCD and case‐matched healthy controls to actively inhibited distractors and to facilitated retrieval stimuli.

High neuronal organization has been found within intrinsic brain activity [Raichle et al., 2001; Shulman et al., 1997]. A consistent network of regions has displayed deactivation in target‐oriented tasks and increased activation during intrinsic monitoring of ones own psychological state [Raichle and Mintun, 2006]. Commonly, this network involves the medial prefrontal area, posterior cingulate, and parahippocampal regions [Raichle and Snyder, 2007; Shulman et al., 1997]. However, task‐induced deactivation represents only one of many varieties of the brain's intrinsic functionality [Buckner et al., 2008; Salinas and Sejnowski, 2001]. Another is the capacity to relate past experience to anticipating and preparing for future events [Ingvar, 1985]. Anticipatory top‐down attentional activity is the focus of this study.

In healthy humans, the pattern of cortical activation linked to anticipation of future events, involves multiple areas of medial brain including the rostral and dorsal anterior cingulate cortex [Petit et al., 1998; Shulman et al., 1997], the superior frontal and pre‐supplementary motor region [Picard and Strick, 1996], and the posterior cingulate/retrosplenial cortex [Maddock, 1999]. Connectivity studies of intrinsic attentional states [Fox et al., 2005] emphasized the highly interconnected posterior‐medial complex, with posterior cingulate/retrosplenial cortex (PCG/Rsp), and inferior parietal area [Buckner et al., 2009; Kobayashi and Amaral, 2003]. Since recurrent anticipation of events is prime in OCD, a model of top‐down intrinsic anticipatory processing provides a conceptual framework well suited to study the underlying neural mechanism.

There are no neuroimaging reports on intrinsic anticipatory processing and medial brain in OCD [Broyd et al., 2009], but neuroimaging studies using evoked‐response paradigms repeatedly emphasize the importance of abnormally increased activation in the prefrontal ventral and anterior cingulate regions as one component of abnormally functioning cortico‐thalamo‐striatal circuitry [Dougherty et al., 2002; Rauch et al., 2001]. Here, we use MEG and a visual‐spatial working memory task, delayed matching‐to‐sample with distractor (DMSTD) to investigate the role of medial cortex in top‐down anticipatory processing in OCD. In DMSTD, the encoded sample‐stimulus to be retained and responded to must compete for limited top‐down attentional resources, engaging both the dorsal fronto‐parietal and the ventral fronto‐temporal pathways. While the former has been associated with early visual‐spatial attentional processing [Corbetta and Shulman, 2002], the latter has been shown to enhance firing rates in the pre‐retrieval anticipatory stage [Gregoriou et al., 2009]. Because of the high similarity between the sample, distractor and retrieval stimuli the matching‐to‐sample tasks require an effortful competitive inhibitory selection processes [Posner and Raichle, 1994]. Further, the anticipation of the retrieval targets in working memory tasks has been described as an active, top‐down‐monitored facilitation [Corbetta and Shulman, 2002]. Thus, using the above framework and the unique high temporal resolution of MEG (>1 ms) we examined cortical activation at four stages of DMSTD: (1) anticipation of to‐be‐inhibited distractor (AD); (2) post‐distractor processing (PD); (3) anticipation of to‐be‐facilitated retrieval stimuli (AR); and (4) post‐retrieval processing (PR).

Anticipatory processing engages attentional inhibitory resources for task‐specific networks [Mishkin et al., 1983; Worden et al., 2000], leading to “biased competition” between stimulus‐related groups of neurons [Desimone, 1998]. In agreement, event‐related fMRI studies found a stronger neuronal activation by to‐be‐inhibited distractors than by target stimuli [Fize et al., 2000]. A prolonged inhibition is found in cells neighboring the areas activated by target stimuli [Duncan et al., 1997]. Single unit studies [Desimone and Duncan, 1995], modeling theories [Salinas and Sejnowski, 2001], and neuroimaging observations [Fox et al., 2006; Pessoa et al., 2009] concur that processing of the anticipated target is facilitated in that a specific set of neurons is primed for processing of a particular stimulus before it is presented. Thus priming signals feed back from higher order brain regions to primary visual regions, to sensitize neurons within the related network, and to enhance and accelerate their responses.

If the top‐down effortful competitive inhibition in delayed sample‐matching tasks is the source of increased activity during anticipation of both distractors and retrieval stimuli, then will the pattern of medial brain spatio‐temporal dynamics reflect a generic widespread increased anticipatory activation in OCD, or will the pattern of activation be differentiated by the contextual identity of DMSTD stages. If the latter is true, we expect that during anticipation of a distractor, activity in the rostral cingulate might be reduced given its link to the amygdala for emotional control and to the striatum for cognitive inhibitory control [Botvinick et al., 2001; Chamberlein et al., 2008]. Meanwhile, anticipation of competitive, effortful processing of distractor stimuli will produce increased activation in the posterior cingulate cortex, the major neural node of intrinsic, self‐regulatory network [Raichle and Snyder, 2007], and dorsal cingulate/premotor cortex associated with top‐down prediction of sample retrieval [Pickard and Strick, 1996]. An increased activation is expected to retrieval stimuli in the dorsal anterior cingulate cortex, given its prominent connectivity to the seed areas of cognitive and memory control, the dorsal prefrontal cortex and hippocampal formation [Corbetta and Schulman, 2002; Devinsky et al., 1995].

MATERIALS AND METHODS

Participants

Eight nonmedicated OCD outpatients and healthy controls (C) were case‐matched for gender (4 males per group) age (OCD: 28.6yrs; C: 28.0yrs), handedness (all right‐handed), years of education (OCD: 13.75; C: 13.00), as well as normal scores on verbal neuropsychological measures. Other psychiatric disorders (The Structured Clinical Interview; DSM‐IV), recreational drug use, and clinical depression symptoms during the preceding 3 months were exclusionary. The level of symptoms in OCD was moderate‐to‐severe (Y‐BOCS; Goodman et al., 1989; obsessions: 14.0, compulsions: 11.2). The clinical history of the control group was unremarkable. The study was approved by the Institutional Review Board at the School of Medicine, UNM and Massachusetts General Hospital.

Visual‐Spatial DMSTD

The DMSTD (see Fig. 1) contained black and white checkerboard patterns. Each sample stimulus (a square containing 9 checks) extended to 1° 40′ vertically × 1° 40′ horizontally. The small stimulus was chosen to keep the total size of the retinal image close to the size of macula lutea, thus within the high and constant level of visual acuity. The task involved: (i) presentation of an encoding sample‐stimulus for 200 ms, (ii) presentation of a distractor for 200 ms; (iii) 1,000 ms post‐encoding, and (iv) presentation of retrieval stimuli: two similar checkerboards for 200 ms. Retrieval stimuli were presented 3,200 ms post‐encoding. The subjects were instructed to match the sample to retrieval stimuli by pressing a corresponding button with the left or right index finger. Speed and accuracy were emphasized. One hundred and sixty‐four trials were divided into two runs. Each run included 82 stimuli.

Figure 1.

Figure 1

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The visual‐spatial delayed matching‐to‐sample task with distractor (DMSTD). The number above each panel indicates the duration of the stimulus presentation in milliseconds. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com.]

To reduce the probability of the automatic preparatory contingent negative variation response that could obscure our frontal MEG measures, 15% of these randomly distributed trials were jittering time trials. These trials were not included in our task‐related data analysis. The jittering trials varied the time between stimuli (inter stimulus intervals, ISI), whereas the presentation time for sample and retrieval stimuli was constant across the study. With variable ISIs in jittering trials, the total time of a single run was about 8 min.

Magnetoencephalography (MEG) Data Acquisition

MEG signals were recorded using a Vectorview™ system (Elekta Neuromag, Finland) in four pairs of OCD and case matched controls, and a Neuromag‐122 system in the remaining four pairs. The measurements were carried out in a magnetically shielded room (Imedco AG, Switzerland). The two systems were calibrated with identical phantoms. Previous evidence suggests that different types of MEG systems provide consistent source estimates [Ou et al., 2007; Weisend et al., 2007]. For comparability between data from both systems, only planar gradiometers were used in the analysis (204 in Vectorview, 122 in Neuromag‐122). The location of the head with respect to the sensors was determined using head‐position indicator coils attached to the scalp [Uutela et al., 2001]. The data were sampled at 300 (Neuromag‐122) or 600 samples/s (Vectorview), with an anti‐aliasing low‐pass filter set at 100 Hz and 200 Hz, respectively. The stimuli were presented with a liquid crystal display projector onto a back‐projection screen placed 1.7 m in front of the subject. Blinks and eye movements were monitored with vertical and horizontal EOG.

Structural Magnetic Resonance Imaging (MRI)

A set of 3‐D T1‐weighted magnetic resonance images (MRI) using a 1.5T Picker or Siemens systems were acquired several days after the MEG session. The MRI and MEG coordinate systems were aligned by matching scalp surface points digitized prior to the MEG acquisition to the scalp surface reconstructed from the MRIs.

MEG‐Evoked Activation Within Task‐Relevant Regions of Interest and Time Windows

Regions of interest (ROIs)

The anatomically predetermined ROIs (see Fig. 2) were transformed to an average cortical surface using a morphing procedure based on sulcal and gyral patterns in FreeSurfer [Dale et al., 1999; Fischl et al., 1999]. The same procedure was applied to the MEG source estimates for group analysis within the task‐relevant TW and ROIs. For statistical comparisons, we employed the actual current strength values given by the depth‐weighted MNE in each ROI.

Figure 2.

Figure 2

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The right medial surface of the brain showing marked ROIs: G indicates gyrus; S, sulcus. Individually reconstructed cortical surfaces were averaged across subjects and inflated for better visualization of sulcal regions. From anterior to posterior brain the MEG activation was measured for: (1) the rostral anterior cingulate cortex, rACC (rACG and rACS); (2) dorsal anterior cingulate cortex, dACC (dACG and ACS); (3) the superior frontal gyrus, SFG; (4) posterior cingulate cortex, PCC (PCS and PCG/Retrospinal cortex Rsp); (5) fusiform gyrus, FG (FG and collateral sulcus, COS); (6) sensory visual regions, SV [calcarine sulcus (CS) and cuneus (Cun)]. The remaining regions: parieto‐occipital sulcus, POS; lingual gyrus, LG; parahippocampal gyrus; PHG and, were not considered for statistical analysis, because of inter‐subjects anatomical variability. The medial orbital frontal cortex MOF was excluded to avoid confound by eye movements.

Time windows (TWs)

Our major study aim was to capture anticipatory activity before the onset of distractor, limiting any confound of this measure by still ongoing processing of the encoded sample. The flow of activation along visual pathways in humans, was shown to set a window of 100–400 ms needed for visual information processing to reach the parietal‐frontal and motor brain regions [Corbetta and Shulman, 2002; Foxe and Simpson, 2002]. To reduce the influence of encoding processing on the measurements of pre‐distractor anticipatory activity, we increased the skip window to 610 ms from the onset of the sample stimulus before we began measurements. Thus, a TW of 590 ms (610–1,200 ms) before the onset of distractor (at 1,200 ms), constitutes “the anticipation of distractor” (AD). A sibling 590 ms long TW was used for measurements of “the anticipation of retrieval” (AR: 2,610–3,200 ms). This window of time was reported by the subjects to be a time of effortful rehearsal of the sample stimulus and preparation for a prompt response. Measures of MEG activity were also obtained during PD active inhibition (PD: 1,410–2,400 ms) and post‐retrieval stimulus processing (Post‐Retrieval, PR: 3,410–3,800 ms). The length of the latter window was determined by the shortest RT in our tested samples, to prevent a response‐motor confound on cognitive brain measures.

MEG Source Estimation

Spatial distributions of cortical currents underlying the MEG signals were estimated using the minimum‐norm approach [Dale et al., 2000; Hamalainen and Ilmoniemi, 1984]. The current locations (3,000 points per hemisphere, 7‐mm spacing) were restricted to the gray‐white matter interface segmented from the MRI using the FreeSurfer software [Dale et al., 1999; Fischl et al., 1999]. No orientation constraint was used. The single‐layer boundary‐element model (BEM) was used in the forward solution. The cortically constratined source estimates were computed using MNE software http://www.nmr.mgh.harvard.edu/martinos/userInfo/data/sofMNE.php.

The continuous raw data were re‐averaged off line with a band‐pass of 0.1–40 Hz. Epochs contaminated by eye movement or blink artifacts were rejected (peak‐to‐peak EOG threshold 150 μV). The noise‐covariance matrix was estimated from the 500‐ms baseline period preceding each encoding stimulus. Depth‐weighted minimum‐norm estimates (MNE) as well as dynamic statistical parametric maps (dSPM) were computed at 10 ms intervals [Dale et al., 2000]. The regularization parameter in the computation of the minimum‐norm solutions was set to correspond to amplitude signal‐to‐noise ratio 3 in the whitened data. The dSPM was obtained by normalizing the MNE solutions by the square root of estimated current variance at each location, obtained by mapping the noise‐covariance matrix, to the source space. We used the dSPM for displaying the spatial distribution of the significance of the activation in a particular task‐relevant area. For statistical comparisons, we employed the actual current strength values given by the depth‐weighted MNE.

RESULTS

Behavior

The performance accuracy was high in both groups and not significantly different [C: 94.1%, SD = 7.0, OCD: 96.4%, SD = 2.4%, t(6) = 0.20, and P = 0.8]. RTs were significantly longer in OCD (OCD: 684 ms; C: 591 ms; t(6) = 4.10; P = 0.006).

MEG

Measures of MEG activation in ROIs and TW

Figure 2 illustrates ROIs on the medial wall of the brain estimated using anatomical sources [Nolte, 2008]. Considering a relatively low 6–8 mm MEG resolution six broader ROIs were analyzed containing neighboring gyrus and sulcus: (1) dorsal anterior cingulate cortex dACC (encompassing activity in dACG and dACS); (2) rostral anterior cingulate cortex rACC (rACG and rACS); (3) posterior cingulate cortex, PCC (PCS and PCG/Rsp); (4) fusiform gyrus, FG (FG and collateral sulcus, COS); (5) superior frontal gyrus, SFG, and (6) sensory visual regions, SV, (calcarine sulcus, CS, and cuneus, Cun).

Figure 3 displays group differences in brain activity for task‐relevant TW. In both distractor related TW, AD (610–1,200 ms) and PD (1,410–2,400 ms), the OCD group has lower activity in the right posterior medial cortex. However, the OCD group has more activity in both the rostral and dorsal regions of the anterior cingulate when anticipating retrieval, (AR: 2,610–3,200 ms). OCD group also displays higher activation during post‐retrieval processing, (PR: 3,410–3,800 ms).

Figure 3.

Figure 3

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Group differences (t‐values) for different latency windows. Red and yellow indicate higher activation in OCD subjects than in C subjects, whereas light blue and dark blue indicate higher activation in C subjects. During the anticipation of distractor (AD) and post‐distractor (PD) epochs, the displayed prominent large blue areas in the right PCC and FG suggest abnormally low engagement of the posterior‐medial cortex in OCD. In contrast, OCD subjects have more MEG activation in many regions, particularly over the left medial surface. In later epochs (retrieval), OCD subjects demonstrate more activation in many regions including sensory visual regions, SV. The exception is the right PCC, which never shows significantly greater activation in OCD than in controls.

Figure 4 shows estimated MNE measures in the sensory visual areas of the medial brain, cuneus, and calcarine sulcus. Four TW were selected (details in Methods) to obtain MEG measurements for statistical analysis: “anticipation of a distractor” (AD: 610–1,200 ms), “anticipation of retrieval” (AR: 2,610–3,200 ms), “PD active inhibition” (PD: 1,410–2,400 ms), and “post‐retrieval active processing” (PR: 3,410–3,800 ms).

Figure 4.

Figure 4

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Estimated cortical activation across the DMSTD epoch. The MNE waveforms were averaged across subjects within each group (blue line: C, red line: OCD). The TW (task stages) of interest are marked in color shades: Red: anticipatory inhibition of distractor, AD; post‐stimulus inhibition of distractor, PD; Blue: anticipatory facilitation of retrieval, AR; and post‐stimulus processing of retrieval, PR. Estimated cortical activation are displayed for two sensory visual ROIs: CS and Cun. The MNE waveforms suggest a trend to lower activation during AD in OCD subjects, and shows higher activation during PR TW.

Figure 5 shows minimum norm estimate (MNE) activity across groups of subjects in the anterior and posterior medial cortex within relevant cognitive TW of the DMSTD.

Figure 5.

Figure 5

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A: Estimated activity (MNE) in three anterior ROIs: rACG, rACS, and dACS. Increased activation is evident in subjects with OCD compared to HC throughout the whole task epoch. The activity during retrieval stage (PR) was higher in OCD than in C in each case. B: Estimated activity in three posterior ROIs: PCS, PCG/Rsp, and FG. The MNE source waveforms display more activation in C than in the OCD subjects, both anticipating distractor (AD) and post‐distractor (PD).

ANOVA was conducted on stages of DMSTD relevant to anticipation of competitive inhibition. Since prior studies linked inhibitory processing with the right‐hemisphere [Garavan et al., 1999], and since visual‐spatial difficulties reported in OCD are commonly related to the right hemisphere [Purcell et al., 1998], we decided to fit the ANOVA model to the MEG MNE measures in the right and left hemisphere separately, with pair‐matched subjects as a random effect, and fixed effects of Group (OCD, C), TW (4 levels, AD, PD, AR, and PR), and ROIs (6 levels: dACC, rACC, PCC, FG, SFG, and SV).

ANOVA for the left hemisphere showed no significant main effect of the Group [F(1,7) = 1.644; P = 0.241], but significant effect for the TW [F(3, 322) = 41.708, P = 0.0001], and the ROIs factor [F(5, 322) = 37.350, P = 0.0001]. Although the interactions Group × ROI reached the statistical significance [F(5,322) = 1.451, P = 0.021], the interactions Group × TW (P = 0.697), TW × ROI factor (P = 0.093), and Group × ROI × TW (P = 0.827) were not statistically significant. Thus, post hoc analysis of particular ROIs or particular TW was not warranted. However, a question was raised about one of the highly predictable a priori findings reported in OCD, the abnormally increased activity in the dorsal anterior cingulate region [Rosenberg and Hanna, 2000]. During the AR TW, the two‐sided paired t‐tests revealed statistically significantly higher activation in the dorsal anterior cingulate for OCD [OCD = 2.44, C = 1.89; t(7) = 2.620, and P = 0.034].

The ANOVA for the right hemisphere showed significant effects for the TW [F(3, 322) 56.144, P < 0.0001], ROI [F(5, 322) 51.548, and P <0.0001], but no significant effect for Group [F(1,7)0.80 and P = 0.80]. Significant interactions included: Group × TW [F(3,322) = 3.666, P = 0.013]; Group × ROI factor [F(5,322)2.648, P = 0.023], TW × ROI [F(15,322)2.597, P = 0.001], and Group × TW × ROI [F(15,322)2.124, P = 0.009]. Considering the significant interactions, the published findings and current neurobiological models of OCD [Dougherty et al., 2002; Rosenberg and Hanna, 2000), we tested several group‐contrasts using two‐sided paired t‐tests. In the AD TW the OCD group displayed lower activation in the right posterior cingulate region [OCD = 3.210, C = 3.480; t(7) = 2.47, and P = 0.043], fusiform gyrus region (OCD = 3.010, C = 3.376; t(7) = 3.537, and P = 0.001], and in the right visual‐sensory region, but the latter did not quite reach statistical significance [OCD = 1.36, C = 1.68; t(7) = 1.178, and P = 0.201]. In the AR TW statistically significantly higher activation in OCD subjects was shown for the right dorsal anterior cingulate area [OCD = 2.301, C = 1.667; t(7) = 3.55, and P = 0.009]. In the PR TW, the OCD displayed significantly larger activation in all regions including visual‐sensory areas [SV: OCD = 5.12, C = 4.17; t(7) = 3.54, and P = 0.003].

DISCUSSION

Medial Brain Dissociation Between Anticipatory Activation for Inhibition and Facilitation

Contrary to our expectations, the posterior medial areas to anticipation of distractors were less active in OCD subjects than in controls, suggesting a failure in engaging inhibitory self‐regulatory networks. In contrast, the anterior cingulate regions extending to supplementary motor areas displayed significantly increased activation in OCD, mostly during top‐down anticipatory facilitation of retrieval. Based on area‐specific group‐differences, we propose the operation of a mechanism compensating for abnormal posterior self‐regulatory networks through increased engagement of the anterior medial regions. Consistent with such proposal is the high performance accuracy in OCD accompanied by the prolonged response time. Thus, we think much of the increase in MEG activation in OCD is not due to a direct failure of inhibition but may result from extra‐compensatory processing.

The Reduced Anticipatory Activation in the Posterior‐Medial and Fusiform Regions Plays a Role in Efficacy of Self‐Regulatory Networks in OCD

The abnormally low anticipatory activation in the right posterior cingulate and fusiform regions in OCD (AD window), suggests dysfunctions in the posterior network related to anticipation of inhibition. To a lesser degree, the reduced activation was also found in PD TW. Yet, the performance accuracy of OCD subjects matched controls. Prior studies have shown that anticipation of a distractor guides specific “cognitive readiness” to be employed in the top‐down inhibitory control for upcoming events. The higher posterior medial MEG activation during the AD window in controls (see Fig. 3B) is consistent with reported by other laboratories an active competitive‐inhibition of task‐irrelevant networks [Jensen et al., 2002]. Consequently, the reduced activation in the medial posterior regions during anticipation and processing of distractor stimuli may suggest a limitation in self‐regulatory inhibitory resources. Concurring, persistent failure in inhibiting trivial visual details plays a central role in cognitive phenomenology of OCD.

The posterior cingulate cortex is an important neural center coordinating efforts of attentional self‐control. It has a strong anatomical and functional relationship with the lateral inferior parietal regions linked to top‐down inhibition of distractors [Friedman‐Hill et al., 2003]. These results, while unexpected, are consistent with accumulating evidence about structural and functional abnormalities within the posterior brain in patients with OCD [Ciesielski et al., 2005; Menzies et al., 2008; Nordahl et al., 1989]. Since the posterior‐medial cortex is richly connected with the medial prefrontal and parahippocampal regions, as well as with lateral prefrontal and parietal areas [Suzuki and Amaral, 1994], its abnormally low intrinsic activity observed here may alter multiple anticipatory and stimulus‐response systems. The present study reveals a possible role of one of them in OCD, the area of fusiform gyrus. FG has been considered a major component of the intrinsic mode processing, attentional modulation, and maintenance of memory [Lepsien and Nobre, 2006]. While associated with context‐based processing of visual memory retrieval, the FG participates in top‐down modulation of recognition patterns [Kourtzi and Kanwisher, 2001]. FG may play, therefore, an important role in the preparatory stage for predictive memory‐based self‐control, and as such it may be a part of an important neural circuit whose malfunction contributes to the phenomenology of OCD.

Increased Anticipatory Activation in the Anterior Cingulate and Pre‐Supplementary Motor Regions May Indicate an Effortful Compensatory Mechanism

How do OCD subjects match normal performance accuracy on the difficult visual‐spatial DMSTD, when the key node of the instrumental network, the posterior cingulate cortex exhibits abnormally low activation? A possible answer comes from the pattern of activation in the anterior medial brain. While our prediction about the leading role of the prefrontal medial cortex in reflecting increased activation in OCD to anticipation of distractor was not supported by the current data, the anterior medial areas did display increased activation across several time‐windows including AD, particularly around retrieval (AR and PR epochs). Such globally increased activation may reflect a task‐nonspecific attentional vigilance and increased motivation. This view is consistent with association of rostral anterior cingulate/orbital cortex with emotion and motivation [Gusnard et al., 2001]. The commonly accepted model of pathophysiology in OCD relies on functional and structural abnormalities in the prefrontal‐striatal‐thalamic circuit. The ventral prefrontal cortex with rich connections to ventral caudate has been found to exhibit an increased activation in almost all OCD studies [but see Chamberlain et al., 2008]. Our present findings are in line with those earlier studies.

One possible view of the increased anticipatory activation in the anterior cingulate and supplementary motor areas is that it may reflect an effortful identification of sample stimulus and motor response. This interpretation is consistent with single unit studies on top‐down prediction of retrieval of a sample in a spatial delayed‐response task, which revealed the presence of anticipatory cells in the anterior cingulate sulcus and caudal premotor area [Picard and Strick, 1996]. Within proposed conflict‐solving networks the prefrontal dorsal‐medial regions have been found in OCD subjects to respond to tasks with high conflict detection and monitoring [Ciesielski et al., 2010; Kerns et al., 2004]. Our data concurs with these earlier findings, but also suggests a novel perspective on the working memory mechanism in OCD. Since the anticipation of retrieval involves multifaceted inhibitory control of conflicts between the encoded sample, distractor, and incoming retrieval stimuli, the enhanced anterior medial activity may be a signature of a compensatory mechanism. Such a mechanism maintains extensive motivational, monitoring, and inhibitory control, providing on‐line amendment to dysfunctional posterior self‐regulatory networks. In line with this, OCD subjects were as accurate as normal controls on the task, whereas their reaction times were delayed by almost a tenth of a second. Thus, OCD subjects appear to be taking noticeably more time and more effort than controls to ensure accurate performance.

In conclusion, the abnormally reduced anticipatory activation to distractors in the posterior medial cortex implicates a disrupted top‐down self‐regulatory control system in subjects with OCD. Yet, they performed as accurately as or better than controls. This profile of behavioral and MEG data implicates the operation of a compensatory processing mechanism in OCD aiming high performance accuracy. The increased anterior medial cortex activation, commonly seen as a marker of inhibitory deficits in OCD, may demonstrate here, at least in part, adaptive brain plasticity.

Acknowledgements

We thank Dr. Julia Stephen, Dr. David Cohen and Dr. Robert Elmasian for their support to this project.

REFERENCES

  1. Botvinick MM, Braver TS, Barch DM, Carter CS, Cohen JD. ( 2001): Conflict monitoring and cognitive control. Psychol Rev 108: 624–652. [DOI] [PubMed] [Google Scholar]
  2. Broyd SJ, Demanuele C, Debener S, Helps SK, James CJ, Sonuga‐Barke EJ. ( 2009): Default‐mode brain dysfunction in mental disorders: A systematic review. Neurosci Biobehav Rev 33: 279–296. [DOI] [PubMed] [Google Scholar]
  3. Buckner RL, Andrews‐Hanna JR, Schacter DL. ( 2008): The brain's default network: Anatomy, function, and relevance to disease. Ann N Y Acad Sci 1124: 1–38. [DOI] [PubMed] [Google Scholar]
  4. Buckner RL, Sepulcre J, Talukdar T, Krienen FM, Liu H, Hedden T, Andrews‐Hanna JR, Sperling RA, Johnson KA. ( 2009): Cortical hubs revealed by intrinsic functional connectivity: Mapping, assessment of stability, and relation to Alzheimer's disease. J Neurosci 29: 1860–1873. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Chamberlain SR, Menzies L, Hampshire A, Suckling J, Fineberg NA, del Campo N, Aitken M, Craig K, Owen AM, Bullmore ET, Robbins TW, Sahakian BJ. ( 2008): Orbitofrontal dysfunction in patients with obsessive‐compulsive disorder and their unaffected relatives. Science 321: 421–422. [DOI] [PubMed] [Google Scholar]
  6. Ciesielski KT, Hamalainen MS, Lesnik PG, Geller DA, Ahlfors SP. ( 2005): Increased MEG activation in OCD reflects a compensatory mechanism specific to the phase of a visual working memory task. Neuroimage 15: 1180–1191. [DOI] [PubMed] [Google Scholar]
  7. Ciesielski KT, Rowland LM, Harris RJ, Kerwin AA, Reeve A, Knight JE. ( 2011): Increased anterior brain activation to correct responses on high‐conflict Stroop task in obsessive‐compulsive disorder. Clin Neurophysiol 122: 107–113. [DOI] [PubMed] [Google Scholar]
  8. Corbetta M, Shulman GL. ( 2002): Control of goal‐directed and stimulus‐driven attention in the brain. Nat Rev Neurosci 3: 201–215. [DOI] [PubMed] [Google Scholar]
  9. Dale AM, Fischl B, Sereno MI. ( 1999): Cortical surface‐based analysis. I. Segmentation and surface reconstruction. Neuroimage 9: 179–194. [DOI] [PubMed] [Google Scholar]
  10. Dale AM, Liu AK, Fischl BR, Buckner RL, Belliveau JW, Lewine JD, Halgren E. ( 2000): Dynamic statistical parametric mapping: Combining fMRI and MEG for high‐resolution imaging of cortical activity. Neuron 26: 55–67. [DOI] [PubMed] [Google Scholar]
  11. Desimone R. ( 1998): Visual attention mediated by biased competition in extrastriate visual cortex. Philos Trans R Soc Lond B Biol Sci 353: 1245–1255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Desimone R, Duncan J. ( 1995): Neural mechanisms of selective visual attention. Annu Rev Neurosci 18: 193–222. [DOI] [PubMed] [Google Scholar]
  13. Devinsky O, Morrell MJ, Vogt BA. ( 1995): Contributions of anterior cingulate cortex to behaviour. Brain 118: 279–306. [DOI] [PubMed] [Google Scholar]
  14. Dougherty DD, Baer L, Cosgrove GR, Cassem EH, Price BH, Nierenberg AA, Jenike MA, Rauch SL. ( 2002): Prospective long‐term follow‐up of 44 patients who received cingulotomy for treatment‐refractory obsessive‐compulsive disorder. Am J Psychiatry 159: 269–275. [DOI] [PubMed] [Google Scholar]
  15. Duncan J, Humphreys G, Ward R. ( 1997): Competitive brain activity in visual attention. Curr Opin Neurobiol 7: 255–261. [DOI] [PubMed] [Google Scholar]
  16. Fischl B, Sereno MI, Tootell RB, Dale AM. ( 1999): High‐resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp 8: 272–284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fize D, Boulanouar K, Chatel Y, Ranjeva J‐P, Fabre‐Thorpe M, Thorpe S. ( 2000): Brain areas involved in rapid categorization of natural images: An event‐related fMRI study. Neuroimage 11: 634–643. [DOI] [PubMed] [Google Scholar]
  18. Fox MD, Corbetta M, Snyder AZ, Vincent JL, Raichle ME. ( 2006): Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci USA 103: 10046–10051. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Foxe JJ, Simpson GV. ( 2002): Flow of activation from V1 to frontal cortex in humans. A framework for defining “early” visual processing. Exp Brain Res 142: 139–150. [DOI] [PubMed] [Google Scholar]
  20. Friedman‐Hill SR, Robertson LC, Desimone R, Ungerleider LG. ( 2003): Posterior parietal cortex and the filtering of distractors. Proc Natl Acad Sci USA 100: 4263–4268. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Garavan H, Ross TJ, Stein EA. ( 1999): Right hemispheric dominance of inhibitory control: An event‐related functional MRI study. Proc Natl Acad Sci USA 96: 8301–8306. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Goodman WK, Price LH, Rasmussen SA, Mazure C, Fleischmann RL, Hill CL, et al. ( 1989): The Yale‐Brown Obsessive Compulsive Scale. I. Development, use, and reliability. Arch Gen Psychiatry 46: 1006–1011. [DOI] [PubMed] [Google Scholar]
  23. Gregoriou GG, Gotts SJ, Zhou H, Desimone R. ( 2009): High‐frequency, long‐range coupling between prefrontal and visual cortex during attention. Science 324: 1207–1210. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Gusnard DA, Akbudak E, Shulman GL, Raichle ME. ( 2001): Medial prefrontal cortex and self‐referential mental activity: Relation to a default mode of brain function. Proc Natl Acad Sci USA 98: 4259–4264. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Hamalainen MS, Ilmoniemi RJ. ( 1984): Interpreting measured magnetic fields of the brain: Estimates of current distributions. Technical Report, TKK‐F‐A559, Helsinki University of Technology, Espoo, Finland. [Google Scholar]
  26. Ingvar DH. ( 1985): “Memory of the future”: An essay on the temporal organization of conscious awareness. Hum Neurobiol 4: 127–136. [PubMed] [Google Scholar]
  27. Jensen O, Gelfand J, Kounios J, Lisman JE. ( 2002): Oscillations in the alpha band (9–12 Hz) increase with memory load during retention in a short‐term memory task. Cereb Cortex 12: 877–882. [DOI] [PubMed] [Google Scholar]
  28. Kobayashi Y, Amaral DG. ( 2003): Macaque monkey retrosplenial cortex: II. Cortical afferents. J Comp Neurol 466: 48–79. [DOI] [PubMed] [Google Scholar]
  29. Kourtzi Z, Kanwisher N. ( 2001): Representation of perceived object shape by the human lateral occipital cortex. Science 293: 1506–1509. [DOI] [PubMed] [Google Scholar]
  30. Lepsien J, Nobre AC. ( 2006): Cognitive control of attention in the human brain: Insights from orienting attention to mental representations. Brain Res 1105: 20–31. [DOI] [PubMed] [Google Scholar]
  31. Maddock RJ. ( 1999): The retrosplenial cortex and emotion: New insights from functional neuroimaging of the human brain. Trends Neurosci 22: 310–316. [DOI] [PubMed] [Google Scholar]
  32. Menzies L, Williams GB, Chamberlain SR, Ooi C, Fineberg N, Suckling J, Sahakian BJ, Robbins TW, Bullmore ET. ( 2008): White matter abnormalities in patients with obsessive‐compulsive disorder and their first‐degree relatives. Am J Psychiatry 165: 1308–1315. [DOI] [PubMed] [Google Scholar]
  33. Mishkin M, Ungerleider LG, Macko KA. ( 1983): Object vision and spatial vision: Two cortical pathways. TINS 378: 408–411. [Google Scholar]
  34. Nolte J. ( 2009): The human brain: An introduction to its functional anatomy. Mosby: Mosby‐Elsevier: p 720. [Google Scholar]
  35. Nordahl TE, Benkelfat C, Semple WE, Gross M, King AC, Cohen RM. ( 1989): Cerebral glucose metabolic rates in obsessive compulsive disorder. Neuropsychopharmacology 2: 23–28. [DOI] [PubMed] [Google Scholar]
  36. Ou W, Golland P, Hamalainen MS. ( 2007): Sources of variability in MEG. Med Image Comput Assist Interv 10: 751–759. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Pessoa L, Rossi A, Japee S, Desimone R, Ungerleider LG. ( 2009): Attentional control during the transient updating of cue information. Brain Res 1247: 149–158. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Petit L, Courtney SM, Ungerleider LG, Haxby JV. ( 1998): Sustained activity in the medial wall during working memory delays. J Neurosci 18: 9429–9437. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Picard N, Strick PL. ( 1996): Motor areas of the medial wall: A review of their location and functional activation. Cereb Cortex 6: 342–353. [DOI] [PubMed] [Google Scholar]
  40. Posner M, Raichle M. 1994. Images of mind. New York: WH Freeman and Company. [Google Scholar]
  41. Purcell R, Maruff P, Kyrios M, Pantelis C. ( 1998): Neuropsychological deficits in obsessive‐compulsive disorder: A comparison with unipolar depression, panic disorder, and normal controls. Arch Gen Psychiatry 55: 415–423. [DOI] [PubMed] [Google Scholar]
  42. Raichle ME, Mintun MA. ( 2006): Brain work and brain imaging. Annu Rev Neurosci 29: 449–476. [DOI] [PubMed] [Google Scholar]
  43. Raichle ME, Snyder AZ. ( 2007): A default mode of brain function: A brief history of an evolving idea. Neuroimage 37: 1083–1090. [DOI] [PubMed] [Google Scholar]
  44. Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. ( 2001): A default mode of brain function. Proc Natl Acad Sci USA 98: 676–682. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Rauch SL, Whalen PJ, Curran T, Shin LM, Coffey BJ, Savage CR, McInerney SC, Baer L, Jenike MA. ( 2001): Probing striato‐thalamic function in obsessive‐compulsive disorder and Tourette syndrome using neuroimaging methods. Adv Neurol 85: 207–224. [PubMed] [Google Scholar]
  46. Rosenberg DR, Hanna GL. ( 2000): Genetic and imaging strategies in obsessive‐compulsive disorder: Potential implications for treatment development. Biol Psychiatry 48: 1210–1222. [DOI] [PubMed] [Google Scholar]
  47. Salinas E, Sejnowski TJ. ( 2001): Correlated neuronal activity and the flow of neural information. Nat Rev Neurosci 2: 539–550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Shulman, GL , Corbetta M, Buckner RL, Raichle ME, Fiez JA, Miezin FM, Petersen SE. ( 1997): Top‐down modulation of early sensory cortex. Cereb Cortex 7: 193–206. [DOI] [PubMed] [Google Scholar]
  49. Suzuki WA, Amaral DG. ( 1994): Perirhinal and parahippocampal cortices of the macaque monkey: Cortical afferents. J Comp Neurol 22: 497–533. [DOI] [PubMed] [Google Scholar]
  50. Uutela K, Taulu S, Hamalainen MS. ( 2001): Detecting and correcting for head movements in neuromagnetic measurements. Neuroimage 14: 1424–1431. [DOI] [PubMed] [Google Scholar]
  51. Weisend MP, Hanlon FM, Montano R, Ahlfors SP, Leuthold AC, Pantazis D, Mosher JC, Georgopoulos AP, Hamalainen MS, Aine C. ( 2007): Paving the way for cross‐site pooling of magnetoencephalography (MEG) data. Elsevier Intern Congress Series 1300: 615–618. [Google Scholar]
  52. Worden MS, Foxe JJ, Wang N, Simpson GV. ( 2000): Anticipatory biasing of visuospatial attention indexed by retinotopically specific alpha‐band electroencephalography increases over occipital cortex. J Neurosci 20: 1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]

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