Psychiatrist’s Perspective on Cingulate Cortexby George Bush, M.D., M.M.Sc.
Assistant Professor of Psychiatry & Research Fellow in Radiology, Harvard Medical School
Assistant Director of Psychiatric Neuroimaging Research, MGH/MIT/HMS Athinoula A. Martinos Center for Functional and Structural Biomedical Imaging, Charlestown, MA
email@example.com My research is focused on using functional neuroimaging in conjunction with cognitive, motor, and emotional activation paradigms in novel ways to study cingulate cortex function in both normal cognitive processing and neuropsychiatric disorders. The principle that guides our work is simplein order to truly understand disease, we must first develop a solid knowledge base about normal brain physiology and mechanisms. Only then can we adequately develop and test neurobiological models for defined neuropsychiatric illness. In the following sections, I will attempt to outline why anterior cingulate cortex (ACC) is vital to our understanding of neuropsychiatric disease and provide some examples of how neuroimaging can be used to help our patients. Please note that I have kept references here to a minimum, but the ones provided contain extensive citation information for those interested in pursuing issues in greater detail. Determining the nature of ACC function, with special attention to the different roles of its sub-territories, is particularly vital to understanding the neurobiology of many neuropsychiatric disorders. Straddling the fence between cognition and emotion, ACC has been suggested to be involved in the pathophysiology of attention deficit/hyperactivity disorder (Bush et al., 1999), post-traumatic stress disorder (Shin et al., in press), depression (Mayberg et al., 1997; Drevets, 2001; Davidson et al., in press), obsessive-compulsive disorder (Jenike et al., 1991), schizophrenia, bipolar disorder, akinetic mutism, panic disorder, Tourettes Syndrome (Benes, 1993), and Alzheimers Disease (Vogt et al., 1997). It is not surprising that ACC has been associated with such a wide variety of disorders, as it encompasses a large territory and its subregions are vital to many cognitive, motor, and emotional processes (Vogt, 1992). Many studies have reported ACC activation as if it were a single homogeneous region, but to truly understand how it may be involved in neuropsychiatric disorders, the functions of different ACC subregions in normal humans must be determined. ACC encompasses two major subdivisions that subserve distinct functions, including (1) dorsal ACC [dACC] (areas 32' and/or 24c'), which has been shown to be involved in cognition (Vogt et al., 1992; Devinsky, Morrell & Vogt, 1995; Bush, Luu & Posner, 2000) and reward-based decision-making (Bush et al., In press), and (2) perigenual ACC [pACC] (rostral areas 24, 25, 32, and 33), which is involved in processing affective/emotional information, including assigning emotional valence to internal and external stimuli, conditioned emotional learning, regulation of autonomic and endocrine functions, vocalizations expressing internal states, assessing motivation, and maternal-infant interactions (Vogt et al., 1992; Devinsky, Morrell & Vogt, 1995; Whalen et al., 1998). These sub-territories are distinguishable based upon cytoarchitectural (Vogt, 1993) and connectivity patterns (Vogt & Pandya, 1987) as well as convergent evidence from studies using a variety of experimental techniques, including lesion studies, electrophysiology, and functional neuroimaging (see Bush, Luu & Posner, 2000). Conversely, pACC has been activated by affectively-related tasks, including studies of obsessive-compulsive disorder symptom provocation, simple phobia, post-traumatic stress disorder, and depression (see Whalen et al., 1998; Bush, Luu & Posner, 2000 and Figure 1). Figure 1. Schematic sagittal views of anterior cingulate cortex with local maxima from studies reporting activations (left) and decreases (right) in reponse to cognitive, motor and emotional tasks. Activations (Increases) Deactivations (Decreases) Left (Activations): The dACC is activated by Stroop and Stroop-like tasks, divided attention tasks, and response selection tasks (red dots, aqua stars/squares, and yellow and orange triangles) and during reward-based decision making (purple triangle/box, see Bush et al, in press). The pACC is generally activated by tasks that relate to affective content or symptom-provocation (blue squares and diamond). Right (Deactivations): Conversely, a meta-analysis of studies reporting deactivations (signal or blood flow decreases) reveals that cognitively-demanding tasks produce deactivation of pACC while emotional information processing leads to a decrease in dACC (figure adapted from Bush, Luu & Posner, 2000). Notably, to assess the dichotomy of dACC/pACC subdivisions more directly, two Stroop-like interference tasks with differing causes of interference (i.e., one cognitive [Bush et al., 1998] and one affective [Whalen et al., 1998]) were validated in fMRI studies run with the same subjects during the same scanning session. In an interesting double-dissociation (depicted in Figure 1, left), the cognitively interfering Counting Stroop produced dACC activation but not pACC activation, whereas the affectively interfering Emotional Counting Stroop produced pACC activation but not dACC activation.
Putting Knowledge to Use: Attention Deficit/Hyperactivity Disorder (ADHD)OK, so now maybe youre convinced that all the animal and human anatomists and the electrophysiologists and the neuroimaging folks are right, and that there are, in fact, distinct functional subdivisions of ACC. So what? How does this help us as psychiatrists? Can we use this information to develop a clinical test for neuropsychiatric disorders? Well, the good news is: perhaps, yes. The bad news is: not quite yet. But were getting closer. The matter is obviously quite complex and there are numerous issues of sensitivity and specificity, but lets take a look at one way to approach the matter. For this, well use ADHD as an example case. Determining the underlying neurobiology of ADHD is of great importance. It has been estimated to affect 3-5% in school-age children, and to persist to a lesser degree, into adulthood. As such, it is a source of great morbidity, and has a huge impact upon society with regard to its impact on affected children and their families, its attendant financial cost, disruption of schools, and its potential to lead to criminality and substance abuse. If one had to guess which of the ACC subdivisions might be abnormal in ADHD, dACC would, of course, be the obvious choiceeven without specifically knowing how dACC works mechanistically. Back in 1997 when we began our first fMRI study of ADHD, we hypothesized that dACC might be dysfunctional in ADHD. Our thinking was that dACC had been hypothesized to play a primary role in (1) stimulus selection when faced with competing streams of input (divided attention situations), and/or (2) response selection tasks requiring the facilitation of correct responses and/or the inhibition of incorrect responses. Furthermore, these functions closely parallel the essential features of ADHD, which include (1) inattention, (2) impulsivity and (3) hyperactivity and both of the latter two signs can be conceptualized as defective inhibition of inappropriate responses. To make a long story short, we tested our hypothesis by scanning 8 ADHD adult subjects and 8 closely-matched normal controls using fMRI and the Counting Stroop (Bush et al., 1999). As predicted (see Figure 2), the expected fMRI activation was observed in the dACC of the normal controls, but no dACC activation was found in the group with ADHD. Furthermore, normal bilateral activation was seen in dorsolateral prefrontal and parietal cortex (Brodmann areas 46/9 and 7, respectively) in the ADHD subjects indicating that the ADHD subjects were able to produce significant activation in other brain regions that have been shown to support performance of the Counting Stroop, and suggesting that ADHD may involve a specific deficit of dACC.
Mechanism of dACC FunctionRelevance to Psychiatric DisordersKnowing that different divisions of anterior cingulate are important for processing different types of information is intrinsically interesting, and having such information can assist one in developing testable models of psychiatric disorder. However, even a meta-analysis of all tasks activating ACC still would not answer how the sub-territories actually function mechanistically—a point that is crucial to elucidating their potential role in disease. Specifically, developing models for how the different ACC sub-divisions support the various cognitive, motor, and emotional functions is vital to determining how they might contribute to the pathophysiology of different neuropsychiatric disorders. Here we will take dACC as an example. DACC has long been associated with the motor system, based upon nonhuman primate studies of cingulate projections/connections (Biber at al., 1978; Vogt et al., 1987; Dum & Strick, 1991, 1993; Morecraft & Van Hoesen, 1992; Bates & Goldman-Rakic, 1993), electrical stimulation (Luppino et al., 1991) and electrophysiology studies (Shima et al., 1991). As seen in Figure 1 above, dACC has also been activated during performance of cognitive interference tasks, divided attention tasks, and response selection/inhibition tasks in normal subjects—supporting the conclusion that dACC plays a critical role in attention (Posner & Petersen, 1990; Mesulam, 1990; Colby, 1991), Notably, in a study of divided and selective attention (Corbetta et al.,1991), dACC was active in the divided attention condition but not active in any of the selective attention conditions. Furthermore, dACC was not activated in PET studies of simple sustained attention tasks (Pardo, 1991) or during the cognitively demanding reading and semantic processing tasks examined by Petersen et al. (1990). These facts combine to show that dACC is not merely recruited any time focused/selective attention is needed, but rather that dACC plays a central role in difficult cognitive interference and attentional tasks. But how ?
The Heterogeneity ModelBased primarily on work in humans, different functions have been ascribed to dACC, including attention-for-action/target selection, motivational valence assignment, motor response selection, error detection/performance monitoring, competition monitoring, anticipation, working memory, novelty detection, and reward assessmentbut no single unifying model has been able to explain the diverse results from neuroimaging and electrophysiological studies (Bush, Luu & Posner, 2000; Bush et al, in press). Single unit recording studies of the putative monkey homologue of dACC (CMAr) have repeatedly shown it is populated by a heterogeneous group of cells. Niki & Watanabe (1979) identified timing (stimulus anticipation) units, and others sensitive to targets, motor responses, rewards, or errors. Nishijo et al (1997) found ACC cells that were anticipatory, stimulus-related, response-related, and reward-relatedadding that subsets responded to novel objects, while others could discriminate rewarding, aversive, and neutral objects. Procyk et al (2000) recorded from dACC cells that reacted to targets, rewards or error cues, and others involved in routine and non-routine motor sequencing behaviors in macaques. Though it has been established in monkeys that dACC is comprised of many different cell types, the problem of how to link this literature to humans remains. Fortunately, a single unit recording study provides a key piece of information. Shima & Tanji (1998) recorded from dACC (CMAr) cells in Macaca fuscata during a reward-based decision-making task. As in the other studies, the authors noted that different populations of cells responded to target detection, motor response, CONrew, and REDrew. Critically, Shima & Tanji (1998) also reported the proportions of each cell type were not equal in dACCfive times as many cells responded specifically to movement selection based on REDrew (37%) as opposed to CONrew (7%). We believed that such a large difference in the proportions of cells could be exploited (i.e., that this would produce measurable, differential fMRI activation). Based on these monkey single unit recordings, we hypothesized that human dACC is comprised of mixed cells that variously anticipate and detect targets, indicate novelty, influence motor responses, encode reward values and signal errors. As an initial test of this conceptualization, we conducted an fMRI study using a reward-based decision-making task (modeled after Shima & Tanjis task) to isolate responses from a subpopulation of dACC cells sensitive to reward reduction. As predicted, seven of eight subjects showed significant (P < 10-4) dACC activation when contrasting reduced reward (REDrew) trials to fixation (FIX). Confirmatory group analyses then corroborated the predicted ordinal relationships of fMRI activation expected during each trial type (REDrew > SWITCH > constant reward (CONrew) 3 FIX (See Figure 3). The data support a role for dACC in reward-based decision-making, and by linking the human and monkey literatures, provide initial support for the existence of heterogeneity within dACC.
Figure 3 Dorsal Anterior Cingulate Cortex fMRI Response and ROI (Left). Activation within dACC in response to REDrew trials (vs. FIX). Pseudocolor-scaled statistical maps are displayed superimposed on the medial surface of the left hemisphere (sagittal view) and a coronal slice (y = +12 mm) in radiological convention (R = L) for subject 1. These areas are enlarged on the left. The dACC region of interest (ROI) included ACC between y = 0 and y = +30 within the cingulate sulcus (CS) for cerebral hemispheres with a single CS (right), and ACC between the paracingulate sulcus (PCS) and CS, inclusive, for cerebral hemispheres with a double parallel cingulate sulcal pattern (left). It refers to the same cortical region that has been called the anterior cingulate cognitive division (ACcd) (Bush, Luu & Posner, 2000), the rostral cingulate zone (Picard & Strick, 1996), or midcingulate cortex (Vogt 1997). The dACC ROI is indicated in aqua on the coronal slice enlargement (bottom left). Schematized Predicted fMRI Response (Upper Right). Schematized representation of components most relevant to the fMRI results. A CONrew cell is depicted in green, cells responsible for the additional demands of performing the SWITCH trials in blue, and cells specific to REDrew trials in red. Per Shima & Tanji (1998), REDrew and CONrew cells are depicted at an approximate 5:1 ratio. Gray cells represent cells that support all trial types (e.g., anticipation, target detection) but are not the subject of immediate focus because they do not serve to differentiate fMRI responses. Qualitative predictions for fMRI responses appear on the right. FIX was predicted to produce no activation, CONrew only minimal activation, and SWITCH trials (recruiting novelty detection cells and placing greater demands on response selection) were predicted to produce significantly greater activation. REDrew trials, recruiting cells involved in all previous trial types plus the very numerous REDrew sensitive cells, were predicted to produce the greatest activation. Dorsal Anterior Cingulate Cortex fMRI Response (Lower Right). Group-averaged (N = 7), normalized, time-locked dACC activity for REDrew, SWITCH, and CONrew trials plotted as percent change from mean MRI signal during the first three images for each condition. Error bars indicate the standard error of the mean. As predicted, at t = 4.5 and 6 seconds (accounting for hemodynamic delay), REDrew > SWITCH trials > CONrew. [Bush et al., PNAS, In Press]. Confirmation of the existence of such a local intracortical network of heterogeneous cell types within dACC would be important, as it would allow us to advance beyond current hypotheses of the pathophysiology of psychiatric disorders by providing a framework for how the brain normally processes cognitive information. The mechanism of how such a local dACC network might operate and contribute to cognition is straightforward and consistent with observed behavior. Signaling from anticipatory/timing cells would have predictive value, and improve the processing of salient stimuli. Novelty detection and target detection cells would similarly enhance attention to relevant stimuli. Motor response cells in dACC have been shown to contribute to complex motor behaviors, especially during non-routine tasks. Of course, reward cells and error cells would provide invaluable feedback that would guide future actions based on experience. Thus, support exists for such a dynamic interaction among heterogeneous cell types within dACC. The reward angle may be particularly germane to psychiatric illnesses. Taken together, the data suggest that dACC may play a role in reward circuitryparticularly in reward-based decision-making, learning, and especially the performance novel (non-automatic) tasksfunctions known to be substantially influenced by dopamine. Moreover, this line of inquiry may have clinical relevance, given the implication of dopaminergic pathways in the pathophysiology of attention deficit disorder (Biederman & Spencer, 1999) and observed dysfunction of dACC in this illness (Bush et al., 1999). Similar arguments could be made and tested with respect to schizophrenia, depression and anxiety disorders.
ACC and Mood DisordersStructural and functional abnormalities have been reported within pACC, and reliably decreased dACC activation has been repeatedly seen in major depressive episodes using a variety of functional imaging techniques, including single photon emission computed tomography (SPECT), positron emission tomography (PET) and fMRI. Remission seems to be associated with normalization of this pattern (for reviews, see Mayberg et al 1999, Drevets 2001, Davidson, in press). Interestingly, increased pre-treatment activity in the rostral portion of pACC may have predictive value, as it seems to be associated with better treatment response (Mayberg et al. 1997; Pizzagalli et al, 2001). Drevets et al. (1997) reported decreased volume of the subgenual portion of pACC, and Ongur et al (1998) followed-up with a post-mortem study finding that both the density and total number of glial cells were reduced in subjects with major depressive disorder or bipolar disorder, as compared to matched normal control subjects. Notably, Ongur et al. reported that neither the density nor the total number of neurons was altered. As it can be seen, ACC is emerging as a region of major importance to mood disorders research.
SummarySo there you have it. By no means do we fully comprehend how this marvelous little collection of functional subdivisions known as ACC operates or interacts, but the combined wisdom of anatomists, electrophysiologists, neurochemists, clinicians, and neuroimagers is drawing an ever-clearer picture of how the various ACC subdivisions work in normal cognitive and emotional processing, as well as how they might fail and thus contribute to psychopathology. Weve seen regional specificity of function in normal humans, and observed how this information can help us develop and test refined disorder-specific hypotheses. Weve also seen how animal work and human studies can inform one another to reveal new understanding of the myterious mechanisms underlying normal human cognition and reward processing. The challenges ahead are to continue extending our knowledge on all of these fronts, and to continually seek to leverage the emerging knowledge in the service of developing clinically meaningful neuroimaging-based diagnostic tests and therapy-monitoring procedures that can be used to help our patients. Stay tuned . . . were in for a bumpy but fascinating ride! . . . References Bates JF and Goldman-Rakic PS (1993): Prefrontal connections of medial motor areas in the rhesus monkey. J Comparative Neurol. 336:211-228. Benes FM (1993): Relationship of cingulate cortex to schizophrenia and other psychiatric disorders. In:Vogt BA and Gabriel M, (eds). Neurobiology of cingulate cortex and limbic thalamus: a comprehensive handbook. Boston. Birkhäuser, pp. 581-605. Biber MP, Kneisley LW, LaVail JH (1978) Cortical neurons projecting to the cervical and lumbar enlargements of the spinal cord in young and adult rhesus monkeys. Exp. Neurol. 59:492-508. Biederman J, Spencer T. (1999) Biol. 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