Cerebral Cortex Advance Access published March 2, 2014 Cerebral Cortex doi:10.1093/cercor/bhu028
Anterior Cingulate Cortex Cells Identify Process-Specific Errors of Attentional Control Prior to Transient Prefrontal-Cingulate Inhibition Chen Shen1, Salva Ardid1, Daniel Kaping1, Stephanie Westendorff1, Stefan Everling2 and Thilo Womelsdorf1,2 1
Department of Biology, Centre for Vision Research, York University, Toronto, Ontario, Canada M6J 1P3 and 2Department of Physiology and Pharmacology, Western University, London, Ontario, Canada N6A 5K8 Address correspondence to Dr Thilo Womelsdorf, Department of Biology, Centre for Vision Research, York University, 4700 Keele Street, Toronto, Ontario, Canada M3J 1P3. Email: [email protected]
Keywords: anterior cingulate cortex, cognitive control, dorsolateral prefrontal cortex, error detection, inhibitory interneurons
Introduction The anterior cingulate cortex (ACC) has been suggested to be of particular relevance to optimize attentional control processes, required for a wide variety of tasks (Hayden et al. 2011; Hyman et al. 2011; Euston et al. 2012; Holroyd and Yeung 2012; Khamassi et al. 2013; Narayanan et al. 2013; Shenhav et al. 2013). Lesion and recording studies have shown that the ACC is critical (1) to optimize choice behavior from integrating reward histories of prior choices, (2) to convey stimulus- and action-specific reward expectations (Kennerley et al. 2006; Quilodran et al. 2008; Kaping et al. 2011; Luk and Wallis 2013), and (3) to provide early onset neuronal task control signals specifically after erroneous or suboptimal choices have been made (Johnston et al. 2007; Quilodran et al. 2008; Womelsdorf et al. 2010; Bryden et al. 2011). According to recent conceptualizations, the ACC accomplishes such a high-level control function by evaluating whether behavioral outcomes indicate the need to adjust control demands for better future performances (Khamassi et al. 2013; Shenhav et al. 2013). Consistent with this view, cell populations in the ACC are sensitive to a variety of different outcomes (Alexander and Brown 2011): Subsets of 7–15% of © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: [email protected]
cells in the ACC encode separable types of outcomes, including positive and negative reward prediction errors (Matsumoto et al. 2007; Sallet et al. 2007; Quilodran et al. 2008), unsigned prediction errors including surprising events (Hayden et al. 2011; Kennerley et al. 2011), and outcome signals that vary with the expected reward magnitude (Amiez et al. 2005; Seo and Lee 2007). These diverse outcome-specific firing-rate modulations in the ACC have in common that they can be used to adjust stimulus- and response-outcome predictions in future trials (Alexander and Brown 2011; Khamassi et al. 2013; see also Holroyd and Coles 2008). However, beyond optimizing reward expectancies, subjects utilize errors to identify whether they allocated sufficient attentional control to task aspects to accomplish desired goals. This perspective emphasizes that errors act as interruptions of ongoing processes, serve as “circuit breakers,” and function as an immediate “vigilance signals” to identify what processing aspects needs refinement (Dehaene et al. 1998; Alexander and Brown 2011; Hayden et al. 2011; Shenhav et al. 2013). This control-adjusting function of errors has recently been formalized in the Expected Value of Control (EVC) framework (Shenhav et al. 2013). The EVC predicts that the ACC is functionally specialized to track information about current processing states, to rapidly detect errors (changes in processing states), and to specify which processes require enhanced control in subsequent trials. According to this framework, separate cells in the ACC should not only detect erroneous outcomes, but should also be specifically tuned to the attentional processing requirements that failed at the time of error commission. Here, we tested this “process specificity” of attentional error signals in the ACC and across a large extent of medial and lateral prefrontal cortices of macaques. Monkeys performed an attention task that dissociated errors originating from failures of attentional focusing, attentional filtering, and stimulusresponse mappings. Our results reveal an apparent functional specialization of ACC (area 24) to encode specific errors of attentional control, and suggest that ACC error detection triggers a widespread posterror inhibition that disengages ACC and lateral PFC from ongoing task processing.
Materials and Methods Experimental Procedures and Paradigm We collected data from 2 male macaques following the guidelines of the Canadian Council of Animal Care on the use of laboratory animals and of Western University’s Council on Animal Care. The following experimental procedures and data acquisition protocols have been described in detail in Kaping et al. (2011).
Downloaded from http://cercor.oxfordjournals.org/ at Universita' degli Studi Roma La Sapienza on April 21, 2014
Errors indicate the need to adjust attention for improved future performance. Detecting errors is thus a fundamental step to adjust and control attention. These functions have been associated with the dorsal anterior cingulate cortex (dACC), predicting that dACC cells should track the specific processing states giving rise to errors in order to identify which processing aspects need readjustment. Here, we tested this prediction by recording cells in the dACC and lateral prefrontal cortex (latPFC) of macaques performing an attention task that dissociated 3 processing stages. We found that, across prefrontal subareas, the dACC contained the largest cell populations encoding errors indicating (1) failures of inhibitory control of the attentional focus, (2) failures to prevent bottom-up distraction, and (3) lapses when implementing a choice. Error-locked firing in the dACC showed the earliest latencies across the PFC, emerged earlier than reward omission signals, and involved a significant proportion of putative inhibitory interneurons. Moreover, early onset errorlocked response enhancement in the dACC was followed by transient prefrontal-cingulate inhibition, possibly reflecting active disengagement from task processing. These results suggest a functional specialization of the dACC to track and identify the actual processes that give rise to erroneous task outcomes, emphasizing its role to control attentional performance.
Extracellular Recordings Extracellular recordings commenced with 1–6 tungsten microelectrodes (impedance 1.2–2.2 MΩ, FHC, Bowdoinham, ME, USA) through standard recording chambers (19 mm inner diameter) implanted over the left hemisphere in both monkeys. The recording chambers allowed access to more anterior aspects of the prefrontal cortex and cingulate sulcus in both animals (see Kaping et al. 2011 and see below the section reconstruction of Recording Sites). Electrodes were lowered in guide tubes with software controlled precision microdrives (NAN Instruments Ltd, Israel) on a daily basis, through a recording grid with 1 mm interhole spacing. Before recordings began, anatomical 7-T magnetic resonance imagings (MRIs) were obtained from both monkey’s to allow reconstruction of electrode trajectories and recording sites.
Visual Stimulation Stimuli were presented on a 19′ CRT monitor placed 57 cm from the monkey’s eyes, running at 1024 × 768 pixel resolution and 85-Hz refresh rate. Behavioral control and visual stimulation were accomplished with Pentium III PCs running the open-source software Monkeylogic (http:// www.monkeylogic.net/), which has been benchmarked and validated in 2 previous publications (Asaad and Eskandar 2008a, 2008b). We used grating stimuli with “rounded off” edges moving within a circular aperture at 1.0° per sec, a spatial frequency of 1.4°, and radius of 1.5–2.2°. Gratings were presented at 4.2° eccentricity to the left and right of fixation. The grating on the left (right) side always moved within the aperture upwards at −45° (+45°) relative to vertical. Monkeys had to detect a transient and smooth clockwise/counterclockwise rotation of the grating movement (see below). The rotation was adjusted to ensure ≥85% of overall correct responses to the grating and ranged between ±13° and ±19°. The rotation proceeded smoothly from standard direction of motion toward maximum tilt within 60 ms, staying at maximum tilt for 235 ms, and rotating back to the standard direction within 60 ms, and continued moving at the standard ±45° thereafter. Experimental Paradigm Monkeys performed a selective attention task requiring a twoalternative, forced-choice discrimination on the attended stimulus (Fig. 1A). Monkeys initiated a trial by directing their gaze to a centrally presented, gray fixation point. Following a fixed 0.4-s period with 2 black/white moving grating stimuli, the moving grating stimuli were colored red/green (“color cue” onset). The location of the red and green grating color was randomized across trials. Within 0.05–0.75 s after color onset, the central fixation point changed to red or green cueing the monkeys to covertly shift attention toward the location with the color-matching stimulus. We label the period of sustained spatial attention the “Attention Epoch” (Fig. 1A, time epoch with red-colored solid line). Errors during the Attention Epoch were fixation breaks— indicating failures to control covert peripheral attention and central fixation—and triggered abortion of the trial. At random times (drawn from a uniform distribution) within 0.05–4 s after cue onset, the cued
2 Error Detection in Anterior Cingulate and Lateral Prefrontal Cortex
•
Shen et al.
Reconstruction of Recording Sites The anatomical site of each recorded neuron was reconstructed and projected onto a flat map representation of a normalized, average macaque brain (see Kaping et al. 2011), which allowed assigning each recorded location to a region on a two-dimensional (2D) flat map (Fig. 1E, see also Supplementary Figs 3 and 4, and Fig. 5). We used the area subdivision scheme outlined by Barbas and Zikopoulus (2007) and refer to prefrontal areas 46, 8, and 9 as “lateral prefrontal cortex” (latPFC), area 24 as the ACC, and area 32 as the (ventro-) medial prefrontal cortex (medPFC) (see also Passingham and Wise 2012). Very similar area assignments would follow when considering 2 other major anatomical subdivision schemes of the prefrontal and cingulate cortex (Petrides and Pandya 2007; Saleem et al. 2013; see Supplementary Fig. 4). To briefly summarize the reconstruction steps, we began by projecting each electrodes trajectory onto the 2D brain slice obtained from 7-T anatomical MRIs, using the open-source OsiriX Imaging software and custom-written Matlab programs (Mathworks, Inc., Natick, MA, USA), and utilizing the iodine visualized electrode trajectory within the electrode grid placed within the recording chamber during MR scanning. We drew the coronal outline of the cortical folding of the MR gray scale image to ease the comparison of the individual monkey brain slices to standard anatomical atlases, and to ease using major landmarks the projection of the electrode tip position into the standardized F99 brain available in Caret (Van Essen et al. 2001). Note that we initially reproduced the individual monkey brains within the Caret software to validate similarity and to derive the scaling factors to match the lower resolution monkey MRs to the higher resolution standard F99 brain. We then projected manually and under visual guidance the electrode position to the matched location in the standard F99 brain in Caret. We estimate that the complete procedure from documenting precisely the recording depth, identification of the recording location in the monkeys MR slice, and up to the placement of the electrode position in the standard F99 brain introduces a potential maximal error of 3 mm. However, we felt that despite this potential distortion, which we cannot rule out despite our confidence that the typical (unsystematic) error is more in the 0.05). In addition, we applied Akaike’s and Bayesian information criteria for the two- versus one-Gaussian model to determine whether using extra parameters in the two-Gaussian model is justified (Fig. 8A). In both cases, the information criteria decreased (from −669.6 to −808.9 and from −661.7 to −788.9, respectively), confirming that the two-Gaussian model is
Downloaded from http://cercor.oxfordjournals.org/ at Universita' degli Studi Roma La Sapienza on April 21, 2014
Analysis of the Behavioral Consequences of Errors To quantify the behavioral consequence of the different types of errors, we calculated for each experimental session the accuracy in trials following the errors relative to the accuracy in the trial that preceded the error. Sparse errors (with a cutoff of
Anterior Cingulate Cortex Cells Identify Process-Specific Errors of Attentional Control Prior to Transient Prefrontal-Cingulate Inhibition Chen Shen1, Salva Ardid1, Daniel Kaping1, Stephanie Westendorff1, Stefan Everling2 and Thilo Womelsdorf1,2 1
Department of Biology, Centre for Vision Research, York University, Toronto, Ontario, Canada M6J 1P3 and 2Department of Physiology and Pharmacology, Western University, London, Ontario, Canada N6A 5K8 Address correspondence to Dr Thilo Womelsdorf, Department of Biology, Centre for Vision Research, York University, 4700 Keele Street, Toronto, Ontario, Canada M3J 1P3. Email: [email protected]
Keywords: anterior cingulate cortex, cognitive control, dorsolateral prefrontal cortex, error detection, inhibitory interneurons
Introduction The anterior cingulate cortex (ACC) has been suggested to be of particular relevance to optimize attentional control processes, required for a wide variety of tasks (Hayden et al. 2011; Hyman et al. 2011; Euston et al. 2012; Holroyd and Yeung 2012; Khamassi et al. 2013; Narayanan et al. 2013; Shenhav et al. 2013). Lesion and recording studies have shown that the ACC is critical (1) to optimize choice behavior from integrating reward histories of prior choices, (2) to convey stimulus- and action-specific reward expectations (Kennerley et al. 2006; Quilodran et al. 2008; Kaping et al. 2011; Luk and Wallis 2013), and (3) to provide early onset neuronal task control signals specifically after erroneous or suboptimal choices have been made (Johnston et al. 2007; Quilodran et al. 2008; Womelsdorf et al. 2010; Bryden et al. 2011). According to recent conceptualizations, the ACC accomplishes such a high-level control function by evaluating whether behavioral outcomes indicate the need to adjust control demands for better future performances (Khamassi et al. 2013; Shenhav et al. 2013). Consistent with this view, cell populations in the ACC are sensitive to a variety of different outcomes (Alexander and Brown 2011): Subsets of 7–15% of © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: [email protected]
cells in the ACC encode separable types of outcomes, including positive and negative reward prediction errors (Matsumoto et al. 2007; Sallet et al. 2007; Quilodran et al. 2008), unsigned prediction errors including surprising events (Hayden et al. 2011; Kennerley et al. 2011), and outcome signals that vary with the expected reward magnitude (Amiez et al. 2005; Seo and Lee 2007). These diverse outcome-specific firing-rate modulations in the ACC have in common that they can be used to adjust stimulus- and response-outcome predictions in future trials (Alexander and Brown 2011; Khamassi et al. 2013; see also Holroyd and Coles 2008). However, beyond optimizing reward expectancies, subjects utilize errors to identify whether they allocated sufficient attentional control to task aspects to accomplish desired goals. This perspective emphasizes that errors act as interruptions of ongoing processes, serve as “circuit breakers,” and function as an immediate “vigilance signals” to identify what processing aspects needs refinement (Dehaene et al. 1998; Alexander and Brown 2011; Hayden et al. 2011; Shenhav et al. 2013). This control-adjusting function of errors has recently been formalized in the Expected Value of Control (EVC) framework (Shenhav et al. 2013). The EVC predicts that the ACC is functionally specialized to track information about current processing states, to rapidly detect errors (changes in processing states), and to specify which processes require enhanced control in subsequent trials. According to this framework, separate cells in the ACC should not only detect erroneous outcomes, but should also be specifically tuned to the attentional processing requirements that failed at the time of error commission. Here, we tested this “process specificity” of attentional error signals in the ACC and across a large extent of medial and lateral prefrontal cortices of macaques. Monkeys performed an attention task that dissociated errors originating from failures of attentional focusing, attentional filtering, and stimulusresponse mappings. Our results reveal an apparent functional specialization of ACC (area 24) to encode specific errors of attentional control, and suggest that ACC error detection triggers a widespread posterror inhibition that disengages ACC and lateral PFC from ongoing task processing.
Materials and Methods Experimental Procedures and Paradigm We collected data from 2 male macaques following the guidelines of the Canadian Council of Animal Care on the use of laboratory animals and of Western University’s Council on Animal Care. The following experimental procedures and data acquisition protocols have been described in detail in Kaping et al. (2011).
Downloaded from http://cercor.oxfordjournals.org/ at Universita' degli Studi Roma La Sapienza on April 21, 2014
Errors indicate the need to adjust attention for improved future performance. Detecting errors is thus a fundamental step to adjust and control attention. These functions have been associated with the dorsal anterior cingulate cortex (dACC), predicting that dACC cells should track the specific processing states giving rise to errors in order to identify which processing aspects need readjustment. Here, we tested this prediction by recording cells in the dACC and lateral prefrontal cortex (latPFC) of macaques performing an attention task that dissociated 3 processing stages. We found that, across prefrontal subareas, the dACC contained the largest cell populations encoding errors indicating (1) failures of inhibitory control of the attentional focus, (2) failures to prevent bottom-up distraction, and (3) lapses when implementing a choice. Error-locked firing in the dACC showed the earliest latencies across the PFC, emerged earlier than reward omission signals, and involved a significant proportion of putative inhibitory interneurons. Moreover, early onset errorlocked response enhancement in the dACC was followed by transient prefrontal-cingulate inhibition, possibly reflecting active disengagement from task processing. These results suggest a functional specialization of the dACC to track and identify the actual processes that give rise to erroneous task outcomes, emphasizing its role to control attentional performance.
Extracellular Recordings Extracellular recordings commenced with 1–6 tungsten microelectrodes (impedance 1.2–2.2 MΩ, FHC, Bowdoinham, ME, USA) through standard recording chambers (19 mm inner diameter) implanted over the left hemisphere in both monkeys. The recording chambers allowed access to more anterior aspects of the prefrontal cortex and cingulate sulcus in both animals (see Kaping et al. 2011 and see below the section reconstruction of Recording Sites). Electrodes were lowered in guide tubes with software controlled precision microdrives (NAN Instruments Ltd, Israel) on a daily basis, through a recording grid with 1 mm interhole spacing. Before recordings began, anatomical 7-T magnetic resonance imagings (MRIs) were obtained from both monkey’s to allow reconstruction of electrode trajectories and recording sites.
Visual Stimulation Stimuli were presented on a 19′ CRT monitor placed 57 cm from the monkey’s eyes, running at 1024 × 768 pixel resolution and 85-Hz refresh rate. Behavioral control and visual stimulation were accomplished with Pentium III PCs running the open-source software Monkeylogic (http:// www.monkeylogic.net/), which has been benchmarked and validated in 2 previous publications (Asaad and Eskandar 2008a, 2008b). We used grating stimuli with “rounded off” edges moving within a circular aperture at 1.0° per sec, a spatial frequency of 1.4°, and radius of 1.5–2.2°. Gratings were presented at 4.2° eccentricity to the left and right of fixation. The grating on the left (right) side always moved within the aperture upwards at −45° (+45°) relative to vertical. Monkeys had to detect a transient and smooth clockwise/counterclockwise rotation of the grating movement (see below). The rotation was adjusted to ensure ≥85% of overall correct responses to the grating and ranged between ±13° and ±19°. The rotation proceeded smoothly from standard direction of motion toward maximum tilt within 60 ms, staying at maximum tilt for 235 ms, and rotating back to the standard direction within 60 ms, and continued moving at the standard ±45° thereafter. Experimental Paradigm Monkeys performed a selective attention task requiring a twoalternative, forced-choice discrimination on the attended stimulus (Fig. 1A). Monkeys initiated a trial by directing their gaze to a centrally presented, gray fixation point. Following a fixed 0.4-s period with 2 black/white moving grating stimuli, the moving grating stimuli were colored red/green (“color cue” onset). The location of the red and green grating color was randomized across trials. Within 0.05–0.75 s after color onset, the central fixation point changed to red or green cueing the monkeys to covertly shift attention toward the location with the color-matching stimulus. We label the period of sustained spatial attention the “Attention Epoch” (Fig. 1A, time epoch with red-colored solid line). Errors during the Attention Epoch were fixation breaks— indicating failures to control covert peripheral attention and central fixation—and triggered abortion of the trial. At random times (drawn from a uniform distribution) within 0.05–4 s after cue onset, the cued
2 Error Detection in Anterior Cingulate and Lateral Prefrontal Cortex
•
Shen et al.
Reconstruction of Recording Sites The anatomical site of each recorded neuron was reconstructed and projected onto a flat map representation of a normalized, average macaque brain (see Kaping et al. 2011), which allowed assigning each recorded location to a region on a two-dimensional (2D) flat map (Fig. 1E, see also Supplementary Figs 3 and 4, and Fig. 5). We used the area subdivision scheme outlined by Barbas and Zikopoulus (2007) and refer to prefrontal areas 46, 8, and 9 as “lateral prefrontal cortex” (latPFC), area 24 as the ACC, and area 32 as the (ventro-) medial prefrontal cortex (medPFC) (see also Passingham and Wise 2012). Very similar area assignments would follow when considering 2 other major anatomical subdivision schemes of the prefrontal and cingulate cortex (Petrides and Pandya 2007; Saleem et al. 2013; see Supplementary Fig. 4). To briefly summarize the reconstruction steps, we began by projecting each electrodes trajectory onto the 2D brain slice obtained from 7-T anatomical MRIs, using the open-source OsiriX Imaging software and custom-written Matlab programs (Mathworks, Inc., Natick, MA, USA), and utilizing the iodine visualized electrode trajectory within the electrode grid placed within the recording chamber during MR scanning. We drew the coronal outline of the cortical folding of the MR gray scale image to ease the comparison of the individual monkey brain slices to standard anatomical atlases, and to ease using major landmarks the projection of the electrode tip position into the standardized F99 brain available in Caret (Van Essen et al. 2001). Note that we initially reproduced the individual monkey brains within the Caret software to validate similarity and to derive the scaling factors to match the lower resolution monkey MRs to the higher resolution standard F99 brain. We then projected manually and under visual guidance the electrode position to the matched location in the standard F99 brain in Caret. We estimate that the complete procedure from documenting precisely the recording depth, identification of the recording location in the monkeys MR slice, and up to the placement of the electrode position in the standard F99 brain introduces a potential maximal error of 3 mm. However, we felt that despite this potential distortion, which we cannot rule out despite our confidence that the typical (unsystematic) error is more in the 0.05). In addition, we applied Akaike’s and Bayesian information criteria for the two- versus one-Gaussian model to determine whether using extra parameters in the two-Gaussian model is justified (Fig. 8A). In both cases, the information criteria decreased (from −669.6 to −808.9 and from −661.7 to −788.9, respectively), confirming that the two-Gaussian model is
Downloaded from http://cercor.oxfordjournals.org/ at Universita' degli Studi Roma La Sapienza on April 21, 2014
Analysis of the Behavioral Consequences of Errors To quantify the behavioral consequence of the different types of errors, we calculated for each experimental session the accuracy in trials following the errors relative to the accuracy in the trial that preceded the error. Sparse errors (with a cutoff of
