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. Author manuscript; available in PMC: 2020 Oct 1.
Published in final edited form as: J Neurosci Res. 2019 Aug 5;98(4):680–691. doi: 10.1002/jnr.24506

Cingulo-opercular adaptive control for younger and older adults during a challenging gap detection task

Kenneth I Vaden Jr 1, Mark A Eckert 1, Judy R Dubno 1, Kelly C Harris 1
PMCID: PMC7000297  NIHMSID: NIHMS1535221  PMID: 31385349

Abstract

Cingulo-opercular activity is hypothesized to reflect an adaptive control function that optimizes task performance through adjustments in attention and behavior, and outcome monitoring. While auditory perceptual task performance appears to benefit from elevated activity in cingulo-opercular regions of frontal cortex before stimuli are presented, this association appears reduced for older adults compared to younger adults. However, adaptive control function may be limited by difficult task conditions for older adults. An fMRI study was used to characterize adaptive control differences while 15 younger (average age = 24 years) and 15 older adults (average age = 68 years) performed a gap detection in noise task designed to limit age-related differences. During the fMRI study, participants listened to a noise recording and indicated with a button-press whether it contained a gap. Stimuli were presented between sparse fMRI scans (TR = 8.6 s) and BOLD measurements were collected during separate listening and behavioral response intervals. Age-related performance differences were limited by presenting gaps in noise with durations calibrated at or above each participant’s detection threshold. Cingulo-opercular BOLD increased significantly throughout listening and behavioral response intervals, relative to a resting baseline. Correct behavioral responses were significantly more likely on trials with elevated pre-stimulus cingulo-opercular BOLD, consistent with an adaptive control framework. Cingulo-opercular adaptive control estimates appeared higher for participants with better gap sensitivity and lower response bias, irrespective of age, which suggests that this mechanism can benefit performance across the lifespan under conditions that limit age-related performance differences.

Keywords: Aging, Attention, Gap Detection in Noise, Adaptive Control, fMRI

Graphical Abstract

graphic file with name nihms-1535221-f0006.jpg

Cingulo-opercular activity was associated with gap detection in noise for younger and older adults. This association increased with better gap sensitivity and lower response bias, regardless of age group. These results suggest that adaptive control can enhance task performance across the lifespan when age-related declines in task ability are controlled.

1. Introduction

Adjustments in cognitive resources, strategy, and behavior are hypothesized to benefit task performance (i.e., adaptive control function) more extensively when task conditions are challenging but not impossibly difficult (Eckert, Teubner-Rhodes, & Vaden, 2016). Because age-related declines in auditory perception (e.g., poorer temporal acuity) and cognition (e.g., attention lapses) may increase the difficulty of challenging auditory task conditions, poorer task performance may limit adaptive control function for older adults. The current gap detection in noise study tested the prediction that adaptive control function can benefit younger and older adults to a similar extent when age-related performance differences are limited.

Attention engagement and maintenance can benefit performance in challenging perceptual tasks (Carter, Macdonald, Botvinick, et al., 2000; Kerns, Cohen, MacDonald, et al., 2004), including speech recognition in noise (Vaden, Kuchinsky, Cute, et al., 2013) and auditory target detection (Coste & Kleinschmidt, 2016; Sadaghiani, Hesselmann, & Kleinschmidt, 2009). Cingulo-opercular regions in frontal cortex are hypothesized to facilitate a domain-general adaptive control function by adjusting attention or behavior, and monitoring outcomes (Eckert et al., 2016; Vaden et al., 2013). These regions appear sensitive to task performance based on robust activations when there is response uncertainty and response errors (Dosenbach, Visscher, Palmer, et al., 2006). Moreover, pre-stimulus activity in these regions is associated with enhanced trial-level task performance for a range of tasks, which include Stroop color-naming (Coste, Sadaghiani, Friston, & Kleinschmidt, 2011; Kerns et al., 2004), auditory target detection (Coste & Kleinschmidt, 2016; Sadaghiani et al., 2009), and speech recognition in noise (Vaden, Kuchinsky, Ahlstrom, Dubno, & Eckert, 2015; Vaden, Kuchinsky, Ahlstrom, Teubner-Rhodes, et al., 2015; Vaden et al., 2013). Complementing those observations, lower cingulo-opercular activity has been linked to lapses in attention and poorer subsequent task performance (Eichele, Debener, Calhoun, et al., 2008; Vaden, Teubner-Rhodes, Ahlstrom, Dubno, & Eckert, 2017; Weissman, Roberts, Visscher, & Woldorff, 2006).

More extensive cingulo-opercular activation is commonly observed for older adults compared to younger adults during the performance of cognitive and perceptual tasks (Eckert et al., 2016; Erb & Obleser, 2013; Reuter-Lorenz & Cappell, 2008). Because these frontal cortex regions also increase activity in more difficult task conditions (Alain, Du, Bernstein, Barten, & Banai, 2018; Dosenbach, Fair, Miezin, et al., 2007; Eckert, Menon, Walczak, et al., 2009), age-related activity increases have been interpreted as reflecting greater task difficulty experienced by older adults compared to younger adults (Erb & Obleser, 2013; Meinzer, Flaisch, Seeds, et al., 2012; Reuter-Lorenz & Cappell, 2008; Stern, Rakitin, Habeck, et al., 2012). That is, cingulo-opercular support of task performance appears to be needed to a greater extent in older adults. However, cingulo-opercular activity appears to provide relatively limited support for speech recognition in noise for older adults than younger adults (Vaden, Kuchinsky, Ahlstrom, Dubno, et al., 2015). Different cortical systems could be engaged for speech recognition by younger and older adults in response to different levels of perceived task demand, consistent with task-dependent differences in peak activity (Alain et al., 2018). Because few studies have examined cingulo-opercular adaptive control for younger and older adults, the extent of age-related change is unclear.

Age-related differences in adaptive control function may be obscured by perceptual or cognitive declines, especially when older adults exhibit poorer performance (Schneider-Garces, Gordon, Brumback-Peltz, et al., 2010). The current study used a gap detection in noise task to limit the influence of age-related perceptual declines (i.e., poorer temporal acuity) by presenting gaps calibrated to each participant’s threshold. Gaps in noise were presented with repetitive and fixed onset timing (see Materials and Methods) to limit the variation in adaptive control function that could reflect age-related differences in task demand and performance. Because auditory target detection studies have previously shown pre-stimulus cingulo-opercular activity related to response accuracy (Coste & Kleinschmidt, 2016; Sadaghiani et al., 2009), we reasoned that adaptive control function was likely to be observed during gap detection in noise. For these reasons, a gap detection in noise task was used to examine adaptive control function for younger and older adults while minimizing age-related task performance differences.

Previous fMRI studies with auditory target detection tasks have produced results that are consistent with a hypothesized cingulo-opercular adaptive control function. For example, cingulo-opercular activity prior to the target presentation was significantly higher on “hit” trials compared to “miss” trials (Coste & Kleinschmidt, 2016; Sadaghiani et al., 2009). Because activity appeared to modulate sensitivity to unpredictable targets with variable onsets and inter-stimulus intervals in both of those studies, these differences in brain activity were interpreted as variation in tonic attention (i.e., “non-selective attention”, Coste and Kleinschmidt, 2016) and related to endogenous feedback between frontal cortex and brainstem structures, such as the locus coeruleus (Sturm, Longoni, Fimm, et al., 2004; Sturm & Willmes, 2001). Based on these studies, we predicted that participants were more likely to detect gaps on trials with elevated pre-stimulus cingulo-opercular activity.

Older adults typically demonstrate longer gap detection thresholds compared to younger adults (John, Hall, & Kreisman, 2012; Ozmeral, Eddins, Frisina, & Eddins, 2016; Palmer & Musiek, 2014; Schneider, Pichora-Fuller, Kowalchuk, & Lamb, 1994; Snell, 1997; Snell & Frisina, 2000; Zendel & Alain, 2012; although c.f. Moore et al., 1992). Attention declines also appear to contribute to poorer gap detection, based on more robust and consistent age effects in studies that present more complex gap stimuli. For example, age-related differences in gap detection are largest when a gap onset is located near the stimulus onset or offset, or varied randomly (He, Horwitz, Dubno, & Mills, 1999), or when there is a frequency disparity between the sounds surrounding a gap (Lister, Besing, & Koehnke, 2002). Attention-modulated neural activity during gap detection also differs with age and processing speed (Harris, Wilson, Eckert, & Dubno, 2012). Because less predictable gap stimuli place greater demands on sustained attention, these observations suggest that attention declines interact with poorer temporal acuity for many older adults (Alain, McDonald, Ostroff, & Schneider, 2004; Harris, Eckert, Ahlstrom, & Dubno, 2010; Harris et al., 2012; He et al., 1999). Based on these findings, the current study presented threshold-calibrated gaps in noise with repetitive and consistent onset timing to minimize task demand for attention and limit age-related performance differences.

The current study was designed to examine cingulo-opercular adaptive control for younger and older adults, while experimentally limiting performance differences. We reasoned that age-related differences in temporal acuity and gap sensitivity can produce differences in cingulo-opercular adaptive control (Meinzer et al., 2012; Shenhav, Botvinick, & Cohen, 2013) and age-related activity differences (Eckert et al., 2016; Schneider-Garces et al., 2010). Gap detection thresholds were measured for each participant prior to the neuroimaging study to limit task performance differences across participants during the fMRI task. That is, cortical activity was measured in the scanner while participants listened to gaps whose durations were equal to their duration threshold and gaps that were slightly longer than their duration threshold (+2.5 ms re: threshold). This allowed us to characterize age-related differences in adaptive control when task performance was similar for younger and older adults.

2. Materials and Methods

Participants

The current study included a total of 30 participants, with a group of 15 younger adults (average age = 24.3 ± 3.3 years) and 15 older adults (average age = 68.2 ± 7.9 years). The sample size (N = 15 per group) was based on power analyses that indicated age-related gap detection threshold differences (Harris et al., 2010; Snell, 1997) would replicate at p = 0.05 significance (one-tailed) with 85% power [Snell: delta = 0.96, sd = 0.87; Harris: delta = 3.33, sd = 3.22]. Both groups in the current study were predominantly female, with 11 females and 4 males in the younger group, and 10 females and 5 males in the older group. This distribution was not suitable for exploratory analyses of differences due to participant sex.

Pure-tone thresholds for both ears were measured at conventional audiometric frequencies using a Madsen OB922 audiometer and TDH-39 headphones (American National Standards Institute, 2004, 2010). Based on the audiometric screening criteria, each participant had pure-tone thresholds ≤ 25 dB HL from 250 to 4000 Hz in the right ear (see Figure 1, left panel), which was the test ear. The right ear average pure-tone thresholds in that frequency range were significantly higher for the older adult participants compared to the younger participants (Welch t25 = 4.88, p < 0.001). Participants were also screened on the basis of a brief cognitive assessment, language background, and medical information. Each participant was a native English speaker. All participants had Mini-Mental State Examination scores ≥ 27 out of 30, indicating little or no cognitive impairment (Folstein, Robins, & Helzer, 1983). All of the participants completed a medical background questionnaire. None of the participants reported a history of neurological or psychiatric events. No study participants were excluded based on incidental findings or excessive head motion. Informed consent was obtained in compliance with the Institutional Review Board at the Medical University of South Carolina (MUSC).

Figure 1.

Figure 1.

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Mean audiograms from the right ear for the younger and older adults are plotted in the left panel. The right panel shows the average psychometric functions for each age group, with mean percent “yes” responses (i.e., gap detection). The error bars in both panels represent the standard error of the mean (SEM).

Experimental design

Behavioral Gap Detection Task

Gap thresholds were measured for each participant using a procedure similar to Harris et al. (2010). Noise stimuli were generated with Labview software (Labview version 8.5, National Instruments), converted to analog with a sampling rate of 50 kHz using a 16-bit digital-to-analog converter (Model 6052E, National Instruments), low pass filtered at 5 kHz (TDT PF1), attenuated (TDT PA4), buffered (TDT HB5), then presented monaurally to the right ear (TDH-39 headphone). Presentation level was calibrated at 80 dB SPL with a Larson-Davis sound level meter, while noise stimuli were monitored electronically with an oscilloscope. The noise was 500 ms in duration and contained gaps with 0, 2, 4, 6, 8 10, or 12 ms durations. Transitions between noise and silent gaps had 0 ms rise/fall times, and were constrained to start and end amplitudes = 0 to limit the spread of spectral energy. Each gap was centered in the noise, with identical noise duration before and after.

Thresholds were measured in a sound-treated room using the method of constant stimuli. Participants were instructed to press a button labeled “yes” when they heard a gap, and “no” if not. Each tested gap duration (0 to 12 ms) was presented five times in a pseudorandomized order, for a total of 35 trials per block. Participants performed 2-3 blocks, with a third block of trials if there was a false alarm response during the first two blocks (i.e., a “yes” response for 0 ms gap duration). Prior to data collection, each participant performed several practice sessions to familiarize them with the stimuli and task. A logistic function was fit to the psychometric function from each participant (i.e., gap durations and response proportion data; Green, 1993), which yielded the following average parameters for each age group: myoung = 4.08, kyoung = 7.2, αyoung = 0.053; mold = 5.24, kold = 7.2, αold = 0.058 (Eq. 1, He et al., 1999). Gap thresholds were derived for each individual as the duration with a 70% probability of a “yes” response (Harris et al., 2010; He et al., 1999). Suprathreshold gap durations for the fMRI task were calculated by adding 2.5 ms to each participant’s gap duration threshold. The average psychometric functions for younger and older adults are shown in Figure 1 (right panel).

fMRI Gaps in Noise Stimuli

During the fMRI experiment, participants were presented noise stimuli with gap durations derived from the behaviorally measured gap detection thresholds described earlier. The gap presentation conditions included: 1) no gaps, 2) gap durations equal to gap detection thresholds (threshold gaps), and 3) gap durations 2.5 ms longer than threshold gaps (suprathreshold gaps). All task trials included a 17.2 s noise stimulus that was presented across two 8.6 s intervals between sparse fMRI scans (see Listening Interval in Figure 2A). Each trial with threshold or suprathreshold gaps included ten regularly spaced gaps in the noise. Gap onset timing was fixed to 3.02, 4.04, 5.06, 6.08, 7.10, 11.62, 12.64, 13.66, 14.68, and 15.70 s, relative to the noise onset. This repetitive gap presentation was designed to increase detection likelihood during the fMRI task trials, further limiting potential age-related variability in task performance. Noise stimuli were produced using Adobe Audition and digitally low pass filtered at 5 kHz to match the noise used in the behavioral task. The spread of spectral energy was limited by presenting gaps with fall and rise times = 0 ms, and start and end amplitudes = 0. Because variable gap timing can increase task demand for attention and potentially confound declines in temporal acuity (Harris et al., 2010; He et al., 1999), gap onset timing was not jittered in the current experiment. Fixed onset timing was used to limit age-related performance differences, similar to the use of calibrated threshold and suprathreshold gaps.

Figure 2.

Figure 2.

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A) Each fMRI trial included separate BOLD measurements for pre-stimulus activity, listening activity, and button-press response activity. During the listening interval, the participant was presented with a noise recording (shown with gaps in this example). During the response interval, the participant was cued to respond when the crosshair onscreen changed colors from white to red. B) The fMRI study included two runs. Each run consisted of a total of 39 task trials: 13 suprathreshold gaps, 13 threshold gaps, and 13 no-gap trials. Each condition is plotted with boxcar functions and response intervals are indicated by the red crosshairs. There were also 10 TRs (86 s) of quiet rest in each run with no task demands (gray), before and after the block of task trials. Together, the quiet rest periods at the end of Run 1 and start of Run 2 provided an approximately 3 m long rest interval between the blocked task trials.

fMRI Gap Detection Task

Participants were instructed to listen to noise presentations, then respond “yes” or “no” by button-press to indicate whether or not the noise contained any gaps during a cued response interval (red crosshair onscreen). Each trial consisted of a 17.2 s listening interval and 8.6 s response interval, based on the 8.6 s inter-scan interval of the sparse fMRI acquisition sequence (Figure 2A). Separate listening and response intervals were used to increase sensitivity to pre-stimulus activity, listening activity, and responding activity, compared to sparse fMRI experiments with shorter trials and fewer images per trial. Because presentation timing was synchronized with the scanner using E-Prime (Psychology Software Tools, Pittsburgh, PA), gaps in noise occurred during a relatively quiet period between fMRI acquisitions. The gap detection experiment was performed over two runs (138 functional images; duration = 19 m 47 s per run). Trials with threshold gaps, suprathreshold gaps, and noise without gaps were presented in a fixed, pseudorandomized condition order. The order of conditions was designed to limit the predictability of task conditions and increase sensitivity to individual differences. At the beginning and end of each run, there were 10 resting acquisitions during which no stimuli were presented (Figure 2B). Noise stimuli were presented monaurally to the right ear with an insert piezoelectric headphone (Sensimetrics) at 80 dB SPL, calibrated in the scanner control room with a Larson-Davis sound level meter. Participants viewed a projection screen through a headcoil-mounted periscope. A BrainLogics fiber optic button response unit (Series 1; Psychology Software Tools, Inc.) was used to record button-press responses during the experiment.

Analysis of Gap Detection Responses

During the fMRI task, the participants responded by button-press to indicate whether or not they detected a gap in the noise on each trial. Signal detection theory measures were used to characterize sensitivity to gaps in noise and response bias for each participant. Depending on whether the presentation included a gap or not, a “yes” response was counted as a hit or false alarm and a “no” response was counted as a correct rejection or a miss. Across participants, the analysis excluded 5.1 ± 6.3% of the trials based on invalid button-press responses (58 trials) or no response (62 trials). There were no significant differences in invalid or missing data across conditions or age groups (P > 0.48).

Nonparametric estimates of sensitivity (A′) and bias (B″D) were used because these are more independent (Donaldson, 1992) compared to parametric estimates (d′ and c) and may be more robust in conditions with non-normal signal and noise distributions (Stanislaw & Todorov, 1999). Sensitivity (A′) represents chance detection when A′ = 0.5 and perfect detection when A′ = 1. The bias estimate (B″D) represents the maximum false-positive bias when B″D = −1, no bias when B″D = 0, and maximum false-negative bias when B″D = 1 (Donaldson, 1992). Sensitivity and bias score differences were significance tested based on bootstrapped 95% confidence intervals (BCI) that were either strictly positive or strictly negative (R V3.3.1, Vienna, Austria; https://www.r-project.org, R Core Development Team, 2016; RRID:SCR_001905; R-package: boot, version 1.3.18).

Image Acquisition

Imaging data were acquired using a 3T MRI scanner (Siemens Trio) with a 32 channel head coil at the Center for Biomedical Imaging at MUSC. A structural image was collected for each participant with a T1-weighted MPRAGE sequence that consisted of 160 sagittal slices, 256 × 256 matrix, TR = 2250 ms, TE = 4.15 ms, inversion time = 900 ms, flip angle = 9°, slice thickness = 1 mm, and slice gap = 0 mm. The 3 mm isomorphic functional images were collected over two runs with 138 volumes each (276 total), using a T2*-weighted, single-shot echo planar image (EPI) sequence with 36 axial slices, 64 × 64 matrix, TR = 8600 ms, TE = 35 ms, flip angle = 90°, acquisition time = 1647 ms, slice thickness = 3 mm, slice gap = 0 mm, sequential order, and GRAPPA (acceleration factor = 2). Four EPI volumes were discarded prior to data collection (i.e., dummy scans).

Image Preprocessing

A study specific brain template was created that represented the average of the younger and older adult brains in the current study with the Advanced Normalization Tools (ANTS version 2.1; Avants and Gee, 2004; RRID:SCR_004757). Structural T1-weighted images and spatially coregistered functional images were spatially warped to match the study specific template using ANTS. The ANTS software was also used to find common atlas coordinates for our results, by warping statistic peaks from maps into Montreal Neurological Institute (MNI) coordinate space with parameters estimated from matching the study specific template to the MNI template.

Preprocessing steps for the BOLD contrast images consisted of 1) realignment, unwarping, and parameter estimation for head motion, 2) spatial coregistration of the functional images to the structural T1 images in native space, 3) spatial warping of the coregistered images into group anatomical space (ANTS), and 4) spatial smoothing with an 8 mm FWHM Gaussian kernel. SPM8 software (www.fil.ion.ucl.ac.uk/spm; RRID:SCR_007037) was used to perform steps 1, 2, and 4. All imaging analyses were performed within the study specific template space, because functional image data were spatially transformed during preprocessing. Each voxel time series was adjusted by removing global BOLD signal fluctuations (Macey, Macey, Kumar, & Harper, 2004). Three-dimensional head position and head movement vectors from the SPM motion correction algorithm were projected into two nuisance regressors based on the Pythagorean Theorem (Kuchinsky et al., 2012; Wilke, 2012; http://www.nitrc.org/projects/pythagoras). A spike detection algorithm was also used to identify BOLD contrast images with voxel or volume intensities ≥ 2.5 standard deviations from the average intensity across the time series (Vaden et al., 2010). The spike detection algorithm produced two binary vectors, which identified extreme values (i.e., spikes) for an average of 4.5 ± 1.1% of the images across participants. The Pythagorean head motion regressors and spike censorship vectors were used to limit artifact-related effects in the general linear model (GLM) and time series analyses, as described below.

Image Analyses

Consistent task-related changes in BOLD contrast were identified by fitting a GLM (SPM8 software) to the fMRI data from each participant and generating subject-level contrast maps. The GLM included event timing for the 1) auditory presentations and 2) button-press responses in a multiple regression model that predicted BOLD contrast. A more complex model was also used to test for effects of task errors on BOLD contrast, however significant effects were not observed. The GLM also included nuisance regressors for head motion and BOLD spikes. Apparent motion artifacts (i.e., activation at edges, skull, and ventricles) were eliminated by excluding trials from the analysis with head motion ≥ 95th percentile across participants and volumes. The volumes excluded from analyses above those criteria included changes in head position between 0.27 – 5.58 mm and angles between 0.004 – 0.08 degrees. The results of the GLM were used to identify increased BOLD contrast when participants listened to stimuli and responded, with the goal of functionally defining cingulo-opercular regions of frontal cortex that typically increase activity during the performance of challenging tasks.

Group level statistics were performed to identify consistent changes in BOLD contrast across participants during the task. The contrast maps produced at the subject-level included 1) Listen > Rest and 2) Respond > Rest, which were submitted to single-sample t-tests within each voxel. The BROCCOLI software (Eklund et al., 2014; https://github.com/wanderine/BROCCOLI; RRID:SCR_014093) was used to implement permutation tests to determine the family-wise error corrected PFWE < 0.05 cluster threshold. This method of multiple comparison correction was chosen to identify significant clusters with a more accurate false positive error rate compared to many commonly used parametric tests (Eklund, Nichols, & Knutsson, 2016). Significant results were defined with an uncorrected cluster-forming voxel statistic threshold of Z = 4.26, PUNC = 0.00001, in combination with an empirical family-wise error cluster extent threshold PFWE = 0.05. Group-level tests were also performed that compared contrast maps across age groups, but significant age differences were not observed in those results, including when the GLM adjusted for task performance.

Time Series Analysis

A functional time series analysis was performed to examine the extent to which pre-stimulus cingulo-opercular BOLD was related to likelihood of a correct response (i.e., hit or correct rejection). Functional regions of interest were defined within cingulo-opercular cortex as clusters that significantly increased activity during both 1) Listen and 2) Response, relative to the implicit resting baseline (Figure 2B). The BOLD contrast time series were averaged across voxels within each region. An average time series was calculated for cingulo-opercular regions that was weighted proportionally by the number of voxels in each region. Potential artifact-related effects were limited by excluding trials (12.9%) from the time series analyses based on extreme BOLD values and excessive head motion cutoffs (above 95th percentiles), which were established for the whole brain GLM analysis. The BOLD contrast in images collected during the gap detection task was normalized for each participant (m = 0, sd = 1). Thus, the analysis of pre-stimulus activity was performed with BOLD measurements that were normalized to indicate the magnitude of activity (in SD) relative to the other pre-stimulus BOLD measurements from the other trials. A generalized linear mixed model (GLMM) was specified for a logistic regression analysis to determine the extent to which the likelihood of a correct response at TR = t (PRFt) was affected by BOLD contrast prior to the stimulus presentation (three TRs earlier, BOLDt-3), with s for subject, r for run, and t for TR number: PRFs,r,t = BOLDs,r,t-3 + (1+BOLDs,r,t-3 | s,r) + error. For each participant, the association between BOLD contrast and response accuracy was transformed from a logit estimate for the probability of a correct response to determine the percent recognition increase for trials with cingulo-opercular BOLD contrast in the top and bottom quartiles (see Figure 5, later).

Control analyses were performed to address the possibility that task difficulty or incorrect responses accounted for the association between BOLD contrast and task performance, by including additional nuisance regressors in the GLMM for the gap duration condition (i.e., suprathreshold gap, threshold gap, no gap) on the current and preceding trial, and incorrect responses on the preceding trial. Consistent with the approach and rationale in earlier studies (Vaden, Kuchinsky, Ahlstrom, Dubno, et al., 2015; Vaden et al., 2013), it was expected that the nuisance regressors would eliminate significant activity-performance associations if prior response errors or uncertainty could account for shared variance.

Additional analyses were performed to determine the extent to which cingulo-opercular activity at other time points before the response was consistently associated with the likelihood of correct responses. Each test used the same regions of interest and method for scaling time-series, but selected BOLD at the time points immediately after the first gap presentation or immediately prior to the button response. The results were expected to clarify whether pre-stimulus cingulo-opercular activity was uniquely associated with subsequent response accuracy. The 8.6 s long intervals between measurements would also provide relatively clear temporal distinctions between activity prior to listening or prior to responding, which are often closer together in time.

4. Results

Behavioral Gap Detection Thresholds

Gap detection thresholds were significantly longer on average for older adults compared to younger adults (younger: 4.19 ms, older: 5.34 ms, difference 95% BCI = [0.64, 1.57]; t28 = 5.15, P < 0.001; see Figure 3). Gap detection thresholds were not significantly related to right ear average pure-tone thresholds from 250 to 4000 Hz (across groups: r28 = 0.35, P = 0.06; older: r13 = −0.18, P = 0.52; younger: r13 = −0.40, P = 0.13).

Figure 3.

Figure 3.

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Measured in a behavioral task prior to the fMRI session, asterisks denote significantly longer gap detection thresholds for the older adults than younger adults (left panel). The duration of the gaps in noise presented during the fMRI session were set to each individual participant’s measured gap detection threshold and 2.5 ms longer than the participant’s threshold. Using threshold and suprathreshold gaps in noise resulted in equivalent gap sensitivity (middle panel) and response bias (right panel) for the younger and older groups. Results were calculated across threshold and suprathreshold conditions, and are shown for the younger adults (red circles) and older adults (black circles). Horizontal lines indicate the average value for each group.

During the fMRI session with stimuli adjusted for participants’ gap detection thresholds, sensitivity to gaps in noise was significantly higher for suprathreshold than threshold gaps (average suprathreshold A′ = 0.72; average threshold A′ = 0.68; difference 95% BCI = [0.02, 0.06]; t29 = 3.74, P < 0.001). A conservative (i.e., false-negative) bias was observed for participant responses, which was greater for threshold compared to suprathreshold gaps (average threshold B″D = 0.86; average suprathreshold B″D = 0.84; difference 95% BCI = [0.04, 0.01]; t29 = 2.72, P = 0.01). Positive B″D values near 1 indicated that a fairly consistent false-negative bias was present, which means that a “no” response was more likely regardless of whether a gap was present. There were no significant age-group differences in A′ (average young: A′ = 0.79, average older: A′ = 0.79, difference 95% BCI = [−0.01, 0.03]; t28 = −0.82, P = 0.42) and B″D (average young: B″D = 0.87, average older: B″D = 0.83, difference 95% BCI = [−0.12, 0.05]; t28 = 0.99, P = 0.33). The age x condition interaction on sensitivity and bias were not significant [P > 0.09], indicating that condition effects were not different across age groups. These results indicate that adjusting the gap durations presented during the task successfully limited age differences in task performance during the fMRI experiment.

Task Effects on BOLD Contrast

During the listening and response portions of each gap detection trial in the fMRI task, significantly increased BOLD was observed in auditory cortices and cingulo-opercular cortices (see Supplementary Table 1 for a complete list). Figure 4A shows the cingulo-opercular regions activated during the listening and response portions of each trial, relative to resting baseline. Additional analyses did not show significant age group differences for these results.

Figure 4.

Figure 4.

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A) Cingulo-opercular activity increased during the gap detection task, during the listening intervals (red) and the response intervals (green) during each task trial (overlap: yellow). Stimuli were presented to the right ear, which may explain why significant listening-related activity only appears in the left primary auditory cortex. A combined voxel statistic: Z = 4.26, PUNC = 0.00001 and permutation-corrected cluster extent PFWE = 0.05 threshold was applied to each statistic map, shown on the average spatially normalized brains. B) Cingulo-opercular cortex (red) with significant Listen and Response activations (yellow overlap regions in 4A) were selected as regions of interest to examine the association between pre-stimulus activity and gap detection performance.

Cingulo-Opercular Activity and Trial-Level Performance

Average BOLD contrast time series were calculated for the cingulo-opercular regions of interest (Figure 4B), which were functionally defined by significant activation during listening and response portions of gap detection trials. The results of the GLMM-based time series analysis showed that correct responses were significantly more likely on trials with increased pre-stimulus cingulo-opercular BOLD contrast (Z = 2.62, P = 0.009). Correct responses were 6.7 ± 1.7% more likely on trials preceded by high activity (top 25% BOLD contrast) compared to low activity (bottom 25%; average percent correct differences from each individual are plotted in the Y-axes of Figure 5). Post-hoc tests within individual cingulo-opercular sub-regions suggested that these associations were driven by the dorsal cingulate/paracingulate region (Z = 2.56, P = 0.01), as the result was not significant for the left or right inferior frontal gyrus (P > 0.11). The GLMM estimates were equivalent for younger and older participants, and increased significantly for the participants with higher gap sensitivity and lower response bias (Figure 5). The GLMM estimates were not significantly correlated with the gap detection duration thresholds measured prior to the fMRI study (r28 = 0.14, P = 0.46).

Figure 5.

Figure 5.

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Top panel: Correct gap detection responses were significantly more likely on trials with relatively high BOLD contrast prior to the stimulus presentation (dark red and gray) compared to relatively low BOLD contrast (light red and gray). In each of the bottom panels, the y-axis shows the percent increase in correct responses for trials with high versus low cingulo-opercular activity for each subject (see Methods). Bottom left panel: Adaptive control estimates did not significantly differ for the younger and older groups: t28 = −1.30, P = 0.20. Horizontal lines in the left panel indicate group averages. Across participants, those BOLD effects increased for participants with higher gap sensitivity (r28 = 0.60, P < 0.001; bottom middle panel) and lower response bias (r28 = −0.58, P < 0.001; bottom right panel). The red and black circles indicate younger adult and older adult participants, respectively.

Control analyses were performed by including nuisance regressors to adjust for the effects of gap duration and incorrect responses on the preceding trial. Results from the control analyses showed that the correct responses were significantly more likely on trials with higher pre-stimulus BOLD contrast than those with lower pre-stimulus BOLD contrast (cingulo-opercular cortices: Z = 2.71, P = 0.007; cingulate: Z = 2.60, P = 0.009). This indicates that task difficulty and errors could not account for the association between pre-stimulus cingulo-opercular BOLD contrast and gap detection task performance. Furthermore, correct responses were not significantly predicted by cingulo-opercular BOLD measurement after the pre-stimulus scan (listening interval 1: Z = −0.98, P = 0.33; listening interval 2: Z = 0.17, P = 0.86). In other words, pre-stimulus cingulo-opercular activity was uniquely associated with task performance.

5. Discussion

After experimentally limiting age-related gap detection task performance differences, younger and older participants demonstrated similar associations between increased cingulo-opercular activity and correct responses. Irrespective of participant age, the association between cingulo-opercular activity and response accuracy was significantly higher for participants with better gap sensitivity and lower response bias. Together, these results suggest that cingulo-opercular adaptive control contributes to variation in gap sensitivity and does not vary with age in conditions with similar performance.

The current study replicated previous observations that auditory temporal processing declines with increasing age (Fitzgibbons & Gordon-Salant, 1996), as older adults demonstrated longer gap detection thresholds compared to younger adults (Harris et al., 2010; John et al., 2012; Kuchinsky et al., 2012; Ozmeral et al., 2016; Palmer & Musiek, 2014; Schneider et al., 1994; Snell, 1997; Snell & Frisina, 2000). To equate task performance, age-related differences in gap detection sensitivity were controlled during the fMRI experiment by presenting stimuli that were adjusted according to individual gap thresholds. These threshold-calibrated gaps resulted in sensitivity and bias measures that did not significantly differ for younger and older adults, indicating that age-related perceptual declines did not contribute to performance differences for the neuroimaging study.

While the influence of age-related declines in gap sensitivity could be mitigated by presenting threshold-calibrated gaps in noise during the fMRI study, differences in attention function could still potentially contribute to performance variability. There is behavioral and neural evidence that sustained attention and processing speed can influence performance in auditory tests of temporal acuity (Harris et al., 2010, 2012; He et al., 1999; Moore et al., 1992). Older adults show larger effects of gap location compared to younger adults, particularly when onset timing varies from trial to trial, which is suggestive of a decline in sustained attention (Harris et al., 2010; He et al., 1999). In the context of these earlier findings, the current results suggest that attention control can benefit gap detection for younger and older adults to a similar extent, at least when gaps are presented with stable onset timing (i.e., low task demand for attention). Furthermore, we speculate that age-related differences in cingulo-opercular function may be observed if gap location was varied across trials to increase the task demand for attention.

The finding that pre-stimulus cingulo-opercular activity was significantly associated with correct responses is consistent with previous neuroimaging auditory target detection studies (Coste & Kleinschmidt, 2016; Sadaghiani et al., 2009). The current results also align with the extant aging literature on cingulo-opercular cortex and its functional significance. Age-related structural declines in cingulo-opercular cortices are associated with slower processing speed on the Connections Test (see review: Eckert, 2011). Older adults with slower processing speed also exhibit poorer gap detection, as well as slower P2 latencies and smaller N2 amplitudes in auditory cortex event-related potentials during gap detection tasks (Harris et al., 2012; although c.f. Humes et al., 2009). These earlier findings suggest that differences in cingulo-opercular function can contribute to gap detection task performance. As noted in the Introduction, pre-stimulus cingulo-opercular activity is also associated with word recognition in noise for older adults (Vaden, Kuchinsky, Ahlstrom, Dubno, et al., 2015; Vaden, Kuchinsky, Ahlstrom, Teubner-Rhodes, et al., 2015). Moreover, age-related declines in cingulo-opercular activity and structure relate to older adults’ poorer word recognition in noise (Erb & Obleser, 2013; Harris, Dubno, Keren, Ahlstrom, & Eckert, 2009).

The current study adds to our understanding of cingulo-opercular function across the lifespan. Despite the differences in the two tasks, elevated pre-stimulus cingulo-opercular activity was associated with 6.7% more accurate gap detection responses and was previously associated with 9.0% more accurate speech recognition in noise (Vaden et al., 2013). In the current study and speech recognition in noise (Vaden, Kuchinsky, Ahlstrom, Dubno, et al., 2015), better task performance was related to higher adaptive control estimates. Significantly lower cingulo-opercular adaptive control estimates were observed for older adults compared to younger adults during speech recognition in noise (Vaden, Kuchinsky, Ahlstrom, Dubno, et al., 2015), but age group differences in adaptive control estimates were not observed in the present study. One important distinction between the studies was significant age-related performance differences for speech recognition in noise in the previous study, but not for gap detection in noise in the current study. Adaptive control may be less likely to provide benefit for older adults than younger adults under task conditions that do not account for age-related perceptual declines. Alternatively, the gap detection task may be less sensitive to age differences than our earlier speech recognition in noise study, perhaps because of relatively lower task demand for attention or processing speed.

The current study also provides information about when cingulo-opercular activity is engaged to benefit task performance. The specificity of these associations was enhanced by separate listening and response intervals over long trials with duration = 25.8 s. While robust cingulo-opercular activity was evident throughout the listening and response portions of the task, the performance benefit from elevated activity was limited to the pre-stimulus time point for each trial. That is, a correct response was significantly more likely on trials with higher pre-stimulus cingulo-opercular BOLD contrast and was unrelated to any other BOLD measurements during the trial. This specificity is consistent with control-related signals in the cingulate cortex that are hypothesized to adjust ongoing task behavior (Carter et al., 2000; Eckert et al., 2016; Kerns et al., 2004), informed by uncertainty or performance on the preceding trial (Dosenbach et al., 2006; Neta, Schlaggar, & Petersen, 2014). For example, cingulo-opercular function is thought to adjust perceptual decision-making strategy for upcoming trials (Gratton, Neta, Sun, et al., 2016; Ploran, Nelson, Velanova, et al., 2007; Ploran, Tremel, Nelson, & Wheeler, 2011; Van Maanen, Brown, Eichele, et al., 2011).

Results from the control analyses showed that pre-stimulus cingulo-opercular activity was significantly related to response accuracy after including covariates for gap durations and incorrect responses on the preceding trial (Vaden, Kuchinsky, Ahlstrom, Dubno, et al., 2015; Vaden et al., 2013), which indicates that this association was not driven by post-error adjustments (Laming, 1979; Rabbitt, 1966). Nonetheless, our results could be consistent with effects of uncertainty on the previous trial. Future studies could include the analysis of separate post-response and pre-stimulus BOLD measurements to further characterize the timing of cingulo-opercular adaptive control, similar to the separate listening intervals and response intervals in the present study.

Our findings are consistent with a sustained attention function proposed for cingulo-opercular activity, based on its association with target detection in the context of unpredictable or infrequently presented stimuli (Coste & Kleinschmidt, 2016; Sadaghiani et al., 2009; Sturm et al., 2004). Within an adaptive control theoretical framework, a failure to engage or maintain task-focused attention could be reflected in lower cingulo-opercular activity prior to trials with task errors. Indeed, attention lapses appear to occur with lower cingulo-opercular activity prior to incorrect responses (Weissman et al., 2006; West & Alain, 2000). We also have observed lower cingulo-opercular activity related to subsequent task errors in earlier studies (Vaden, Kuchinsky, Ahlstrom, Dubno, et al., 2015; Vaden et al., 2017). Cingulo-opercular activity did not appear to lapse prior to errors in the current gap detection study.

Cingulo-opercular adaptive control appears to involve adjustments in behavior (Gratton et al., 2016; Van Maanen et al., 2011) and tonic attention (Coste & Kleinschmidt, 2016; Sadaghiani & D’Esposito, 2015) that can optimize task performance, which can also be influenced by factors such as task difficulty and motivation (Eckert et al., 2016; Shenhav et al., 2013). Coordinated and modular cingulo-opercular activity also appears to facilitate performance in listening tasks (Alavash, Tune, & Obleser, 2019; Vaden et al., 2013). Although the current investigation was not optimized to investigate connectivity, adaptive control may reflect interactions across the cingulo-opercular and other distributed cortical networks. Regardless of whether cingulo-opercular activity engages attention or other resources, the current finding of effects related to pre-stimulus activity supports the importance of its engagement before each trial begins.

We note some limitations of our current study, which again was designed to limit age-related performance differences. Repetitive and fixed gap onset timing was used to minimize task demand for sustained attention that could have contributed to age group effects. Our findings were consistent with other studies that demonstrated correct responses associated with higher cingulo-opercular activity prior to unpredictably timed auditory task presentations (Coste & Kleinschmidt, 2016). However, this design does not allow us to determine when a participant detected a gap (or gaps) in noise during the two listening intervals. Attention could also vary during the stimulus presentations with little consequence for performance, which potentially limited sensitivity to an association between post-stimulus activity and task performance. Important associations between the sustained cingulo-opercular activity observed throughout each trial and gap detection in noise could be more evident with a different task design.

Conclusions

The current study was designed to minimize age-related declines in gap detection and thus characterize age-related differences in cingulo-opercular function within a fixed range of operation for younger and older adults. Consistent with behavioral and electrophysiological results that support attentional influences on gap detection, we showed that cingulo-opercular engagement increased the likelihood of correct responses and this association was highest for individuals with better gap sensitivity and lower response bias. These results support an adaptive control framework in which cingulo-opercular activity can adjust attention and behavior to optimize task performance. Cingulo-opercular adaptive control estimates were unrelated to age group, at least in the context of our experiment that limited the influence of gap sensitivity declines by presenting gaps with fixed onset timing and durations adjusted for each participant’s threshold. Adaptive control appears to have a modest influence on auditory perceptual task performance across the lifespan and may explain why some listeners perform better than others when task difficulty is equated.

Supplementary Material

1

Significance Statement.

Elevated attention can facilitate performance in difficult listening tasks, although this benefit appears to decline for older adults. An fMRI study was performed with a threshold-calibrated gap detection task to characterize age-related differences in attention control independently from poorer temporal acuity. Correct responses were significantly more likely when cingulo-opercular regions of frontal cortex increased activity prior to presentations, consistent with adaptive control of attention. This association increased significantly for individuals with better gap sensitivity, regardless of age group. This provides additional evidence that cingulo-opercular adaptive control supports performance across the lifespan, under conditions that limit age-related differences in task performance.

Other Acknowledgments:

We thank the study participants.

Support or grant information: This work was supported (in part) by NIH/NIDCD (P50 DC 000422, R01 DC014467, K23 DC008787), MUSC Center for Biomedical Imaging, South Carolina Clinical and Translational Research (SCTR) Institute, NIH/NCRR (UL1 RR029882). This investigation was conducted in a facility constructed with support from the NIH/NCRR Research Facilities Improvement Program (C06 RR014516).

Footnotes

Conflict of Interest Statement: The authors have no conflict of interest to declare.

Data Accessibility: Data from this study can be requested from the corresponding author and accessed with appropriate IRB and institutional approvals.

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