DesignWe examined age-related and individual differences in cortical event-related potential (ERP) amplitudes and latencies, processing speed, and gap detection from twenty-five younger and twenty-five older adults with normal hearing. ERPs were elicited by brief silent periods (gaps) in an otherwise continuous broadband noise and were measured under two listening conditions, passive and active. During passive listening, participants ignored the stimulus and read quietly.
FAQs or Ask Our Trainers Here are answers to some of the questions training alumni have asked The Grantsmanship Center. If you're enrolled in The Grantsmanship Center's Membership Program, click here to send our trainers your question.
During active listening, participants button pressed each time they detected a gap. Gap detection (percent detected) was calculated for each gap duration during active listening (3, 6, 9, 12 and 15 ms). Processing speed was assessed using the Purdue Pegboard test and the Connections Test. Repeated measures ANOVAs assessed effects of age on gap detection, processing speed, and ERP amplitudes and latencies. An “attention modulation” construct was created using linear regression to examine the effects of attention while controlling for age-related differences in auditory processing.
![100003457 100003457](/uploads/1/2/5/6/125626973/560931964.jpg)
Pearson correlation analyses assessed the extent to which attention modulation, ERPs, and processing speed predicted behavioral gap detection. Results: Older adults had significantly poorer gap detection and slower processing speed than younger adults.
Even after adjusting for poorer gap detection, the neurophysiological response to gap onset was atypical in older adults with reduced P2 amplitudes and virtually absent N2 responses. Moreover, individual differences in attention modulation of P2 response latencies and N2 amplitudes predicted gap detection and processing speed in older adults. That is, older adults with P2 latencies that decreased and N2 amplitudes that increased with active listening had faster processing speed and better gap detection than those older adults whose P2 latencies increased and N2 amplitudes decreased with attention. ConclusionsResults from the current study are broadly consistent with previous findings that older adults exhibit significantly poorer gap detection than younger adults in challenging tasks.
Even after adjusting for poorer gap detection, older and younger adults showed robust differences in their electrophysiological responses to sound offset. Furthermore, the degree to which attention modulated the ERP was associated with individual variation in measures of processing speed and gap detection. Taken together, these results suggests an age-related deficit in early or automatic levels of auditory temporal processing and that some older adults may be less able to compensate for declines in processing by attending to the stimulus. These results extend our previous findings and support the hypothesis that age-related differences in cognitive or attention-related processing, including processing speed, contribute to an age-related decrease in gap detection. IntroductionProcessing information in a timely manner is essential for communication. Age-related slowing is well documented across both cognitive and sensory domains (e.g.,;; ) and has led to the hypothesis that age-related slowing of information processing is the result of a global dysfunction in the aging brain. When assessed in the same population, perceptual and psychomotor speed has been shown to share a common variance, supporting the notion of a general slowing of perceptual, motor, attentional, and cognitive processes (;; ).
Age-related slowing of processing speed has been defined as a reduction in the speed with which cognitive operations can be executed and is measured using speeded tests of attention, sequencing, mental flexibility, visual search, and motor function. Moreover, age-related differences in processing speed, have been associated with age-related differences in auditory temporal processing. However, age-related differences in gap detection are often attributed to changes in earlier or automatic levels of processing. Indeed, Humes et al.
(2009) found limited associations in older adults between measures of cognitive function and auditory temporal processing. The goal of the current study was to examine the degree to which age-related differences in early or automatic levels of auditory processing and attention-related processes explain age-related differences in auditory temporal processing.
Understanding how aging affects the interaction between automatic and attention-related processing will provide better explanations of the extent to which older adults are able to compensate for processing declines.Event-related potentials (ERPs) provide a powerful tool for examining the effects of age and attention on auditory temporal processing because they can be used to compare responses to the same stimuli when they are ignored (passive) or attended to (active). ERPs may also identify cortical levels at which differences in neuronal activity relate to age-related differences in auditory temporal processing. To date, most studies using ERPs to examine temporal processing and aging have focused on ERPs from the brainstem or cortex during passive listening (e.g,;;; ) and support the hypothesis that the perceptual difficulties of older adults may be attributed to age-related differences in early or automatic levels of processing of temporal information.
Additional detrimental or compensatory effects of processing speed and attention on auditory temporal processing in older adults remain unknown.The most consistent and robust effects of age on temporal processing are revealed when using complex tasks or stimuli. For example, robust age-related differences in gap detection are observed when the markers surrounding a silent gap are shorter than 10 ms and when the location of the gap falls near the onset or offset of the stimuli or is varied randomly. These results suggest that age-related differences in temporal processing may be more dependent on cognitive or attention-related factors than previously thought (;;; ).
Moreover, with relatively simple tasks, older adults may compensate for declines in early stages of auditory processing by exerting increased cognitive control or attention (; ). Consistent with this hypothesis, observed age-related differences in ERP amplitudes during passive listening in a simple gap detection task, but not with active listening, which may reflect a decline in automatic processing of temporally modulated stimuli that can be compensated by attentional processes.When faced with challenging listening tasks, older adults may be less able to compensate for age-related differences in auditory processing. Indeed, age-related declines in processing speed and attention may exaggerate deficits seen at automatic levels of processing. In support of this cognitive-driven hypothesis, reported that age-related differences in complex measures of gap detection were related to age-related differences in processing speed and attention and that older adults reported increased effort. These findings are consistent with a large literature on age-related cognitive and neurobiologic changes in attention-related systems (e.g.
).In the current study, we examined age-related differences in and associations among gap detection, processing speed, and cortical ERP latencies and amplitudes elicited by gaps in a continuous noise under passive and active listening. We hypothesized that age-related differences in attention and cognition compound age-related differences at automatic levels of processing, contributing to the robust age effects observed during challenging listening tasks. Consistent with our hypothesis, we predicted that (1) older adults would require longer gaps than younger adults to detect a gap and elicit an ERP, (2) age-related differences in ERP response latencies and amplitudes would occur during passive and active listening, and (3) older adults with slower processing speed would be less able to compensate for changes at automatic levels of processing by exerting increased attention. 2.1 ParticipantsParticipants included two groups of adults: younger n=25; mean age=24.19 (3.42) years; 17 females and older n=25; mean age=69.82 (7.11) years; 17 females. All participants were right-handed monolingual native speakers of American English. Each participant completed the Mini Mental State Examination with three or fewer errors, indicating little or no cognitive impairment (as reviewed in ).
Pure-tone thresholds at conventional frequencies were measured with a Madsen OB922 clinical audiometer, calibrated to appropriate ANSI standards and equipped with TDH-39 headphones. All participants had normal hearing (defined as thresholds ≤ 25 dB HL at 250, 500, 1000defined as thresholds ≤ 25 dB HL at 250, 500, 2000, 3000 and 4000 Hz); differences in thresholds between right and left ears did not exceed 15 dB at each frequency. Mean pure-tone audiometric thresholds for the test ear (right ear) (±1 S.E.M.) are shown in. Thresholds for younger and older subjects were significantly different at 3000, 4000, and 8000 Hz. Individual differences in pure-tone thresholds at each frequency were used to assess associations between tonal detection, gap detection, processing speed, and ERP latencies and amplitudes.
Participants provided written informed consent before participating in this MUSC Institutional Review Board approved study. 2.2 ParadigmThe duration and type of leading and lagging markers in gap stimuli may affect the magnitude of age-related differences in gap detection. Gap detection is typically measured using short durations of noise or tones separated by a very short silent gap. With these stimuli, an ERP is generated in response to the onset of the leading marker and the onset of the gap. The response to the marker onset can overlap with the response to the gap onset, making it difficult to assess the response to the gap alone.
Possible solutions are to: (1) compute difference waves, where the ERP to the stimulus with a gap is subtracted from the response to a similar stimulus without a gap, or (2) increase the duration of the leading marker to separate in time the response to marker onset from the response to the gap onset. To further reduce the effect of the response to the marker onset, we used a continuous noise that was interrupted every ∼2 seconds ; the responses to the onset of each gap are averaged (; ).
The potentials generated by the gap onset differ in their morphology and neural generators from those to the marker onset (; ) and the effects of age are unknown. The current study examined age-related differences in ERPs to gap onset. 2.3 StimuliA broadband noise, low-pass filtered at 5 kHz, was presented continuously, with silent gaps of 3, 6, 9, 12, or 15 ms distributed every 2 to 2.2 s. Each gap duration was presented sequentially 250 times, followed by the next gap duration. The order of presentation was randomized across listening conditions and participants. Stimuli were digitally created using Tucker Davis Technologies (TDT) system 3 hardware. Stimuli were attenuated (TDT PA4), passed through a headphone buffer (TDT HB5), presented monaurally to the right ear through a TDH-39 headphone at an overall level of 80 dB SPL.
Stimuli were monitored by oscilloscope at the input to the earphone and acoustically calibrated with the earphone placed in a NBS-9A coupler using a Larson-Davis Model 800B sound level meter equipped with a 1 in pressure microphone (Model 2575). 2.4 Data Acquisition and AnalysisParticipants were seated in a comfortable reclining chair in a sound-treated room. During passive listening, participants were instructed to ignore the stimulus and read quietly. During active listening, participants were instructed to button press each time they detected a gap. The passive and active listening experiments were conduction during the same test session.
Active listening followed passive listening after a short break to reduce the effects of attention during passive listening. Electrode impedances were examined prior to the start of each phase of data collection.Percent detected and reaction time (RT) was measured during active listening. To quantify gap detection, percent detected was calculated for each gap duration (3, 6, 9, 12 and 15 ms).
Percent detected was defined as the percentage of correct detections of gaps divided by the total number of gaps presented. RT was calculated as the time post gap onset to button press. Behavioral responses that occurred less than 75 ms or more than 1500 ms after stimulus presentation were excluded. The intraindividual coefficient of variation (ICV) was calculated by dividing the SD across trials by the mean RT across trials for each participant to adjust for the relationship between intraindividual variability and mean RT (Stuss, et al., 2003).Electrophysiological (EEG) signals were recorded with a 64-channel Neuroscan QuickcapTM based on the international 10-20 system and connected to a SynAmpsRT A/D rate=1000 Hz, bandpass filtered 0.1 Hz to 200 Hz. Bipolar electrodes were placed on the skin above and below the left eye for recording vertical electro-oculogram (VEOG) activity.
SCAN version 4.4 acquisition software was used to record and analyze the EEG signal. Offline, raw EEG recordings were subjected to an automatic artifact rejection algorithm. Trials contaminated by excessive peak-to-peak deflections (± 150 μV) at the channels not adjacent to the eyes were automatically rejected.
For each individual, ocular artifacts were corrected by means of ocular source components (; ). Individual trials were then segmented into epochs surrounding the onset of the gap (-100 ms to 1000 ms). Epochs were baseline corrected and band-pass filtered from 1 to 30 Hz.
For each subject, average waveforms (ERPs) were computed relative to gap onset for each gap duration. Group average waveforms were computed for both passive and active listening for younger and older subjects.The effects of age and attention were examined in a cluster of electrodes where the ERP showed maximal activity (Fz, FCz, Cz, FC1, FC2) (e.g. Scalp topography maps for time periods surrounding peaks of interest are provided in. As described in previous studies using a similar continuous noise paradigm, P1 is greatly reduced or absent in response to the gap onset (; ).
Accordingly, P1 will not be discussed further. The mean global field power (mGFP) has a local maximum when the amplitude difference between electrodes is strongest; its maxima reflect components, such as N1, P2, and N2, in the ERP and were used to determine peak latency windows for identification of N1, P2, and N2. Response windows were consistent with previously reported literature (e.g. Harkrider et al., 2006; ). N1 was described as the largest negativity occurring between 90 and 150 ms at frontocentral electrodes (Fz, FCz, Cz, FC1, FC2). P2 was defined as the largest positivity following N1 occurring between 130 and 220 ms.
Visual inspection of the group average and individual waveforms showed that N2 was well-defined in younger adults but was absent, or was a broad negativity, in many older adults. Therefore, mean N2 amplitude was calculated as the largest mean amplitude from amplitudes within the 240-340 ms latency range in younger adults and within 260-360 ms in older adults at frontocentral electrodes (Fz, FCz, Cz, FC1, FC2). Latency windows were determined by inspection of peak latencies in group and individual waveforms and were defined later in older adults to avoid P2 amplitudes in the calculation of N2.
These methods are consistent with previous literature examining N2 in younger and older adults. While this technique may provide an accurate estimate of mean N2 amplitude for the two age groups, we were unable to calculate N2 latencies. Results from one younger participant were excluded due to excess artifact. The active condition was not included for one older participant due to time constraints. Waveforms and scalp voltage maps. Group average waveforms for younger and older adults during passive (A) and active (B) listening for each gap duration recorded and averaged from a cluster of fronto-central electrodes (Fz, FCz, Cz, FC1, FC2). Group average waveforms for younger and older adults during passive and active listening at the gap duration nearest to the asymptotic threshold (see text).
Group average scalp voltage maps for younger and older adults elicited by the gap duration closest to asymptotic threshold during active listening. Voltage maps display scalp topography of the ERP components at their peak latencies. Given its anterior scalp distribution and negative deflection, an argument can be made that the component labeled as N2 is in fact a second N1 response to the onset of the second marker.
However, pronounced differences in the voltage maps for N1 and N2, and the differential effects of attention and gap duration on the N2, as compared to the N1, support the identity of the N2 response. 2.5 Processing SpeedProcessing speed was assessed using the Purdue Pegboard test and the Connections Test. For the Purdue Pegboard test, participants completed three time trials, during which they placed as many pegs in the board as possible within 30 s using their right hand, then left hand, then both hands.
Participants also completed an assembly task, in which they alternate hands, to construct as many “assemblies” as possible within 60 s; an assembly consists of a pin, a washer, a collar, and another washer. Results were averaged across the three time trials to provide single estimates of processing speed for the right hand (dominant), left hand, both hands, and assembly. The Connections test consists of two parts, Connections Simple and Connections Complex. The Connections Simple portion of the test requires participants to connect as many circled letters or numbers in alphabetic and numeric sequence as possible in 20 s.
The letters and/or numbers are pseudorandomly organized in a 7 × 7 array of circles on a sheet of paper, with the next target located at one of the adjacent locations. Participants completed 2 trials of the letter form and 2 trials of the number form. Trial results were averaged to provide the Connections Simple score. Connections Complex requires participants to alternate between drawing a line between a number and a letter (i.e., 1 to A to 2 to B). Participants completed 2 trials starting with a number and 2 trials starting with a letter. Trial results were averaged to provide the Connections Complex score. 2.6 Data AnalysesFor the behavioral measure of gap detection, age-related differences in percent detected were assessed with a repeated measures analysis of variance (ANOVA) with age as the grouping variable and gap duration as the repeated measure.
Differences in ERP latencies were assessed by ANOVA for N1, and P2, and differences in ERP amplitudes for N1, P2, and N2, with age as the grouping variable and listening condition (active and passive) as the repeated measure. Differences in measures of processing speed were assessed using independent t-tests. Associations among variables were assessed using Pearson correlation coefficients to determine the extent to which latencies and amplitudes for active and passive listening and processing speed predicted behavioral gap detection.
For all analyses, p values of. 3.1 Percent detected and reaction time to gaps in continuous noiseGap detection increased with increasing gap duration. The 3-ms step size generated steep behavioral response curves for most subjects. The ANOVA revealed a significant main effect of age F (1,46) = 8.9, p=.005 and a significant gap duration by age interaction F (1,46) = 7.6, p1500 ms post gap onset, were minimal (.
Percent detected (percent correct) as a function of gap duration (ms) for younger adults (open) and older adults (filled). Significant differences in percent detected between younger and older adults were observed only for 6 ms and 9 ms gaps.To account for individual differences in gap detection and examine age-related differences in ERPs elicited by equally discriminable stimuli , age-related differences were assessed at the shortest gap that produced a percent detected within 5% of the asymptote.
Asymptote was estimated from the function relating percent detected to gap duration: (1- a)/ (1+ Exp- s (d- m), where a is required to be greater than or equal to 0, d is gap duration, s determines the slope of the function, m is the point where the slope reaches its maximum value, and (1- a) is the asymptotic value of the function. The gap duration nearest to asymptotic detection during active listening was also used for analyses of passive listening. Across subjects, the shortest gap corresponding to asymptotic performance ranged from 6 to 15 ms.
Older adults required significantly longer gaps than younger adults to reach asymptotic performance t (1,23) = -3.1, p=.004, but there were no significant differences with respect to percent detected at asymptote t (1,23) = -1.5, p=.143.Mean RT and the RT ICV were calculated for younger and older subjects. Differences in RT between younger subjects (mean=351.77ms, SD=124.31) and older subjects (mean=434.68, SD=177.41). Differences in ICV between younger (.53) and older subjects (.40) were not statistically significant.
In addition to targeting gap duration closest to asymptote, age-related differences in ERPs were assessed with 15 ms gaps (results not shown). 3.2 ERPs to gaps in continuous noiseDuring both passive and active listening, clear N1-P2-N2 components were obtained in response to gaps of 6 ms or longer in younger adults.
In older adults, the averaged ERP was dominated by the N1 component and was present in response to gaps as short as 9 ms, although in some subjects the ERP was not present until gap duration increased to 12 or 15 ms. The presence of the ERP was consistent with the subject's gap detection; that is, an ERP was more likely to be present for gap durations corresponding to percent detected ≥ 60%. Group average ERP waveforms are provided for passive listening and active listening for younger adults (, left) and older adults (, right). Regardless of listening condition (passive and active), grand average ERP waveforms for the younger adults differed from those for the older adults during the initial ∼400 ms following gap onset. As seen in the scalp voltage maps , these differences were most pronounced over fronto-central electrodes, where the response was largest. Response latencies and amplitudes were measured from a subset of fronto-central electrodes (Fz, FCz, Cz, FC1, FC2). Given that there were no significant interactions between electrode and age or experiment, data were collapsed across electrodes and averaged for further analysis.Waveforms elicited in response to the gap duration nearest asymptote are provided in for passive (solid) and active listening (dotted) for younger adults (left) and older adults (right).
Age-related differences remain significant even after adjusting for differences in behavioral gap detection. The P2 response appears greatly diminished and the N2 response is largely absent in older adults compared to younger adults during both passive and active listening. The P2 and the N2 response are most affected by attention, showing increases in response amplitude and age-dependent changes in response latency in active compared to passive listening. 3.3 N1, P2, and N2 amplitudes: Effects of age and attentionSeparate repeated-measures ANOVAs were conducted for N1, P2, and N2 amplitudes in response to the gap duration nearest to asymptotic detection.
Similar results were obtained in response to gaps of 15 ms. ANOVA results are summarized in. Box plots showing response amplitudes are provided in for younger and older subject groups during passive listening (white) and active listening (gray).
Amplitudes for N1, P2 and N2 were robustly affected by age with significantly larger response amplitudes for younger than older adults Main effect of age: (N1: F (1,46)=13.76, p=.001),(P2: F (1,46)=29.73, p. 3.4 N1 and P2 response latencies: Effects of age and attentionN1 latencies did not differ significantly between younger and older adults F (1,46)= 1.10, p=.299, whereas, P2 latencies were significantly shorter in younger than in older adults F (1,46)= 29.71, p. 3.5 Processing speed: Effects of ageMean scores and standard deviations for Connections and Purdue Pegboard for younger and older participants are provided in. Measures of processing speed were strongly positively correlated with each other, with Pearson r values ranging from 0.63 to 0.84, (p. 3.6 Associations among behavioral gap detection, processing speed, and ERP latencies and amplitudesProcessing speed, as measured by simple and complex Connections, was positively correlated with behavioral gap detection after controlling for differences in processing speed due to age Connections simple: ( partial r =.35, p=.02); Connections Complex ( partial r =.37, p=.01). Adults with faster processing speed had better gap detection.
The association among P2 response latencies during active listening, processing speed, and gap detection were driven primarily by results for older adults. P2 response latencies during active listening were negatively correlated with gap detection partial r=.31, p=.04, such that subjects with shorter P2 response latencies had better gap detection. For older subjects, P2 latencies during active but not passive listening were negatively correlated with processing speed, including Connections complex, and the right, left, and assembly conditions on the Pegboard test r (24)=.48 to.50, p.
3.7 Attention modulation, gap detection, and processing speedThe attention modulation construct was used to clarify the role of attention on ERPs, behavioral gap detection, and processing speed (see Section 2.6 Data Analyses). For P2 latencies, attention modulation was negatively correlated with gap detection partial r=.39, p=.009.
In older adults P2 latency attention modulation was negatively correlated with processing speed r (23)=.40 to.57, p. Pegboard measure of processing speed plotted against P2 latency (top) and N2 amplitude (bottom) estimates of attention modulation for younger adults (open) and older adults (filled). Attention modulation was defined as the standardized residual, or the remaining variance in the attended response latencies or amplitudes after removing the variance attributed to passive listening. Significant negative correlations reveal that older adults with P2 latencies that decreased with attention (more negative residuals) had faster processing speeds, whereas older adults with P2 latencies that increased with attention (more positive residuals) had slower processing speeds (top). Similar relations between attention modulation and processing speed were obtained for N2 amplitudes for younger and older subjects (bottom).
Age-related differences in attention may compound age-related differences at automatic levels of processing, contributing to the age effects observed during challenging listening tasks. We examined age-related differences in and associations among gap detection, processing speed, and cortical ERP elicited by gaps in a continuous noise under passive and active listening. Age-related differences in gap detection and ERPs during active and passive listening suggest that early or automatic levels of auditory temporal processing are reduced for older adults, and that some older adults may be less able to compensate for declines in processing by attending to the stimulus. DiscussionWe hypothesized that age-related differences in auditory temporal processing are the result of age-related changes in automatic and attention-related processing and that some older adults would be less able to compensate for age-related differences at automatic levels of processing by exerting increased cognitive control and attention. Consistent with this hypothesis, our results show that:(1) older adults are less able than young adults to detect small gaps,(2) neurophysiological response patterns to gap onset were atypical for older adults for active and passive listening, with reduced P2 responses and virtually absent N2 responses, and (3) neurophysiological estimates of attention modulation accounted for a significant portion of the variance in gap detection and processing speed such that faster processing speed in old age appears to limit significant aging effects on gap detection. 4.1 Aging affects both automatic and cognitive processesNeurobiological explanations for age-related differences in auditory temporal processing have focused largely on age-related differences in early or automatic levels of processing during passive listening, and have attributed age-related differences in auditory temporal processing to impaired inhibition, temporal jitter, declines in neural synchrony, and/or neuronal loss or disintegration of synaptic connectivity in auditory cortices (e.g.,;; ). Age-related changes in the structure and function of the central auditory system are well documented (e.g.,;;;; ).
The robust differences between younger and older adults ERPs observed during passive listening are consistent with the premise that aging affects the neural representation of temporal cues at automatic stages of auditory processing.Older adults who maintain normal performance on relatively simple listening tasks may recruit cognitive control systems to compensate for changes at automatic levels of processing (; ). However, even with focused attention, large age-related differences in ERP amplitude and latency of the ERP and gap detection were observed in the current study, suggesting that the gap detection task used in the current study may have been more difficult for older than younger adults. With increased task difficulty greater demands are placed on cognitive resources to compensate for changes in auditory processing.
With increased task difficulty, the cognitive control systems of some older adults may not be able to compensate for changes in automatic levels of processing, resulting in declining performance (Harris et al. These results suggest a complex relationship between the effects of age on automatic and cognitive processing.
4.2 N2 as a marker of sustained attentionThe most obvious difference in ERP waveforms of younger and older adults was the absence of the N2 response in many older subjects. Although larger or similar N2 amplitudes have been observed in older adults , the N2 peak most often shows a marked decrease in amplitude with age (;; ). Unlike previous studies where gap duration was randomized within an experimental block (; ), gap durations in the present study were held constant within a stimulus block, such that the same gap duration was presented sequentially for 250 consecutive trials, followed by the next gap duration for 250 trials, and so on. When gap duration varies from trial to trial, a robust P3 or P300 is generated in response to the attended gap stimuli (; ).
Presenting the same gap duration throughout an experimental block had profound effects on the physiological response, including a lack of, or significant reduction in a P3 response, and possible effects on the N2 response. By grouping the stimuli in this manner the task becomes more monotonous and the gaps may be less exogenously alerting. Similar effects on ERP components have been reported in the go/no-go N2-P3 literature, particularly while participants performed the Sustained Attention to Response Task (SART) (; ). Differences observed between the SART fixed versus the SART random are relevant to the current study. The SART is a continuous performance task in which a series of digits (from 1 to 9) is presented visually and subjects are required to press a button upon seeing each digit, except when the digit 3 appears, in which case the subjects must withhold a response.
In the random version, the numbers are presented randomly and the number 3 occurs infrequently. In the fixed version, the numbers are presented sequentially with equal likelihood of occurrence (e.g., 1, 2, 3, 4). An N2-P3 component is generated during the SART random condition in comparison to the fixed condition were only an N2 response is observed (Dockree et al. By making the task more routine demands on the frontally driven sustained attention networks may have increased.
Thus, the N2 may be an index of these proactive control processes and may represent performance monitoring (; ), whereas the P3 may reflect later stages of processing related to response selection and/or response inhibition.The N2 is thought to be generated from medial frontal areas, including anterior cingulate cortex (; ), and the right ventral and dorsolateral prefrontal cortex. Conducted a simultaneous EEG and fMRI study and reported that the N2 amplitude was associated with BOLD responses in medial frontal brain regions and the putamen.
Furthermore, differences in brain activation patterns (PET and fMRI studies) elicited by SART fixed and random suggest that SART fixed places increased demands on sustained attention networks, including right dorsolateral prefrontal cortex , left inferior frontal cortex, right angular gyrus, left insula, and medial frontal gyrus. These studies collectively emphasize the importance of frontal attention-related brain regions in the generation of the N2.
The large age-related differences in N2 amplitude and/or absence of the N2 in older adults, and the increased variability in attention modulation of N2 amplitudes in older adults, may represent an age-related change in the structure and function of frontal attention networks.N2 amplitudes may also be affected by temporal expectations and/or implicit cueing that was generated by the regular spacing of the gaps in the continuous noise. The temporal regularity of an auditory signal may automatically engage ventral frontal parietal areas used in search tasks , generating the frontally dominant N2 response.
Thus, age-related differences in N2 amplitudes could relate to differences in temporal cuing. However, reaction times collected from button presses by older adults to gap detection remained relatively constant within a block, which rules out effects of temporal expectations or anticipation. 4.2 Associations among gap detection, processing speed, and ERPsThe associations among attention modulation, processing speed, and gap detection variables support our previous findings and suggest that age-related differences in gap detection stem in part from differences in attention networks. The auditory P2 has been associated with sensory arousal and attentional orienting and is likely susceptible to top-down modulatory control. More so, P2 latency may be a salient marker for attention-related decrements with age and has been shown to correlate with accuracy during attention and working memory tasks. This is consistent with the hypothesis that modality-specific attentional processing and brain processes supporting attention can occur even at very early sensory levels (;;; ). The auditory N2 is proposed to reflect higher order sensory processing (; ) and may be affected by age-related changes in local connectivity and top-down modulations.
Larger N2 amplitudes have been shown to be associated with better auditory target detection and better performance on measures of sustained attention (Dockree et al. The relationship between the N2 response and processing speed suggest that similar structural and functional cortical networks may be involved in N2 response generation (;; ) and processing speed (; ). Our future studies will examine the extent to which age-related differences in ERPs and gap detection are the result of changes in the structure of attention networks or related to changes in auditory cortex that may limit top-down modulation. 4.5 Challenges in studies of gap detection, attention, and agingThe neurophysiological response to gap onset may have unique morphology and underlying generators that differ from those to marker onset. The ERP elicited by a gap is generated in response to the change from noise against no input; however, our current understanding of the physiological response to gap stimuli is largely influenced by the response to the marker onset. The brains ability to signal the offset of a signal is important in many aspects of speech recognition. Results from the current study suggest that older adults exhibited markedly different responses to gap onset than younger adults.
Although responses to marker onset were not available, little to no age-related differences have been observed in cortical responses to the onset of simple stimuli, such as tones even for a similar continuous presentation paradigm (; ) when a slow inter-stimulus interval is used. ConclusionsResults from the current study are broadly consistent with previous findings that older adults exhibit significantly poorer gap detection than younger adults in challenging tasks. Task difficulty in the current study may have been increased in older adults by the regularity of the task, which may have increased demands on sustained attention networks.
Even after adjusting for poorer gap detection, older and younger adults showed robust differences in their electrophysiological responses to sound offset. These differences were present with both passive and active listening.
Furthermore, the degree to which attention modulated the ERP was associated with individual variation in measures of processing speed and gap detection. Taken together, these results suggests an age-related deficit in early or automatic levels of auditory temporal processing and that some older adults may be less able to compensate for declines in processing by attending to the stimulus. These results extend our previous findings (Harris et al. 2010b) and support the hypothesis that age-related differences in cognitive or attention-related processing, including processing speed, contribute to an age-related decrease in gap detection. Alain C, McDonald KL, Ostroff JM, et al. Aging: a switch from automatic to controlled processing of sounds? Psychol Aging.
2004; 19:125–133. Specification for Audiometrics. New York: American National Standards Institute; 2004. Bekker EM, Kenemans JL, Verbaten MN. Source analysis of the N2 in a cued Go/NoGo task.
Brain Res Cogn Brain Res. 2005; 22:221–231. Berg P, Scherg M.
A multiple source approach to the correction of eye artifacts. Electroencephalogr Clin Neurophysiol. 1994; 90:229–241.
Bertoli S, Heimberg S, Smurzynski J, et al. Mismatch negativity and psychoacoustic measures of gap detection in normally hearing subjects. 2001; 38:334–342.
Bertoli S, Probst R. Lack of standard N2 in elderly participants indicates inhibitory processing deficit. 2005; 16:1933–1937. Boettcher FA, Mills JH, Swerdloff JL, et al. Auditory evoked potentials in aged gerbils: responses elicited by noises separated by a silent gap. 1996; 102:167–178.
Caspary DM, Milbrandt JC, Helfert RH. Central auditory aging: GABA changes in the inferior colliculus. Exp Gerontol. 1995; 30:349–360. Ceponiene R, Alku P, Westerfield M, et al. ERPs differentiate syllable and nonphonetic sound processing in children and adults. 2005; 42:391–406.
Ceponiene R, Westerfield M, Torki M, et al. Modality-specificity of sensory aging in vision and audition: evidence from event-related potentials.
2008; 1215:53–68. Cerella J. Information processing rates in the elderly. Psychol Bull. 1985; 98:67–83. Craik FI. Changes in memory with normal aging: a functional view.
1990; 51:201–205. De Chicchis AR, Carpenter M, Cranford JL, et al. Electrophysiologic correlates of attention versus distraction in young and elderly listeners. J Am Acad Audiol. 2002; 13:383–391. Quiz 400-381. Dockree PM, Kelly SP, Robertson IH, et al.
Neurophysiological markers of alert responding during goal-directed behavior: a high-density electrical mapping study. 2005; 27:587–601. Eckert MA. Slowing down: age-related neurobiological predictors of processing speed.
Front Neurosci. Eckert MA, Keren NI, Roberts DR, et al. Age-related changes in processing speed: unique contributions of cerebellar and prefrontal cortex. Front Hum Neurosci.
Eckert MA, Walczak A, Ahlstrom J, et al. Age-related Effects on Word Recognition: Reliance on Cognitive Control Systems with Structural Declines in Speech-responsive Cortex. J Assoc Res Otolaryngol. 2008; 9:252–259.
Fassbender C, Murphy K, Foxe JJ, et al. A topography of executive functions and their interactions revealed by functional magnetic resonance imaging. Brain Res Cogn Brain Res. 2004; 20:132–143.
Finnigan S, O'Connell RG, Cummins TD, et al. ERP measures indicate both attention and working memory encoding decrements in aging.
2011; 48:601–611. Folstein MF, Robins LN, Helzer JE. The Mini-Mental State Examination. Arch Gen Psychiatry. 1983; 40:812.
Gazzaley A, Clapp W, Kelley J, et al. Age-related top-down suppression deficit in the early stages of cortical visual memory processing. Proc Natl Acad Sci U S A. Gazzaley A, Cooney JW, Rissman J, et al.
Top-down suppression deficit underlies working memory impairment in normal aging. Nat Neurosci. 2005; 8:1298–1300. Harkrider AW, Plyler PN, Hedrick MS. Effects of age and spectral shaping on perception and neural representation of stop consonant stimuli.
Clin Neurophysiol. 2005; 116:2153–2164. Harris KC, Dubno JR, Keren NI, et al.
Speech recognition in younger and older adults: a dependency on low-level auditory cortex. 2009; 29:6078–6087.
Harris KC, Eckert MA, Ahlstrom JB, et al. Age-related differences in gap detection: effects of task difficulty and cognitive ability. 2010; 264:21–29. Harris KC, Mills JH, Dubno JR. Electrophysiologic correlates of intensity discrimination in cortical evoked potentials of younger and older adults.
2007; 228:58–68. Harris KC, Mills JH, He NJ, et al. Age-related differences in sensitivity to small changes in frequency assessed with cortical evoked potentials. 2008; 243:47–56.
He NJ, Horwitz AR, Dubno JR, et al. Psychometric functions for gap detection in noise measured from young and aged subjects. J Acoust Soc Am. 1999; 106:966–978.
Humes LE. Do ‘auditory processing’ tests measure auditory processing in the elderly? 2005; 26:109–119.
Jonkman LM, Sniedt FL, Kemner C. Source localization of the Nogo-N2: a developmental study. Clin Neurophysiol. 2007; 118:1069–1077. Karch S, Feuerecker R, Leicht G, et al.
Separating distinct aspects of the voluntary selection between response alternatives: N2- and P3-related BOLD responses. 2010; 51:356–364. Karhu J, Herrgard E, Paakkonen A, et al. Dual cerebral processing of elementary auditory input in children. 1997; 8:1327–1330.
Kennedy KM, Raz N. Aging white matter and cognition: differential effects of regional variations in diffusion properties on memory, executive functions, and speed. 2009; 47:916–927. Lavric A, Clapp A, Rastle K. ERP evidence of morphological analysis from orthography: a masked priming study. J Cogn Neurosci.
2007; 19:866–877. Lehmann D, Skrandies W. Reference-free identification of components of checkerboard-evoked multichannel potential fields. Electroencephalogr Clin Neurophysiol.
1980; 48:609–621. Lister J, Besing J, Koehnke J. Effects of age and frequency disparity on gap discrimination. J Acoust Soc Am. 2002; 111:2793–2800. Lister JJ, Roberts RA. Effects of age and hearing loss on gap detection and the precedence effect: narrow-band stimuli.
J Speech Lang Hear Res. 2005; 48:482–493.
Manly JJ, Touradji P, Tang MX, et al. Literacy and memory decline among ethnically diverse elders. J Clin Exp Neuropsychol. 2003; 25:680–690.
Manly T, Owen AM, McAvinue L, et al. Enhancing the sensitivity of a sustained attention task to frontal damage: convergent clinical and functional imaging evidence. 2003; 9:340–349.
Mesulam M. Brain, mind, and the evolution of connectivity.
2000; 42:4–6. Michalewski HJ, Starr A, Nguyen TT, et al. Auditory temporal processes in normal-hearing individuals and in patients with auditory neuropathy.
Clin Neurophysiol. 2005; 116:669–680.
O'Connell RG, Bellgrove MA, Dockree PM, et al. Self-Alert Training: volitional modulation of autonomic arousal improves sustained attention. 2008; 46:1379–1390.
Pichora-Fuller MK, Schneider BA, Benson NJ, et al. Effect of age on detection of gaps in speech and nonspeech markers varying in duration and spectral symmetry. J Acoust Soc Am.
2006; 119:1143–1155. Picton TW, Bentin S, Berg P, et al.
Guidelines for using human event-related potentials to study cognition: recording standards and publication criteria. 2000; 37:127–152. Poth EA, Boettcher FA, Mills JH, et al. Auditory brainstem responses in younger and older adults for broadband noises separated by a silent gap.
2001; 161:81–86. Pratt H, Bleich N, Mittelman N. The composite N1 component to gaps in noise. Clin Neurophysiol.
2005; 116:2648–2663. Rimmele J, Jolsvai H, Sussman E.
Auditory target detection is affected by implicit temporal and spatial expectations. J Cogn Neurosci. 2011; 23:1136–1147. Roland PE. Cortical regulation of selective attention in man.
A regional cerebral blood flow study. J Neurophysiol. 1982; 48:1059–1078. Salthouse TA. Aging and measures of processing speed. Biol Psychol. 2000; 54:35–54.
Salthouse TA, Ferrer-Caja E. What needs to be explained to account for age-related effects on multiple cognitive variables? Psychol Aging.
2003; 18:91–110. Schneider BA, Hamstra SJ. Gap detection thresholds as a function of tonal duration for younger and older listeners. J Acoust Soc Am. 1999; 106:371–380.
Shulman GL, Pope DL, Astafiev SV, et al. Right hemisphere dominance during spatial selective attention and target detection occurs outside the dorsal frontoparietal network. 2010; 30:3640–3651. Tombaugh TN, McIntyre NJ. The mini-mental state examination: a comprehensive review. J Am Geriatr Soc. 1992; 40:922–935.
Tremblay KL, Billings C, Rohila N. Speech evoked cortical potentials: effects of age and stimulus presentation rate. J Am Acad Audiol.
2004; 15:226–237. Tremblay KL, Piskosz M, Souza P. Aging alters the neural representation of speech cues. 2002; 13:1865–1870. van Veen V, Carter CS. The anterior cingulate as a conflict monitor: fMRI and ERP studies. Physiol Behav.
2002; 77:477–482. Walton JP, Frisina RD, Ison JR, et al. Neural correlates of behavioral gap detection in the inferior colliculus of the young CBA mouse. J Comp Physiol A 1997; 181:161–176. Walton JP, Simon H, Frisina RD. Age-related alterations in the neural coding of envelope periodicities. J Neurophysiol.
2002; 88:565–578.