International Journal of Psychophysiology 96 (2015) 125–133

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Cortical configuration by stimulus onset visual evoked potentials (SO-VEPs) predicts performance on a motion direction discrimination task Bojan Zalar a, Tim Martin b, Voyko Kavcic a,c,⁎ a b c

Biomedical Research Institute, Ljubljana, Slovenia Department of Psychology, Kennesaw State University, Kennesaw, GA, USA Institute of Gerontology, Wayne State University, Detroit, MI, USA

a r t i c l e

i n f o

Article history: Received 6 October 2014 Received in revised form 7 April 2015 Accepted 7 April 2015 Available online 16 April 2015 Keywords: Aging Stimulus onset VEPs P1 N1 P2 Configuration Diffusion model

a b s t r a c t The slowing of information processing, a hallmark of cognitive aging, has several origins. Previously we reported that in a motion direction discrimination task, older as compared to younger participants showed prolonged nondecision time, an index of an early perceptual stage, while in motion onset visual evoked potentials (MO-VEPs) the P1 component was enhanced and N2 was diminished. We did not find any significant correlations between behavioral and MO-VEP measures. Here, we investigated the role of age in encoding and perceptual processing of stimulus onset visually evoked potentials (SO-VEPs). Twelve healthy adults (age b 55 years) and 19 elderly (age N 55 years) performed a motion direction discrimination task during EEG recording. Prior to motion, the stimulus consisted of a static cloud of white dots on a black background. As expected, SO-VEPs evoked well defined P1, N1, and P2 components. Elderly participants as compared to young participants showed increased P1 amplitude while their P2 amplitude was reduced. In addition elderly participants showed increased latencies for P1 and N1 components. Contrary to the findings with MO-VEPs, SO-VEP parameters were significant predictors of average response times and diffusion model parameters. Our electrophysiological results support the notion that slowing of information processing in older adults starts at the very beginning of encoding in visual cortical processing, most likely in striate and extrastriate visual cortices. More importantly, the earliest SO-VEP components, possibly reflecting configuration of visual cortices and encoding processes, predict subsequent prolonging and tardiness of perceptual and higher-level cognitive processes. © 2015 Elsevier B.V. All rights reserved.

1. Introduction We have recently shown that aging impacts motion perception at an early perceptual stage (Kavcic et al., 2013). Behaviorally, elderly participants showed prolonged response times, while electrophysiologically, the elderly exhibited an enhancement of the first motion onset visual evoked potential (MO-VEP) P1 component and diminished response to the subsequent MO-VEP N2 component in terms of smaller amplitude and prolonged onset latency. Given that electrophysiological agerelated changes occurred at the very first MO-VEP P1 component, the remaining question is if this early sensory decline is motion onset specific, or if these changes were propagated from earlier stimulusonset visual evoked potentials (SO-VEPs). In this regard, SO-VEPs could be considered as an event alerting the participant about subsequent stimulus occurrence (Fernandez-Duque and Posner, 1997) and/ or as an index of configuration of visual cortices, i.e., proactive recruiting ⁎ Corresponding author at: Institute of Gerontology, Wayne State University, 87 E. Ferry Street, Detroit, MI 48202, USA. Tel.: +1 313 664 2613; fax: +1 313 664 2667. E-mail address: [email protected] (V. Kavcic).

http://dx.doi.org/10.1016/j.ijpsycho.2015.04.004 0167-8760/© 2015 Elsevier B.V. All rights reserved.

of obligatory neural resources and set-up of as-needed neuronal pathways (e.g., synchronization) in anticipation of impending motion onset (Levy and Reyes, 2011; Prat and Just, 2011; Serrien et al., 2004). 1.1. Stimulus onset VEPs (SO-VEPs) and motion onset VEPs (MO-VEPs) Both discrete stimulus onset and motion onset generate well defined VEPs. Painting of a visual stimulus on a computer monitor evokes an early positive voltage deflection, the P1 component, with onset between 65 and 80 ms and peak latency between 100 and 130 ms (Di Russo et al., 2002; Hillyard and Kutas, 1983). The subsequent N1 component is a negative voltage deflection in the range of 130–200 ms (Di Russo et al., 2002; Hillyard and Kutas, 1983), followed by the P2 component, a positive deflection 200 ms after the stimulus onset. If stimulus onset requires an immediate response then it has been suggested that P1 and N1 index “gain control” of sensory processing (Hillyard et al., 1998), while P2 indexes working memory (Finnigan et al., 2011; Lefebvre et al., 2005; Taylor et al., 1990; Wolach and Pratt, 2001). In addition, P2 appears to be an index of stimulus salience (Riis et al., 2009) and it is enhanced in novel stimuli response (Knight, 1997).

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Motion onset also evokes well defined VEP responses in humans, dominating with a negativity between 150 and 220 ms (N2), preceded by an earlier positivity between 100 and 130 ms (P1) (Kuba and Kubova, 1992; Bach and Ullrich, 1997). It has been suggested that N2 reflects the processing of visual motion, and that P1 can be attributed to local pattern processing (Kuba and Kubova, 1992; Bach and Ullrich, 1997). It has also been shown (Kubova et al., 1995) that P1 is strongly affected by luminance contrast change, while N2 is almost insensitive to luminance contrast changes. Thus, both MO-VEPS P1 and N2 reflect summed potentials evoked by different motion processes that overlap in time (Heinrich et al., 2005). Hong et al. (2009) showed that the N2 component is as reliable as the most well known cognitive ERP component P300. In our previous studies (Martin et al., 2010; Kavcic et al., 2013) we showed that MO-VEP P1 is not sensitive to a manipulation of coherence of motion in moving dots: coherently moving dots and dots moving randomly within a 320 degree range evoked almost identical P1 responses. We interpreted this finding as reflecting the luminance change in local motion detectors. These detectors are presumably located in V1 and directly connected to exstrastriate motion specific areas (Movshon and Newsome, 1996). Area MT, the primary motion processing area, receives direct input from early V1 local motion detectors and it integrates ambiguous motion information into global coherent perception (Newsome et al., 1986).

1.2. Aging effects on stimulus onset VEPs (SO-VEPs) There have been several studies documenting age-related changes in early transient sensory VEPs (for review see De Sanctis et al., 2008). While several studies showed that aging enhanced and delayed stimulus onset P1 and N1 (De Sanctis et al., 2008, 2009; Falkenstein et al., 2006; Yordanova et al., 2004; Diaz and Amenedo, 1998), a few studies did not find age-related changes in early sensory VEPs (Dustman and Beck, 1966; Stothart et al., 2013), and one study even reported a decrease in VEP amplitudes (Czigler and Balazs, 2005). De Sanctis et al. (2008) found substantially increased N1 amplitude and latency in elderly as compared to younger subjects, while they did not find significant differences associated with the earlier P1 component. They suggest that increased and broader N1 may be indicative of compensatory processing. They also found age-related changes in scalp topography and underlying sources of N1 in the form of reduced hemispheric asymmetry for early sensory processing activated even for the very simplest stimulus settings. There remains some controversy regarding the neural origins of the P1, N1, and P2 components of stimulus onset responses. The P1 stimulus onset responses are localized more medially in occipital cortex than the N1 stimulus onset response, suggesting substantial striate cortical contributions to P1 and more extrastriate origins of N1 and the later motion dependent components (Probst et al., 1993; Bach and Ullrich, 1997). Alternative formulations cast the P1 as having stronger contributions from the parvocellular visual subsystem and the N1 as having stronger magnocellular contributions (Kubova et al., 1995; Niedeggen and Wist, 1999). Current understanding considers the origins of VEPs P1 and N1 in extrastriate cortex (e.g., Herrmann and Knight, 2001; Natale et al., 2006) and are modulated by attention (e.g.,Gazzaley et al., 2008; Hackley et al., 1990; Luck et al., 1990; Jiang et al., 2009; O'Connell et al., 2009; Zanto et al., 2010). Our reporting (Fernandez et al., 2007) of strong effects of stimulus luminance on the P1 responses is similar to some earlier reports (Di Russo et al., 2002) and supports the view that P1 is substantially generated by striate mechanisms given the prominent role of striate cortex in luminance dependent response effects (Doty, 1977; McCourt and Foxe, 2004). Nevertheless, caution must be exercised in inferring links to specific cortical areas based on scalp recorded neuroelectric signals.

1.3. Configuration Given that enhancement of an early MO-VEP was associated with age (Kavcic et al., 2013), we wondered if that change might not be subsequent to an even earlier differentiation by age related to brain responses to the luminance onset of the dot cloud. We initially separated onset of the dots and onset of motion to decouple luminance and motion onset responses and obtain a more pure measure of motion-related potentials. In effect, this created a warning stimulus to alert observers of the coming motion onset (Woodrow, 1914; Fernandez-Duque and Posner, 1997). As reviewed above, SO-VEPs have been linked primarily to the attention or working memory. Here we are proposing an alternative explanation. It is possible that the differences we observed between elderly and young participants in response to motion originated in differences between elderly and young participants in configuration of neural processes in preparation for the expected motion. In our view, configuration relates to how the brain establishes, organizes, maintains and modifies its internal connectivity structure in space and time. In other words, configuration consists of preparation and activation of sensory, working memory and motor neuronal networks needed for subsequent task related decision making and motor response. One can easily conceptualize configuration as simultaneous appropriation of neuronal resources and/or assembly of proper neuronal networks, one in which there is an increased sensitivity (lower thresholds and/or increased gain) for target detection and suppression of external noise and internal extraneous neuronal operations. In such conceptualization, configuration is driven by bottom up (e.g., stimulus onset) and topdown (e.g. setting of inhibitory network) processes. At present the configuration mechanisms are poorly understood: e.g., it is not clear how much of excitatory vs. inhibitory capacities is needed for proper configuration. Thus, stimulus onset warns an observer of an imminent target, triggering configuration. Another view here is that configuration is also involved in suppression of default networks (Damoiseaux et al., 2008). Default brain networks, an intrinsic brain activity, have been proposed to represent a more fundamental property of brain functional organization that could serve to stabilize brain ensembles, consolidate the past memories, and available for immediate future demands (Buckner and Vincent, 2007; Raichle and Snyder, 2007). The present study is a secondary analysis of data previously reported in Kavcic et al. (2013). Kavcic et al. (2013) found greater P1 amplitude and smaller N2 amplitude in elderly observers in response to motion onset, but these measures did not explain significant variance in the response latency of elderly observers. In the present study, we compared early visual sensory components between healthy young and elderly participants to the stimulus onset, when a response was not yet required. The stimulus onset was the cloud of the white dots on black background which started to move after no earlier than 1 s. Here, because no task was performed, age-related stimulus onset VEP differences could not easily be accounted for by task-related factors such as differences in the deployment of attentional resources between young and old adults, but these differences can be rather accounted by configuration processes. We already reported age-related effects on motion onset MO-VEPs. Here, in addition to compare early visual components between young and old participants, we also investigate if early sensory components are related to subsequent MO-VEPs and motion direction discrimination performance. 2. Materials and methods 2.1. Participants We recruited 31 neurologically and ophthalmologically intact adults (14 males and 17 females), including two of the authors (TM and VK). Based on their age, participants were divided into a younger group (age b55 years of age) and older group (age N 55 years of age). The

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young group was comprised of 12 participants (M = 5 and F = 7, average age = 25 years, SD = 4.93, age range 21–37), while the elderly group was comprised of 19 participants (M = 11 and F = 8, average age = 70.9 years, SD = 9.11, age range 58–89). All participants had normal or corrected-to-normal visual acuity. Participants were recruited from employees and the student body of the University of Rochester, and from the broader Rochester community. We obtained informed consent from all subjects before their enrollment. All procedures were carried out in accordance with the Declaration of Helsinki and were approved by the University of Rochester Medical Center, Institutional Review Board. 2.2. Procedures 2.2.1. Neuropsychological test battery All participants underwent the evaluation of cognitive status with the battery of neuropsychological tests in the following order: Rey Auditory Verbal Learning Test (AVLT) (Rey, 1964), trail making test (TMT) (Reitan and Wolfson, 1985), Digit Span subtest of the Wechsler Adult Intelligence Scale — III (WAIS-III) (Wechsler, 1987), Categorical Name Retrieval, and Visual Search test (Lewis and Rennick, 1979). Detailed descriptions of these tests are provided in Kavcic et al. (2013). 2.2.2. Motion direction discrimination task Participants performed a left-right, global motion direction discrimination task (Fig. 1) with random dot stimuli. Trials began after a random inter-trial interval that varied between 1 and 2 s with a uniform distribution. On each trial, a fixation spot subtending approximately 0.5° of visual angle was followed 1000 ms later by the onset of a random dot stimulus. The stimulus had a circular aperture 10° in radius and consisted of white dots on a black background, each subtending 0.125° of visual angle. Dot density was approximately 0.6 dots/deg2. After a uniformly distributed random interval between 1 and 2 s, the dots began to move either to the left or the right for half a second. On half of the trials, all dots moved in the same direction (i.e. coherently, with the range of dot directions = 0°). On the other half of the trials, the direction of each dot was randomized within a range of 320° about the mean direction (always to the right or left). We refer to these conditions as direction range 0° (DR0) and direction range 320° (DR320) respectively. There were 50 trials for each combination of direction range and direction of motion. Participants identified the direction of motion by pressing one of two buttons on standard computer mouse: they pressed the left button for leftward motion and the right button for rightward motion as quickly and accurately as possible. For the direction of motion in the 320° condition, if uncertain they were instructed to guess if the dots moved left or right. To familiarize themselves with task and to adapt visual motion mechanisms, participants completed 15 practice trials. 2.2.3. Neurophysiological recording Scalp electroencephalographic (EEG) activity was recorded using Brain Vision (Brain Vision, Inc.) equipment. We used the high density Acti Cap (64 electrodes) modified according to the International 10–20 System. The recording locations included eight midline sites,

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with the FCz electrode as an on-line reference and a ground at midline location AFz. Low and high pass filter settings were 70 Hz and 0.1 Hz, respectively. The cutoff frequencies for these filters were set at 3 dB down; the roll off was 12 dB per octave at both sides. Impedances were maintained below 10 kΩ for each channel and balanced across all channels within a 5 kΩ range. The sampling rate was 500 Hz with 32 bit resolution.

2.3. Data processing and analyses 2.3.1. VEP data analysis We used off-line inspection to identify and remove segments of EEG contaminated either by excessive noise, saturation or lack of EEG activity. An independent components analysis (ICA) was used to remove eye blinks: initially, ICA was performed to identify the independent component related to eye blinks, and then the EEG was reconstructed by removing the eye blink component.

2.3.1.1. The stimulus-onset visual evoked potentials (SO-VEPs). SO-VEPs were derived from all artifact free trials in the task, with a maximum of 200 repetitions. Average waveforms were computed for each subject for each electrode and the four stimulus conditions (collapsing across left/right direction of motion and DR0/DR320 direction ranges). Average waveforms were baseline-corrected to the mean of the 100 ms pre stimulus onset time, and extended to 1 s post-stimulus onset. Averaged responses were used to identify waveform components. In order to determine whether the P1, N1 and P2 components differed between groups, we measured amplitude and latency in a time window of these components seen in prior studies (Murray et al., 2008). We specifically looked for the following, standard stimulus onset VEP components (see Fig. 2 for graphical illustration of these components): The P1 component: defined as the positive peak between 70 and 120 ms after stimulus onset. The N1 component: defined as the peak negative amplitude in the range of 100 to 200 ms after the stimulus onset. The P2 component: defined as a positive peak between 170 and 320 ms positive deflection after the stimulus onset, following the P1 and N1 components. For all the above listed components, we obtained peak maximal/ minimal amplitude and peak latency within a predetermined time range. As peak latencies are sensitive to high frequency noise, the data were filtered with a 20 Hz low-pass filter before peak extraction and amplitude measurement. For each group separately, we also computed topographical voltage maps in order to examine possible changes in the scalp distribution of stimulus onset VEPs. Due to the exploratory nature of this research, amplitudes and latencies of SO-VEP components P1, N1 and P2 from vertex electrodes POz, and OZ were used for statistical evaluations of group differences. Analyses were collapsed across all trials for the motion direction discrimination task.

Fig. 1. Schematic illustration of global motion direction discrimination task and stimuli. On each trial, after a uniformly random inter-trial interval of 1–2 s, a fixation spot appeared for 1 s, followed by a cloud of stationary random dots. After a uniformly random interval between 1 and 2 s, the randomly positioned dots began to move to the right or left.

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2.3.2. EZ-diffusion model We used reaction times (RTs), variances to correct trials, and accuracy (proportion correct) to evaluate parameters of the EZ-diffusion model separately for the DR0 and DR320 conditions with the formulas provided in Wagenmakers et al. (2007). This model of perceptual decisions estimates the rate at which information about a stimulus is accumulated by a decision process (drift rate), the amount of information that an observer chooses to accumulate before initiating a response (boundary separation or simply boundary), and a summary of both perceptual and motor non-decision process times, the non-decision time (NDT). A more detailed description of the derivation of the EZdiffusion model is available in Martin et al. (2010). 2.4. Statistical analyses We performed all statistical analyses with SPSS software (IBM Corp). The group difference between young and old participants on SO-VEP measures was assessed by t tests. Relationships between behavioral measures and EZ diffusion model parameter estimates and stimulus onset VEP measures were assessed with exploratory forward regressions. In order to control for number of predictors, we pooled 16 posterior electrodes into two composite variables: for the left hemisphere we averaged amplitudes and latencies across P7, P5, P3, P1, PO9, PO7, PO3, and O1 electrodes, and for the right hemisphere we averaged amplitudes and latencies across P8, P6, P4, P2, PO10, PO8, PO4, and O2 electrodes. For each of these composites of electrodes, we averaged the P1, N1, and P2 amplitudes and latencies. In addition, age was used as a candidate predictor in each regression, for a total of 13 variables considered as candidate predictors in each regression. In forward regression, the variable with the strongest relationship to the criterion variable is entered first, and then the variable with the strongest relationship to the residuals is entered next, and so on. Entry stops when no variable among the candidate predictors changes the coefficient of determination (R2) significantly (α = .05). 3. Results 3.1. Neuropsychological performance Means and standard deviations for the performance on tests are given in Table 1. Older participants performed significantly lower only

on the AVLT memory test [t(25) = 3.01, p = .006] and trail making tests: Trail Making A [t(25) = 2.54, p = .02] and Trail Making B [t(25) = 3.03, p = .006]. Overall, our older adults were generally highly functioning from the cognitive point of view. 3.2. Stimulus onset visual evoked potential (SO-VEP) analyses The grand-averaged waveforms for stimulus onset VEPs of younger and older participants from the occipital midline electrode locations Oz and POz are presented in Fig. 2. Means and standard errors are presented in Table 2 for P1, N1 and P2 amplitudes and latencies. 3.2.1. P1 SO-VEP component For SO-VEPs P1 amplitudes were significantly greater for old as compared to young adults at Oz electrode [t(29) = 2.43, p = .02, 5.91 uV vs. 1.12 uV] and POz electrode [t(29) = 2.83, p = .009, 5.28 uV vs. 1.36 uV]. For P1 latencies we also found statistically significant differences where younger participants showed faster peak latencies at POz electrode [t(29) = 3.69, p = .001, 85 ms vs. 100 ms] and Oz electrode [t(29) = 2.77, p = .01, 93 ms vs. 98 ms]. 3.2.2. N1 SO-VEP component For SO-VEP N1 amplitudes there were no statistically significant differences between old as compared to young adults. However, for N1 latencies we found statistically significant differences where younger participants showed faster peak latencies at POz electrode [t(29) = 2.94, p = .006, 120 ms vs. 138 ms] and Oz electrode [t(29) = 2.21, p = .03, 121 ms vs. 140 ms]. 3.2.3. P2 SO-VEP component For SO-VEPs P2 amplitudes were significantly greater for young as compared to old adults at POz electrode [t(29) = 2.85, p = .008, 6.75 uV vs. 4.11 uV]. For P2 latencies we did not find statistically significant differences between the two groups. 3.2.4. Topographic brain maps The topographic voltage maps presented in Fig. 3 show scalp distribution differences between two groups over occipital sites parallel peak analyses: greater activation of elderly as compared to young participants for P1 and N1 components, while young as compared to elderly showed greater activation for P2 component. In addition, for P1 component young subjects showed two distinct activation clusters separately over each hemisphere, while for elderly group there was a single cluster extending over both hemispheres. 3.2.5. Correlations between neuropsychological performance and SO-VEPs There were few statistically significant correlations between performance on neuropsychological tests and P1, N1, and P2 SO-VEP peak amplitudes and latencies. For young there were statistically significant negative correlations between performance on AVLT memory test and P2 latency over left hemisphere (r = − .75, p = .02) and over right hemisphere (r = −.74, p = .02): subjects with better performance on Table 1 Means and standard deviations for neuropsychological tests of young and elderly subject groups. Neuropsychological test

Fig. 2. Average waveforms for stimulus onset VEPs for young (red line) and old (black line) participants from POz and Oz electrodes. The vertical line at 0 ms represents the stimulus onset. The stimulus onset VEP (SO-VEP) components P1, N1 and P2 are labeled.

AVLT Trail Making A Trail Making B Verbal fluency Digit Span total Visual Search ⁎ p b .05. ⁎⁎ p b .01.

Young (n = 12)

Elderly (n = 19)

Mean

SD

Mean

SD

99.07 25.30 48.17 29.78 10.78 103.11

14.85 5.51 14.68 7.03 1.56 39.61

74.72⁎⁎ 36.29⁎ 78.60⁎⁎ 24.56 10.28 122.20

21.75 12.27 28.10 6.30 2.02 65.25

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Table 2 Stimulus onset VEP mean ± standard error for P1, N1, and P2 amplitudes and latencies for younger and older participants, computed separately for POz and Oz electrodes. P1

N1

P2

Amplitude (μV)

Latency (ms)

Amplitude (μV)

Latency (ms)

Amplitude (μV)

Latency (ms)

POz Young Elderly

1.36 ± 0.84 5.28 ± 1.14

85 ± 3.93 100 ± 2.01

−3.18 ± 0.31 −3.27 ± 0.68

120 ± 5.57 138 ± 3.61

6.75 ± 0.88 4.11 ± 0.49

212 ± 7.76 232 ± 7.22

Oz Young Elderly

1.12 ± 0.80 5.91 ± 1.47

93 ± 5.13 98 ± 2.59

−3.05 ± 0.95 −4.69 ± 0.82

129 ± 6.84 135 ± 2.38

4.88 ± 0.71 4.20 ± 0.55

224 ± 4.94 224 ± 7.80

memory test showed shorter latencies for P2 component. For elderly, however, we found that there was statistically significant positive correlation between performance on Trail Making B test and P1 and N1 latencies over both hemispheres (P1 left r = .51, p = .03, P1 right r = .56, p = .01, N1 left r = .54, p = .02, N1 right r = .48, p = .04): older subject who finished Trail Making B faster showed significantly slower P1 and N1 latencies.

report in which the onset of the stimulus was dissociated from acting upon the stimulus. The closest passive paradigms to ours are the selective delayed-recognition and three stimulus oddball paradigms. In both

3.2.6. Forward regression analyses Means and standard errors for RT, variance of RT for correct responses, and percent correct are given in Table 3. Table 4 presents the same statistics for the diffusion model parameter estimates. Parameter estimates from the forward regressions are given in Table 5. One elderly participant was removed from the models because this person's left P1 amplitude was an outlier (z = 4.69). The forward models predicting reaction time (RT) and NDT in both the DR0 and DR320 conditions were significant. The amplitude of the P1 response on the left side was a significant predictor of RT in both conditions (see Fig. 4). In the DR0 condition, left P1 amplitude was a significant predictor of NDT, but not in the DR320 condition. In both conditions, age was a significant predictor of NDT, despite not accounting for significant variance in RT. Notably, age accounted for significant unique variance in NDT in the DR0 condition even with P1 amplitude included in the model, indicating that the relationship between age and RT cannot be fully explained by the relationship between age and P1 amplitude. There were no significant predictors of accuracy among our electrophysiological measures or age for either condition. Finally, latency of the P2 response on the left side accounted for over 20% of the variance in both drift rate and boundary in the DR320 condition, but none of the predictors were significantly related to these parameters in the DR0 condition. 4. Discussion In this study the focal question was whether age-related effects we observed in the motion-onset VEPs are of perceptual/attentional origin or can they be traced to the earlier sensory/encoding processing stages. Results unequivocally showed that the initial age-related VEP differences can be traced to the earliest stimulus-onset VEPs: we found enhanced and prolonged P1, prolonged N1, and diminished P2 components for older as compared to younger participants. Our motion direction discrimination paradigm uniquely enabled us to separately investigate motion-onset from stimulus-onset VEPs. The present study also provide even more surprising results, namely that the SO-VEP components, P1, N1, and P2, to the entirely passive painting of white dots on black background were highly predictive of subsequent (at least with a delay of 1 s) motion direction discrimination of these dots and performance on some neuropsychological tests. And for the first time agerelated changes in VEPs were attributed to a configuration mechanism, proactive assembly of needed neuronal networks for proper processing. Age-related changes in our study are difficult to compare with existing reports, since we intentionally separated stimulus presentation from onset of the motion in order to independently extract stimulus onset from motion onset VEP responses. We could not find any VEP

Fig. 3. Topographic maps: Voltage topographic maps showing the amplitude distribution over the time window of 20 ms centered at peak of P1, N1, and P2 components separately for young participant (left) and elderly participants (right). The color scales depict the P1, N1, and P2 amplitudes in uVs.

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Table 3 Means and standard errors for the behavioral dependent measures. Group

Condition

DV

M

Se

L95

U95

Young

DR0

RT s2 %C RT s2 %C RT s2 %C RT s2 %C

.53 .01 .99 .60 .02 .98 .63 .01 .99 .71 .03 .98

.03 .00 .00 .03 .01 .01 .03 .00 .00 .03 .01 .01

.46 .01 .98 .53 .01 .97 .57 .01 .98 .65 .01 .96

.60 .02 .99 .67 .03 1.00 .69 .02 1.00 .77 .04 .99

DR320

Elderly

DR0

DR320

Note. RT = reaction time, s2 = variance, M = mean, se = standard error, L95 = lower 95% confidence limit, U95 = upper 95% confidence limit.

paradigms the immediate behavioral response is not required, whereas internal cognitive processing is needed, e.g., memorization of stimuli or inhibition of non-target stimuli. In the selective delayed-recognition task used for testing working memory the stimuli set is presented to be memorized for subsequent probe matching (Zanto et al., 2010). They reported that in elderly participants there was a delay in the N1 latency during the encoding of initial stimuli. In the three-stimulus oddball task (Squires et al., 1975) there are infrequent target stimuli, frequent nontarget and novel, deviant stimuli; the participant is to discriminate the target stimulus and ignore irrelevant and novel stimuli. Even though irrelevant and novel stimuli should be ignored/inhibited they elicit an automatic VEP response. Most of the research with this paradigm was done by Czigler and his colleagues (Czigler and Balazs, 2005; Pato and Czigler, 2011; Weisz and Czigler, 2006). They used a letter-matching task with pictures as novel, irrelevant stimuli that were to be ignored. Their studies were focused primarily on N2b and P3a, exogenous fronto-central ERP components, indices of inhibitory processes, occurring after 200 ms post stimulus onset. Even though in their reports there are visible exogenous VEP components as well as aging effects on these components they did not evaluate age-related changes on P1, N1 or P2. In only one study (Czigler and Balazs, 2005) they reported amplitude reduction of the P1, anterior N1 and P2 components in the older group. These findings are contrary to ours and may reflect the nature of novel stimuli in the oddball task, e.g., active withholding of response engaging inhibitory processes. 4.1. Stimulus-onset VEPs Since behavioral data indexes the outcome of many sensory, perceptual, and cognitive processes involved in visual motion direction discrimination, we also recorded VEPs, providing us a high-resolution temporal measure of visual information processing. We separately Table 4 Means and standard errors of the diffusion model parameter estimates. Group

Condition

DV

M

Se

L95

U95

Young

DR0

Drift rate Boundary NDT Drift rate Boundary NDT Drift rate Boundary NDT Drift rate Boundary NDT

.40 .13 .35 .34 .14 .39 .37 .15 .43 .31 .15 .47

.02 .01 .03 .02 .01 .03 .02 .01 .02 .02 .01 .03

.35 .12 .30 .29 .12 .33 .33 .13 .39 .28 .13 .41

.45 .15 .40 .38 .15 .45 .41 .16 .48 .35 .17 .52

DR320

Elderly

DR0

DR320

Note. RT = reaction time, s2 = variance, M = mean, se = standard error, L95 = lower 95% confidence limit, U95 = upper 95% confidence limit.

recorded EEG for stimulus onset (SO-VEPs) and motion onset VEPs (MO-VEPs) to separate VEPs generated by stimulus onset, i.e., global luminance change, from motion onset, i.e., integration of local luminance change due to the sequential dots displacement. With such separation VEPs can provide electrophysiological correlates of different visual processes and provide an opportunity to establish which of visual processes are more sensitive to aging. 4.1.1. P1 component Visual processing can be characterized by rapid, widespread activation through the visual sensory pathways, which allow time for feedback to earlier sensory areas well before the P1 peak latency (Bullier, 2001a; Foxe and Simpson, 2005). Thus, the effects on P1 are very likely the result of feed forward and feedback, interactive processing that can impact very early stages of visual cortical processing (Bullier, 2001b). In other words, P1 presumably reflects the intersection of bottom-up and top-down processes. By the nature of our passive task paradigm, i.e., the absence of immediate action upon stimulus presentation, we assume that the P1 in our paradigm reflects primarily bottom-up encoding process. As reviewed in De Sanctis et al. (2008), a majority of reports showed an enhanced and prolonged P1 in elderly observers. Our findings are in agreement with these findings: elderly as compared to young showed an at least 3 uV enhancement and 15 ms prolongation of P1 component. Increased P1 amplitude in the elderly has different interpretations: while some claim that enhanced P1 represents compensatory processes or resistance to cognitive decline (De Sanctis et al., 2008), others maintain that enhanced P1 reflects impairments in inhibitory mechanisms in visual processing (Gazzaley et al., 2008), i.e., selective deficit in older adults' top-down suppression of visual stimulus processing. Our findings unequivocally support the assertion that the enhanced P1 observed in normal aging subjects is indicative of compensatory recruitment. As discussed later, enhanced P1 in elderly was related to faster, more efficient performance. Regarding prolonged P1 latency, we agree with Natale et al. (2006) that the slower P1 latencies in older adults may reflect at least a degree of delayed sensory processing, perhaps concomitant with delayed attentional suppression. The delayed P1 peak latency could be a notable ERP marker of configuration deficits in aging (defined as proactively recruitment of obligatory neural processors needed for anticipated task demands). This notion is also supported by significant positive correlations between P1 amplitudes but not latencies with motion direction discrimination performance. These novel correlation outcomes extend the previously reported age effects on P1 to indicate that P1 amplitudes acquired from an appropriate perceptual paradigm may constitute a salient ERP marker of perceptual aging. Overall, we believe that P1 deficits obtained in our VEP study reflect general diminution in visual processing. 4.1.2. N1 component A number of recent studies that focused on age-related VEP differences have confirmed increased N1 amplitude and/or prolonged N1 latencies (see De Sanctis et al., 2008 for review). Our results are showing primarily an 19 ms increased latency of N1 component in older as compared to younger participants. Slowing of N1 is in agreement with several reports (Ceponiene et al., 2008; Curran et al., 2001; Finnigan et al., 2011). According to the majority of the accounts, delay of N1 is indicative of age-related decline in attention. The only index that N1 could be related to attention is the correlation between N1 latency from right hemisphere and non-decision time for DR320 task. However, as already indicated we consider all observe VEP age-related changes to be related to configuration. 4.1.3. P2 component As for P2 component, it is usually not considered an exogenous, sensory visual VEP. P2 has been related to working memory in studies in

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Table 5 Statistics of forward regression analyses predicting behavioral performance and diffusion model parameters from SO VEPs across all subjects. Condition

Criterion

Predictor(s)

DR0

RT Accuracy Drift rate Boundary NDT

Left P1 amplitude X X X Left P1 amplitude Age Left P1 amplitude X Left P2 latency Left P2 latency Age

DR320

RT Accuracy Drift rate Boundary NDT

df

p

β

Adj R2

SY|X

8.222

(1,28)

0.008

0.476

0.199

0.109

9.992

(2,27)

0.001

0.383

0.073

11.743

(1,28)

0.002

0.473 .342 0.544

0.27

0.114

10.578 8.223 5.356

(1,28) (1,28) (1,28)

0.003 0.008 0.028

0.524 0.477 0.401

0.248 0.2 0.131

0.065 0.029 0.104

F

Note: β = standardized regression weight, Adj R2 = adjusted coefficient of determination, SY|X = standard error of estimate, RT = reaction time, NDT = non-decision time.

which action was required upon stimulus appearance (Finnigan et al., 2011; Riis et al., 2009). Since the P2 was prominently evoked by passive stimulus onset in our task we consider it a genuine VEP. The present results showed that aging attenuated P2. Attenuation of P2 amplitude is in agreement with the finding of Ceponiene et al. (2008) and Finnigan et al. (2011), who also reported diminished P2 amplitude in older as compared to younger adults. Finnigan et al. (2011) linked P2 to semantic operations during memory encoding, while Ceponiene et al. (2008) considered P2 as reflecting attentional processes. Our results that P2 latency was correlated with boundary and drift rate for DR320 task are also in agreement with Finnigan et al. (2011) who reported correlation between P2 latency and accuracy independent of age during a modified Sternberg word recognition task. To our knowledge our findings of the diminution of P2 amplitude in older adults to passive stimulus onset are the first of its kind. Previous researchers attributed diminution of P2 amplitude to age-related declines in working memory (Finnigan et al., 2011) or attention (Ceponiene et al., 2008). Linking our finding to working memory is problematic since the onset of the cloud of dots does not involve any specific information for manipulation to serve the subsequent task. Admittedly, onset of the cloud of dots has alerting information and could be linked to attention, however, as discussed later, we attributed P2 together with P1 and N1 SO-VEPs to configuration. Overall, we found robust aging effects on SO-VEPs in terms of prolonged latencies for P1 and N1 components, enhanced P1 amplitude, and diminished P2 amplitude, representing genuine processing differences in the elderly. We assume that observed differences in SO-VEPs reflect compensatory mechanisms. Age-related changes in VEP components reported in the current study cannot be explained by a global slowing (Salthouse, 1996) of neural responses to visual stimuli nor by inhibitory deficit because a) we did not observed global changes in SO-VEPs as predicted by global slowing theory, and b) the increase of P1 amplitude was associated with behavioral outcome measures, contrary to prediction of inhibition model. An alternative explanation

could be that observed changes in SO-VEPs are due to an age-related compensatory neural response to impoverished sensory input. It has been shown that sensory input from retina to visual cortex is impoverished in older adults (Freund et al., 2011). The reduced input to the striate cortex may be reflected in this study in an increased P1 amplitude and delayed P1 latency, the first delay we observed in the older group. Thus compensatory neural processes occur in response to the deterioration of early sensory processing mechanisms. We hypothesize that, in order to retain adequate performance, the brain compensates by increasing activation in visual cortices in response to external stimulation. 4.2. Configuration idea As defined in the Introduction, configuration relates to brain's automatic establishment, organization, maintenance, and modification of its internal connectivity. Simultaneous appropriation and synchronization of neuronal processing resources are obligatory for stimuli encoding, target detection, decision making, and response execution in concert with other neuronal processes related to suppression of external noise and internal extraneous neuronal operations. Applying to our task, configuration relates to preparation and activation of sensory, working memory, decision making, and motor neuronal networks needed for successful motion direction discrimination. An alternative explanation to configuration is alerting. In our motion direction discrimination task the onset of the stimulus, e.g., cloud of white dots on black background, has informative value that sometime between 1 and 2 s the dots will start to move. In other words, stimulus onset has warning, orienting feature triggering an alerting that was outlined by Fernandez-Duque and Posner (1997) as a process of “change in the internal state that follows the presentation of a warning signal”. It has been shown that alerting evokes the contingent negative variation (CNV) (Walter et al., 1964; Gomez et al., 2003), a sustained negative deflection preceding a target stimulus. However, in our task the dot

Fig. 4. Scatterplot showing relationships between reaction times on motion direction discrimination task (y axis) and SO-VEP P1 (x axis) for DR0 stimuli (left panel) and DR320 stimuli (right panel) at Oz electrode.

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cloud onset did not trigger a CNV, whereas SO-VEPs P1, N1 and P2 reliably predicted subsequent motion direction discrimination. In other words, the expected CNV response was not observed, yet neurophysiological activity between warning stimulus and response predicted subsequent behavior. In terms of aging, our SO-VEP results unequivocally showed that even to the passive presentation of the stimuli the evoked responses of older brains differ from those of the young with higher P1 amplitude, prolonged P1 and N1 latencies, and smaller P2 amplitude. More importantly, particularly the increased P1 amplitude, first detectable neuronal response to luminance change by EEG for foveally presented stimuli, can significantly predict subsequent better performance in motion direction discrimination task. These findings could reflect the agerelated changes in configuration. In this regard, we agree with Foxe and Simpson (2005) who demonstrated that this P1 deficit is not simply a shift in the amplitude of an otherwise intact process, but that P1, N1, and P2 age-related changes most likely reflect the configuration of the underlying brain network as revealed by modulations in the VEP. Agerelated changes in configuration would most likely reflect compensatory mechanism of allocating increasing neural resources to continue to successfully perform certain cognitive tasks. We somewhat agree with De Sanctis et al. (2008) who suggested that the functional relevance of alteration in early VEP could be indicative of resistance to cognitive decline or rather evidence for inefficiency of inhibitory processing in normal aging. Due to the passive nature of stimulus presentation attentional and/or inhibitory effects are less likely to play a role, whereas we consider compensation as one way of resistance to cognitive decline. 4.3. Forward regression analyses We found that age affected SO-VEP parameters even before the onset of a target stimulus, and of course it is well known, and confirmed in our data (Kavcic et al., 2013), that age affects response times. We addressed the degree to which the relationship between age and RT depends on SO-VEPs by beginning with an exploratory forward regression. We used both SO-VEP parameters and age as predictors of RT, accuracy, and parameters of the diffusion model. We found that left P1 amplitude was the sole predictor of RT in both DR0 and DR320 conditions, indicating that left P1 amplitude was more strongly related to RT than age and that age could not account for unique variance in RT once the contribution of P1 amplitude was taken out. Nevertheless, when RT was parsed into drift rate, boundary and NDT using the diffusion model, both P1 and age predicted unique variance in NDT in the DR0 condition, and age alone could account for significant variance in NDT in the DR320 condition. This suggests that age is related particularly to NDT and less so to drift rate and boundary, and that the effect of age on NDT is not entirely accounted for by P1 amplitude. The fact that P2 latency was predictive for the DR320 condition but not DR0 may point to an increased range of either the drift rate and boundary parameters, or P2 latency itself, when the direction range of motion is higher, i.e., the motion discrimination task is more demanding. Finally, the fact that each of the significant predictors was from summary measures taken from the left side of the electrode array likely reflects the fact that all participants responded with their right hands, which would have several effects on hemispheric activation. In particular, the significant SO-VEP predictors recorded over left hemisphere might reflect the configuration process: presentation of the random dot cloud informed subjects about a subsequent event which required right hand response. Thus, proper configuration of neuronal networks for right hand response represents an advantageous strategy. Future research can empirically test this assertion by using left hand or bimanual response. For use of left hand response we should find right hemispheric SO-VEPs predicting behavioral performance if our assertion is correct. And for bimanual responses, if the left hand were used to signal leftward motion and the right hand for rightward motion, it will be reasonable to expect that hemispherically evoked SO-VEPs will be less

predictive of behavioral performance since stimulus onset would not carry any information regarding subsequent direction of motion. The series of exploratory forward regressions point to several directions for future research. First, it is clear that the P1 amplitude accounts for much of the association between age and RT. Although age was significantly related to RT in the DR0 condition and NDT in the DR320 condition, P1 amplitude was more highly correlated, and when it entered the model first, age could not account for significant variance in these criterion variables. Nevertheless, the situation is not as clear cut as that, because age did enter the models predicting NDT, suggesting that the relationship between age and RT is not fully mediated by P1 amplitude. It would be beneficial to follow up this exploratory analysis with a planned mediation analysis (c.f. Baron and Kenny, 1986; MacKinnon and Dwyer, 1993). From the results of this exploratory analysis, we would predict that P1 amplitude would be a significant mediator of the correlation between age and RT, but that a significant direct effect between age and RT would remain. 4.4. Limitations While advancing our understanding of the nature of perceptual declines in aging, the present study does have methodological limitations. Although we attempted to recruit a representative aging sample, our elderly volunteers showed equivalent accuracy but they showed significantly prolonged RT, so that they represent an optimal aging sample. This would mainly have the effect of limiting the variability between young and old participants and thus attenuating age-related correlations, so that the population correlations among the variables reported here are likely stronger than we estimated. On the other hand, this sample is somewhat small for the number of analyses we performed, so inconsistencies with other similar studies should be interpreted with caution. Furthermore there is large difference in age between the 2 groups. Such large differences between the groups makes it impossible to investigate when the age-related change in SOVEPs occurs. Does it progress gradually in linear fashion or more abruptly after a certain age? Future studies on the influence of aging on VEP activity should include greater number of subjects within and across groups. Finally, this study shares the limited internal validity of all cross-sectional designs that differences between age groups may reflect something other than aging. Nevertheless, our main result is the relationship between response latency and the evoked response to a warning stimulus, and this is independent of aging effects. 5. Conclusion Prior studies, typically using active stimuli, have predominantly shown that aging was accompanied by alterations in early VEP components. Here we show that age-related changes in early VEP components are apparent with passive stimulus presentation, e.g., without an explicit task requirement: older as compared to younger adults showed increased P1 amplitude whereas their P2 amplitude was reduced, while they showed slowing of P1 and N1 components. The current data are consistent with other neurobiological evidence that attentional decrements can manifest in aging. However, we suggest that the perseverance of these VEP findings in paradigms without task requirements is indicative of genuine age-related sources in early sensory processing, most likely reflecting initial configuration processes. Thus, VEPs may be particularly useful measures for assessing visual neurocognitive processes in older adults. Our electrophysiological results support the notion that slowing of information processing in older adults starts at the very beginning of system configuration and continues in initial encoding in visual cortical processing, most likely in striate and extrastriate visual cortices. Furthermore, the earliest SO-VEP components reflecting configuration of visual cortices and encoding processes predict subsequent prolonging of perceptual, higher-level cognitive and motor processes. The functional

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Cortical configuration by stimulus onset visual evoked potentials (SO-VEPs) predicts performance on a motion direction discrimination task.

The slowing of information processing, a hallmark of cognitive aging, has several origins. Previously we reported that in a motion direction discrimin...
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