The Effects of Amnestic Mild Cognitive Impairment on Go/NoGo Semantic Categorization Task Performance and Event-Related Potentials

Abstract

We examined the effects of amnestic mild cognitive impairment (aMCI) on behavioral (response times and error rates) and scalp-recorded event-related potential (ERP) measures of response execution and inhibition, using Go/NoGo tasks involving basic and superordinate semantic categorization. Twenty-five aMCI (16 F; 68.5±8 years) and 25 age- and gender-matched normal control subjects (16 F; 65.4±7.1 years) completed two visual Go/NoGo tasks. In the single car task, responses were made based on single exemplars of a car (Go) and a dog (NoGo) (basic). In the object animal task, responses were based on multiple exemplars of objects (Go) and animals (NoGo) (superordinate). The aMCI subjects had higher commission errors on the NoGo trials compared to the control subjects, whereas both groups had comparable omission errors and reaction times during the Go trials. The aMCI subjects had significantly prolonged N2 ERP latency during Go and NoGo trials across tasks compared to the controls. Both groups showed similar categorization effects and response type effects in N2/P3 ERP latencies and P3 amplitude. Our findings indicate that altered early neural processing indexed by N2 latency distinguishes subjects with aMCI from controls during the Go/NoGo task. Prolonged Go-N2 latency in aMCI appears to precede behavioral changes in response execution, whereas prolonged NoGo-N2 latency underlies behavioral deterioration in response inhibition.

Keywords

Categorization cognition electroencephalography event-related potentials Go/NoGo mild cognitive impairment semantics

INTRODUCTION

It is well recognized that individuals with a diagnosis of amnestic mild cognitive impairment (aMCI) are at higher risk of developing Alzheimer’s disease (AD), one of the most common forms of dementia [1, 2]. While impairment in episodic memory is a hallmark symptom of aMCI, there is accumulating evidence that individuals with aMCI also have impairments in other cognitive domains, including executive function [3 –9].

Executive function, also referred to as cognitive control, encompasses top-down cognitive processes that guide behavior. These cognitive processes include stimulus evaluation, response execution and inhibition, conflict monitoring, planning, organizing, and decision making [10 –13], all of which play a role in day-to-day activities. To characterize cognitive control and its deficits in individuals with MCI, a variety of clinical (e.g., Behavior Rating Inventory of Executive Function, the Stroop color-word task, Wisconsin Card Sorting Test, Trails B, Letter fluency) and experimental measures (e.g., the Brown-Peterson task, the Hayling task, Erickson flanker task, the Simon task, Go/NoGo task, the stop signal task) have been used [14 –24]. Here we report on electrophysiological correlates of cognitive control involved in execution and inhibition of a response in a cohort with aMCI and a group of cognitively normal controls.

Response inhibition entails overriding an ongoing course of action in favor of an alternative course [25]. As we navigate our ever-changing environment, we evaluate stimuli, monitor conflict between current actions or intentions in the face of upcoming and competing sources of information, and appropriately execute or withhold a response [26, 27]. For instance, when walking down a street, we stop if we see an approaching car but continue to walk if we simply see other people walking by. There is general consensus that individuals in early stages of AD have impaired response inhibition (see [28] for review; [29 –36]). What remains unclear is whether response inhibition deficits occur in the pre-dementia stage, as existing studies have yielded inconsistent results [14]. While some behavioral studies in MCI patients have found response inhibition deficits on Erickson flanker [23], Stroop [15, 22], and stop signal tasks [24], others have observed no differences between normal controls and individuals with aMCI on negative priming, Stroop, Go/NoGo tasks [37], and the classic Hayling task [16]. Differences in tasks and stimuli across studies, in addition to the heterogeneity in the diagnosis of MCI, may explain discrepancies in the behavioral findings. Given that neural alterations in aMCI and mild AD precede and eventually lead to behavioral deficits [38 –42], we sought to evaluate neural responses underlying behavioral changes in response inhibition using electroencephalography (EEG) to better characterize changes in cognitive control in individuals with aMCI [43, 44].

Many EEG-based studies of normally aging individuals have used the Go/NoGo paradigm to study response inhibition [45 –50]. The standard Go/NoGo task requires subjects to respond to a certain type of stimuli (Go) and inhibit/refrain from responding to another type of stimuli (NoGo), pre-defined by a specific set of rules. Typically, the Go/NoGo paradigm yields two event-related potential (ERP) components: an early fronto-central negative deflection developing between 200 and 400 ms post-stimulus onset (N2) and a later positive component (P3) between 300 and 600 ms [51 –55], which are differentially modulated by the Go and NoGo trials. The exact functional significance of each of these two ERP components is debated. The suggested roles of N2 vary from being a marker of response inhibition ([56 –58]; also see [46, 59] for reviews) to stimulus evaluation and conflict monitoring [26 , 61]. On the other hand, the suggested roles of P3 vary from being an index of evaluation of stimuli and attentional allocation [62, 63] to response monitoring (e.g., [52]). Nevertheless, there is general consensus that both N2 and P3 are modulated during response execution and inhibition [64, 65].

To the best of our knowledge, only one EEG study has thus far examined response execution and inhibition in aMCI using the Go/NoGo paradigm [19]. Cid-Fernández et al. [19] compared ERPs of 63 healthy controls and 30 aMCI participants obtained during a Go/NoGo task. They found that aMCI patients had smaller Go-N2 and NoGo-N2 amplitudes, longer response times (on Go trials), and more errors compared to controls, suggesting an overall impairment in performing the Go/NoGo task. This Go/NoGo study and other ERP studies that have used the oddball paradigm in aMCI [66 –68] have predominantly examined perceptual categorization using stimuli such as tones of different frequencies, numbers, letters, and shapes. Although response inhibition in the real world is often based on how objects we encounter are categorized (e.g., when walking through the woods, deciding whether to take a step or not when a rope versus a snake is encountered), to our knowledge, none have studied the Go/NoGo paradigm in aMCI in the context of semantic categorization. Given that semantic memory deficits have been observed in behavioral [69 –74] and ERP [75, 76] studies in aMCI, examining response execution and inhibition in the context of semantic categorization could provide useful information as to whether these operations interact in individuals with aMCI.

Semantic categorization (e.g., categorizing items as animate versus inanimate) allows for meaningful organization of objects in our surrounding environment [77 –80]. In general, objects are categorized at three levels: basic, superordinate, and subordinate [81]. For example, a ‘dog’ at the basic level can be categorized at the superordinate level as an ‘animal’ and at the subordinate level as a ‘golden retriever.’ Basic and superordinate categorizations are more frequently involved in day-to-day functioning than subordinate categorization, which involves more in-depth/expert knowledge (e.g., knowledge used by dog experts, car experts, bird experts). Previously, we examined the effects of age on cognitive control using Go/NoGo tasks involving basic and superordinate categorization in an EEG study by comparing cognitively normal young and older adults [47]. We found that cognitively normal older adults had reduced NoGo-N2 and NoGo-P3 amplitudes compared to younger adults, suggesting alterations in neural processing during response inhibition. Additionally, we found that older adults had longer N2 latency for superordinate compared to basic categorization; in contrast, younger adults showed no N2 latency differences between the two categorization tasks, suggesting that complexity of categorization modulates neural processing in older adults in early stages of processing. However, both groups exhibited similar task effects in later stages of neural processing with shorter P3 latency and larger P3 amplitude for basic compared to superordinate categorization. The logical next step was to examine how age-related cognitive control mechanisms differ from disease-related mechanisms such as in individuals withaMCI.

The goal of the current study was to determine whether and how aMCI affects the ERP correlates (N2/P3) of cognitive control (response inhibition and/or response execution) during semantic categorization, using the same paradigm from our earlier studies [47 , 83]. The objective ERP markers isolated in this study may help detect early neural alterations corresponding to changes in cognitive control in individuals with aMCI, and in the future, could be applied as a measure to monitor disease progression and treatment response given the high predictive value of cognitive control deficits in everyday functioning [7 , 84–86]. Based on our previous work using the Go/NoGo paradigm and in consideration of the work by others cited above, we predicted delayed latency and/or reduced amplitude in N2 and/or P3 components in aMCI participants compared to controls. We also expected aMCI-related ERP changes (delayed latency and/or reduced amplitude) to be more prominent during superordinate compared to basic categorization and during NoGo compared to Go trials.

METHODS

Participants

Twenty-five individuals with aMCI (16 females; mean age = 68.5 years, SD = 8; 54–86 years) and 25 normal control subjects (16 females; mean age = 65.4 years, SD = 7.1; 57–82 years) participated in this study. All participants were Caucasian, right handed (except for one aMCI subject), and native English speakers, with no history of learning disabilities, stroke, major psychiatric illness, alcoholism or substance abuse, uncorrected hearing or vision loss, or elevated depressive symptoms (Beck Depression Inventory-II [87] or Geriatric Depression Scale >10 [88]). All aMCI subjects met the Petersen et al. [89] criteria, including the following: (a) memory concerns reported by the patient and/or corroborated by a reliable informant, (b) episodic memory impairments verified by objective measures, (c) relative independence in performing daily functions, and (d) did not meet dementia criteria. Control subjects had no subjective memory or cognitive complaints and performed normally on neuropsychological evaluations (Table 1).

Cognitive screening was done initially using the Mini-Mental State Evaluation (MMSE; [90]) or the Montreal Cognitive Assessment (MoCA; [91]) to rule out dementia. All participants in the aMCI group received MMSE, while a small subset of controls received MMSE (n = 5) and others received MOCA. We are mainly reporting MMSE and MOCA scores to show that participants in the aMCI and control groups had no global cognitive deficits. The Clinical Dementia Rating (CDR) [92] was administered to participants in the aMCI group [CDR = 0.5], and results of the neuropsychological assessment for both groups are listed in Table 1. Three of the 25 aMCI patients were taking cholinesterase inhibitors when tested (all were on stabilized doses of donepezil for at least 3 months). Informed consent was obtained from all participants in accordance with the protocols approved by the Institutional Review Boards of The University of Texas at Dallas and The University of Texas Southwestern Medical Center. Experiments with human subjects were performed in accordance with the ethical standards of the Committee on Human Experimentation of these institutions and with the Helsinki Declaration of 1975.

Experimental paradigm and procedures

Participants completed two Go/NoGo tasks [47 , 83], which required varying levels of perceptual and semantic processing. Both tasks were completed during a single visit with a short break between the tasks.

In the single car task (SiC), participants made Go/NoGo decisions based on a line drawing of a single exemplar of a car (Go) or a single exemplar of a dog (NoGo). The images of the car and the dog were presented 160 and 40 times respectively. The following instructions were given to participants: “You are going to see some dogs and cars. When you see a dog, do not push the button. Press the button for anything that is not a dog. Be as quick and as accurate as possible.” The basic-level labels of ‘car’ and ‘dog’ were used to encourage correct discrimination using basic classification (car vs. dog) as opposed to superordinate classification (vehicle vs. animal). As images of the same car and dog were utilized throughout the entire task, the perceptual properties of the images remained identical, facilitating decisions based on coarse perceptual discrimination.

In the object animal task (ObA), participants made Go/NoGo decisions based on multiple exemplars (line-drawings) of objects (Go) and animals (NoGo), which involved superordinate categorization rather than basic categorization. The images included 160 different exemplars of objects for Go trials (40 food items, 40 cars, 20 clothing items, 20 kitchen items, 20 human body parts, and 20 tools) and 40 different exemplars of animals of varying visual typicality for NoGo trials (e.g., butterfly, lobster, snake, dog). Participants were given the following instructions: “You are going to see some objects and animals. When you see an animal, do not push the button. Press the button for anything that is not an animal. Be as quick and as accurate as possible.” Each image was displayed only once during the course of the task.

Overall, each task consisted of 200 trials: 160 (80%) Go trials that required response through button pressing and 40 (20%) NoGo trials that required inhibition/withholding of response. This 4:1 trial type ratio was used in order to accentuate the tendency for pre-potent responses. Each stimulus was presented for 300 ms followed by a 1700 ms fixation period (with ‘+’ presented in the center of the display). To mitigate order or practice effects, the sequence of the stimuli in each task was pseudo-randomized, and the task order was counterbalanced for each participant. A button box was placed under the right index finger to register Go responses and record reaction times (RT). The details regarding the development of these tasks can be found in Maguire et al. [82].

EEG data acquisition and processing

Continuous EEG data were recorded from a 64-electrode elastic cap (Neuroscan Quikcap) using a Neuroscan SynAmps2 amplifier and Scan 4.5 software (sampling rate: 1 kHz, DC-200 Hz), with electrode impedances typically below 10 kΩ. The reference electrode was located at the vertex between Cz and CPz. Vertical electroocculogram (VEOG) was recorded at sites above and below the left eye. Data were processed off-line using Neuroscan Edit software. Poorly functioning electrodes were identified by visual inspection and excluded from analysis (2.6% in normal and 2.3% in MCI subjects). The continuous EEG data were high-pass filtered at 0.15 Hz and corrected for eye blinks using spatial filtering in Neuroscan. The data were epoched between 200 ms before the onset of the stimuli until 1500 ms after the presentation of the stimuli. Additionally, a digital low-pass filter was applied with a cutoff value of 20 Hz (linear finite impulse response function) to minimize high frequency noise, such as muscle activity. Epochs with peak signal amplitudes of more than 75μV were rejected. The rejection rates were 13.5% /10.1% in Go and 11% /10.6% in NoGo trials for control/aMCI subjects. Factorial analyses including group, task, and response type on the rejection rates did not show any significance (p > 0.05). The EEG data were re-referenced to the average potential over the entire scalp. Baseline correction was done by subtracting the mean amplitude of the pre-stimulus interval (–200 ms to 0 ms) from each time point. An algorithm computing the average based on spherical splines fitted to the data (as described in [93]) was then applied to interpolate EEG data to the sites of the bad electrodes. Individual ERP data were averaged for the two tasks separately (both Go and NoGo response types). Only correct trials were included in the ERP averages, and trials with RT shorter than 200 ms or longer than 1000 ms were excluded from further analysis.

ERP analysis

We focused on N2 and P3 components around the midline. Electrode sites and time windows were selected based on (1) previous N2/P3 studies, in particular the Maguire et al. [82, 83] studies (from which the two tasks were adopted) and the Mudar et al. [47] study, and (2) the consistency with which N2 and P3 were observed across participants at each electrode location. For both Go and NoGo trials, we first measured peak latency between 150 and 300 ms for N2 at 3 midline electrodes (Fz, FCz, Cz) and between 250 and 650 ms for P3 at (FCz, Cz, CPz). We calculated peak latency-adjusted (time window: mean latency±1 SD) mean amplitude for each group to better estimate amplitude differences independent of latency variability across aMCI and control groups, as in our previous study [47]. We averaged mean amplitude and peak latency across the three midline frontal, fronto-central, and central (Fz, FCz, Cz) electrodes for examining N2 and across the three midline fronto-central, central, and centro-parietal (FCz, Cz, CPz) electrodes for examining P3.

Statistical analysis

A standard general linear model (GLM) was applied to each behavioral and ERP outcome measure to assess the effects of response type (Go/NoGo), task (SiC/ObA), and group (aMCI/control), as well as all interactions among these experimental factors on the means. The full GLM was used for analyses of all outcome measures (including response type, group, and condition) with the exception of a behavioral measure (i.e., RT). Our primary focus was differences between the groups and any higher-order interactions from the GLMs of the behavioral and ERP measures because we hypothesized differential means that depended on group.

Behavioral outcome measures included RT and error rate. Go and NoGo errors are also referred to as omission errors (i.e., a subject incorrectly inhibits during Go trials/misses) and commission errors (i.e., a subject fails to inhibit during NoGo trials/false alarms), respectively. Because RT was measured only for Go trials, the GLM for RT did not include response type (Go/NoGo). ERP outcome measures included peak latency and latency adjusted mean amplitude for N2 and P3.

The GLMs were implemented in SAS (Cary, NC), using the mixed model procedure with the Kenward-Roger degree-of-freedom method and default residual maximum likelihood estimation of variance components. The GLMs included subject as a random term to account for within- and between-subject sources of error variability. Additionally, due to the unequal number of Go/NoGo trials (160 versus 40 trials) and the subject-specific attrition rates for trials themselves, the variance of trial-averaged responses was unequal. Therefore, we employed weights in the GLMs for the ERP measures to take into account the unequal variances of subjects’ measured responses to each level of experimental factor. Weights were determined by the number of trials used for the calculation of each ERP measure in each individual separately (trial types separately including SiC-Go, SiC-NoGo, ObA-Go, and ObA-NoGo). P-values reported in the Results section were derived from F- and t-statistics of contrasts of experimental factor means, including interaction contrasts.

RESULTS

Behavioral results

Behavioral data for both RT and error rate are reported in Table 2. Statistical analyses for RT revealed only a main effect of task, F(1,48) = 153.8, p < 0.00001, with ObA RT significantly longer than SiC (459 > 369 ms). No other effects were significant (p > 0.1). Statistical analyses for error rate revealed a significant main effect of response type, F(1,144) = 68.58, p < 0.00001, with lower accuracy on NoGo than Go trials (error rate 12.7 > 4.2%). In addition, the interaction between group and response type was significant, F(1,144) = 11.27, p = 0.001. This interaction was driven by increased commission errors (false alarms/failure to inhibit during NoGo trials) in the aMCI group compared to controls (error rate 15.8 > 9.7%), t(99) = –3.42, p = 0.0009, but no group difference was observed in omission errors (misses during Go trials) (p > 0.1). All statistical results are reported in Table 3.

ERP results

Group average scores on ERP measures are reported in Table 4. Group average ERPs and topographies are presented in Figs. 1 and 2.

N2 latency

There was a significant main effect of group, F(1,55.4) = 7.38, p = 0.0088, with aMCI having longer latency than controls (250.1 > 233.5 ms). Additionally, significant main effects of task, F(1,144) = 37.97, p < 0.00001, with ObA longer than SiC (249.8 > 233.8 ms), and response type, F(1,144) = 16.31, p = 0.00009, with NoGo longer than Go (247 > 236.6 ms) were observed. No other effects were significant (p > 0.1) and all are reported in Table 5.

N2 amplitude

There was a significant main effect of task, F(1,144) = 14.5, p = 0.0002, with ObA more negative than SiC (–1.25 < –0.94μV). No other effects were significant (p > 0.1) and all results are reported inTable 5.

P3 latency

There were significant main effects of task, F(1,144) = 64.6, p < 0.00001, with ObA longer than SiC (508.4 > 458.1 ms), and response type, F(1,144) = 5.17, p = 0.025, with NoGo longer than Go (491.9 > 474.6 ms). Other effects were not significant (p > 0.1) and all are reported in Table 5.

P3 amplitude

There were significant main effects of task, F(1,144) = 4.61, p = 0.033, with SiC more positive than ObA (1.33 > 1.09μV), and response type, F(1,144) = 41.35, p < 0.00001, with NoGo more positive than Go (1.56. >0.85μV). Other effects were not significant (p > 0.1) and are reported in Table 5.

DISCUSSION

We compared a group of individuals diagnosed with aMCI to a group of cognitively normal controls in order to examine the effects of aMCI on behavioral measures and neural markers related to response execution and inhibition (the N2 and the P3 ERP components) across two Go/NoGo tasks that varied in levels of semantic categorization (basic and superordinate). Behavioral data revealed that individuals with aMCI made more commission errors on the NoGo trials compared to controls, whereas both groups had comparable omission errors during the Go trials. Individuals with aMCI had significantly prolonged N2 ERP latency compared to controls in both tasks across conditions. Both groups had prolonged N2 and P3 latency and reduced P3 amplitude during superordinate compared to basic categorization, and both had prolonged N2 and P3 latency and increased P3 amplitude during the NoGo trials compared to the Go trials.

More commission errors or false alarms on the NoGo trials in individuals with aMCI compared to the controls suggests a potential deficit in response inhibition ([56]; also see [59]). However, since we used unequal trial distribution (80% Go and 20% NoGo) to enhance pre-potent response tendencies, difficulty in identifying the less frequent NoGo trials or degraded ability to resolve response conflict during the less frequent NoGo trials [26 , 99] may have also contributed to these findings. The study by Cid-Fernández et al. [19] that examined response inhibition using a Go/NoGo task found an overall reduction in response accuracy in the aMCI group compared to the control group. However, they did not examine commission and omission errors separately, and thus, our findings cannot be directly compared to their study. The reduced NoGo accuracy findings in the aMCI group were corroborated by the deficits observed on the neuropsychological measures of cognitive control. Overall, the aMCI group performed significantly less well compared to the control group not only on neuropsychological measures of episodic memory (logical memory immediate and delayed) but also on measures of cognitive control (Trails B and letter fluency), as has been observed by others [18 , 66]. A study by Traykov et al. [22] found that aMCI participants had increased errors during the Stroop interference condition compared to controls, supporting deficits in cognitive control similar to that observed in our NoGo accuracy data.

We found prolonged N2 latency in the aMCI group compared to the control group. A recent meta-analysis by Howe [100] of ERP studies in MCI and AD patients revealed that N2 latency is a consistent marker of cognitive deterioration, in line with our findings. For instance, Bennys et al. [101] found that frontal N2 latency discriminated between MCI and controls in 80% of the cases. Furthermore, studies have also revealed that N2 latency is an accurate and sensitive measure of conversion from MCI to AD [67 , 102]. Papaliagkas et al. [67] found that an N2 latency cut-off of 287 ms for auditory evoked potentials obtained using an oddball paradigm predicted conversion from MCI to AD with 100% sensitivity and 91% specificity in their sample. However, our N2 latency findings diverge from that of the Go/NoGo ERP study by Cid-Fernández et al. [19], who observed N2 amplitude but not N2 latency differences between the aMCI and control groups. Several reasons might have led to the difference in findings. The visual Go/NoGo stimuli in the Cid-Fernández et al. [19] study were preceded by auditory stimuli. Participants were required to ignore the distracting auditory stimuli as they actively responded to the visual Go/NoGo stimuli, which would have engaged cognitive processes other than those involved in performing a Go/NoGo task without distraction such as ours. Furthermore, their study examined perceptual categorization using letters, numbers, and shapes, whereas we examined categorization of objects.

Although we observed an interaction between response type and group for error rates (significant group difference in commission error rates but comparable omission error rates in both groups), we did not observe such an interaction between response type and group for N2 latency. The overall N2 latency was delayed in both Go and NoGo conditions in the aMCI group compared to the controls. While delayed NoGo-N2 latency may explain increased commission errors (false alarms) in the aMCI group compared to the controls, delayed Go-N2 latency in the absence of increased omission errors or prolonged RT on Go trials in the aMCI group is puzzling. It is possible that the neural mechanisms that support cognitive operations common to both Go and NoGo trials are affected in aMCI. Different cognitive operations, including inhibitory processing and conflict monitoring [26 , 61], have been suggested in relation to N2 associated with the Go/NoGo task [26 , 61]. In particular, an independent component analysis of N2 by Kropotov et al. [58] has revealed three independent cognitive operations. These include sensory preparatory set, conflict monitoring, and response inhibition. While our Go/NoGo tasks were not designed to parse out the exact functional correlate that corresponds to N2 in relation to these three cognitive operations, a common cognitive operation involving Go and NoGo trials in our task was the sensory preparatory set or initial evaluation of the stimuli based on the pre-set rules. It is plausible that aMCI slows neural processing related to the initial evaluation of the stimuli resulting in prolonged overall N2 latency during both Go and NoGo trials compared to controls. Prolonged Go-N2 latency in the absence of corresponding Go accuracy deficits may be an early sign of neural degradation. This decline in function may affect response execution at the behavioral level as the disease progresses or may affect performance when conflict monitoring during response execution is more demanding (e.g., using more complex response-stimulus criteria). These speculations are based on the suggestion that neural alterations in aMCI and mild AD precede and eventually lead to behavioral deficits [38–40 , 42]. Also, prolonged NoGo-N2 latency and increased false alarm rates suggest that additional neural mechanisms that support response inhibition may also be affected by aMCI. Although this mechanism is indexed by N2, it might involve a different neural generator [58]. We cannot determine these from our data, and future studies involving paradigms that can parse out the functional cognitive operations related to the Go/NoGo task and their source are necessary to evaluate these possibilities.

Both groups exhibited similar task effects for the reaction time measure. Behavioral response in the basic categorization task (SiC) was faster compared to the superordinate categorization task (ObA) in both groups, supporting the findings of previously conducted behavioral studies on categorization of typical objects [103 –107] with the exception of those that have examined ultra-rapid object categorization (e.g., [108 –111]). It has been suggested that basic categorization is faster since it depends on global, coarse perceptual discrimination [112] compared to superordinate categorization, which depends on semantic information as well as perceptual information, as long as sufficient time is allowed to process information.

The ERP findings related to task effects including prolonged N2 and P3 latencies and reduced P3 amplitude in the superordinate compared to basic categorization task suggest that both normal older adults and individuals with MCI respond similarly to task modulations. While younger adults exhibit task modulations in P3 latency and P3 amplitude similar to normal older adults and individuals with MCI, they do no exhibit task related N2 latency differences [47, 82]. These findings suggest that N2 latency provides unique insights into the differential effects of age on neural processing related to categorization, as observed previously [47].

Our prediction of greater impairment in superordinate compared to basic categorization in MCI was not supported by the findings. Although we observed impairment in letter fluency on neuropsychological assessment, we did not observe any deficits on category fluency in our cohort. It is possible that the aMCI individuals included in our study did not have semantic memory impairment that was severe enough to interfere with performance on the superordinate task more so than the basic task. The impact of categorization level may become more obvious with disease progression or if response time limitations are placed.

Also, in the current study, higher N2 amplitude was observed during superordinate compared to basic categorization in both control and aMCI groups, whereas in our previous study, the N2 amplitude did not significantly differ across tasks although a comparable trend was noted. Larger N2 amplitude in the superordinate categorization condition could be due to a strategy preference toward meaning-based processing of information [113] in the early stages of processing in older adults. However, studies that have examined semantic categorization in young adults have observed similar N2 amplitude differences between superordinate and basic categorizations, suggesting that this is not just a strategy preference. A study by Tanaka et al. [114] that required participants to make true/false decisions by matching between an individual category name (basic, superordinate, and subordinate) and a subsequently presented image found larger N2 amplitudes for superordinate compared to basic categorization in the frontal sites. They suggested that the frontal N2 amplitude difference reflects differential semantic processing between superordinate and basic levels. It is possible that N2 amplitude task effects were not observed in our previous study due to relatively smaller subject numbers in each group compared to our current study. Shorter P3 latency and larger P3 amplitude were observed in SiC compared to ObA in both groups irrespective of the response types, consistent with the findings of the Maguire et al. [82] and Mudar et al. [47] studies, suggesting that irrespective of age effects or early disease effects in MCI, the categorization levels modulate P3 similarly, and the same follows for N2.

As far as the effects of response type, both groups exhibited prolonged N2 and P3 latency and increased P3 amplitude during NoGo trials compared to Go trials. While these findings are consistent with what is typically observed in a Go/NoGo paradigm [45 , 115], we had anticipated that the aMCI group would show more pronounced changes during NoGo trials compared to Go trials, consistent with our behavioral data. N2 latency differentiated the aMCI and control groups irrespective of the response type. It is likely that accuracy differences between Go and NoGo trials may be better captured by other EEG measures such as event-related spectral perturbations [116]. ERPs are time-locked neural responses obtained by averaging across multiple trials of interest. The neural activity captured by ERPs constitutes only a limited dimension of the total neural response to a stimulus and does not include the non-phase-locked activity at different frequencies [117 –120]. Perhaps underlying differences in spectral perturbations at a given frequency band (e.g., alpha) may characterize underlying accuracy differences observed between Go and NoGo trials in the aMCI group. Subsequent studies are needed to explore if other EEG measure can extend our ERP findings. In general, our results related to response type suggest that neural processing is slower (indexed by prolonged NoGo-N2 and NoGo-P3 latency) and more effortful during response inhibition trials (enhanced NoGo-P3 amplitude). Whether this is related to more careful stimulus evaluation, conflict monitoring or inhibitory processing indexed by N2, or more rigorous response evaluation indexed by P3 during NoGo trials awaits clarification in future studies. Consistent with the findings of Mudar et al. [47], no differences were observed between Go-N2 and NoGo-N2 amplitude. Similar to normally aging adult controls, individuals with aMCI appeared to process both Go and NoGo trials with comparable effort, resulting in a lack of difference between Go-N2 and NoGo-N2 amplitudes. Although studies have observed differences in distribution of N2 and P3 across Go and NoGo trials [57], we did not examine differences in electrode distribution per se. Studies that examine N2 and P3 topographical distribution and source for Go and NoGo trials across MCI and control groups are further warranted.

In conclusion, individuals with aMCI manifest behavioral deficits and altered neural processing associated with cognitive control as related to the Go/NoGo task during semantic categorization. In particular, increases in commission errors and prolonged N2 latency differentiated the aMCI group from the controls. While this study provides an initial characterization of behavioral and electrophysiological correlates of cognitive control in a cohort with aMCI, changes with disease progression and changes as a result of clinical interventions await future investigations, as cognitive control is closely tied to functional abilities.

Footnotes

ACKNOWLEDGMENTS

This work was supported by grants from the National Institute of Health (RC1-AG035954, P30AG12300), the RGK foundation, Alzheimer’s Association New Investigator Grant (NIRG-11-173815), and the Berman Research Initiative at the Center for BrainHealth. The authors thank Audette Rackley, Erin Venza, Dr. Elizabeth Bartz, Rajen Patel, Monique Salinas, Molly Keebler, and Claire Gardner for their invaluable assistance in data collection.

Authors’ disclosures available online ().

References

Petersen

, Caracciolo

, Brayne

, Gauthier

, Jelic

, Fratiglioni

(2014) Mild cognitive impairment: A concept in evolution. J Intern Med 275, 214–228.

Petersen

, Roberts

, Knopman

, Boeve

, Geda

, Ivnik

, Smith

, Jack

Jr. (2009) Mild cognitive impairment: Ten years later. Arch Neurol 66, 1447–1455.

Arnaiz

, Almkvist

, Ivnik

, Tangalos

, Wahlund

, Winblad

, Petersen

(2004) Mild cognitive impairment: A cross-national comparison. J Neurol Neurosurg Psychiatry 75, 1275–1280.

Brandt

, Aretouli

, Neijstrom

, Samek

, Manning

, Albert

, Bandeen-Roche

(2009) Selectivity of executive function deficits in mild cognitive impairment. Neuropsychology 23, 607–618.

Johns

, Phillips

, Belleville

, Goupil

, Babins

, Kelner

, Ska

, Gilbert

, Massoud

, de

Boysson C

, Duncan

, Chertkow

(2012) The profile of executive functioning in amnestic mild cognitive impairment: Disproportionate deficits in inhibitory control. J Int Neuropsychol Soc 18, 541–555.

Liu-Ambrose

, Ashe

, Graf

, Beattie

, Khan

(2008) Increased risk of falling in older community-dwelling women with mild cognitive impairment. Phys Ther 88, 1482–1491.

Marshall

, Rentz

, Frey

, Locascio

, Johnson

, Sperling

(2011) Executive function and instrumental activities of daily living in mild cognitive impairment and Alzheimer’s disease. Alzheimers Dement 7, 300–308.

Saunders

, Summers

(2011) Longitudinal deficits to attention, executive, and working memory in subtypes of mild cognitive impairment. Neuropsychology 25, 237–248.

Zheng

, Dong

, Sun

, Xu

, Ma

, Wang

(2012) The overall impairment of core executive function components in patients with amnestic mild cognitive impairment: A cross-sectional study. BMC Neurology 12, 1–10.

10.

Anderson

, Tranel

(2002) Neuropsychological consequences of dysfunction in human dorsolateral pre-frontal cortex. In Handbook of Neuropsychology: The Frontal Lobes, Grafman

, ed. New York, Elsevierpp, 145–156.

11.

Botvinick

, Braver

, Barch

, Carter

, Cohen

(2001) Conflict monitoring and cognitive control. Psychol Rev 108, 624–652.

12.

Inzlicht

, Bartholow

, Hirsh

(2015) Emotional foundations of cognitive control. Trends Cogn Sci 19, 126–132.

13.

Miyake

, Friedman

, Emerson

, Witzki

, Howerter

, Wager

(2000) The unity and diversity of executive functions and their contributions to complex “frontal lobe” tasks: A latent variable analysis. Cogn Psychol 41, 49–100.

14.

Belanger

, Belleville

(2009) Semantic inhibition impairment in mild cognitive impairment: A distinctive feature of upcoming cognitive decline?. Neuropsychology 23, 592–606.

15.

Belanger

, Belleville

, Gauthier

(2010) Inhibition impairments in Alzheimer’s disease, mild cognitive impairment and healthy aging: Effect of congruency proportion in a Stroop task. Neuropsychologia 48, 581–590.

16.

Belleville

, Chertkow

, Gauthier

(2007) Working memory and control of attention in persons with Alzheimer’s disease and mild cognitive impairment. Neuropsychology 21, 458–469.

17.

Cespon

, Galdo-Alvarez

, Diaz

(2013) Electrophysiological correlates of amnestic mild cognitive impairment in a simon task.e. PLoS One 8, 81506.

18.

Chang

, Jacobson

, Fennema-Notestine

, Hagler

Jr. , Jennings

, Dale

, McEvoy

, Alzheimer’s

Disease Neuroimaging I

(2010) Level of executive function influences verbal memory in amnestic mild cognitive impairment and predicts prefrontal and posterior cingulate thickness. Cereb Cortex 20, 1305–1313.

19.

Cid-Fernandez

, Lindin

, Diaz

(2014) Effects of amnestic mild cognitive impairment on N2 and P3 Go/NoGo ERP components. J Alzheimers Dis 38, 295–306.

20.

Doi

, Shimada

, Makizako

, Yoshida

, Shimokata

, Ito

, Washimi

, Endo

, Suzuki

(2013) Characteristics of cognitive function in early and late stages of amnestic mild cognitive impairment. Geriatr Gerontol Int 13, 83–89.

21.

Grambaite

, Selnes

, Reinvang

, Aarsland

, Hessen

, Gjerstad

, Fladby

(2011) Executive dysfunction in mild cognitive impairment is associated with changes in frontal and cingulate white matter tracts. J Alzheimers Dis 27, 453–462.

22.

Traykov

, Raoux

, Latour

, Gallo

, Hanon

, Baudic

, Bayle

, Wenisch

, Remy

, Rigaud

(2007) Executive functions deficit in mild cognitive impairment. Cogn Behav Neurol 20, 219–224.

23.

Wylie

, Ridderinkhof

, Eckerle

, Manning

(2007) Inefficient response inhibition in individuals with mild cognitive impairment. Neuropsychologia 45, 1408–1419.

24.

Zheng

, Sun

, Dong

, Liu

, Xu

, Chen

, Song

, Zhang

, Wang

(2014) Executive dysfunction and gray matter atrophy in amnestic mild cognitive impairment. Neurobiol Aging 35, 548–555.

25.

Nigg

(2001) Is ADHD a disinhibitory disorder?. Psychol Bull 127, 571–598.

26.

Nieuwenhuis

, Yeung

, van den Wildenberg

, Ridderinkhof

(2003) Electrophysiological correlates of anterior cingulate function in a go/no-go task: Effects of response conflict and trial type frequency. Cogn Affect Behav Neurosci 3, 17–26.

27.

Simmonds

, Pekar

, Mostofsky

(2008) Meta-analysis of Go/No-go tasks demonstrating that fMRI activation associated with response inhibition is task-dependent. Neuropsychologia 46, 224–232.

28.

Amieva

, Phillips

, Della Sala

, Henry

(2004) Inhibitory functioning in Alzheimer’s disease. Brain 127, 949–964.

29.

Amieva

, Lafont

, Auriacombe

, Le Carret

, Dartigues

, Orgogozo

, Colette

(2002) Inhibitory breakdown and dementia of the Alzheimer type: A general phenomenon?. J Clin Exp Neuropsychol 24, 503–516.

30.

Binetti

, Magni

, Padovani

, Cappa

, Bianchetti

, Trabucchi

(1996) Executive dysfunction in early Alzheimer’s disease. J Neurol Neurosurg Psychiatry 60, 91–93.

31.

Bondi

, Serody

, Chan

, Eberson-Shumate

, Delis

, Hansen

, Salmon

(2002) Cognitive and neuropathologic correlates of Stroop Color-Word Test performance in Alzheimer’s disease. Neuropsychology 16, 335–343.

32.

Collette

, Van der Linden

, Delrue

, Salmon

(2002) Frontal hypometabolism does not explain inhibitory dysfunction in Alzheimer disease. Alzheimer Dis Assoc Disord 16, 228–238.

33.

Fisher

, Freed

, Corkin

(1990) Stroop Color-Word Test performance in patients with Alzheimer’s disease. J Clin Exp Neuropsychol 12, 745–758.

34.

Koss

, Ober

, Delis

, Friedland

(1984) The Stroop color-word test: Indicator of dementia severity. Int J Neurosci 24, 53–61.

35.

Perry

, Watson

, Hodges

(2000) The nature and staging of attention dysfunction in early (minimal and mild) Alzheimer’s disease: Relationship to episodic and semantic memory impairment. Neuropsychologia 38, 252–271.

36.

Spieler

, Balota

, Faust

(1996) Stroop performance in healthy younger and older adults and in individuals with dementia of the Alzheimer’s type. J Exp Psychol Hum Percept Perform 22, 461–479.

37.

Zhang

, Han

, Verhaeghen

, Nilsson

(2007) Executive functioning in older adults with mild cognitive impairment: MCI has effects on planning, but not on inhibition. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 14, 557–570.

38.

Bateman

, Xiong

, Benzinger

, Fagan

, Goate

, Fox

, Marcus

, Cairns

, Xie

, Blazey

, Holtzman

, Santacruz

, Buckles

, Oliver

, Moulder

, Aisen

, Ghetti

, Klunk

, McDade

, Martins

, Masters

, Mayeux

, Ringman

, Rossor

, Schofield

, Sperling

, Salloway

, Morris

(2012) Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med 367, 795–804.

39.

Jack

, Lowe

, Weigand

, Wiste

, Senjem

, Knopman

, Shiung

, Gunter

, Boeve

, Kemp

, Weiner

, Petersen

(2009) Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer’s disease: Implications for sequence of pathological events in Alzheimer’s disease. Brain 132, 1355–1365.

40.

Reiman

, Quiroz

, Fleisher

, Chen

, Velez-Pardo

, Jimenez-Del-Rio

, Fagan

, Shah

, Alvarez

, Arbelaez

, Giraldo

, Acosta-Baena

, Sperling

, Dickerson

, Stern

, Tirado

, Munoz

, Reiman

, Huentelman

, Alexander

, Langbaum

JBS

, Kosik

, Tariot

, Lopera

(2012) Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer’s disease in the presenilin 1 E280A kindred: A case-control study. Lancet Neurol 11, 1048–1056.

41.

Rowan

, Klyubin

, Cullen

, Anwyl

(2003) Synaptic plasticity in animal models of early Alzheimer’s disease. Philos Trans R Soc Lond B Biol Sci 358, 821–828.

42.

Villemagne

, Burnham

, Bourgeat

, Brown

, Ellis

, Salvado

, Szoeke

, Macaulay

, Martins

, Maruff

, Ames

, Rowe

, Masters

(2013) Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: A prospective cohort study. Lancet Neurol 12, 357–367.

43.

Galli

, Ragazzoni

, Viggiano

(2010) Atypical event-related potentials in patients with mild cognitive impairment: An identification-priming study. Alzheimers Dement 6, 351–358.

44.

Olichney

, Yang

, Taylor

, Kutas

(2011) Cognitive event-related potentials: Biomarkers of synaptic dysfunction across the stages of Alzheimer’s disease. J Alzheimers Dis 26(Suppl 3), 215–228.

45.

Beste

, Willemssen

, Saft

, Falkenstein

(2010) Response inhibition subprocesses and dopaminergic pathways: Basal ganglia disease effects. Neuropsychologia 48, 366–373.

46.

Huster

, Enriquez-Geppert

, Lavallee

, Falkenstein

, Herrmann

(2013) Electroencephalography of response inhibition tasks: Functional networks and cognitive contributions. Int J Psychophysiol 87, 217–233.

47.

Mudar

, Chiang

, Maguire

, Spence

, Eroh

, Kraut

, Hart

Jr. , (2015) Effects of age on cognitive control during semantic categorization. Behav Brain Res 287, 285–293.

48.

Pfefferbaum

, Ford

(1988) ERPs to stimuli requiring response production and inhibition: Effects of age, probability and visual noise. Electroencephalogr Clin Neurophysiol 71, 55–63.

49.

Tachibana

, Aragane

, Sugita

(1996) Age-related changes in event-related potentials in visual discrimination tasks. Electroencephalogr Clin Neurophysiol 100, 299–309.

50.

Vallesi

(2011)Neurosci Lett 487, 313–317 Targets and non-targets in the aging bra, A go/nogo event-relatedotential study.

51.

Eimer

(1993) Effects of attention and stimulus probability on ERPs in a Go/Nogo task. Biol Psychol 35, 123–138.

52.

Falkenstein

, Hoormann

, Hohnsbein

(1999) ERP components in Go/Nogo tasks and their relation to inhibition. Acta Psychol (Amst) 101, 267–291.

53.

Kok

(1986) Effects of degradation of visual stimulation on components of the event-related potential (ERP) in go/nogo reaction tasks. Biol Psychol 23, 21–38.

54.

Pfefferbaum

, Ford

, Weller

, Kopell

(1985) ERPs to response production and inhibition. Electroencephalogr Clin Neurophysiol 60, 423–434.

55.

Simson

, Vaughan

, Ritter

(1977) The scalp topography of potentials in auditory and visual discrimination tasks. Electroencephalogr Clin Neurophysiol 42, 528–535.

56.

Falkenstein

(2006) Inhibition, conflict and the Nogo-N2. Clin Neurophysiol 117, 1638–1640.

57.

Kropotov

, Ponomarev

(2009) Decomposing N2 NOGO wave of event-related potentials into independent components. Neuroreport 20, 1592–1596.

58.

Kropotov

, Ponomarev

, Hollup

, Mueller

(2011) Dissociating action inhibition, conflict monitoring and sensory mismatch into independent components of event related potentials in GO/NOGO task. Neuroimage 57, 565–575.

59.

Folstein

, Van Petten

(2008) Influence of cognitive control and mismatch on the N2 component of the ERP: A review. Psychophysiology 45, 152–170.

60.

Botvinick

, Cohen

, Carter

(2004) Conflict monitoring and anterior cingulate cortex: An update. Trends Cogn Sci 8, 539–546.

61.

Donkers

FCL

, van Boxtel

GJM

(2004) The N2 in go/no-go tasks reflects conflict monitoring not response inhibition. Brain Cogn 56, 165–176.

62.

Donchin

, Karis

, Bashore

, Coles

, Gratton

(1986) Cognitive psychophysiology and human information processing. In Psychophysiology: Systems, Processes, and Applications, Coles

MGH

, Donchin

, Porges

, eds. Guilford Press, New York.

63.

Polich

, Herbst

(2000) P300 as a clinical assay: Rationale, evaluation, and findings. Int J Psychophysiol 38, 3–19.

64.

Smith

, Johnstone

, Barry

(2007) Response priming in the Go/NoGo task: The N2 reflects neither inhibition nor conflict. Clin Neurophysiol 118, 343–355.

65.

Smith

, Johnstone

, Barry

(2008) Movement-related potentials in the Go/NoGo task: The P3 reflects both cognitive and motor inhibition. Clin Neurophysiol 119, 704–714.

66.

Bennys

, Rondouin

, Benattar

, Gabelle

, Touchon

(2011) Can event-related potential predict the progression of mild cognitive impairment?. J Clin Neurophysiol 28, 625–632.

67.

Papaliagkas

, Kimiskidis

, Tsolaki

, Anogianakis

(2008) Usefulness of event-related potentials in the assessment of mild cognitive impairment. BMC Neurosci 9, 107.

68.

Papaliagkas

, Kimiskidis

, Tsolaki

, Anogianakis

(2011) Cognitive event-related potentials: Longitudinal changes in mild cognitive impairment. Clin Neurophysiol 122, 1322–1326.

69.

Ahmed

, Arnold

, Thompson

, Graham

, Hodges

(2008) Naming of objects, faces and buildings in mild cognitive impairment. Cortex 44, 746–752.

70.

Borg

, Bedoin

, Basaglia-Pappas

, Laroche

, Minvielle

, Chesnoy

, Thomas-Anterion

(2012) Semantic knowledge of newly coined words in mild cognitive impairment and Alzheimer’s disease. J Neurolinguistics 25, 263–275.

71.

Greene

, Hodges

(1996) Identification of famous faces and famous names in early Alzheimer’s disease. Relationship to anterograde episodic and general semantic memory. Brain, 119(Pt 1), 111–128.

72.

Joubert

, Brambati

, Ansado

, Barbeau

, Felician

, Didic

, Lacombe

, Goldstein

, Chayer

, Kergoat

(2010) The cognitive and neural expression of semantic memory impairment in mild cognitive impairment and early Alzheimer’s disease. Neuropsychologia 48, 978–988.

73.

Kraut

, Cherry

, Pitcock

, Anand

, Li

, Vestal

, Henderson

, Hart

Jr. (2007) The Semantic Object Retrieval Test (SORT) in amnestic mild cognitive impairment. Cogn Behav Neurol 20, 62–67.

74.

Price

, Kinsella

, Ong

, Storey

, Mullaly

, Phillips

, Pangnadasa-Fox

, Perre

(2012) Semantic verbal fluency strategies in amnestic mild cognitive impairment. Neuropsychology 26, 490–497.

75.

Chiang

, Mudar

, Pudhiyidath

, Spence

, Womack

, Cullum

, Tanner

, Eroh

, Kraut

, Hart

Jr. (2015) Altered neural activity during semantic object memory retrieval in amnestic mild cognitive impairment as measured by event-related potentials. J Alzheimers Dis 46, 703–717.

76.

Olichney

, Taylor

, Gatherwright

, Salmon

, Bressler

, Kutas

, Iragui-Madoz

(2008) Patients with MCI and N400 or P600 abnormalities are at very high risk for conversion to dementia. Neurology 70, 1763–1770.

77.

Cicirelli

(1976) Categorization behavior in aging subjects. J Gerontol 31, 676–680.

78.

Kincses

, Chadaide

, Varga

, Antal

, Paulus

(2006) Task-related temporal and topographical changes of cortical activity during ultra-rapid visual categorization. Brain Res 1112, 191–200.

79.

Long

, Liu

, Qiu

, Shen

, Li

(2010) Neural signs of flexible categorization: Evidence from the flexibility of inclusion of humans in animal/non-animal categorization. Brain Res 1337, 64–73.

80.

Mack

, Palmeri

(2011) The timing of visual object categorization. Front Psychol 2, 165–165.

81.

Mervis

, Rosch

(1981) Categorization of Natural Objects. Annu Rev Psychol 32, 89–115.

82.

Maguire

, Brier

, Moore

, Ferree

, Ray

, Mostofsky

, Hart

, Kraut

(2009) The influence of perceptual and semantic categorization on inhibitory processing as measured by the N2-P3 response. Brain Cogn 71, 196–203.

83.

Maguire

, White

, Brier

(2011) How semantic categorization influences inhibitory processing in middle-childhood: An Event Related Potentials study. Brain Cogn 76, 77–86.

84.

Cahn-Weiner

, Malloy

, Boyle

, Marran

, Salloway

(2000) Prediction of functional status from neuropsychological tests in community-dwelling elderly individuals. Clin Neuropsychol 14, 187–195.

85.

Griffith

, Belue

, Sicola

, Krzywanski

, Zamrini

, Harrell

, Marson

(2003) Impaired financial abilities in mild cognitive impairment: A direct assessment approach. Neurology 60, 449–457.

86.

Tabert

, Albert

, Borukhova-Milov

, Camacho

, Pelton

, Liu

, Stern

, Devanand

(2002) Functional deficits in patients with mild cognitive impairment: Prediction of AD. Neurology 58, 758–764.

87.

Beck

(1996) Beck Depression Inventory Manual, The Psychological Corporation, San Antonio, TX.

88.

Almeida

, Almeida

(1999) Short versions of the geriatric depression scale: A study of their validity for the diagnosis of a major depressive episode according to ICD-10 and DSM-IV. Int J Geriatr Psychiatry 14, 858–865.

89.

Petersen

, Doody

, Kurz

, Mohs

, Morris

, Rabins

, Ritchie

, Rossor

, Thal

, Winblad

(2001) Current concepts in mild cognitive impairment. Arch Neurol 58, 1985–1992.

90.

Folstein

, Folstein

, McHugh

(1975) Mini-mental state. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12, 189–198.

91.

Nasreddine

, Phillips

, Bedirian

, Charbonneau

, Whitehead

, Collin

, Cummings

, Chertkow

(2005) The Montreal Cognitive Assessment, MoCA: A brief screening tool for mild cognitive impairment. J Am Geriatr Soc 53, 695–699.

92.

Morris

(1993) The Clinical Dementia Rating (CDR): Current version and scoring rules. Neurology 43, 2412–2414.

93.

Ferree

, Brier

, Hart

Jr. , Kraut

(2009) Space-time-frequency analysis of EEG data using within-subject statistical tests followed by sequential PCA. Neuroimage 45, 109–121.

94.

Benton

, Hamsher

(1976) Multilingual Aphasia Examination 2nd ed, AJA Associates, Iowa City, IA.

95.

Goodglass

, Kaplan

, Barresi

(2001) Boston Diagnostic Aphasia Examination 3rd Ed., Lippincott Williams & Wilkins, Philadelphia, PA.

96.

Reitan

(1958) Validity of the Trail Making Test as an indicator of organic brain damage. Percept Mot Skills 8, 271–276.

97.

Wechsler

(1997) Wechsler Memory Scale - Third Edition, The Psychological Corporation, San Antonio, TX.

98.

Wechsler

(2008) Wechsler Adult Intelligence Scale - Fourth Edition: Technical and Interpretive Manual, Pearson, San Antonio, TX.

99.

Braver

, Barch

, Keys

, Carter

, Cohen

, Kaye

, Janowsky

, Taylor

, Yesavage

, Mumenthaler

, Jagust

, Reed

(2001) Context processing in older adults: Evidence for a theory relating cognitive control to neurobiology in healthy aging. J Exp Psychol Gen 130, 746–763.

100.

Howe

(2014) Meta-analysis of the endogenous N200 latency event-related potential subcomponent in patients with Alzheimer’s disease and mild cognitive impairment. Clin Neurophysiol 125, 1145–1151.

101.

Bennys

, Portet

, Touchon

, Rondouin

(2007) Diagnostic value of event-related evoked potentials N200 and P300 subcomponents in early diagnosis of Alzheimer’s disease and mild cognitive impairment. J Clin Neurophysiol 24, 405–412.

102.

Papaliagkas

, Anogianakis

, Tsolaki

, Koliakos

, Kimiskidis

(2009) Progression of mild cognitive impairment to Alzheimer’s disease: Improved diagnostic value of the combined use of N200 latency and beta-amyloid(1-42) levels. Dement Geriatr Cogn Disord 28, 30–35.

103.

Grill-Spector

, Kanwisher

(2005) Visual recognition: As soon as you know it is there, you know what it is. Psychol Sci 16, 152–160.

104.

Johnson

, Mervis

(1997) Effects of varying levels of expertise on the basic level of categorization. J Exp Psychol Gen 126, 248–277.

105.

Murphy

, Brownell

(1985) Category differentiation in object recognition: Typicality constraints on the basic category advantage. J Exp Psychol Learn Mem Cogn 11, 70–84.

106.

Murphy

, Lassaline

(1997) Hierarchical structure in concepts and the basic level of categorization. In Knowledge, Concepts and Categories, Shanks

KLDR

, ed. The MIT Press, Cambridge, MA, US, pp. 93–131.

107.

Tanaka

, Taylor

(1991) Object categories and expertise: Is the basic level in the eye of the beholder?. Cogn Psychol 23, 457–482.

108.

Fabre-Thorpe

, Delorme

, Marlot

, Thorpe

(2001) A limit to the speed of processing in ultra-rapid visual categorization of novel natural scenes. J Cogn Neurosci 13, 171–180.

109.

Large

, Kiss

, McMullen

(2004) Electrophysiological correlates of object categorization: Back to basics. Brain Res Cogn Brain Res 20, 415–426.

110.

Rogers

, Patterson

(2007) Object categorization: Reversals and explanations of the basic-level advantage. J Exp Psychol Gen 136, 451–469.

111.

Thorpe

, Fize

, Marlot

(1996) Speed of processing in the human visual system. Nature 381, 520–522.

112.

Biederman

, Gerhardstein

(1993) Recognizing depth-rotated objects: Evidence and conditions for three-dimensional viewpoint invariance. J Exp Psychol Hum Percept Perform 19, 1162–1182.

113.

Radvansky

, Dijkstra

(2007) Aging and situation model processing. Psychon Bull Rev 14, 1027–1042.

114.

Tanaka

, Luu

, Weisbrod

, Kiefer

(1999) Tracking the time course of object categorization using event-related potentials. Neuroreport 10, 829–835.

115.

Falkenstein

, Hoormann

, Hohnsbein

(2002) Inhibition-related ERP components: Variation with modality, age, and time-on-task. J Psychophysiol 16, 167–175.

116.

Makeig

, Debener

, Onton

, Delorme

(2004) Mining event-related brain dynamics. Trends Cogn Sci 8, 204–210.

117.

Galambos

(1992) A comparison of certain gamma band (40-HZ) Brain rhythms in cat and man. In Induced Rhythms in the Brain, Başar

, Bullock

, eds. Birkhäuser, Boston, pp. 201–216.

118.

Herrmann

, Grigutsch

, Busch

(2005) EEG oscillations and wavelet analysis. In Event-related potentials: A methods handbook, Handy

, ed. MIT Press, Cambridge, MA, pp. 229–259.

119.

Makeig

(1993) Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalogr Clin Neurophysiol 86, 283–293.

120.

Tallon-Baudry

, Bertrand

, Delpuech

, Pernier

(1996) Stimulus specificity of phase-locked and non-phase-locked 40 Hz visual responses in human. J Neurosci 16, 4240–4249.