Parkinsons disease reaction time

Parkinsons disease reaction time DEFAULT

Altered Inhibitory Mechanisms in Parkinson&#x;s Disease: Evidence From Lexical Decision and Simple Reaction Time Tasks

Introduction

Although the motor symptoms of Parkinson&#x;s disease (PD) are well defined and described, nonmotor features have been increasingly recognized in recent years as being inherent to the disease (Chaudhuri and Schapira, ; Zis et al., ). Cognitive deterioration is a common, progressive and disabling feature of PD, arising from neuropsychological, neurochemical, structural, and pathophysiological changes (Pagonabarraga and Kulisevsky, ). However, important questions about cognitive disorders in patients without dementia have yet to be addressed (Barone et al., ). Research over the past two decades on the various processes specific to language impairment in PD (for reviews, see Murray, ; Altmann and Troche, ; Colman and Bastiaanse, ; Auclair-Ouellet et al., ) indicates that language disorders should be viewed as part of the spectrum of cognitive deficits in patients with PD without dementia, as also recommended by the Movement Disorder Society task force on cognitive impairment (Litvan et al., ). For example, higher-level language processes have been shown to be impaired in patients with PD, affecting various aspects of language comprehension such as complex sentence structure understanding (Lieberman et al., ; Lee et al., ; Hochstadt et al., ; Angwin et al., a), metaphor and ambiguous sentence comprehension (Berg et al., ; Monetta and Pell, ), inference generation (Monetta et al., ), and irony comprehension (Monetta et al., ). In terms of language production, individuals with PD appear to produce mainly simple sentences (Illes et al., ; Murray, ; Murray and Lenz, ), punctuated by numerous pauses and presenting many acoustic variations associated with motor speech difficulties (Illes et al., ; Illes, ). Whether these deficits are caused by a language-specific impairment or more general deficits in other cognitive functions, such as executive functioning or working memory, is an ever present issue for researchers (Lee et al., ; Longworth et al., ; Terzi et al., ; Angwin et al., b; Hochstadt et al., ).

One of these higher-level language processes is word recognition, which is commonly tested. Word recognition can be estimated by measuring access to the mental lexicon, classically by using a lexical decision task (LDT; Moret-Tatay and Perea, ). Participants have to decide as quickly as possible whether a word (auditory or visual presentation) is a real word or not (i.e., a pseudoword). The response (i.e., manual button press) is faster for a word than for a pseudoword, and the time difference can be interpreted as the result of a lexicality effect, also called word superiority effect (e.g., Cattell, ; Henderson, ). According to psycholinguistic models of lexical access (e.g., Coltheart et al., ; Hauk et al., ), word recognition can be viewed as a series of processes occurring in cascade, where lexical access precedes meaning processing. In LDTs, with visual stimuli, it is commonly assumed that word/pseudoword reading involves two parallel and complementary routes: a direct, lexical (lexicosemantic) route, and an indirect, sublexical (phonological) one (Coltheart, ). Roughly speaking, the lexical pathway affords direct access to orthographic information about the words, and thence to the semantic network. This route makes it possible to recognize visually familiar words, but it is less helpful for visually deciphering unfamiliar words, including pseudowords. It is therefore the indirect sublexical pathway that underlies the process of connecting the orthographic and phonological features of unfamiliar words or pseudowords, allowing them to be read. The use of this circuit relies on the segmentation of words into graphemes, and then the matching of these graphemes with their related phonemes. As the name of this route implies, words are identified according to their phonological coding. This dual-route model of visual word recognition was inspired by the interactive activation (IA) model (McClelland and Rumelhart, ), which itself was based on a concept put forward by Morton (). According to the IA model, written word recognition involves three levels of parallel processing: (1) visual features (e.g., horizontal, vertical, and diagonal lines), (2) letters, and (3) words. Letters are coded according to their position within the word and processed simultaneously. The different units are interconnected within and between levels. The connections are excitatory between two compatible units, and inhibitory between two incompatible units. At the word level, there is a mechanism of mutual lexical inhibition of all active lexical candidates, to allow for recognition of the target word. This is commonly referred to as lateral inhibition. In the same vein, based on the concept of spreading activation (McClelland and Rumelhart, ), auditory word recognition can be interpreted according to the TRACE model (McClelland and Elman, ). It again involves subsystems processed in parallel, with three distinct levels: (1) acoustic features (e.g., intensity, timbre, duration, and pitch), (2) phonemes, and (3) words. Acoustic information activates phonemic representations containing the acoustic characteristics, which in turn activate words that contain them (lexical entries) in the right order. This takes place automatically, while the acoustic information is being processed. Each processing level is linked by excitatory connections to other levels, and the selection of the word to be recognized is made possible by inhibitory links between competing units, where the most active unit (i.e., the one most compatible with the perceived acoustic features) inhibits the less active ones. Together, the IA (McClelland and Rumelhart, ) and TRACE (McClelland and Elman, ) models predict direct access to the mental lexicon during the visual or auditory presentation of words. This explains the faster recognition of words compared with pseudowords, subtended by the pairing between the signal and the information contained in the mental lexicon. Here again, this process is strongly modulated by activation and inhibition mechanisms within the processing levels, in the form of lexical competition / lateral inhibition between words. Thus, after the visual or auditory presentation of a word, different competitors sharing traits with the target word are automatically activated. Lateral inhibition between these competitors allows those with the highest activation levels, including the target word, to predominate and eliminate those with a lower activation level. Accordingly, not only does the target word try to inhibit its competitors, but the latter also send inhibitory information to the target word (Dufour and Frauenfelder, ).

Some versions of the LDT feature semantic priming. This involves enhancing lexical access by presenting a semantically related word prime beforehand, in order to test structural/functional connections between words in the mental lexicon. The result is faster recognition when the word is preceded by a semantically related prime word (e.g., apple-fruit) rather than by an unrelated one (e.g., paper-fruit). Phonological (auditory presentation of stimuli) and orthographic (visual presentation of stimuli) priming can be used to test other levels of connections between words. Lexical access has seldom been investigated in patients with PD, and most studies have focused on the processes underlying access to semantic representations in these patients (Hines and Volpe, ; Spicer et al., ; McDonald et al., ; Copland, ; Filoteo et al., ; Angwin et al., ; Marí-Beffa et al., ; Ito and Kitagawa, ; Pederzolli et al., ; Ehlen et al., ). LDT with semantic priming has often been performed for this purpose. The very first study among patients with PD reported that the amplitude of the semantic priming effect was comparable to that achieved by healthy individuals (Hines and Volpe, ). Patients had longer reaction times (RTs) than controls when primes were unrelated, but not when they were semantically related (Spicer et al., ; McDonald et al., ), leading to the so-called hyperpriming concept, which has sometimes come in for criticism for methodological reasons (Arnott and Chenery, , ). This hyperpriming could be regarded as part of the semantic processing deficits in PD (Copland, ), possibly caused by the abnormal persistence of lexical activation of primes in memory (Filoteo et al., ), or poor inhibition of irrelevant information from distractors (Angwin et al., ; Marí-Beffa et al., ). These findings point to the involvement of the basal ganglia in the facilitation and inhibition processes. The semantic priming effects observed in patients with or without dopaminergic treatment suggest that dopamine depletion leads to both a lower level of activation during automatic semantic processing, and a decrease in the intensity of this activation in the semantic network, restored by the medication (Copland et al., ; Arnott et al., , ; Angwin et al., b, ; Castner et al., ). Therefore, altered semantic activation in patients with PD seems to stem from dopamine loss. Furthermore, it should be noted that most studies so far have featured visual stimuli, with only a few investigations considering the auditory presentation modality (e.g., Copland, ; Ehlen et al., ). There have been even fewer studies using phonological priming (e.g., Elorriaga-Santiago et al., ).

In the present study, we explored lexical access by investigating the lexicality effect with an LDT task without any priming (i.e., semantic, phonological, or orthographic) in participants with PD and age-matched controls. This version of the LDT appeared to be the most appropriate one, as we wanted to study lexical access per se, rather than enhance it. Our first objective was to determine whether the motor execution of the task used to measure lexical access was affected by bradykinesia. Our second objective was to look for differences and similarities in responses to the visual vs. auditory stimuli in the LDT, in order to identify the mechanisms of lexical access in patients with PD and in these two modalities.

Materials and Methods

Participants

Participants were 34 nondemented patients with PD, either without (off; n = 16; mean age = years, and SD = ), or under optimum (on; n = 18; mean age = years, and SD = ) medication. The patients attended the Neurology Department of Aix-en-Provence Hospital (France). They met all the Parkinson&#x;s UK Brain Bank criteria (Hughes et al., ) for the diagnosis of idiopathic PD. Dopaminergic denervation was objectified by striatal dopamine transporter visualization using single-photon emission computed tomography (ioflupane iodine injection, DaTscanTM). Patients were not recruited if they had any history of stroke, depression, impulse control disorder, drug or alcohol abuse, as this might have interfered with their ability to perform the tasks. Patients with deep brain stimulation were also excluded. Levodopa equivalence daily dose (LEDD) was calculated according to standard formulae (Tomlinson et al., ; Schade et al., ). Motor disability was assessed with Part III (Items 18&#x;31) of the Unified Parkinson&#x;s Disease Rating Scale (UPDRS; Fahn et al., ), and cognitive impairment with the Mattis Dementia Rating Scale (MDRS; Mattis, ). As recommended by Llebaria et al. (), the MDRS cut-off score was set at / for the screening of dementia in patients.

A third group of participants consisted of 19 age-matched controls (mean age = years, SD = years), recruited via a call for participation and with the same exclusion criteria as for patients. Table 1 summarizes participants&#x; characteristics.

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Table 1. Participants&#x; demographic and clinical data.

All participants were right-handed (Edinburgh Handedness Inventory 75%; Oldfield, ) and native French speakers. They had normal or corrected-to-normal vision and self-reported normal-for-age hearing. The study was approved by the local institutional review board (Ethical Research Committee Sud Méditerranée 1, protocol no. 12 42). In accordance with the Declaration of Helsinki (World Medical Association, ), all participants provided their written informed consent.

Analyses of variance (ANOVAs) for between-group comparisons with Tukey&#x;s HSD post hoc test and Bonferroni correction revealed (1) similar mean ages for participants in all three groups, F(2, 32) = , p = ), (2) significantly lower MDRS scores for patients both off (pBonferroni , Cohen&#x;s d = ) and on (pBonferroni , Cohen&#x;s d = ) medication than controls, F(2, 32) = , p Welch&#x;s t tests showed that neither disease duration (t = , p = , and Cohen&#x;s d = ) nor LEDD (t = , p = , and Cohen&#x;s d = ) differed between patients off and on medication. Additionally, patients off medication had higher UPDRS III scores (t = , p , and Cohen&#x;s d = ) than patients on medication.

Protocol and Stimulus Validation

Prior to running the experiment with the patients and age-matched controls, we tested and validated the stimuli we had selected (words) or created (pseudowords) with a group of 40 young adults (men/women = 20/20; mean age = years, and SD = ). The objective of this validation experiment was to confirm that (1) RTs for words vs. pseudowords in the simple reaction time task (SRTT) did not differ, and (2) the stimuli we used in the LDT elicited a lexicality effect. Exclusion criteria were the same as those for the patients with PD and age-matched controls.

Experimental Design

The off medication group was assessed after an overnight medication fast (i.e., after 12 h without any treatment), in order to be as close as possible to the Parkinsonian state. The on medication group was also assessed in the morning, after the usual morning dose treatment (i.e., after min). Participants were seated at a comfortable viewing distance from a computer screen in a quiet room at the hospital. To maximize the lexicality effect and avoid any familiarity with the items, they started the experiment with the LDT in the two modalities. The order of presentation (visual vs. auditory stimuli) was counterbalanced across participants. Participants then performed a SRTT in the two modalities, to estimate their distal motor state, as proposed by the Movement Disorder Society task force on cognitive impairment (Litvan et al., ). This enabled us to pinpoint the impact of motor execution on LDT performance.

For both tasks in the visual modality, the sequence of experimental trials was as follows: (a) a fixation cross (+) was displayed for ms; (b) this was followed by a white screen with a random duration of , ms (this interstimulus interval served to maintain the participant&#x;s attention); (c) a stimulus was displayed in the centre of the screen until the participant responded; and (d) the following trial then began automatically after ms. All items were randomly presented, in black capital letters (point Arial font) against a white background, on a 20 CRT monitor (60 Hz).

For both tasks in the auditory modality, there was a similar sequence of trials, except that the fixation cross was replaced with a ms auditory signal (beep). Words and pseudowords were played via a headset (Sennheiser PC ; volume adjusted to each participant prior to the experiment).

For both the visual and auditory versions of the LDT, participants indicated whether the stimulus was a real word or not as quickly as possible, but without compromising accuracy, by pressing the word or pseudoword buttons of a serial response box (model A, Psychology Software Tools) with the index or middle finger of their right hand. To avoid any possible difference in movement initiation latency between the two fingers (Wilimzig et al., ), the associations between response buttons and fingers were counterbalanced, as is commonly done (Fernandino et al., ).

For the SRTT, participants had to press a button of the response pad as quickly as possible whenever a visual or auditory stimulus was presented (i.e., immediately after stimulus onset), with the index (for half the trials, n = 10), or middle finger (for the remaining trials, n = 10). Three lists of 20 stimuli randomly extracted from the original set were used in this task, counterbalanced across participants.

In both the visual and auditory modalities, the tasks were preceded by four practice trials. RTs were digitally recorded by dedicated software (E-Prime®, Psychology Software Tools), starting from the onset of the stimulus.

Stimuli

We selected 30 five-letter, bisyllabic words with the same CVCVC (C: consonant; V: vowel) phonological and orthographic pattern from a French database (Lexique, v; New et al., ). No other characteristics (e.g., frequency, lexical neighborhood) of these items could be controlled (see Supplementary Material). We also constructed 30 orthographically legal and pronounceable pseudowords. In order to match these pseudowords with the real words as closely as possible, in terms of number of letters and bigram frequency, we generated them using syllabic segmentation: the second syllable of one selected word was randomly associated with the first syllable of another selected word, taking care to avoid contructing a real French word (e.g., the real words lapin [rabbit] and melon [melon] could be used to create the pseudowords lalon and mepin).

Auditory stimuli were recorded in a soundproof room by a trained native French speaker, and were then segmented and preprocessed (Praat software, version ; Boersma and Weenink, ). The words ( ± 52 ms) and pseudowords ( ± ms) did not differ significantly on duration (Welch&#x;s t test, t = , ns).

These stimuli were used in both experimental tasks (LDT and SRTT) in both sensory modalities (visual and auditory).

Statistical Analyses

We only analyzed RTs for correct trials. All temporal errors were removed from analyses (i.e., RTs below ms or above 3, ms for the two LDTs, and RTs below ms or above 1, ms for the two SRTTs). Following this preprocessing, individual and group outliers (defined as any RT more than two SDs above or below the mean) were also excluded from the analyses. This procedure ensured that the results were not driven by a small number of atypical data points (Ratcliff, ). In the visual modality, errors (control group = %; PD group = %), and outlier RTs resulted in the removal of a total of % of the dataset for the LDT, and % for the SRTT. In the auditory modality, errors (controls = %, PD group = %) and outlier RTs resulted in the removal of % of the dataset for the LDT, and % for the SRTT.

Two separate repeated-measures ANOVAs with group (control vs. PD off and control vs. PD on) as a between-groups factor and lexicality (words, pseudowords) as a within-participants factor were performed on RTs. They were conducted with participants (F1) and items (F2) as random variables. Another ANOVA with group (control vs. PD off vs. PD on) as between-group factor and lexicality (words, pseudowords) as within-subject factor has been performed on number of errors for both LDTs. Estimated effect sizes are reported as partial eta squared (η2p; Lakens, ; Wasserstein and Lazar, ). Tukey HSD post hoc comparisons were also performed when appropriate, with Bonferroni correction (Zar, ) for multiple comparisons. The statistical significance level was set at p &#x; The data were preprocessed in the RStudio environment (v. ), implementing R software (v; R Development Core Team, ), and analyses were performed using Jamovi (version ; The Jamovi Project, ).

Results

Stimulus Validation

For the LDT in the visual modality, RTs were significantly shorter for words than for pseudowords ( ± 73 ms vs. ± 86 ms), F1(1, 39) = , p , and η2p = and F2(1, 58) = , p , and η2p = In the auditory modality, RTs were also shorter for words (mean = ± 73 ms) than for pseudowords (mean = ± ms), F1(1, 39) = , p , and η2p = and F2(1, 58) = , p , and η2p =

For the SRTT, mean RT was ms (±26) for both words and pseudowords in the visual modality, and ms (±68) for both words and pseudowords in the auditory modality. No lexicality effect was observed in either the visual, F1 (1, 39) = , p = , and η2p = and F2(1, 58) = , p = , and η2p = , or auditory modality, F1(1, 39) = , p = , and η2p = and F2(1, 58) = , p = , and η2p =

Lexical Decision Task

Concerning accuracy, omissions appear to be null in the present experiment, potentially because the maximum cut-off response time was rather long (i.e, fixed to 5 s). In the visual modality, no significant effect was observed, i.e, the number of errors was equal between groups, F(2, 50) = , p = , and η2p = , and lexicality status, F(1, 50) = , p = , and η2p = In the auditory modality, a lonely main effect of lexicality, F(1, 50) = , p = , and η2p = , i.e, more errors for pseudo-words than for words is observed, but Tukey HSD post-hoc testing failed to show any significant effect within groups.

Patients off Medication vs. Controls

In the visual modality, we observed a main effect of lexicality, with longer RTs for pseudowords (mean RT = ± ms) than for real words (mean RT = ± ms), F1(1, 33) = , p , and η2p = and F2(1, 58) = , p , and η2p = This main effect was statistically significant in both groups. The ANOVA also revealed a main effect of group, F1(1, 33) = , p = , and η2p = and F2(1, 58) = , p , and η2p = , and a lexicality &#x; group interaction, F1(1, 33) = , p = , and η2p = and F2(1, 58) = , p , and η2p = The Tukey HSD post hoc test revealed a significant difference between groups for pseudowords (pBonferroni = ), but not for words (pBonferroni = 1.): the mean value of the lexicality effect was greater in the PD group ( ± ms) than in the control group (68 ± 64 ms).

In the auditory modality, we also observed a significant main effect of lexicality in both groups, with longer RTs for pseudowords (mean RT = ± ms) than for real words (mean RT = ± ms), F1(1, 33) = , p , and η2p = and F2(1, 58) = , p , and η2p = There was no main effect of group, F1(1, 33) = , p = , and η2p = and F2(1, 58) = , p = , and η2p = , and no lexicality &#x; group interaction, F1(1, 33) = , p = , and η2p = and F2(1, 58) = , p = , and η2p = (Table 2).

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Table 2. Mean reaction times (±standard deviation) in ms for word and pseudoword responses, for age-matched controls (CO) and patients with PD on or off medication, in the lexical decision task (LDT) and simple reaction time task (SRTT) in the visual and auditory modalities.

Patients on Medication vs. Controls

We observed a main effect of lexicality in the visual modality, with longer RTs for pseudowords (mean RT = ± ms) than for real words (mean RT = ± ms), F1(1, 35) = , p , and η2p = and F2(1, 58) = , p , and η2p = This main effect was statistically significant in both groups. The ANOVA did not reveal a main effect of group, F1(1, 35) = , p = , and η2p = and F2(1, 58) = , p , and η2p = , or lexicality &#x; group interaction, F1(1, 35) = , p = , and η2p = and F2(1, 58) = , p = , and η2p =

We observed a significant main effect of lexicality in the auditory modality for both groups, with longer RTs for pseudowords (mean RT = ± ms) than for words (mean RT = ± ms), F1(1, 35) = , p , and η2p = and F2(1, 58) = , p , and η2p = There was no main effect of group, F1(1, 35) = , p = , and η2p = and F2(1, 58) = , p , and η2p = , or lexicality &#x; group interaction, F1(1, 35) = , p = , and η2p = and F2(1, 58) = , p = , and η2p = (Table 2).

Simple Reaction Time Task

Patients off Medication vs. Controls

In the visual modality, we observed main effects of lexicality, F1(1, 33) = , p = , and η2p = and F2(1, 58) = , p = , and η2p = , and group, F1(1, 33) = , p = , and η2p = and F2(1, 58) = , p , and η2p = There was also a lexicality &#x; group interaction, F1(1, 33) = , p = , and η2p = and F2(1, 58) = , p = , and η2p = ). The Tukey HSD post hoc test showed a significant difference between groups for pseudowords (pBonferroni = ), but not for words (pBonferroni = ). There was a lexicality effect of 27 ms in the PD group, but not in the control group (Table 2).

In the auditory modality, there was no main effect of lexicality, F1(1, 33) = , p = , and η2p = and F2(1, 58) = , p = , and η2p = , and the lexicality &#x; group interaction was not significant, F1(1, 33) = , p = , and η2p = and F2(1, 58) = , p = , and η2p = We did, however, observe a main effect of group, F1(1, 33) = , p = , and η2p = and F2(1, 58) = , p , and η2p = , as patients were slower ( ± ms) than controls ( ± ms).

Patients on Medication vs. Controls

In the visual modality, there was no main effect of either lexicality, F1(1, 35) = , p = , and η2p = and F2(1, 58) = , p = , and η2p = , or group, F1(1, 35) = , p = , and η2p = and F2(1, 58) = , p , and η2p = There was no lexicality &#x; group interaction, F1(1, 35) = , p = , and η2p = and F2(1, 58) = , p = , and η2p =

In the auditory modality, we found no effect of lexicality, F1(1, 35) = , p = , and η2p = and F2(1, 58) = , p = , and η2p = The lexicality &#x; group interaction was not significant, F1(1, 35) = , p = , and η2p = and F2(1, 58) = , p = , and η2p = There was no main effect of group, F1(1, 35) = , p = , and η2p = and F2(1, 58) = , p = , and η2p = (Table 2).

Discussion

The goal of the present study was to investigate motor execution and lexical access in patients with PD, on or off medication, and age-matched controls. Besides well documented motor symptoms, recent studies have highlighted cognitive impairments in patients with PD. However, a possible impairment of linguistic processes (e.g., access to mental lexicon) has seldom been investigated in PD. To determine which processes (perceptual, motor, or linguistic) might be affected in patients when it comes to lexical access, we used two tasks that differed on the cognitive/linguistic processes they elicit: an SRTT in which participants simply had to respond as quickly as possible when the stimulus appeared, whatever its lexical status (word or pseudoword), and an LDT, where they had to decide whether the stimulus was a real word or a pseudoword. The SRTT gives an estimate of the temporal costs of perceptual and motor processes, independently of any linguistic features. In the LDT, additional temporal costs are generated by the lexical processing of the stimuli. Within this general word recognition framework, we administered the tasks in either a visual or an auditory modality, to determine whether none, one or both types of perceptual input are modulated in PD.

After discussing the results of the preliminary experiment conducted among young participants to validate our methodological choices (e.g., tasks, stimuli), we discuss the comparison between patients off medication and controls, starting with the most peripheral (i.e., motor and sensory) aspects, then the cognitive-linguistic ones. We then compare patients on medication and controls. We end by identifying several limitations of this study.

Experimental Validation in Young Participants

Before the main experiment conducted among patients with PD and age-matched controls, we ran a validation experiment in which we tested the stimuli we had created among young adults, who are usually recruited as participants in studies such as ours. A total of 40 participants therefore underwent both tasks (LDT and SRT) in the same order as the older participants, and with the same visual and auditory stimuli.

In the SRTT, as expected, the young participants responded just as quickly for words as they did for pseudowords: no lexicality effect was observed. Auditory stimuli gave rise to slightly longer RTs (+47 ms) than visual stimuli did, probably because the onset of visual stimuli was instantaneous, whereas more time was needed to detect the onset of the auditory stimuli.

In the LDT, RTs were longer than they were for the SRTT, the additional duration ( ms) corresponding to the time needed for lexical access and decision making. Once again, RTs were longer for the auditory modality ( ms) than for the visual one ( ms), as the word or pseudoword had to be listened to until the offset (mean duration: approx. ms) before a lexical decision could be made. A lexicality effect was expected and observed in the LDT. This effect was of equal duration in both modalities (about 70 ms), as the decision-making process was the same. Taken together, these results in young participants validated the methodology we used in our experiment, in terms of both stimulus construction and protocol design.

Motor Deficits in Patients With PD?

The SRTT is a relevant means of estimating possible motor deficits (akinesia and bradykinesia) in patients with PD, as it requires very few cognitive resources. In the visual modality, all the patients responded to real words as quickly as controls. These fast responses suggest that their performance was not hindered by bradykinesia. Interestingly, this may seem to run counter to descriptions in the literature (Gauntlett-Gilbert and Brown, ; Favre et al., ), as increased RTs attributed to akinesia have often been reported in patients with PD (Evarts et al., ). However, this effect has not been systematically observed, and probably depends on several parameters, in particular, patients&#x; age and age at onset of the disease (Reid et al., ; Fimm et al., ), and the presence/absence of bradyphrenia (Mayeux et al., ). Patients&#x; slowdown is also related to deficits in attentional processes (Goodrich et al., ). The results of the present study confirm that motor execution per se is not systematically slowed in patients with PD either off or on medication, especially not in the kinds of task we used here.

Hearing Deficits in Patients With PD off Medication?

In the auditory modality, the SRTT revealed longer RTs in patients with PD off medication, compared with control participants. Since this was not the case in the visual modality, in which patients responded as quickly as controls, this slowness responding to auditory stimuli suggests that patients have hearing loss, compared with age-matched controls. Specific hearing loss has recently been recognized as an additional nonmotor feature in patients with PD (Vitale et al., ), even in de novo patients (Pisani et al., ). From a pathophysiological point of view, the natural aging process, combined with the intrinsic neurodegenerative changes in PD, could interfere with cochlear transduction mechanisms, contributing to presbycusis (Vitale et al., ). However, we did not specifically measure participants&#x; hearing acuity, and further research is required to elucidate the involvement of an auditory perceptual deficit in PD in higher-order language processes.

Inhibition Deficits in Patients With PD off Medication?

Patient groups both off and on medication had significantly lower MDRS scores than controls, as previously observed (Schmidt et al., ; McDermott et al., ). When we set an MDRS cut-off score of / for PD with mild cognitive impairment, in line with Matteau et al. (), a total of 58% of patients on medication and 81% of patients off medication fell within this category. This confirmed that the MDRS is a sensitive instrument for evaluating the general decrease in cognitive functioning in PD (Kulisevsky and Pagonabarraga, ), but lacks sufficient specificity to precisely estimate inhibitory ability. Inhibition deficits have already been reported in patients with PD (Gauggel et al., ; Favre et al., ) as part of a more global executive dysfunction (for a review, see Dirnberger and Jahanshahi, ).

In our study, results on both SRTT and LDT pointed to inhibition deficits in patients off medication. In the SRTT in the visual modality, RTs for pseudowords, but not real words, were significantly longer in the PD off medication group than in the control group, inducing an unexpected but significant lexicality effect. This effect could be interpreted as reflecting patients&#x; difficulty inhibiting irrelevant processing. An alternative interpretation is that patients had difficulty switching from the LDT to the SRTT, and therefore incorrectly applied the strategy used for the first task to the second task. This is a plausible interpretation, as patients with PD have been shown to have difficulty switching from one task to another (Witt et al., ; Cameron et al., ). Nevertheless, it can be ruled out in the present case, for if patients had applied the same strategy in the SRTT as they had done in the LDT, their RTs would have been much longer. As it was, their RTs ( ms) were fully compatible with those expected in an SRTT and comparable to those of controls for real words. We therefore think that the problem came from elsewhere and was specific to pseudowords.

The visual presentation of a word is known to automatically trigger access to the mental lexicon (McClelland and Rumelhart, ). Event-related potential studies have shown that this process can take place very rapidly after the presentation of the visual stimulus ( ms), and the detection of word/pseudoword differences occurs just ms after stimulus onset (e.g., Hauk et al., ). Some cognitive resources are allocated to this automatic processing, and when the task requires the inhibition or deactivation of this processing, additional resources are required. It is therefore likely that patients off medication struggled to inhibit the reading of the items in the SRTT. This slowdown is reminiscent of the classic Stroop effect (Stroop, ), in which irrelevant information interferes with the performance of a cognitive task. Similar interpretations have previously been proposed (Taylor et al., ; Gotham et al., ), whereby patients with PD have difficulty ignoring irrelevant information or inhibiting its processing (Hietanen and Teräväinen, ; Henik et al., ).

For the SRTT in the auditory modality, patients did not exhibit a lexicality effect, but their RTs (around ms) suggest that they made their manual responses before the end of the auditory stimulus, when they did not yet know what the latter was. They presumably accessed the mental lexicon too late for it to slow down their response. This may explain why no lexicality effect was observed in the auditory modality, contrary to the visual modality in which lexical access was very fast because the word was instantaneously displayed.

For the LDT in the visual modality, patients off medication had slower RTs than control participants for pseudowords, but not for real words. Patients therefore exhibited a greater lexicality effect than controls, whereas Marí-Beffa et al. () reported similar lexicality effects in both groups. As mentioned earlier, pseudowords have not always been treated as stimuli of interest in lexical decision studies. Rather, they have often been regarded as mere fillers, and the linguistic processes subtending their processing have rarely been modelled. According to the conventional dual-route model of reading (Morton and Patterson, ; Coltheart et al., ), the identification of the lexical status of the stimuli depends on the activation of the direct (lexical) pathway for words, which is faster than the indirect (sublexical) pathway used for the recognition of pseudowords, which requires grapheme-phoneme conversion. This implies the lateral inhibition of competitors (McClelland and Rumelhart, ). The more similar the words in the lexicon, the greater the competition between them and the slower the response. We can guess that, owing to the close orthographic proximity of the two kinds of stimuli in this study, pseudowords also activated similar neighboring words. In patients, this activation turned into overactivation because of the deficit/dysfunction of the process needed to inhibit competitive words, and therefore slowed down the responses of patients more than controls. Focusing on the idea of competition and lateral inhibition between ambiguous words (Watters and Patel, ), Gurd and Oliveira () showed that patients with PD have difficulty choosing an appropriate word from a list of semantically competitive words.

This hypothesis also fits with the computational modelling of lexical decision, which tries to determine how participants respond negatively when the stimulus is not a real word (Dufau et al., ). The leaky competing accumulator model of the LDT, derived from the multiple read-out model (Grainger and Jacobs, ), represents an alternative way of understanding lexical decision mechanisms (Usher and McClelland, ; Dufau et al., ). In this model, a no response is generated if insufficient evidence for a yes response has been accumulated before the deadline is reached. It is composed of yes and no nodes, both activated constantly and equally before the trial. In the absence of any evidence for a nonword, the no response node is equal to the constant total input value minus the evidence for a real word extracted from the stimulus. This model also features mutually inhibitory connections between the two response nodes, such that a rise in activity in one automatically causes a reduction in activity in the other, and vice versa. From this point of view, and as mentioned above, the patients in our experiment may have had difficulty correctly inhibiting the yes response when a no response was needed.

Concerning the LDT in the auditory modality, the participants&#x; RTs were much longer than they were in the visual modality, owing to the need to hear enough auditory information to perform the task. The expected lexicality effect was observed whatever the group. Patients off medication did not exhibit any difficulty with phonological processing, and performed similarly to controls. We can conclude that, owing to the slow processing of the auditory signal, this experimental condition is not ideal for revealing difficulty with the cognitive-linguistic processes involved in lexical access.

Comparison Between Patients on Medication and Age-Matched Controls

Overall, in both tasks and both sensory modalities, the performances of patients on medication were no different from those of age-matched controls. As was already the case in patients off medication, no bradykinesia was noted in their responses to the SRTT. In addition, and contrary to the patients off medication, no unexpected lexicality effect was exhibited in the visual modality, and no slowness in the auditory modality, compared with controls. Therefore, patients on medication and controls did not differ on the motor and auditory processes elicited by the SRTT. Finally, in the LDT, the magnitude of the lexicality effect was no different from that of controls. We may thus conclude that dopaminergic medication was able to restore motor, perceptual and cognitive functioning close to normal in the patients with PD in the present study. However, to confirm this medication effect and draw a more robust conclusion, a further study involving a single set of patients tested both off and on medication is required, as confounding factors (education, sex, and verbal IQ, etc.) may have influenced the results in the present between-participants experimental design.

Limitations of the Study

Several limitations have already been mentioned in specific parts of the Discussion. An additional one is the small sample size, as this reduced the power and generalizability of the results. As already mentioned, the same participants with PD should have been tested both on and off medication, to precisely evaluate its effect. Finally, patients&#x; hearing should probably be performed systematically before any study featuring sound stimuli.

Conclusion

We found that motor execution per se was not slowed in patients with PD either on or off medication, as they were just as fast as controls in the visual modality when the stimuli were real words. At the sensory level, however, the hearing acuity of patients off medication seemed to be deficient, compared with that of the age-matched controls and patients on medication. In addition, the unmedicated patients were slower than controls when the stimuli were pseudowords, even when the task (SRTT) did not require them to differentiate between the stimuli. Finally, the classic lexicality effect was of the same magnitude in patients on medication and controls, but amplified in PD patients off medication. We conclude that patients with PD have difficulty inhibiting a cognitive-linguistic process (i.e., reading) when not necessary (SRTT) and exhibit a particular deficit in pseudoword processing, which may be related to impaired lateral word inhibition within the mental lexicon. This raises the question of whether this lack of inhibition is specific to lexical processing, or whether it reflects a more general deficit that affects other types of linguistic features. The basal ganglia are acknowledged to be key substrates of high-level cognitive domains. This role reflects their complex organization and multiple circuitries, including pathways through cortico-subcortical loops (Leh et al., ; Haber and Calzavara, ). More specifically, a network involving the basal ganglia, thalamus, and Broca&#x;s area is involved in language processing (Ford et al., ; see also Moro et al., ; Crosson et al., ). However, it has yet to be ascertained whether language impairments following basal ganglia damage are primary or epiphenomenal to other cognitive dysfunctions, and further dedicated studies are therefore needed in this field.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by Ethical Research Committee Sud Méditerranée 1, France. The patients/participants provided their written informed consent to participate in this study.

Author Contributions

AL, J-LV, FV, and SP designed the study. AL performed the data acquisition. FV was in charge of patient recruitment and performed all the clinical assessments. AL performed statistical analyses of the data. AL, J-LV, and SP analyzed, interpreted, and drew conclusions from the results. AL wrote the first draft of the manuscript. J-LV, FV, and SP revised and participated in the writing of the article. All the authors read and approved the final draft.

Funding

AL wishes to thank the French Research and Education Ministry for its financial support (Ph.D. grant scheme) and Aix-Marseille University (teaching and research assistant; ). This study was supported by a grant from the association France Parkinson.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors would like to thank Prof. Marion Tellier (LPL, Aix-en-Provence) for her help recording the auditory stimuli used in the study, and Mrs. Portier for the English revision of the manuscript.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles//fnhum/full#supplementary-material

Footnotes

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Keywords: Parkinson&#x;s disease, lexical access, inhibition capability, language, cognitive impairment

Citation: Letanneux A, Velay J-L, Viallet F and Pinto S () Altered Inhibitory Mechanisms in Parkinson&#x;s Disease: Evidence From Lexical Decision and Simple Reaction Time Tasks. Front. Hum. Neurosci. doi: /fnhum

Received: 30 October ; Accepted: 22 March ;
Published: 26 April

Copyright © Letanneux, Velay, Viallet and Pinto. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Alban Letanneux, [email protected]; Serge Pinto, [email protected]

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Quantitative Electromyographic Analysis of Reaction Time to External Auditory Stimuli in Drug-Naïve Parkinson’s Disease

Evaluation of motor symptoms in Parkinson’s disease (PD) is still based on clinical rating scales by clinicians. Reaction time (RT) is the time interval between a specific stimulus and the start of muscle response. The aim of this study was to identify the characteristics of RT responses in PD patients using electromyography (EMG) and to elucidate the relationship between RT and clinical features of PD. The EMG activity of 31 PD patients was recorded during isometric muscle contraction. RT was defined as the time latency between an auditory beep and responsive EMG activity. PD patients demonstrated significant delays in both initiation and termination of muscle contraction compared with controls. Cardinal motor symptoms of PD were closely correlated with RT. RT was longer in more-affected side and in more-advanced PD stages. Frontal cognitive function, which is indicative of motor programming and movement regulation and perseveration, was also closely related with RT. In conclusion, greater RT is the characteristic motor features of PD and it could be used as a sensitive tool for motor function assessment in PD patients. Further investigations are required to clarify the clinical impact of the RT on the activity of daily living of patients with PD.

1. Introduction

Progressive degeneration of the nigrostriatal pathway results in a deficit of dopaminergic neurons and an imbalance in the corticobasal ganglia-thalamocortical circuit, causing motor dysfunctions in PD [1, 2]. Objective measurement of PD symptoms is problematic, because the PD motor symptoms are usually quantified clinically with UPDRS scores that rely on a physician’s subjective scoring. Reaction time (RT) is the time interval between a specific stimulus and the reaction to it. RT is occupied by a train of processes or stages, which are composed of mental processing and motor reaction. RT measurement has been shown by several investigators to be a useful tool for the assessment of motor response and cognitive function evaluation [3–8]. Previous RT studies using electromyography (EMG) in stroke patients demonstrated a significantly longer RT for the initiation and termination of muscle responses [9, 10]. Because cardinal motor dysfunctions in PD are under the control of basal ganglia, defective basal ganglia function might be reflected in RT values; however, the correlation between RT and clinical symptoms and the underlying mechanism is unclear [3, 5, 7, 11–15]. Also, it is assumed that the presence of nonmotor symptoms in PD possibly has an adverse effect on motor functions [16–18]. The primary purpose of this study was to describe the relationship between RT [RTi (delay in initiation of muscle contraction) and RTt (delay in termination of muscle contraction)] and clinically measured motor/nonmotor scores in untreated de novo PD patients. In this study, we quantified motor functions by RT measurements based on EMG signals of PD. Furthermore, we examined whether nonmotor PD symptoms had an effect on RT by comparing clinical measurements and acquired EMG signals. The second aim of this study was to evaluate if RT varied according to the PD stages.

2. Methods

Subjects and Methods

We recruited thirty-one untreated de novo PD patients from an outpatient Parkinson’s disease and movement disorder clinic of an academic medical center. Clinical diagnosis of PD was made according to the clinical criteria described by the United Kingdom Parkinson’s Disease Society Brain Bank [19]. Age-matched healthy control subjects () were recruited from among respondents to an advertisement in the hospital. Subjects having significant comorbid systemic disorders, previous motor defects, or taking medications that could affect cognitive function were excluded from the study. Dementia patients who could not follow a simple three-step command were also excluded. Written informed consent for study participation was obtained from all subjects. This study was approved by the Institutional Ethics Review Board. All PD patients underwent magnetic resonance imaging of the brain to rule out symptomatic organic lesions and nerve conduction study was performed to exclude peripheral neuropathy.

Drug-naïve de novo PD patients were enrolled in this study because clinically measured scores can be affected by dopaminergic drugs [20]. Motor function was assessed clinically using UPDRS part III [21] and the H&Y stages [22]. Patients were subclassified according to the Hoehn and Yahr (H&Y) stage: Hoehn and Yahr (H&Y) stage 1, H&Y stage 2, and H&Y stage greater than Each side of the patient was evaluated to confirm which the more- and less-affected sides were according to the UPDRS score. Motor function assessment was performed within 1 hour before the measurement of RT to avoid possible discrepancies due to diurnal symptom fluctuations. To assess neuropsychiatric function of the patients, expert psychologist evaluated detailed neuropsychological battery [23]; patients who scored below the 9% of normal values were considered to be abnormal group. The age of the 31 de novo PD patients (19 men and 12 women) ranged between and years (median years). The healthy controls (5 men and 10 women) ranged in age from 53 to 73 years (median years). All participants were right hander. There were no statistical differences between patients and controls in terms of age or sex distribution. The H&Y stage of all participants with PD was (range: 1–4) and the average UPDRS part III (motor function) score was (total ).

RT Measurements Using Surface EMG

RT values (RTi and RTt) were measured from both upper and lower extremities using surface EMG during isometric muscle contraction. We used a 4-channel EMG machine and conductive adhesive foam electrocardiogram-disposable Ag/AgCl transcutaneous surface recording electrodes (Meditrace , Tyco healthcare, USA) for signal recording. A subject was seated on a chair and the extremities were placed in an apparatus using established methods to stabilize and fix during isometric muscle contraction (Figures 1(a) and 1(b)) [9, 10]. For the upper extremities, the surface electrode was placed over the flexor carpi radialis and the extensor carpi radialis (Figure 1(a)), and for the lower extremities, specially-designed shoe-type apparatus with the sole attached to the wooden board was worn to record isometric ankle dorsiflexion (Figure 1(b)). The surface electrode was placed over the belly of the tibialis anterior muscle. Subjects were instructed to contract the corresponding muscle quickly and strongly against the backboard of the apparatus as soon as possible when they heard an audible beep and then terminate muscle contraction as quickly and completely as possible when they recognized that the beep terminated. The auditory beep signal consisted of a total of six audible beeps with three beeps of 3 seconds and three beeps of 6 seconds presented in a random order to minimize the participant’s anticipation. The time between beeps was also randomized by the computer to be either 3, 4, or 5 seconds to minimize anticipation.

(a)
(a)
(b)
(b)

Data acquisition hardware included a 4-channel EMG amplifier QEMG-4 (LMX, LAXTHA, KOREA) and National Instruments data acquisition board: PCI that was interfaced with a personal desktop computer (X-pion TKG X, LG electronics, Korea). Data was processed through LabView software. The amplifier gain setting was , and the bandpass filter frequencies were set to 8~ Hz. A sampling frequency of  Hz. was used. Two blinded examiners analyzed each tracing of the EMG signal visually and investigated as follows. The beginning and end points were marked manually on the screen. RTi (delay in initiation) was determined as the time interval between the start of an auditory beep and the detection of compound muscle action potential (CMAP) from baseline. RTt (delay in termination) was defined as the time interval between stop of the beep and termination of the EMG muscle activity to the baseline (Figure 2).


Data Analysis

Independent -tests were used to compare basic demographic factors and the chi-square test to evaluate differences in sex ratios between groups. Because PD is basically an asymmetric disorder, RT parameters of the more- and less-affected side were compared using a paired -test. To confirm the relationship between RT with clinical data, Spearman’s correlation was used. One-way ANOVA was conducted to compare differences in clinical degree, RT between the three different PD groups classified according to H&Y stage. A value less than was considered statistically significant. SPSS version for Windows (SPSS Inc, Chicago, IL, USA) was used for all statistical data analyses.

3. Results

Both RTi and RTt were significantly greater in the PD group () than the controls (). Among the 31 PD patients, the right side was more affected in 19 patients, while the left side was more affected in 11 patients. One PD patient could not be categorized as the patient showed symmetric involvement. Both RTi and RTt were significantly greater in the more-affected side than the less-affected side; in particular, RTt was more prominent in the more-affected side (Table 1). Most of the representative motor deficits of PD, that is, bradykinesia, rigidity, and resting tremor, showed significant correlation with RT (Table 2). Both RT values (RTi and RTt) also increased significantly as the sum of the UPDRS part III scores increased. In particular, RT of the less-affected side was more strongly correlated with cardinal motor symptoms and the sum of the UPDRS scores than the more-affected side.


Control ()PD () valueWithin PD group () value
More-affected side
(Rt 19, Lt 11)
Less-affected side
(Rt 11, Lt 19)

RTiWrist flexion<****
Wrist extension<****
Ankle flexion<***

RTtWrist flexion<***
Wrist extension<***
Ankle flexion<**

The mean difference is significant at the level.
**The mean difference is significant at the level.
Data are presented as mean ± SD (msec).
PD: Parkinson’s disease; RTi: reaction time (initiation delay of muscle contraction); RTt: reaction time (termination delay of muscle contraction).

Spearman’s correlation coefficient
between RT and UPDRS part III motor scores
More-affected sideLess-affected side
Wrist flexorWrist extensorAnkle flexorWrist flexorWrist extensorAnkle flexor

RTiSum of UPDRS III******
 Bradykinesia***
 Rigidity**
 Tremor***

RTtSum of UPDRS III**********
 Bradykinesia******
 Rigidity*******
 Tremor*

The mean difference is significant at the level.
**The mean difference is significant at the level.
UPDRS: Unified Parkinson’s Disease Rating Scale; UPDRS part III: sum of motor part score of UPDRS; RTi: reaction time (initiation delay of muscle contraction); RTt: reaction time (termination delay of muscle contraction).

PD patients were categorized into three groups according to their Hoehn and Yahr (H&Y) stage. Seven patients were in Hoehn and Yahr (H&Y) stage 1 (%), 16 were in H & Y stage 2 (%), and 8 had Hoehn and Yahr (H&Y) stage greater than (%). Both RTi and RTt were delayed as PD stages advanced (Table 3).


Mean latency ± SD (msec) value
H&Y stage 1
(, 14 limbs)
H&Y stage 2
(, 32 limbs)
H&Y stage ≥
(, 16 limbs)

RTiWrist flexion*
Wrist extension<**
Ankle flexion*

RTtWrist flexion
Wrist extension**
Ankle flexion*

The mean difference is significant at the level.
**The mean difference is significant at the level.
H&Y stage: Hoehn and Yahr stage; RTi: reaction time (initiation delay of muscle contraction); RTt: reaction time (termination delay of muscle contraction); msec: milliseconds.

More than half of the PD patients (52%) were classified as having abnormal frontal lobe function despite most patients being in the early, untreated state of the disease. PD patients with abnormal Fist-Edge-Palm and alternating hand movement tests, reflecting defects in motor programming and motor set-shifting ability, had significantly greater RT values (). Patients who had abnormal Luria loop test results, reflecting the presence of perseveration, had also significantly higher RT values than the normal group () (Figure 3). Attention and prefrontal mental set shifting domains were not significantly correlated with RT.


4. Discussion

The principal findings of this study are the following: (1) the RT values for both initiation and termination of muscle contraction are significantly longer in PD patients than healthy controls, (2) the degree of RT correlates well with clinically measured motor and cognitive scores in PD patients, (3) RT has some unique features in PD patients (the delay in termination is greater than the delay in initiation and RTt is also more strongly correlated with clinical motor scores than RTi), and (4) RT is longer in the more-affected side and in more-progressed disease states.

Correlation of RT and Clinical Data in Patients with PD

Completion of a motor response requires complex interactions between neural circuits and synaptic connections. In this study, we adopted simple RT tasks that involved one stimulus and one predetermined motor response. The basic processes involved in performing this simple voluntary motor response are signal detection, central signal processing, and motor execution. Delay in muscle contraction of the paretic limbs in stroke survivors has been documented previously [10, 24]. Defects in motor processing may contribute to this delay in stroke patients [10]. The pathophysiology of motor deficits in PD is somewhat different from that in paretic stroke, which involves the pyramidal tract. PD is basically a disease of basal ganglia, which are involved in motor programming and maintenance of motor activity. The motor circuit of the basal ganglia consists of a variety of functional feedback circuits between the basal ganglia and cerebral cortex [25]. Motor deficits in PD are due primarily to failure of the basal ganglia to energize the cortical mechanisms that prepare the muscle for movement [26]. Because of defective basal ganglia function, the cortical networks that enable the corticospinal system to execute voluntary movement fail to adequately control the motor circuit in PD [26]. And Müller et al. [2] previously investigated that the degree of dopaminergic nigrostriatal degeneration measured by

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Response slowing in Parkinson’s disease: A psychophysiological analysis of premotor and motor processes

Abstract

The mechanisms responsible for reaction time slowing in Parkinson’s disease were investigated using movement‐related potentials in a choice reaction time task. Parkinson’s disease patients and control subjects were required to respond with the left or right hand to indicate whether a visual stimulus was relatively large or small. The difficulty of the size discrimination was manipulated, as was the complexity of the manual response (single key press versus sequence of three key presses). Behavioural responses of Parkinson’s disease patients were slower than those of control subjects, especially when complex responses were required. Moreover, the timing of movement‐related potentials indicated that motor processes clearly required extra time, relative to control subjects, for Parkinson’s disease patients making complex responses. In addition, delayed onset of the movement‐related potentials indicated that one or more premotor processes are also slowed in these patients.

Keywords: Parkinson’s disease; lateralized readiness potential; response slowing; stimulus discrimination; sequential movement

Abbreviations: ERP = event‐related potential; LRP = lateralized readiness potential; MT = movement time; R‐LRP = response‐locked lateralized readiness potential; RT = reaction time; S‐LRP = stimulus‐locked lateralized readiness potential

Introduction

Across a wide variety of experimental paradigms, people with Parkinson’s disease tend to have slower reactions compared with healthy adults of a similar age (e.g. Rafal et al., ; Brown and Marsden, ; Daum and Quinn, ; Wascher et al., ). Within an information processing framework, the slowing associated with Parkinson’s disease is usually conceptualized in terms of the processing stages that must occur between the time a stimulus is presented and the subsequent response is performed. For example, a choice reaction time task might include identifying and evaluating the stimulus, selecting the appropriate response, and programming and executing the movement. Thus, when Parkinson’s disease patients are slower to respond, any one or all of these stages may be contributing to the delayed reaction time (RT).

The processing stages related to motor function are perhaps the most obvious stages in which to expect some degree of slowing in Parkinson’s disease. After all, impaired motor activity is a defining feature of the disease. Even in tasks with minimal perceptual and decision requirements (e.g. simple RT task), Parkinson’s disease patients tend to have slower reactions than control subjects (Heilman et al., ; Evarts et al., ; Bloxham et al., ). In addition, Parkinson’s disease patients are especially slow and inaccurate at executing more complex movements (e.g. simultaneous or sequential movements), which presumably pose greater demands on motor processing compared with single element movements (Benecke et al., ; Agostino et al., ; Martin et al., ; Low, ). There is also evidence from transcranial magnetic stimulation studies to suggest that, in Parkinson’s disease, a longer time is needed for the motor cortex to reach the threshold necessary for emitting an overt response (e.g. Pascual‐Leone et al., b). Thus, in a basic choice RT task, it seems reasonable to believe that there is some degree of slowing in motor processing.

It is less clear whether processing stages prior to the motor system are affected by Parkinson’s disease. Cooper and colleagues had subjects make a go/no‐go decision based on one, two or three stimulus dimensions and found that, relative to control subjects, Parkinson’s disease patients showed increasingly prolonged RTs as the complexity of the decision increased (Cooper et al., ). They concluded that cognitive speed is slowed in Parkinson’s disease and that this slowing is proportional to the increase in decision complexity. Zimmerman and colleagues came to a similar conclusion when varying the complexity of stimulus‐response associations (Zimmermann et al., ). Contrary to these findings, however, other researchers have found no exaggerated slowness in Parkinson’s disease patients when performing an increasingly complex stimulus matching task (Russ and Seger, ) or when performing tasks which included distractor elements designed to increase the complexity of the stimulus configuration (Dubois et al., ). Therefore, it remains difficult to determine the contribution of cognitive slowing to the RT delays typically seen in Parkinson’s disease patients.

The present study was designed to investigate both motor and cognitive processing in Parkinson’s disease patients. The lateralized readiness potential (LRP) is a component derived from motor‐related potentials that seems ideal for this purpose. It has proven particularly useful in the study of information processing in healthy populations (e.g. Smulders et al., ; Leuthold et al., ; Miller and Ulrich, ). In brief, the LRP is derived from event‐related potentials (ERPs) recorded at electrodes overlying the hand areas of the primary motor cortex, and it reflects the increase in EEG activity that occurs in the contralateral hemisphere prior to a unimanual hand response (see Methods for details on the LRP derivation). A variety of experimental findings converge to support the notion that LRP onset marks the beginning of hand‐specific, central motor activation (for reviews see Coles, ; Miller and Hackley, ; Leuthold et al., ; Eimer, ).

Three recent studies have compared LRPs for Parkinson’s disease patients and control subjects in order to investigate differences in motor function. First, Praamstra and colleagues investigated the hypothesis that Parkinson’s disease patients have a selective deficit in preparation of a to‐be‐executed response—a hypothesis suggested among other things by evidence that Parkinson’s disease patients are usually slowed more in simple RT tasks than in choice RT tasks (Praamstra et al., ). To elucidate the hypothesized preparation deficit using the LRP, Praamstra and colleagues studied the development of motor potentials during a 1 s interval following a cue that indicated which hand would be required to make a subsequent speeded response. Somewhat surprisingly, they found only minor differences between the preparatory motor potentials of patients and controls, and they concluded that Parkinson’s disease patients are indeed able to prepare responses if sufficient time is allowed for that preparation (cf., Stelmach et al., ). Secondly, Wascher and colleagues also used a variety of psychophysiological measures, including the LRP and response force, to check for abnormal motor preparation in Parkinson’s disease patients (Wascher et al., ). In one experiment, the to‐be‐prepared response was required at unpredictable times, and in a second experiment the to‐be‐prepared response was required with varying probabilities. They also found comparable preparatory LRPs for Parkinson’s disease patients and control subjects, supporting the idea of reasonably normal response preparation in Parkinson’s disease. Thirdly, Praamstra and colleagues used the LRP to check for another type of abnormality in response preparation (Praamstra et al., ). Specifically, they examined the automatic build‐up of response activation resulting from response‐related but irrelevant distractor stimuli in a version of the flankers task (Eriksen and Eriksen, ). They found Parkinson’s disease patients more susceptible than control subjects to motor influences of the distractors, which they interpreted as evidence of greater reliance on external response cues in Parkinson’s disease.

The present study also investigated motor preparation in Parkinson’s disease using the LRP, but we used a different technique—described below—that allows the RT interval to be partitioned into premotor and motor components. That is, rather than assessing preparation in advance of stimulus onset, like Praamstra and colleagues (Praamstra et al., ) and Wascher and colleagues (Wascher et al., ), we used the LRP to monitor response preparation during the crucial interval between the onset of a reaction stimulus and the speeded response. This is precisely the interval often observed to be prolonged in Parkinson’s disease. In particular, we sought to isolate the interval consumed by motor processes so that we could establish their duration and study the motor delays thought to be characteristic of Parkinson’s disease. In addition to measuring the interval consumed by motor processes, this technique also allows measurement of the interval consumed by premotor processes, including sensory registration, perceptual discrimination and response selection. Thus, this study was designed to assess both premotor (cognitive) and motor contributions to the response slowing observed in Parkinson’s disease.

To partition the RT interval and thereby estimate the durations of the motor and premotor components, researchers have used the procedure of time‐locking the LRP to two different events, the onset of the stimulus and the onset of the response (e.g. Miller and Ulrich, ). When the LRP is aligned with respect to the moment of stimulus onset (stimulus‐locked LRP or S‐LRP), the interval between stimulus onset and LRP onset can be used as an estimate of the duration of premotor processes (i.e. cognitive processes which occur prior to the activation of the motor system). In contrast, when the LRP is aligned with respect to the moment of the behavioural response (response‐locked LRP or R‐LRP), the interval from the onset of the LRP to the onset of the response can be used to estimate the duration of motor processes (Smulders et al., ; Leuthold et al., ; Miller and Ulrich, ). Thus, the S‐LRP latencies and R‐LRP latencies observed in a speeded RT task can be used to assess the separate contributions of premotor versus motor processes to the overall RTs. Then, by comparing the onset latency of the S‐LRP for Parkinson’s disease patients versus control subjects, we can assess group differences in the durations of premotor processes. Analogously, by comparing the onset latencies of the R‐LRPs, we can assess group differences in the durations of motor processes.

One virtue of this partitioning method is that R‐LRP onset latency includes the time needed for fairly central motor processes. In contrast, partitioning the RT interval with respect to EMG onset (e.g. Praamstra et al., ) limits the estimate of motor time to only the relatively peripheral motor processes following the moment of EMG onset.

A second virtue of this partitioning method is its generality. Separate time‐locking of EEG to stimuli and responses can be used in almost any task with an overt response to a presented stimulus. The present study, for example, used a choice RT paradigm in which participants were asked to respond with either the right or the left hand depending on the size of a visual stimulus. Of course, the durations of both motor and premotor processes must depend on the task requirements, and so too may the group differences (cf., Brown et al., b). In particular, group differences may be especially pronounced when the stages affected by Parkinson’s disease are challenged by difficult processing requirements. Moreover, the effects of different types of task difficulty manipulations (e.g. perceptual, motor) can be used to help isolate the processing stages responsible for slowing in Parkinson’s disease (e.g. Stelmach et al., ).

We varied the difficulty of the task in two ways in order to explore the generality of the results, to help identify the processes responsible for slowing and to maximize the chances of finding group differences. First, in an effort to challenge a premotor processing stage, we manipulated the difficulty of the size discrimination (e.g. Sternberg, ; Sanders, ). Secondly, to challenge the motor processing stage, we manipulated the difficulty of the response (e.g. Sanders, ; Rosenbaum et al., ). We chose to manipulate these two particular factors because of their selective effects on the S‐ and R‐LRP latencies. In healthy young adults, increasing the difficulty level of either the discrimination or the response tends to result in slower responses. Moreover, manipulations of stimulus discriminability affect the latency of the S‐LRP, but have no influence on the R‐LRP. This is consistent with the notion that harder discriminations require more time for cognitive operations, such as stimulus evaluation, but do not affect later motor processing (Osman et al., ; Smulders et al., ). In contrast, manipulations of response complexity affect the timing of the R‐LRP but not the S‐LRP, suggesting that more complex reactions require more time for motor processing without affecting the duration of earlier cognitive operations. By including manipulations that are known to challenge premotor and motor processes independently in healthy adults, we can determine whether Parkinson’s disease patients show a qualitatively similar pattern of results as task difficulty increases, and whether these patients have any selective deficits in either discrimination or response processes.

In sum, the LRP can be useful in the study of Parkinson’s disease because it can be used to separate the premotor and motor components comprising the RT interval. By including this measure along with more traditional measures (i.e. P, EMG, RT and movement time), we can more directly assess how much of the RT slowing typically seen in Parkinson’s disease patients is due to cognitive deficits arising before the motor system has been engaged and how much is due to subsequent motor deficits.

Methods

Participants

Twelve patients with mild Parkinson’s disease (six females and six males) and 12 healthy control subjects (seven females and five males) participated in this study. Participants were recruited through a local Parkinson’s disease support group and from the general community. The average age of the patients was  years (range 55–76 years) while the average age of the control group was  years (range 58–74 years). All were right‐handed as determined by the Edinburgh Handedness Inventory (Oldfield, ). Neither group demonstrated signs of dementia or depression; scores on both the Geriatric Depression Inventory Short Form (Sheikh and Yesavage, ) and the Mini‐Mental State Examination (Folstein et al., ) were within the normal range for all participants. Control participants had no known neurological disorders and had not been prescribed any neurological medications.

All the patients were under their normal medication routine at the time of testing. Seven of the patients were taking levodopa as their only form of parkinsonian therapy. Three others were taking some combination of levodopa plus selegiline and bromocriptine, or amantadine. One patient was on selegiline and bromocriptine, and one other patient was tested prior to being placed on any parkinsonian medication. The motor subscale of the Unified Parkinson’s Disease Rating Scale (Lang and Fahn, ) was used to measure the level of motor impairment on the day of testing. Scores ranged from 4 to 19 with a mean of 11 (postural instability was not assessed). This research was approved by the Human Ethics Committee of the University of Otago, New Zealand, and all participants provided written informed consent in accordance with the Declaration of Helsinki.

Apparatus and stimuli

Subjects were seated ∼60 cm from the display monitor in a dimly lit room. The stimulus set consisted of four squares varying in size. The squares were white outline images on a black background and were presented at fixation with the visual angle of one side measuring ∼°, °, ° or °. Responses consisted of either a single key press (simple response) or a three‐finger key press sequence (complex response). For complex responses, subjects were required to press with the index, ring and middle fingers, in that order, using the c, z and x keys of a standard computer keyboard for left‐hand responses and the comma, slash and period keys for right‐hand responses. For simple responses, only the index finger of each hand was used to press either the c or the comma key. Feedback was provided following incorrect responses and consisted of a visual presentation of the word ‘wrong’ in the centre of the screen and ∼3 cm below fixation. Stimulus presentation and recording of behavioural and electrophysiological responses were controlled by an IBM‐PC compatible microcomputer (PC General Corp., Dunedin, NZ).

Procedure

On each trial, a fixation cross was presented for  ms followed by a blank period of  ms before the appearance of one of the four squares. The reaction stimulus remained on the screen until a response was made or  ms had elapsed, whichever came first. The four square sizes were presented randomly and with equal probability. Following trials with an incorrect first key press, the word ‘wrong’ would appear for 2 s. There was ∼ s between the response and the onset of the next stimulus.

Prior to the experimental blocks, participants were first shown the complete stimulus set of four different sizes of squares, which were referred to as extra‐small, small, large and extra‐large. Subjects were then told to try to remember the stimulus set because only one of the four squares would appear on a given trial. The task was to determine whether the presented square was relatively small or relatively large (i.e. one of the smaller two versus one of the larger two) and to indicate this decision by making a right‐ or left‐hand response, respectively (counterbalanced across subjects). Clearly, however, the ease of identifying whether a given square was large or small depended on its particular size. The extra‐small and extra‐large squares were ‘easy’ to identify as small and large (respectively), whereas the two intermediate sized squares were ‘hard’ to classify as small or large.

After familiarizing themselves with the stimulus set, participants were given at least one 20‐trial block of practice with both the simple and complex response requirements. All but two of the Parkinson’s disease patients required additional training on the complex response. Our procedure was to allow these individuals as many repetitions of the complex response sequence as necessary to feel comfortable with the order of the finger movements. This additional practice was performed in a self‐paced fashion (i.e. without the reaction signals), but once comfortable, these individuals were given an additional block of practice with complex responding to the reaction signals. This was because performing this response under the moderate constraints of the timed reaction task (i.e. a reaction had to be made within  ms or an error message would appear) sometimes posed additional difficulties.

Following the practice blocks, each subject performed 16 experimental blocks. Both speed and accuracy were emphasized, with subjects asked to respond as quickly as possible without making too many mistakes. The type of response (simple versus complex) alternated across the blocks, but remained consistent within a given block. Half of the subjects started with a simple response block, and the other half started with a complex response block. Each block consisted of 40 trials for a total of  trials with each response type. Subjects were encouraged to take rest breaks between blocks as needed to avoid fatigue.

Electrophysiological recording

Electrophysiological activity was recorded using Ag–AgCl electrodes attached to the scalp with EC‐2 (Astro‐Med Inc., Rhode Island) paste at sites 1 cm anterior and superior to positions C3 and C4 of the International 10–20 System (designated C3′ and C4′) and at the midline parietal site Pz. Horizontal eye movements were monitored with facial electrodes placed ∼2 cm lateral to the left and right outer canthi. These electrodes were all referenced to an electrode clipped on the left earlobe and were recorded with a band pass of – Hz. Blinks and vertical eye movements were monitored with electrodes placed ∼2 cm above and below the left eye referenced to one another using a band pass of – Hz. EMG activity from the muscles controlling finger flexion was also recorded bipolarly at sites that roughly trisected the wrist–elbow distance on the ventral forearm. A band pass of – Hz was used for the EMG recordings and these signals were then full‐wave rectified off‐line. All electrophysiological signals were digitized at a rate of  Hz. Electrode impedances were <5 kΩ on the scalp and face and <15 kΩ on the forearms.

Data reduction

The EEG recordings were first examined for artefacts (excessive EMG activity, amplifier saturation and slow linear drift) that occurred between the start of the baseline period and the 95th percentile of the participant’s RT distribution for the condition being tested on that trial. This resulted in the rejection of ∼5% of the trials. The remaining EEG waveforms were then corrected for contamination due to blinks and eye movements using the procedure developed by Gratton and colleagues (Gratton et al., ). Trials with RTs < ms (%) or > ms (%) were also excluded from further analyses.

Signal‐averaged ERPs were computed for each subject and trial type with time‐locking to the onset of the reaction signal (i.e. stimulus‐locked) or to the moment of the key press (i.e. response‐locked). The LRP was then computed using the average ERPs at C3′ and C4′ in a manner consistent with prior investigations (cf., Smid et al., ; Gratton et al., ). For each time point and trial type, the activity at the site contralateral to the responding hand was subtracted from that at the ipsilateral site (e.g. C3′ was subtracted from C4′ for trials in which the right hand was signalled). These difference waveforms were then averaged across right‐ and left‐hand trial types for each subject separately. This derivation produces a positive deflection in the LRP waveform when there is relatively more negativity recorded at the scalp site contralateral to the responding hand.

Electrophysiological readings were scored relative to the average value during the  ms baseline interval immediately preceding the reaction stimulus. The ERP (including LRP) and EMG waveforms shown in the figures were digitally smoothed using a 12 Hz finite impulse response low‐pass filter using weights from the program developed by Cook and colleagues (Cook et al., ; Cook and Miller, ). This filter attenuates the amplitude of an 8 Hz signal by ∼2% and a 16 Hz signal by 98%. In all cases, however, peak and mean amplitudes used in statistical analyses were computed on unfiltered waveforms.

LRP onset latencies were analysed using the jackknifing procedure recommended by Miller and colleagues (Miller et al., ). For this analysis, the waveforms were digitally smoothed using a 4 Hz finite impulse response low‐pass filter (attenuates 1 and 6 Hz signals by ∼ and 80%, respectively). The onset latency of the LRP is then taken as the point in time when the grand average LRP for a given condition reaches a criterion amplitude, in this case  µV. A jackknifing method was then used to estimate the standard error of the latency differences (Mosteller and Tukey, ; Efron, ). Based on extensive simulations, Miller and colleagues concluded that this procedure has lower bias and higher power than other measures of LRP onset latency used previously (Miller et al., ). Interested readers should consult that article for a more detailed description and evaluation of the procedure. In the present study, the criterion value of  µV was selected as the lowest cut‐off that yielded small standard errors of estimate for each group and condition. Similar LRP latency results were also obtained with cut‐offs of , and  µV. A factorial analysis of variance (ANOVA) of the jackknifed LRP onset latencies was conducted using the extension developed by Ulrich and Miller () of the analysis described by Miller and colleagues (Miller et al., ).

EMG onset was scored on individual trials for each subject separately. To estimate the onset latency, we first calculated the mean and standard deviation of EMG activity during the baseline period for each trial and set the criterion amplitude at SD above the baseline mean. EMG onset was then defined as the first point in time during which the EMG amplitude reached the criterion and persisted such that the average amplitude during the next two 50 ms epochs also exceeded this criterion. Because EMG activity must start before the key press, trials that failed to reach the criterion amplitude within the RT interval were excluded from the analysis of EMG latency.

Results

Behavioural measures

Mean RTs and percentage correct for each group are displayed in Table 1. Preliminary analyses indicated that the only significant effect of practice was a reduction in RTs for the control subjects following the first two blocks (i.e. one block with each response requirement), so the main ANOVAs were conducted collapsing across practice levels. A mixed ANOVA was conducted with group (Parkinson’s disease versus control subjects) and order of response requirements as between subjects factors and response complexity, stimulus discriminability, and hand of response as within subjects factors. Responses were reliably slower for complex compared with simple movements [F(1,20) = , P < ], and for hard relative to easy discriminations [F(1,20) = , P < ]. Parkinson’s disease patients were  ms slower overall, but the difference only approached statistical significance [F(1,20) = , P = ]. There was, however, a highly reliable group by response complexity interaction [F(1,20) = , P < ]. On complex response trials, Parkinson’s disease patients were  ms slower than control subjects [F(1,22) = , P < ], but the 41 ms group difference on simple trials was not statistically significant, F < 1. The difference between Parkinson’s disease patients and control subjects was somewhat larger when the discrimination was easy ( ms) than when it was hard ( ms), but this interaction was not significant.

Accuracy for the first key press was fairly high overall with correct responses on 93% and 95% of trials for the Parkinson’s disease and control groups, respectively [F(1,20) = , P > ]. Trials involving easy stimulus discriminations were more accurate than trials with hard discriminations [F(1,20) = , P < ], but this factor did not interact with group, F = 1. Thus, both groups were quite accurate with the initial key press. However, because the complex response condition provided more opportunity for error, a further exploration of sequence accuracy was conducted on these trials. Accuracy for performing all three key presses of the sequence was substantially higher for the control group (%) than for the Parkinson’s disease patients (90%) [F(1,22) = , P < ], indicating that the complex response requirement was especially difficult for the patients.

The complex trials also afforded the opportunity to investigate movement time (MT). We reanalysed initial RT using the more strict accuracy criterion (i.e. entire sequence correct). The average initial RT for Parkinson’s disease patients dropped by ∼88 ms, suggesting that when Parkinson’s disease patients made mistakes with the sequence, they were also more likely to be delayed in initiating that sequence. The change in criterion did not alter the initial RT in the control group, but this is likely due to the fact that this group made very few sequence errors so a similar number of trials are contributing to both analyses. Despite this difference, Parkinson’s disease patients were still ∼ ms slower than control subjects for initial RT [F(1,22) = , P < ].

Including only those trials for which the entire sequence was correct, a group by discriminability by hand‐mixed ANOVA was computed for the intervals between the first and second key presses (MT‐1) and the second and third key presses (MT‐2). As can be seen in Table 2, Parkinson’s disease patients were slower on both MT‐1 and MT‐2 [F(1,22) =  and , P < ]. The sequence was generally performed faster with the right hand [F(1,22) =  and , P <  for MT‐1 and MT‐2, respectively]. An unexpected effect of stimulus discriminability was found for MT‐2. Initiation of the final key press was 5 ms faster following a hard compared with an easy discrimination trial [F(1,22) = , P < ], and we suspect that this is a Type I error.

Psychophysiological measures

LRP

The effects of stimulus discriminability and response complexity on the LRP waveforms are shown separately for each group in Fig. 1. In both groups, S‐LRP onsets were later for hard discriminations than for easy ones, and R‐LRP onsets were earlier for complex responses than for simple ones. Thus, both task–difficulty manipulations appear to have affected the expected stages of information processing.

Group differences for each condition, however, can be seen more easily in Fig. 2. This figure suggests a delay in the onset of the S‐LRP for the Parkinson’s disease group (solid line) compared with the control group (dashed line), and this delay appears to persist under all conditions. Furthermore, when performing the complex response the Parkinson’s disease patients also seem to show an increase in the duration of the R‐LRP.

These observations were tested statistically using the jackknife procedure (see Methods) in a factorial design that included the between‐subjects factor of group and the within‐subjects factors of stimulus discriminability and response complexity. In the analysis of S‐LRP waveforms, there was a robust effect of stimulus discriminability [F(1,22) = , P < ], with lateralization beginning 44 ± 12 ms earlier in the easy compared with the hard stimulus condition. This suggests that at least a portion of the RT difference between easy‐ and hard‐to‐discriminate stimuli can be attributed to premotor processes. In contrast, response complexity had no effect on the interval between stimulus onset and S‐LRP onset (P > ). This pattern of S‐LRP effects for stimulus discriminability and response complexity is consistent with that found by Smulders and colleagues (Smulders et al., ), and therefore extends their findings to include older adults.

The group difference in S‐LRP onset that is apparent in Fig. 2 was also confirmed [F(1,22) = , P < ]. Relative to control subjects, Parkinson’s disease patients showed a delay in onset of 66 ± 59 ms. However, the group factor did not interact with either stimulus discriminability or response complexity for the S‐LRP waveforms (P > ). The lack of interaction of group with stimulus discriminability suggests that, although Parkinson’s disease patients appear to have a delay in some premotor process, the speed of stimulus discrimination processes is normal.

In the analysis of R‐LRP waveforms, there were reliable main effects of both stimulus discriminability and response complexity [F(1,22) =  and , respectively, P < ]. The LRP‐to‐key press interval lasted 45 ± 33 ms longer in the hard relative to the easy discrimination condition and was  ± 49 ms longer prior to complex compared with simple responses. Whereas the effect of response complexity on the R‐LRP is consistent with prior research (e.g. Smulders et al., ), the effect of stimulus discriminability on the duration of motor processes is unexpected because it has not been observed in previous studies with normals (e.g. Smulders et al., ). Moreover, the effect was not simply produced by the inclusion of the Parkinson’s disease patients, because it is present in the control group as well (cf., Fig. 1). The reasons for and implications of this unexpected finding are considered in the Discussion.

Although there was not a main effect of group on the duration of the R‐LRP (F < 1), there was a reliable group by response complexity interaction [F(1,22) = , P < ]. Fig. 2 suggests that the duration of motor processes prior to the complex response was longer for patients compared with control subjects, whereas for the simple response the duration of motor processes was approximately the same for the two groups. To test this observation statistically, we compared the groups at each level of response complexity using the jackknife procedure. For complex responses, the duration of the R‐LRP was  ±  ms longer in the Parkinson’s disease compared with the control group, and this difference was statistically reliable [F(1,22) = , P < ]. In contrast, the groups did not differ with simple responses (F < 1). So, the increased disadvantage in RT for Parkinson’s disease patients performing the complex response can be attributed in large part to the additional time needed by the central motor system to reach a level of activation sufficient to generate the initial key press in this condition.

EMG

As described above, the mean latency of EMG onset was based on single trial measurements. The group means for each condition are presented in Table 3. A mixed ANOVA was conducted on these latency estimates with group as a between‐subjects factor and stimulus discriminability, response complexity, and responding hand as within‐subjects factors. The results were redundant with those obtained for RT. EMG onset occurred earlier for easy compared with hard discriminations and for simple compared with complex key presses [F(1,22) =  and , respectively, P < ]. Furthermore, there was a statistically reliable group by complexity interaction [F(1,22) = , P < ], with group comparisons indicating a delay in EMG onset for Parkinson’s disease patients performing the complex, but not the simple response [F(1,22) = , P <  and F < 1, respectively].

Figure 3 displays the grand average response‐locked EMG activity recorded from the responding and non‐responding arms (top and bottom panels, respectively). To evaluate whether the groups differed with respect to EMG amplitude, for each subject and condition we calculated the mean amplitude of the response‐locked EMG during four consecutive  ms epochs (from  ms prior to  ms after the initial key press). A mixed ANOVA was then conducted on these mean amplitudes with the between‐subjects factor of group and the within‐subjects factors of stimulus discriminability, response complexity, response hand and epoch. Overall, EMG amplitude on the responding arm was greater for Parkinson’s disease patients than for control subjects ( and  µV, respectively), with this difference approaching statistical significance [F(1,22) = , P < ]. Also for the responding arm, there was a reliable effect of epoch [F(3,66) = , P < ], with mean amplitudes increasing across the four epochs. EMG mean amplitude was greater on complex compared with simple responses [F(1,22) = , P < ] and this effect interacted with epoch [F(3,66) = , P  ]. Figure 3 shows that EMG amplitude began to increase much earlier preceding complex responses compared with simple responses. Although this difference appears to be more pronounced in the Parkinson’s disease group, the three‐way interaction of group, complexity and epoch was not significant (F < 1). There was an interaction between stimulus discriminability and epoch [F(3,66) = , P < ], but pairwise comparisons during each epoch failed to reveal any consistent pattern. During epochs 1 and 4, there was no difference in EMG amplitude for easy versus hard stimuli. During epoch 2 (– to – ms), EMG mean amplitude was  µV larger on hard trials, and during epoch 3 (– to – ms) this pattern reversed with larger ( µV) mean amplitude on easy trials. Finally, there was a reliable three‐way interaction of group, hand and epoch [F(3,66) = , P < ]. Inspection of the means indicated that during the last epoch (– to  ms), the Parkinson’s disease group tended to have larger EMG activity on the left hand while the control group showed more activity on the right hand.

A parallel analysis was conducted on the EMG activity of the non‐responding hand. Again, Parkinson’s disease patients produced greater levels of EMG activity compared with the control group [F(1,22) = , P < ]. This effect interacted with epoch [F(3,66) = , P < ], such that the difference between groups was most pronounced in the  ms epoch surrounding the key press (i.e. movement of the opposite hand). This difference between groups was slightly more pronounced for complex movements as indicated by a trend towards the three‐way interaction of group, epoch and complexity [F(3,66) = , P = ]. Thus, it appears that Parkinson’s disease patients were not able to fully inhibit motor activity on the non‐responding hand, especially when performing complex movements.

P

Figure 4 displays the grand‐average ERP waveforms recorded at Pz for patients (left panel) and control subjects (right panel), separately. These waveforms show a positive peak at ∼ ms followed by a negative component that peaks ∼ ms after the onset of the reaction signal. Scrutiny of the individual subject averages indicated that this P1–N2 complex varied considerably across subjects, with some individuals showing peak‐to‐peak amplitude changes as great as 14 µV while others showed no detectable change during the interval between 50 and  ms. This rather high variability may be due to the fact that our recording locations were not ideal for investigating these early components. When stimuli are presented visually, these components are typically largest at sites overlying the occipital cortex (Hillyard et al., ). Furthermore, there were no systematic differences between the two groups in terms of this variability. Although previous research has reported differences in these components in Parkinson’s disease patients compared with controls (e.g. Bodis‐Wollner and Yahr, ; Wright et al, ), we were unable to investigate these potential group differences adequately due to the large variability across subjects in identifying these early components in the present study.

All subjects displayed a prominent P component that peaked ∼ ms after the onset of the reaction signal. For each subject and condition, the maximum amplitude recorded at Pz between and  ms was identified, and the amplitude and corresponding latency of this maximum was scored. A mixed ANOVA was then conducted on both peak latency and peak amplitude with the factors of group, stimulus discriminability and response complexity. We failed to find any statistically reliable effects on P peak latency (all P > ). The lack of an effect of stimulus discriminability was particularly surprising given the number of studies which have reported that P peaks earlier for easy compared with hard discriminations (e.g. Ritter et al., ; McCarthy and Donchin, ; Duncan‐Johnson, ; Ford et al., ). We have since twice replicated the null effect in this paradigm with independent samples of young adults, and we have some evidence that it is caused by varying discriminability within blocks rather than between blocks as is more commonly done. Furthermore, a comparison of these young adults with our healthy older control group provided evidence that P latency was at least sensitive to the effects of age. Consistent with numerous studies on aging (e.g. Mullis et al., ; Barrett et al., ; Pfefferbaum and Ford, ; Verleger et al., ), P peaked earlier for the young adults than the older ones.

For the analysis of peak amplitude, the P component was smaller on hard compared with easy discriminations and with complex as opposed to simple responses [F(1,22) =  and , respectively, P < ]. Thus, as the overall difficulty of the task increased, the amplitude of P decreased (easy/simple > easy/complex > hard/simple > hard/complex). Moreover, there was a reliable interaction between stimulus discriminability and response complexity [F(1,22) = , P < ]. Inspection of the means indicated that the amplitude difference between easy and hard discriminations was larger with simple responses than with complex ones.

Careful inspection of Fig. 4 also suggests differential activity for the two groups during the baseline window with the control group, showing a negative trend that was not apparent in the Parkinson’s disease waveforms. It is possible that this negative‐going waveform represents the tail end of the contingent negative variation, a component that has been shown to be reduced in amplitude in Parkinson’s disease patients (e.g. Wascher et al., ; Cunnington et al., a). To test whether this group difference in baseline activity was reliable in our study, we calculated the slope of the linear trend of Pz amplitude over the interval from – to 0, and found that the slope was statistically greater in the control group compared with the Parkinson’s disease group [F(1,22) = , P < ]. Although the present study was not specifically designed to look at prestimulus preparatory differences, this finding is compatible with the notion of strategic differences in attention between Parkinson’s disease patients and control subjects reported by Cunnington and colleagues (Cunnington et al., a). We would like to thank an anonymous reviewer for calling this difference to our attention.

Discussion

The present study used behavioural and psychophysiological measures—particularly the LRP—to investigate the RT slowing typically seen in Parkinson’s disease patients. In particular, we sought to isolate the slowing of motor processes and separate it from slowing due to cognitive deficits arising before the motor system has been activated by using a psychophysiological measure of motor preparation—the LRP. To that end, we also manipulated the difficulty of the choice reaction task both in terms of the processing required for stimulus identification (i.e. stimulus discriminability) and the processing required for performing the response (i.e. response complexity). We reasoned that increasing the difficulty of specific processes would be more likely to reveal any deficit in those processes present in Parkinson’s disease and that the differential effects of these manipulations on Parkinson’s disease patients versus control subjects would help to elucidate the specific processes affected by Parkinson’s disease.

Overall, Parkinson’s disease patients were slower to initiate responses than were age‐matched control subjects: the electrophysiological data allowed us to explore the processes responsible for this slowing. Because LRP onset reflects the moment that one response hand becomes more activated than the other hand, the onset latencies of the LRP relative to the stimulus (i.e. S‐LRP onset) and to the response (i.e. R‐LRP) can be used as estimates of the duration of premotor and motor processes, respectively. In the present study, the stimulus‐locked LRP began later for Parkinson’s disease patients relative to control subjects in all conditions. This indicates a deficit in some process or processes that occur prior to the engagement of the motor system when Parkinson’s disease patients are performing choice reactions. Thus, slower premotor processing contributed to the overall delay in RT for the Parkinson’s disease group. Given that the patients included in this study were in relatively early stages of the disease, this strongly suggests a cognitive component to Parkinson’s disease that is not attributable to a dementing process sometimes associated with the disease. Given the focused‐attention nature of the task, it is also unlikely to reflect stringent attentional demands.

In contrast to their general premotor slowing, Parkinson’s disease patients showed a deficit in motor processing speed only when performing the complex movement. With simple responses (i.e. single key presses), motor processing time for the Parkinson’s disease patients was equivalent to that of the control group. Thus, deficits in motor processing only became apparent when the motor requirements of the task were stringent. This pattern is no doubt due in part to the fact that the patients were in the relatively early stages of the disease.

The effects of discriminability provide further clues about which premotor stages are affected by Parkinson’s disease. First, there was no interaction of group with stimulus discriminability for either RT or S‐LRP onset latency. Secondly, P peak latency, which is often used as an estimate of the relative timing of stimulus evaluation processes, was approximately the same for Parkinson’s disease patients and control subjects across all conditions. Together, these findings suggest that Parkinson’s disease patients have no specific impairment in the perceptual processes responsible for size discrimination. Instead, it appears that the delay in premotor processing in Parkinson’s disease patients involves a deficit in some stage after stimulus evaluation but before hand‐specific motor activation.

One plausible interpretation of the premotor slowing is that Parkinson’s disease patients were slower at mapping the stimuli to their associated responses. More specifically, Parkinson’s disease patients may be quite able to distinguish among the different sizes but then might be slow at retrieving the correct response hand. Consistent with this possibility, Brown and colleagues (Brown et al., a) found that Parkinson’s disease patients were slowed more than control subjects by an incompatible assignment of stimuli to responses. Response selection may have been particularly difficult in the present experiment, because the assignment of stimuli to responses was arbitrary. There is evidence to suggest, however, that Parkinson’s disease patients are not selectively impaired in tasks with arbitrary stimulus‐response mappings (Zimmermann et al., ; Brown et al., b). Also contrary to the hypothesis of difficulties with response selection are findings that Parkinson’s disease patients are not selectively impaired by conflicting response‐related information (e.g. Cope et al., ). Clearly, further research is needed to isolate exactly which premotor stages of processing are slowed in Parkinson’s disease.

Two aspects of our results are at odds with a previously suggested explanation of the fact that Parkinson’s disease patients are sometimes, but not always slower, than control subjects in choice RT tasks. Based on their findings with transcranial magnetic stimulation, Pascual‐Leone and colleagues (Pascual‐Leone et al., a) suggested that ‘the main abnormality of Parkinson’s disease patients in a RT situation is the abnormally slow build‐up of premovement excitability’ (Pascual‐Leone et al., a; for similar suggestions, see Cunnington et al., b; Praamstra et al., ). Pascual‐Leone and colleagues went on to suggest that this could account for differences in the effects of Parkinson’s disease on RT:

In a cRT situation, the influence of this process on the RT will depend on the difficulty of the go‐signal discrimination … If the go‐signal is difficult to discriminate, it will take longer to complete the stimulus‐evaluation system and there will be enough time available for activation of the response channel, so that cRT will be normal. However, if the go‐signal is easy to discriminate, the abnormally slow activation of the response channel will become the main determinant of the RT and the cRT will be prolonged. (Pascual‐Leone et al., a)

According to the model that they suggested, Parkinson’s disease patients should be much slower, relative to control subjects, when the discrimination is easy compared with when it is difficult. This was clearly not the case in our data, because the patient/control RT difference was essentially the same regardless of discrimination difficulty. Moreover, with simple responses, the R‐LRPs were approximately equivalent for patients versus control subjects, suggesting that activation of the response channel was not prolonged in the present study. Thus, our data suggest that the complexity of the response is much more important than the difficulty of the stimulus discrimination in determining whether Parkinson’s disease patients will be slower than or equal to control subjects in a choice‐RT situation.

Stimulus discriminability

As expected, it took longer for subjects to respond to the intermediate‐sized squares (i.e. hard discrimination) compared with the more extreme, extra‐small and extra‐large squares (i.e. easy discrimination). This manipulation of stimulus discriminability, however, produced some surprising results for the electrophysiological data. Based on prior research, we expected to find an effect of discriminability on S‐LRP onset latency and no effect on R‐LRP duration—consistent with the notion that stimulus discriminability affects a relatively early premotor process (Smulders et al., ). Although we did find a later S‐LRP onset on hard than on easy trials, this difference could only account for a portion of the total RT effect. The remainder of the RT difference showed up as an effect on the duration of motor processes, with the duration of the R‐LRP being greater on hard compared with easy trials. As mentioned above, this pattern of LRP activity is not simply due to the inclusion of Parkinson’s disease patients because the effects were found in both groups. This unexpected result can most easily be interpreted in terms of a model in which preliminary partial output about stimulus size is transmitted from perceptual processes to motor processes so that motor activation can begin before perceptual analysis is finished (cf., Trevena and Miller, ). In that case, the duration of motor activation will be increased if the motor process must wait for final perceptual output before a response is actually made (cf., Osman et al., ; Miller and Ulrich, ). With the difficult discrimination, it is easy to imagine that the final perceptual decision would be quite slow, perhaps due to rechecking, and that the response would be delayed even though preliminary motor preparation had been carried out. The main difficulty with this account is to explain why Smulders and colleagues (Smulders et al., ) did not find the same effect. One potentially important difference between the present study and that of Smulders and colleagues was that we intermixed easy and difficult discriminations randomly within each block, whereas they presented easy and difficult discriminations in separate blocks. This blocked versus mixed difference could cause various differences in processing, including strategy changes (Los, ). In the present case, it could be that participants transmit partial perceptual information when perceptual difficulty is mixed, but wait for full information when it is blocked.

The data indicate that early discrimination processes are normal in Parkinson’s disease patients tested while on medication, at least for discriminations based on object size. [There is evidence to suggest Parkinson’s disease patients may have problems discriminating small variations in contrast (e.g. Tebartz van Elst et al., ).] The RT difference between easy and hard trials was approximately equivalent for the two groups. Moreover, stimulus discriminability affected the LRP latencies similarly for patients and control subjects. The P component peaked at approximately the same time for the two groups as well, but this null result is somewhat difficult to interpret because P latency was also not affected by stimulus discriminability as was expected, and therefore calls into question the functional significance of P peak latency in the present experiment. Still, the RT and LRP data provide reasonable evidence that Parkinson’s disease patients do not have a specific deficit for stimulus discrimination processes.

Response complexity

The complexity of the response varied across blocks such that on some blocks subjects responded with a simple, one key press response and on the other blocks they responded with a more complex, three key press sequence. Consistent with prior research, RT for the complex response was longer than for the simple response (Henry and Rogers, ; Sternberg et al., ). The increase in RT on complex trials was related to an increase in the duration of motor‐related processing. The duration of the R‐LRP was longer prior to complex as opposed to simple movements. Moreover, the onset of the S‐LRP occurred with the same latency for both levels of response complexity suggesting that response complexity had no effect on earlier premotor processing. This pattern of results extends the findings of Smulders and colleagues (Smulders et al., ) to include older adults and supports the notion that complex movements require more time for motor‐related activity than is necessary for simple movements, and that these processes occur prior to the initiation of the first movement.

The complex response requirement clearly created greater difficulties for the Parkinson’s disease patients than for the control subjects. First, as noted in Methods, most of the patients required additional training to learn the sequential pattern adequately. Secondly, the group difference in RT was much more pronounced when subjects were performing the complex, three key press sequence than when performing the simpler one key press response. Thirdly, R‐LRP data showed clearly that complex responses slowed motor processing to a much greater extent for patients than for controls. Finally, patients were slower, and more error prone, when executing the second and third key presses of the sequence—a finding that is consistent with one of the cardinal features of Parkinson’s disease known as bradykinesia or slowness of movement. Together, these results support previous reports indicating that Parkinson’s disease patients may have a selective impairment for performing sequential movements (Stelmach et al., ; Agostino et al., ; Weiss et al., ; Low, ; in contrast, see Rafal et al., ).

In conclusion, the motor‐related potentials observed in this study clearly reveal increases in the duration of both premotor and motor processes in Parkinson’s disease patients performing a choice RT task, as compared with control subjects. These results provide clear evidence that previously observed RT slowing in Parkinson’s disease patients stems from both motor and premotor or cognitive components, even in early stages of the disease. The technique of partitioning the RT interval into components before as against after the onset of motor‐related activity appears to be a very promising tool in the quest to understand the nature of this and other disease processes that affect RT.

Acknowledgement

This research was supported by an Otago Research Grant to J.M.

Fig. 1 The effects of stimulus discriminability (easy versus hard) and response complexity (one key press versus three key press sequence) on grand average S‐LRP and R‐LRP for each group separately. In the stimulus‐locked figures, time 0 corresponds to the onset of the reaction stimulus. In the response‐locked figures, time 0 corresponds to the moment of the initial key press. PD patients = Parkinson’s disease patients.

Fig. 1 The effects of stimulus discriminability (easy versus hard) and response complexity (one key press versus three key press sequence) on grand average S‐LRP and R‐LRP for each group separately. In the stimulus‐locked figures, time 0 corresponds to the onset of the reaction stimulus. In the response‐locked figures, time 0 corresponds to the moment of the initial key press. PD patients = Parkinson’s disease patients.

Fig. 2 Comparisons of Parkinson’s patients (PD, solid lines) and control subjects (CTL, dashed lines) on S‐LRPs and R‐LRPs for each level of stimulus discriminability and response complexity.

Fig. 2 Comparisons of Parkinson’s patients (PD, solid lines) and control subjects (CTL, dashed lines) on S‐LRPs and R‐LRPs for each level of stimulus discriminability and response complexity.

Fig. 3 Grand average response‐locked EMG activity for Parkinson’s patients (PD patients) and control subjects. Time 0 corresponds to the moment of the initial key press. Responding arm (top panels) refers to the muscle corresponding to the hand indicated by the reaction stimulus, whereas non‐responding arm (bottom panels) refers to the muscle activity of the hand opposite to that indicated by the reaction stimulus.

Fig. 3 Grand average response‐locked EMG activity for Parkinson’s patients (PD patients) and control subjects. Time 0 corresponds to the moment of the initial key press. Responding arm (top panels) refers to the muscle corresponding to the hand indicated by the reaction stimulus, whereas non‐responding arm (bottom panels) refers to the muscle activity of the hand opposite to that indicated by the reaction stimulus.

Table 1

Mean reaction time and percentage of correct responses as a function of discrimination difficulty and response complexity for Parkinson’s patients and control subjects

Discrimination 
Easy Hard 
Simple response Complex response Simple response Complexresponse 
Reaction time (ms) Parkinson’s patients     
Control subjects     
Percentage of correct responses Parkinson’s patients     
Control subjects     
Discrimination 
Easy Hard 
Simple response Complex response Simple response Complexresponse 
Reaction time (ms) Parkinson’s patients     
Control subjects     
Percentage of correct responses Parkinson’s patients     
Control subjects     

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Table 1

Mean reaction time and percentage of correct responses as a function of discrimination difficulty and response complexity for Parkinson’s patients and control subjects

Discrimination 
Easy Hard 
Simple response Complex response Simple response Complexresponse 
Reaction time (ms) Parkinson’s patients     
Control subjects     
Percentage of correct responses Parkinson’s patients     
Control subjects     
Discrimination 
Easy Hard 
Simple response Complex response Simple response Complexresponse 
Reaction time (ms) Parkinson’s patients     
Control subjects     
Percentage of correct responses Parkinson’s patients     
Control subjects     

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Table 2

Mean movement times and percentage of correct movement sequences in blocks with complex responses for Parkinson’s patients and control subjects

Discrimination 
Easy Hard 
Mean movement time from the first to second key press MT‐1 (ms) Parkinson’s patients   
Control subjects   
Mean movement time from the second to third key press MT‐2 (ms) Parkinson’s patients   
Control subjects   
Percentage of correct responses Parkinson’s patients   
Control subjects   
Discrimination 
Easy Hard 
Mean movement time from the first to second key press MT‐1 (ms) Parkinson’s patients   
Control subjects   
Mean movement time from the second to third key press MT‐2 (ms) Parkinson’s patients   
Control subjects   
Percentage of correct responses Parkinson’s patients   
Control subjects   

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Table 2

Mean movement times and percentage of correct movement sequences in blocks with complex responses for Parkinson’s patients and control subjects

Discrimination 
Easy Hard 
Mean movement time from the first to second key press MT‐1 (ms) Parkinson’s patients   
Control subjects   
Mean movement time from the second to third key press MT‐2 (ms) Parkinson’s patients   
Control subjects   
Percentage of correct responses Parkinson’s patients   
Control subjects   
Discrimination 
Easy Hard 
Mean movement time from the first to second key press MT‐1 (ms) Parkinson’s patients   
Control subjects   
Mean movement time from the second to third key press MT‐2 (ms) Parkinson’s patients   
Control subjects   
Percentage of correct responses Parkinson’s patients   
Control subjects   

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Table 3

Mean latency of EMG as a function of discrimination difficulty and response complexity for Parkinson’s patients and control subjects

Discrimination 
Easy Hard 
Simple response Complex response Simple response Complex response 
EMG (ms) Parkinson’s patients     
Control subjects     
Discrimination 
Easy Hard 
Simple response Complex response Simple response Complex response 
EMG (ms) Parkinson’s patients     
Control subjects     

Open in new tab

Table 3

Mean latency of EMG as a function of discrimination difficulty and response complexity for Parkinson’s patients and control subjects

Discrimination 
Easy Hard 
Simple response Complex response Simple response Complex response 
EMG (ms) Parkinson’s patients     
Control subjects     
Discrimination 
Easy Hard 
Simple response Complex response Simple response Complex response 
EMG (ms) Parkinson’s patients     
Control subjects     

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Sours: https://academic.oup.com/brain/article//9//

Reaction times and attention in Parkinson's disease.

Abstract

Ten patients with Parkinson's disease performed a simple reaction time task in which, on hearing a tone, they pressed a button with the left thumb. In the first experiment tones sometimes occurred unannounced and at other times were preceded (by between 0 and ms) by a warning signal. The second experiment was identical to the first except that the subject had simultaneously to perform a simple continuous task with his right hand. Patients had slower reaction times than controls under all circumstances. In general, however, the effect of a warning signal and the effect of a second task were the same for both groups. In the control group the effect of a warning signal depended on whether or not a second task was being performed. Specifically, the advantage of a warning signal for reaction time was lost after long intervals (greater than ms) when a second task was being performed. Parkinson's disease patients lost this advantage even when they were not performing a second task. Animal studies have suggested that dopamine deficiency results in an increase in neural "noise" in the basal ganglia. The behavioural consequences of this may be that Parkinson's disease patients always perform as if they were carrying out another task at the same time. In contrast, their ability to benefit from a warning signal and to allocate attentional resources are unimpaired.

Full text

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Selected References

These references are in PubMed. This may not be the complete list of references from this article.

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Reaction parkinsons time disease

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Parkinson's Disease: The Basics

A rented apartment in Kiev is not a problem. I have prepared everything from champagne with fruits to gentle music. We drank not long and a little.

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I slowly entered the room: Seryozha, what are you doing. Let her go immediately. - I exclaimed, but inside me everything shrank. For a moment I thought that I could have been in Lena's place.



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