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Functional compensation or pathology in cortico-subcortical interactions in preclinical Huntington’s disease? Christian Beste a,b,∗,1 , Carsten Saft a,1 , Juliana Yordanova c , J¨urgen Andrich a , Ralf Gold a , Michael Falkenstein b , Vasil Kolev c b
a Department of Neurology, Huntington Centre NRW, St. Josef Hospital, Ruhr-University Bochum, Germany Leibniz Research Centre for Working Environment and Human Factors, WHO Collaborating Centre for Occupational Health and Human Factors, Dortmund, Germany c Institute of Physiology, Bulgarian Academy of Science, Sofia, Bulgaria
Received 5 February 2007; received in revised form 18 May 2007; accepted 12 June 2007 Available online 24 June 2007
Abstract Huntington’s disease (HD) is an autosomal dominant neurological disorder, with degeneration amongst others affecting the basal ganglia dopaminergic system. Recent findings suggest compensatory as well as pathogenetic mechanisms mediated via the adenosine receptor system in the presymptomatic stage (pHD) of HD. The adenosine receptor system is functionally related to the dopaminergic system. In this study, we assessed error processing, a dopamine-dependent cognitive function, using an event-related potential the error negativity (Ne/ERN) in pHD and controls. This was done by means of a flanker task. The Ne consists of a cognitive and a motor component, expressed via different frequency bands. Time–frequency decomposition of the Ne into delta and theta sub-components was applied to assess if degeneration or compensation predominantly involve cognitive or motor processes. No parameter of the behavioral data (reaction times, error frequency, corrections, post-error slowing) differed between the groups. A selective increase in the power of the cognitive delta-Ne component was found in pHD relative to controls inversely related to the estimated age of onset (eAO). Thus, the increase in the power of the cognitive delta-Ne component was stronger in pHD with an earlier eAO. An earlier eAO implies stronger pathogenetic mechanisms. Due to the behavioral data our results speak for a solely cognitive compensating-mechanism controlling performance monitoring in pHD. In contrast, correlations with eAO suggest that the increase in delta-Ne activity is also related to pathogenesis. It is proposed that compensation is a transient effect of the whole pathogenetic dynamics of HD, with these two processes not foreclosing each other. © 2007 Elsevier Ltd. All rights reserved. Keywords: Event-related potential (ERP); Error negativity (Ne/ERN); Time–frequency decomposition; Performance monitoring; Huntington’s disease; Flanker task
1. Introduction Huntington’s disease (HD) is an autosomal, dominant inherited neuropsychiatric disorder typically characterized by chorea and complex involuntary movements. Cognitive deterioration and neuropsychiatric symptoms including depression, suicidal behavior, mania, psychotic symptoms and apathy are also evident (see for review, Craufurd & Snowden, 2002). Genetically, ∗ Corresponding author at: Leibniz Research Centre for Working Environment and Human Factors, WHO Collaborating Centre for Occupational Health and Human Factors, Ardeystr. 67, D-44139 Dortmund, Germany. Tel.: +49 231 1084 212; fax: +49 231 1084 401. E-mail address:
[email protected] (C. Beste). 1 These authors equally to contributed this work.
0028-3932/$ – see front matter © 2007 Elsevier Ltd. All rights reserved. doi:10.1016/j.neuropsychologia.2007.06.004
HD is accompanied by an extension of the CAG-repeat length at the 4th chromosome encoding a large protein, huntingtin (Huntington’s Disease Collaborative Research Group, 1993). This protein accumulates and causes apoptotic neuronal death mainly in the striatum (Paulsen et al., 2001). Neuroanatomical pathology is not limited to the striatum but is also seen in other cortico-subcortical brain regions (for review: Gutekunst, Norflus, & Hersch, 2002). Among several neurotransmitter changes (Yohrling & Cha, 2002), a hallmark of HD is a reduction in D1 and D2 receptor density up to 60% (Turjanski, Weeks, Dolan, Harding, & Brooks, 1995), which is detected in both symptomatic and presymptomatic HD suggesting that degenerative processes already take place at early stages of HD (Augood, Faull, & Emson, 1997; Backman, Robins-Wahlin, Lundin, Ginovart, & Farde, 1997; Van Oostrom et al., 2005).
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In progressive movement disorders such as HD and Parkinson’s disease (PD), but also in neurodegenerative diseases in general, neurodegeneration can be counteracted by compensatory mechanisms (rev. Bezard, Gross, & Brotchie, 2003) delaying the clinical onset of (motor) symptoms. In HD, they have been recently suggested to take place at the adenosine A2A-receptor level (Tarditi et al., 2006), which is supported by several lines of research (e.g., Blum, Hourez, Galas, Popoli, & Schiffmann, 2003a; Blum et al., 2003b; Maglione et al., 2005), but A2A-receptors also have been proposed to play a role in HD pathogenesis (Jarabek, Yasuda, & Wolfe, 2004; see also Tarditi et al., 2006). From a pathophysiological perspective, it is important to mention that adenosine A2A-receptors and dopamine D2receptors are functionally related (e.g., Short, Ledent, Borrelli, Drago, & Lawrence, 2006; Svenningsson, Le Moine, Fisone, & Fredholm, 1999). Processes attributable to compensation are not only found at a neurobiological level, but also at a neurophysiological/neurocognitive one in association with the amount and regional distribution of functional activation. Using PET, Feigin et al. (2006) pointed out that additional brain areas are recruited in pHD to compensate for deficits and maintain performance in a motor sequence learning task. Also, Voermans et al. (2004), using functional MRI (fMRI), have accounted for a similar mechanism compensating for caudate atrophy. As patients enter the symptomatic stage, these compensatory mechanisms may ultimately fail and performance declines (Rosas, Feigin, & Hersch, 2004). Recently, another fMRIstudy has indicated that in a time-discrimination task, the medial prefrontal cortex of pHD was hyperactivated in pHD far from estimated age of onset (eAO), while it was hypoactivated in patients close to eAO (Paulsen et al., 2004). These results suggest for cortico-subcortical compensatory processes in HD. Another major function most likely relying on corticosubcortical interactions and especially on the medial prefrontal cortex is action monitoring and processing of errors (Gehring & Knight, 2000; Ridderinkhof, Ullsperger, Crone, & Nieuwenhuis, 2004). Error processing can be examined by means of event-related (response-related) potentials (ERPs, RRPs). Different types of errors generate a phasic negative component with fronto-central scalp maximum, the error negativity (Ne: Falkenstein, Hohnsbein, Hoormann, & Blanke, 1991) or errorrelated negativity (ERN: Gehring, Goss, Coles, Meyer, & Donchin, 1993). The Ne has classically been interpreted as the detection of a mismatch or conflict between response representations (Falkenstein, Hoormann, Christ, & Hohnsbein, 2000). In this respect, the Ne reflects an automatic mismatch between the neural representations of the actual error response and the planned correct response. Another theory, the reinforcement learning hypothesis (Holroyd & Coles, 2002) assumes a more general functional significance of the Ne and states that the midbrain DA-system evaluates evolving events including responses. If an event is worse than expected (i.e. an error), the basal ganglia detect this event and send a signal to the anterior cingulate cortex (ACC) via the DA-system, which in turn elicits the Ne. Multiple research lines confirm the importance of the
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dopaminergic system in the generation of the Ne (Holroyd & Coles, 2002; Holroyd & Yeung, 2003; Ridderinkhof et al., 2002). Given these associations of Ne with midbrain DA-system and medial frontal functions, the objective of the present study was to evaluate error processing in pHD as reflected by Ne. Regarding the processes (degeneration and/or compensation) that possibly occur in pHD, two hypothesis can be formulated: (i) The dopamine striatal system (D2-system) is dysfunctional in pHD (Augood et al., 1997; Backman et al., 1997; Van Oostrom et al., 2005) and/or the adenosine A2A-receptors express their pathogenic effects. Accordingly, the Ne should be reduced in pHD compared to healthy controls, since a suppression of the DA system and cortico-subcortical interaction by pathological or pharmacological factors has been consistently shown to decrease the Ne (de Bruijn, Sabbe, Hulstjin, Ruigt, & Verkes, 2006; Zirnheld et al., 2004) as has recently been shown in symptomatic HD (Beste, Saft, Andrich, Gold, & Falkenstein, 2006; Beste et al., in press). (ii) On the contrary, compensatory mechanisms mediated via the A2A-receptors in pHD may enhance the DA-mediated activation, which could be accompanied by a larger Ne in pHD. In relation to these major hypotheses, the following specific questions were addressed: (1) Does compensation or degeneration in pHD occur primarily at the level of cognitive control, motor response control, or both? This is of special interest, since it has been suggested by Smith, Brandt, and Shadmehr (2000) that deficits in error-feedback processing may be the origin of motor dysfunction in HD. To approach this question a time–frequency decomposition (TF) of Ne was applied because a recent study (Yordanova, Falkenstein, Hohnsbein, & Kolev, 2004a) has indicated that the Ne consists of two subcomponents. A sub-component from the delta frequency band (1.5–3.5 Hz) was related to error-specific monitoring at the cognitive level, and a second sub-component from the theta frequency band (4–8 Hz) was associated with motor response monitoring. (2) Do putative compensatory mechanisms involve the recruitment of additional brain areas (Feigin et al., 2006; Voermans et al., 2004), which may produce specific scalp-topography patterns of the Ne in pHD? In addition to analysis of Ne scalp distribution, regional compensation was assessed at the level of movement activation processes by analyzing the lateralised readiness potentials (LRPs) (Yordanova, Kolev, Hohnsbein, & Falkenstein, 2004b). (3) Is there a relationship between the Ne and parameters of neurodegeneration? In the presymptomatic phase, the point in time when the disease becomes manifest is of clinical relevance (Aylward et al., 2004). This “estimated age of onset (eAO)” is calculated with respect to the CAG-repeat length and the parents’ age of onset (Aylward et al., 2004; Ranen et al., 1995). 2. Materials and methods 2.1. Participants A group of 11 right-handed presymptomatic gene mutation carriers defined by a positive gene test and absence of clinical symptoms (pHD, N = 11) from 22
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Table 1 Descriptive analysis of CAG-repeat number, estimated age of onset (eAO), duration until eAO, TFC-, IS- and motor score (MS), ages, sex as well as a synopsis of performance to the control group in different neuropsychological tests Test
pHD
Controls
Significance
Mean (S.D.)
Range
Mean (S.D.)
Range
Age Sex CAG Estimated age of onset (eAO) Duration until estimated age of onset Motor score (UHDRS) (MS) IQ (MWT-B)
33.70 (9.69) 6 males/5 males 42.81 (1.77) 45.54 (4.87) 10.81 (7.96) 0.55 (0.52) 110 (12.12)
22–50
23–49
ns
39–46 37.5–53.28 1.28–23.28 0–1 95–130
34.00 (9.89) 5 males/4 males NA NA NA NA 114 (10.11)
NA NA NA NA 98–130
ns
Stroop test (UHDRS) Color naming Color reading Interference
79.70 (4.54) 96.60 (6.78) 50.00 (5.05)
72–86 80–100 42–59
80.11 (3.01) 94.66 (5.70) 53.33 (3.33)
75–85 85–100 48–59
ns ns ns
Symbol-digit test (WAIS) Word fluency (Benton)
54.50 (5.94) 44.40 (15.51)
45–65 30–85
55.11 (4.04) 48.55 (10.12)
50–61 31–61
ns ns
Digit span (WMS-R) Forward Backward
9.11 (2.02) 8.22 (2.04)
5–12 6–11
9.33 (1.11) 8.33 (0.86)
8–11 7–10
ns ns
Block span (WMS-R) Forward Backward
8.00 (1.87) 8.00 (1.80)
5–11 6–12
8.88 (0.92) 8.22 (0.66)
7–10 7–9
ns ns
Benton test (visual memory)
14.00 (0.70)
13–15
13.77 (0.83)
13–15
ns
6–10 10–15 6–10 8–15 8–15
7.66 (1.73) 13.88 (0.92) 8.22 (0.97) 12.22 (1.56) 11.77 (1.30)
5–10 12–15 7–10 9–14 9–13
ns ns ns ns ns
27–30 0–14
29.66 (0.50) 3.44 (3.74) NA NA
29–30 0–12 NA NA
ns ns
American verbal learning test (AVLT) [German equivalent was used; VLMT] Immediate word span 7.33 (1.32) Final acquisition level 13.77 (1.56) Interference list 7.88 (1.26) Postintergference recall I 12.33 (2.06) Postintergference recall II 12.77 (1.98) Mini mental status examination (MMSE) Beck depression inventory TFC IS
29.18 (0.87) 4.45 (4.03) 13 100
NA, not applicable.
to 50 years of age (M = 33.70; S.D. = 9.69) were recruited. All patients agreed to be videotaped in order to document their neurological status. Values of CAGrepeat length, the estimated age of onset (eAO) (Aylward et al., 2004; Ranen et al., 1995), duration until eAO, scores of neurological and psychiatric investigation (UHDRS motor score, TFC, IS, BDI, MMSE) as well as performance in neuropsychological investigation is given in Table 1 in comparison to healthy controls. Furthermore nine healthy controls (N = 9) from 23 to 49 years of age were recruited (mean = 34.35; S.D. = 5.19) and underwent the same neuropsychological and psychiatric assessment. For description and comparison of results see Table 1. Both groups had a comparable educational background. All participants gave written informed consent. The study was approved by the Ethics Committee of the Ruhr-University Bochum, Germany. Table 2.
2.2. Stimuli and procedure To measure error-processing we used a Flanker Task (Kopp, Rist, & Mattler, 1996) which reliably yields a high percentage of errors. In here vertically arranged visual stimuli were presented on a PC monitor. The target-stimulus (white arrowhead or circle) was presented in the center of a black background with the arrowhead pointing to the right or left. These target-stimuli were flanked by two vertically adjacent arrowheads which pointed in the same (compatible) or opposite (incompatible) direction of the target stimulus. The flankers preceded
the target by 100 ms to maximize premature responding to the flankers, which would result in errors in the incompatible and Nogo condition. The target was displayed for 300 ms. The response-stimulus interval was 1600 ms. Flankers and target were switched off simultaneously. Time pressure was administered by asking the subjects to respond within 550 ms, which additionally enhances the likelihood of errors. In trials with reaction times exceeding this deadline a feedback stimulus (1000 Hz, 60 dB SPL) was given 1200 ms after the response; this stimulus had to be avoided by the subjects. Four blocks of 105 stimuli each were presented in this task. Compatible (60%) and incompatible stimuli (20%) and Nogo-stimuli (circle) (20%) were presented randomly. The subjects had to
Table 2 Numerical values pf the Ne delta- and theta-frequencies as well as r-LRP peak amplitude Test
pHD
Controls
Significance
Ne (peak amplitude) (V/m2 ) r-LRP (peak amplitude) (V/m2 ) Theta-Ne (power) Delta-Ne (power)
136.22 (82.91)
95.08 (50.14)
ns
23.35 (0.43)
20.17 (0.47)
ns
4.86 (0.18) 5.14 (0.14)
4.40 (0.19) 4.60 (0.16)
ns .2) between the pHD (340.9 ± 8.45 ms) and the control group (329.2 ± 5.17 ms). The same was found for error trials (pHD: 284.9 ± 13.93 ms; controls: 260.6 ± 13.79 ms; F(1,18) = 1.50, p > .2). Both groups showed comparable error rates (pHD: 25.72 ± 2.04; controls: 29.11 ± 2.89; F(1,18) = 0.95; p > .3). RTs of correct responses after an error (post-RT) can be used to assess the behavioral consequences of an error. Post-error slowing is seen as an indicator for behavioral adaptation after errors. Therefore we subjected the mean RT of all correct responses (c-RT) and those after an error (post-RT) as two levels of a withinsubjects factor to a repeated measures ANOVA with group as a between-subjects factor. Post-RTs (344.51 ± 8.59 ms) were significantly longer than c-RTs (331.25 ± 6.68 ms; F(1,18) = 6.54; p = .02). No significant interaction with the factor group was obtained (F(1,18) = 1.61; p > .2). Group means of c-RTs were 340.97 ± 8.97 ms for pHD and 321.54 ± 9.91 ms for controls. Group means of post-RTs were 347.65 ± 11.53 ms for pHD and 341.37 ± 12.74 ms for controls. The behavioral relevance of errors can also be measured by the number of error corrections. In the present study, the frequency of corrections did not differ (F(1,18) = 0.01; p > .9) between groups (pHD: 5.81 ± 2.23 ms; controls: 5.44 ± 2.46 ms). In sum, behavioral indices did not differ across the groups.
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Fig. 1. (A) Grand average RRPs (time domain) from FCz for the groups of controls (n = 9) and pHD (n = 11). Positivity upwards, CSD values. The vertical line at time 0 represents the moment of response production (error response). Note the expression of Ne about 100 ms after the response. (B) Time–frequency representation of RRP wavelet power (lower panel) and amplitude (upper panel) for two groups: controls and pHD. Response production appears at time 0. (C) Maps of Ne power at its maximum for two groups (controls, pHD) and frequency ranges (delta, theta). log10 -transformed power values are presented. For the sake of best topography visualization, scaling is different for separate maps and is limited between the values a and b, designated under the corresponding plots.
3.2. Response-related potentials: Ne For description of the data the mean and standard error of the mean (SEM) are given. The Ne of pHD and controls is illustrated in Fig. 1A. Despite the larger Ne seen in Fig. 1 for pHD (136.22 ± 82.91 V/m2 ) relative to controls (95.08 ± 50.14 V/m2 ), the statistical analysis of Ne peak amplitudes revealed that the groups did not differ (F(1,18) = 1.69; p > .2). As stated in Section 1, the Ne is supposed to consist of frequency-specific sub-components, a motor and a cognitive one. Response-related potentials were decomposed in the time–frequency (TF) domain by means of wavelet transform (WT, Yordanova et al., 2004a). TF decomposition plots are presented in Fig. 1B. The maps of the Ne in the delta- and theta-frequency band are illustrated in Fig. 1C.
As can be seen from these maps, the topography of the delta-Ne and theta-Ne was characterized by a frontal-central maximum and did not differ between the groups. For further analysis, the maximal log-transformed power values of delta and theta Ne components at FCz were subjected to univariate ANOVAs with one between-subjects factor “group” (control versus pHD). The power of the delta-Ne differed significantly between the groups (F(1,18) = 5.97; p = .025), with the pHDgroup showing greater power (5.14 ± 0.14) than the control group (4.60 ± 0.16). For the theta-Ne this analysis did not reveal a significant group difference (F(1,18) = 2.91; p > .1), although numerical values differed in the same direction (pHD: 4.86 ± 0.18; controls: 4.40 ± 0.19). To explore a possible relationship of Ne and parameters of neurodegeneration (i.e. estimated age of onset (eAO)) correlation analyses were performed. For this analysis the power of
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Fig. 2. Response-related lateralized readiness potentials (LRPs) of the two groups (controls, pHD) for correct (left panel) and error (right panel) responses. Time 0 represents the moment of response production.
the extracted delta-component of the Ne was used. The power of the theta-component was ommitted, because of the lack of difference from the control group. The power of the isolated delta-frequency-spectra was inversely related to the estimated age of onset (eAO) (delta-Ne: r = −.787; p = .002), indicating that Ne was more prominent in patients showing an early eAO. When using the duration until the eAO no relation was found (delta-Ne: r = −.156; p > .3). 3.3. Response-related potentials: LRP Response-locked LRPs (r-LRPs) for correct and error trials were analyzed using the same ANOVA design. R-LRPs in correct and error trials are presented in Fig. 2. The analysis revealed that for the correct trials, the peak amplitude reflecting the strength of activation did not differ between the groups (F(1,18) = 0.001; p > .9; pHD: 22.35 ± 0.48 V; control: 22.70 ± 0.53 V). The same statistical outcome (F(1,18) = 0.001; p > .9) was obtained for the onsetlatency, giving the point in time when the movement is initiated (pHD: −143.81 ± 1.12 ms; control: −142.8 ± 1.24 ms). The analysis of the error trials revealed the same result. The peak amplitudes (pHD: 23.35 ± 0.43 V; control: 20.17 ± 0.47 V) did not differ between the groups (F(1,18) = 0.068; p > .7). The onset-latencies (pHD: −155.45 ± 3.19 ms; control: −164.44 ± 3.52 ms) did not differ between the groups (F(1,18) = 0.010; p > .9). The following table summarizes numerical values for Ne, delta and theta frequencies, R-LRP. 4. Discussion In the current study, we assessed error-processing in preclinical HD and controls by analyzing the Ne, which depends on cortico-subcortical interactions. In the time domain, the Ne did not differ between the groups. Time–frequency decomposition of the Ne by means of wavelet transform (Yordanova et al., 2004a) revealed that the groups differed significantly in the power of the delta-band, with the pHD-group showing greater power than the control group. No difference was found
in the theta-band, although a trend was seen. These results imply that the groups differed with respect to behavioral/cognitive monitoring, but not in motor response monitoring. This dissociation is additionally underlined by the fact that the r-LRPs (reflecting motor generation) did not differentiate the groups. Further, the groups did not differ with respect to any behavioral parameters. Such a dissociation between an increased electrophysiological activity reflecting enhanced cognitive monitoring and comparable behavioral performance may be interpreted in terms of compensation occurring in functional cortico-subcortical circuits. Such a compensatory mechanism may counteract preclinical neuronal dysfunction or even neuron loss detectable by MRI in pHD (e.g., Aylward et al., 2004; Kassubek et al., 2004; Thieben et al., 2002). Residual neurons may perform functions previously carried out by the entire population (Bezard & Gross, 1998) so that performance (behavior) is maintained at a level comparable to that of healthy controls (see also Feigin et al., 2006; Paulsen et al., 2004). The maps (Fig. 1C) denote that the regional distribution of activation did not differ between the groups, suggesting that a compensation is not further modulated by a reorganization of the interplay between different brain areas. Yet, the groups included in this study are not extended. Therefore, the generalizability should be treated cautious, due to a possible weakness in statistical power. Even though compensation in motor function cannot be fully excluded, it may not be as advanced and functionally relevant as the cognitive one. Additionally, the behavioral data was very stable, justifying the interpretation put forward here. Our finding of an error-related compensatory mechanism underlines the assumed importance of this cognitive process in HD (Smith et al., 2000). Yet, in contrast to our results, Smith et al. (2000) reported a dysfunction of error-processing in their pHD-group. This interpretation was based on the observation of performance differences in late stages of a complex reaching movement. At the early stages of movements there were no group differences. In our study we concentrated on simple reactions and not on complex movements and also on early processing stages. This might be a reason for the observed difference.
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4.1. Possible mechanisms
4.2. Perspectives and conclusion
According to the major results, the Ne delta-component was increased in pHD patients. The correlational analysis revealed that this increase was more expressed in patients with an earlier estimated age of HD onset. Due to comparable behavioral data of the groups, the enhancement of Ne delta power can be treated as a compensating mechanism occurring in functional cortico-subcortical circuits. In this perspective, the compensation is more intense in subjects with an earlier expected onset of the disease. However, Paulsen et al. (2004) argued that a hyperactivation may reflect early abnormal cell processes that are direct negative effects of the genetic alteration (Paulsen et al., 2004). Hyperactivation may therefore also be regarded as an expression of pathogenesis. Paulsen et al. (2004) state that this interpretation is supported by electrophysiological findings in the R6/2 transgenetic mice model of HD accounting for an increased electrophysiological activity coupled to pathogenic factors. In contrast to eAO, no relation was found between the “duration until eAO” and Ne. One reason may be that in neurodegenerative diseases (like HD), progression may not be a steady, continuous linear process, which is supported by the fact that neurodegeneration may follow exponential rather than linear decline as revealed in animal models (Clarke et al., 2000) as well as in man (Peruz & Windle, 2001). Since the increase in Ne-power may be attributed to both compensation and pathogenesis, a paradoxon appears. Tarditi et al. (2006) theorized about a similar paradoxon regarding the possible compensatory (Blum et al., 2003a,b; Maglione et al., 2005) but also pathogenic role (Blum et al., 2003b; Jarabek et al., 2004) of the adenosine system. Convergent findings suggest that compensatory mechanisms in pHD may be mediated via the adenosine A2A- or A1-receptors (Blum et al., 2003b; Maglione et al., 2005; Tarditi et al., 2006). However, adenosine A2Areceptors are also supposed to exhibit pathogenic effects (e.g., Blum et al., 2003b; Jarabek et al., 2004). Accordingly, Tarditi et al. (2006) suggest that an upregulation of A2A-receptors may in time result in an increased vulnerability of striatal neurons to degeneration. Thus, the upregulation mechanism may cause its own decline. Carried over to the current results it may be hypothesized that an increase in neuronal activity as reflected by Ne may have transiently “compensating effects” for the neurodegenerative processes, but over time, this compensation may induce its own decline causing the beginning of the symptomatic stage (see, relation to the eAO). As such, the increase in neuronal activity may be considered as both a compensatory and a pathogenic mechanism. Compensatory effects may be a transient phenomenon of the whole pathogenic process. Since the Ne most likely relies on the dopaminergic system (e.g., Holroyd & Coles, 2002; Holroyd & Yeung, 2003; Ridderinkhof et al., 2002), we further suggest that the increase of the Ne deltacomponent might be mediated via the adenosine–dopamine interactions. Yet, these considerations about the possible mechanisms and relationships must remain theoretical, since they cannot be directly resolved by the methods of the study.
Based upon the current observations, several lines of further research can be outlined. Like Paulsen et al. (2004), we evaluated a function of the medial prefrontal cortex (Gehring & Knight, 2000; Ridderinkhof et al., 2004) and accounted for a regionally localized increased neuronal activity likely associated with compensation. Other studies, assessing different brain functions, point to compensation via recruitment of additional brain areas (Feigin et al., 2006; Voermans et al., 2004). Future research should address the basic principles of these differences in compensation. As the increase in neuronal activity also implies a relation with pathogenesis (relation to the eAO) current research on the pathogenesis of HD (for review, Beal & Ferrante, 2004) should be intensified with special focus on possible presymptomatic “compensation” mechanisms and cognition. These different lines of interdisciplinary research may reasonably be conducted in a longitudinal manner in humans and animals to gain more insight into the time course of such mechanisms in disease development thereby combining different assessment techniques. In summary, we accounted for an increase in power of the cognitive component of the Ne. With regard to the possible contribution of both compensation and pathogenic degeneration to Ne increase, it is suggested that compensatory mechanisms in pHD may be a temporary phenomenon/effect as a part of the whole pathogenic process and that compensation and pathology are not processes that exclude each other. Acknowledgements This work was supported by a grant from FoRUMAZ-F479 2005, Ruhr University Bochum and a grant from the National Research Council by the Ministry of Education and Science, Sofia, Bulgaria (Project L-1501/2005 to JY). The previous support of Prof. Przuntek and his great enthusiasm in founding the HD unit are gratefully acknowledged. We thank all participants for their participation. We thank L. Blanke for committed technical assistance and V. Boyd for linguistic improvements to the manuscript. References Augood, S. J., Faull, R. L., & Emson, P. C. (1997). Dopamine D1 and D2 receptor gene expression in the striatum in Huntington’s disease. Annals of Neurology, 42, 215–221. Aylward, E. H., Sparks, B. F., Field, K. M., Yallapragada, V., Shpritz, B. D., Shpritz, B. D., et al. (2004). Onset and rate of striatal atrophy in preclinical Huntington disease. Neurology, 63, 66–72. Backman, L., Robins-Wahlin, T. B., Lundin, A., Ginovart, N., & Farde, L. (1997). Cognitive deficits in Huntington’s disease are predicted by dopaminergic PET markers and brain volumes. Brain, 120, 2207–2217. Beal, M. F., & Ferrante, R. J. (2004). Experimental therapeutics in transgenic mouse models of Huntington’s disease. Nature Reviews Neuroscience, 5, 373–384. Beste, C., Saft, C., Andrich, J., Gold, R., & Falkenstein, M. (2006). Error processing in Huntington’s disease. PloS One, e86. Beste, C., Saft, C., Konrad, C., Andrich, J., Habbel, A., Schepers, I., et al. (2007). Levels of error processing in Huntington’s disease: A combined
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