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Benign hereditary chorea as an experimental model to investigate the role of medium spiny neurons for response adaptation
Neuropsychologia ∎ (∎∎∎∎) ∎∎∎–∎∎∎
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Benign hereditary chorea as an experimental model to investigate the role of medium spiny neurons for response adaptation Christian Beste a,n, Carsten Saft b a Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine of the TU Dresden, Schubertstrasse 42, D-01309 Dresden, Germany b Department of Neurology, St. Josef Hospital, Ruhr-University Bochum, Germany
art ic l e i nf o
a b s t r a c t
Article history: Received 10 January 2014 Received in revised form 28 April 2014 Accepted 5 May 2014
Processing errors is a major requirement for behavioral adaptation. While it has been assumed that the basal ganglia play an important role in initiating these processes, the role of the striatal microstructure for these processes remains to be uncovered. Previous studies in basal ganglia diseases could not elucidate the relevance of the striatal medium spiny neuron (MSN) microstructure unambiguously because structural alterations occur together with alterations in various neurotransmitter systems. We present and examine a possible model that allows the examination of MSN dysfunction unbiased by other modulations, i.e. a case of ‘benign hereditary chorea’ (BHC) in comparison to healthy controls. We apply event-related potentials (ERPs) to uncover the underlying neurophysiological mechanisms underlying post-error behavioral adaptation. The BHC patient revealed a smaller error-related negativity (ERN) together with almost absent behavioral adaptation after an error and generally more error-prone behavior. Performance monitoring processes unrelated to errors, as well as response inhibition processes, were not affected in the BHC patient. The results suggest that the striatal MSN microstructural integrity is more important for error-related behavioral adaptation than for other response monitoring processes unrelated to errors. & 2014 Published by Elsevier Ltd.
Keywords: Medium spiny neurons (MSNs) Performance monitoring Benign hereditary chorea EEG Basal ganglia
1. Introduction The striatum plays a pivotal role in response selection (Redgrave, Prescott, & Gurney, 1999) and behavioral adaptation after response errors. From a neurophysiological point of view, the error-related negativity (ERN) (Gehring, Goss, Coles, Meyer, & Donchin, 1993; Falkenstein, Hohnsbein, Hoormann, & Blanke, 1991) has been shown to drive processes of post-error behavioral adaptation (Debener, Ullsperger, Siegel, Fiehler, Von Cramon & Engel, 2005). It has been assumed that the basal ganglia compare neural representations of the actual and the desired outcome of an action (e.g. Falkenstein, Hoormann, Christ, & Hohnsbein, 2000; Gehring et al., 1993; Scheffers, Coles, Bernstein, Gehring, & Donchin, 1996). In case of a mismatch, an error signal is sent to the anterior cingulate cortex (ACC) via the dopamine system, which in turn elicits the ERN and induces corrective actions (Huster, Enriquez-Geppert, Wollbrink, Kugel, Konrad & Pantev, 2011; Debener et al., 2005; Holroyd & Coles, 2002). In this sense it is a comparison between the desired and the actual outcome occurring in the basal ganglia that builds the basis for error-related behavioral adaptation. Consequently, diseases affecting
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Corresponding author. Tel.: þ 49 351 458 7072; fax: þ49 351 458 7318. E-mail address: [email protected] (C. Beste).
the basal ganglia, such as Parkinson's and Huntington's disease, have been shown to compromise error processing (e.g. Beste, Willemssen, Saft, & Falkenstein, 2009; Willemssen, Müller, Schwarz, Falkenstein, & Beste, 2009). However, besides such structural changes, the dopamine system also shows strong concomitant alterations in these diseases. Since the dopamine system is of known importance for error processing (Jocham & Ullsperger, 2008; Frank, D'Lauro, & Curran, 2007; Krämer et al., 2007), there is no clear-cut experimental evidence for a role of the striatal microstructure for these processes. A possible experimental model disease to examine the contribution of striatal microstructure for cognitive functions in humans unbiased of influences by other neurotransmitter systems may be benign hereditary chorea (BHC) (Beste, Humphries & Saft, 2014; Beste & Saft, 2013). Benign hereditary chorea (BHC) is a rare autosomal dominant neurological disease (prevalence 1– 2:1.000.000) related to mutations in the TTF1 gene on chromosome 14q13 encoding the thyroid transcription factor-1 (also known as TITF1, TEBP or NKX2-1) (for review: Inzelberg & Weinberger, 2011; Kleiner-Fisman & Lang, 2007). A hallmark of this disease is a circumscribed dysgenesis of striatal structures and medium spiny neurons (MSNs) (Yoshida, Nunomura, Shimohata, Nanjo, & Miyata, 2012; Sussel, Marin, Kimura, & Rubenstein, 1999). This is supported by post-mortem studies which also show that other brain structures are not or only unsystematically affected
http://dx.doi.org/10.1016/j.neuropsychologia.2014.05.004 0028-3932/& 2014 Published by Elsevier Ltd.
Please cite this article as: Beste, C., & Saft, C. Benign hereditary chorea as an experimental model to investigate the role of medium spiny neurons for response adaptation. Neuropsychologia (2014), http://dx.doi.org/10.1016/j.neuropsychologia.2014.05.004i
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Table 1 Neuropsychological test data (raw scores) of the BHC case and the group of healthy controls. Testing possible differences between BHC case and controls revealed no difference in any of the tests applied. The UHDRS motor score is necessarily different, as the controls do not suffer under the genetic mutation causing BHC. Test
BHC
Controls
UHDRS (motor score) IQ score (MWT-B) Stroop test (UHDRS) Color naming Color reading Interference (Stroop condition) Symbol-digit test (WAIS) correct items (Härting et al., 2000) Word fluency (Benton) number of words Digit span (WMS-R) number of items (Härting et al., 2000) Forward Backward Block Span (WMS-R) number of items (Härting et al., 2000) Forward Backward Benton Test (visual memory) (Benton, Benton-Sivan, & Steck, 2009) Mini Mental Status examination (MMSE) (Folstein, Folstein, & McHugh, 1975)
26 105
0 113 (9)
(for review: Inzelberg & Weinberger, 2011). Clinically, BHC is associated with choreatic movements. As MSN dysfunctions are not modulated by otherwise dysfunctional neurotransmitters in BHC, this disease may serve as a possible experimental model drawn from disease to uncover the relevance of the striatal microstructure for cognitive functions. The striatal microstructure may be important because a fundamental step in error processing is the comparison between the desired and the actual outcome (Gehring et al., 1993; Scheffers et al., 1996). To do so, the basal ganglia must be able to represent different outcomes in order to compare them to each other. Several computational basal ganglia models suggest that MSN is important for this process (Bar-Gad et al., 2003; Plenz, 2003). In BHC it is possible that error processing functions are diminished because a comparison of the actual outcome of an action against the desired outcome of action may not be possible at the basal ganglia level because of low microstructural integrity of MSNs. Therefore, behavioral adaptation after an error may be diminished. The current study sets out to test this hypothesis in a clinical case-control study.
61 98 36 51 39
75 (6) 101 (15) 40 (10) 59 (12) 42 (8)
8 5
9 (3) 5 (4)
8 7 11 30
9 (3) 7 (3) 13 (3) 30
2011) and was structured as follows: vertically arranged visual stimuli were presented. The target-stimulus (arrowhead or circle) was presented in the center with the arrowhead pointing to the left or right. The central stimuli were flanked by two vertically adjacent arrowheads which pointed in the same (compatible) or opposite (incompatible) direction as the target. In case of target stimuli (arrowheads pointing to the left or right) participants were required to press a response button with their left or right thumb. A circle as the central stimulus indicates a Nogo trial, where the subject was required to inhibit the response. The flankers preceded the target by 100 ms. The target (arrowhead or circle) 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 600 ms. 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 (cf.Beste et al., 2011). Importantly, for the analysis of error processing only trials with incorrect button presses, but not trials with response times exceeding the response deadline of 600 ms were used. This was done to avoid the fact that the errors observed in BHC may be due to the motor problems of the patient and indeed would reflect error-prone choice behavior.
2.3. EEG recording and analysis and MRI scanning 2. Materials and methods 2.1. Patient and controls A sample of N ¼ 20 healthy controls between 20 and 28 years of age (mean age 24.6; SD ¼ 3.6) was recruited for comparison to a single individual with benign hereditary chorea (BHC) using single-case bootstrap statistics. This individual who was a 24-year old female with genetically confirmed mutation in the TTF1 gene was recruited. The BHC patient was unmedicated so that the results obtained are unbiased with respect to that factor. Controls and the BHC case were investigated clinically with a neuropsychological test battery (see Table 1). The BHC patient underwent neurological assessment of motor symptoms using the Unified Huntington's Disease Rating Scale (UHDRS) Huntington Study Group, 1996). This scale was used because here choreatic movements can reliably be assessed and quantified. The BHC case showed severe signs of choreatic movement disturbances. The neuropsychological and neurological assessment of the patient and controls is summarized in Table 1. Using these standard neuropsychological tests, no difference was evident between the BHC case and the controls using Craufurd and Howell's method (p 4.3) (cf. Crawford & Garthwaite, 2012). The study was approved by the Ethics Committee of the Ruhr-University of Bochum. The study was conducted according to the Declaration of Helsinki. All healthy participants and the BHC patient gave written informed consent.
During the task an EEG was recorded from 64 Ag–AgCl electrodes against a reference electrode located at Cz at a sampling rate of 500 Hz applying a filter bandwidth 0–80 Hz to the EEG (Quickamp, Brain Products Inc.). To re-reference the data, a CSD transformation was applied, which eliminates the reference potential (Nunez and Pilgreen, 1991). Electrode impedances were kept below 5 kΩ. The EEG was filtered off-line from .5 to 20 Hz (48 db/oct)1. A raw data inspection was applied and technically occurring artifacts were discarded by manual inspection of the data. Afterwards, independent component analysis (ICA, Infomax algorithm) was applied. Independent components of blinks, saccades and pulse artifacts were discarded by visual inspection. After segmenting the data into correct and error trials, an automated artifact rejection procedure was applied with an amplitude threshold of 7 80 mV. The response was set to time point 0 and a baseline correction procedure was applied from 200 ms till button press. The ERN and response related potentials after correct responses (Nc) were quantified in amplitude and latency at electrode FCz against the pre-response baseline. The ERN and the correct-related negativity (Nc) were defined as the most negative peak within 50–120 ms after the response. As can be seen in scalp topography maps, ERN was maximal at electrodes FCz and Cz. We only quantified the ERN at electrode FCz because electrode Cz was used as reference electrode during data acquisition. The error positivity (Pe) was quantified at electrode Pz (Falkenstein et al., 2000) and defined as the most positive peak in the window between 300 and 600 ms after an erroneous response. As the task also contained Nogo-trials, neurophysiological processes underlying inhibition (i.e., Nogo-N2 and Nogo-P3, see: Huster,
2.2. Task To examine error monitoring we used a flanker task. The paradigm is identical to other studies done by our group (Beste, Gunturkun, Baune, Falkenstein & Konrad,
1 We also analyzed the data with a more liberal filter (i.e., from .5 to 40 Hz including a 50 Hz notch filter; 48 db/oct). This did not change the pattern of results.
Please cite this article as: Beste, C., & Saft, C. Benign hereditary chorea as an experimental model to investigate the role of medium spiny neurons for response adaptation. Neuropsychologia (2014), http://dx.doi.org/10.1016/j.neuropsychologia.2014.05.004i
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Fig. 1. (A) Degree of post-error slowing (in ms) for the control group and the BHC case. (B) Event-related potentials (ERPs) including scalp topography plots on correct and error trials at electrode FCz. Time point 0 denotes the time point of response execution. As can be seen, ERN is only evident in controls but not in the BHC case. Please note the different scaling for the scalp topography plots. The scalp topography plots denote the mean value of the amplitude at its peak. (C) Event-related potentials (ERPs) including scalp topography plots on correct and error trials at electrode Pz to show the error-positivity (Pe) including the scalp topography of the Pe (all-in-one map). (D) MRI transversal section (T2 turbo spin echo) of the BHC case showing the striatum. A smaller caudate nucleus is evident. Enriquez-Geppert, Lavallee, Falkenstein, & Herrmann, 2013) were analyzed in stimulus-locked data at electrode FCz. The Nogo-N2 was defined as the most negative deflection in the time interval between 200 and 300 ms after the stimulus and the Nogo-P3 were defined as the most positive deflection in the time window between 300 and 600 ms after Nogo-stimulus presentation. In order to obtain an estimate about the reliability of the neurophysiological data, which is of special importance in light of single-case data, we calculated the signal-to-noise (SNR) in BHC and controls as implemented in the Brain Vision Analyzer II software package (BrainProducts Inc.). Calculation of the SNR is important for single-case data, since usually SNR is increased by averaging over a number of participants in an experimental group. As this is not possible in single-case data, SNR gives important information about the reliability of the data for the single case. For an anatomical MRI scan, a T2 turbo spin echo scan was acquired on 3 T Philips Intera System using a SENSE head coil (TR¼ 3721 ms; TE¼ 80 ms; 901 flip angle). A transversal section showing the striatum is shown in Fig. 1D.
2.4. Statistics To compare the BHC case with the control group, single case statistics were run using Crawford and Howell's methods (for review: Crawford & Garthwaite, 2012). This method offers the best way to compare single cases with groups of control subjects (for review: Crawford & Garthwaite, 2012). For within-subject effects in the control group, usual t-tests were used and Bonferroni-corrected wherever necessary.
3. Results 3.1. Error processing For all statistics the mean and standard deviations are given. Error rates were higher in the BHC patient (28%), compared to controls (10.15%7 3.1) (t ¼5.63, p ¼.00001; 95% confidence interval 3.89 to 7.64). In controls an error trial was followed by an anew error in the following trial in 7% of cases (72.2). In the BHC patient an error trial was followed by an anew error in the following trial in 30% of cases (t¼10.20; p o.00001; 95% confidence interval 7.12 to 13.77). For the control cohort, RTs were longer on correct trials (389 ms 787) compared to error trials (290 ms 785) (t(19) ¼ 23.33; p o.001). The BHC patient did not differ from the control group in these RTs (error: 315 ms; correct: 402 ms; all p4 .5). In controls it is further shown that correct responses succeeding error trials were longer (425 ms 773) than responses succeeding a correct trial (386 ms 7 70) (t(19) ¼ 17.90; p o.001). This posterror slowing effect was thus 43 ms ( 79) for controls (refer Fig. 1A). In the BHC patient this post-error slowing effect was 5 ms and was hence significantly lower than the effect in controls
Please cite this article as: Beste, C., & Saft, C. Benign hereditary chorea as an experimental model to investigate the role of medium spiny neurons for response adaptation. Neuropsychologia (2014), http://dx.doi.org/10.1016/j.neuropsychologia.2014.05.004i
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(t ¼ 4.21, and p ¼.0003; 95% confidence interval 5.61 to 2.81) and not different from zero (p 4.5). The neurophysiological data (i.e., ERN and Nc) (Fig. 1B) showed that ERN was larger ( 25.57 mV/m2 7 4.45) than Nc ( 11.7 mV/ m2 74.13) in the control group (t(19) ¼ 13.34; p o.001). The BHC patient showed a much smaller ERN ( 11.2 mV/m2), compared to controls (t¼ 3.10, and p ¼.003; 95% confidence interval 4.26 to 2.08). Nc in the BHC patient was 10.5 mV/m2 and thus not different from controls (p4 .7). ERN and Nc in the BHC patient did not differ from Nc in controls (p 4.5). There were generally no latency differences between BHC patient and controls (all p 4.7). Also when using the difference between ERN and Nc, this difference was smaller for the BHC patient (3.1 mV/m2) than for healthy controls (14.8 mV/m2 7 6.44) (t¼ 1.89, and p ¼.04; 95% confidence interval 2.25 to 1.08). Yet, it may be argued that the smaller ERN is an artifact of the higher rates of errors (Endrass, Klawohn, Gruetzmann, Ischebeck, & Kathmann, 2012) in the BHC patient. To control the effect of error frequency on the modulation of ERN, we used the same amount of trials in the BHC patient to compare ERN against ERN of healthy subjects. This controls for a possible biasing effect of error frequency on the differential modulation of ERN in the BHC patient and healthy controls. In this analysis, ERN was still lower in BHC ( 14.2 mV/m2) compared to controls (t¼ 2.66, and p ¼.009). Similarly, in the BHC patient the post-error slowing effect was 12 ms and hence lower than slowing in controls (t ¼ 3.88, and p ¼.001). However, to control these effects it may be even more important to take the relative probability of errors into account. This is the case because post-error slowing effects can be explained by differences in error rate (Notebaert, Houtman, Opstal, Gevers, Flas, &Verguts, 2009) and high error rates lead to diminished post-error slowing (Nunez-Castellar, Houtman, Gevers, Morrens, Vermeylen, Sabbe et al., 2012). To control this effect we re-analyzed ERN and the post-error slowing effect on the first 210 trials of the experiment. The post-error slowing was still smaller in the BHC patient (11 ms) compared to controls (49 ms 79) (t ¼ 4.12, and p ¼.0003; 95% confidence interval 5.61 to 2.81), while the error rate in these first 210 trials was not different between controls and BHC patient (p 4.6). Similarly, ERN was smaller in BHC patient ( 12.1 mV/m2) than in controls ( 26.11 mV/m2 73.11) (t ¼ 4.39, and p ¼.0002; 95% confidence interval 5.98 to 3.01). Nc did not differ between BHC patient and controls (p 4.6) and there was no difference between Nc in the BHC patient and ERN in controls (p 4.7). All analysis strategies therefore lead to the same pattern of effects2. The error positivity is also shown in Fig. 1C. The analysis of the Pe amplitude did not show differences between BHC patient and controls (p4 .5). Calculation of the SNR, as implemented in the Brain Vision Analyzer II software package, revealed that for correct trials SNR was (.157.05) in controls, and for error trials the SNR was (.347.08) in controls. In the BHC patient, SNR was .16 for correct trials and .32 for error trials. The SNRs did not differ between controls and BHC patient on correct and error trials (p4.8) showed that the EEG signals compared are similarly reliable in controls and the BHC patient. The same was evident for the error positivity (Pe) (p4.6).
3.2. Response inhibition The flanker task contained 20% Nogo trials. To examine response inhibition processes, the data was analyzed stimulus2 When not using the peak amplitude but the mean amplitude of the ERN and Nc (calculated over the time window between 30 and 90 ms) the results remained the same.
Fig. 2. Event-related potentials on Nogo-trials in BHC and controls and compatible Go-trials. No differences are evident for the Nogo-N2 and Nogo-P3 between BHC and controls. The maps denote the scalp topography plots at the peak of the respective components.
locked on Nogo trials. The neurophysiological data is shown in Fig. 2. Although numerically different, the rate of Nogo errors (i.e., button presses on Nogo-stimuli) did not differ between BHC patient (7.6) and controls (5.17 1.9) (p 4.6). Similarly, there were no differences in the response times on Go trials (p4 .5). There was no difference in the amplitude of the Nogo-N2 (p 4.7) and the Nogo-P3 (p 4.6), between the BHC patient and the control group. An analysis of the SNRs revealed no difference between BHC patient (.22) and controls (.247.1) (p4 .4).
4. Discussion The current study aimed to investigate the relevance of the microstructural integrity of striatal MSN for error-related behavioral adaptation processes. To this end, benign hereditary chorea (BHC), a possible experimental model (Beste & Saft, 2013) of circumscribed MSN dysfunction was investigated in a case-control study. The results show almost absent behavioral adaptation processes after an error in the BHC case as indicated by the post-error slowing parameter. Similarly, error rates were higher in the BHC patient, which may reflect a consequence of diminished post-error slowing. ERN has been shown to be associated with corrective action and post-error slowing (Debener et al., 2005). The reduced post-error slowing effect observed for the BHC patient may be interpreted as a lack of increase in response caution that is observed in controls and is likely to be reflected by post-error slowing as suggested by data on reaction time modeling (Dutilh, Vendekerckhove, Forstmann, Keuleers, Brysbaert & Wagenmakers, 2012). However, in line with other conceptions on post-error slowing this may also suggest deficits in an orienting response, due to the rareness of errors (Notebaert et al., 2009). Yet, as post-error slowing effects were not biased with respect to the frequency of errors in the present data, our data speaks for an effect at the level of response caution. The absence of post-error slowing in the BHC patient together with decrease in the post-error accuracy may be a consequence of an absent ERN in BHC: The results show that the ERN is smaller in the BHC patient, compared to controls. The lack of difference between the ERN in BHC and the Nc in controls suggests that ERN is even absent in the BHC patient. Despite of a single case for BHC, the
Please cite this article as: Beste, C., & Saft, C. Benign hereditary chorea as an experimental model to investigate the role of medium spiny neurons for response adaptation. Neuropsychologia (2014), http://dx.doi.org/10.1016/j.neuropsychologia.2014.05.004i
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analysis of the SNR shows that the neurophysiological data obtained from the BHC patient is as reliable as the data obtained from the control cohort. The results suggest that MSNs may constitute an important element in error processing and behavioral adaptation. As stated in (Section 1), it has been assumed that a comparison between the desired and the actual outcome of an action forms the basis for post-error behavioral adaptation (Falkenstein et al., 2000; Gehring et al., 1993; Scheffers et al., 1996). The data suggests that a dysfunctional MSNs network is not able to differentiate between a desired and an actual erroneous outcome of a response. The results suggest that processes of response control per se (as reflected by the Nc) are not deficient in a condition of MSN dysfunction. What seems to be particularly affected are behavioral adaptation processes after errors, which is underlined by the higher error rates in the BHC patient compared to controls. In this regard the analysis of response inhibition performance (behavioral and neurophysiological data) does not show differences in the BHC patient, compared to healthy controls. All this data suggests that MSN dysfunctions, as observed in the BHC patient, do not have a generally compromising effect on response monitoring processes, but are of importance for error monitoring processes as reflected by ERN. This suggests that these response monitoring functions related to the error-related correction of actions, response inhibition processes and general response monitoring functions unrelated to errors have different functional neuroanatomical correlates. The finding that ERN and Nc were differentially modulated in the BHC patient adds to research suggesting that both processes may be implemented via different neurobiological and neurophysiological mechanisms (e.g. Hoffmann, Labrenz, Themann, Wascher, & Beste, 2014; Beste et al., 2010a,b; Jocham & Ullsperger, 2008). Interestingly, a lack of modulation between the ERN and the Nc (as found in BHC) has previously been shown to exist in patients with focal dorsolateral prefrontal lesions (Gehring & Knight, 2000). As such, it seems that structural damage to dorsolateral prefrontal cortex and basal ganglia MSNs both entail a lack of differentiation between desired and undesired outcomes of an action. In this regard it is interesting that other processes of error monitoring (reflected by the parietal Pe) did not change in the BHC patient, compared to controls. Opposed to the ERN, Pe has been suggested to reflect conscious error recognition processes (e.g. Overbeeck, Nieuwenhuis, & Ridderinkhof, 2005; Falkenstein et al., 2000; Leuthold & Sommer, 1999). The current results suggest that MSN function is not important for these processes. This is line with other data also showing no modulations of the Pe in a neurodegenerative disease affecting MSNs (Beste, Saft, Konrad, Andrich, Habbel, Schepers et al., 2008) and is in line with data suggesting an important role of the anterior insula, but not of the striatum, for processes related to the Pe (Ullsperger, Harsay, Wessel, & Ridderinkhof, 2010). An obvious limitation of the study is that only a single-case was examined, despite usage of robust single-case statistics. Future studies should provide data on larger sample sizes and also knockout animal models may be investigated to examine the relevance MSNs for the examined cognitive control functions in more detail. In summary, the results provide first insights into the involvement of striatal MSNs for post-error behavioral adaptation processes. The study relates genetically determined structural microneuroanatomical changes with electrophysiological mechanisms of behavioral adaptation processes. It shows that in a clinical condition previously thought not to display cognitive disturbances (for review: Inzelberg & Weinberger, 2011) dysfunctions in executive control are evident.
Acknowledgments This research was supported by a Grant from the Deutsche Forschungsgemeinschaft (DFG) BE4045/10-1 to C.B.
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Please cite this article as: Beste, C., & Saft, C. Benign hereditary chorea as an experimental model to investigate the role of medium spiny neurons for response adaptation. Neuropsychologia (2014), http://dx.doi.org/10.1016/j.neuropsychologia.2014.05.004i
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Neuropsychologia journal homepage: www.elsevier.com/locate/neuropsychologia
Benign hereditary chorea as an experimental model to investigate the role of medium spiny neurons for response adaptation Christian Beste a,n, Carsten Saft b a Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine of the TU Dresden, Schubertstrasse 42, D-01309 Dresden, Germany b Department of Neurology, St. Josef Hospital, Ruhr-University Bochum, Germany
art ic l e i nf o
a b s t r a c t
Article history: Received 10 January 2014 Received in revised form 28 April 2014 Accepted 5 May 2014
Processing errors is a major requirement for behavioral adaptation. While it has been assumed that the basal ganglia play an important role in initiating these processes, the role of the striatal microstructure for these processes remains to be uncovered. Previous studies in basal ganglia diseases could not elucidate the relevance of the striatal medium spiny neuron (MSN) microstructure unambiguously because structural alterations occur together with alterations in various neurotransmitter systems. We present and examine a possible model that allows the examination of MSN dysfunction unbiased by other modulations, i.e. a case of ‘benign hereditary chorea’ (BHC) in comparison to healthy controls. We apply event-related potentials (ERPs) to uncover the underlying neurophysiological mechanisms underlying post-error behavioral adaptation. The BHC patient revealed a smaller error-related negativity (ERN) together with almost absent behavioral adaptation after an error and generally more error-prone behavior. Performance monitoring processes unrelated to errors, as well as response inhibition processes, were not affected in the BHC patient. The results suggest that the striatal MSN microstructural integrity is more important for error-related behavioral adaptation than for other response monitoring processes unrelated to errors. & 2014 Published by Elsevier Ltd.
Keywords: Medium spiny neurons (MSNs) Performance monitoring Benign hereditary chorea EEG Basal ganglia
1. Introduction The striatum plays a pivotal role in response selection (Redgrave, Prescott, & Gurney, 1999) and behavioral adaptation after response errors. From a neurophysiological point of view, the error-related negativity (ERN) (Gehring, Goss, Coles, Meyer, & Donchin, 1993; Falkenstein, Hohnsbein, Hoormann, & Blanke, 1991) has been shown to drive processes of post-error behavioral adaptation (Debener, Ullsperger, Siegel, Fiehler, Von Cramon & Engel, 2005). It has been assumed that the basal ganglia compare neural representations of the actual and the desired outcome of an action (e.g. Falkenstein, Hoormann, Christ, & Hohnsbein, 2000; Gehring et al., 1993; Scheffers, Coles, Bernstein, Gehring, & Donchin, 1996). In case of a mismatch, an error signal is sent to the anterior cingulate cortex (ACC) via the dopamine system, which in turn elicits the ERN and induces corrective actions (Huster, Enriquez-Geppert, Wollbrink, Kugel, Konrad & Pantev, 2011; Debener et al., 2005; Holroyd & Coles, 2002). In this sense it is a comparison between the desired and the actual outcome occurring in the basal ganglia that builds the basis for error-related behavioral adaptation. Consequently, diseases affecting
n
Corresponding author. Tel.: þ 49 351 458 7072; fax: þ49 351 458 7318. E-mail address: [email protected] (C. Beste).
the basal ganglia, such as Parkinson's and Huntington's disease, have been shown to compromise error processing (e.g. Beste, Willemssen, Saft, & Falkenstein, 2009; Willemssen, Müller, Schwarz, Falkenstein, & Beste, 2009). However, besides such structural changes, the dopamine system also shows strong concomitant alterations in these diseases. Since the dopamine system is of known importance for error processing (Jocham & Ullsperger, 2008; Frank, D'Lauro, & Curran, 2007; Krämer et al., 2007), there is no clear-cut experimental evidence for a role of the striatal microstructure for these processes. A possible experimental model disease to examine the contribution of striatal microstructure for cognitive functions in humans unbiased of influences by other neurotransmitter systems may be benign hereditary chorea (BHC) (Beste, Humphries & Saft, 2014; Beste & Saft, 2013). Benign hereditary chorea (BHC) is a rare autosomal dominant neurological disease (prevalence 1– 2:1.000.000) related to mutations in the TTF1 gene on chromosome 14q13 encoding the thyroid transcription factor-1 (also known as TITF1, TEBP or NKX2-1) (for review: Inzelberg & Weinberger, 2011; Kleiner-Fisman & Lang, 2007). A hallmark of this disease is a circumscribed dysgenesis of striatal structures and medium spiny neurons (MSNs) (Yoshida, Nunomura, Shimohata, Nanjo, & Miyata, 2012; Sussel, Marin, Kimura, & Rubenstein, 1999). This is supported by post-mortem studies which also show that other brain structures are not or only unsystematically affected
http://dx.doi.org/10.1016/j.neuropsychologia.2014.05.004 0028-3932/& 2014 Published by Elsevier Ltd.
Please cite this article as: Beste, C., & Saft, C. Benign hereditary chorea as an experimental model to investigate the role of medium spiny neurons for response adaptation. Neuropsychologia (2014), http://dx.doi.org/10.1016/j.neuropsychologia.2014.05.004i
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Table 1 Neuropsychological test data (raw scores) of the BHC case and the group of healthy controls. Testing possible differences between BHC case and controls revealed no difference in any of the tests applied. The UHDRS motor score is necessarily different, as the controls do not suffer under the genetic mutation causing BHC. Test
BHC
Controls
UHDRS (motor score) IQ score (MWT-B) Stroop test (UHDRS) Color naming Color reading Interference (Stroop condition) Symbol-digit test (WAIS) correct items (Härting et al., 2000) Word fluency (Benton) number of words Digit span (WMS-R) number of items (Härting et al., 2000) Forward Backward Block Span (WMS-R) number of items (Härting et al., 2000) Forward Backward Benton Test (visual memory) (Benton, Benton-Sivan, & Steck, 2009) Mini Mental Status examination (MMSE) (Folstein, Folstein, & McHugh, 1975)
26 105
0 113 (9)
(for review: Inzelberg & Weinberger, 2011). Clinically, BHC is associated with choreatic movements. As MSN dysfunctions are not modulated by otherwise dysfunctional neurotransmitters in BHC, this disease may serve as a possible experimental model drawn from disease to uncover the relevance of the striatal microstructure for cognitive functions. The striatal microstructure may be important because a fundamental step in error processing is the comparison between the desired and the actual outcome (Gehring et al., 1993; Scheffers et al., 1996). To do so, the basal ganglia must be able to represent different outcomes in order to compare them to each other. Several computational basal ganglia models suggest that MSN is important for this process (Bar-Gad et al., 2003; Plenz, 2003). In BHC it is possible that error processing functions are diminished because a comparison of the actual outcome of an action against the desired outcome of action may not be possible at the basal ganglia level because of low microstructural integrity of MSNs. Therefore, behavioral adaptation after an error may be diminished. The current study sets out to test this hypothesis in a clinical case-control study.
61 98 36 51 39
75 (6) 101 (15) 40 (10) 59 (12) 42 (8)
8 5
9 (3) 5 (4)
8 7 11 30
9 (3) 7 (3) 13 (3) 30
2011) and was structured as follows: vertically arranged visual stimuli were presented. The target-stimulus (arrowhead or circle) was presented in the center with the arrowhead pointing to the left or right. The central stimuli were flanked by two vertically adjacent arrowheads which pointed in the same (compatible) or opposite (incompatible) direction as the target. In case of target stimuli (arrowheads pointing to the left or right) participants were required to press a response button with their left or right thumb. A circle as the central stimulus indicates a Nogo trial, where the subject was required to inhibit the response. The flankers preceded the target by 100 ms. The target (arrowhead or circle) 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 600 ms. 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 (cf.Beste et al., 2011). Importantly, for the analysis of error processing only trials with incorrect button presses, but not trials with response times exceeding the response deadline of 600 ms were used. This was done to avoid the fact that the errors observed in BHC may be due to the motor problems of the patient and indeed would reflect error-prone choice behavior.
2.3. EEG recording and analysis and MRI scanning 2. Materials and methods 2.1. Patient and controls A sample of N ¼ 20 healthy controls between 20 and 28 years of age (mean age 24.6; SD ¼ 3.6) was recruited for comparison to a single individual with benign hereditary chorea (BHC) using single-case bootstrap statistics. This individual who was a 24-year old female with genetically confirmed mutation in the TTF1 gene was recruited. The BHC patient was unmedicated so that the results obtained are unbiased with respect to that factor. Controls and the BHC case were investigated clinically with a neuropsychological test battery (see Table 1). The BHC patient underwent neurological assessment of motor symptoms using the Unified Huntington's Disease Rating Scale (UHDRS) Huntington Study Group, 1996). This scale was used because here choreatic movements can reliably be assessed and quantified. The BHC case showed severe signs of choreatic movement disturbances. The neuropsychological and neurological assessment of the patient and controls is summarized in Table 1. Using these standard neuropsychological tests, no difference was evident between the BHC case and the controls using Craufurd and Howell's method (p 4.3) (cf. Crawford & Garthwaite, 2012). The study was approved by the Ethics Committee of the Ruhr-University of Bochum. The study was conducted according to the Declaration of Helsinki. All healthy participants and the BHC patient gave written informed consent.
During the task an EEG was recorded from 64 Ag–AgCl electrodes against a reference electrode located at Cz at a sampling rate of 500 Hz applying a filter bandwidth 0–80 Hz to the EEG (Quickamp, Brain Products Inc.). To re-reference the data, a CSD transformation was applied, which eliminates the reference potential (Nunez and Pilgreen, 1991). Electrode impedances were kept below 5 kΩ. The EEG was filtered off-line from .5 to 20 Hz (48 db/oct)1. A raw data inspection was applied and technically occurring artifacts were discarded by manual inspection of the data. Afterwards, independent component analysis (ICA, Infomax algorithm) was applied. Independent components of blinks, saccades and pulse artifacts were discarded by visual inspection. After segmenting the data into correct and error trials, an automated artifact rejection procedure was applied with an amplitude threshold of 7 80 mV. The response was set to time point 0 and a baseline correction procedure was applied from 200 ms till button press. The ERN and response related potentials after correct responses (Nc) were quantified in amplitude and latency at electrode FCz against the pre-response baseline. The ERN and the correct-related negativity (Nc) were defined as the most negative peak within 50–120 ms after the response. As can be seen in scalp topography maps, ERN was maximal at electrodes FCz and Cz. We only quantified the ERN at electrode FCz because electrode Cz was used as reference electrode during data acquisition. The error positivity (Pe) was quantified at electrode Pz (Falkenstein et al., 2000) and defined as the most positive peak in the window between 300 and 600 ms after an erroneous response. As the task also contained Nogo-trials, neurophysiological processes underlying inhibition (i.e., Nogo-N2 and Nogo-P3, see: Huster,
2.2. Task To examine error monitoring we used a flanker task. The paradigm is identical to other studies done by our group (Beste, Gunturkun, Baune, Falkenstein & Konrad,
1 We also analyzed the data with a more liberal filter (i.e., from .5 to 40 Hz including a 50 Hz notch filter; 48 db/oct). This did not change the pattern of results.
Please cite this article as: Beste, C., & Saft, C. Benign hereditary chorea as an experimental model to investigate the role of medium spiny neurons for response adaptation. Neuropsychologia (2014), http://dx.doi.org/10.1016/j.neuropsychologia.2014.05.004i
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Fig. 1. (A) Degree of post-error slowing (in ms) for the control group and the BHC case. (B) Event-related potentials (ERPs) including scalp topography plots on correct and error trials at electrode FCz. Time point 0 denotes the time point of response execution. As can be seen, ERN is only evident in controls but not in the BHC case. Please note the different scaling for the scalp topography plots. The scalp topography plots denote the mean value of the amplitude at its peak. (C) Event-related potentials (ERPs) including scalp topography plots on correct and error trials at electrode Pz to show the error-positivity (Pe) including the scalp topography of the Pe (all-in-one map). (D) MRI transversal section (T2 turbo spin echo) of the BHC case showing the striatum. A smaller caudate nucleus is evident. Enriquez-Geppert, Lavallee, Falkenstein, & Herrmann, 2013) were analyzed in stimulus-locked data at electrode FCz. The Nogo-N2 was defined as the most negative deflection in the time interval between 200 and 300 ms after the stimulus and the Nogo-P3 were defined as the most positive deflection in the time window between 300 and 600 ms after Nogo-stimulus presentation. In order to obtain an estimate about the reliability of the neurophysiological data, which is of special importance in light of single-case data, we calculated the signal-to-noise (SNR) in BHC and controls as implemented in the Brain Vision Analyzer II software package (BrainProducts Inc.). Calculation of the SNR is important for single-case data, since usually SNR is increased by averaging over a number of participants in an experimental group. As this is not possible in single-case data, SNR gives important information about the reliability of the data for the single case. For an anatomical MRI scan, a T2 turbo spin echo scan was acquired on 3 T Philips Intera System using a SENSE head coil (TR¼ 3721 ms; TE¼ 80 ms; 901 flip angle). A transversal section showing the striatum is shown in Fig. 1D.
2.4. Statistics To compare the BHC case with the control group, single case statistics were run using Crawford and Howell's methods (for review: Crawford & Garthwaite, 2012). This method offers the best way to compare single cases with groups of control subjects (for review: Crawford & Garthwaite, 2012). For within-subject effects in the control group, usual t-tests were used and Bonferroni-corrected wherever necessary.
3. Results 3.1. Error processing For all statistics the mean and standard deviations are given. Error rates were higher in the BHC patient (28%), compared to controls (10.15%7 3.1) (t ¼5.63, p ¼.00001; 95% confidence interval 3.89 to 7.64). In controls an error trial was followed by an anew error in the following trial in 7% of cases (72.2). In the BHC patient an error trial was followed by an anew error in the following trial in 30% of cases (t¼10.20; p o.00001; 95% confidence interval 7.12 to 13.77). For the control cohort, RTs were longer on correct trials (389 ms 787) compared to error trials (290 ms 785) (t(19) ¼ 23.33; p o.001). The BHC patient did not differ from the control group in these RTs (error: 315 ms; correct: 402 ms; all p4 .5). In controls it is further shown that correct responses succeeding error trials were longer (425 ms 773) than responses succeeding a correct trial (386 ms 7 70) (t(19) ¼ 17.90; p o.001). This posterror slowing effect was thus 43 ms ( 79) for controls (refer Fig. 1A). In the BHC patient this post-error slowing effect was 5 ms and was hence significantly lower than the effect in controls
Please cite this article as: Beste, C., & Saft, C. Benign hereditary chorea as an experimental model to investigate the role of medium spiny neurons for response adaptation. Neuropsychologia (2014), http://dx.doi.org/10.1016/j.neuropsychologia.2014.05.004i
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(t ¼ 4.21, and p ¼.0003; 95% confidence interval 5.61 to 2.81) and not different from zero (p 4.5). The neurophysiological data (i.e., ERN and Nc) (Fig. 1B) showed that ERN was larger ( 25.57 mV/m2 7 4.45) than Nc ( 11.7 mV/ m2 74.13) in the control group (t(19) ¼ 13.34; p o.001). The BHC patient showed a much smaller ERN ( 11.2 mV/m2), compared to controls (t¼ 3.10, and p ¼.003; 95% confidence interval 4.26 to 2.08). Nc in the BHC patient was 10.5 mV/m2 and thus not different from controls (p4 .7). ERN and Nc in the BHC patient did not differ from Nc in controls (p 4.5). There were generally no latency differences between BHC patient and controls (all p 4.7). Also when using the difference between ERN and Nc, this difference was smaller for the BHC patient (3.1 mV/m2) than for healthy controls (14.8 mV/m2 7 6.44) (t¼ 1.89, and p ¼.04; 95% confidence interval 2.25 to 1.08). Yet, it may be argued that the smaller ERN is an artifact of the higher rates of errors (Endrass, Klawohn, Gruetzmann, Ischebeck, & Kathmann, 2012) in the BHC patient. To control the effect of error frequency on the modulation of ERN, we used the same amount of trials in the BHC patient to compare ERN against ERN of healthy subjects. This controls for a possible biasing effect of error frequency on the differential modulation of ERN in the BHC patient and healthy controls. In this analysis, ERN was still lower in BHC ( 14.2 mV/m2) compared to controls (t¼ 2.66, and p ¼.009). Similarly, in the BHC patient the post-error slowing effect was 12 ms and hence lower than slowing in controls (t ¼ 3.88, and p ¼.001). However, to control these effects it may be even more important to take the relative probability of errors into account. This is the case because post-error slowing effects can be explained by differences in error rate (Notebaert, Houtman, Opstal, Gevers, Flas, &Verguts, 2009) and high error rates lead to diminished post-error slowing (Nunez-Castellar, Houtman, Gevers, Morrens, Vermeylen, Sabbe et al., 2012). To control this effect we re-analyzed ERN and the post-error slowing effect on the first 210 trials of the experiment. The post-error slowing was still smaller in the BHC patient (11 ms) compared to controls (49 ms 79) (t ¼ 4.12, and p ¼.0003; 95% confidence interval 5.61 to 2.81), while the error rate in these first 210 trials was not different between controls and BHC patient (p 4.6). Similarly, ERN was smaller in BHC patient ( 12.1 mV/m2) than in controls ( 26.11 mV/m2 73.11) (t ¼ 4.39, and p ¼.0002; 95% confidence interval 5.98 to 3.01). Nc did not differ between BHC patient and controls (p 4.6) and there was no difference between Nc in the BHC patient and ERN in controls (p 4.7). All analysis strategies therefore lead to the same pattern of effects2. The error positivity is also shown in Fig. 1C. The analysis of the Pe amplitude did not show differences between BHC patient and controls (p4 .5). Calculation of the SNR, as implemented in the Brain Vision Analyzer II software package, revealed that for correct trials SNR was (.157.05) in controls, and for error trials the SNR was (.347.08) in controls. In the BHC patient, SNR was .16 for correct trials and .32 for error trials. The SNRs did not differ between controls and BHC patient on correct and error trials (p4.8) showed that the EEG signals compared are similarly reliable in controls and the BHC patient. The same was evident for the error positivity (Pe) (p4.6).
3.2. Response inhibition The flanker task contained 20% Nogo trials. To examine response inhibition processes, the data was analyzed stimulus2 When not using the peak amplitude but the mean amplitude of the ERN and Nc (calculated over the time window between 30 and 90 ms) the results remained the same.
Fig. 2. Event-related potentials on Nogo-trials in BHC and controls and compatible Go-trials. No differences are evident for the Nogo-N2 and Nogo-P3 between BHC and controls. The maps denote the scalp topography plots at the peak of the respective components.
locked on Nogo trials. The neurophysiological data is shown in Fig. 2. Although numerically different, the rate of Nogo errors (i.e., button presses on Nogo-stimuli) did not differ between BHC patient (7.6) and controls (5.17 1.9) (p 4.6). Similarly, there were no differences in the response times on Go trials (p4 .5). There was no difference in the amplitude of the Nogo-N2 (p 4.7) and the Nogo-P3 (p 4.6), between the BHC patient and the control group. An analysis of the SNRs revealed no difference between BHC patient (.22) and controls (.247.1) (p4 .4).
4. Discussion The current study aimed to investigate the relevance of the microstructural integrity of striatal MSN for error-related behavioral adaptation processes. To this end, benign hereditary chorea (BHC), a possible experimental model (Beste & Saft, 2013) of circumscribed MSN dysfunction was investigated in a case-control study. The results show almost absent behavioral adaptation processes after an error in the BHC case as indicated by the post-error slowing parameter. Similarly, error rates were higher in the BHC patient, which may reflect a consequence of diminished post-error slowing. ERN has been shown to be associated with corrective action and post-error slowing (Debener et al., 2005). The reduced post-error slowing effect observed for the BHC patient may be interpreted as a lack of increase in response caution that is observed in controls and is likely to be reflected by post-error slowing as suggested by data on reaction time modeling (Dutilh, Vendekerckhove, Forstmann, Keuleers, Brysbaert & Wagenmakers, 2012). However, in line with other conceptions on post-error slowing this may also suggest deficits in an orienting response, due to the rareness of errors (Notebaert et al., 2009). Yet, as post-error slowing effects were not biased with respect to the frequency of errors in the present data, our data speaks for an effect at the level of response caution. The absence of post-error slowing in the BHC patient together with decrease in the post-error accuracy may be a consequence of an absent ERN in BHC: The results show that the ERN is smaller in the BHC patient, compared to controls. The lack of difference between the ERN in BHC and the Nc in controls suggests that ERN is even absent in the BHC patient. Despite of a single case for BHC, the
Please cite this article as: Beste, C., & Saft, C. Benign hereditary chorea as an experimental model to investigate the role of medium spiny neurons for response adaptation. Neuropsychologia (2014), http://dx.doi.org/10.1016/j.neuropsychologia.2014.05.004i
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analysis of the SNR shows that the neurophysiological data obtained from the BHC patient is as reliable as the data obtained from the control cohort. The results suggest that MSNs may constitute an important element in error processing and behavioral adaptation. As stated in (Section 1), it has been assumed that a comparison between the desired and the actual outcome of an action forms the basis for post-error behavioral adaptation (Falkenstein et al., 2000; Gehring et al., 1993; Scheffers et al., 1996). The data suggests that a dysfunctional MSNs network is not able to differentiate between a desired and an actual erroneous outcome of a response. The results suggest that processes of response control per se (as reflected by the Nc) are not deficient in a condition of MSN dysfunction. What seems to be particularly affected are behavioral adaptation processes after errors, which is underlined by the higher error rates in the BHC patient compared to controls. In this regard the analysis of response inhibition performance (behavioral and neurophysiological data) does not show differences in the BHC patient, compared to healthy controls. All this data suggests that MSN dysfunctions, as observed in the BHC patient, do not have a generally compromising effect on response monitoring processes, but are of importance for error monitoring processes as reflected by ERN. This suggests that these response monitoring functions related to the error-related correction of actions, response inhibition processes and general response monitoring functions unrelated to errors have different functional neuroanatomical correlates. The finding that ERN and Nc were differentially modulated in the BHC patient adds to research suggesting that both processes may be implemented via different neurobiological and neurophysiological mechanisms (e.g. Hoffmann, Labrenz, Themann, Wascher, & Beste, 2014; Beste et al., 2010a,b; Jocham & Ullsperger, 2008). Interestingly, a lack of modulation between the ERN and the Nc (as found in BHC) has previously been shown to exist in patients with focal dorsolateral prefrontal lesions (Gehring & Knight, 2000). As such, it seems that structural damage to dorsolateral prefrontal cortex and basal ganglia MSNs both entail a lack of differentiation between desired and undesired outcomes of an action. In this regard it is interesting that other processes of error monitoring (reflected by the parietal Pe) did not change in the BHC patient, compared to controls. Opposed to the ERN, Pe has been suggested to reflect conscious error recognition processes (e.g. Overbeeck, Nieuwenhuis, & Ridderinkhof, 2005; Falkenstein et al., 2000; Leuthold & Sommer, 1999). The current results suggest that MSN function is not important for these processes. This is line with other data also showing no modulations of the Pe in a neurodegenerative disease affecting MSNs (Beste, Saft, Konrad, Andrich, Habbel, Schepers et al., 2008) and is in line with data suggesting an important role of the anterior insula, but not of the striatum, for processes related to the Pe (Ullsperger, Harsay, Wessel, & Ridderinkhof, 2010). An obvious limitation of the study is that only a single-case was examined, despite usage of robust single-case statistics. Future studies should provide data on larger sample sizes and also knockout animal models may be investigated to examine the relevance MSNs for the examined cognitive control functions in more detail. In summary, the results provide first insights into the involvement of striatal MSNs for post-error behavioral adaptation processes. The study relates genetically determined structural microneuroanatomical changes with electrophysiological mechanisms of behavioral adaptation processes. It shows that in a clinical condition previously thought not to display cognitive disturbances (for review: Inzelberg & Weinberger, 2011) dysfunctions in executive control are evident.
Acknowledgments This research was supported by a Grant from the Deutsche Forschungsgemeinschaft (DFG) BE4045/10-1 to C.B.
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