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November 05, 2013; 81 (19) Article

Poor reward sensitivity and apathy after stroke

Implication of basal ganglia

Lucien Rochat, Martial Van der Linden, Olivier Renaud, Jean-Benoît Epiney, Patrik Michel, Roman Sztajzel, Lucas Spierer, Jean-Marie Annoni
First published October 9, 2013, DOI: https://doi.org/10.1212/01.wnl.0000435290.49598.1d
Lucien Rochat
From the Cognitive Psychopathology and Neuropsychology Unit (L.R., M.V.d.L.), Swiss Centre for Affective Sciences (L.R., M.V.d.L.), Methodology & Data Analysis (O.R.), and Department of Neuroscience, University Medical Center (R.S.), University of Geneva, Switzerland; Cognitive Psychopathology Unit (M.V.d.L.), University of Liège, Belgium; Neurology Unit (L.R., J.-B.E., L.S., J.-M.A.), Department of Medicine, Faculty of Sciences, University of Fribourg; and Department of Neurology (P.M.), Centre Hospitalier Universitaire Vaudois and University of Lausanne, Switzerland.
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Martial Van der Linden
From the Cognitive Psychopathology and Neuropsychology Unit (L.R., M.V.d.L.), Swiss Centre for Affective Sciences (L.R., M.V.d.L.), Methodology & Data Analysis (O.R.), and Department of Neuroscience, University Medical Center (R.S.), University of Geneva, Switzerland; Cognitive Psychopathology Unit (M.V.d.L.), University of Liège, Belgium; Neurology Unit (L.R., J.-B.E., L.S., J.-M.A.), Department of Medicine, Faculty of Sciences, University of Fribourg; and Department of Neurology (P.M.), Centre Hospitalier Universitaire Vaudois and University of Lausanne, Switzerland.
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Olivier Renaud
From the Cognitive Psychopathology and Neuropsychology Unit (L.R., M.V.d.L.), Swiss Centre for Affective Sciences (L.R., M.V.d.L.), Methodology & Data Analysis (O.R.), and Department of Neuroscience, University Medical Center (R.S.), University of Geneva, Switzerland; Cognitive Psychopathology Unit (M.V.d.L.), University of Liège, Belgium; Neurology Unit (L.R., J.-B.E., L.S., J.-M.A.), Department of Medicine, Faculty of Sciences, University of Fribourg; and Department of Neurology (P.M.), Centre Hospitalier Universitaire Vaudois and University of Lausanne, Switzerland.
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Jean-Benoît Epiney
From the Cognitive Psychopathology and Neuropsychology Unit (L.R., M.V.d.L.), Swiss Centre for Affective Sciences (L.R., M.V.d.L.), Methodology & Data Analysis (O.R.), and Department of Neuroscience, University Medical Center (R.S.), University of Geneva, Switzerland; Cognitive Psychopathology Unit (M.V.d.L.), University of Liège, Belgium; Neurology Unit (L.R., J.-B.E., L.S., J.-M.A.), Department of Medicine, Faculty of Sciences, University of Fribourg; and Department of Neurology (P.M.), Centre Hospitalier Universitaire Vaudois and University of Lausanne, Switzerland.
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Patrik Michel
From the Cognitive Psychopathology and Neuropsychology Unit (L.R., M.V.d.L.), Swiss Centre for Affective Sciences (L.R., M.V.d.L.), Methodology & Data Analysis (O.R.), and Department of Neuroscience, University Medical Center (R.S.), University of Geneva, Switzerland; Cognitive Psychopathology Unit (M.V.d.L.), University of Liège, Belgium; Neurology Unit (L.R., J.-B.E., L.S., J.-M.A.), Department of Medicine, Faculty of Sciences, University of Fribourg; and Department of Neurology (P.M.), Centre Hospitalier Universitaire Vaudois and University of Lausanne, Switzerland.
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Roman Sztajzel
From the Cognitive Psychopathology and Neuropsychology Unit (L.R., M.V.d.L.), Swiss Centre for Affective Sciences (L.R., M.V.d.L.), Methodology & Data Analysis (O.R.), and Department of Neuroscience, University Medical Center (R.S.), University of Geneva, Switzerland; Cognitive Psychopathology Unit (M.V.d.L.), University of Liège, Belgium; Neurology Unit (L.R., J.-B.E., L.S., J.-M.A.), Department of Medicine, Faculty of Sciences, University of Fribourg; and Department of Neurology (P.M.), Centre Hospitalier Universitaire Vaudois and University of Lausanne, Switzerland.
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Lucas Spierer
From the Cognitive Psychopathology and Neuropsychology Unit (L.R., M.V.d.L.), Swiss Centre for Affective Sciences (L.R., M.V.d.L.), Methodology & Data Analysis (O.R.), and Department of Neuroscience, University Medical Center (R.S.), University of Geneva, Switzerland; Cognitive Psychopathology Unit (M.V.d.L.), University of Liège, Belgium; Neurology Unit (L.R., J.-B.E., L.S., J.-M.A.), Department of Medicine, Faculty of Sciences, University of Fribourg; and Department of Neurology (P.M.), Centre Hospitalier Universitaire Vaudois and University of Lausanne, Switzerland.
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Jean-Marie Annoni
From the Cognitive Psychopathology and Neuropsychology Unit (L.R., M.V.d.L.), Swiss Centre for Affective Sciences (L.R., M.V.d.L.), Methodology & Data Analysis (O.R.), and Department of Neuroscience, University Medical Center (R.S.), University of Geneva, Switzerland; Cognitive Psychopathology Unit (M.V.d.L.), University of Liège, Belgium; Neurology Unit (L.R., J.-B.E., L.S., J.-M.A.), Department of Medicine, Faculty of Sciences, University of Fribourg; and Department of Neurology (P.M.), Centre Hospitalier Universitaire Vaudois and University of Lausanne, Switzerland.
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Poor reward sensitivity and apathy after stroke
Implication of basal ganglia
Lucien Rochat, Martial Van der Linden, Olivier Renaud, Jean-Benoît Epiney, Patrik Michel, Roman Sztajzel, Lucas Spierer, Jean-Marie Annoni
Neurology Nov 2013, 81 (19) 1674-1680; DOI: 10.1212/01.wnl.0000435290.49598.1d

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Abstract

Objective: To examine the relationship between reward sensitivity and self-reported apathy in stroke patients and to investigate the neuroanatomical correlates of both reward sensitivity and apathy.

Methods: In this prospective study, 55 chronic stroke patients were administered a questionnaire to assess apathy and a laboratory task to examine reward sensitivity by measuring motivationally driven behavior (“reinforcement-related speeding”). Fifteen participants without brain damage served as controls for the laboratory task. Negative mood, working memory, and global cognitive functioning were also measured to determine whether reward insensitivity and apathy were secondary to cognitive impairments or negative mood. Voxel-based lesion-symptom mapping was used to explore the neuroanatomical substrates of reward sensitivity and apathy.

Results: Participants showed reinforcement-related speeding in the highly reinforced condition of the laboratory task. However, this effect was significant for the controls only. For patients, poorer reward sensitivity was associated with greater self-reported apathy (p < 0.05) beyond negative mood and after lesion size was controlled for. Neither apathy nor reward sensitivity was related to working memory or global cognitive functioning. Voxel-based lesion-symptom mapping showed that damage to the ventral putamen and globus pallidus, dorsal thalamus, and left insula and prefrontal cortex was associated with poorer reward sensitivity. The putamen and thalamus were also involved in self-reported apathy.

Conclusions: Poor reward sensitivity in stroke patients with damage to the ventral basal ganglia, dorsal thalamus, insula, or prefrontal cortex constitutes a core feature of apathy. These results provide valuable insight into the neural mechanisms and brain substrate underlying apathy.

GLOSSARY

BG=
basal ganglia;
CRRT=
Cued Reinforcement Reaction Time;
RT=
response time;
VLSM=
voxel-based lesion-symptom mapping;
WM=
working memory

Apathy, conceptualized as a motivational disturbance evidenced by reduced overt acts, cognitive activity, and affective responses, is a frequent and disabling condition in stroke patients.1,–,6 Although apathy is frequently associated with global cognitive impairment and depression, it might also be an isolated manifestation that specifically predicts poor outcome.2,4,7,–,9 According to lesion studies, apathy has been associated with damage to subcortical-frontal circuits, including the medial and dorsolateral prefrontal cortex, anterior cingulate, basal ganglia (BG), caudate nucleus, thalamus, and white matter tracts connecting these regions.10,–,15 This diversity of cerebral structures supports the view that a variety of processes underlies apathy. In particular, recent studies provide preliminary evidence for reward processing abnormalities, such as difficulties in modulating behavior in response to incentives that have been specifically related to apathy in patients with BG lesions.16,–,18 In contrast to demonstrated motivational deficits, the hedonic response to reward (consummatory pleasure) appeared to be intact in these patients.16

Although reward insensitivity constitutes a core feature of apathy, no studies have so far examined whether this process relates to self-reported apathy in a consecutive series of stroke patients. Thus, the aims of the study were to examine i) the contribution of reward insensitivity to self-reported apathy in stroke patients, after controlling for negative mood, cognitive functioning, lesion size, functional outcomes, and demographic variables; and ii) the neuroanatomical correlates of reward sensitivity and self-reported apathy by using voxel-based lesion-symptom mapping (VLSM).

METHODS

Participants.

Eighty-eight consecutive referrals to the cerebrovascular outpatient center, University Hospitals of Geneva and Lausanne (Switzerland), were invited to participate in this study from November 2009 through July 2010. Inclusion criteria were as follows: i) younger than 75 years; ii) first single ischemic or hemorrhagic stroke documented by MRI and/or CT scan; iii) poststroke delay ≥3 months; and iv) a modified Rankin score of ≤3 at the time of assessment to exclude confounding effects of motor or sensorial disorders. Exclusion criteria were a history of neurologic or psychiatric disorders, vascular or degenerative dementia, or signs of aphasia or dysarthria. Among the 88 patients who were initially contacted, 55 gave their consent to participate in the study. The 55 patients were aged 56.4 ± 13.1 years (mean ± SD; range = 23–75 years) and had 13.3 ± 3.5 years of education (4–21 years). The neuropsychological assessment was conducted 24.1 ± 36.1 months after lesion onset (3–148 months). The mean score on the NIH Stroke Scale at hospital admission was 4.68 ± 4.20 (0–17), and the modified Rankin score at the time of assessment was 0.62 ± 0.88 (0–3). Age, sex, and initial severity of the stroke did not differ between the 33 patients who declined and the 55 who participated in the study (all p values >0.05). A power analysis suggests that 55 participants are sufficient to detect a medium‐sized effect with an α of 0.05 and a desired power of 0.80 in a correlation analysis.

Fifteen healthy participants (9 males; aged 56.1 ± 5.8 years; 13.9 ± 3.3 years of education) served as a control group. Control participants were recruited from the community through advertisements or personal contacts.

Standard protocol approvals, registrations, and patient consents.

All participants provided written informed consent to participate in the study. The study was approved by the ethics committees of the University Hospitals of Geneva and Lausanne, Switzerland.

Assessment of apathy.

Apathy was assessed with the self-reported version of the Apathy Inventory.19 This scale assesses 3 dimensions of apathy: emotional blunting, lack of initiative, and lack of interest. This score ranges from 0 to 36, with a higher score indicating more severe apathy. Because this scale is standardly used to evaluate apathy changes since the onset of neurologic diseases, it was not administered to control participants.

Assessment of negative mood.

Negative mood was assessed with the Short Depression and Happiness Scale, which consists of 6 items that assess the levels of positive and negative mood, happiness, and depression.20 The total score was computed by averaging the responses obtained across the 6 items. Scores of the happiness items were reversed so that a high score reflected a tendency to experience negative mood, a characteristic of depression that does not overlap with the apathy dimensions.

Assessment of working memory and global cognitive functions.

Working memory (WM) was assessed with the Letter-Number Sequencing task,21 a measure of verbal WM that involves both retention and manipulation of information and constitutes a good predictor of fluid intelligence.22 A higher score indicates better WM performances. Global cognitive functioning was assessed with the Mini-Mental State Examination.23

Reward sensitivity task.

The Cued Reinforcement Reaction Time (CRRT) task assesses reward sensitivity by measuring the speed of responses after the presentation of cues that indicate a higher probability of reward.24 In the CRRT, participants had to perform a rapid “odd-one-out” judgment on 3 shapes, one of which was distinct from the others. The presentation of stimuli was preceded by a cue (a colored rectangle of 1 of 3 colors) indicating the likelihood that a correct response would be followed by a reward. The 3 colors of the cue were associated with a 10%, 50%, and 90% reinforcement probability (hereafter called conditions 10, 50, and 90, respectively). The association between colors and probability of reinforcement was counterbalanced across participants. Participants were naive to the association between the probability of reward and each color. No feedback was presented and no points were obtained on the unreinforced trials. An implicit associative learning task was selected inasmuch as this type of task does not excessively rely on higher order controlled cognitive processes, which are often impaired in patients with brain damage.

The CRRT consists of 96 trials, with 32 trials of each cue type. Participants responded with their dominant hand on the keyboard and reinforced trials were immediately followed by feedback: a green smiley face was displayed after the correct identification of the odd one out, whereas a red sad face followed incorrect responses. The magnitude of reinforcement depended on both the accuracy and the response time (RT): a fast correct response was rewarded with 100 points, a slow correct response with 1 point, and an incorrect response with 0 points. Feedback and points were simultaneously delivered on the screen. The goal of the task was to obtain as many points as possible. After task completion, a debriefing questionnaire was administered to supply a measure of explicit awareness of stimulus reinforcement contingencies. Participants were asked to provide an estimate of the reinforcement probability for each color corresponding to the various conditions of the task and a degree of confidence about their responses.

RT thresholds were adjusted for each individual participant during a practice session, which consisted of 2 blocks of 20 trials of the same odd-one-out circle judgment, but where no cue or feedback was provided. The first block of 20 trials served to familiarize the participants with the task, whereas the mean RT and SD for the second practice block were used to compute a cutoff for reward delivery in the main task. The cutoff for reward was calculated by subtracting the SD from the mean RT. Consequently, reward attainment levels in the test phase were adjusted to an individual participant's psychomotor speed.

Data analyses.

Group comparisons (t tests) of stroke patients and controls were performed for demographic, cognitive, and affective variables. A multilevel model25 was conducted on the CRRT task, with the subjects at level 2 and trials at level 1. This analysis fully corresponds to the design and allows us to take into account every effect that may influence the RT (group, condition, learning, and autocorrelation). Because learning showed nonlinear trends, a quadratic learning effect (over the trials) crossed with the group membership and the condition were used as predictors. The condition effect was dummy-coded in 2 variables: condition 90 against the other 2 conditions, and condition 10 vs 50. In addition, the linear and quadratic learning effects were centered at the last trial, so that the other effects corresponded to the differences at the end of the task. Furthermore, to measure reinforcement independently of the general learning process that is also present in the CRRT task, we computed a reinforcement index for each patient. It corresponds to the response difference in the predicted RT at the end of the task (i.e., at the end of the learning process) between the high reinforcement probability condition 90 and the low condition 10. The greater the reinforcement index, the lower the insensitivity to reward. Correlation and a hierarchical regression analysis were finally performed to examine the extent to which sensitivity to reward (assessed with the reinforcement index) contributed to apathy after other variables significantly associated with apathy were controlled for. Nonparametric tests were used when necessary. A 2-sided p value <0.05 was considered significant for all statistical tests. A specific procedure was used to control for type I error in the regression analysis.26

Voxel-based lesion-symptom mapping.

Brain lesions were manually reported on axial slices of the standard Montreal Neurological Institute's brain template by using MRIcro software27,28 according to previously described methods.29 Lesions were reported on the template brain by an experienced neurologist (J.-M.A.) who was naive to the clinical profiles of the patients.30 To determine brain areas where damage had an impact on our dependent variables, we submitted these normalized lesions to statistical mapping analyses by using VLSM algorithms implemented in MRIcron and NPM software.31,32 Voxel-wise Brunner-Munzel nonparametric tests were performed to compare continuous scores between patients with and without a lesion in each voxel. All analyses included only voxels that were damaged in at least 3 patients. Only voxels surviving a significance threshold of p < 0.05, uncorrected, were considered in the results, but, where necessary, voxels surviving a significance threshold of p < 0.10, uncorrected, were also considered.

RESULTS

Group comparisons.

Independent sample t tests between the scores of patients and controls revealed that control participants had higher global cognitive functioning and WM scores than patients did (p < 0.01), whereas age, years of education, and negative mood did not differ between the 2 groups (table 1). For the CRRT task, the multilevel model showed a main quadratic effect of learning (t6,164 = 6.55, p < 0.001) and its interaction with group (t6,164 = −2.90, p = 0.004), a main effect of group (t68 = 4.81, p < 0.001), and a main effect of condition 90 vs the other 2 conditions (t6,164 = −3.05, p = 0.002) and their interaction (t6,164 = 2.33, p = 0.020). No other effect reached significance. These results indicated that participants decreased their RT in the highly reinforced condition compared with the other conditions. The interaction (figure 1) highlighted that this effect was significant only in the control group (simple effects for the controls: (t6,164 = −3.03, p = 0.002); and for the patients: (t6,164 = −1.24, p = 0.216). Other models incorporating temporal correlation and more complex random effects, as well as extensive diagnostic analyses, showed that the presented model fits the data very well. Note that the analysis has been performed on the logarithm of RT, but analyses with the untransformed RT showed very similar significant results.

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Table 1

Means, SDs, and results of group comparisons for stroke and control groups

Figure 1
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Figure 1 Interaction between group and condition in the Cued Reinforcement Reaction Time task

The prediction at the last trial is depicted, representing the effect of condition and group at the end of the learning process. C10, C50, and C90 represent color cues associated with 10%, 50%, and 90% reinforcement probability, respectively. The 2 main effects and the interaction are significant.

On the debriefing questionnaire, neither patients nor controls revealed reliable explicit knowledge of the cue-outcome contingencies for the explicit ratings on the CRRT task. In addition, group comparisons (Mann-Whitney U test) showed that the 2 groups did not differ on the identification of the reinforcement probability (z = −1.77, p = 0.08) or the degree of certainty (z = −1.74, p = 0.08).

Correlation analysis.

Correlation analysis performed on the patient group revealed that greater self-reported apathy was associated with greater negative mood, larger lesion size, and lower reinforcement-related speeding (table 2). No other correlation reached statistical significance. There was no association between the degree of explicit knowledge of the cue-outcome contingencies in the CRRT task and the reinforcement index for patients and controls (p > 0.05).

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Table 2

Correlation between self-reported apathy and other variables in patients with stroke (n = 55)

Hierarchical regression analysis.

A hierarchical regression analysis (table 3) was performed to examine the specific contribution of reward insensitivity to the total apathy score while controlling for lesion size and negative mood. Because lesion size and negative mood significantly correlated with apathy, they were entered in the first step of the regression, followed by the reinforcement index in a second step.

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Table 3

Hierarchical regression for total score of self-reported apathy

In the first step, only negative mood was found to be a significant predictor of the total apathy score. In the second step, negative mood and the reinforcement index were both significant predictors of apathy. In addition, the R2 change of the second step was significant, suggesting that the reinforcement index explains a significant additional part of variance (9%) regarding lesion size and negative mood.

Lesion analysis.

Lesion overlay plots of patients are displayed in figure 2A. VLSM analysis was performed to reveal lesion sites associated with reward sensitivity as assessed by the reinforcement index of the CRRT task. Lesions to the bilateral ventral putamen and globus pallidus, dorsal thalamus, and left insula and prefrontal dorsolateral cortex were associated with poorer reward sensitivity (figure 2B). Self-reported apathy was associated with bilateral thalamic, right putaminal, insular, and dorsolateral prefrontal cortex damage (figure 2C).

Figure 2
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Figure 2 Neuroanatomical data

Brain slices are displayed from z-coordinates (labeled under each template) of the Montreal Neurological Institute space, with the left hemisphere on the right side. Only voxels surviving a significance threshold of p < 0.10, uncorrected, were considered in the results for the total apathy score. CRRT = Cued Reinforcement Reaction Time.

DISCUSSION

First, multilevel analysis indicated that in addition to learning effects, participants decrease their RT in the highly reinforced condition compared with the other conditions, providing evidence that this task elicits reinforcement. However, this difference reached significance for the controls only. Second, we examined the association between self-reported apathy and reward sensitivity, as well as their neuroanatomical correlates, in 55 stroke patients. Regression analysis showed that patients who did not decrease their RT when a cue indicated a high probability of reward reported more severe apathy, even when negative mood and lesion size were controlled for.

Our results corroborate previous evidence that reward insensitivity constitutes a key component of apathy.12,16,17 At a neuroanatomical level, the VLSM analysis revealed that the ventral putamen, globus pallidus, and dorsal thalamus were associated with poor reward sensitivity, even if participants were not aware of the cue-outcome contingencies. These results are in line with studies indicating that the BG is a key network for motivation processes that enable expected rewards to energize behavior without the need for the participants' awareness.33 However, the data point to other brain areas, such as the insula or the prefrontal cortex, which may also be relevant. In particular, the insula is thought to be involved in the representation of bodily (interoceptive) states,34 a process that might be at play in approach behavior and reward sensitivity. Finally, there was a partial overlap between lesions associated with self-reported apathy and reward insensitivity, thus corroborating the view that apathy is not a unitary construct, but rather depends on different types of mechanisms.12

Our study also extends previous work by specifically examining the influence of incentives on RT while controlling for potentially interacting variables, such as negative mood, lesion size, and cognitive performances. From this perspective, we showed that poor reward sensitivity was not confounded by these variables because WM, global cognitive functioning, lesion size, and negative mood were not related to the reinforcement index. This absence of a significant relationship between basic cognitive variables and reward sensitivity may be explained by the implicit nature of the task that requires few cognitive controlled processes to be adequately completed, as well as by the reward attainment levels in the test phase that were adjusted to the participant's own psychomotor speed.

Numerous studies show that the subcortical dopamine systems have a central role in reward and motivational processing, as well as in apathetic manifestations.12,35,36 Dopamine has been associated with motivation, anticipation of reward, and prediction error signaling rather than with consummatory behaviors.36 Because dopamine acts as a modulating system facilitating reward-dependent learning, performances on the CRRT task may be secondary to dopamine dysfunction.12,24 In this context, apathetic syndrome resulting from a focal lesion within the BG and/or thalamic structures can be explained by disruption of a functional circuit leading to difficulties in selection and/or signal amplification in the prefrontal cortex, resulting in a decreased effort to reach a goal or a reward.12 These data are consistent with the hypothesis that apathy may at least partially result from fronto-BG dysfunction, a network involved in the generation and control of self-generated purposeful behaviors.12 Additional studies are necessary to determine whether such results could help distinguish patients who might benefit from pharmacologic agents that potentiate dopamine release and/or delayed dopamine reuptake.37

Our results also showed a strong association between negative mood and apathy. Indeed, negative mood may contribute to apathy by increasing the subjective difficulty of tasks or goals, which in turn results in a disengagement of the task.38 However, poor reward sensitivity explains a significant additional part of the variance in the total apathy score with respect to negative mood. Thus, our data add new evidence for motivation abnormalities in stroke patients with difficulties in modulating their behavior in response to incentives. In this context, the CRRT task may be considered a relevant measure of apathy that might easily be implemented in clinical settings. Several limitations of the study should, however, be mentioned. First, our sample of patients was small and constituted only those with overall good recovery or with minor to moderate functional impairment. Second, there was a wide range in the stroke-to-test interval. Third, the precision of the VLSM analyses was limited by the fact that they were based on clinical MRI and CT scans that do not reach the quality and stability of dedicated research scanning at high resolutions. Finally, with a small sample size such as in the current study, the VLSM results are highly dependent on the spatial distribution of the lesion, which not only influences where in the brain the probability of finding an effect is the highest, but also where false-negative results are more likely to manifest.32 Therefore, these results should be interpreted with caution.

AUTHOR CONTRIBUTIONS

L. Rochat provided the design of the study, participated in the statistical analyses and their interpretations, and wrote the manuscript. M. Van der Linden provided the design of the study and contributed to the interpretations of the results, as well as to the editing and review of the final manuscript. O. Renaud participated in the statistical treatment of the data, contributed to the interpretations of the results, and reviewed the final manuscript. J.-B. Epiney conducted the data acquisition for both the patients and control participants and participated in the statistical treatment of the data. P. Michel and R. Sztajzel assisted in the recruitment of patients and reviewed the final manuscript. L. Spierer participated in the statistical treatment of the data and reviewed the final manuscript. J.-M. Annoni provided the design of the study and contributed to the interpretations of the results, as well as to the editing and review of the final manuscript.

STUDY FUNDING

Supported by the Swiss National Science Foundation to Prof. J.-M. Annoni (grant 325130_138497).

DISCLOSURE

L. Rochat, M. Van der Linden, O. Renaud, J.-B. Epiney, P. Michel, R. Sztajzel, and L. Spierer report no disclosures. J.-M. Annononi is supported by the Swiss National Science Foundation (grant 325130_138497). Go to Neurology.org for full disclosures.

ACKNOWLEDGMENT

The authors thank A. Blum, C. Bendahan, R. Bressoud, and P. Mayer for their help in collecting the data, and Barbara Every, ELS, at BioMedical Editor for copyediting services.

Footnotes

  • Go to Neurology.org for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.

  • Received November 10, 2012.
  • Accepted in final form August 13, 2013.
  • © 2013 American Academy of Neurology

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