J Neural Transm DOI 10.1007/s00702-014-1271-6
NEUROLOGY AND PRECLINICAL NEUROLOGICAL STUDIES - ORIGINAL ARTICLE
Impaired cognitive control in Parkinson’s disease patients with freezing of gait in response to cognitive load Courtney C. Walton • James M. Shine • Loren Mowszowski • Moran Gilat • Julie M. Hall • Claire O’Callaghan • Sharon L. Naismith • Simon J. G. Lewis
Received: 31 May 2014 / Accepted: 3 July 2014 Ó Springer-Verlag Wien 2014
Abstract Freezing of gait is a frequent and disabling symptom experienced by many patients with Parkinson’s disease. A number of executive deficits have been shown to be associated with the phenomenon suggesting a common underlying pathophysiology, which as of yet remains unclear. Neuroimaging studies have also implicated the role of the cognitive control network in patients with freezing. To explore this concept, the current study examined errormonitoring as a measure of cognitive control. Thirty-four patients with and 38 without freezing of gait, who were otherwise well matched on disease severity, completed a colour-word interference task that allowed the specific assessment of error monitoring during conflict. Whilst both groups performed colour-naming and word-reading tasks equally well, those patients with freezing showed a pattern between conditions whereby they were better able to monitor performance and self-correct errors in the pure inhibition task but not after a switching rule was introduced. The novel results shown here provide insight into possible pathophysiological mechanisms involved in cognitive load and error
C. C. Walton J. M. Shine L. Mowszowski M. Gilat J. M. Hall S. L. Naismith S. J. G. Lewis (&) Parkinson’s Disease Research Clinic, Brain and Mind Research Institute, University of Sydney, Sydney, NSW, Australia e-mail: [email protected] C. C. Walton L. Mowszowski S. L. Naismith Healthy Brain Ageing Program, Brain and Mind Research Institute, University of Sydney, Sydney, NSW, Australia C. O’Callaghan Neuroscience Research Australia and School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
monitoring in patients with freezing of gait. These results provide further evidence for the role of functional frontostriatal circuitry impairments in patients with freezing of gait and have implications for future studies and possible therapeutic interventions. Keywords Freezing of gait Parkinson’s disease Executive function Error monitoring Stroop task Cognitive control
Introduction Freezing of gait (FOG) is a debilitating symptom experienced by many patients with Parkinson’s disease (PD) (Giladi et al. 2001). The complex phenomenon is clinically defined as the sudden inability to generate effective stepping and forward progression despite the intention to do so and is often described as feeling as though one’s feet are glued to the ground (Nutt et al. 2011). The current treatment options for FOG are limited (Walton et al. 2014), and its occurrence leads to a significantly increased risk of falls and reduced quality of life (Gray and Hildebrand 2000; Moore et al. 2007) highlighting the importance of gaining an increased understanding of its manifestation. At present the pathophysiology underlying FOG is poorly understood (Heremans et al. 2013b), although dysfunction of frontostriatal pathways is thought to be critically involved (Hall et al. 2014; Lewis and Barker 2009; Shine et al. 2013d). One area of increasing interest in the literature is the distinct pattern of neuropsychological deficits demonstrated by PD patients with FOG, which may inform our understanding of the underlying neural mechanisms of freezing. A range of specific executive impairments are greater in patients with FOG than in those
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without. This has led many authors to suggest that particular cognitive functions may be intimately related to freezing behaviour (For reviews see: Heremans et al. 2013a; Walton et al. 2014). General executive deficits have been reported in patients with FOG (Amboni et al. 2010, 2008), more specifically in the domains of set-shifting (Naismith et al. 2010; Shine et al. 2013e), conflict resolution and inhibitory control (Cohen et al. 2014; Matar et al. 2013; Vandenbossche et al. 2011, 2012) and implicit learning (Vandenbossche et al. 2013b). These findings have been further validated by recent functional neuroimaging studies, which have implicated the dysfunctional role of a putative cognitive control network (CCN) operating through both fronto-parietal cortices and subcortical structures including the subthalamic nucleus (STN) and striatum (Shine et al. 2013a, b, c). These neuroanatomical insights raise the possibility that the executive deficits documented in FOG may be underpinned to an extent by impairments in cognitive control, which refers to the ability to control goal directed behaviour by organizing and optimising appropriate information processing to flexibly respond to changes in predicted behavioural outcomes (Narayanan et al. 2013; Ridderinkhof et al. 2004). Taking these findings together leads to an intriguing proposition: given FOG has been hypothesised to occur as a result of a decreased neural reserve for coping with demands on the frontostriatal system (Lewis and Barker 2009), it could be suggested that under highly demanding cognitive situations, patients with FOG display an inefficient recruitment of cognitive control. Therefore, the aim of the current study was to further explore impairments in cognitive control using increasingly demanding conditions of a verbal Stroop task. By using a verbal rather than computerized format, the stimulus used in the task matches the form of response needed (i.e., word– word rather than word-button) leading to more automatic responding (Kornblum et al. 1990). This is important in adequately differentiating between automatic versus effortful processing (Vandenbossche et al. 2013a). Further, the current Stroop task records both corrected and uncorrected errors. Thus, different interpretations regarding cognitive control can be recorded than in previous measures of Stroop performance in FOG (Cohen et al. 2014; Vandenbossche et al. 2012). An error that is corrected would imply cognitive control has been enforced, albeit late. Conversely, uncorrected errors are suggestive of poor cognitive control. In the current study, we sought to investigate novel measures of error making and monitoring in PD patients with and without FOG. We propose that if the CCN underpins the pathophysiology of FOG, patients with FOG should produce more uncorrected errors than patients without FOG during the most demanding condition of the task which requires set-shifting. Such a finding would
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demonstrate inefficient recruitment of cognitive control regions under high cognitive load, specifically occurring under switching demands.
Experimental procedures Participants Participants were recruited from a larger cohort of patients prospectively evaluated at the Parkinson’s Disease Research Clinic at the Brain and Mind Research Institute, University of Sydney. The diagnosis of idiopathic PD was based on the UK brain bank clinical criteria (Hughes et al. 1992) and was confirmed by a neurologist (SJGL). The 109 patients originally included in this study were divided into two groups based on their score on the FOG-Questionnaire item 3 [FOGQ-3: ‘‘Do you feel that your feet get glued to the floor while walking, making a turn, or trying to initiate walking (freezing)?’’], which has been shown to be a reliable screening tool to identify freezers (Giladi et al. 2000). Higher scores on FOGQ-3 indicate increased freezing severity and patients who scored 2 or higher were included in the FOG group, while those scoring 0 were placed in the no-FOG group. Patients scoring 1 were not included in the study to minimise the possibility of misclassification. Exclusion criteria included the presence of other neurological diseases, other conditions that would impair gait and a score of B24 on the mini mental state examination (MMSE) (Folstein et al. 1975). All patients were assessed on their regular medication. This research was approved by the Human Research Ethics Committee of The University of Sydney, and written informed consent was obtained from all participants. Clinical assessments A series of demographic details were recorded, including age, gender, and predicted premorbid intellectual functioning (revised national adult reading test; NART-R) (Nelson and Willison 1991). Clinical features including disease duration, Hoehn & Yahr stage (H&Y) (Hoehn and Yahr 1967), dopamine dose equivalence (DDE; mg/day) (Tomlinson et al. 2010) and disease severity using the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) (Goetz et al. 2008), of which the total score and section III (motor sub-score) were assessed. Section ‘‘Demographic and clinical data’’ of the MDS-UPDRS was also individually recorded to ensure verbal responses to the task did not simply reflect problems with speech output. Additionally, self-reported mood disturbance using the beck depression inventory (BDI-II) (Beck et al. 1996) and global cognitive functioning using
Impaired cognitive control in Parkinson’s disease patients
the MMSE (Folstein et al. 1975) were used to assess for depression and dementia, respectively. Inhibition and switching tasks The task used in the current study was the Delis–Kaplan executive function system (D-KEFS) colour-word interference test (Delis et al. 2001). This task contains four conditions: colour-naming (condition 1), word-reading (condition 2), inhibition (condition 3) and inhibition/ switching (condition 4). In all conditions, the patient is asked to read the colours or words aloud depending on the rule given, as quickly as possible while minimising errors. There are 50 words presented on the page in each condition. Participants practice on ten word trials prior to the recorded version to ensure they understand the rules. During the task, participants are prompted if they make three consecutive errors and reminded of the rules. This occurs only once during each condition, however. Participants are told they can correct any errors as they progress through the task. In condition 1, participants must name aloud the colour of red, green or blue squares on the page. In condition 2, participants must read through a page containing the words ‘‘red’’, ‘‘green’’ and ‘‘blue’’ printed in black ink. In condition 3, the same words in differing order are presented in incongruent ink colour (e.g., the word ‘‘red’’ appears in blue ink). The participant is asked to read aloud the colour of the ink as quickly as possible without making mistakes. This task, therefore, requires the participant to inhibit a more automatic response (word reading). In condition 4, the participant is presented with incongruent colour words as in the previous condition but a selection of the colour words are surrounded by a black rectangular outline. For these selected words, the patient is asked to read the word written and not to name the colour of the ink, thus requiring them to set-shift in addition to maintaining inhibitory control (condition 4 requires 37 switches). Outcome measures include the total time taken to complete each condition (raw and age scaled scores) (Delis et al. 2001) as well as the number of uncorrected (UC), self-corrected (SC) and total errors. In this study, contrast scores between conditions were not calculated due to their known unreliability for this test (Crawford et al. 2008). Statistical analysis This study employed a matched-groups sample to address common critiques of other neuropsychological assessments of FOG, which do not often account for disease severity (Heremans et al. 2013a). Therefore, cases were removed prior to task analysis where outliers for descriptive and clinical variables led to significantly differing groups. This was done in a stepwise fashion, removing patients with
particularly high or low MDS-UPDRS scores until there was no significant group difference on this variable. Subsequently, 34 patients with FOG and 38 without were retained in the sample, matched on most key demographic variables (see Table 1). Data analyses were conducted using the Statistical Package for the Social Sciences (SPSS) version 21. Due to the non-parametric nature of the task data, Mann–Whitney U tests were conducted to assess for differences between groups. Due to unequal variances for error scores, the data were additionally analysed by categorising the data and using Pearson’s Chi Square test to ensure the robustness and reliability of the results. For these analyses, error scores were split into those who scored 0 or 1 error, and those who scored 2 or more errors (multiple errors). Where the assumption of expected counts was violated, Fisher’s exact test statistic was used (FET). Where data met parametric assumptions for descriptive statistics, t-tests were used. Wilcoxon signed ranks test was used to assess within-groups differences across conditions. All analyses used an alpha of 0.05 and were two-tailed. The effect size for differences between groups using Mann– Whitney U and Wilcoxon signed ranks tests was produced using the following formula (r = Z/HN) while Crame´r’s V was used for the Chi Square statistic. For both values, 0.10 represents a small, 0.30 a medium, and 0.50 a large effect. One patient with FOG was unable to complete condition 4 despite sufficiently completing the prior three conditions, and, therefore, a maximum time score was applied in accordance with standard D-KEFS procedure and their error scores were not included in the analysis of this condition.
Results Demographic and clinical data Table 1 displays the median, range, mean and standard deviations for demographic and clinical data. Colour-word interference test For condition 1 and 2, there were no significant differences between groups for any measure, suggesting no difference between groups in simple reading or naming speed. Table 2 displays the median, range, mean, standard deviation and effect sizes for condition 3 and 4 representing the critical trials in the study. For condition 3, there was no significant difference between groups in completion time for raw (U = 513.5, Z = -1.50, p = 0.135) or age-scaled time scores (U = 502, Z = -1.63, p = 0.102). There was also no significant difference between groups in UC errors (U = 576, Z = -1.06, p = 0.288). However, there were significant differences in SC errors (U = 473.5, Z = -2.04,
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C. C. Walton et al. Table 1 Descriptive and clinical data for patients with and without FOG FOG
No FOG
Significance
N
34
38
–
Gender (% male)
68 %
63 %
0.81a
Age (years)
66.44 ± 8.8
66.76 ± 8.1
0.87b
FOGQ-3
3 (2)/2.82 ± 0.8
–
–
NART-R
106 (35)/107.88 ± 9.4
115 (55)/112.00 ± 10.5
0.06
MMSE
28 (5)/28.21 ± 1.5
29 (3)/29.05 ± 1.1
0.01
Disease duration (years)
6.54 ± 3.9
5.7 ± 3.6
0.37b
Hoehn & Yahr stage
2 (3)/2.18 ± 0.6
2 (1)/2.08 ± 0.2
0.40
DDE
650 (1820)/723.44 ± 452.9
575 (1508)/605.53 ± 310.4
0.42
MDS-UPDRS total
51.39 ± 20.4
44.79 ± 18.9
0.16b
MDS-UPDRS-III MDS-UPDRS-speech
22 (58)/28.06 ± 15.3 1 (2)/0.75 ± 0.7
23.5 (68)/26.47 ± 13.0 1 (4)/0.86 ± 0.9
0.95 0.71
BDI-II
9 (30)/11.81 ± 7.4
7 (25)/8.63 ± 6.1
0.04
Scores represent median (range)/mean ± standard deviation FOGQ-3 Freezing of gait questionnaire-question 3, NART-R revised national adult reading test, MMSE minimental state examination, DDE dopamine dose equivalence, MDS-UPDRS movement disorder society-unified Parkinson’s disease rating scale: total, motor score (III), and part 3.1 (speech), BDI-II beck depression inventory a
Pearson’s Chi Square test used
b
Independent samples t test used
Table 2 Descriptive data and effect sizes for conditions 3 and 4
Effect size FOG
No FOG
Crame´r’s V
r
Condition 3 (Inhibition)
Scores represent median (range)/mean ± standard deviation a
Independent samples t test used ns
p [ 0.05
* p B 0.05 ** p B 0.01
Time
69 (130)/75.50 ± 27.9
61 (126)/68.11 ± 27.1
–
0.18
Age scaled score Uncorrected errors
9.53 ± 3.5 0 (12)/0.85 ± 2.3
10.66 ± 3.6 0 (3)/0.37 ± 0.9
– 0.02ns
0.07ns,a 0.12ns
Self-corrected Errors
1 (13)/2.15 ± 2.9
1 (4)/0.92 ± 1.2
0.28*
0.24*
Total errors
2 (14)/3.00 ± 4.0
1 (6)/1.29 ± 1.6
0.30**
0.26*
Time
74.50 (142)/86.21 ± 33.3
64.5 (96)/74.66 ± 25.9
–
0.23*
Age scaled score
8.97 ± 4.0
10.42 ± 3.5
–
0.24*,a
Uncorrected errors
1 (11)/2.18 ± 3.0
0 (7)/0.87 ± 1.6
0.23*
0.28*
1 (8)/1.29 ± 1.5
0.04
ns
0.06ns
0.12
ns
0.17ns
Condition 4 (Switching)
Self-corrected errors Total errors
1 (5)/1.42 ± 1.4 2 (14)/3.61 ± 3.9
p = 0.042) and total errors (U = 457, Z = -2.20, p = 0.028), with patients with FOG producing more. In condition 4, patients with FOG were significantly slower in both raw (U = 474, Z = -1.94, p = 0.052) and agescaled time scores (U = 470, Z = -2.01, p = 0.045). Patients with FOG also committed significantly more UC errors than those without FOG (U = 434.5, Z = -2.39, p = 0.017). However, groups did not differ significantly on SC errors (U = 585.0, Z = -0.50, p = 0.614) or total errors (U = 507, Z = -1.41, p = 0.158). In order to confirm these results, a Chi Square test was used to compare error rates between groups. Again, no
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2 (9)/2.16 ± 2.4
significant differences were found for any measures on condition 1 and 2. In condition 3, the number of UC errors was not significantly different between groups (FET (1), p = 1.00), while patients with FOG produced significantly more SC [v2 (1) = 5.46, p = 0.019] and total errors [v2 (1) = 6.55, p = 0.010]. In condition 4, participants with FOG recorded significantly more UC errors [v2 (1) = 3.77, p = 0.052], while SC [v2 (1) = 0.09, p = 0.761] and total errors did not differ [v2 (1) = 0.96, p = 0.327] between groups. Thus, all previous results were confirmed. To further explore the pattern of error-making across conditions, a within-group analysis using Wilcoxon signed
Impaired cognitive control in Parkinson’s disease patients
Fig. 1 Mean error scores across conditions 3 and 4 for patients with and without FOG. Error bars represent 1 SE. * p B 0.05, ** p B 0.01
ranks test was conducted. As shown in Fig. 1, there was a medium to strong sized significant difference in UC errors for patients with FOG between conditions 3 and 4 (Z = -2.89, p = 0.004, r = 0.38). Conversely, UC errors for patients without FOG (Z = -1.61, p = 0.108), and the SC errors for those with (Z = -1.47, p = 0.143) and without FOG (Z = -1.20, p = 0.230) between conditions were not significant. Both patients with FOG (Z = -2.20, p = 0.028) and those without FOG (Z = -2.15, p = 0.031) showed significantly more total errors in condition 4.
Discussion This study presents the first investigation into self-monitoring of conflict-induced errors in FOG under differing levels of cognitive load. The key finding was that the introduction of switching demands to the task impaired performance in patients with FOG, particularly in their UC error making. More precisely, in condition 3, freezers produced significantly more errors than non-freezers (automatic responding), but also demonstrated the cognitive control necessary to self-correct immediately after responding. However, when the additional switching element was introduced in condition 4, patients with FOG displayed a large and significant increase in UC errors while also being less likely to self-correct. These novel results suggest that in the presence of intensified cognitive
load induced by switching, patients with FOG may no longer be able to recruit appropriate monitoring mechanisms to adjust response selection (Brown 2013; Lewis and Barker 2009). This finding of a threshold-crossing is similar to that of PD in general when compared to healthy controls (Brown and Marsden 1988). This finding is consistent with the understanding that patients with FOG have deficits in both conflict interference (Cohen et al. 2014; Matar et al. 2013; Vandenbossche et al. 2011, 2012) and switching ability (Naismith et al. 2010; Shine et al. 2013e). However, the current results extend this understanding by further exploring the precise inability of patients with FOG to track and adapt their responses under increased cognitive demand (combination of conflict interference and switching). This raises important questions regarding the underlying anatomical regions associated with performance monitoring and how these may be related to pathophysiological mechanisms underlying FOG. Understanding the processes involved in error monitoring is complex. For example, separating differing aspects such as monitoring when aware and/or unaware of errors, which may be self-made and/or external (incongruent stimuli) in nature, in addition to the subsequent detection and correction of these errors is extremely complex, and not well understood (Orr and Hester 2012; Wessel 2012). Generally, the process of detection and correction of errors has been shown to involve the anterior cingulate, medial frontal, posterior parietal, pre-supplementary motor area (pSMA) and insular cortices (Garavan et al. 2003; Klein et al. 2007; Narayanan et al. 2013; Orr and Hester 2012; Ridderinkhof et al. 2004; Sharp et al. 2010). However, the precise mechanism for how these areas interact in error detection and correction remains unclear (Narayanan et al. 2013). These cortical regions are known to commonly coactivate as part of a large-scale network (Niendam et al. 2012), and this network is thought to be involved in the processing of response conflict and the effective switching of attention during cognitively demanding tasks (Wager et al. 2004). Additionally, sub-cortical circuitry has also been associated with cognitive demands of inhibitory and switching control, with the STN known to play key roles in such processes (Aron and Poldrack 2006; Frank 2006), including in PD patients (Alegre et al. 2013; Brittain et al. 2012; Obeso et al. 2014). Interestingly, dysfunction within these regions is consistent with findings from recent functional neuroimaging experiments demonstrating impaired processing through the pSMA, anterior insula, ventral striatum and STN during cognitive processing in patients with FOG (Shine et al. 2013b). Furthermore, an additional study showed that episodes of freezing were associated with increased activity within the fronto-parietal regions which are required to
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perform goal-directed behaviours (Shine et al. 2013a). Expanding on these results, it has since been shown that in FOG, there is a paroxysmal functional decoupling of activity between multiple neural networks, including the basal ganglia and CCN, and cross-talk between left and right portions of the CCN (Shine et al. 2013c). Together, these studies suggest that patients with FOG may require activation of more cognitive resources to effectively perform challenging tasks, leaving less reserve available for the ongoing monitoring of errors. This suggestion is similar to that proposed of Pieruccini-Faria et al. (2014) who found cognitive overload to impede on complex motor planning in patients with FOG and that these deficits were associated with executive dysfunction. Alternatively, the inability to properly recruit appropriate cortical regions during high cognitive load may be due to the role of the STN. One hypothesis is that as cognitive load increases in response to these executive tasks, it drives an overwhelming increase in STN firing through the hyperdirect pathway (Aron and Poldrack 2006), leading to increased inhibition of basal ganglia output nuclei to the thalamus. Although the hyper-direct pathway of the basal ganglia was originally hypothesised as important for purely motor cessation (Nambu et al. 2002), recent work has implicated this network in stopping behaviour in executive domains (Aron, Robbins and Poldrack 2014; Haynes and Haber 2013). As such, overactivity in the executive regions of this hyper-direct pathway may result in thalamo-cortical inhibition, which would then mean that error-monitoring hubs, including areas of medial and prefrontal cortex, are unable to process information efficiently during periods of high cognitive load (DeLong and Wichmann 2007). This would be reflected in increased uncorrected errors, as shown in the current study. Conclusions and future directions Further research employing behavioural and neuroimaging techniques should be employed to help disentangle these preliminary interpretations. In future studies, the role of switching and inhibitory control along with the influence of cognitive load could be manipulated using novel paradigms to provide additional insight into these processes and how they may relate to such functional networks. The current findings may have implications for understanding the role of performance monitoring during gait (Amboni et al. 2013; Cohen et al. 2014). For example, the current results may well relate to the impaired ability of patients to appropriately monitor and plan gait while dealing with cognitive overload in overwhelming situations (PierucciniFaria et al. 2014). Virtual reality studies may be employed for this goal (Mirelman et al. 2013). Finally, the current
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findings help advise on specific targets for cognitive training in patients with FOG (Walton et al. 2014). Overall, these novel findings demonstrate that in patients with FOG, there is a specific deficit in monitoring for self-made errors under high cognitive load. They raise important questions regarding the underlying anatomical regions associated with performance monitoring and the pathophysiological mechanisms underlying FOG. Future studies are needed to untangle this complex area of cognitive control in patients with FOG. Acknowledgments The authors wish to thank Dr. Dafydd Llewelyn for his contribution to data collection that made up part of this study. Additionally, we thank the patients of the Parkinson’s Disease Research Clinic for being so generous with their time and efforts. Financial disclosures CC Walton is supported by an Australian Postgraduate Award at the University of Sydney. M Gilat is supported by an International Postgraduate Scholarship at the University of Sydney. C O’Callaghan is supported by an Alzheimer’s Australia PhD Scholarship at the University of New South Wales. SL Naismith is supported by a National Health and Medical Research Council Career Development Award No. 1008117. SJG Lewis is supported by a National Health and Medical Research Council Practitioner Fellowship No. 1003007. JM Shine, L Mowszowski, JM Hall have no financial disclosures. Conflict of interest
The authors report no Conflicts of interest.
References Alegre M et al (2013) The subthalamic nucleus is involved in successful inhibition in the stop-signal task: a local field potential study in Parkinson’s disease. Exp Neurol 239:1–12. doi:10.1016/j.expneurol.2012.08.027 Amboni M, Cozzolino A, Longo K, Picillo M, Barone P (2008) Freezing of gait and executive functions in patients with Parkinson’s disease. Mov Disord 23:395–400. doi:10.1002/ mds.21850 Amboni M, Barone P, Picillo M, Cozzolino A, Longo K, Erro R, Iavarone A (2010) A two-year follow-up study of executive dysfunctions in parkinsonian patients with freezing of gait at onstate. Mov Disord 25:800–802. doi:10.1002/mds.23033 Amboni M, Barone P, Hausdorff JM (2013) Cognitive contributions to gait and falls: evidence and implications. Mov Disord 28:1520–1533. doi:10.1002/mds.25674 Aron AR, Poldrack RA (2006) Cortical and subcortical contributions to Stop signal response inhibition: role of the subthalamic nucleus. J Neurosci 26:2424–2433. doi:10.1523/JNEUROSCI. 4682-05.2006 Aron AR, Robbins TW, Poldrack RA (2014) Inhibition and the right inferior frontal cortex: one decade on. Trends Cogn Neurosci 18(4):177–185. doi:10.1016/j.tics.2013.12.003 Beck A, Steer R, Brown G (1996) Manual for the BDI-II. Psychological Corporation, San Antonio Brittain JS et al (2012) A role for the subthalamic nucleus in response inhibition during conflict. J Neurosci 32:13396–13401. doi:10. 1523/JNEUROSCI.2259-12.2012 Brown JW (2013) Beyond conflict monitoring: cognitive control and the neural basis of thinking before you act. Curr Dir Psychol Sci 22:179–185. doi:10.1177/0963721412470685
Impaired cognitive control in Parkinson’s disease patients Brown RG, Marsden CD (1988) Internal versus external cues and the control of attention in Parkinson’s disease brain. J Neurol 111(Pt 2):323–345 Cohen RG et al (2014) Inhibition, executive function, and freezing of gait. J Parkinson’s Dis 4:111–122. doi:10.3233/JPD-130221 Crawford JR, Sutherland D, Garthwaite PH (2008) On the reliability and standard errors of measurement of contrast measures from the D-KEFS. J Int Neuropsychol 14:1069–1073. doi:10.1017/ S1355617708081228 Delis DC, Kaplan E, Kramer JH (2001) Delis-Kaplan executive function system (D-KEFS). Psychological Corporation, San Antonio, TX DeLong MR, Wichmann T (2007) Circuits and circuit disorders of the basal ganglia. Arch Neurol 64:20–24. doi:10.1001/archneur.64.1. 20 Folstein MF, Folstein SE, McHugh PR (1975) Mini-mental state A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198 Frank MJ (2006) Hold your horses: a dynamic computational role for the subthalamic nucleus in decision making. Neural Netw 19:1120–1136. doi:10.1016/j.neunet.2006.03.006 Garavan H, Ross TJ, Kaufman J, Stein EA (2003) A midline dissociation between error-processing and response-conflict monitoring. Neuroimage 20:1132–1139. doi:10.1016/S10538119(03)00334-3 Giladi N, Shabtai H, Simon ES, Biran S, Tal J, Korczyn AD (2000) Construction of freezing of gait questionnaire for patients with Parkinsonism. Parkinsonism Relat Disord 6:165–170 Giladi N et al (2001) Freezing of gait in patients with advanced Parkinson’s disease. J Neural Transm 108:53–61 Goetz CG et al (2008) Movement Disorder Society-sponsored revision of the unified Parkinson’s disease rating scale (MDSUPDRS): scale presentation and clinimetric testing results. Mov Disord 23:2129–2170. doi:10.1002/mds.22340 Gray P, Hildebrand K (2000) Fall risk factors in Parkinson’s disease. J Neurosci Nurs 32:222–228 Hall JM, Shine JM, Walton CC, Gilat M, Kamsma YP, Naismith SL, Lewis SJ (2014) Early phenotypic differences between Parkinson’s disease patients with and without freezing of gait. Parkinsonism Relat Disord. doi:10.1016/j.parkreldis.2014.02.028 Haynes WI, Haber SN (2013) The organization of prefrontalsubthalamic inputs in primates provides an anatomical substrate for both functional specificity and integration: implications for Basal Ganglia models and deep brain stimulation. J Neurosci 33:4804–4814. doi:10.1523/JNEUROSCI.4674.12.2013 Heremans E et al (2013a) Cognitive aspects of freezing of gait in Parkinson’s disease: a challenge for rehabilitation. J Neur Transm 120:543–557. doi:10.1007/s00702-012-0964-y Heremans E, Nieuwboer A, Vercruysse S (2013b) Freezing of gait in Parkinson’s disease: where are we now? Curr Neurol Neurosci Rep 13:350. doi:10.1007/s11910-013-0350-7 Hoehn MM, Yahr MD (1967) Parkinsonism: onset, progression and mortality. Neurology 17:427–442 Hughes AJ, Daniel SE, Kilford L, Lees AJ (1992) Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinicopathological study of 100 cases. J Neurol Neurosurg Psychiatry 55:181–184 Klein TA, Endrass T, Kathmann N, Neumann J, von Cramon DY, Ullsperger M (2007) Neural correlates of error awareness. Neuroimage 34:1774–1781. doi:10.1016/j.neuroimage.2006.11.014 Kornblum S, Hasbroucq T, Osman A (1990) Dimensional overlap: cognitive basis for stimulus-response compatibility—a model and taxonomy. Psychol Rev 97:253–270 Lewis SJ, Barker RA (2009) A pathophysiological model of freezing of gait in Parkinson’s disease. Parkinsonism Relat Disord 15:333–338. doi:10.1016/j.parkreldis.2008.08.006
Matar E, Shine JM, Naismith SL, Lewis SJ (2013) Using virtual reality to explore the role of conflict resolution and environmental salience in Freezing of Gait in Parkinson’s disease. Parkinsonism Relat Disord. doi:10.1016/j.parkreldis.2013.06.002 Mirelman A, Maidan I, Deutsch JE (2013) Virtual reality and motor imagery: promising tools for assessment and therapy in Parkinson’s disease. Mov Disord 28:1597–1608. doi:10.1002/mds. 25670 Moore O, Peretz C, Giladi N (2007) Freezing of gait affects quality of life of peoples with Parkinson’s disease beyond its relationships with mobility and gait. Mov Disord 22:2192–2195. doi:10.1002/ mds.21659 Naismith SL, Shine JM, Lewis SJ (2010) The specific contributions of set-shifting to freezing of gait in Parkinson’s disease. Mov Disord 25:1000–1004. doi:10.1002/mds.23005 Nambu A, Tokuno H, Takada M (2002) Functional significance of the cortico-subthalamo-pallidal ‘hyperdirect’ pathway. Neurosci Res 43:111–117 Narayanan NS, Cavanagh JF, Frank MJ, Laubach M (2013) Common medial frontal mechanisms of adaptive control in humans and rodents. Nat Neurosci 16:1888–1895. doi:10.1038/nn.3549 Nelson H, Willison J (1991) The revised national adult reading test– test manual Windsor: NFER-Nelson Niendam TA, Laird AR, Ray KL, Dean YM, Glahn DC, Carter CS (2012) Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cogn Affect Behav Neurosci 12:241–268. doi:10.3758/s13415-0110083-5 Nutt JG, Bloem BR, Giladi N, Hallett M, Horak FB, Nieuwboer A (2011) Freezing of gait: moving forward on a mysterious clinical phenomenon. Lancet Neurol 10:734–744. doi:10.1016/S14744422(11)70143-0 Obeso I et al (2014) The subthalamic nucleus and inhibitory control: impact of subthalamotomy in Parkinson’s disease. Brain 137:1470–1480. doi:10.1093/brain/awu058 Orr C, Hester R (2012) Error-related anterior cingulate cortex activity and the prediction of conscious error awareness. Front Hum Neurosci 6:177. doi:10.3389/fnhum.2012.00177 Pieruccini-Faria F, Jones JA, Almeida QJ (2014) Motor planning in Parkinson’s disease patients experiencing freezing of gait: the influence of cognitive load when approaching obstacles. Brain Cogn 87:76–85. doi:10.1016/j.bandc.2014.03.005 Ridderinkhof KR, Ullsperger M, Crone EA, Nieuwenhuis S (2004) The role of the medial frontal cortex in cognitive control. Science 306:443–447. doi:10.1126/science.1100301 Sharp DJ, Bonnelle V, De Boissezon X, Beckmann CF, James SG, Patel MC, Mehta MA (2010) Distinct frontal systems for response inhibition, attentional capture, and error processing. Proc Natl Acad Sci USA 107:6106–6111. doi:10.1073/pnas.1000175107 Shine JM et al (2013a) Exploring the cortical and subcortical functional magnetic resonance imaging changes associated with freezing in Parkinson’s disease. Brain 136:1204–1215. doi:10. 1093/brain/awt049 Shine JM, Matar E, Ward PB, Bolitho SJ, Pearson M, Naismith SL, Lewis SJ (2013b) Differential neural activation patterns in patients with Parkinson’s disease and freezing of gait in response to concurrent cognitive and motor load. PloS One 8:e52602. doi:10.1371/journal.pone.0052602 Shine JM et al (2013c) Freezing of gait in Parkinson’s disease is associated with functional decoupling between the cognitive control network and the basal ganglia. Brain 136:3671–3681. doi:10.1093/brain/awt272 Shine JM, Moustafa AA, Matar E, Frank MJ, Lewis SJ (2013d) The role of frontostriatal impairment in freezing of gait in Parkinson’s disease Frontiers in Systems Neuroscience 7 doi:10.3389/ fnsys.2013.00061
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C. C. Walton et al. Shine JM, Naismith SL, Palavra NC, Lewis SJ, Moore ST, Dilda V, Morris TR (2013e) Attentional set-shifting deficits correlate with the severity of freezing of gait in Parkinson’s disease. Parkinsonism Relat Disord 19:388–390. doi:10.1016/j.parkreldis.2012. 07.015 Tomlinson CL, Stowe R, Patel S, Rick C, Gray R, Clarke CE (2010) Systematic review of levodopa dose equivalency reporting in Parkinson’s disease. Mov Disord 25:2649–2653. doi:10.1002/ mds.23429 Vandenbossche J, Deroost N, Soetens E, Spildooren J, Vercruysse S, Nieuwboer A, Kerckhofs E (2011) Freezing of gait in Parkinson disease is associated with impaired conflict resolution. Neurorehabil Neural Repair 25:765–773. doi:10.1177/1545968311 403493 Vandenbossche J et al (2012) Conflict and freezing of gait in Parkinson’s disease: support for a response control deficit. Neuroscience 206:144–154. doi:10.1016/j.neuroscience.2011.12. 048
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Vandenbossche J et al (2013a) Freezing of gait in Parkinson’s disease: disturbances in automaticity and control. Front Hum Neurosci 6:356. doi:10.3389/fnhum.2012.00356 Vandenbossche J et al (2013b) Impaired implicit sequence learning in Parkinson’s disease patients with freezing of gait. Neuropsychology 27:28–36. doi:10.1037/a0031278 Wager TD, Jonides J, Reading S (2004) Neuroimaging studies of shifting attention: a meta-analysis. NeuroImage 22:1679–1693. doi:10.1016/j.neuroimage.2004.03.052 Walton CC, Shine JM, Mowszowski L, Naismith SL, Lewis SJ (2014) Freezing of gait in Parkinson’s disease: current treatments and the potential role for cognitive training. Restor Neurology Neurosci 32:411–422. doi:10.3233/rnn-130370 Wessel JR (2012) Error awareness and the error-related negativity: evaluating the first decade of evidence. Front Hum Neurosci 6:88. doi:10.3389/fnhum.2012.00088
NEUROLOGY AND PRECLINICAL NEUROLOGICAL STUDIES - ORIGINAL ARTICLE
Impaired cognitive control in Parkinson’s disease patients with freezing of gait in response to cognitive load Courtney C. Walton • James M. Shine • Loren Mowszowski • Moran Gilat • Julie M. Hall • Claire O’Callaghan • Sharon L. Naismith • Simon J. G. Lewis
Received: 31 May 2014 / Accepted: 3 July 2014 Ó Springer-Verlag Wien 2014
Abstract Freezing of gait is a frequent and disabling symptom experienced by many patients with Parkinson’s disease. A number of executive deficits have been shown to be associated with the phenomenon suggesting a common underlying pathophysiology, which as of yet remains unclear. Neuroimaging studies have also implicated the role of the cognitive control network in patients with freezing. To explore this concept, the current study examined errormonitoring as a measure of cognitive control. Thirty-four patients with and 38 without freezing of gait, who were otherwise well matched on disease severity, completed a colour-word interference task that allowed the specific assessment of error monitoring during conflict. Whilst both groups performed colour-naming and word-reading tasks equally well, those patients with freezing showed a pattern between conditions whereby they were better able to monitor performance and self-correct errors in the pure inhibition task but not after a switching rule was introduced. The novel results shown here provide insight into possible pathophysiological mechanisms involved in cognitive load and error
C. C. Walton J. M. Shine L. Mowszowski M. Gilat J. M. Hall S. L. Naismith S. J. G. Lewis (&) Parkinson’s Disease Research Clinic, Brain and Mind Research Institute, University of Sydney, Sydney, NSW, Australia e-mail: [email protected] C. C. Walton L. Mowszowski S. L. Naismith Healthy Brain Ageing Program, Brain and Mind Research Institute, University of Sydney, Sydney, NSW, Australia C. O’Callaghan Neuroscience Research Australia and School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
monitoring in patients with freezing of gait. These results provide further evidence for the role of functional frontostriatal circuitry impairments in patients with freezing of gait and have implications for future studies and possible therapeutic interventions. Keywords Freezing of gait Parkinson’s disease Executive function Error monitoring Stroop task Cognitive control
Introduction Freezing of gait (FOG) is a debilitating symptom experienced by many patients with Parkinson’s disease (PD) (Giladi et al. 2001). The complex phenomenon is clinically defined as the sudden inability to generate effective stepping and forward progression despite the intention to do so and is often described as feeling as though one’s feet are glued to the ground (Nutt et al. 2011). The current treatment options for FOG are limited (Walton et al. 2014), and its occurrence leads to a significantly increased risk of falls and reduced quality of life (Gray and Hildebrand 2000; Moore et al. 2007) highlighting the importance of gaining an increased understanding of its manifestation. At present the pathophysiology underlying FOG is poorly understood (Heremans et al. 2013b), although dysfunction of frontostriatal pathways is thought to be critically involved (Hall et al. 2014; Lewis and Barker 2009; Shine et al. 2013d). One area of increasing interest in the literature is the distinct pattern of neuropsychological deficits demonstrated by PD patients with FOG, which may inform our understanding of the underlying neural mechanisms of freezing. A range of specific executive impairments are greater in patients with FOG than in those
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without. This has led many authors to suggest that particular cognitive functions may be intimately related to freezing behaviour (For reviews see: Heremans et al. 2013a; Walton et al. 2014). General executive deficits have been reported in patients with FOG (Amboni et al. 2010, 2008), more specifically in the domains of set-shifting (Naismith et al. 2010; Shine et al. 2013e), conflict resolution and inhibitory control (Cohen et al. 2014; Matar et al. 2013; Vandenbossche et al. 2011, 2012) and implicit learning (Vandenbossche et al. 2013b). These findings have been further validated by recent functional neuroimaging studies, which have implicated the dysfunctional role of a putative cognitive control network (CCN) operating through both fronto-parietal cortices and subcortical structures including the subthalamic nucleus (STN) and striatum (Shine et al. 2013a, b, c). These neuroanatomical insights raise the possibility that the executive deficits documented in FOG may be underpinned to an extent by impairments in cognitive control, which refers to the ability to control goal directed behaviour by organizing and optimising appropriate information processing to flexibly respond to changes in predicted behavioural outcomes (Narayanan et al. 2013; Ridderinkhof et al. 2004). Taking these findings together leads to an intriguing proposition: given FOG has been hypothesised to occur as a result of a decreased neural reserve for coping with demands on the frontostriatal system (Lewis and Barker 2009), it could be suggested that under highly demanding cognitive situations, patients with FOG display an inefficient recruitment of cognitive control. Therefore, the aim of the current study was to further explore impairments in cognitive control using increasingly demanding conditions of a verbal Stroop task. By using a verbal rather than computerized format, the stimulus used in the task matches the form of response needed (i.e., word– word rather than word-button) leading to more automatic responding (Kornblum et al. 1990). This is important in adequately differentiating between automatic versus effortful processing (Vandenbossche et al. 2013a). Further, the current Stroop task records both corrected and uncorrected errors. Thus, different interpretations regarding cognitive control can be recorded than in previous measures of Stroop performance in FOG (Cohen et al. 2014; Vandenbossche et al. 2012). An error that is corrected would imply cognitive control has been enforced, albeit late. Conversely, uncorrected errors are suggestive of poor cognitive control. In the current study, we sought to investigate novel measures of error making and monitoring in PD patients with and without FOG. We propose that if the CCN underpins the pathophysiology of FOG, patients with FOG should produce more uncorrected errors than patients without FOG during the most demanding condition of the task which requires set-shifting. Such a finding would
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demonstrate inefficient recruitment of cognitive control regions under high cognitive load, specifically occurring under switching demands.
Experimental procedures Participants Participants were recruited from a larger cohort of patients prospectively evaluated at the Parkinson’s Disease Research Clinic at the Brain and Mind Research Institute, University of Sydney. The diagnosis of idiopathic PD was based on the UK brain bank clinical criteria (Hughes et al. 1992) and was confirmed by a neurologist (SJGL). The 109 patients originally included in this study were divided into two groups based on their score on the FOG-Questionnaire item 3 [FOGQ-3: ‘‘Do you feel that your feet get glued to the floor while walking, making a turn, or trying to initiate walking (freezing)?’’], which has been shown to be a reliable screening tool to identify freezers (Giladi et al. 2000). Higher scores on FOGQ-3 indicate increased freezing severity and patients who scored 2 or higher were included in the FOG group, while those scoring 0 were placed in the no-FOG group. Patients scoring 1 were not included in the study to minimise the possibility of misclassification. Exclusion criteria included the presence of other neurological diseases, other conditions that would impair gait and a score of B24 on the mini mental state examination (MMSE) (Folstein et al. 1975). All patients were assessed on their regular medication. This research was approved by the Human Research Ethics Committee of The University of Sydney, and written informed consent was obtained from all participants. Clinical assessments A series of demographic details were recorded, including age, gender, and predicted premorbid intellectual functioning (revised national adult reading test; NART-R) (Nelson and Willison 1991). Clinical features including disease duration, Hoehn & Yahr stage (H&Y) (Hoehn and Yahr 1967), dopamine dose equivalence (DDE; mg/day) (Tomlinson et al. 2010) and disease severity using the Movement Disorder Society-Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) (Goetz et al. 2008), of which the total score and section III (motor sub-score) were assessed. Section ‘‘Demographic and clinical data’’ of the MDS-UPDRS was also individually recorded to ensure verbal responses to the task did not simply reflect problems with speech output. Additionally, self-reported mood disturbance using the beck depression inventory (BDI-II) (Beck et al. 1996) and global cognitive functioning using
Impaired cognitive control in Parkinson’s disease patients
the MMSE (Folstein et al. 1975) were used to assess for depression and dementia, respectively. Inhibition and switching tasks The task used in the current study was the Delis–Kaplan executive function system (D-KEFS) colour-word interference test (Delis et al. 2001). This task contains four conditions: colour-naming (condition 1), word-reading (condition 2), inhibition (condition 3) and inhibition/ switching (condition 4). In all conditions, the patient is asked to read the colours or words aloud depending on the rule given, as quickly as possible while minimising errors. There are 50 words presented on the page in each condition. Participants practice on ten word trials prior to the recorded version to ensure they understand the rules. During the task, participants are prompted if they make three consecutive errors and reminded of the rules. This occurs only once during each condition, however. Participants are told they can correct any errors as they progress through the task. In condition 1, participants must name aloud the colour of red, green or blue squares on the page. In condition 2, participants must read through a page containing the words ‘‘red’’, ‘‘green’’ and ‘‘blue’’ printed in black ink. In condition 3, the same words in differing order are presented in incongruent ink colour (e.g., the word ‘‘red’’ appears in blue ink). The participant is asked to read aloud the colour of the ink as quickly as possible without making mistakes. This task, therefore, requires the participant to inhibit a more automatic response (word reading). In condition 4, the participant is presented with incongruent colour words as in the previous condition but a selection of the colour words are surrounded by a black rectangular outline. For these selected words, the patient is asked to read the word written and not to name the colour of the ink, thus requiring them to set-shift in addition to maintaining inhibitory control (condition 4 requires 37 switches). Outcome measures include the total time taken to complete each condition (raw and age scaled scores) (Delis et al. 2001) as well as the number of uncorrected (UC), self-corrected (SC) and total errors. In this study, contrast scores between conditions were not calculated due to their known unreliability for this test (Crawford et al. 2008). Statistical analysis This study employed a matched-groups sample to address common critiques of other neuropsychological assessments of FOG, which do not often account for disease severity (Heremans et al. 2013a). Therefore, cases were removed prior to task analysis where outliers for descriptive and clinical variables led to significantly differing groups. This was done in a stepwise fashion, removing patients with
particularly high or low MDS-UPDRS scores until there was no significant group difference on this variable. Subsequently, 34 patients with FOG and 38 without were retained in the sample, matched on most key demographic variables (see Table 1). Data analyses were conducted using the Statistical Package for the Social Sciences (SPSS) version 21. Due to the non-parametric nature of the task data, Mann–Whitney U tests were conducted to assess for differences between groups. Due to unequal variances for error scores, the data were additionally analysed by categorising the data and using Pearson’s Chi Square test to ensure the robustness and reliability of the results. For these analyses, error scores were split into those who scored 0 or 1 error, and those who scored 2 or more errors (multiple errors). Where the assumption of expected counts was violated, Fisher’s exact test statistic was used (FET). Where data met parametric assumptions for descriptive statistics, t-tests were used. Wilcoxon signed ranks test was used to assess within-groups differences across conditions. All analyses used an alpha of 0.05 and were two-tailed. The effect size for differences between groups using Mann– Whitney U and Wilcoxon signed ranks tests was produced using the following formula (r = Z/HN) while Crame´r’s V was used for the Chi Square statistic. For both values, 0.10 represents a small, 0.30 a medium, and 0.50 a large effect. One patient with FOG was unable to complete condition 4 despite sufficiently completing the prior three conditions, and, therefore, a maximum time score was applied in accordance with standard D-KEFS procedure and their error scores were not included in the analysis of this condition.
Results Demographic and clinical data Table 1 displays the median, range, mean and standard deviations for demographic and clinical data. Colour-word interference test For condition 1 and 2, there were no significant differences between groups for any measure, suggesting no difference between groups in simple reading or naming speed. Table 2 displays the median, range, mean, standard deviation and effect sizes for condition 3 and 4 representing the critical trials in the study. For condition 3, there was no significant difference between groups in completion time for raw (U = 513.5, Z = -1.50, p = 0.135) or age-scaled time scores (U = 502, Z = -1.63, p = 0.102). There was also no significant difference between groups in UC errors (U = 576, Z = -1.06, p = 0.288). However, there were significant differences in SC errors (U = 473.5, Z = -2.04,
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C. C. Walton et al. Table 1 Descriptive and clinical data for patients with and without FOG FOG
No FOG
Significance
N
34
38
–
Gender (% male)
68 %
63 %
0.81a
Age (years)
66.44 ± 8.8
66.76 ± 8.1
0.87b
FOGQ-3
3 (2)/2.82 ± 0.8
–
–
NART-R
106 (35)/107.88 ± 9.4
115 (55)/112.00 ± 10.5
0.06
MMSE
28 (5)/28.21 ± 1.5
29 (3)/29.05 ± 1.1
0.01
Disease duration (years)
6.54 ± 3.9
5.7 ± 3.6
0.37b
Hoehn & Yahr stage
2 (3)/2.18 ± 0.6
2 (1)/2.08 ± 0.2
0.40
DDE
650 (1820)/723.44 ± 452.9
575 (1508)/605.53 ± 310.4
0.42
MDS-UPDRS total
51.39 ± 20.4
44.79 ± 18.9
0.16b
MDS-UPDRS-III MDS-UPDRS-speech
22 (58)/28.06 ± 15.3 1 (2)/0.75 ± 0.7
23.5 (68)/26.47 ± 13.0 1 (4)/0.86 ± 0.9
0.95 0.71
BDI-II
9 (30)/11.81 ± 7.4
7 (25)/8.63 ± 6.1
0.04
Scores represent median (range)/mean ± standard deviation FOGQ-3 Freezing of gait questionnaire-question 3, NART-R revised national adult reading test, MMSE minimental state examination, DDE dopamine dose equivalence, MDS-UPDRS movement disorder society-unified Parkinson’s disease rating scale: total, motor score (III), and part 3.1 (speech), BDI-II beck depression inventory a
Pearson’s Chi Square test used
b
Independent samples t test used
Table 2 Descriptive data and effect sizes for conditions 3 and 4
Effect size FOG
No FOG
Crame´r’s V
r
Condition 3 (Inhibition)
Scores represent median (range)/mean ± standard deviation a
Independent samples t test used ns
p [ 0.05
* p B 0.05 ** p B 0.01
Time
69 (130)/75.50 ± 27.9
61 (126)/68.11 ± 27.1
–
0.18
Age scaled score Uncorrected errors
9.53 ± 3.5 0 (12)/0.85 ± 2.3
10.66 ± 3.6 0 (3)/0.37 ± 0.9
– 0.02ns
0.07ns,a 0.12ns
Self-corrected Errors
1 (13)/2.15 ± 2.9
1 (4)/0.92 ± 1.2
0.28*
0.24*
Total errors
2 (14)/3.00 ± 4.0
1 (6)/1.29 ± 1.6
0.30**
0.26*
Time
74.50 (142)/86.21 ± 33.3
64.5 (96)/74.66 ± 25.9
–
0.23*
Age scaled score
8.97 ± 4.0
10.42 ± 3.5
–
0.24*,a
Uncorrected errors
1 (11)/2.18 ± 3.0
0 (7)/0.87 ± 1.6
0.23*
0.28*
1 (8)/1.29 ± 1.5
0.04
ns
0.06ns
0.12
ns
0.17ns
Condition 4 (Switching)
Self-corrected errors Total errors
1 (5)/1.42 ± 1.4 2 (14)/3.61 ± 3.9
p = 0.042) and total errors (U = 457, Z = -2.20, p = 0.028), with patients with FOG producing more. In condition 4, patients with FOG were significantly slower in both raw (U = 474, Z = -1.94, p = 0.052) and agescaled time scores (U = 470, Z = -2.01, p = 0.045). Patients with FOG also committed significantly more UC errors than those without FOG (U = 434.5, Z = -2.39, p = 0.017). However, groups did not differ significantly on SC errors (U = 585.0, Z = -0.50, p = 0.614) or total errors (U = 507, Z = -1.41, p = 0.158). In order to confirm these results, a Chi Square test was used to compare error rates between groups. Again, no
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2 (9)/2.16 ± 2.4
significant differences were found for any measures on condition 1 and 2. In condition 3, the number of UC errors was not significantly different between groups (FET (1), p = 1.00), while patients with FOG produced significantly more SC [v2 (1) = 5.46, p = 0.019] and total errors [v2 (1) = 6.55, p = 0.010]. In condition 4, participants with FOG recorded significantly more UC errors [v2 (1) = 3.77, p = 0.052], while SC [v2 (1) = 0.09, p = 0.761] and total errors did not differ [v2 (1) = 0.96, p = 0.327] between groups. Thus, all previous results were confirmed. To further explore the pattern of error-making across conditions, a within-group analysis using Wilcoxon signed
Impaired cognitive control in Parkinson’s disease patients
Fig. 1 Mean error scores across conditions 3 and 4 for patients with and without FOG. Error bars represent 1 SE. * p B 0.05, ** p B 0.01
ranks test was conducted. As shown in Fig. 1, there was a medium to strong sized significant difference in UC errors for patients with FOG between conditions 3 and 4 (Z = -2.89, p = 0.004, r = 0.38). Conversely, UC errors for patients without FOG (Z = -1.61, p = 0.108), and the SC errors for those with (Z = -1.47, p = 0.143) and without FOG (Z = -1.20, p = 0.230) between conditions were not significant. Both patients with FOG (Z = -2.20, p = 0.028) and those without FOG (Z = -2.15, p = 0.031) showed significantly more total errors in condition 4.
Discussion This study presents the first investigation into self-monitoring of conflict-induced errors in FOG under differing levels of cognitive load. The key finding was that the introduction of switching demands to the task impaired performance in patients with FOG, particularly in their UC error making. More precisely, in condition 3, freezers produced significantly more errors than non-freezers (automatic responding), but also demonstrated the cognitive control necessary to self-correct immediately after responding. However, when the additional switching element was introduced in condition 4, patients with FOG displayed a large and significant increase in UC errors while also being less likely to self-correct. These novel results suggest that in the presence of intensified cognitive
load induced by switching, patients with FOG may no longer be able to recruit appropriate monitoring mechanisms to adjust response selection (Brown 2013; Lewis and Barker 2009). This finding of a threshold-crossing is similar to that of PD in general when compared to healthy controls (Brown and Marsden 1988). This finding is consistent with the understanding that patients with FOG have deficits in both conflict interference (Cohen et al. 2014; Matar et al. 2013; Vandenbossche et al. 2011, 2012) and switching ability (Naismith et al. 2010; Shine et al. 2013e). However, the current results extend this understanding by further exploring the precise inability of patients with FOG to track and adapt their responses under increased cognitive demand (combination of conflict interference and switching). This raises important questions regarding the underlying anatomical regions associated with performance monitoring and how these may be related to pathophysiological mechanisms underlying FOG. Understanding the processes involved in error monitoring is complex. For example, separating differing aspects such as monitoring when aware and/or unaware of errors, which may be self-made and/or external (incongruent stimuli) in nature, in addition to the subsequent detection and correction of these errors is extremely complex, and not well understood (Orr and Hester 2012; Wessel 2012). Generally, the process of detection and correction of errors has been shown to involve the anterior cingulate, medial frontal, posterior parietal, pre-supplementary motor area (pSMA) and insular cortices (Garavan et al. 2003; Klein et al. 2007; Narayanan et al. 2013; Orr and Hester 2012; Ridderinkhof et al. 2004; Sharp et al. 2010). However, the precise mechanism for how these areas interact in error detection and correction remains unclear (Narayanan et al. 2013). These cortical regions are known to commonly coactivate as part of a large-scale network (Niendam et al. 2012), and this network is thought to be involved in the processing of response conflict and the effective switching of attention during cognitively demanding tasks (Wager et al. 2004). Additionally, sub-cortical circuitry has also been associated with cognitive demands of inhibitory and switching control, with the STN known to play key roles in such processes (Aron and Poldrack 2006; Frank 2006), including in PD patients (Alegre et al. 2013; Brittain et al. 2012; Obeso et al. 2014). Interestingly, dysfunction within these regions is consistent with findings from recent functional neuroimaging experiments demonstrating impaired processing through the pSMA, anterior insula, ventral striatum and STN during cognitive processing in patients with FOG (Shine et al. 2013b). Furthermore, an additional study showed that episodes of freezing were associated with increased activity within the fronto-parietal regions which are required to
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perform goal-directed behaviours (Shine et al. 2013a). Expanding on these results, it has since been shown that in FOG, there is a paroxysmal functional decoupling of activity between multiple neural networks, including the basal ganglia and CCN, and cross-talk between left and right portions of the CCN (Shine et al. 2013c). Together, these studies suggest that patients with FOG may require activation of more cognitive resources to effectively perform challenging tasks, leaving less reserve available for the ongoing monitoring of errors. This suggestion is similar to that proposed of Pieruccini-Faria et al. (2014) who found cognitive overload to impede on complex motor planning in patients with FOG and that these deficits were associated with executive dysfunction. Alternatively, the inability to properly recruit appropriate cortical regions during high cognitive load may be due to the role of the STN. One hypothesis is that as cognitive load increases in response to these executive tasks, it drives an overwhelming increase in STN firing through the hyperdirect pathway (Aron and Poldrack 2006), leading to increased inhibition of basal ganglia output nuclei to the thalamus. Although the hyper-direct pathway of the basal ganglia was originally hypothesised as important for purely motor cessation (Nambu et al. 2002), recent work has implicated this network in stopping behaviour in executive domains (Aron, Robbins and Poldrack 2014; Haynes and Haber 2013). As such, overactivity in the executive regions of this hyper-direct pathway may result in thalamo-cortical inhibition, which would then mean that error-monitoring hubs, including areas of medial and prefrontal cortex, are unable to process information efficiently during periods of high cognitive load (DeLong and Wichmann 2007). This would be reflected in increased uncorrected errors, as shown in the current study. Conclusions and future directions Further research employing behavioural and neuroimaging techniques should be employed to help disentangle these preliminary interpretations. In future studies, the role of switching and inhibitory control along with the influence of cognitive load could be manipulated using novel paradigms to provide additional insight into these processes and how they may relate to such functional networks. The current findings may have implications for understanding the role of performance monitoring during gait (Amboni et al. 2013; Cohen et al. 2014). For example, the current results may well relate to the impaired ability of patients to appropriately monitor and plan gait while dealing with cognitive overload in overwhelming situations (PierucciniFaria et al. 2014). Virtual reality studies may be employed for this goal (Mirelman et al. 2013). Finally, the current
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findings help advise on specific targets for cognitive training in patients with FOG (Walton et al. 2014). Overall, these novel findings demonstrate that in patients with FOG, there is a specific deficit in monitoring for self-made errors under high cognitive load. They raise important questions regarding the underlying anatomical regions associated with performance monitoring and the pathophysiological mechanisms underlying FOG. Future studies are needed to untangle this complex area of cognitive control in patients with FOG. Acknowledgments The authors wish to thank Dr. Dafydd Llewelyn for his contribution to data collection that made up part of this study. Additionally, we thank the patients of the Parkinson’s Disease Research Clinic for being so generous with their time and efforts. Financial disclosures CC Walton is supported by an Australian Postgraduate Award at the University of Sydney. M Gilat is supported by an International Postgraduate Scholarship at the University of Sydney. C O’Callaghan is supported by an Alzheimer’s Australia PhD Scholarship at the University of New South Wales. SL Naismith is supported by a National Health and Medical Research Council Career Development Award No. 1008117. SJG Lewis is supported by a National Health and Medical Research Council Practitioner Fellowship No. 1003007. JM Shine, L Mowszowski, JM Hall have no financial disclosures. Conflict of interest
The authors report no Conflicts of interest.
References Alegre M et al (2013) The subthalamic nucleus is involved in successful inhibition in the stop-signal task: a local field potential study in Parkinson’s disease. Exp Neurol 239:1–12. doi:10.1016/j.expneurol.2012.08.027 Amboni M, Cozzolino A, Longo K, Picillo M, Barone P (2008) Freezing of gait and executive functions in patients with Parkinson’s disease. Mov Disord 23:395–400. doi:10.1002/ mds.21850 Amboni M, Barone P, Picillo M, Cozzolino A, Longo K, Erro R, Iavarone A (2010) A two-year follow-up study of executive dysfunctions in parkinsonian patients with freezing of gait at onstate. Mov Disord 25:800–802. doi:10.1002/mds.23033 Amboni M, Barone P, Hausdorff JM (2013) Cognitive contributions to gait and falls: evidence and implications. Mov Disord 28:1520–1533. doi:10.1002/mds.25674 Aron AR, Poldrack RA (2006) Cortical and subcortical contributions to Stop signal response inhibition: role of the subthalamic nucleus. J Neurosci 26:2424–2433. doi:10.1523/JNEUROSCI. 4682-05.2006 Aron AR, Robbins TW, Poldrack RA (2014) Inhibition and the right inferior frontal cortex: one decade on. Trends Cogn Neurosci 18(4):177–185. doi:10.1016/j.tics.2013.12.003 Beck A, Steer R, Brown G (1996) Manual for the BDI-II. Psychological Corporation, San Antonio Brittain JS et al (2012) A role for the subthalamic nucleus in response inhibition during conflict. J Neurosci 32:13396–13401. doi:10. 1523/JNEUROSCI.2259-12.2012 Brown JW (2013) Beyond conflict monitoring: cognitive control and the neural basis of thinking before you act. Curr Dir Psychol Sci 22:179–185. doi:10.1177/0963721412470685
Impaired cognitive control in Parkinson’s disease patients Brown RG, Marsden CD (1988) Internal versus external cues and the control of attention in Parkinson’s disease brain. J Neurol 111(Pt 2):323–345 Cohen RG et al (2014) Inhibition, executive function, and freezing of gait. J Parkinson’s Dis 4:111–122. doi:10.3233/JPD-130221 Crawford JR, Sutherland D, Garthwaite PH (2008) On the reliability and standard errors of measurement of contrast measures from the D-KEFS. J Int Neuropsychol 14:1069–1073. doi:10.1017/ S1355617708081228 Delis DC, Kaplan E, Kramer JH (2001) Delis-Kaplan executive function system (D-KEFS). Psychological Corporation, San Antonio, TX DeLong MR, Wichmann T (2007) Circuits and circuit disorders of the basal ganglia. Arch Neurol 64:20–24. doi:10.1001/archneur.64.1. 20 Folstein MF, Folstein SE, McHugh PR (1975) Mini-mental state A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 12:189–198 Frank MJ (2006) Hold your horses: a dynamic computational role for the subthalamic nucleus in decision making. Neural Netw 19:1120–1136. doi:10.1016/j.neunet.2006.03.006 Garavan H, Ross TJ, Kaufman J, Stein EA (2003) A midline dissociation between error-processing and response-conflict monitoring. Neuroimage 20:1132–1139. doi:10.1016/S10538119(03)00334-3 Giladi N, Shabtai H, Simon ES, Biran S, Tal J, Korczyn AD (2000) Construction of freezing of gait questionnaire for patients with Parkinsonism. Parkinsonism Relat Disord 6:165–170 Giladi N et al (2001) Freezing of gait in patients with advanced Parkinson’s disease. J Neural Transm 108:53–61 Goetz CG et al (2008) Movement Disorder Society-sponsored revision of the unified Parkinson’s disease rating scale (MDSUPDRS): scale presentation and clinimetric testing results. Mov Disord 23:2129–2170. doi:10.1002/mds.22340 Gray P, Hildebrand K (2000) Fall risk factors in Parkinson’s disease. J Neurosci Nurs 32:222–228 Hall JM, Shine JM, Walton CC, Gilat M, Kamsma YP, Naismith SL, Lewis SJ (2014) Early phenotypic differences between Parkinson’s disease patients with and without freezing of gait. Parkinsonism Relat Disord. doi:10.1016/j.parkreldis.2014.02.028 Haynes WI, Haber SN (2013) The organization of prefrontalsubthalamic inputs in primates provides an anatomical substrate for both functional specificity and integration: implications for Basal Ganglia models and deep brain stimulation. J Neurosci 33:4804–4814. doi:10.1523/JNEUROSCI.4674.12.2013 Heremans E et al (2013a) Cognitive aspects of freezing of gait in Parkinson’s disease: a challenge for rehabilitation. J Neur Transm 120:543–557. doi:10.1007/s00702-012-0964-y Heremans E, Nieuwboer A, Vercruysse S (2013b) Freezing of gait in Parkinson’s disease: where are we now? Curr Neurol Neurosci Rep 13:350. doi:10.1007/s11910-013-0350-7 Hoehn MM, Yahr MD (1967) Parkinsonism: onset, progression and mortality. Neurology 17:427–442 Hughes AJ, Daniel SE, Kilford L, Lees AJ (1992) Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinicopathological study of 100 cases. J Neurol Neurosurg Psychiatry 55:181–184 Klein TA, Endrass T, Kathmann N, Neumann J, von Cramon DY, Ullsperger M (2007) Neural correlates of error awareness. Neuroimage 34:1774–1781. doi:10.1016/j.neuroimage.2006.11.014 Kornblum S, Hasbroucq T, Osman A (1990) Dimensional overlap: cognitive basis for stimulus-response compatibility—a model and taxonomy. Psychol Rev 97:253–270 Lewis SJ, Barker RA (2009) A pathophysiological model of freezing of gait in Parkinson’s disease. Parkinsonism Relat Disord 15:333–338. doi:10.1016/j.parkreldis.2008.08.006
Matar E, Shine JM, Naismith SL, Lewis SJ (2013) Using virtual reality to explore the role of conflict resolution and environmental salience in Freezing of Gait in Parkinson’s disease. Parkinsonism Relat Disord. doi:10.1016/j.parkreldis.2013.06.002 Mirelman A, Maidan I, Deutsch JE (2013) Virtual reality and motor imagery: promising tools for assessment and therapy in Parkinson’s disease. Mov Disord 28:1597–1608. doi:10.1002/mds. 25670 Moore O, Peretz C, Giladi N (2007) Freezing of gait affects quality of life of peoples with Parkinson’s disease beyond its relationships with mobility and gait. Mov Disord 22:2192–2195. doi:10.1002/ mds.21659 Naismith SL, Shine JM, Lewis SJ (2010) The specific contributions of set-shifting to freezing of gait in Parkinson’s disease. Mov Disord 25:1000–1004. doi:10.1002/mds.23005 Nambu A, Tokuno H, Takada M (2002) Functional significance of the cortico-subthalamo-pallidal ‘hyperdirect’ pathway. Neurosci Res 43:111–117 Narayanan NS, Cavanagh JF, Frank MJ, Laubach M (2013) Common medial frontal mechanisms of adaptive control in humans and rodents. Nat Neurosci 16:1888–1895. doi:10.1038/nn.3549 Nelson H, Willison J (1991) The revised national adult reading test– test manual Windsor: NFER-Nelson Niendam TA, Laird AR, Ray KL, Dean YM, Glahn DC, Carter CS (2012) Meta-analytic evidence for a superordinate cognitive control network subserving diverse executive functions. Cogn Affect Behav Neurosci 12:241–268. doi:10.3758/s13415-0110083-5 Nutt JG, Bloem BR, Giladi N, Hallett M, Horak FB, Nieuwboer A (2011) Freezing of gait: moving forward on a mysterious clinical phenomenon. Lancet Neurol 10:734–744. doi:10.1016/S14744422(11)70143-0 Obeso I et al (2014) The subthalamic nucleus and inhibitory control: impact of subthalamotomy in Parkinson’s disease. Brain 137:1470–1480. doi:10.1093/brain/awu058 Orr C, Hester R (2012) Error-related anterior cingulate cortex activity and the prediction of conscious error awareness. Front Hum Neurosci 6:177. doi:10.3389/fnhum.2012.00177 Pieruccini-Faria F, Jones JA, Almeida QJ (2014) Motor planning in Parkinson’s disease patients experiencing freezing of gait: the influence of cognitive load when approaching obstacles. Brain Cogn 87:76–85. doi:10.1016/j.bandc.2014.03.005 Ridderinkhof KR, Ullsperger M, Crone EA, Nieuwenhuis S (2004) The role of the medial frontal cortex in cognitive control. Science 306:443–447. doi:10.1126/science.1100301 Sharp DJ, Bonnelle V, De Boissezon X, Beckmann CF, James SG, Patel MC, Mehta MA (2010) Distinct frontal systems for response inhibition, attentional capture, and error processing. Proc Natl Acad Sci USA 107:6106–6111. doi:10.1073/pnas.1000175107 Shine JM et al (2013a) Exploring the cortical and subcortical functional magnetic resonance imaging changes associated with freezing in Parkinson’s disease. Brain 136:1204–1215. doi:10. 1093/brain/awt049 Shine JM, Matar E, Ward PB, Bolitho SJ, Pearson M, Naismith SL, Lewis SJ (2013b) Differential neural activation patterns in patients with Parkinson’s disease and freezing of gait in response to concurrent cognitive and motor load. PloS One 8:e52602. doi:10.1371/journal.pone.0052602 Shine JM et al (2013c) Freezing of gait in Parkinson’s disease is associated with functional decoupling between the cognitive control network and the basal ganglia. Brain 136:3671–3681. doi:10.1093/brain/awt272 Shine JM, Moustafa AA, Matar E, Frank MJ, Lewis SJ (2013d) The role of frontostriatal impairment in freezing of gait in Parkinson’s disease Frontiers in Systems Neuroscience 7 doi:10.3389/ fnsys.2013.00061
123
C. C. Walton et al. Shine JM, Naismith SL, Palavra NC, Lewis SJ, Moore ST, Dilda V, Morris TR (2013e) Attentional set-shifting deficits correlate with the severity of freezing of gait in Parkinson’s disease. Parkinsonism Relat Disord 19:388–390. doi:10.1016/j.parkreldis.2012. 07.015 Tomlinson CL, Stowe R, Patel S, Rick C, Gray R, Clarke CE (2010) Systematic review of levodopa dose equivalency reporting in Parkinson’s disease. Mov Disord 25:2649–2653. doi:10.1002/ mds.23429 Vandenbossche J, Deroost N, Soetens E, Spildooren J, Vercruysse S, Nieuwboer A, Kerckhofs E (2011) Freezing of gait in Parkinson disease is associated with impaired conflict resolution. Neurorehabil Neural Repair 25:765–773. doi:10.1177/1545968311 403493 Vandenbossche J et al (2012) Conflict and freezing of gait in Parkinson’s disease: support for a response control deficit. Neuroscience 206:144–154. doi:10.1016/j.neuroscience.2011.12. 048
123
Vandenbossche J et al (2013a) Freezing of gait in Parkinson’s disease: disturbances in automaticity and control. Front Hum Neurosci 6:356. doi:10.3389/fnhum.2012.00356 Vandenbossche J et al (2013b) Impaired implicit sequence learning in Parkinson’s disease patients with freezing of gait. Neuropsychology 27:28–36. doi:10.1037/a0031278 Wager TD, Jonides J, Reading S (2004) Neuroimaging studies of shifting attention: a meta-analysis. NeuroImage 22:1679–1693. doi:10.1016/j.neuroimage.2004.03.052 Walton CC, Shine JM, Mowszowski L, Naismith SL, Lewis SJ (2014) Freezing of gait in Parkinson’s disease: current treatments and the potential role for cognitive training. Restor Neurology Neurosci 32:411–422. doi:10.3233/rnn-130370 Wessel JR (2012) Error awareness and the error-related negativity: evaluating the first decade of evidence. Front Hum Neurosci 6:88. doi:10.3389/fnhum.2012.00088
