Abstract
Introduction:
Parkinson’s disease (PD) is a neurodegenerative disorder that commonly results in cognitive impairments and dementia. Intra-individual variability of neuropsychological performance is a sensitive marker of cognitive decline in other neurologic populations. However, studies have not examined the longitudinal utility of intra-individual variability in predicting future cognitive impairments among individuals with PD. In the current study we hypothesized that increased intra-individual variability would predict future cognitive decline independent of traditional neuropsychological markers of cognitive impairment.
Methods:
The sample included 423 newly diagnosed PD patients and 175 healthy controls, who were followed up to five years (baseline, 1st, 2nd, 3rd, 4th and 5th annual follow up). Participants underwent tests of learning, memory, processing speed, attention, verbal fluency and visuospatial functioning. Cognitive status (cognitive intact, mild cognitive impairment, and dementia) was classified based on previously established criteria. Multilevel models were computed to examine the longitudinal relationship between intra-individual variability, cognitive status and general cognitive functioning.
Results:
Analyses revealed that increased intra-individual variability was predictive of incident cognitive decline among individuals with PD. Specifically, greater dispersion in neuropsychological performance was associated with greater risk of transitioning from cognitively intact to mild cognitive impairment or transitioning from mild cognitive impairment to dementia. Additional analyses revealed a significant intra-individual variability by Group (PD or control) interaction, meaning that intra-individual variability was predictive of declines in cognitive functioning among PD participants only, but not healthy controls.
Conclusion:
Intra-individual variability may be a harbinger for future cognitive decline among individuals with PD.
Keywords: Parkinson’s disease, mild cognitive impairment, dementia, intra-individual variability, dispersion
INTRODUCTION
Parkinson’s Disease (PD) is a neurological disorder typically marked with the degradation of motor skills, partially due to dysfunctions in frontal-subcortical circuits (Alexander, Delong & Strick, 1986). These circuits are also important for cognitive functioning (Bonelli & Cummings, 2007). Cognitive impairments are common among PD patients. The pattern of cognitive impairment in PD is heterogenous with reports of deficits in several domains including processing speed, executive functioning, visuospatial, learning and memory. (Camicioli, Weiler, de Frias, Martin, 2008; Curtis, Masellis, Camicioli, Davidson, & Tierney, 2018; Litvan et al., 2011). The risk of developing dementia greatly increases throughout the progression of PD, with up to 80% of patients developing Parkinson’s disease dementia (PDD) within 15–20 years of initial diagnosis (Hely, Reid, Adena, Halliday, & Morris, 2008). Furthermore, a large portion of patients experience more subtle signs of cognitive impairment, termed mild cognitive impairment (PD-MCI; Litvan et al., 2011). Cognitive impairments are important and have been linked to important outcomes such as depression, quality of life, and mortality (Butterfield, Cimino, Oelke, Hauser, Sanchez-Ramos, 2010; Jones, Mangal, Lafo, Okun, & Bowers, 2016; Szymkowicz, Dotson, Jones, Okun, & Bowers 2018; Hely et al., 2008).
Despite the evidence that cognitive impairment is an important non-motor symptom in PD, prediction and detection of cognitive impairment in PD is difficult (Litvan et al, 2011; Emre et al., 2007). This is partially due to the heterogeneous pattern of cognitive impairment. There have been inconsistent findings regarding which cognitive domain is most predictive of PDD. Specifically, some studies have suggested a “posterior” pattern of cognitive impairment, including deficits in language and visuospatial functioning, is predictive of dementia (Williams-Gray et al., 2013). Conversely, other studies found a “frontal-executive” pattern is predictive of dementia (Caviness et al., 2007; Janvin et al., 2006), and others have reported a mixed pattern (tests of episodic memory, semantic fluency, mental flexibility, and visuospatial domains) to be predictive of PDD (Domellöf, Ekman, Forsgren, & Elgh, 2015).
One potential marker of neuropsychological impairment that has received minimal interest in PD is intra-individual variability. Intra-individual variability characterizes cognitive performance by observing the within-subject variability. One benefit of intra-individual variability is that it provides clinicians an additional metric of cognitive functioning without administrating additional tests and increasing patient/clinician burden. Intra-individual variability is generally measured by either examining inconsistency or dispersion (Stuss, Murphy, Binns, & Alexander, 2003). Inconsistency is variability across multiple trials within a single task, most frequently involving a reaction time task (Stuss, Murphy, Binns, & Alexander, 2003). Alternatively, dispersion is variability across multiple test scores within a single testing occasion. In studies, low intra-individual variability (i.e., high consistency across scores) is hypothesized to represent better neurologic integrity, whereas high intra-individual variability (i.e., low consistency across scores) is indicative of neurologic compromise (Slifkin & Newell, 1998; de Frias, Dixon, & Camicioli, 2013).
The utility of intra-individual variability has been investigated in other neurological populations. In the context of aging, older adults with greater dispersion in neuropsychological performance at baseline were at greater risk for developing MCI and Alzheimer’s disease five years later (Vaughan et al., 2013). Longitudinal studies of individuals with HIV showed that greater intra-individual variability was predictive of greater risk of white matter compromise (decline in fractional anisotropy), neurocognitive impairment and mortality at subsequent visits (Anderson, et al., 2018; Jones et al., 2018). Intra-individual variability has also been linked to markers of HIV disease severity, medication adherence and brain disruption/cortical thinning in cross-sectional studies (Ettenhofer et al., 2010; Hines et al., 2015; Thaler et al., 2015).
IIV may be a particularly useful metric among individuals with PD due to the fact that the pattern of cognitive impairment may be heterogeneous. Cognitive impairments may present in certain domains, but other domains may be spared. Therefore, examining the variability across scores may be a more sensitive marker than examining individual scores or averaging all scores (Jones et al., 2018). There have been limited studies of intra-individual variability in PD. In general, studies have shown that intra-individual variability is greater among PD participants relative to controls (Burton et al., 2006; Frias et al., 2007). However, these studies were limited to cross-sectional samples and limited in the number of cognitive domains assessed. Specifically, these studies focused on inconsistency across trials in a single task of attention/processing speed, and did not examine dispersion among various cognitive domains.
The aim of the current study was to examine the longitudinal relationship between intra-individual variability and neurocognitive outcomes. Specifically, we hypothesized that greater intra-individual variability would be predictive of increased risk of developing future PD-MCI and PDD. Furthermore, we predicted that intra-individual variability would be associated with cognitive decline among PD participants but to a lesser extent among healthy older adult controls.
METHODS
Study Design
The current study utilized data from the Parkinson’s Markers Initiative (PPMI) database (www.ppmi-info.org/data). The PPMI is a longitudinal multisite-study of untreated and newly diagnosed PD patients. For more information regarding the study, see Marek et al., 2011. The study received institutional review board approval from all study locations and all participants provided informed consent.
The current study consisted a sample of 423 participants newly diagnosed with PD and 175 healthy control participants. All participants completed neuropsychological measures during at least one assessment period. We excluded participants who were recruited as part of the prodromal PD or genetic PD cohorts. All participants were followed up to 5 years (baseline, 1st, 2nd, 3rd, 4th, and 5th annual follow-up).
Neuropsychological Measures
All participants completed five neuropsychological tests at each assessment period. Tests assessed domains of working memory (Letter-Number Sequencing; LNS), processing speed (Symbol Digit Modalities Test; SDMT), visuospatial functioning (Judgement of Line Orientation; JOLO), verbal fluency (Animal Fluency), learning and delayed recall (Hopkins Verbal Learning Test-Revised; HVLT-R trials 1–3 and delayed recall). All tests were normed on demographic variables and converted into z-scores. Intra-individual variability was calculated as the standard deviation of the six scores derived from the five tests. In addition to intra-individual variability, a mean-cognition term was created by calculating the average of each neurocognitive test. Both intra-individual variability and mean-cognition terms were calculated at each occasion. Participants also completed a test of global cognitive functioning (Montreal Cognition Assessment; MoCA). Importantly, the MoCA was not used in calculation of intra-individual variability but was utilized as a measure of cognitive functioning that is independent from intra-individual variability and mean-cognitive.
Cognitive Status
Cognitive status (cognitively intact, PD-MCI and PDD) was defined according to the Movement Disorder Society (MDS) criteria and consistent with past PPMI studies (Dawson, Fereshtehnejad & Anang, 2018; Emre et al., 2007; Litvan et al., 2011). Participants were classified as MCI if there was evidence of cognitive impairment but no subjective report of functional impairments related to cognitive symptoms. PDD was classified if there was evidence of both cognitive impairment and functional impairments related to cognitive symptoms. Cognitive impairment was defined as neuropsychological performance 1.5 SDs below the mean on at least two tests.
Statistical Analyses
Multi-level models (MLM) were used to analyze the longitudinal relationship between intra-individual variability and cognitive impairment.
For Aim 1, MLM analyses included cognitive status (cognitively intact, PD-MCI, or PDD) as the outcome. Due to restricted range of cognitive status among control participants (see Supplemental Figure 1), analyses were only computed using the subsample of PD participants. Cognitive status was treated as an ordinal variable. Fixed predictors included age, gender, years of education, occasion (baseline, 1st, 2nd, 3rd, 4th, or 5th annual follow-up), motor severity (UPDRS-III motor score), between-person mean-cognition, within-person mean-cognition, between-person intra-individual variability and within-person intra-individual variability. Random effects were modeled for all time-varying predictors, including: occasion, motor severity, within-person mean-cognition and within-person intra-individual variability.
Within-person and between-person effects were modeled separately for mean-cognition and intra-individual variability. This allowed us to examine if 1) longitudinal changes in mean-cognition/ intra-individual variability associate with cognitive status, or 2) average level of mean-cognition/intra-individual variability throughout the duration of the study associate with cognitive status.
Additional MLM analyses were computed to examine if intra-individual variability predicted cognitive status above and beyond performance on individual cognitive tests. Analyses were similar to the above MLMs but mean-cognition was replaced with performance on the six individual cognitive tests: working memory, learning, delayed recall, processing speed, animal fluency and visuospatial functioning.
Aim 2 examined if the relationship between intra-individual variability and global cognitive functioning (MoCA) differed among both PD and control participants. Essentially, this aim examines if the ability of intra-individual variability to predict cognitive is unique to PD participants. MoCA scores were entered as the dependent variable. Predictors were similar to aim 1, but also included the main effect of Group (PD or control) and a Group X intra-individual variability interaction. The Group X intra-individual variability interaction examines if the relationship between intra-individual variability and MoCA performance differs among PD or control participants (i.e., is intra-individual variability a stronger predictor of MoCA performance among PD participants?).
RESULTS
Sample Characteristics
Sample characteristics at baseline are shown in table 1. At baseline, PD and control groups did not significantly differ in age, education (trend, p=0.05), or gender. As expected, the PD group experienced greater severity of motor symptoms and depression. The PD group performed worse than controls on tests of verbal learning, verbal delay recall, and processing speed. PD patients and controls did not significantly differ in terms of intra-individual variability or the occurrence of MCI.
Table 1.
Sample Characteristics at Baseline
| PD | Control | t/Mann-Whitney U | p | |
|---|---|---|---|---|
| Mean Age (SD) | 61.2 (9.7) | 60.3 (11.1) | 0.98 | 0.330 |
| Mean Education | 15.5 (3.0) | 16.0 (2.9) | 1.96 | 0.050 |
| %Male | 65.5 | 64.3 | 40957 | 0.771 |
| % Caucasian | 94.8 | 93.9 | 41104 | 0.668 |
| Mean UPDRS Motor | 20.9 (8.9) | 1.2 (2.2) | 30.60 | <0.001 |
| MeanGDS | 2.3 (2.4) | 1.3 (2.1) | 5.11 | <0.001 |
| Mean HVLT Learning Trials | −0.17 (.95) | 0.12 (.88) | 3.62 | <0.001 |
| Mean HVLT Delay | −0.20 (.95) | 0.08 (.87) | 3.54 | <0.001 |
| Mean JOLO | 0.09 (.94) | 0.23 (.89) | 1.71 | 0.088 |
| MeanLNS | 0.02 (.88) | 0.11 (.90) | 1.15 | 0.253 |
| Mean Animal Fluency | −0.11 (.90) | 0.04 (.94) | 1.87 | 0.062 |
| MeanSDMT | −0.19 (.83) | 0.30 (1.0) | 6.38 | <0.001 |
| Mean IIV | 0.78 (.30) | 0.77 (.32) | 0.36 | 0.715 |
| % Cognitively Intact | 96.0 | 97.9 | 34028 | 0.242 |
| % MCI | 4.0 | 2.1 | −− | −− |
Standard deviations are listed in parentheses. Normative z-scores are presented for cognitive tests.
UPDRS = Unified Parkinson’s Disease Rating Scale- part III; GDS = Geriatric Depression Scale; HVLT = Hopkins Verbal Learning Test; JOLO = Judgement of Line Orientation; SDMT = Symbol Digit Modalities Test; IIV = intra-individual variability; MCI = mild cognitive impairment.
Supplemental Figure 1 displays the distribution in cognitive status at each year for PD and control participants.
Intra-individual Variability and Risk of PD-MCI and PDD
MLM analyses revealed that cognitive status was significantly predicted by the between-person and within-person terms for both intra-individual variability and mean-level cognitive functioning (Table 2). Specifically, the between-person effect means that participants who, on average across the entire study period, displayed worse mean-cognition performance and more variability in cognitive performance were more likely to be classified as either PD-MCI or PDD (Figure 1). Additionally, the within-person effect revealed that longitudinal changes in mean-cognition and intra-individual variability were predictive of transitioning to a greater stage of cognitive impairment (i.e., transition from cognitively intact to PD-MCI, or from PD-MCI to PDD). Importantly, the within-person and between-person effects of intra-individual variability were independent of the effects of mean-cognition. There was also a significant effect of occasion, meaning that participants were more likely to be classified as either PD-MCI or PDD in later assessments.
Table 2.
IIV and Mean Cognition Predict Cognitive Status
| Coefficient | p | |
|---|---|---|
| Age | 0.01 | 0.920 |
| Education | 0.01 | 0.898 |
| Occasion | 0.25 | 0.001 |
| Motor Severity | 0.02 | 0.862 |
| Mean Cognition-Between Person | −1.81 | <0.001 |
| Mean Cognition-Within Person | −1.98 | <0.001 |
| IIV-Between Person | 0.66 | <0.001 |
| MV-Within Person | 0.66 | <0.001 |
| Between Pseudo R2: 0.713 | ||
| Within Pseudo R2: 0.462 | ||
Cognitive Status (Cognitive Intact, Mild Cognitive Impairment or Parkinson’s Disease Dementia) entered as dependent variable. IIV = intra-individual variability.
Figure 1.
Relationship between IIV and Cognitive Status at Each Annual Assessment. Note: model-implied cognitive status is depicted. IIV = intra-individual variability; CI = cognitively intact; MCI = mild cognitive impairment; PDD = Parkinson’s disease dementia; BL = baseline.
Separate MLMs were conducted to examine if intra-individual variability predicted cognitive status independent from the individual neuropsychological tests (Table 3). Results revealed both the within-person effect and the between-person effect of intra-individual variability predicted cognitive status independent of the individual neuropsychological test scores. Similar to the above analyses, individuals who on average had higher intra-individual variability across the entire study period were more likely to be classified as either PD-MCI or PDD. Increases in intra-individual variability (greater dispersion over time) were predictive of future transitions to a greater stage of cognitive impairment.
Table 3.
IIV and Individual Neuropsychological Test Performance Predict Cognitive Status
| Coefficient | p | |
|---|---|---|
| Age | −0.11 | 0.150 |
| Education | 0.01 | 0.929 |
| Occasion | 0.29 | <0.001 |
| Motor Severity | 0.02 | 0.869 |
| Learning | −0.66 | <0.001 |
| Delayed Recall | −0.54 | <0.001 |
| Visuospatial | −0.23 | 0.002 |
| Working Memory | −0.21 | 0.006 |
| Animal Fluency | −0.39 | <0.001 |
| Processing Speed | −0.46 | <0.001 |
| IIV-Between Person | 0.62 | <0.001 |
| NV-Within Person | 0.73 | <0.001 |
| Between Pseudo R2:1.000 | ||
| Within Pseudo R2: 0.592 | ||
Cognitive Status (Cognitive Intact, Mild Cognitive Impairment or Parkinson’s Disease Dementia) entered as dependent variable. IIV = intra-individual variability.
Exploratory analyses were conducted to replicate the above findings, but using a 1 SD cut-off threshold for cognitive impairment (Supplemental Table 1). In general, findings were consistent. Both the between-person and the within-person effects of intra-individual variability were significantly predictive of cognitive status.
Intra-Individual Variability and Cognitive Decline among PD and Controls.
MLM analyses examined if the relationship between intra-individual variability and a separate/independent measure of global cognitive functioning (MoCA) differed among PD and control participants (Table 4). Results revealed the Group X intra-individual variability interaction term predicted MoCA performance independent from mean-cognition (Figure 2). Specifically, greater intra-individual variability was predictive of worse MoCA performance among PD participants, but there was only a minimal relationship among controls. Worse MoCA performance was also significantly predicted by male gender, older age, fewer years of education, occasion (decline in performance over time), more severe motor symptoms, and worse mean-cognitive performance.
Table 4.
IIV X Group Interaction Predicts Cognitive Functioning
| Coefficient | p | |
|---|---|---|
| Gender | 0.14 | 0.001 |
| Age | −0.21 | <0.001 |
| Education | 0.08 | 0.001 |
| Occasion | −0.05 | <0.001 |
| Motor Severity | −0.09 | <0.001 |
| Mean Cognition | 0.29 | <0.001 |
| IIV | −0.01 | 0.984 |
| Group XIIV | −0.08 | 0.022 |
| Between Pseudo R2: 0.772 | ||
| Within Pseudo R2: 0.236 | ||
Global cognitive performance (Montreal Cognitive Assessment) entered as dependent variable. IIV - intra-individual variability; PD = Parkinson’s disease
Figure 2.
Depiction of Group X Intra-Individual Variability Interaction.
DISCUSSION
Findings from the current study provide preliminary support for intra-individual variability as a useful metric for detecting PD patients at risk for future cognitive impairment. Specifically, findings revealed that participants with higher intra-individual variability (i.e., greater dispersion among neuropsychological scores) were more likely to develop PD-MCI or PDD relative to their counterparts with lower intra-individual variability. Additionally, intra-individual variability may be a particularly useful metric for patients with PD. Findings revealed that intra-individual variability was predictive of a separate measure of cognitive functioning among PD patients, but only minimally predictive among controls.
The finding of intra-individual variability being predictive of future cognitive declines is consistent with past studies involving other neurologic populations. To date, many investigations of intra-individual variability have focused on HIV populations. Findings from longitudinal studies have shown that higher intra-individual variability is predictive of important out comes including severity of cognitive impairment, mortality, disrupted white matter integrity and worse medication adherence (Anderson et al., 2018; Jones et al., 2018; Thaler et al., 2015). Similar to the current study, these studies showed that intra-individual variability was predictive of outcomes independently of separate markers of cognitive functioning, such as mean-level/global cognitive functioning.
Obviously, there are many important differences between the HIV population and the PD population, including. differences in typical age of onset, stigma, psychiatric/substance/medical comorbidities, pathophysiology and more. However, one commonality is that both HIV and PD are considered to have heterogeneous cognitive profiles and are sometimes conceptualized as “subcortical” neurocognitive disorders. This is a potentially important shared feature because intra-individual variability has been hypothesized to be particularly sensitive to frontal-subcortical dysfunction (Bellgrove, Hester & Garavan, 2004). Specifically, frontal-subcortical dysfunction may lead to attentional and self-monitoring deficits, which in turn lead to fluctuations in performance across tests/trials. Indeed, neuroimaging studies have shown that intra-individual variability is associated with frontal gray and white matter disintegrity (Hines et al., 2016; MacDonald, Li & Backman, 2009).
The current study found that intra-individual variability was a stronger predictor of cognitive decline among PD participants relative to elderly controls. This finding may be explained by the hypothesis that frontal-subcortical disruption is a mechanism underlying intra-individual variability, and frontal-subcortical disruption is a common feature of PD. On the other hand, intra-individual variability has also been shown to be predictive of future Alzheimer’s disease among older adults (a condition typically conceptualized as a “cortical” disorder; Vaughan et al., 2013). Therefore, frontal-subcortical dysfunction is unlikely to be the only mechanism driving intra-individual variability. It is important to note that cognitive impairments in PD possibly represent disruptions in frontal-subcortical circuitry and/or posterior-cortical impairments (Williams-Gray et al., 2013). Future studies are needed to elucidate the neural mechanisms underlying intra-individual variability in PD.
One unique finding in the current study was that intra-individual variability not only predicted cognitive impairment independent of mean-level cognition (i.e. averaging separate neuropsychological measures together to obtain a “grand mean”), but the predictive effect was also independent of the individual neuropsychological tests. This raises the question “what is intra-individual variability measuring that is unique from the other neuropsychological tests?” As mentioned above, intra-individual variability is conceptualized as being sensitive to frontal-executive disruptions (Bellgrove, Hester & Garavan, 2004). The current study was a secondary data-analysis of the PPMI. The PPMI study did administered tests of cognitive constructs that are sometimes subsumed under the term “executive functioning” (i.e., working memory, semantic fluency). However, tests of other aspects of executive functioning, such as set-shifting, letter fluency, behavioral inhibition and novel-task problem solving, were not administered as part of the parent study. It is possible these other constructs of executive functioning may fully mediate the relationship between intra-individual variability and risk of future cognitive impairment; however this is unlikely because past studies utilizing a wider variety of executive-functioning tasks also find that intra-individual variability is a unique predictor of important outcomes (Anderson et al., 2018; Hines et al., 2016; Jones et al., 2018; Morgan et al., 2011).
Furthermore, it should be highlighted that intra-individual variability is calculated from tests administered as part of a routine neuropsychological evaluation. No additional tests are needed to calculate intra-individual variability. Therefore, it may provide an additional marker of neurologic compromise without increasing patient/provider burden.
Regarding limitations, the current study consisted of newly diagnosed PD patients. Findings may not generalize to the entire PD population or other neurologic populations. As mentioned above, the study was limited in the number of tests administered, particularly tests of executive functioning. This may be considered a weakness because the battery was not optimally reflective of important neuropsychological domains. Alternatively, findings provide evidence that intra-individual variability can be a useful clinical marker even when intra-individual variability is calculated from a relatively brief battery. In the current study intra-individual variability was calculated from six scores, administered from five tests, and likely can be administered in less than 1 hour. Clinical markers that minimize patient/provider burden may prove to be particularly valued in the current economic climate. Intra-individual variability was predictive of MoCA performance in the PD group but not the Control group. The lack of a relationship in the Control group may be partially driven by restricted range in MoCA scores (Supplemental Figure 2). Although a greater range of MoCA scores in the PD group is not surprising, because a diagnosis of PD is a risk factor for cognitive impairment, caution should be taken when interpreting the lack of a relationship between intra-individual variability and MoCA scores in the control group. The current study did not account for acute cognitive fluctuations. Similar to motor fluctuations in PD and cognitive fluctuations in Lewy body disease, cognitive performance may fluctuate in PD (Witjas et al., 2002). Although little is known about cognitive fluctuations and its relationship to intra-individual variability, it is plausible that dispersion in cognitive performance may be related to cognitive fluctuations. Mean-cognition was a strong predictor of cognitive status. However, the same cognitive variables were used to calculate both cognitive status and mean-cognition. Therefore, the relationship between mean-cognition and cognitive status should be interpreted with caution due to incomplete independence between the variables.
Intra-individual variability has been an unheeded metric in the clinical neuropsychological assessment. This likely reflects the lack of research into the psychometrics and underlying mechanisms of intra-individual variability. However, the fact that initial evidence suggests intra-individual variability is sensitive to future cognitive decline, and the intra-individual variability metric does not increase provider/participant burden, makes intra-individual variability an attractive potential clinical marker. Ultimately, further psychometric studies of intra-individual variability (across multiple neurologic populations), and investigations into the underlying mechanism are needed to establish the clinical utility of intra-individual variability.
Supplementary Material
Public Significance:
Adults with Parkinson’s disease may be at risk for cognitive impairment. The current study examined 423 newly diagnosed individuals with Parkinson’s disease followed for up to five years. Findings demonstrated that variability in neuropsychological performance was predictive of cognitive impairment over a five-year period.
Acknowledgements/ Study Funding
Joseph Bunch & Matthew Apodaca were supported by NIH T34GM083883
Data used in the preparation of this article were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database (www.ppmi-info.org/data). For up-to-date information on the study, visit www.ppmi-info.org.
PPMI – a public-private partnership – is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including AbbVie, Avid Radiopharmaceuticals, Biogen Idec, BioLegend, Bristol-Meyers Squibb, GE Healthcare, Genentech, GlaxoSmithKline, Eli Lilly and Company, Lundbeck, Merck, Meso Scale Discovery, Pfizer Inc., Piramal Imaging, Roche group, Sanofi-Genzyme, Servier, Takeda, TEVA, and UCB.
We would like to thank the CSUSB Institute for Child Development and Family Relations, and the Faculty Center for Excellence for supporting the publication of this paper with funded writing time.
References
- Alexander GE, DeLong MR, & Strick PL (1986). Parallel organization of functionally segregated circuits linking basal ganglia and cortex. Annual review of neuroscience, 9(1), 357–381. [DOI] [PubMed] [Google Scholar]
- Anderson AE, Jones JD, Thaler NS, Kuhn TP, Singer EJ, & Hinkin CH (2018). Intraindividual variability in neuropsychological performance predicts cognitive decline and death in HIV. Neuropsychology, 32(8), 966. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Bellgrove MA, Hester R, & Garavan H (2004). The functional neuroanatomical correlates of response variability: evidence from a response inhibition task. Neuropsychologia, 42(14), 1910–1916. [DOI] [PubMed] [Google Scholar]
- Bonelli RM, & Cummings JL (2007). Frontal-subcortical circuitry and behavior. Dialogues in clinical neuroscience, 9(2), 141–51. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Butterfield LC, Cimino CR, Oelke LE, Hauser RA, Sanchez-Ramos J (2010). The Independent Influence of Apathy and Depression on Cognitive Functioning in Parkinson’s Disease. Neuropsychology 24(6). doi: 10.1037/a0019650 [DOI] [PubMed] [Google Scholar]
- Camicioli RM, Weilder M, de Frais CM, & Martin WR (2008). Early, Untreated Parkinson’s Disease patients show reaction time variability. Neurosci Lett 441(1). [DOI] [PubMed] [Google Scholar]
- Caviness JN, Driver-Dunckley E, Connor DJ, Sabbagh MN, Hentz JG, Noble B, … & Adler CH (2007). Defining mild cognitive impairment in Parkinson’s disease. Movement Disorders, 22(9), 1272–1277. [DOI] [PubMed] [Google Scholar]
- Curtis AF, Masellis M, Camicioli R, Davidson H, & Tierney MC (2018). Cognitive Profiles of Non-Demented Parkinson’s Disease: Meta-Analysis of Domain and Sex-Specific Deficits Parkinsonism & Related Disorders. In Press.doi: 10.1016/j.parkredis.2018.10.014 [DOI] [PubMed] [Google Scholar]
- Domellöf ME, Ekman U, Forsgren L, & Elgh E (2015). Cognitive function in the early phase of Parkinson’s disease, a five-year follow-up. Acta Neurologica Scandinavia, 132(2). [DOI] [PubMed] [Google Scholar]
- de Frias CM, Dixon RA, & Camicioli R (2012). Neurocognitive speed and inconsistency in Parkinson’s disease with and without incipient dementia: an 18-month prospective cohort study. Journal of the International Neuropsychological Society: JINS, 18(4), 764–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Emre M, Aarsland D, Brown R, Burn DJ, Duyckaerts C, Mizuno Y, & Goldman J (2007). Clinical diagnostic criteria for dementia associated with Parkinson’s disease. Movement disorders, 22(12), 1689–1707. [DOI] [PubMed] [Google Scholar]
- Hely MA, Reid WG, Adena MA, Halliday GM, & Morris JG (2008). The Sydney Multicenter Study of Parkinson’s disease: The Inevitability of Dementia at 20 Years. Mov Disord. 23(6). doi: 10.1002/mds.21956. [DOI] [PubMed] [Google Scholar]
- Hines LJ, Miller EN, Hinkin CH, Alger JR, Barker P, Goodkin K, … & Sanders J (2016). Cortical brain atrophy and intra-individual variability in neuropsychological test performance in HIV disease. Brain imaging and behavior, 10(3), 640–651. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Hultsch DF, MacDonald SWS, Hunter MA, Levy-Bencheton J, Strauss E. (2002). Intra-individual variability in cognitive performance in older adults: comparison of adults with mild dementia, adults with arthritis, and healthy adults. Neuropsychol. (14)588–598. [DOI] [PubMed] [Google Scholar]
- Jackson JD, Balota D, Ducheck J & Head D (2012) White Matter Integrity and Reaction Time Intraindividual Variability in Healthy Aging and Early-Stage Alzheimer Disease. Neuropsycholoica 50(3). [DOI] [PMC free article] [PubMed] [Google Scholar]
- Janvin CC, Larsen JP, Aarsland D, & Hugdahl K (2006). Subtypes of mild cognitive impairment in Parkinson’s disease: progression to dementia. Movement disorders: official journal of the Movement Disorder Society, 21(9), 1343–1349. [DOI] [PubMed] [Google Scholar]
- Jones JD, Kuhn T, Mahmood Z, Singer EJ, Hinkin CH, & Thames AD (2018). Longitudinal intra-individual variability in neuropsychological performance relates to white matter changes in HIV. Neuropsychology, 32(2), 206. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Jones JD, Mangal P, Lafo J, Okunm MS, & Bowers D (2016). Mood Differences Among Parkinson’s Disease Patients with Mild Cognitive Impairment. J Neuropsychiatry Clin Neurosci. 28(3). doi: 10.1176/appi.neuropsych.15090221 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Litvan I, Aarsland D, Adler CH, Goldman JG, Kulisevsky J, Mollenhauer B, … & Weintraub D (2011). MDS Task Force on mild cognitive impairment in Parkinson’s disease: critical review of PD-MCI. Movement disorders, 26(10), 1814–1824. [DOI] [PMC free article] [PubMed] [Google Scholar]
- MacDonald SW, Li SC, & Bäckman L (2009). Neural underpinnings of within-person variability in cognitive functioning. Psychology and aging, 24(4), 792. [DOI] [PubMed] [Google Scholar]
- MacDonald SWS, Nyberg L, Backman L. Intra-individual variability in behavior: links to brain structure, neurotransmission and neuronal activity. Trends Neurosci. 29:474–480. [DOI] [PubMed] [Google Scholar]
- Morgan EE, Woods SP, Delano-Wood L, Bondi MW, & Grant I (2011). Intraindividual variability in HIV infection: Evidence for greater neurocognitive dispersion in older HIV seropositive adults. Neuropsychology, 25(5), 645. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Samat N, Abdul Murad N, Mohamad K, Abdul Razak M, & Mohamed Ibrahim N (2017). Apolipoprotein Eε4: A Biomarker for Executive Dysfunction among Parkinson’s Disease Patients with Mild Cognitive Impairment. Frontiers in Neuroscience: Neurodegeneration 20,doi.org/ 10.3389/fnins.2017.00712 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Slifkin AB, Newell KM. (1998). Is variability in human performance a reflection of system noise? Curr Direct Psychol Sci. 7 170–177. [Google Scholar]
- Stuss DT, Murphy KJ, Binns MA, & Alexander MP (2003). Staying on the job: The frontal lobes control individual performance variability. Brain: A Journal of Neurology, 126, 2363–2380. 10.1093/brain/awg237 [DOI] [PubMed] [Google Scholar]
- Szymkowics SM, Dotson VM, Jones JD, Okun MS, & Bowers D (2018). Symptom Dimensions of Depression and Apathy and Their Relationship With Cogntion in Parkinson’s Disease. J Int Neuropsychol Soc. 24(3). doi: 10.1017/s1355617717001011 [DOI] [PMC free article] [PubMed] [Google Scholar]
- Vaughan L, Leng I, Dagenbach D, Resnick SM, Rapp SR, Jennings JM, … & Gaussoin SA(2013). Intraindividual variability in domain-specific cognition and risk of mild cognitive impairment and dementia. Current gerontology and geriatrics research, 2013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Williams-Gray CH, Mason SL, Evans JR, Foltynie T, Brayne C, Robbins TW, & Barker RA (2013). The CamPaIGN study of Parkinson’s disease: 10-year outlook in an incident population-based cohort. J Neurol Neurosurg Psychiatry, 84(11), 1258–1264. [DOI] [PubMed] [Google Scholar]
- Witjas T, Kaphan E, Azulay JP, Blin O, Ceccaldi M, Pouget J, … & Chérif AA (2002). Nonmotor fluctuations in Parkinson’s disease: frequent and disabling. Neurology, 59(3), 408–413. [DOI] [PubMed] [Google Scholar]
- Wojtowicz M, Berrigan LI, & Fisk JD (2012). Intra-individual variability as a measure of information processing difficulties in multiple sclerosis. International journal of MS care, 14(2), 77–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
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