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Early stage Parkinson's disease patients and normal volunteers: comparative mechanisms of sequence learning.

ABSTRACT: Early-stage nondemented Parkinson's disease (PD(es)) patients can learn short but not long sequences as well as controls. We have previously shown that to achieve normal performance, PD(es) patients activated the same right-sided cortical regions as controls plus the homologous left sided cortex and bilateral cerebellum. In this study, we evaluated two related hypotheses to explain the behavioral abnormalities and the increased bilateral brain activation observed in the PD(es) group. Hypothesis 1 proposed that PD(es) patients recruit regions from a normal bilateral network specialized for sequence learning that healthy controls would activate if performing difficult tasks. Thus, PD(es) patients can learn short sequences as well as controls. Hypothesis 2 proposed that information processing within the network in the PD(es) group is impaired. Thus, PD(es) patients cannot learn as difficult a sequence as controls. To test hypothesis 1, we increased task difficulty and statistical power in the control group and showed that the control and the PD(es) groups activated the same regions. To test hypothesis 2, we analyzed the equal performance data using two partial least squares (PLS) multivariate analyses. The task-PLS analysis showed that to perform equally with controls, the PD(es) group expressed the normal bilateral network more than the control group. The behavior-PLS analysis showed that the correlation between learning performance and regional activation was significantly different between the groups. We conclude that PD(es) patients have near normal learning if task difficulty is moderate because they can recruit additional regions from a normal bilateral network specialized for sequence learning. However, when a difficult task would normally require bilateral activation, PD(es) patients fail to learn because information processing within the network is impaired. Hum. Brain Mapp. 20:246-258, 2003.

SUBMITTER: Mentis MJ

PROVIDER: S-EPMC6871797 | biostudies-literature | 2003 Dec

REPOSITORIES: biostudies-literature

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Early stage Parkinson's disease patients and normal volunteers: comparative mechanisms of sequence learning.

Mentis Marc J MJ Dhawan V V Feigin Andrew A Delalot Dominique D Zgaljardic Dennis D Edwards Christine C Eidelberg David D

Human brain mapping 20031201 4

Early-stage nondemented Parkinson's disease (PD(es)) patients can learn short but not long sequences as well as controls. We have previously shown that to achieve normal performance, PD(es) patients activated the same right-sided cortical regions as controls plus the homologous left sided cortex and bilateral cerebellum. In this study, we evaluated two related hypotheses to explain the behavioral abnormalities and the increased bilateral brain activation observed in the PD(es) group. Hypothesis ...[more]

PMID: 14673808

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Project description:Empirical evidence suggests that levodopa medication used to treat the motor symptoms of Parkinson's disease (PD) may either improve, impair or not affect specific cognitive processes. This evidence led to the 'dopamine overdose' hypothesis that levodopa medication impairs performance on cognitive tasks if they recruit fronto-striatal circuits which are not yet dopamine-depleted in early PD and as a result the medication leads to an excess of dopamine. This hypothesis has been supported for various learning tasks including conditional associative learning, reversal learning, classification learning and intentional deterministic sequence learning, on all of which PD patients demonstrated significantly worse performance when tested on relative to off dopamine medication. Incidental sequence learning is impaired in PD, but how such learning is affected by dopaminergic therapy remains undetermined. The aim of the current study was to investigate the effect of dopaminergic medication on incidental sequence learning in PD. We used a probabilistic serial reaction time task (SRTT), a sequence learning paradigm considered to make the sequence less apparent and more likely to be learned incidentally rather than intentionally. We compared learning by the same group of PD patients (n=15) on two separate occasions following oral administration of levodopa medication (on state) and after overnight withdrawal of medication (off state). Our results demonstrate for the first time that levodopa medication enhances incidental learning of a probabilistic sequence on the serial reaction time task in PD. However, neither group significantly differed from performance of a control group without a neurological disease, which indicates the importance of within group comparisons for identifying deficits. Levodopa medication enhanced incidental learning by patients with PD on a probabilistic sequence learning paradigm even though the patients were not aware of the existence of the sequence or their acquired knowledge. The results suggest a role in acquiring incidental motor sequence learning for dorsal striatal areas strongly affected by dopamine depletion in early PD.

Project description:ObjectiveThe purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson's disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms.BackgroundCurrently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD.MethodsNineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation.ResultsThe ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task).ConclusionsThis pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.

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Early stage Parkinson's disease patients and normal volunteers: comparative mechanisms of sequence learning.

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Early stage Parkinson's disease patients and normal volunteers: comparative mechanisms of sequence learning.

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