Implicit sequence learning in Parkinson’s disease
Katherine R.
1
Gamble ,
Steven E.
4
Lo ,
Thomas J. Cummings,
4
Jr.
James H. Howard,
1,2,3
Jr. ,
Darlene V.
1
Howard
1Department
of Psychology, Georgetown University; 2Department of Psychology, The Catholic University
of America; 3Department of Neurology, Georgetown University Medical Center, 4Medstar Georgetown University Hospital
1
Results: Accuracy and Reaction Time
.8
Raw Scores
Accuracy difference score
(High prob – Low prob)
Accuracy (% correct)
.98
HC,
High
probability
Control,
High
HC,
LowLow
probability
Control,
PD, High
PD,
High probability
PD,
Low probability
PD, Low
.96
.94
.92
.9
e2
e3
e4
e5
•
•
•
•
•
50 trials/block
250 trials/epoch
500 trials/session
3 sessions
80%
20%
.2
0
.6 .6.6 .6
0
Control
.01
Control
HC
Reaction time difference score
(Low prob – High prob)
550
HC,
High
probability
Control,
High
HC,
LowLow
probability
Control,
PD, High
PD,
High probability
PD,
Low probability
PD, Low
530
510
490
470
e2
e3
e4
e5
e6
Repetition
Trill
PD
0
•
•
No significant effects
Participants
were not aware
High
High
High
High
Low
Low
Low
Low
that
high probability triplets
Repetition
Repetition
Repetition
Repetition
occurred
more frequently
TrillTrill
Trill
Trill
during training in either
group
00 0
Control
Control
Control
Control
Healthy
Control
PD PD
PD PD
Parkinson’s
disease
-.03
Conclusions & Implications
-.05
e2
e3
e4
e5
e6
•
22.5
20
17.5
15
12.5
Control
HC
PD
PD
10
7.5
5
2.5
0
e1
e2
e3
e4
e5
e6
Epoch
No significant differences
Main effects of Epoch: p < .01, and
Triplet type: p < .01
LowProbability
Low
Repetition
Repetition
Trill
Trill
-.01
No significant differences
570
PD
PD
PD
Epoch
590
High
HighProbability
.4 .4.4 .4
Reaction Time
Calculating Associative Learning Scores
No Group differences were seen in learning
• Accuracy revealed no evidence of sequence learning (i.e., no triplet
type effect), likely due to a ceiling effect
• There was significant skill-learning (epoch) and sequence learning
(triplet type) seen in the reaction time measure
• Associative learning scores revealed learning in all epochs (all
significantly greater than 0)
• A significant difference in AL scores at Epoch 6 suggests that PD
participants were no longer able to acquire as much new information
about the pattern as HC
• This was likely not due to fatigue, as accuracy and reaction time were
maintained throughout training
• People with Parkinson’s disease have extensive dopamine denervation
in the striatum, well beyond healthy aging (Kish et al., 1988)
• The caudate, which is involved in TLT learning, shows dopamine loss
later in the disease
• People with Parkinson’s disease may lack the neural resources to
support learning in late training, likely due to dopamine declines
• People with PD may not have the neural resources in the hippocampus
to support learning in late training
• Dopaminergic medication helps performance on tasks that rely on
brain regions with dopamine denervation, but “floods” and harms
other areas of the brain, such as the hippocampus (Cools, 2006)
• The hippocampus has been shown to compensate for prefrontal and
striatal deficiencies in PD, but this compensation may not be possible
as the disease progresses (Carbon et al., 2010)
•
Correlate each individuals’ reaction time for each triplet with that
triplet’s frequency within a window of time (epoch)
• Higher frequency  Faster RT
• High learning = Negative correlation
• Multiply correlation by -1 to get a + Associative Learning score for
each person
Results: Associative Learning Scores
Associative Learning Score
•
.2
.8 .8.8 .8
Control
e1
Epoch
Two red cues are shown, followed by a green target to which
participants respond
Unbeknownst to participants, the 1st red cue predicts the location of
the green target
16 triplets occur with high probability (80% of the time), while 32
triplets occur with low probability (20% of the time)
.4
Low
11 1
.2 .2.2 .2
No significant effects
Participants
•
.4
.03
Epoch
e1
Triplets Learning Task
1
.05
e6
450
27 people with Parkinson’s disease (PD; 10 females)
• Aged 64.55 ± 5.77
• Diagnosed with mild to moderate PD by a neurologist; Hoehn and
Yahr stage Range 1 – 2.5, UPDRS Motor score 8.55 ± 6.53 (0 - 29),
disease duration of 6.59 ± 4.55 (1 – 18) years
• All participants were receiving dopaminergic medication and were
tested while ON
27 healthy older adult controls (HC; 18 females)
• Aged 66.07 ± 5.27
.6
.6
1
e1
High
Learning Difference Scores
Accuracy
Reaction time (ms)
Parkinson’s disease is characterized by motor deficits
• Motor deficits allow categorization into Hoehn and Yahr stages
(Hoehn and Yahr, 1967) and ratings on the Unified Parkinson’s
disease Rating Scale (UPDRS; Fahn and Elton, 1987)
• Motor symptoms do not appear until there is an 80% loss of
dopamine in the striatum (Bernheimer et al., 1973), a region of the
brain involved in implicit sequence learning (Rieckmann and
Bäckman, 2009)
• The disease presents with sequencing deficits, such as problems
with sentence comprehension (Hockstadt et al., 2006) and
increased falls (Loftus, 2009)
• Results of studies investigating implicit sequence learning in people
with Parkinson’s disease are mixed (Siegert et al., 2006), with some
showing that learning is impaired (Muslimovic et al., 2007) and
others showing that it is spared (Kwak et al., 2012)
Triplets Learning Task (TLT) measures implicit associative learning
• The reduced motor component is important, as Parkinson’s diseaserelated motor impairments may affect sequence learning in some
tasks (Helmuth et al., 2000)
• The amount of learning is related to variations in a gene related to
dopamine availability (DAT1; Simon et al., 2011)
• Learning relies on the caudate in young adults, particularly late in
training (Simon et al., 2012), a region of the brain where dopamine
denervation occurs later in Parkinson’s disease (Kish et al., 1988)
• In healthy older adults, learning relies on the caudate and
hippocampus early in training, with the hippocampus showing more
learning-related activation in late training (Simon et al., 2012)
Results: Explicit Awareness
.8
Proportion Rated as
“Occurred More Often”
Introduction
1
.23
*
.2
.17
.15
.13
Control
HC
PD
PD
.1
.08
•
•
Marginal main effect of Group: p = .062
Significant difference between Group at Epoch 6:
p= .028
References
Carbon, M., Reetz, K., Ghilardi, M. F., Dhawan, V., & Eidelberg, D. (2010). Neurobiology of disease, 37,
455-460.
Cools, R. (2006). Neuroscience & Biobehavioral Reviews, 30, 1-23.
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Developments in Parkinson's Disease (Vol. 2, pp. 153-163, 293-304). Florham Park, NJ:
Macmillan Health Care Information.
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Hochstadt, J., Nakano, H., Lieberman, P., & Friedman, J. (2006). Brain and Language, 97, 243-257.
Hoehn, M. M., & Yahr, M. D. (1967). Neurology, 17, 427-442.
Howard Jr., J. H., Howard, D. V., Dennis, N. A., & Kelly, A. J. (2008). Journal of Experimental PsychologyLearning Memory and Cognition, 34(5), 1139-1157.
Kish, S. J., Shannak, K., & Hornykiewicz, O. (1988). The New England Journal of Medicine, 318, 876-880.
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Research, 230, 116-124.
Loftus, S. (2009). Fall Prevention Strategies for People Living with Parkinson's. Understanding Parkinson's,
from http://www.pdf.org/en/fall09_fall_prevention
Muslimovic, D., Post, B., Speelman, J. D., & Schmand, B. (2007). Brain, 130, 2887-2897.
Rieckmann, A., & Backman, L. (2009). Neuropsychology Review, 19(4), 490-503.
Siegert, R. J., Taylor, K. D., Weatherall, M., & Abernethy, D. A. (2006). Neuropsychology, 20, 490-495.
Simon, J. R., Stollstorff, M., Westbay, L. C., Vaidya, C. J., Howard Jr., J. H., & Howard, D. V. (2011).
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Simon, J. R., Vaidya, C. J., Howard Jr., J. H., & Howard, D. V. (2012).. Journal of Cognitive
Neuroscience, 24, 451-463.
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43rd Annual Meeting of the Society for Neuroscience, 2013
Supported by: NIH/NIA Grant RO1AG036863
krg27@georgetown.edu