Dopamine and Consolidation of Episodic Memory:
Timing Is Everything

John Grogan1, Rafal Bogacz2, Demitra Tsivos3, Alan Whone1,3, and Elizabeth Coulthard1,3

Abstract

■ Memory consolidation underpins adaptive behavior and do-
paminergic networks may be critical for prolonged, selective in-
formation storage. To understand the time course of the
dopaminergic contribution to memory consolidation in hu-
mans, here we investigate the effect of dopaminergic medica-
tion on recall and recognition in the short and longer term in
Parkinson disease (PD). Fifteen people with PD were each
tested on or off dopaminergic medication during learning/early
consolidation (Day 1) and/or late consolidation (Day 2). Fif-
teen age-matched healthy participants were tested only once.
On Day 1 participants learned new information, and early ep-
isodic memory was tested after 30 min. Then on Day 2, recall
and recognition were retested after a 24-hr delay. Participants
on medication on Day 1 recalled less information at 30 min

and 24 hr. In contrast, patients on medication on Day 2 (8–
24 hr after learning) recalled more information at 24 hr than
those off medication. Although recognition sensitivity was un-
affected by medication, response bias was dependent on dopa-
minergic state: Medication during learning induced a more
liberal bias 24 hr later, whereas patients off medication during
learning were more conservative responders 24 hr later. We
use computational modeling to propose possible mechanisms
for this change in response bias. In summary, dopaminergic
medication in PD patients during learning impairs early consol-
idation of episodic memory and makes delayed responses
more liberal, but enhances late memory consolidation presum-
ably through a dopamine-dependent consolidation pathway
that may be active during sleep. ■

INTRODUCTION

Traditionally, memory impairment in neurodegenerative
disease has often been considered to be explained by
loss of cholinergic neurons (e.g., Bartus, Dean, Beer, &
Lippa, 1982). The role of dopamine in memory is more
controversial as are the nature and degree of memory
impairments in patients with dopaminergic loss such as
those with Parkinson disease (PD; Foerde, Braun, &
Shohamy, 2013; Foerde & Shohamy, 2011; Shohamy,
Myers, Geghman, Sage, & Gluck, 2006; Cools, Barker,
Sahakian, & Robbins, 2001). Conventional accounts of
memory posit three main stages: encoding (initial learn-
ing of the information), consolidation (maintenance of
the stored memory over a period of minutes, hours, days,
or years), and retrieval (accessing this memory). Dopami-
nergic effects have been reported across these stages.

Dopamine antagonist infusion during or immediately
after encoding worsens delayed recall in animals (Bethus,
Tse, & Morris, 2010; O’Carroll, Martin, Sandin, Frenguelli,
& Morris, 2006), suggesting a benefit of dopaminergic ac-
tivity on encoding and perhaps early consolidation. This is
also supported by a recent observation that optogenetic
stimulation of dopaminergic neurons during learning en-
hances memory retention (McNamara, Tejero-Cantero,

1University of Bristol, 2University of Oxford, 3North Bristol NHS
Trust

Trouche, Campo-Urriza, & Dupret, 2014). Importantly this
recent study found that stimulating the dopaminergic pro-
jections from the ventral tegmental area (VTA) to the hip-
pocampal CA1 subfield during learning did not speed up
learning but did increase memory at a 1-hr delayed test, as
well as increasing the reactivation of memory traces during
sleep after learning. The authors suggested that dopami-
nergic input to the CA1 increases the reactivation of newly
formed neuronal assemblies to allow for consolidation of
the memories they encode.

Dopamine appears to improve memory consolidation
in animals, but only at certain time points after initial
learning. A dopamine D1/D5 agonist infused into the
CA1 in the hippocampus of rats before inhibitory avoid-
ance learning had no effect when tested 24 hr later, nor
when it was infused 9 hr after learning (Bernabeu et al.,
1997). However, when it was infused 3 or 6 hr after learn-
ing, it improved (increased) the step-down latencies of
the rats significantly. A dopamine D1/D5 antagonist had
the opposite effect, decreasing the latency only when in-
fused 3 or 6 hr after learning. Adenylyl cyclase activator
infusion 3 or 6 hr after learning also increased memory,
and a PKA inhibitor decreased it, suggesting that this
dopamine-dependent consolidation effect is mediated
by the cAMP/PKA pathway in the hippocampus. Further in-
sights into the molecular mechanisms come from Rossato
and colleagues, who demonstrated that NMDAR antagonist

© 2015 Massachusetts Institute of Technology. Published under a
Creative Commons Attribution 3.0 Unported (CC By 3.0) license.

Journal of Cognitive Neuroscience 27:10, pp. 2035–2050
doi:10.1162/jocn_a_00840

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infusion to the VTA 12 hr after learning impaired memory
14 days later, and this was reversed by dopamine agonist
infusion to the CA1 (Rossato, Bevilaqua, Izquierdo, Medina,
& Cammarota, 2009). The authors propose that NMDAR
activation in the VTA 12 hr after learning excites those cells
and causes greater dopaminergic activity projected to the
hippocampus, which causes dopamine-dependent
consolidation.

Very recent work demonstrated that a D2 inverse ago-
nist (similar to an antagonist, but it decreases the recep-
tor activity below baseline activity) injected postlearning
decreased novelty preference 24 hr later (França et al.,
2015). Importantly, a putative mechanism for such an ef-
fect stems from the findings that the decrease in memory
was accompanied by a decrease in CaMKII from 3 to
12 hr after learning, Zif-268 6 hr after learning, and brain-
derived neurotrophic factor (BDNF) 12 hr after learning,
along with decreased REM sleep. These proteins are all
activated at different times after learning, are involved
in plasticity and consolidation, and are affected by dopa-
mine just after the time of learning, as well as at their
time of activation (Rossato et al., 2009). This suggests
that dopamine affects plasticity protein levels, possibly
via REM sleep. Perhaps not surprisingly given the known
dose dependence of dopamine responses and complex-
ity of dopaminergic networks, somewhat conflicting
results have emerged when different experimental para-
digms are used (e.g., Furini, Myskiw, Schmidt, Marcondes,
& Izquierdo, 2014; Péczely et al., 2014; Rossato et al., 2009,
2013). However, the overall picture is that dopamine-
dependent networks have a time-critical role in animal
memory consolidation that may to some extent depend
on a period of sleep.

One theory of consolidation that incorporates a role of
dopamine is the synaptic tagging and capture theory
(Clopath, 2012; Redondo & Morris, 2011; Clopath, Ziegler,
Vasilaki, Büsing, & Gerstner, 2008; Sajikumar & Frey,
2004; Frey & Morris, 1997, 1998). This theory states that
an input to a synapse can cause early synaptic plasticity
and can also “tag” the synapse so that when plasticity-
related products are synthesized they can be captured
by the tagged synapse and used to stabilize the early plas-
ticity changes to allow consolidation to take place. In the
computational model of this theory (Clopath et al., 2008),
the threshold for production of the plasticity-related
products is set by tonic dopamine levels. As this theory
states that the tags only last a few hours, which could
account for some of the findings mentioned above, it
cannot easily account for the consolidation effects of
dopamine at later time points found in other studies,
without extensions made to the model.

Although these models of memory consolidation have
been developed based on animal experimentation, the
focus of our work is the contribution of dopamine to hu-
man memory. The global application of dopaminergic
drugs in humans limits the conclusions that can be drawn
and poses a challenge when designing studies in people.

However, it is critical to establish how dopamine influ-
ences human memory, particularly given that memory
loss is such a prominent problem affecting our increas-
ingly elderly population.

Older adults given levodopa, a dopamine precursor,
before learning showed no benefits on memory after
2 hr, but did show dose-dependent improvement in scene
recognition 6 hr after learning (Chowdhury, Guitart-Masip,
Bunzeck, Dolan, & Düzel, 2012). Such an inverted U-shaped
correlation between dose and performance perhaps im-
plies that low concentrations were insufficient to have an
impact, whereas high concentrations “overdosed” the
brain (Cools, 2006; Cools et al., 2001). The delayed benefi-
cial effects of levodopa suggest dopamine might be impor-
tant for human late memory consolidation, but effects of
dopamine over longer timescales in humans have not been
investigated.

Genetic studies suggest that increased levels of dopa-
minergic activity improve memory (Wittmann, Tan, Lisman,
Dolan, & Düzel, 2013; De Frias et al., 2004). However, work
in humans has not normally fully dissociated encoding,
consolidation, and retrieval, and results have conflicted
with some studies finding that dopamine replacement
medications given before learning improve memory
(Chowdhury et al., 2012; Coulthard et al., 2012) whereas
another found that they impaired encoding (Macdonald
et al., 2013). Thus, it is not clear exactly how dopamine con-
tributes to the earliest stages of memory processing and
whether human dopaminergic contributions to memory
mirror those observed in animals.

We designed a paradigm to differentiate the effects
of dopaminergic activity on encoding, consolidation and
retrieval in PD patients. We aimed to see whether exoge-
nous dopamine present during encoding/early consolida-
tion or late consolidation would enhance 24-hr delayed
recall and recognition memory in people with PD. Impor-
tantly, we were able to distinguish between the stages of
memory by probing (i) initial learning, (ii) early consolida-
tion and retrieval (at 30 min), and (iii) late consolidation
and retrieval at 24 hr (after overnight consolidation) sepa-
rately. By withdrawing patients from their dopaminergic
medications before learning on Day 1 and/or testing on
Day 2, we could see the effects of dopaminergic activity
on each component process within the memory system.

METHODS

Participants

Fifteen patients with PD and 15 age-matched healthy par-
ticipants were tested (see Table 1 for details). Patients
were identified through movement disorder and general
neurology clinics in North Bristol NHS Trust, and all had a
clinical diagnosis of PD and no other neurological diagno-
ses. The PD patients were all on dopaminergic medica-
tion (levodopa and/or dopamine agonists) and were
not taking cholinesterase inhibitors, monoamine oxidase

2036

Journal of Cognitive Neuroscience

Volume 27, Number 10

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Table 1. Demographics and Questionnaires Scores for Patients and Healthy Participants

Group

Number

Age

Years since diagnosis

Number on levodopa/

dopamine agonists/both

Levodopa dose equivalency

(mg/day)

MMSE

UPDRS on meds

UPDRS off meds

DASS

(Depression/

Anxiety/

Stress)

BIS

REI (Rationality/

Experientiality)

LARS

Number of Missing
Patients/HPP

PD Patients

Healthy
Participants

One-way ANOVA: Patients vs. Healthy
Participants (df, F, p)

15

15

71.53 (2.40)

71.00 (2.63)

(1, 28), 0.076, .785

5.20 (1.38)

9/1/5

603.00 (71.64)

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27.00 (0.31)

27.90 (0.34)

(1, 28), 5.000, .033

19.90 (3.17)

24.90 (3.86)

1/0

22.50 (2.57)

10.30 (3.17)

(1, 27), 10.943, .003

1/0

0/4

6.53 (0.84)

2.94 (0.82)

(1, 27), 16.079, <.001 8.18 (1.28) 3.176 (0.83) (1, 27), 14.312, .001 6.41 (0.96) 5.06 (0.98) (1, 27), 2.640, .116 63.40 (1.68) 62.10 (1.37) (1, 27), 0.736, .398 3.33 (0.15) 3.67 (0.21) (1, 24), 1.828, .189 2.89 (0.12) 2.91 (0.15) (1, 24), 0.10, .920 −19 (1.56) −26 (1.32) (1, 28), 13.094, .001 Means with SEM in parentheses unless stated otherwise. MMSE = Mini Mental State Exam; UPDRS = Unified Parkinson Disease Rating Scale; DASS = Depression, Anxiety and Stress Scale; BIS = Barratt Impulsivity Scale; REI = Rational–Experiential Inventory; LARS = Lille Apathy Rating Scale. inhibitors, or anti-psychotic medications or treated with deep brain stimulation or other functional neurosurgery. Aside from Parkinsonian medications, two patients were tak- ing medications for cholesterol (simvastatin), and one of these was also on amlodipine and clopidogrel, which are an- tihypertensive and antiplatelet medications. One was taking alfuzosin for enlarged prostate, one an SSRI (sertraline) and calcium supplements, one quinine sulfate (for nocturnal leg cramps), and one latanoprast and dorzolamide for glau- coma and salazopyrin for arthritis. Levodopa dose equiva- lency was calculated to get a measure of total daily dopaminergic medication taken (Tomlinson et al., 2010). Healthy participants were either the spouses of the PD patients who accompanied them to the session or were recruited from the BRACE Centre’s Healthy Volunteer da- tabase. Healthy participants taking any dopaminergic or noradrenergic medications were excluded from the study. None had any neurological diagnoses or reported memory problems. Of the healthy participants, one was taking simvastatin and aspirin (cholesterol and antiplate- let medication), one an ACE inhibitor (ramipril), and one fluoxetine (SSRI). Ethics approval was granted by the North Bristol NHS Trust Research Ethics Committee, and all participants gave written consent in accordance with the Declaration of Helsinki. Task The Hopkins Verbal Learning Task-Revised (HVLT-R) is an episodic memory test with immediate and 30-min de- layed recall and recognition components (www4.parinc. com/Products/Product.aspx?ProductID=HVLT-R). The experimenter reads aloud 12 words at a rate of one per second, after which the participant recalls as many words as they can in any order. These words are drawn from three semantic categories (four words from each) such as mammals, fuels, and tools. The list is read out twice more in the same order, each time followed by immedi- ate recall to give a total of three immediate recall trials. After a 30-min delay, there is another recall trial, along with a recognition trial where the experimenter reads aloud the 12 target words and 12 new distractor words. Six of these distractors are from the same three semantic categories as the target words (two from each category), and six are not. The words are presented in a randomized order, and no feedback is given. Participants respond “yes” if they think the word was on the learning list and “no” if they think it is a new word. In an addition to the standard HVLT, at 24 hr another recall test was performed along with a further recognition test, which was the same as the 30-min test only with new distractor words. A different version of the task was given in each of the four conditions (Versions 1, 3, 4, 5) in a randomized Grogan et al. 2037 l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 7 / 1 2 0 7 / 2 1 0 0 3 / 5 2 1 0 9 3 4 5 9 / 4 1 5 2 7 8 o 3 c 3 n 4 _ 1 a / _ j 0 o 0 c 8 n 4 0 _ a p _ d 0 0 b 8 y 4 g 0 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j . f t / u s e r o n 1 7 M a y 2 0 2 1 order between participants. Each word appeared in only one version of the task. As patients completed the HVLT four times, the delayed tests could not be kept a surprise, so all patients were told at the beginning that they would be tested on the list both later on in the current session and in the session the next day. Procedure Testing took place over two consecutive days (see Figure 1 for details). On Day 1 participants completed the immedi- ate recall and 30-min delayed recall and recognition tests, along with paper-based questionnaires: Barratt Impul- sivity Scale (Patton, Stanford, & Barratt, 1995), Rational– Experiential Inventory (REI; Pacini & Epstein, 1999), Depression Anxiety Stress Scale (DASS; Lovibond & Lovibond, 1995), Mini Mental State Exam (MMSE; Folstein, Folstein, & McHugh, 1975), and Lille Apathy Rating Scale (LARS; Zahodne et al., 2009; Sockeel et al., 2006). The PD patients also completed a standard motor symptom rating (Unified Parkinson Disease Rating Scale-III; Goetz et al., 2008). These assessments were completed during the 30-min delay. On Day 2 participants completed the 24-hr delayed re- call and recognition tasks and a sleep questionnaire about their sleep the previous night (St. Mary’s Hospital Sleep Questionnaire; Leigh, Bird, Hindmarch, Constable, & Wright, 1988; Ellis et al., 1981)). PD patients were on or off their medications on each of the 2 days, giving four conditions for each subject (Day1/Day2 = on/on, on/off, off/on, off/off ). When the patients were in the “off” condition, they did not take any dopaminergic medications for a minimum of 15 hr before testing (usually none after 6 pm for testing at 10 am the next day). After the session had finished, they took a dose and then carried on with their usual dose schedule. For example, in the “off–off” condition (bot- tom bar in Figure 1), patients were on medication until 6 pm Day 0, then off medication until after testing on Day 1 (∼11 am), then on medication until 6 pm Day 1, then off medication until after testing on Day 2 (∼10:30 am), after which they resumed their normal medication sched- ule. All patients were on their medication for at least a few hours after learning on Day 1 (see Figure 1). This meant that in Day 1 off conditions they took a dose after the session finished and continued their normal dosage until the evening where if they were to be off medication on Day 2 they stopped their doses. This was to minimize discomfort to the patients and to reduce the chance of neuroleptic malignant syndrome, which can occur when dopaminergic medications are stopped (Keyser & Rodnitzky, 1991). The order of the four conditions was counterbalanced across participants. Healthy participants were tested only once. Data Analysis Data were analyzed using within-subject repeated- measures ANOVAs to test the effects of Day 1 and Day 2 medication state on each separate measure in the patient group. Between-subject ANOVAs were used to compare patients and healthy participants. Raw de- layed recall scores were measured at 30 min and 24 hr, as well as the percentage retention, which was the de- layed recall score divided by the highest of the second and third immediate recall trial scores, multiplied by 100. This was calculated for the 30-min and 24-hr delayed recall scores. The change between the two delayed scores was also expressed as a percentage (24 hr/30 min × 100). For the recognition tests, the raw measures were num- ber of hits, misses, false alarms, and correct rejections. Signal detection methods (Stanislaw & Todorov, 1999; Macmillan, 1993; Green & Swets, 1966) were used to es- 0 measure of sensitivity between the targets timate the d and distractors, d0 ¼ Z p hits ð ð Þ Þ−Z p false alarms ð ð Þ Þ (1) where Z is the inverse cumulative density of the standard normal distribution and the hits and false alarms are D o w n l o a d e d f r o m l l / / / / j t t f / i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 7 / 1 2 0 7 / 2 1 0 0 3 / 5 2 1 0 9 3 4 5 9 / 4 1 5 2 7 8 o 3 c 3 n 4 _ 1 a / _ j 0 o 0 c 8 n 4 0 _ a p _ d 0 0 b 8 y 4 g 0 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j / f . t u s e r o n 1 7 M a y 2 0 2 1 Figure 1. Timeline of the testing procedure from 6 pm Day 0 to 12 pm Day 2. Blue denotes when patients were on their dopaminergic medication, red when they were withdrawn from it, and yellow the time of testing sessions. To have patients off their medication during the testing at 10 am, patients had to be withdrawn from their dose the night before because of the long washout times of the medications. 2038 Journal of Cognitive Neuroscience Volume 27, Number 10 expressed as probabilities, and response bias (a measure of where the threshold for a “yes” response is set), The change in weights after presentation of a novel stimulus is calculated as ð c ¼ − Z p hits ð Þ Þ þ Z p false alarms ð ð 2 Þ Þ (2) Δwj ¼ −η ð Na 1−a (cid:3) (cid:2) Þ h xj−a (4) Negative response biases indicate that less evidence is needed for a “yes” response, which corresponds to more “yes” responses—a liberal response bias. Positive response biases mean more evidence is needed for a “yes,” which leads to more “no” responses—a con- servative response bias. Nonparametric measures of sen- sitivity and response bias were also calculated (Stanislaw & Todorov, 1999) but are not reported here as they gave the same results as the more common parametric measures. The response bias score takes into account the propor- tion of hits and false alarms. We use this measure rather than discussing the raw hits and false alarms scores be- cause there was no clear pattern of results emerging from them. All SEM bars for figures have been corrected for between-subject variance using the Cousineau–Morey method (O’Brien & Cousineau, 2014; Morey, 2008; Cousineau, 2005), which removes the variance from between-subject differences and only shows the variance due to within-subject differences in a similar manner to the way a repeated-measures ANOVA removes between- subject variance. Methods of Simulation To illustrate what changes in synaptic connections could underlie the observed effects of dopaminergic medica- tion on recognition memory (i.e., higher tendency to classify both previously seen and unseen stimuli as famil- iar when on medication during encoding; see Figure 4B), we used a simple anti-Hebbian model of familiarity discrimination (Bogacz & Brown, 2003a). The intuitive description of model is provided in the Results section, whereas here we describe details of the model (it is recommended to read the Results section before these details). The model is a simple layer network with N input neurons and novelty neurons in separate layers. For the version we used, we simplified the model to only have one novelty neuron and N = 100 input neurons. The model assumes the novelty neuron receives con- nections from all input neurons. The weights of the con- nections from input neuron j to the novelty neuron are denoted as wj. The activity of each input neuron is de- noted by xj, and it is 1 if the neuron is active and 0 oth- erwise. The activity of the novelty neuron (h) is thus calculated, h ¼ XN j¼1 wjxj (3) D o w n l o a d e d f r o m l l / / / / j t t f / i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 7 / 1 2 0 7 / 2 1 0 0 3 / 5 2 1 0 9 3 4 5 9 / 4 1 5 2 7 8 o 3 c 3 n 4 _ 1 a / _ j 0 o 0 c 8 n 4 0 _ a p _ d 0 0 b 8 y 4 g 0 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j t f / . u s e r o n 1 7 M a y 2 0 2 1 where η is the learning rate and a is the sparseness pa- rameter controlling the fraction of input neurons active in the patterns. In particular for each input pattern (x), the number of active neurons is determined by the sparseness parameter a such that XN j¼1 xj ¼ aN (5) In all our simulations, parameters were fixed at a = 0.2 and η = 0.2. After learning a series of randomly generated pat- terns, we present the now-familiar patterns and some ran- domly generated novel patterns. For each presented pattern, the neuron’s activity is compared against a thresh- old T: if h < T it is classified as familiar, and if h > T as novel.
We further denote the thresholds on 2 days of testing
by T1 and T2, respectively. In all our simulations, T1 = 1.
To simulate the effects of high and low dopaminergic
state, we proposed two versions of the model. In the first
version (the decay model), dopaminergic medications
would affect the decay of the weights. Dopamine has
been implicated in consolidation in animals (Bethus
et al., 2010; Rossato et al., 2009; Bernabeu et al., 1997),
and models have been proposed in which dopamine sets
the threshold for consolidation of weight changes at syn-
apses (Clopath et al., 2008; Frey & Morris, 1997). We
model this with two coefficients that control how much
of the weight change is expressed:

(cid:4)

if Δwij > 0; Δwij ¼ Δwijαþ
if Δwij < 0; Δwij ¼ Δwijα− (6) There is a separate coefficient for the decreases in weights caused by coactivation of the input and novelty neurons and the increase in weights caused by the acti- vation of the input neurons alone. This change was only applied after the 30-min test as consolidation takes time to have an effect. This model has four parameters that have been optimized: α+ and α− for PD patients on and off medication on Day 1, respectively (α+,On, α−,Off, α+,Off, α−,Off). In this model, the thresholds are assumed to be the same on both days T2 = T1 = 1. In the second version of the model (the threshold model), the dopamine state during learning does not affect the decay of the weight changes but instead the threshold T2 for the 24-hr test. We assumed that T2 depends upon Day 1 dopamine state; thus, the model has four free param- eters, two of which correspond to the value of T2 when the dopamine was on and off during Day 1 (T2,On, T2,Off). This is akin to a consolidation or decay of the threshold inputs dependent on dopamine 30–120 min after learning. Two other free parameters correspond to α+ and α−, but these were the same for Day 1 on and off conditions. Grogan et al. 2039 We simulated the models and then compared the rec- 0 and response bias measures from ognition accuracy d the model with the behavioral data (Figure 4) using root mean square deviation (RMSD), RMSD ¼ s ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Xn Þ2 ð yi−di n i¼1 (7) where yi is the behavioral data, di is the simulated data for measure i, and n is the number of measures (n = 0 and response bias on and off med- 8; 30 min and 24 hr d ications on Day 1). We used 12 familiar patterns, each presented three times in the same order (as in the HVLT-R), and for the recognition tests all 12 familiar patterns along with 12 novel patterns were presented (different novel patterns were used for each test). We repeated this 1000 times with newly generated patterns and random starting weights each time. We used Matlab’s fminsearch function to find free pa- rameters of each model that minimize this RMSD. The ini- tial values of the free parameters (i.e., the starting point for the search) were randomly generated from uniform distri- butions between 0 and 2. For each model, the whole opti- mization was repeated 300 times with different randomly generated initial sets of parameters to avoid local minima. RESULTS No Effects of Dopaminergic Medication on Immediate Recall Patients and healthy participants show an increase in the number of words recalled over the three immediate re- call trials (Figure 2A), although healthy participants recall significantly more words at each trial than patients (one- way multivariate ANOVAs, p < .001). Healthy participants only show a change in number of correctly recalled words between the first and second recalls (one-way repeated-measures ANOVA with Bonferroni-corrected pair- wise comparisons, p = .005), with no further increases or decreases ( p = 1). A two-way repeated-measures ANOVA (Day 1 medication state × Time) on the PD patients’ immediate recall trials revealed a significant effect of Time (F(2, 28) = 24.870, p < .001) but no significant effect of Day 1 medication state (F(1, 14) = .008, p = .931; see Figure 2B). PD patients take longer to learn information and retain less information than healthy par- ticipants, with their final immediate recall score about the same as the healthy participants’ first, and show no do- pamine medication effects. Medication Impairs Early Consolidation but Improves Late Consolidation Healthy participants retain information across both 30-min and 24-hr delays (Figure 2A). Patients, however, show a decrease across both delays: 30-min delayed recall is significantly lower than the third immediate recall (one- way repeated-measures ANOVA with Bonferroni-corrected pairwise comparisons; p = .001), and 24-hr recall score is significantly lower than both the third immediate re- call score and the 30-min delayed recall score ( p < .001 for both). On average, 1–1.5 words are lost across both 30-min and 24-hr delays in patients. The recall retention scores in different medication condi- tions are shown in Figure 3, and the patients were statisti- cally analyzed with a three-way repeated-measures ANOVA (Delay × Day 1 medication state × Day 2 medication D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 7 / 1 2 0 7 / 2 1 0 0 3 / 5 2 1 0 9 3 4 5 9 / 4 1 5 2 7 8 o 3 c 3 n 4 _ 1 a / _ j 0 o 0 c 8 n 4 0 _ a p _ d 0 0 b 8 y 4 g 0 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j f . t / u s e r o n 1 7 M a y 2 0 2 1 Figure 2. The mean number of correct words recalled at each recall test. (A) The red line is the patients’ raw recall scores averaged across all conditions (SEM bars). Healthy participants showed significantly higher accuracy on all recall trials ( p < .001 for all) and no decrease over the delays (F(1, 14) = 2.507, p = .136). PD patients learn more slowly and forget more over 30-min and 24 hr-delays. (B) Each PD condition separately, there were no significant differences for the immediate recall trials. 2040 Journal of Cognitive Neuroscience Volume 27, Number 10 ing that whereas the off–on condition may contribute majorly to the effect, the on–on condition does also. Response Bias, but Not Recognition Sensitivity, Is Affected by Dopamine As shown in Figure 4A, the patients had significantly 0) on the 24-hr recognition task than lower sensitivity (d on the 30-min test (effect of delay: F(1, 14) = 14.089, p = .002), but there was no significant effect of medica- 0 scores at tion state. Healthy participants have the same d both time delays (paired t test: T = 0.363, p = .722), which are much higher than the patients’ scores in both cases. Figure 4B shows that when patients were on medica- tion on Day 1 they had a more liberal (negative) response bias after 24 hr, but when patients were off medication on Day 1 they had a more conservative (positive) bias 24 hr later, regardless of Day 2 medication (interaction of time and Day 1 medication: F(1, 14) = 15.083, p = .002). In other words, dopaminergic medication during learning and early consolidation led to more “yes” re- sponses 24 hr later, whereas a lack of dopaminergic med- ication during learning led to more “no” responses 24 hr later. In contrast, response bias of healthy participants increased over 24 hr; they were more conservative at the 24-hr recognition task, responding “no” more often and decreasing the number of hits and false alarms. This is the same pattern as PD patients off medication on Day 1. Modeling the Response Bias Effects The response bias effects were the most unexpected re- sults we found, and we were unable to come up with a simple explanation, so we turned to computational models of familiarity discrimination to see if an interac- tion of delay and Day 1 medication state could be found in the models. We have used an abstract recognition model to at- tempt to replicate our recognition memory results. The anti-Hebbian model (Bogacz & Brown, 2002, 2003b; Kohonen, Oja, & Rouhonen, 1974) was originally devel- oped to capture the finding that perirhinal cortex neu- rons that discriminate on the basis of familiarity show high firing activity for novel, unfamiliar stimuli, but low firing activity for familiar stimuli (Brown & Aggleton, 2001). For example, when presenting the same stimulus twice, in the first time the neurons will fire wildly, and in the second time the neurons will fire at a lower rate. This differential activity allows discrimination between novel and familiar stimuli. In the model, when a novel stimulus is presented as a pattern of activity to the novelty neuron, it elicits an out- put activity (Figure 5A). Consequently, the synaptic weights from the activated inputs are decreased, which results in lower output activity for the same stimulus pre- sented again (Figure 5B; see Banks, Bashir, & Brown, Grogan et al. 2041 Figure 3. The mean percentage retention over 30-min and 24-hr delays. Healthy participants (black line) do not show a significant decrease over 24 hr (F(1, 14) = 2.507, p = .136). For PD patients (red and blue lines), there is a significant effect of delay (F(1, 14) = 49.885, p < .0001) and a clear effect of Day 1 medication state (F(1, 14) = 7.329, p = .017), with patients off medication on Day 1 during learning (red lines) having higher retention of words at 30 min and 24 hr than patients on medication on Day 1 (blue lines). It also shows a significant interaction of Delay and Day 2 medication state (F(1, 14) = 11.4, p = .005), with patients on medication on Day 2 (solid lines) having higher retention on Day 2 than patients off dopamine on Day 2 (dashed lines). All bars show SEM. state). The recall scores after 24 hr were significantly lower than scores after 30 min (effect of delay: F(1, 14) = 49.855, p < .0001). Patients who were on dopaminergic medica- tions on Day 1 recalled less at both 30-min and 24-hr recall, which can be seen in Figure 3 where both blue lines are below the red lines (effect of Day 1 medication state: F(1, 14) = 7.320, p = .017). In stark contrast, patients on dopaminergic medication on Day 2 retained significantly more words between 30 min and 24 hr, which can be seen in Figure 3 where both solid lines are flatter than the dashed lines (interaction of delay and Day 2 medication: F(1, 14) = 11.4, p = .005; note that Day 2 medication effect could only be an interaction, as Day 2 medication could not possibly affect the 30-min recall score on Day 1). Thus, do- paminergic medication impairs delayed memory when present during learning (without affecting immediate re- call) but improves it when present overnight after learning and during testing the next day. Looking at Figure 3B, it seems that the Delay × Day 2 medication state effects may be driven mostly by the off–on condition and that this may be significantly differ- ent to the other three conditions by itself. However, the three-way interaction from the ANVOA (Delay × Day 1 medication state × Day 2 medication state) did not return a significant result (F(1, 14) = 1.274, p = .278), which means that the significant differences are not due just to the off–on condition. There is only a significant effect when delay and Day 2 medication state are factors, mean- D o w n l o a d e d f r o m l l / / / / j t t f / i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 7 / 1 2 0 7 / 2 1 0 0 3 / 5 2 1 0 9 3 4 5 9 / 4 1 5 2 7 8 o 3 c 3 n 4 _ 1 a / _ j 0 o 0 c 8 n 4 0 _ a p _ d 0 0 b 8 y 4 g 0 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j / t f . u s e r o n 1 7 M a y 2 0 2 1 D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 7 / 1 2 0 7 / 2 1 0 0 3 / 5 2 1 0 9 3 4 5 9 / 4 1 5 2 7 8 o 3 c 3 n 4 _ 1 a / _ j 0 o 0 c 8 n 4 0 _ a p _ d 0 0 b 8 y 4 g 0 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j t f / . u s e r o n 1 7 M a y 2 0 2 1 Figure 4. Recognition memory. (A) d0 measure of sensitivity and (B) response bias for 30-min and 24-hr delayed tests (SEM bars). Healthy 0 across the delay whereas patients do (F(1, 14) = 14.089, p = .002). Response bias has a significant interaction of participants show no decrease in d Day 1 medication state and delay (F(1, 14) = 15.083, p = .002), with patients on dopamine on Day 1 (blue lines) showing a decrease in response bias over 24 hr (more “yes” responses) and patients off dopamine during learning (red lines) and healthy participants showing an increase in response bias (more “no” responses) irrespective of Day 2 medication (solid/dashed lines). 2012, for review of experimental evidence, suggesting that such anti-Hebbian synaptic plasticity in the perirhinal cortex underlies familiarity discrimination). Additionally, during learning the weights from the inactivate inputs are increased to balance the overall excitability for the neuron. When a new novel pattern is presented, it is likely to return a similar level of output activity as the original novel pattern (here shown by the output number 6; Figure 5C), which would not be the case without this in- crease. The new novel pattern has some overlapping in- put activity because of the finite number of inputs. The output activity of the novelty neuron in the model is a simple number, as this is a phenomenological model, but could correspond to either the number of spikes fired, the level of depolarization, or the probability of fir- ing (if the model were to include more than one novelty neuron). We considered two ways of modeling the effect of medication to which we refer as the decay and threshold models. The decay model assumes that PD patients on medication on Day 1 would show a decay in the in- creased weights (Figure 5D), which does not affect the output for familiar stimuli but decreases it for novel stim- uli (Figure 5E). This would lead to more false alarms and a lower response bias. PD patients off medication on Day 1 were expected to have a decay in all weight changes (Figure 5F), which heightens the activity from familiar stimuli and overall keeps it the same for novel patterns (Figure 5G), meaning more rejections of familiar stimuli (i.e., a more conservative, positive response bias). The threshold model assumes that the dopaminergic medications on Day 1 affect the threshold for the novelty decision on Day 2. In the threshold model, there is still a decay of the weight changes (Equation 6), but this is the same for the two simulated medication conditions. The threshold can be thought of as either the spiking threshold for the novelty neuron or for a “decision neu- ron” that receives input from the novelty neuron. Either way, dopamine-dependent consolidation mechanisms could affect the threshold. We simulated each model with a variety of randomly generated parameters and minimized the RMSD between the simulated data and the PD patients’ behavioral data. The best fitting parameters (with the lowest RMSD) for 0 data each model are shown along with the simulated d in Figure 6. We compare the two models directly using the RMSD as both models have the same number of pa- rameters, meaning that measures like the Akaike Infor- mation Criterion are not needed. Both models can provide good fits to the patient data 0 (Figure 6A and B), reproducing the similar decrease in d in both conditions (Figure 6C and E), but with Day 1 on condition leading to a lower, more liberal response bias and Day 1 off medication leading to a higher, more con- servative response bias (Figure 6D and F). Although the RMSD for the threshold model is slightly lower than for the decay model, the behavior of participants does not differ from the predictions of either model; thus, given the uncertainty in exact values of experimental data, we feel that it is not justified to categorically say that one of the models fit better. The best fitting parameters for the decay model were α+,On = 0.1515, α−,On = 0.8787, α+,Off = 0.4079 and α−,Off = 0.8702. This represents greater decay of positive weight changes and slightly lesser decay of negative weight changes for PD on medication on Day 1 when 2042 Journal of Cognitive Neuroscience Volume 27, Number 10 compared to PD Day 1 off. This means that PD patients on dopamine during learning may be better preserving the weight decreases associated with the active inputs from the patterns, but at the cost of poorer consolida- tion of the weight increases for the inactive inputs. The best fitting parameters for the threshold model were T2,On = 1.1594, T2,Off = 1.1108, α+ = 0.3685, α− = 0.7562. This means that, even though this model is nomi- nally the threshold model, there were only very slight dif- ferences between the two conditions’ thresholds. The slightly higher threshold when on medication on Day 1 means that 24 hr later patterns with higher (more novel) activities are accepted as familiar, leading to a more liberal response bias. Both conditions shared the same decay parameters (α+ and α−), but these two were not the same, meaning that both on and off conditions had a large de- crease in inactive connection weight increases and a smaller decrease in active connection weight decreases, which would decrease the novelty neuron’s activity and then the slight differences in thresholds would be enough to separate the two response biases. In summary, both models can account for the current data, and we mention in the Discussion additional exper- iments that could distinguish between the models. Questionnaires A one-way ANOVA was run on the questionnaire data (PD vs. healthy participants), which revealed that PD patients scored significantly lower on the MMSE (see Table 1 for p values) and higher on the DASS and LARS than healthy participants. The DASS breaks down into subscores for depression, anxiety, and stress, and there were significant differences only for the depression and anxiety sub- scores, not for stress. The groups did not differ significantly on Rational– Experiential Inventory, Barratt Impulsivity Scale, or age. This means that the PD patients were more apathetic, de- pressed, and anxious and had poorer memory as com- pared to the healthy participants, which may have biased the between-group comparisons, but the critical results from this study are within subjects. D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 7 / 1 2 0 7 / 2 1 0 0 3 / 5 2 1 0 9 3 4 5 9 / 4 1 5 2 7 8 o 3 c 3 n 4 _ 1 a / _ j 0 o 0 c 8 n 4 0 _ a p _ d 0 0 b 8 y 4 g 0 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j . t f / u s e r o n 1 7 M a y 2 0 2 1 Figure 5. Anti-Hebbian model with one novelty neuron. Numbers to the left show the activity of inputs (for simplicity just four are shown), the numbers over the four connections indicate their synaptic weights, and the number on the right is the level of neuron’s activity. (A) A novel input pattern is presented to the neuron, and it outputs an activity of 6. Anti-Hebbian learning takes place decreasing the weights of the active connections and increasing the inactive weights to balance overall excitation leading to the weights shown in B. (B) The same pattern (now familiar) is presented again, this time eliciting a lower output activity. (C) Presenting a new novel pattern returns the same output as the original novel pattern because of the increased weights of the inactive connections balancing the excitation. (D) Simulated PD patients on dopamine during learning show decays in the weight increase for the inactive connections, but the activated connections remain decreased so the output for the familiar pattern remains the same. (E) Presentation of the novel pattern from C would now elicit a lowered output, meaning it is more likely to be accepted as familiar. (F) Simulated PD patients off their medication during learning show decays in both increased and decreased weights, leading to higher activity for the familiar stimuli and (G) regular activity for novel patterns, corresponding to an increased response bias. Grogan et al. 2043 D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 7 / 1 2 0 7 / 2 1 0 0 3 / 5 2 1 0 9 3 4 5 9 / 4 1 5 2 7 8 o 3 c 3 n 4 _ 1 a / _ j 0 o 0 c 8 n 4 0 _ a p _ d 0 0 b 8 y 4 g 0 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j f . t / u s e r o n 1 7 M a y 2 0 2 1 0 and response bias results for the patients and decay and threshold models. The results of the model Figure 6. The behavioral and simulated d fittings; (A) Behavioral d0 and (B) response bias data for PD patients split by Day 1 medications state (Day 1 on vs. Day 1 off ). (C) The simulated d0 for the best fitting decay model (blue lines are simulated PD Day 1 on medication patients, and red lines are Day 1 off medication). (D) The simulated response biases for the best fitting decay model (RMSD = 0.0778), showing the same pattern as the behavioral data. The best fitting parameters were 0 for the best fitting threshold model as follows: Day 1 on: α+ = .1515, α− = 0.8787; Day 1 off: α+ = 0.4079, α− = 0.8702. (E) The simulated d (RMSD = 0.0620) and (D) the simulated response bias results. The best fitting parameters were as follows: α+ = 0.3685, α− = 0.7562, Day 1 on: T2 = 1.1594; Day 1 off: T2 = 1.1108. The threshold model has a better fit to the data, based on the lower RMSD, which may be due to Day 1 on d0 being slightly higher than Day 1 off, as in the behavioral data. 2044 Journal of Cognitive Neuroscience Volume 27, Number 10 From the St. Mary’s Hospital Sleep Questionnaire ad- ministered on every Day 2 session, we had self-reported measures of sleep latency, sleep quality, number of hours of sleep during the night and day, and the total number of hours. We compared each of these measures with a two-way repeated-measures ANOVA (Day 1 medication state × Day 2 medication state). There were no signifi- cant effects of Day 1 or Day 2 medication states on any of the sleep measures. Repetition Effects As the patients completed the HVLT four times, albeit with different versions of the HVLT each time, it was pos- sible that the practice improved their recall and recog- nition scores so that they performed better during their fourth condition than their first. We ran repeated- measures ANOVAs on the 30-min and 24-hr delayed recall and recognition tests as well as the learning score (third immediate recall trial – first) to test for this. The ANOVAs revealed that there were no significant effects of order on 30-min recognition (F(3, 42) = 0.035, p = .991), 24-hr recognition (F(3, 42) = 2.671, p = .06), 30-min recall (F(3, 42) = 0.887, p = .456), 24-hr recall (F(3, 42) = 2.516, p = .071), or learning score (F(3, 42) = 1.444, p = .897). The two 24-hr scores show p values ap- proaching significance, but this is not due to an increase in accuracy with repetition but instead a “U”-shaped curve with the final value similar to the first. DISCUSSION We investigated the effect of dopaminergic medication on memory consolidation in PD patients tested on and off dopaminergic medications during learning/early con- solidation and late consolidation/recall. Compared to age- matched healthy participants, PD patients retain less in- formation over 30 min and 24 hr than healthy elderly par- ticipants. Remarkably, for free recall, dopamine during learning impaired recall at 30 min and 24 hr (but not im- mediate memory), whereas dopamine between 8 and 24 hr after learning (including a period of sleep) enhanced recall at 24 hr. Thus, we have demonstrated a benefit of dopaminergic medication on long-term memory storage over 24 hr in PD patients. Although dopaminergic medi- cation state had no overall effect on recognition sensitiv- ity, dopamine during learning led to a more liberal response bias 24 hr after learning regardless of dopamine state during recognition. Dopaminergic Medication during Learning Impairs Early Consolidation of Memory PD patients on medication during learning retained less af- ter 30-min and 24-hr delays, suggesting that dopaminergic medication is interfering with early consolidation of infor- mation within 30 min of encoding and that this deleterious effect persists despite subsequent changes to dopamine levels. The effects of dopaminergic medication are unlikely to reflect a role in encoding as there were no effects of do- pamine state on immediate memory, which would be ex- pected if encoding were compromised by dopaminergic drugs. Also, we used the retention scores for the ANOVAs on the delayed memory tests, which normalize the number of words recalled by the maximum number recalled in the immediate recall trials. This should have removed any ef- fects of worse encoding of words. The results are not con- sistent with a Day 1 effect of dopaminergic medication purely on retrieval processes; such an effect would be sim- ilar across immediate recall and 30 min and would not affect performance after a 24-hr delay. Thus, the critical time point at which dopaminergic activity appears to impair both 30-min and 24-hr recall is early consolidation (within 30 min of encoding). Note that this memory impairment on dopamine medication is seen despite improvement of motoric symptoms (see Unified Parkinson’s Disease Rating Scale in Table 1) and so does not represent a general delete- rious effect of dopamine medication on the patients’ overall function, rather dopaminergic medication appears to specifi- cally interfere with early consolidation of new information. The only theory that predicts poorer performance when dopamine is restored in PD is the dopamine over- dose hypothesis (Cools et al., 2001). This posits that the brain regions that are relatively spared from the dopami- nergic cell loss in PD are flooded with dopamine from the medication, and this impairs normal function. Thus, a possible explanation is that encoding/early consolidation mechanisms in brain regions spared the dopaminergic depletion in PD and are therefore harmed by excess do- pamine replacement therapy. Although the VTA is usually implicated in situations such as these because of its rela- tively preserved dopaminergic projections in PD (when compared to the substantia nigra; Wittmann et al., 2013; Cools, 2006; Gasbarri, Sulli, & Packard, 1997; Agid et al., 1989), in this case the VTA is thought to underpin improved memory performance when on medication overnight (see below). Although the VTA can underlie both encoding/early consolidation and sleep consolida- tion mechanisms, we would expect that dopamine would exert either positive or negative effects on both, not the different directions of effects seen here. Therefore, al- though the dopamine overdose hypothesis may fit with the finding of impaired learning when on dopaminergic medication, the exact underlying neural substrates neces- sary for this effect are unknown. Neuroimaging experi- ments offer a way to investigate this by examining blood flow changes in brain regions during learning when on and off dopaminergic medication. Dopaminergic Activity Improves Late Consolidation of Memory We found that the number of items retained between 30 min and 24 hr decreased in the patients. However, Grogan et al. 2045 D o w n l o a d e d f r o m l l / / / / j f / t t i t . : / / h t t p : / D / o m w i n t o p a r d c e . d s f i r o l m v e h r c p h a d i i r r e . c c t . o m m / j e o d u c n o / c a n r a t r i t i c c l e e - p - d p d 2 f 7 / 1 2 0 7 / 2 1 0 0 3 / 5 2 1 0 9 3 4 5 9 / 4 1 5 2 7 8 o 3 c 3 n 4 _ 1 a / _ j 0 o 0 c 8 n 4 0 _ a p _ d 0 0 b 8 y 4 g 0 u . e p s t d o f n b 0 y 8 S M e I p T e m L i b b e r r a 2 r 0 2 i 3 e s / j / f . t u s e r o n 1 7 M a y 2 0 2 1 patients who were on medication between 8 and 24 hr after learning showed a smaller decrease, irrespective of Day 1 medication state. Thus, dopaminergic medication during late consolidation and retrieval enhances memory in PD patients. It appears more likely that dopamine is exerting effects during consolidation rather than re- trieval, as retrieval was also required on Day 1 tests, when dopaminergic medication did not enhance performance. Although this is the first demonstration in humans of an effect of dopamine on memory consolidation over 24 hr, the finding is consistent with animal work (Bethus et al., 2010; O’Carroll et al., 2006). When coming off medication, patients spend a mini- mum of 15 hr without dopaminergic medications before testing, meaning they are off medication overnight dur- ing sleep. In addition, on Day 1 all patients are on their medications from 11 am until 6 pm regardless of Day 1 or two condition (see Figure 1), meaning that the only time Day 2 off patients differed from Day 2 on patients was from 6 pm on Day 1 until testing on Day 2 (around 10 am). This suggests that any dopaminergic medication effects on 24-hr recall are occurring within this window (8–24 hr after learning), which coincides with a period of sleep. It has been proposed that sleep plays a role in the con- solidation of memories, and therefore, a lack of dopa- mine during sleep could impair this process. PD patients often complain of sleep disturbances (Dhawan, Healy, Pal, & Chaudhuri, 2006), and dopamine has been suggested to play a role in the sleep–wake cycle (Rye, 2004) possibly because of the increased phasic firing of VTA neurons during REM sleep (Lima, 2013; Dahan et al., 2007). Indeed, D2 antagonist infusion has been found to decrease REM sleep as well as the levels of plas- ticity related proteins and memory performance (França et al., 2015), and the VTA has been shown to activate the dopaminergic projections to the hippocampus and in- crease BDNF levels there (Rossato et al., 2009). McNamara et al. (2014) also found dopaminergic effects on sleep, but during SWS. They found that optogenetic stimulation of VTA neurons or their axons onto hippocampal CA1 cells increased memory performance on a spatial learning task. This coincided with SWS reactivation of the firing patterns present during awake exploration. This effect was removed when a D1/D5 antagonist was infused before the explora- tion, suggesting that dopaminergic activity during learning can affect later SWS consolidation via VTA–hippocampal connections. It may be that a similar process occurs when dopamine is present during sleep itself. So dopaminergic consolidation effects have been found in both REM and SWS; an interesting focus for future work would be unpick- ing the relative contribution of these two sleep stages to memory consolidation. If dopaminergic activity is restored during postlearning sleep, this would allow the VTA firing to have its full effect and, because of the VTA-hippocampal projections, affect consolidation via increased dopamine release onto the hippocampus. Our self-reported sleep questionnaires did not yield any significant effects of Day 2 medication state on sleep (the night when consolidation takes place after learning); thus, it seems that, although dopamine may effect overnight consolidation, it does this without affecting overall self-reported sleep measures such as du- ration and quality. An alternative although not mutually exclusive mecha- nism of overnight dopamine could involve selection of memories to be consolidated. Evidence for this comes from a placebo-controlled dopamine agonist study in hu- mans, which found that a dopamine D2 receptor agonist given overnight after learning increased memory for pic- tures associated with low value rewards to the level of ac- curacy seen for pictures associated with high value rewards (Feld, Besedovsky, Kaida, Münte, & Born, 2014). This suggests that the greater dopaminergic activ- ity mimicked the dopamine-dependent consolidation usually only seen with items associated with rewards ( Wittmann et al., 2005). In other words, the greater do- paminergic activity selected the low-reward stimuli to be consolidated in the same way that the high-reward stim- uli were selected by the dopamine bursts associated with the rewards. Although our words were not associated with rewards, it may be that the increased dopaminergic activity overnight is still selecting these words as though they had been reward related and thus consolidating them. One possible molecular mechanism for the overnight consolidation effect of dopamine is suggested by the synaptic-tagging and capture model (Clopath, 2012; Clopath et al., 2008; Frey & Morris, 1997). In this model, synap- tic activity induces potentiation or depression of the synapse, and in order for this to persist, the number of “tagged” synapses must exceed the threshold that is set by dopamine levels. This triggers protein synthesis and causes consolidation of the synaptic weight change. In this TagTriC model, dopamine lowers the threshold for the synthesis of a consolidation protein. In the mod- el, there is only a fairly short time window for this pro- tein to be synthesized—before the number of tagged synapses has decayed to below the dopamine threshold (<4 hr). However, dopamine consolidation over these longer timescales could operate similarly to that pro- posed by the TagTriC model, perhaps through a protein with much slower decay rate, genetic modi- fications synapses, or downstream proteins do not depend on number of tagged synapses (e.g., BDNF), perhaps depending instead previous protein levels change in weight which has already oc- curred. Extending current models cover effects seen at longer help explain our findings. One interesting thing is see PD medication condition most similar healthy participants’ behav- ior. We might expect on–on condition would have best overall performance as this has 2046 Journal Cognitive Neuroscience Volume 27, Number 10 D o w n l o a d e d f r o m l l >

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