Arm immobilization causes cortical plastic changes ... - Semantic Scholar

© 2006 Nature Publishing Group http://www.nature.com/natureneuroscience

ARTICLES

Arm immobilization causes cortical plastic changes and locally decreases sleep slow wave activity Reto Huber1, M Felice Ghilardi2, Marcello Massimini1, Fabio Ferrarelli1, Brady A Riedner3, Michael J Peterson1 & Giulio Tononi1 Sleep slow wave activity (SWA) is thought to reflect sleep need, increasing after wakefulness and decreasing after sleep. We showed recently that a learning task involving a circumscribed brain region produces a local increase in sleep SWA. We hypothesized that increases in cortical SWA reflect synaptic potentiation triggered by learning. To further investigate the link between synaptic plasticity and sleep, we asked whether a procedure leading to synaptic depression would cause instead a decrease in sleep SWA. We show here that if a subject’s arm is immobilized during the day, motor performance deteriorates and both somatosensory and motor evoked potentials decrease over contralateral sensorimotor cortex, indicative of local synaptic depression. Notably, during subsequent sleep, SWA over the same cortical area is markedly reduced. Thus, cortical plasticity is linked to local sleep regulation without learning in the classical sense. Moreover, when synaptic strength is reduced, local sleep need is also reduced.

There is increasing evidence for a connection between sleep and learning1. Both rapid eye movement (REM) sleep and non-REM sleep (NREM) can improve performance after various learning tasks2–11. NREM sleep, which represents about 80% of human sleep, is characterized by SWA (0.5–4.5 Hz) in the electroencephalogram (EEG). SWA reflects near-synchronous slow oscillations in the membrane potential of cortical neurons, which alternate between a depolarized up state with irregular firing and a hyperpolarized down state with no synaptic activity12. Cortical SWA is thought to be closely connected to the function of sleep, as it increases globally over the cortex in relation to the duration of prior waking, and returns to baseline during sleep13. However, the factors responsible for the homeostatic changes in SWA are currently unknown. Recently, it was shown that learning a visuomotor task involving a circumscribed cortical region produces a local increase in SWA during subsequent sleep14. In this task, subjects reached for visual targets displayed on a computer screen using a hand-held cursor, while unconsciously adapting to systematic rotations imposed on the perceived cursor trajectory. After sleep, performance on the task was enhanced, and such enhancement was strongly correlated with the local SWA increase. This finding suggests that sleep SWA can be regulated locally and that such regulation is related to plastic changes induced by learning. What could be the connection between cortical plasticity and sleep slow waves? It has been hypothesized that cortical SWA may reflect the amount of synaptic potentiation triggered by experience-dependent plasticity during wakefulness15,16. More specifically, the hypothesis predicts that increased synaptic strength after certain learning tasks

leads to stronger synchronization among cortical neurons and thereby to increased SWA (S.L. Hill et al., Soc. Neurosci. Abstr. 314.7, 2005). If this hypothesis is correct, the converse should also hold: manipulations leading to a depression of synaptic strength should be associated with a reduction in SWA. Moreover, if synaptic depression is localized to a specific region of the cortex, the EEG should reveal a decrease in SWA that is localized to the same region. To test these predictions, we used short-term arm immobilization— a procedure that, as indicated by studies in animal models, leads to a local synaptic depression in sensorimotor areas17,18. Specifically, we investigated whether 12 h of immobilization of one arm, a form of sensorimotor deprivation, leads (i) to a deterioration of motor performance, as measured by kinematic indices during a brief task; (ii) to a reduction of transcranial magnetic stimulation (TMS)-evoked motor potentials (MEP); (iii) to a depression of somatosensory evoked potentials (SEP) over sensorimotor cortex, as measured by electrical stimulation of the median nerve and high-density EEG; and (iv) in accordance with our predictions, to a local reduction in SWA over the same area, as measured by high-density EEG during subsequent sleep. We found that, just as visuomotor learning improves task performance and induces local increases in SWA, arm immobilization induces a deterioration in motor performance, a decrease in MEPs and SEPs, and a local decrease in sleep SWA. RESULTS We used a simple reaching task to evaluate subjects’ motor performance before immobilization (Fig. 1), after which we recorded SEPs of the right and left median nerve with high-density EEG. Thereafter, we

1Department of Psychiatry, University of Wisconsin, Madison, Wisconsin 53719, USA. 2Department of Physiology and Pharmacology, CUNY Medical School at City College, New York, New York 10031, USA. 3Neuroscience Training Program, University of Wisconsin, Madison, Wisconsin 53719, USA. Correspondence should be addressed to G.T. ([email protected]) or M.F.G. ([email protected]).

Received 5 July; accepted 4 August; published online 27 August 2006; doi:10.1038/nn1758

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ARTICLES Postsleep

Presleep

Baseline

Motor task Task SEP Motor task Task SEP Motor task Task SEP

Sleep

Control condition 9 a.m.

9 p.m.

9 a.m.

Figure 1 Study design. During the baseline, presleep and postsleep sessions, subjects performed a simple motor task, after which SEPs were recorded. During the night, sleep was recorded using high-density EEG (hd-EEG). Between the baseline and presleep sessions, the left arm and hand were immobilized with a sling. A control experiment without immobilization was performed at least 1 week earlier or later.

immobilized the left arm and hand with a sling and bandage for 12 h and monitored the efficacy of the immobilization with a wrist actigraph. The motor task and SEP recording were repeated before sleep time after removing the sling, and again the next morning as soon as the subjects woke up. We obtained high-density EEG recordings during sleep. The same procedures were repeated during a control session without immobilization. Arm immobilization induces motor performance deterioration Wrist actigraphy recordings demonstrated that left arm immobilization resulted in a significant reduction of motor activity in the left arm compared to the control condition (mean ± s.e.m. of 12 h, in counts per min: immobilization, 85.0 ± 7.5; control, 246.8 ± 27.6; P o 0.0005, two-tailed paired t-test), while right arm activity was the same in both conditions (immobilization, 243.5 ± 29.9; control, 253.7 ± 31.2; P ¼ 0.25, two-tailed paired t-test). To determine whether immobilization induced changes in motor performance, we had subjects perform a simple reaching task with their left arm before and after 12 h of arm immobilization. Before immobilization, movements were straight with sharp reversals and

Arm immobilization reduces TMS-evoked motor responses To evaluate cortical changes underlying the observed deterioration of motor performance, we first investigated changes in the MEPs triggered by TMS. In a separate group of subjects (n ¼ 6), we applied TMS to both left and right motor cortex in the morning before and in the evening after 12 h of left arm immobilization. In the immobilized arm, the peak-to-peak amplitude of MEPs decreased by 57% (± 9%, P o 0.05, two-tailed paired t-test) from the baseline (morning) session to the presleep (evening) session (Fig. 3). We observed no significant change for the nonimmobilized arm (P 4 0.1).

Figure 2 Performance decrement after Baseline 0.3 immobilization. (a) Representative hand paths 90° Immobilization condition 0.2 for one subject at baseline (top) and after 135° 45° Control condition 0.1 immobilization (bottom) for the three targets 0 (at 1351, 901 and 451). (b) Top, normalized 50 –0.1 hand-path area (area enclosed by the hand path 40 –0.2 divided by the squared path length) for the three 45° 90° 135° 30 movements in a at baseline (empty circles) and Target direction after immobilization (filled circles). Note that 20 Baseline Presleep after immobilization, the hand-path area increases After immobilization 10 (after left arm immobilization) mostly for target directions at 451 and 1351. 0 Bottom, variability of the normalized hand-path 0.2 –10 area (s.d. of the mean) across the three target directions in a at baseline (empty bar) and after 0.1 –20 immobilization (filled bar). This variability Presleep Postsleep reflects an error in adapting motor commands 0 to directional changes. (c) Increase in the normalized hand-path area variability after immobilization. For both the immobilization and control conditions, changes of the average variability of the normalized hand-path area in the pre- and postsleep sessions are expressed as a percentage of the corresponding value at baseline (error bars indicate s.e.m.). An ANOVA disclosed a significant difference between conditions (F1,22 ¼ 15.9, P o 0.0006). There was no significant difference between time of test (F1,22 ¼ 2.7, P ¼ 0.12), but there was an interaction between time of test and condition (F1,22 ¼ 4.8, P ¼ 0.04). Post-hoc t-tests revealed a significant difference between the immobilization and the control conditions at both presleep (P ¼ 0.0002) and postsleep (P ¼ 0.023) test times. In addition, there was a significant difference between presleep and postsleep tests for the control condition (P ¼ 0.001), but not for the immobilization condition (P ¼ 0.6).

b

c

Variability of normalized hand-path area (% of baseline)

Normalized hand-path area

a

Variability of normalized hand-path area


Hd-EEG Immobilization condition or

out-and-back overlapping strokes (Fig. 2a, upper panel). After immobilization, reversals were less sharp, with an increase in curvature and in the area enclosed by the hand path, and there was great variability of these parameters across different target directions (Fig. 2a, lower panel). We captured these changes by measuring the variability of the hand-path area normalized by path length—an index of interjoint coordination (Fig. 2b, upper panel; and refs. 19,20). Hand-path area variability increased by 43.1% (± 7.7%, P o 0.0003, two-tailed paired t-test) after immobilization compared to baseline (Fig. 2b, lower panel). Other kinematic parameters, including movement time, peak velocity and peak acceleration did not change significantly (P 4 0.1). The increase in hand-path area variability after immobilization was similar to that observed in deafferented individuals or in subjects adapting to new inertial loads19,20. By contrast, in the control condition with no immobilization (at least 1 week apart), hand-path area variability decreased by 13.7% (± 6.9%, P ¼ 0.10, two-tailed paired t-test) in the presleep session compared to the baseline. Thus, as little as 12 h of arm immobilization induces plastic changes in motor function. To determine whether hand-path area variability was affected by sleep, subjects repeated the motor task the next morning after a full night of sleep. In the immobilization condition, postsleep hand-path area variability was as high as it was presleep (Fig. 2c). In the control session, variability increased slightly compared to presleep levels (Fig. 2c). These results suggest that limb inactivity during sleep may also lead to plastic changes, and that these changes are in the same directions as those observed after immobilization during wakefulness, albeit of smaller magnitude.

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ARTICLES Figure 3 Decreased MEP amplitude after Immobilized arm Nonimmobilized arm Immobilized arm immobilization. (a) Representative MEP for Nonimmobilized arm one subject at baseline (gray) and after 12 h –70 * of arm immobilization (presleep, black) for the immobilized (left) and nonimmobilized (right) –60 Baseline arm. Each trace is an average of 20 single evoked Presleep 10 ms –50 responses. (b) Change in the average (n ¼ 6) peak-to-peak amplitude of MEPs for both arms –40 from baseline to the presleep condition (error TMS TMS bars indicate s.e.m.). A two-way ANOVA with –30 time (baseline, presleep) and arm (immobilized, nonimmobilized) as factors disclosed a significant –20 difference between baseline and presleep (F ¼ –10 17.4, P o 0.01) and a significant interaction (F ¼ 10.5, P o 0.05). Post-hoc t-tests revealed 0 a significant difference between the baseline and presleep sessions in the immobilized arm (P o 0.01). *P o 0.05 (significant MEP amplitude decrease in the immobilized arm, two-tailed paired t-test, presleep percentage of baseline). Individual average MEP amplitude varied between 30% and 50% of the group mean. Each subject showed a significant decrease in MEP amplitude size after immobilization when the peak-to-peak amplitudes of the 20 single MEPs were compared.

b

Arm immobilization results in SEP changes In a subgroup of subjects, we recorded SEPs elicited by stimulation of the median nerve before and after arm immobilization (as well as at the corresponding time of day in the control session). SEPs reflect the function of somatosensory pathways and the processing of proprioceptive information along the peripheral nerve and the spinal-cortical pathways. In the control condition, the SEP cortical component with the largest amplitude occurred between 35 and 45 ms (P45 component) at electrode 132 for left arm stimulation and at electrode 45 for right

a

Left arm stimulation

Right arm stimulation

arm stimulation (Fig. 4a). For each subject, we localized the corresponding anatomical sources with electrode and magnetic resonance imaging (MRI) coregistration. The site at electrode 132 projected onto right Brodmann areas (BA) 3 and 4, corresponding to sensorimotor cortex; electrode 45 projected onto the border of the left BA 4. After immobilization, compared to the control condition, the P45 component recorded over contralateral sensorimotor cortex showed increased latency (by 2.0 ± 0.8 ms, P o 0.05, two-tailed paired t-test) and decreased amplitude (by –67.3 ± 9.1% between 35 and 45 ms, P o 0.005,

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Right arm stimulation Immobilization condition Control condition

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0.5 0.0 –0.5 0.000 0.025 0.050

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Figure 4 Changes in SEPs in the presleep session after Time (ms) Time (ms) immobilization. (a) Source localization of the average SEP at the Left arm Right arm largest deflection from 35 ms to 45 ms after electrical stimulation of stimulation stimulation Immobilization condition the left and right median nerves (n ¼ 7). Activity is color-coded and Control condition Immobilization condition Control condition projected onto the MNI brain. Green dots, electrode positions. White 60 dots, electrodes of interest (left, electrode 132; right, electrode 45). 1.4 40 (b) Average SEP in the presleep session after the immobilization and 1.2 the control condition for left and right median nerve stimulation 20 1.0 (n ¼ 7). Black bars below the curves indicate significant differences 0 between the immobilization and control conditions (P o 0.05, 0.8 –20 two-tailed paired t-test corrected for multiple comparisons—see 0.6 –40 Methods; the first 12 ms after the stimulus onset are not displayed 0.4 –60 due to electrical stimulation artifacts). (c) Average SEP amplitude 0.2 –80 integrated between 38 ms and 43 ms after median nerve stimulation for the immobilization and control conditions in the morning –100 0.0 –40 –20 0 20 40 60 80 100 Baseline Presleep Baseline Presleep (baseline) and in the evening before sleep (presleep). To account for Change in hand path area variability subject variability in SEP amplitude, the values were normalized for (presleep % of baseline) each subject to the average over all recordings (error bars indicate s.e.m.). *P o 0.05 (significant difference between immobilization and control conditions, two-tailed paired t-test, n ¼ 7). (d) Correlation between the changes in motor performance and SEP after immobilization and in the control sessions (r ¼ 0.815, P o 0.0005, n ¼ 14; r ¼ 0.22, P ¼ 0.2, n ¼ 7 for immobilization only). The change in motor performance is given as the percent change in hand-path variability between the baseline and presleep sessions. The change in SEP is given as the percent change of the integrated SEP amplitude for latencies between 35 ms and 45 ms at electrode 132 after left median nerve stimulation.

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Change in SEP amplitude (presleep % of baseline)

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Contralateral SEP (µV)


0.1 mV

Decrease in motor evoked potential amplitude (% of baseline)

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ARTICLES Table 1 Sleep architecture for the first 60 minutes of sleep


Control

Immobilization

(n ¼ 14)

Mean

s.e.m.

Mean

s.e.m.

Sleep latency (min) Waking after sleep onset (%)

11.4 6.0

4.1 3.0

17.6 7.5

5.3 2.2

Stage 1 (%) Stage 2 (%)

5.8 48.8

0.8 2.0

10.3 43.4

2.0 3.4

Slow-wave sleep (%) NREM sleep (%)

22.5 71.3

4.5 4.8

18.3 61.7

5.1 4.8

REM sleep (%)

3.6

1.6

3.1

1.6

Movement time (%)

1.4

0.3

1.8

0.3

No significant differences were observed between the control and immobilization condition.

two-tailed paired t-test; data for the electrode with peak deflections in Fig. 4b,c). As expected, SEPs did not change significantly for stimulation of the right, nonimmobilized arm (P 4 0.1). The evoked activity recordings from Erb’s point (peripheral) and Cv7 (spinal), as well as early components recorded over the scalp, were unmodified after immobilization, suggesting a cortical locus for plasticity. Motor performance and SEP changes are correlated Both the observed increase in hand-path area variability and the reduction in SEP amplitude may reflect the plastic changes induced by arm immobilization. To test this hypothesis, we regressed the percentage change in hand-path area variability between the morning and evening session against the changes in SEP amplitude, considering both the immobilization and control conditions. We found a significant correlation (r ¼ 0.82; P o 0.0005) between the kinematic and electrophysiological changes (Fig. 4d). These results show that increases in hand-path variability, which reflect poor interjoint coordination, are related to decreases in SEP amplitude. They further suggest that the changes in motor performance observed after arm immobilization are probably due to plastic changes in sensorimotor areas.

a

b

Immobilization condition

Arm immobilization leaves a local trace in the sleep EEG We recorded high-density EEG during sleep after both the immobilization and the control conditions. Subjects showed the usual progression of sleep stages in both conditions (Table 1). Average power spectra of consecutive 20-s epochs during the first 20 min of NREM sleep showed that SWA was prevalent in anterior regions, in accordance with previous studies21,22 and that the topographic pattern of SWA was highly reproducible across nights and subjects (Fig. 5a). However, in the immobilization condition, compared to the control condition, there was a significant decrease of SWA at three electrodes (132, 45 and 89, Fig. 5b; P o 0.005). The peak SWA decrease, the precise location of which varied slightly between subjects, occurred around electrode 132 and amounted to –22.1% (± 5.9%, P o 0.005, two-tailed paired t-test). Electrode 132, corresponding to right BA 3 and 4, was the same electrode from which peak SEP responses were recorded after median nerve stimulation, and at which the strongest decreases in SEP amplitude occurred after immobilization. Electrode 45 corresponded to ipsilateral sensorimotor cortex, whereas electrode 89 projected onto left BA 7, a parietal area involved in sensorimotor integration (Fig. 5c). A slight increase in SWA power on BA 19—an area involved in visual associative processes—did not reach statistical significance. Thus, short-term immobilization leaves a local trace in the sleep EEG, and the trace corresponds topographically to the site of SEP depression. SWA changes are consistent with reduced sleep pressure Visuomotor learning produces a local increase in sleep EEG power mainly in the low SWA range, although some changes are also detected in the theta and just above the spindle range (15–16.5 Hz)14. In the current study, immobilization produced a decrease in sleep EEG power over contralateral sensorimotor cortex that was also mainly concentrated in the low SWA range, although, again, some changes were detected in the theta and high spindle range (Fig. 6a). The changes in the high spindle range after immobilization appeared to be mirrorsymmetric with those observed after learning (ref. 14 and Fig. 6a), although they were statistically significant only after immobilization (P o 0.05). As it is known that sleep spindles are tightly related to the

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Immobilization/control % +20

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+10

+60

+5

+40

Control condition

+20

45

132

0 –20

45

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89

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Figure 5 Changes in local SWA homeostasis during sleep after immobilization. (a) Topographic distribution of SWA after the immobilization (top) and the control (bottom) conditions. Average EEG power density at 1–4.5 Hz (n ¼ 14 subjects) for the first 20 min of NREM sleep. Values were normalized by total power for the recording, color coded, plotted at the corresponding position on the planar projection of the scalp surface, and interpolated (biharmonic spline) between electrodes (dots). (b) Topographic distribution of the ratio of SWA power between the immobilization and control condition. White dots, significant differences (SnPM). (c) Anatomical localization of the three electrodes showing a significant difference in SWA during the first 20 min of NREM sleep between the immobilization and control conditions. All 256 electrodes (red pins) were digitized and coregistered with the subject’s MRI. Blue dots, the three electrodes showing a decrease in SWA. The left anterior electrode (45) projects onto left area 3/4 (Talairach coordinates: x ¼ – 3, y ¼ – 26, z ¼ 70), the left posterior electrode (89) projects onto area 7 (– 10, 59, 96), and the right electrode (132) projects onto right area 3/4 (20, – 32, 70).

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ARTICLES Figure 6 Characteristics of the local SWA change 20 after immobilization. (a) Frequency specificity of 20 power changes. EEG power density spectrum for the first 20 min of NREM sleep. Average power 10 10 change across subjects for the electrode yielding the peak SWA decrease for each subject in 0 the region surrounding electrode 132. 0 Values represent the percent change of EEG –10 power after the immobilization with respect to after the control condition (mean ± s.e.m. for –10 –20 0.25-Hz bins, n ¼ 14). Bottom bars indicate * frequency bins for which power in the –30 –20 immobilization condition differed significantly 0.00 0.01 from that in the control condition (paired t-test). Power in the following frequency bins was –30 significantly reduced: 0.5–1.5 Hz, 2–2.5 Hz, * 0.05 5.5–6.0 Hz, 13.25–14 Hz. (b) Time course of 0 5 10 15 20 25 1 2 3 Frequency (Hz) SWA changes after immobilization. The change in 20-min interval average EEG power in the 1–4.5 Hz band was calculated for three consecutive 20-min intervals during the first NREM sleep episode. As in a, we selected the electrode yielding the peak SWA decrease for each subject in the region surrounding electrode 132. The increasing trend in power across the three 20-min intervals was significant (P o 0.01, ANOVA). *P o 0.05 (significant reduction of SWA after the immobilization compared to the control condition, two-tailed paired t-test). EEG power density (%)

Percentage change in SWA (immobilization-control)

b

P-value


a

depolarizing phase of the slow oscillation23, a parallel behavior of SWA and spindle activity after immobilization is to be expected, especially considering that spindle activity is prominent over sensorimotor areas21. This parallelism was confirmed by the topographic similarity of power changes in the SWA and high-spindle range (14–15 Hz, Supplementary Fig. 1 online). The close relationship between activity in the SWA and spindle frequency ranges is consistent with the finding that spindle activity is grouped by slow oscillations23,24. After visuomotor learning, the local increase in SWA returns to baseline within 1 h of sleep14, as expected of a homeostatic response to increased sleep pressure13. After immobilization, the local decrease of SWA and spindle activity (Supplementary Fig. 1) also returned to baseline in the course of the first NREM sleep episode (Fig. 6b). Such behavior is to be expected when sleep pressure is decreased: for example, reducing sleep pressure by taking an afternoon nap produces a marked decrease in EEG power in SWA and neighboring frequencies at the beginning of sleep, a decrease progressively attenuated in the course of the night25. Thus, the local changes in SWA observed after arm immobilization are mirror-symmetric with those observed after a learning task. In addition, they parallel the global changes caused by a reduction of sleep pressure, just as the local changes after learning parallel the global changes caused by an increase in sleep pressure. Motor performance changes predict changes in sleep EEG We then asked whether the change in SWA over the right sensorimotor area was predicted by the change in hand-path area variability induced by immobilization of the left arm. Indeed, we found a negative correlation between these two variables (r ¼ –0.76, P o 0.02), suggesting that increases in hand-path variability, which reflect decreased interjoint coordination, are related to decreases in local SWA. The negative correlation of hand-path variability with the local decrease in EEG power was specific to the SWA frequency range (data not shown). There was no correlation between increases in hand-path variability and SWA changes at other electrodes. DISCUSSION As shown here, a few hours of arm immobilization result in plastic changes in the sensorimotor cortex that are reflected in a deterioration of motor performance and a depression of somatosensory and motor evoked potentials. Notably, these experience-dependent plastic mod-


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ifications leave a clearcut local trace during subsequent sleep: SWA, a reliable indicator of sleep need in many species26, is strongly reduced over the affected sensorimotor areas. These results suggest that local sleep need is directly related to changes in synaptic strength and, specifically, that sleep need is reduced if synaptic efficacy is depressed. Performance, MEP, SEP changes reflect cortical plasticity The present results indicate that 12 h of arm immobilization are sufficient to induce a marked and persistent deterioration in motor performance as demonstrated using a simple reaching task. The significant correlation between kinematic and SEP changes suggests that the increase in hand-path variability following immobilization is due to a change in proprioceptive information processing. Indeed, the kinematic effects of immobilization are similar to those observed in individuals with proprioceptive deafferentation and are consistent with an impairment of interjoint coordination20. Notably, by examining the control session, we found that a night of sleep was also associated with a slight but significant deterioration of performance (P o 0.05). This effect might be explained by the immobility of the arms during much of the night, although sleep immobility may be less effective because it occurs in both arms, lasts for 7–8 h rather than for 12, and is interrupted by occasional movements. Other factors to consider include circadian time and sleep-dependent synaptic depression16. Plastic changes induced by arm immobilization are also evidenced by the decreased amplitude and increased latency of the P45 components of the median nerve SEP. The depression of these mid-latency SEP components, in the absence of changes to earlier components, indicates a cortical site for plasticity. Indeed, source modeling revealed reduced currents in the right sensorimotor cortex, in accordance with the results of intracortical recordings27. Some ipsilateral effects have also been observed, presumably resulting from the strong transcallosal connections between sensorimotor areas28. As shown in imaging studies, motor learning is often accompanied by increased activity in bilateral motor cortex (for example, ref. 29). It is therefore reasonable to assume that the opposite happens during immobilization. The current finding of plasticity of cortical SEPs after short-term immobilization in humans complements and extends work using experimental syndactyly30,31, ischemic block32,33 and pharmacological manipulations34. The nature of SEP changes (reduced amplitude, increased latency) suggests that the plastic changes induced by arm immobilization are

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ARTICLES probably due to synaptic depression. In animal studies, the amplitude of evoked potentials is decreased and their latency increased after the induction of long-term depression (LTD) by direct electrical stimulation35. This interpretation is directly supported by a recent study in animals that demonstrated LTD of synapses from layer 4 to layers 2/3 following acute whisker deprivation in slices of primary sensory cortex17. In addition, this study showed that the changes in spike rate and spike timing induced by whisker deprivation were suitable to drive LTD. Of note, sensory deprivation produces LTD in other systems also: for instance, physiological and molecular evidence for LTD at synapses from layer 4 to layers 2/3 has been obtained in primary visual cortex after monocular deprivation18. Further support for the induction of synaptic depression during arm immobilization is evidenced by the amplitude reduction of MEPs after 12 h of arm immobilization. Numerous studies have used TMS and MEPs to quantify changes in synaptic strength36. Synaptic plasticity and sleep SWA The results of this study demonstrate that manipulations leading to plastic changes in cortical circuits are reflected in local SWA changes during subsequent sleep. Specifically, by using short-term arm immobilization, we demonstrated that an experimental protocol that promotes synaptic depression causes a local decrease of SWA. Moreover, the local decrease in SWA was correlated with the deterioration in motor performance. These findings are mirror-symmetric with those obtained using activation or learning protocols, which cause an increase of SWA over one hemisphere37,38 or in specific cortical areas14. Notably, the last study showed that SWA increases when the task requires learning (adaptation to an imposed rotation) compared to a kinematically equivalent, subjectively indistinguishable motor control task. Moreover, the increase in SWA was strongly correlated with an enhancement in performance. Thus, it is likely that changes in SWA reflect neural plasticity, rather than just activity. The link between waking plasticity and sleep SWA is reinforced by other recent studies. Dark rearing of mice and cats results in a reversible decrease of SWA over visual cortex39. In turn, loss of sleep SWA by reversible silencing of visual cortices results in reduced ocular dominance plasticity40. Adult rats who show more exploratory activity (for the same duration of wakefulness) have an increased expression of genes associated with synaptic potentiation and show a larger increase in SWA during subsequent sleep (R. Huber et al., Am. Prof. Sleep Soc. Abstr. 28, 13, 2005). Conversely, after chronic lesions of the noradrenergic system, the expression of molecular markers of synaptic potentiation is reduced, and the SWA response is blunted41. Altogether, these results support the proposal that the level of sleep SWA reflects the level of synaptic strength15,16. Whereas the underlying mechanisms have yet to be explored experimentally, computer simulations indicate that weaker/stronger connections among cortical neurons lead to a decrease/increase in sleep SWA because they result in weaker/stronger synchronization (S.L. Hill and G. Tononi, Am. Prof. Sleep Soc. Abstr. 29, 11, 2006). Consistent with this interpretation, the coherence of slow oscillations increases after a learning task in humans42. Nevertheless, it should be emphasized that the local regulation of sleep SWA is compatible with other mechanisms and alternative accounts are possible. For example, use-dependent changes in the efficacy of inhibitory circuits, accumulation or depletion of substances altering neuronal excitability, or alterations of intrinsic excitability may have similar consequences for the generation and synchronization of sleep slow waves or may at least contribute to the observed effects. The finding that an experimental procedure leading to synaptic depression leads to a local reduction of sleep SWA suggests the

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possibility that reductions of SWA found in certain neuropsychiatric disorders, such as major depression, schizophrenia, Parkinson disease and Alzheimer disease43, as well as in normal aging43, may represent a sensitive marker of an underlying reduction in synaptic efficacy16. It also suggests that the exponential decrease of SWA in the course of sleep observed in healthy subjects may reflect a parallel decrease of synaptic strength throughout the night15,16. In all mammals studied so far, wakefulness leads to an increase in sleep SWA, sleep is accompanied by a decrease in SWA, and global levels of SWA reflect global sleep pressure or need (for example, refs. 13,44). In conjunction with the previous results demonstrating a local increase in SWA after a learning task, the present findings indicate that sleep need may be tightly linked to synaptic plasticity and that this relationship occurs at the level of local neural circuits. More specifically, sleep need may be increased by synaptic potentiation, and decreased by synaptic depression—a suggestion that can now be tested using other plasticity protocols (R. Huber et al., Soc. Neurosci. Abstr. 308.14, 2005). A key function of sleep, thus, may be closely related to the metabolic and cellular costs of neural plasticity16. METHODS Design. Fifteen healthy right-handed male subjects (mean age 25.4 ± 1.3 years) participated in the study. The study was approved by the University of Wisconsin Institutional Review Board. After completely describing the study to the subjects, we obtained written informed consent. For the immobilization session, all subjects performed a brief reaching task in the morning (between 8 a.m. and 9 a.m.), which we used to assess motor performance. After the task, we recorded SEPs in seven subjects using high-density EEG (256 electrode nets, Electrical Geodesics) after both right and left median nerve stimulation. Then, the left arm was immobilized using a regular arm sling. To reduce further somatosensory input from the hand, the subject’s left hand and wrist were bandaged. Subjects wore an actigraph on each wrist to confirm the efficacy of the immobilization, and they were allowed to pursue their normal daily routine. A second visit took place in the evening (between 9 p.m. and 11 p.m.). Subjects were first hooked up with the 256 electrode nets, then the arm sling and bandage were removed. Thereafter, all subjects performed the same motor task as in the morning, and we recorded SEPs in the same subset of seven subjects. All subjects were then allowed to sleep and their EEG was recorded in an adjacent room. All reported satisfactory, restful sleep. As soon as they awoke (between 7 a.m. and 8 a.m.), we asked them to perform the reaching task and we recorded SEPs in the same seven subjects. For the control session, which took place at least 1 week earlier or later, we applied the exact same procedure with the exception of the immobilization. The order of the sessions was randomized to control for order effect. Actigraphy. To verify that the immobilized arm was not being used, subjects wore an actigraph (Actiwatch 64, Mini Mitter) on each wrist for 24 h starting at the morning visit on both experimental days. Average motor activity was calculated for the period between the morning and the evening visit on both days for both arms. Motor task. In the days before the first session, all subjects were familiarized with the testing apparatus and performed the motor task with their left hand. Subjects moved a cursor with the left hand on a digitizing tablet to one of three targets presented on a screen. The three targets were at the same distance (8 cm) from a common starting point and were separated by 451 (Fig. 2a). Hand positions (calibrated to the location of the fingertip) were monitored using a digitizing tablet, which streams the cursor position to the computer at 200 Hz (ref. 29). Subjects were instructed to make a straight uncorrected movement to the target, as fast and as accurately as possible, to reverse direction within the target and to return to the start position (out-and-back movements). To prevent learning and corrections, the cursor indicating the hand position was not displayed during the movement. The subjects received no feedback about their performance either during or after the motor task. Each test session lasted less than 5 min. During each movement cycle, the three

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ARTICLES targets were presented in a predictable sequence. There were 12 cycles for a total of 36 movements. To measure performance, we processed hand trajectories as previously described29. Normalized hand-path area was measured as the area enclosed by the hand path divided by the squared path-length direction. For each movement cycle, we computed the standard deviation of the mean normalized area between the three target directions. We then computed the variability of normalized area for each testing session as the mean of those standard deviations (n ¼ 12). This is an index of interjoint coordination that is altered in individual with proprioceptive deficits and in healthy subjects learning to move with new inertial masses19,20. We also measured movement times, peak velocities, peak accelerations and derived measures of task performance29. MEPs. We administered single-pulse TMS (130% of motor threshold, 20 pulses, interstimulus interval 5–10 s) to the left and right motor cortex before and after 12 h of left arm immobilization (n ¼ 6), using a monophasic stimulator (Magstim BiStim2 stimulator with a figure-of-eight coil, outer diameter 10 cm). To ensure precision and reproducibility of stimulation, we used a Navigated Brain Stimulation system (Nexstim, details in ref. 45). We recorded MEPs from the first dorsal interosseum muscle (sampling frequency 1,450 Hz, high-pass filter at 4 Hz). SEPs. Median nerve SEPs were evoked by electric stimulation at the right and left wrist using constant current squarewave pulses (0.3–0.5 ms, 512 stimuli), with intensity just above the thenar motor threshold. The interstimulus interval was set at 250 ms (4 Hz). The afferent peripheral volley was detected at Erb’s point, over the brachial plexus, and over C7. The cortical evoked potential was recorded on the scalp by means of the 256-electrode system. Filters were set between 10 Hz and 250 Hz for evoked potentials. Sampling frequency was 1 kHz. Source localization. Source localization was performed on the average SEP using the Curry software package (Curry 5.0, Neuroscan). Electrode positions were digitized and coregistered to each subject’s MRI by means of an infrared positioning system (Nexstim). We then estimated the current density on the cortical surface by using the sLORETA algorithm46. The current density of the average SEP was then projected onto the Montre´al Neurological Institute (MNI) standard brain. Sleep recording. Sleep recordings were performed by means of a 256-channel EEG amplifier (Geodesics). Seven subjects were recorded for the entire night. For technical reasons, we recorded only the first sleep episode in the other seven subjects; then the cap was removed and subjects were allowed to sleep undisturbed for the remaining part of the night. Due to unreliable recording quality, one subject’s data had to be discarded from the analysis of the sleep EEG. EEG recordings were sampled at 500 Hz and band-pass filtered between 0.5 Hz and 40 Hz. Sleep stages were visually scored for 20-s epochs according to standard criteria47. For a qualitative analysis of the sleep EEG, we performed a spectral analysis of consecutive 20-s epochs (FFT routine, Hanning window, averages of five 4-s epochs) for all 256 channels after visual and semiautomatic artifact removal48. Statistics. To assess significant topographical differences in the high-density EEG recordings, we used statistical nonparametric mapping (SnPM, Supplementary Methods online). For the correction of multiple testing in the comparison of SEP in the immobilization and control conditions, we used a suprathreshold cluster test49. In all other comparisons, we performed analyses of variance (ANOVAs). To test contrasts, we used post-hoc two-tailed t-tests only if the main factor or interaction of the ANOVA reached significance. ACKNOWLEDGMENTS We thank M. Bove, C. Moisello, N. Garre, L. Rondi and F. Battaglia for help with the selection of the immobilization protocol; C. Cirelli for comments on the manuscript; S. Maata and F. Ferreri for help with the collection of MEPs; and R. Davidson and A. Alexander at the Keck Center for support with EEG and MRI facilities. This work was supported by grants from the Swiss Foundation for Fellowships in Biology and Medicine (R.H.), the National Parkinson Foundation (M.F.G.) and the US National Institutes of Health (1RO1 NS 055 185-01 to G.T.); and by a McDonnell Foundation grant (G.T. and M.F.G.).


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AUTHOR CONTRIBUTIONS R.H. discussed the study design, tested subjects, analyzed electrophysiological data, performed statistics, prepared figures and drafted the manuscript. M.F.G. developed the behavioral protocol, discussed the study design, analyzed the behavioral data, performed statistics, prepared figures and contributed to the manuscript preparation. M.M., F.F., B.A.R. and M.J.P. helped with subject testing and data analysis. G.T. suggested the study design, supervised the experiments and worked on the manuscript. COMPETING INTERESTS STATEMENT The authors declare that they have no competing financial interests. Published online at http://www.nature.com/natureneuroscience Reprints and permissions information is available online at http://npg.nature.com/ reprintsandpermissions/ 1. Stickgold, R. Sleep-dependent memory consolidation. Nature 437, 1272–1278 (2005). 2. Datta, S. Avoidance task training potentiates phasic pontine-wave density in the rat: a mechanism for sleep-dependent plasticity. J. Neurosci. 20, 8607–8613 (2000). 3. Frank, M.G., Issa, N.P. & Stryker, M.P. Sleep enhances plasticity in the developing visual cortex. Neuron 30, 275–287 (2001). 4. Gais, S. & Born, J. Declarative memory consolidation: mechanisms acting during human sleep. Learn. Mem. 11, 679–685 (2004). 5. Gais, S., Plihal, W., Wagner, U. & Born, J. Early sleep triggers memory for early visual discrimination skills. Nat. Neurosci. 3, 1335–1339 (2000). 6. Karni, A., Tanne, D., Rubenstein, B.S., Askenasy, J.J. & Sagi, D. Dependence on REM sleep of overnight improvement of a perceptual skill. Science 265, 679–682 (1994). 7. Maquet, P. The role of sleep in learning and memory. Science 294, 1048–1052 (2001). 8. Marshall, L., Molle, M., Hallschmid, M. & Born, J. Transcranial direct current stimulation during sleep improves declarative memory. J. Neurosci. 24, 9985–9992 (2004). 9. Stickgold, R., James, L. & Hobson, J.A. Visual discrimination learning requires sleep after training. Nat. Neurosci. 3, 1237–1238 (2000). 10. Walker, M.P. & Stickgold, R. Sleep-dependent learning and memory consolidation. Neuron 44, 121–133 (2004). 11. Walker, M.P. & Stickgold, R. Sleep, memory, and plasticity. Annu. Rev. Psychol. (2005). 12. Steriade, M., McCormick, D.A. & Sejnowski, T.J. Thalamocortical oscillations in the sleeping and aroused brain. Science 262, 679–685 (1993). 13. Borbe´ly, A.A. & Achermann, P. Homeostasis of human sleep and models of sleep regulation. in Principles and Practice of Sleep Medicine (eds. Kryger, M.H., Roth, T. & Dement, W.C.) 377–390 (W.B. Saunders, Philadelphia, 2000). 14. Huber, R., Ghilardi, M.F., Massimini, M. & Tononi, G. Local sleep and learning. Nature 430, 78–81 (2004). 15. Tononi, G. & Cirelli, C. Sleep and synaptic homeostasis: a hypothesis. Brain Res. Bull. 62, 143–150 (2003). 16. Tononi, G. & Cirelli, C. Sleep function and synaptic homeostasis. Sleep Med. Rev. 10, 49–62 (2006). 17. Allen, C.B., Celikel, T. & Feldman, D.E. Long-term depression induced by sensory deprivation during cortical map plasticity in vivo. Nat. Neurosci. 6, 291–299 (2003). 18. Heynen, A.J. et al. Molecular mechanism for loss of visual cortical responsiveness following brief monocular deprivation. Nat. Neurosci. 6, 854–862 (2003). 19. Krakauer, J.W., Ghilardi, M.F. & Ghez, C. Independent learning of internal models for kinematic and dynamic control of reaching. Nat. Neurosci. 2, 1026–1031 (1999). 20. Sainburg, R.L., Ghilardi, M.F., Poizner, H. & Ghez, C. Control of limb dynamics in normal subjects and patients without proprioception. J. Neurophysiol. 73, 820–835 (1995). 21. Finelli, L.A., Borbe´ly, A.A. & Achermann, P. Functional topography of the human nonREM sleep electroencephalogram. Eur. J. Neurosci. 13, 2282–2290 (2001). 22. Werth, E., Achermann, P. & Borbe´ly, A.A. Fronto-occipital EEG power gradients in human sleep. J. Sleep Res. 6, 102–112 (1997). 23. Contreras, D. & Steriade, M. Cellular basis of EEG slow rhythms: a study of dynamic corticothalamic relationships. J. Neurosci. 15, 604–622 (1995). 24. Molle, M., Marshall, L., Gais, S. & Born, J. Grouping of spindle activity during slow oscillations in human non-rapid eye movement sleep. J. Neurosci. 22, 10941–10947 (2002). 25. Werth, E., Dijk, D.J., Achermann, P. & Borbely, A.A. Dynamics of the sleep EEG after an early evening nap: experimental data and simulations. Am. J. Physiol. 271, R501–R510 (1996). 26. Tobler, I. Phylogeny of sleep regulation. in Principles and Practice of Sleep Medicine (eds. Kryger, M.H., Roth, T. & Dement, W.C.) 72–81 (W.B. Saunders, Philadelphia, 2000). 27. Mauguiere, F. Somatosensory evoked potentials: normal responses, abnormal waveforms and clinical applications in neurological diseases. in Electroencephalography: Basic Principles, Clinical Applications, and Related Fields (eds. Niedermeyer, E. & Lopes Da Silva, F.) 1014–1058 (Lippincott Williams & Wilkins, Hagerstown, Maryland, 1999).

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