While the term “impact peak” can still refer to a transient that

While the term “impact peak” can still refer to a transient that exhibits a local maximum, a local maximum is not a necessary condition for existence of an impact transient. The current study demonstrates that novice BF runners are successful at immediately reducing loading parameters when they are provided with instruction and real-time feedback on transitioning to an FFS pattern. We reported a large decrease in VALR and VILR during the BF run compared to

the shod. This is contrary to other studies that reported increases in loading rates in novice BF16 and minimalist26 runners without instruction; suggesting that instruction may be playing a role. Kinematics indicate that these participants often continued to RFS while running BF or in minimal shoes. These studies, combined with additional literature,3 and 27 IWR-1 molecular weight selleckchem demonstrate that not all runners convert to an FFS pattern when transitioning to BF running. Studies report that BF runners who RFS, have significantly higher loading rates than forefoot strikers and shod runners who

RFS.3, 24 and 25 Increased loading rates of the VGRF have been linked to a number of common running-related injuries.6, 7 and 8 Additionally, it has been shown that RFS runners are 2.5 times as likely to had a retrospective, repetitive stress injury than FFS runners.28 The use of feedback and instruction likely encouraged an FFS pattern as runners transitioned to BF running. Evaluation of high-speed video indicated that 96% of novice BF runners were able to adopt an FFS pattern. This altered strike pattern likely contributed to decreased loading rates during the BF condition. Literature reporting loading rates between BF runners who employ an FFS pattern and shod runners is extremely limited.25 Lieberman et al.3 reported no overall difference about in loading rates between BF FFS and shod runners. This

is in contrast to the current study and a recent study by Shih et al.24 where habitually shod RFS runners were asked to run BF and shod with both an FFS and RFS pattern. Confidence intervals on each condition indicated that loading rates were significantly reduced for an FFS pattern while BF and shod compared to an RFS pattern while shod. Importantly, participants in this study were told to use a specific strike pattern and given a brief time to practice an FFS before data were collected. Considered collectively, the results of these recent studies imply that not all FFS patterns are equivalent. Not all BF runners who make initial contact with the ground on the front of the foot may necessarily be successful at significantly reducing loading rates. The marked decrease in loading rates observed in this study may be partly attributed to the use of real-time feedback.

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Since advancing age gradually increases stressor load as long as

Since advancing age gradually increases stressor load as long as underlying causes are present, strategies targeting cellular stressor pathways should aim at processes that are critical to drive vicious circles of increasing cellular stress and misfolding protein acumulation. Such critically important stressors may be identified through modifier screens in genetic model organisms and patient-derived this website stem cells, as well as through sequencing strategies in selected groups of patients. Potential

targets may include specific regulators of neuronal excitability, and reagents to reduce the accumulation of specific misfolding species, such as chaperon-like molecules or specific antibodies. Key disease-relevant processes

may differ among NDDs, and possibly also among NDD subtypes, suggesting that it may be important to establish and apply sensitive biomarkers of disease subtypes. Should conversion processes indeed be critical to progress from prodromic dysfunction to advancing degeneration, then these would be potentially valuable targets for treatments, as they may prevent, and possibly even reverse conversion to disease. Candidate targets may involve local interactions within the neuronal environment in the CNS, e.g., inflammatory processes and/or vascular lesions. Finally, at least in sporadic forms of the diseases, effective treatments may target disease spreading, Ribociclib in vitro thereby restricting degeneration to CNS regions initially affected by disease. Again, potential targets include vascular integrity, inflammation, and the immune Rebamipide system, and interventions aimed at reducing extracellular misfolding protein levels, e.g., through specific antibodies. An evaluation of the potential value of such treatment strategies in well characterized animal models of NDDs should at the same time represent a valuable strategy to test and revise postulated causality relations in

these diseases, which, in turn, should lead to further improvement of candidate treatment strategies. It seems reasonable to hope that such an iterative testing process in improved disease models will provide a rational path to develop treatments that will at last show efficacy in the clinics. We thank Mathias Jucker (Hertie-Institut für klinische Hirnforschung, Tübingen, Germany), Patrick Brundin (Wallenberg Institute, Lund Univ., Sweden), and Chris Henderson (Motor Neuron Center, Columbia University, New York, USA) for valuable comments on the manuscript. The Friedrich Miescher Institut is part of the Novartis Research Foundation. “
“Converging evidence from neuroimaging studies indicates that the ventral visual pathway is important for object recognition (Grill-Spector et al., 1999 and Malach et al., 1995).

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Spike sorting was performed by a semiautomatic clustering procedu

Spike sorting was performed by a semiautomatic clustering procedure (see Supplemental Experimental Procedures and Figure S5), yielding a total of 140 active pyramidal neurons during sleep (n = 4

animals), 573 in the maze (n = 3 animals), 569 during wheel running (n = 3 animals) and 193 in the open field (n = 4 animals). The determination of place fields was basically performed as in Leutgeb et al. (2007). For each cell, spatial distributions of firing rates (ratio of total number of spikes to occupancy duration in a given spatial bin) during locomotion (>5 cm/s) were calculated for each bin of the environment (50 × 50 bins) and then boxcar-averaged over the 25 neighboring bins (instead of a Gaussian kernel). Place fields included the bins with the highest firing rates (at least 2 Hz) and all contiguous bins in which the firing rate exceeded 20% buy Ulixertinib of the peak firing rate. Place fields smaller selleck than 16 bins or larger

than half the environment were discarded. Episode fields (EpF) in the wheel were defined as periods <3 s with peak firing rate >5 Hz and 4SD above mean firing rate, EpF limits were set at 10% of the peak firing rate, as described in Pastalkova et al. (2008). Because theta is reliably expressed when the animal is moving, we have selected for analysis all awake periods with running speed >10 cm/s (although including brief interruptions of less than 200 ms). REM sleep periods were determined from manual threshold on theta/delta ratio, which provides a well contrasted estimation of REM sleep versus slow-wave state (Figure S6). Periods shorter than 3.5 s were discarded. Time-frequency spectrograms of continuous LFP traces (EEG, sampling rate 1.25 kHz, hardware Resminostat high-pass filter 1 Hz) were computed by using the multitaper method (1 s time window and

4 tapers) from the Chronux toolbox (Mitra and Bokil, 2008; http://chronux.org/). Theta power signal was measured from the raw EEG trace as the integrated power in the 4–11 Hz frequency band by using the multitaper method. For second-order analysis, theta power was also measured as the peak-to-trough amplitude of individual theta cycles in the 2–30 Hz filtered (“raw theta”) EEG trace or by using Morlet-based wavelet analysis (Bruns, 2004). These methods yielded similar results (Figures 2, S2, and S6). Local maxima (peaks) and minima (troughs) of theta power were detected and measured on the theta power trace, within 200 ms of the peaks and troughs obtained from the low-pass filtered theta power signal (fourth-order Chebychev, cutoff 1.8 Hz; Figure S7). TPSM cycles were defined as the intervals between successive theta power minima and a linear phase (from 0 to 2π) was defined between the successive troughs of TPSM cycles (see Supplemental Experimental Procedures).

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In these instances, it is now technically possible to conduct ChI

In these instances, it is now technically possible to conduct ChIP and DNA methylation studies after selecting fluorescently tagged cell types with flow cytometry (Guo et al., 2011 and Jiang et al., 2008). In addition to basic research,

there is the potential for the study of epigenetics to reveal novel drug targets for the treatment of pain. It is already clear from only a Bortezomib few experiments that HDAC inhibitors might be an interesting group of drugs to explore. Currently, these compounds display limited selectivity or, more precisely, a selectivity bias toward the ubiquitously expressed class I HDACs (Bradner et al., 2010). As a consequence, if given systemically, many adverse side effects, such as fatigue, nausea, and diarrhea, can occur. Nevertheless, two HDAC inhibitors (vorinostat and romidepsin) are already approved for use in the clinic against T cell lymphoma, and many more are being trialed as chemotherapeutic agents (Lemoine and Younes, 2010). Because of their relevance in the fight against cancer,

development of more selective and hence more tolerable HDAC inhibitors is a high priority not only CB-839 concentration for research but also for the pharmaceutical industry. In the pain field, drugs specifically targeting class IIa HDACs would be of particular interest. This class is expressed less widely, and some evidence suggests that one of its members (HDAC4) is implicated in pain processing. Thus, Rajan et al. (2009) reported that knockout of the HDAC4 deacetylase domain in mice decreases their thermal sensitivity on a hot plate (Rajan et al., 2009). It will also be of great interest to test the effects of other groups of compounds, for instance, those interfering with the actions of histone acetyltransferases or lysine methyltransferases. These enzymes add acetyl or methyl groups to histones (as well as other proteins) and tend to be more selective in the residues they modify, potentially making them better targets for drug development than deacetyl-

or demethylases (Copeland et al., Mephenoxalone 2009 and Dekker and Haisma, 2009). Finally, many of the epigenetic “reader proteins” (Table 1) could be viable drug targets, since their precise involvement is likely to be disease-process and situation specific. Hence, a deeper knowledge of epigenetic processes in chronic pain is needed to gauge their therapeutic potential. The currently available data suggest that epigenetic mechanisms may be important contributors to chronic pain states. Descriptive studies, for instance examination of genome-wide histone acetylation or methylation in various models of chronic pain, will be useful and are certainly feasible. Causal interactions may take longer to establish, but a wide variety of compounds, targeting specific epigenetic proteins, are being developed and will greatly facilitate this effort.

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, 2011), leading to the features of the disease (Schon and Area-G

, 2011), leading to the features of the disease (Schon and Area-Gomez, 2010). One other neurodegenerative disease that may be associated with MAM dysfunction is CMT, which can be caused by mutations both in MFN2 (Chen and Chan, 2009) and in ganglioside-induced

selleck chemicals llc differentiation-associated protein 1 (GDAP1) (Pedrola et al., 2005), which interacts with MFN2 (Niemann et al., 2005). MFN2, like mitofusin-1 (MFN1), is required for mitochondrial fusion (Chen and Chan, 2009). However, a portion of MFN2 is enriched in the MAM, where it is required for the tethering of ER to mitochondria (de Brito and Scorrano, 2008). We note that CMT mutant MFN2 expressed in cultured dorsal root ganglion neurons induced abnormal clustering of fragmented mitochondria, as well as impaired axonal transport of mitochondria (Baloh et al., 2007). Perhaps these abnormalities resulted from an underlying defect in ER-mitochondrial communication. The study of MAM is a nascent field that has just begun to be recognized as a contributor to neurodegeneration, and likely will expand beyond the diseases cited above. For example, there may be a “MAM connection” in at least two other diseases in which the relevant proteins—both involved in phospholipid metabolism—appear to be enriched

in the MAM. These are SCA due to mutations in PPP2R2B, a regulatory subunit of protein phosphatase 2A (Giorgi et al., 2010) that promotes mitochondrial fission (Dagda et al., 2008a), presumably via MAM-localized Paclitaxel clinical trial FIS1 (Iwasawa et al., 2011), and PD due to mutations in subunit β of the calcium-independent phospholipase A2 (iPLA2β; gene

PLA2G6), which plays a key role in ER-mitochondrial all crosstalk during ER stress-induced apoptosis ( Lei et al., 2008). It would thus be fascinating to see if future studies on PPP2R2B and iPLA2β provide insight into a potential link between MAM and neurodegeneration in SCA and PD, and perhaps even beyond. Besides alterations in trafficking and in ER-mitochondrial communication, mitochondria can also fail to reach their destinations due to dysregulation of quality control systems. The cell has surveillance mechanisms to eliminate mutated, unfolded, and otherwise unwanted proteins, via autophagic and ubiquitin-proteasome systems located in the cytosol. In a similar manner, unwanted mitochondria can be disposed of, and their contents recycled, by mitophagy. Although there is currently no evidence that mitochondria contain proteasomes, they do have mechanisms to eliminate misfolded or unneeded polypeptides, via, for example, the AAA (ATPase associated with diverse cellular activities) protease paraplegin/SPG7 and the paraplegin-related protease AFG3L2, and their regulators, the prohibitins PHB and PHB2 ( Osman et al., 2009).

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, 1997]) and impaired corticosteroid receptor signaling (Holsboer

, 1997]) and impaired corticosteroid receptor signaling (Holsboer, 2000), more recent hypotheses include the involvement of neurotrophins (Samuels and Hen, 2011), fibroblast growth factors (both ligands and receptors) (Turner et al., 2012), GABAergic deficits (Luscher et al., 2011), and epigenetic changes, specifically alterations in methylation and acetylation profiles at the promoters of glucocorticoid receptors and brain-derived neurotrophic factor (McGowan et al., this website 2009). Genetics does not support the primacy of one theory over another; indeed as our Review of the candidate gene

literature indicates, genetics does not support any of the biological theories put forward to date. Our Review indicates two pathways forward. First, there is no reason to suppose that undifferentiated MD is intractable to GWAS, but success will require very large sample sizes (Figure 3). However, interpreting the results of such a study is likely to be challenging. We have seen that MD is highly comorbid with anxiety, and etiologically heterogeneous, at both genetic and environmental levels. Without information on comorbidity, known risk factors, and clinical phenotypes, the role of each locus will be unclear. Some will be sex specific, some will act only in situations of environmental stress, and others will predispose to anxiety. Genetic selleck studies will need to include

an extensive amount of phenotypic information if we are to make sense of hard-won mapping results. Second, our Review indicates that we should not abandon attempts to concentrate

on subtypes of MD. So far, studies using recurrent and early-onset MD have been no more successful than those that examine undifferentiated MD, but this may be due to lack of power. If we consider MD as part of all a quantitative trait (representing liability to depression), then using a sample of more extreme cases would be equivalent to analyzing a rare disease (as Figure 3 demonstrates). Even a small improvement in genetic tractability could result in a large saving in the number of samples that need to be analyzed (reducing from 50,000 to 20,000, for example). The problem is that we do not know for sure how to determine the scale on which severity is measured: is it the number of episodes of MD, the length of episodes, the number of symptoms, or some other feature or combination of features? Furthermore, the severity scale needs to differentiate cases with higher genetic risk, not those cases resulting largely from environmental adversities. Alternatively, subdividing MD on the basis of one or more clinical features (e.g., typical versus atypical vegetative features, standard versus postpartum onset), sensitivity to environmental stress, or sex, might identify a rarer, or at least a more genetically homogenous, subtype. At present, deciding which features to investigate is likely to be an ad hoc enterprise.

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In plots of power spectral density (Figure 2C), each rat had peak

In plots of power spectral density (Figure 2C), each rat had peak BG beta frequency slightly below 20 Hz (range: 17.9–19.5 Hz) with cortical frequency consistently a touch higher (18.4 Hz

to 20.4 Hz). If beta oscillations represent a distinct, network-wide coordinated BG state, this should CH5424802 in vivo be apparent in analyses of phase and power relationships between structures. Coherence between all BG structures consistently showed a peak at ∼20 Hz for all rats (Figures 2D and Figures S3B). By contrast, we have previously shown that coherence between striatum and dorsal hippocampus in behaving rats is close to zero at 20 Hz (Berke et al., 2004 and Berke, 2009). We next constructed comodulograms, which illustrate the extent to which moment-to-moment oscillatory power covaries between structures (Buzsáki et al., 2003). Coordinated power changes within the BG network were observed especially at ∼20 Hz (Figures 2D and Figures S3B).

Modulation of beta power relative to behavioral events was essentially identical throughout the BG, and similar between BG and ECoG (Figure S2B). There was no consistent difference in the modulation of beta power for ipsilateral RG7204 order versus contralateral movements (Figure S4). We have previously reported that striatal LFPs show mutually exclusive dynamic states, characterized by combinations of either ∼20 Hz beta and ∼50 Hz low-gamma rhythms, or ∼8 Hz theta and ∼80 Hz high-gamma rhythms, respectively (Berke, 2009; see also Dejean et al., 2011). These distinct states were visible in our current comodulograms: in STR, GP, and STN ∼50 Hz power was positively correlated with beta power SB-3CT and negatively correlated with ∼80 Hz activity. These relationships were absent or diminished for SNr. BG beta rhythms were tightly coordinated between structures, but not identical in all respects. We consistently observed a significant difference in beta phase between simultaneously recorded subregions (28 pairwise comparisons, p < 0.05 in every case; see Experimental Procedures).

Although the specific set of recording regions varied between subjects, for all four rats we were able to compare beta phases between frontal ECoG, STR, and GP (Figure 2E). STR beta was always phase-advanced relative to the ECoG, (by an average of 97°), and GP was always slightly phase-advanced relative to the striatum (by an average of 4.8°). These results rule out some nonphysiological explanations for coordinated beta rhythms throughout the BG—for example, if the beta oscillations were on the common reference electrode, they would show no phase shift across regions. However, phase differences do not necessarily indicate where an ERS/ERD occurs first, especially as beta has a different phase at the cortical surface compared to deep layers (Murthy and Fetz, 1992).

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The Shh gradient rapidly stimulated the repulsion of axons with t

The Shh gradient rapidly stimulated the repulsion of axons with turning commencing within 1 hr of application of the gradient, indicating that the effect of Shh is direct. Quantification of the angle turned (Figure 3D) indicated that axons in a control gradient have no net turning (angle turned of −0.82° ± 4.3°, mean ± SEM; Figures 3E and 3F). In a Shh gradient, however, axons from commissural neurons at 3–4 DIV had a significant bias toward negative angles turned (−14.8° ± 5.0°; p < 0.05, one-way ANOVA; Figures 3E and 3F), indicating repulsion by Shh. The degree of repulsion by Shh was even Crizotinib in vitro more dramatic when those axons oriented toward increasing Shh concentrations, i.e.,

with initial angles between 0° and 90°, were considered. In this case, the mean angle turned was −23.9°. Shh appeared to only affect turning, not growth, of these axons because the Shh gradient did not significantly change the growth rate of the axons compared to the control (p = 0.8287) (Figure 3G). Furthermore, the net axon growth in a Shh gradient showed no correlation with the angle turned (Figure 3H). As previously shown (Yam et al., 2009), commissural axons at 2 DIV were attracted up a Shh gradient, with a mean angle

turned of 11.1° ± 4.6° (Figures 3E and 3F). This contrasts sharply with the repulsion by Shh that we observed at 3–4 DIV and suggests that the response of commissural neurons in vitro to Shh gradients changes over time. The length of the axon had no bearing on the degree of repulsion by Shh (Figure 3I), suggesting that the switch from attraction to ABT-737 repulsion by Shh is independent of axon length. This change in response to Shh over time is reminiscent of the change in response of commissural neurons in vivo to Shh gradients during development, with younger precrossing axons attracted to Shh along the DV axis and older postcrossing axons repelled by Shh along the AP axis. That isolated commissural neurons in culture maintain the ability

to switch their response to Shh gradients suggests that the switch is cell intrinsic and temporally regulated. Unlike the switch in commissural axon response to Shh, silencing of the Netrin-1 response at the floorplate is not cell intrinsic and depends on physical Levetiracetam encounter with the floorplate (Shirasaki et al., 1998). Indeed, we found that commissural neurons do not change their response to Netrin-1 over time in vitro. Commissural axons were attracted to Netrin-1 both at 2 DIV (mean angle turned of 17.4° ± 4.0°) and 3–4 DIV (mean angle turned of 12.8° ± 3.6°) (Figure 3J). Thus, the cell-intrinsic switch in the polarity of the response to guidance cues regulates the response to Shh, but not to Netrin-1. We next looked for endogenous proteins that are expressed in a time-dependent manner and that could mediate the switch in Shh response.

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, 2010), such as might arise if naive animals experienced greater

, 2010), such as might arise if naive animals experienced greater fluctuations in arousal during the session. Signal correlation (rsignal) was computed as the Pearson correlation coefficient (ranging between −1 and 1) between the tuning curves from two simultaneously recorded neurons. Tuning curves for each stimulus condition were constructed by computing the mean response (average firing rate during the middle 1 s of the stimulus Selleck BKM120 duration) across trials for each heading direction. Permutation tests were applied to test for significant differences between trained and naive animals with

respect to: the difference in time courses of noise correlation (Figure 2C), mean response (Figures 3A and 3C), and Fano factor (Figures 3B and 3D). We first computed the sum of squared differences between two time courses: equation(1) x2=∑i=1n(Ttrained,i−Tnaive,i)2using a 500 ms sliding window moved in 50 ms steps, for a total of 31 data points. We then created permuted naive and trained groups by randomly drawing data from the original groups, pooled together. Within each cell, all of the responses were preserved (no shuffling across trials). We computed a new x2 value for each permutation (x2permuted), and this process was repeated 10,000 times. A p

value was computed as the proportion of x2permuted > x2. A difference between the two groups of animals was considered significant if p < 0.05. Fano factor, http://www.selleckchem.com/products/BI6727-Volasertib.html or the variance/mean ratio, was computed from log-log scatter plots of the variance of the spike count against the mean spike count, and this was done for each 500 ms time window used to compute time

courses. The data were fit by minimizing the orthogonal distance to the fitted line (type II regression). The slope was generally close to 1 and was thus forced to be 1 for convenience, such that variance scaled linearly with mean spike count. The Fano factor was then computed as 10∧intercept (see Figure S3). Fisher information (IF) provides an upper limit on the precision with which an unbiased estimator can discriminate between small variations in a variable (x) around a reference value (xref) (Pouget et al., 1998 and Seung and Sompolinsky, 1993). before We computed the smallest deviation in heading around straight ahead (threshold, Δx) that could be reliably discriminated (at 84% correct) by an ideal observer: equation(2) Δx=2Ifwhere IF was computed according to (Abbott and Dayan, 1999): equation(3) IF(xref)=f′T(xref)Q−1(xref)f′(xref)+0.5Tr[Q′(xref)Q−1(xref)Q′(xref)Q−1(xref)]IF(xref)=f′(xref)TQ−1(xref)f′(xref)+0.5Tr[Q′(xref)Q−1(xref)Q′(xref)Q−1(xref)]Here, f′ denotes the derivative of a matrix of tuning curves; superscript T denotes the matrix transpose, Tr represents the trace operation, and superscript −1 indicates the matrix inverse. The reference heading was straight ahead in our simulations (xref = 0°).

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This was the case in every trial for all animals (Figure 2A) Whi

This was the case in every trial for all animals (Figure 2A). Which aspects of the motor and sensory

activity determine the timing of the jump? We found that the time at which the cocontraction ended (triggering) was highly correlated with take-off (ρ = 0.95, p < 10−9). Moreover, this correlation exists regardless of l/|v|, since the partial correlation coefficient between these two variables controlling for l/|v| remained high (ρpart = 0.94, p < 10−9). On average take-off occurred 36 ms after triggering (SD: 15, nL = 4, nT = 29; Figure 2B, dashed line) and 90% of the variance in the timing of take-off could be explained by the timing of triggering. At the sensory level, we found that the timing of the DCMD peak firing Dolutegravir concentration rate and take-off were highly correlated as well (ρ = 0.87, p < 10−9) and that the partial correlation coefficient between these variables controlling for l/|v| also remained high (ρpart = 0.73, p = 9.2 × 10−8). Rucaparib chemical structure Locusts took off on average 70 ms (SD: 13) after the DCMD firing rate peaked, regardless of l/|v| (Figure 2C, dashed line) and the timing of the peak accounted for 75% of the variance of the take-off time. Not all looming stimuli led to a final take-off. Thus,

locusts jumped with a median probability of 32%. The jump probability was significantly reduced compared to that of animals without a telemetry backpack (Fotowat and Gabbiani, 2007; median: 64%, pKWT = 0.003). Figure 3 shows a trial in which the same locust as in Figure 1 did not jump (Movie S2). It started preparing by cocontracting its hindleg

flexor and extensor muscles. However, compared to jump trials, the cocontraction started late, such that after a few spikes in the extensor, the unless looming stimulus reached its full size, the DCMD firing rate declined, and the cocontraction ended. This was the case in 85% of trials without take-off, whereas in the remaining 15% the cocontraction failed to initiate altogether. Across animals, we found that cocontraction onset occurred significantly earlier relative to collision in jump trials (Figure 4A), whereas the timing of the DCMD peak itself did not change (Figure 4B). Thus, while the DCMD peak time predicts the time of take-off, it fails to predict its occurrence. Since cocontraction started earlier in jump trials, the number of extensor spikes was also significantly higher (Figure 4C). In contrast, there was no difference in the total number of DCMD spikes between jump and no-jump trials (Figure 4D), although the peak DCMD firing rate was higher in jump trials (Figure S2A). However, we found that if we started counting the DCMD spikes from cocontraction onset rather than stimulus onset (shaded areas in Figure 1 and Figure 3), their number was significantly higher in jump trials (Figure 4E). Furthermore, the number of DCMD spikes from cocontraction onset was highly correlated with the number of extensor spikes (ρ = 0.73, p < 10−9, Figure 4F), such that on average 4.

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