However, as a worst-case scenario, the model was parametrized with a much smaller estimate of the probit slope, 1.095, an estimate obtained by Saeglitz et al. ( 2006a,b) for O. nubilalis over a number of exper- iments.

This compares reasonably well with estimates made by Farinós et al. ( 2004), which for O. nubilalis were in the range 1.33-3.15, and for S. nonagrioides were in the range 0.92-2.96.

The value 1.095 was used through- out for all three species considered.

Since the slope is invariant under the change of scale for probit analysis, the estimated slope applies equally both to concentrations expressed in units of pollen grains per square centimetre leaf area and to doses in units of nanogram of truncated toxin used by Saeglitz et al. ( 2006a,b).


Hence, if p B represents the proportion of I. io and V. atalanta individuals that suffer mortality as a result of a concentration, d, expressed as the number of maize MON810 pollen grains cm 22 , then probitð p B Þ ¼ À4:121 þ 1:095 log 10 d; and if p M represents the proportion of the P. xylostella indi- viduals that suffer mortality, then probitð p M Þ ¼ À3:898 þ 1:095 log 10 d:


Working with logits rather than probits yields an exact solution for expressions involving p. The above probit- regression relationships are well approximated by the following logit-regression relationships:


The concentration, d, of pollen grains adhering to the leaves of host plants declines rapidly with increasing dis- tance in metres, E, from the maize source (Eastham & Sweet 2002; Jarosz et al. 2004; Devos et al. 2005).

This decline in pollen deposition within the margins of maize fields was modelled through a linear regression of log 10 d on E, using the extensive data of Wraight et al. ( 2000, especially fig. 2).

Wraight et al. ( 2000) measured maize pollen falling on microscope slides covered with a coat of petroleum jelly.

The slides provided an accurate measure of total pollen deposition but probably overesti- mated the amount of pollen retained on foliage by about threefold (Pleasants et al. 2001; Lang et al. 2004).

Allowance for this results in the following relationship:


This relationship is consistent with results of Jesse & Obrycki (2000) from a smaller set of data, and is appro- priate for P. xylostella larvae feeding on Brassicaceae.

However, for larvae of I. io and V. atalanta, the results of Darvas et al. ( 2004) suggest that about 2.85 times as much pollen adheres to the hairy leaves of its host plant U. dioica, suggesting that the relationship be amended to log 10 d ¼ 2:346 À 0:145E:


For larvae of I. io and V. atalanta, equations (2.1) and (2.4) may be combined to express mortality within the field margin directly in terms of distance from the edge of the crop, thus yielding the parameter denoted g(E) above:


Note that at the very edge of a maize MON810 crop, where E ¼ 0, the estimated mortality rate is g(0) ¼ p B ¼ 0.0292 (equivalent to one individual in 34.25), and that 2 m into the margin, this rate itself is approximately halved.

Numerical integration shows that average mor- tality within a 2 m band within the margin is 0.0209 (cf.

with the value of 0.075 for the corresponding par- ameter conjectured by the EFSA GMO Panel; EFSA 2009).


For larvae of P. xylostella, equations (2.2) and (2.3) may be combined similarly to yield


Estimates of the parameter h may be derived by noting that pollen deposition within a maize crop is approxi- mately 2.7 times that at the edge ( Jesse & Obrycki 2000; Wraight et al. 2000; Pleasants et al. 2001).

For larvae of I. io and V. atalanta, the adjusted equations (2.1) and (2.4) are combined to give h ¼ 0:0805; ð2:7Þ and for larvae of P. xylostella a value of h ¼ 0:0486:


The expected proportion of all the larvae in a margin that suffer worst-case mortality, before allowing for effects that reduce exposure, is denoted by m ¼ m(D), and is obtained by averaging the value of g(E) over the margin.

In practice, m is obtained by the numerical integration of equation (2.5) or (2.6), between the values E ¼ 0 and E ¼ D. Values of m are shown for various margin 1420 J. N. Perry et al. Modelling exposure of Lepidoptera Proc.

R. Soc.

B (2010) widths, for the butterflies and the moth, in figure 1.

For all species considered, the parameter m(D) is about double the value for small values of D (very narrow mar- gins) than for D ¼ 5, and there is a further approximate halving of m(D) at D ¼ 10.

Estimates of ax are typically about one-third, so the estimated actual within-crop mortality of individual larvae is about 2- 3% for I. io and V. atalanta and about 1 -2% for P. xylostella, and the estimated actual within-margin mortality of individual larvae, for any width of margin, is always less than 1 per cent for I. io and V. atalanta and less than 0.6 per cent for P. xylostella.


For an assumed specific square field of size C (ha) with a margin of D (m), the within-margin area is approximately 400D p C (m 2 ), so the expected number of host plants within the field margins is 400Df p C. The expected number of host plants within the crop is 10 000Ce.

Then, of the population that is potentially exposed within the crop and field margins, an approximate pro- portion (10 000Ceh þ 400fD p Cm)/(10 000Ce þ 400fD p C) suffers mortality, which may be simplified to (25eh p C þ fDm)/(25e p C þ fD).

After allowance for large-scale demo- graphic effects, physical effects, temporal overlap and spatial overlap, the estimated proportion of the population that suffers mortality is


For regions where margins have a bimodal distribution for which there is a probability of q that a field will have no margin at all, then this proportion becomes


Values of parameters other than g, m, and h were chosen by the authors separately for each region mod- elled.

Information on the parameter v for maize MON810 is only available from the larger Bt-maize culti- vation areas in Spain, where it has reached 0.65 in some regions.

Hence, for the regions modelled in Germany, Hungary and Italy, the maximum limit for Bt-maize (v ¼ 0.8) was chosen (based on current insect resistance management requirements for lepidopteran target pests, requiring 20% non-Bt-maize refuge areas), to ensure that a worst-case scenario was modelled that would yield relatively large estimates of mortality.

A summary of all chosen parameter values is given in table 1.


While it is generally agreed that densities of pollen grains decline rapidly with increasing distance from the maize source (Eastham & Sweet 2002; Jarosz et al. 2004; Devos et al. 2005), few studies have measured the varia- bility of pollen concentrations.

Pollen deposition may vary greatly spatially depending upon weather conditions (Jarosz et al. 2004; Hofmann 2009).

Owing to vertical wind movements or gusts, particularly in thundery con- ditions on summer afternoons, a particular small area may experience a larger than average concentration of pollen, even many tens of metres away from a maize field (Vogler et al. 2009).

However, such larger than aver- age pollen concentrations are balanced by smaller than average values elsewhere, where effects are diluted.


The frequency distributions given in table 2 of Pleasants et al. ( 2001) demonstrate that this variability may be con- siderable.

This effect has been quantified in fig. 4 of Hofmann (2007), where log 10 d is shown to decline line- arly with log 10 E, as expected, but with variability about the regression line that may be represented by a normal random variable with variance about 0.175, so that the 99% confidence interval around the line encompasses about 12-fold variation in concentration, at any given dis- tance, in both directions.

For linear systems, the average expected effect of such variation would be zero, but the model considered here is highly nonlinear; therefore, it is necessary to assess what is the effect of the measured variability on the deterministic estimates of mortality.


This was done by simulating, for each of a range of dis- tances E, a thousand random variables N, where N is a normal random variable with zero mean and variance 0.175, and computing log 10 d ¼ 1.891 - 0.145E þ N, the stochastic analogue of equation (2.4) and, in separate simulations, log 10 d ¼ 2.346 - 0.145E, the stochastic ana- logue of equation (2.6).

From that set of values, equations (2.1) and (2.2) were used to derive estimates of mortality that allowed for stochastic variability in pollen deposition, for comparison with predictions from the deterministic case derived in equations (2.5) and (2.6).


Modelling exposure of Lepidoptera J. N. Perry et al. 1421 Proc.

R. Soc.

B (2010) et al. 2004; Lang & Vojtech 2006) is defined as a reduction in larval weight gain, then observations in Spain on the lepidopteran pests O. nubilalis and S. nonagrioides indicate that mortality rates caused by maize MON810 pollen of about 25 and 10 per cent correspond to sublethality rates of about 100 and 50 per cent, respectively.

These values are broadly consistent with the data of Lang & Vojtech (2006) and Felke et al. ( 2002).

Therefore, sub- lethal effects were modelled by assuming that, for any given width of margin D, and the average worst-case mor- tality rate within the margin of m(D), the proportion of larvae suffering sublethal effects, before allowance for any effects that reduced exposure, was 4m(D).

Similarly, within the crop, worst-case sublethality rates before allow- ance for other effects were assumed to be four times worst-case mortality rates, i.e. 0.322 for larvae of I. io/ V. atalanta and 0.194 for larvae of P. xylostella.

A key effect of Cry toxins on lepidopteran larvae is reduced feeding, which can lead to a greatly reduced rate of devel- opment and a level of functional mortality much greater than estimates of direct mortality measured in laboratory assays over short periods.

Under a worst-case scenario, all larvae suffering sublethal effects would be assumed to die without completing their development, although this would most probably greatly overestimate the actual mortality.


Estimates of population mortality and sublethality rates are given for all species and regions considered in table 2.

For the two butterflies, estimates for Spain were zero because for those regions, at the time of maize polli- nation, there are almost no host plants for larvae to feed on in the fields or field margins, since weed control and local irrigation culture suppress weed and field margin vegetation strongly.

Indeed, intensive field surveys in 2009 recorded no U. dioica plants and only one species, C. draba (L.), of Cruciferae, with no lepidopteran larvae feeding on this weed (G. P. Farinó s 2009, personal com- munication).

For each species, the minimum, maximum and median rates, excluding Spain, were computed over the regions considered.

The mean of the stochastic ana- logues of the parameter g(E ), calculated using the variability described in Hofmann (2007), was, for I. io and V. atalanta, between 1.53 (for E ¼ 0) and 1.63 (for E ¼ 4.5) times greater than the deterministic value shown in figure 1.

For P. xylostella, the mean of the sto- chastic analogue was between 1.71 (for E ¼ 0) and 1.79 (for E ¼ 4.5) times greater than the deterministic value.

After allowance for all the effects modelled, the estimated median stochastic mortality rate over regions (excluding Spain) for both I. io and V. atalanta was 2.66 Â 10 24 (1.33 times the corresponding deterministic value), and for P. xylostella was 2.67 Â 10 24 (1.17 times the corresponding deterministic value).


The conclusions from this modelling exercise are that a full exposure assessment is possible for several non- target lepidopteran species exposed to Cry1Ab expressing pollen in or near maize MON810 fields, but it requires many factors to be taken into account, some of which have had to be modelled with restricted available data.

However, we believe that the predictions made here are relatively robust for the following reasons.

First, the esti- mates for larvae of non-target Lepidoptera reported here represent worst-case scenarios, in which any assumptions would tend towards overestimation, rather than underes- timation of mortality.

The estimates were probably most sensitive to the variable measuring the rate of change of mortality with concentration, i.e. the slope in the probit analysis.

Indeed, had the estimate of Felke et al. ( in press) been used instead of that of Saeglitz et al. ( 2006a,b) the mortality rates reported would have been about 8 Â 10 27 smaller.

Furthermore, while future utiliz- ation rates measured by parameter v, cannot now be known accurately, the assumed rate of 0.8 is most unlikely to be achieved even if cultivation were unrestricted.

Therefore, population mortality rates would most prob- ably be even smaller than reported here.


Second, the stochastic simulations suggested that the increase in mor- tality rates owing to aggregation of pollen deposition into heterogeneous clumps will be no more than one-third of those reported in table 2.


Third, for P. xylostella, while there can be marked variance in the sensitivity of different unselected populations to Cry toxins and while the species, cultivar and age of the leaf of the brassica used can also significantly influence the toxicity in bioassays, the sensitivity of the population used by Felke & Langenbruch (2005), from which our data derives, was relatively high.


Fourth, the model used here suggests that population sublethality rates would be likely to be proportionally larger than population mortality rates by the same multiplicative factor that sublethality was assumed to exceed mortality for individual larvae.

In that case, future estimates of population sublethality might usefully be based on estimates for individuals from laboratory studies.


Predicted environmental impact on the studied non- target lepidopteran larvae owing to exposure to potentially harmful amounts of pollen deposited on host plants in or near maize MON810 fields was low.

In all regions: Aachen, Berkatal, Bonn, Grebbin, Oderbruch and the Upper Rhine Valley from Germany; the Po Valley (central) and the Po Valley (coastal) from Italy; Tolna County in Hungary; and the Ebro Valley and Madrid from Spain, the calculated mortality rate was less than 6.36 Â 10 24 (one individual in every 1572) for the butterflies I. io and V. atalanta and 2.55 Â 10 23 (one individual in every 392) for the moth P. xylostella.

The median (typical) rates across all regions excluding Spain were 2 Â 10 24 (one individual in every 5000) for the but- terflies and 2.29 Â 10 24 (one individual in every 4366) for the moth.

Our results suggest that previous estimates (EFSA 2009), using similar techniques but relying on experts' estimates for parameters g(E) and h rather than calculated values, were overly cautious and that mortality and sublethality are about four times less than they estimated.


To place the above results into a quantified population- dynamic context, it would be necessary to predict the precise effects of mortality owing to Bt-maize MON810 in a particular generation(s) on All modelling exercises are subject to uncertainties; as with any ecological model, further data would refine the estimates reported here.

The variability in our reported estimates arises from: (i) natural variation between areas, reflecting expected agronomic and environmental heterogeneity, such as those relating to parameters C, D, e, f, etc.; ( ii) differences between experts' interpretation in parameters other than g(E) and h; and (iii) uncertain- ties arising from incomplete availability of data, particularly regarding sublethal effects.


We thank Stefan Rauschen, Ralf Wilhelm, Gema P. Farinó s, two referees and an anonymous colleague for helpful discussions.

We thank the European Food Safety Authority for paying page charges.


Perry et al. [ 1] developed a model to simulate the field exposure and adverse effects for three European non-target Lepidoptera species (Inachis io L., Vanessa atalanta L., and Plutella xylostella L.) to pollen of the Bacillus thuringiensis (Bt) maize MON810 containing lepidopteran-targeting Cry1Ab toxin.

Perry et al. explicitly modelled the worst case scenario and came to hard quantitative predictions.

However, the incomplete and uncertain input data cause a higher uncertainty than Perry et al. indicate, and we are specifically concerned with the possibility that the effects might be worse than they predict.

Here, we specify this uncertainty by addressing some of the basic model assumptions and input data regarding the toxic effects of Cry1Ab to lepidopteran larvae.

We do not address the hypotheses of the model regarding maize pollen dispersal and deposition, or population-dynamic effects.


A key problem of the modelling study of Perry et al. is that virtually no studies exist in the peer-reviewed literature reporting on the dose–response effects of MON810 pollen to European non-target Lepidoptera [2].

In consequence, the authors had to substitute data obtained from experiments with another transgenic event expressing Cry1Ab, the Bt176 maize [3,4].

In our view, the use of these studies is problematic for several reasons.


First, the authors set the toxicity of MON810 pollen to 31-fold less than Bt176 maize pollen, but did not provide data supporting the implied assumption of a linear relationship between Cry1Ab dose and adverse effects.

The dose–effect relation may be nonlinear, possibly resulting in a disproportionally higher effect of low Cry1Ab concentrations [5,6].


Second, the assumption of a 31-fold difference in Cry1Ab concentration of pollen of MON810 and Bt176 is an average value, which Perry et al. seemingly derived from four papers (but three of them refer to the same source, the US Environmental Protection Agency).

However, Cry1Ab content in pollen varies substantially even within the same event depending on the year, the site, the plant and possibly the cultivar [7].

For example, Cry1Ab concentration has been recorded to be as low as 389 ng g21 in Bt176 pollen and as high as 97 ng g21 in MON810 pollen, which is only a four-fold difference [8].


Third, Perry et al. claim that larvae of the red admiral, Vanessa atalanta, are equally susceptible to Cry1Ab as those of Inachis io.

The evidence cited [9] did not test or report Cry1Ab susceptibility of V. atalanta, and indeed, no toxicological tests with Cry1Ab are published for this species so far.

Susceptibility to Bt can vary greatly among lepidopteran species, e.g. 10-fold between close relatives within a genus, making a prediction of the sensitivity to Bt for any given species difficult [3,10], and extrapolating model data from one species to another may be inappropriate [11,12].


Fourth, the values for mortality incorporated into the model are likely to underestimate the real values owing to two experimental limitations in the studies used: Felke & Langenbruch [3] and Felke et al. [ 4] exposed the lepidopteran larvae to Bt maize pollen for 2 days and terminated the trials after 7 days.

Exposure in the field is likely to last longer as maize fields shed pollen over a prolonged period, on average 8 days or even longer [13].

Also, the larvae will continue to suffer lethal and sublethal effects in later stages (older than 7 days) owing to long-term effects following a short acute dose of Cry1Ab [6].

Both will result in lower LC/EC50 values than applied in the model.

Several other conditions of the above laboratory studies also do not reflect realistic field situations, which have the potential to underestimate a Cry1Ab effect [2], e.g. using cut leaf disks instead of whole host plants, providing ample food to the larvae, keeping larvae under favourable abiotic conditions and excluding multiple environmental stressors exacerbating a Bt effect, e.g. bacterial infections of lepidopteran larvae [14].


Acute toxicity may not be a reliable predictor of sub-lethal effects [15].

Owing to lack of data, Perry et al. assume sublethality rate to be four times the mortality rate.

The justification of this assumed relationship remains to be clarified as the papers ‘broadly consistent’ with this assumption [4,6] do not allow deriving such a ratio of sub-lethal effects to mortality.

For example, a Bt maize pollen density of 18.7 grains cm22 caused a significant average reduction in body mass of caterpillars of the common swallowtail (Papilio machaon), while the same pollen density caused no increased mortality [6].

The notion of the authors that the reduction of larval body mass would be representative for all other types of sublethal parameters seems to lack conclusive support.

Especially, effects on fecundity can be as important as effects on survival [16].


Any Cry1Ab effect will be moderated in the field by variation of exposure.

However, all publications cited in Perry et al. in support of a possible reduction in exposure through behaviour of the larvae refer to Danaus plexippus, the American monarch butterfly, but none to the three modelled European species which show different (feeding) behaviours.

Likewise, several other parameters were also set by personal (unreferenced) estimates.

These include the proportion and density of host plants in and near maize fields, or the temporal overlap of larval occurrence and Bt-maize cultivation (see parameters in table 1 of Perry et al. [ 1]).


Perry et al. provided the first model study for Bt-maize effects on Lepidoptera.

The assessment of such effects is complex, thus modelling approaches are welcomed.

Model studies are especially helpful to better identify and understand complex interactions of key parameters and basic processes.


However, risk assessment of genetically modified organisms is a sensitive area, and any quantitative conclusions should be drawn and published with greatest care, as these could have significant policy and regulatory implications.


As specified above, the claim of Perry et al. to have simulated the worst-case scenario must be challenged.

Moreover, their model study involves considerable uncertainty in values of key ingredients, and their chosen approach is not sufficiently cautious to reflect this uncertainty.


A full uncertainty/sensitivity analysis would need to be performed before making detailed quantitative predictions, and certainly for predictions outside the original application.


We respond to the Comment of Lang et al. [ 1] regarding our mathematical model [2] of exposure of non-target Lepidoptera to Bt-maize pollen expressing Cry1Ab within Europe.

Lang et al. remark on the degree to which the model was subject to uncertainty.

Perry et al. [ 2] did indeed emphasize precaution: they made four separate decisions to model worst-case scenarios; identified six distinct sources of variability to which their results might be sensitive; and emphasized six different bases for the uncertainty of predictions.


Lang et al. rightly emphasize the importance of identifying to which parameters the results of a model are most sensitive; Perry et al. should perhaps have emphasized more that the parameter to which their estimates of mortality were most sensitive was undoubtedly the variable measuring the rate of change of mortality with concentration/dose of the Cry1Ab protein (i.e. the slope of the probit/logit regression line; see below).


Regarding the relationship between the toxicity of MON810 and Bt176 pollen, Lang et al. imply that the relationship between mortality and dose may be nonlinear.

Regressions from bioassay should always be checked for nonlinearity, but there was no evidence of this in any of the extensive number of regressions of Saeglitz et al. [ 3,4] upon which our slope estimate was based.

Of course, the standard transformation of mortality to probits (or logits) and the logarithmic transformation of concentration [5,6] are designed to achieve a linear regression; both papers cited by Lang et al. use this method.

Data from one of these papers [7] were tested for nonlinearity; none was found, and no disproportionately higher mortality at low Cry1Ab concentrations could be verified (figure 1).


Lang et al. are correct that Perry et al. used the range of published [8,9] values (12.2 – 78.9) to derive an average of the ratio of the concentration of the Cry1Ab protein expressed in pollen of maize Bt176 relative to that in maize MON810 for which a 31.05-fold difference was assumed.

This is already likely to be a worst-case underestimate because, as Sears et al. [ 8] noted, the value of expression for MON810 was near the current level of detection by immunoassay.


We do not agree with Lang et al. ’s interpretation of the data from Nguyen [10]: they compared the smallest Bt176 value from 2002 to the largest MON810 value from 2003.

The within-year ratio of Bt176 : MON810 was 64.8 in 2002 and 30.5 in 2003.

The latter value, very close to that adopted by Perry et al., leads to larger mortality estimates for MON810 than the former, a further example of Perry et al. ’s use of worst-case scenarios.


Figure 2 shows the importance of the distinction between the intercept and slope of the probit/logit line in this issue.

The conclusions of the model for risk management depend on the degree of estimated mortality.

The conclusions are clearly highly sensitive to assumptions concerning the slope.

They are sensitive, but much less so, to the intercept; it is governed by the Bt176 : MON810 ratio.

Figure 2 demonstrates the effect of choosing an alternative worst-case value of the ratio (12.2) at the end of the range.


Regarding the assumption of equal susceptibility for the butterflies Inachis io and Vanessa atalanta, we apologize for the incorrect citation given by Perry et al. We acknowledge that data to compare the sensitivities of I. io and V. atalanta are very limited.

We are aware of no evidence that the sensitivities differ; unpublished data from a single field experiment appear to suggest that they may not.

Both Perry et al. and Lang et al. remarked on the need for further data on European Lepidoptera of conservation concern.


Lang et al. argue that the experimental methodology of Felke et al. [ 11] was likely to give results that underestimated true mortality.

However, in many experiments with MON810, larvae have been exposed to longer time periods than those of Felke et al. [ 11]: 10 days [12]; 14 – 22 days [13]; 7 days [14]; 10 – 14 days [15].


Lang et al. claim that fig. 1 of [7] demonstrates long-term effects (longer than 7 days) following a short acute dose of Bt.

However, comparison of treatment and control in that figure appears to contradict rather than support their claim.


In the period between 7 and 27 days, the mortality for a dose of 2.5 mg of Bt is only marginally (approx. 2%) greater than that of the control, while the mortalities for all other doses (1, 5, 7.5, 10, 20, 30 mg) are at least 5 per cent less than that of the control.


We fully agree with Lang et al. that sublethal effects should encompass fecundity and other parameters.

However, many studies neglect to test parameters other than larval or pupal weight (e.g. [7]).

Perry et al. emphasized that ‘our methods are subject to considerable uncertainty’, a caveat repeated in the final sentence of the Discussion.


Lang et al. stated that ‘all publications cited in Perry et al. in support of a possible reduction in exposure through behaviour of the larvae refer to Danaus plexippus’, but have perhaps overlooked Perry et al. ’s text: ‘Both species [V. atalanta and I. io] are somewhat protected from pollen deposition; the former species creates “leaf bags”, the latter builds webs (e.g. [17])’.

We agree that the extent to which exposure is reduced through such behaviour is variable but it is surely not contentious to state that there is evidence for this for neonate larvae within Europe (see e.g. http://tristram.squarespace.com/home/2009/6/9/peacock-butterfly-caterpillars.html).


Regarding sensitivity analysis, it is important to allow for the fact that depositions of pollen in the field occur at far lesser concentrations than the LC50s for the three species considered by Perry et al. In consequence, as shown in figure 2, differing assumptions for the slope of the assumed probit (or logit) line will have little effect on the results for concentrations close to the LC50, but result in very large differences at concentrations around those expected within the crop or in the margin.

Within the crop, the estimated mortality using the slope estimated by Felke et al. [ 11] is vanishingly small and such a value would be impossible to measure in field conditions.

Even a doubling of the Saeglitz et al. [ 3,4] slope of close to 1.1 to the moderately small average 2.25 estimated by Farino´s et al. [ 18] would result in roughly 10-fold decrease in estimated mortality.


It is for these reasons that we consider that the consequence of this sensitivity completely outweighs any of the several effects claimed by Lang et al. to engender uncertainty and affect mortality estimates.

We regard them as minor compared with the ‘safety margin’ factor of 8 x 10^-7 by which Perry et al. inflated the estimated mortality through deliberate choice of a small value of the logit slope, designed to give worst-case mortality.


The value of the Perry et al. model is that it provides a transparent, structured and simple approach to exposure analysis that may be followed for other species and taxa if sufficient data become available.

Further, in other settings, it offers the opportunity for accurate estimation of mortality – dose relationships to supplement relatively inaccurate determinations of mortality in the field.

We agree with Lang et al. that species’ sensitivity to particular GM events that express different forms of Cry1 proteins is an important determinant of mortality; also that further data would be welcome on the mortality – dose relationships (particularly regarding the slopes) for a range of species, especially those of conservation concern.

However, we disagree that we have been incautious regarding the implications of our results for conclusions regarding regulatory policy.

We therefore reaffirm the robustness of our conclusion from our model that, after accounting for large-scale exposure effects, the ‘estimated environmental impact of MON810 pollen on non-target Lepidoptera is low’.


We describe the clinical progression in this unique case supplemented with video and imaging findings in the form of magnetic resonance imaging and brain single photon emission computed tomography.

Final diagnosis is via pathological analysis at autopsy.


We present an elderly gentleman who manifested a clinical syndrome consisting of subacute onset of chorea that at presentation was distinctly unilateral and a frontal behavioral syndrome in the setting of mild thrombocytopenia and elevated anticardiolipin antibodies.

Positive antiphospholipid antibodies resulted in an initial antemortem diagnosis of primary antiphospholipid syndrome as a cause of his chorea.

Longitudinal follow-up over 5 years demonstrated a progression of clinical features with hemi-motor impersistence/chorea, disinhibition and impulsivity, and eventually corticospinal distribution weakness on the initially affected side.

He required nursing home care and falls necessitated wheelchair use.

Postmortem neuropathological study revealed a diagnosis of frontotemporal lobar degeneration-tau, PSP-PNLA.


This case broadens the phenotype of PSP-PNLA and to our knowledge is the only case presenting with unilateral chorea.


Pathologically, progressive supranuclear palsy (PSP) is considered a tauopathy because of the presence of abnormally phosphory- lated tau in the neurons and glia in subcortical and cortical areas.

1 The substantia nigra, subthalamic nucleus, and globus pallidus consistently contain tau pathology.

PSP-pallido-nigro-luysian atrophy (PNLA) is a rare neurodegenerative disease defined by bilateral degeneration of these same structures.

Although PNLA and PSP share prominent degeneration of the pallidonigroluysian region, PNLA has less widespread degeneration, few or absent neurofibrillary changes, absent or rare tufted astrocytes, and fewer/shorter argyrophilic threads compared with PSP.

Thus, important pathological differences exist.


There have been cases described that fulfill the criteria for both PSP and PNLA, and these are called PSP-PNLA.


Pathologically the same types of lesions are observed in PSP and PSP-PNLA but with differences in the distribution and density of tau pathology, with less tau in the motor cortex, striatum, pontine nuclei, and cerebellum in PSP-PNLA.

2 Clinical presentation of PSP-PNLA includes a younger onset age and longer disease duration than patients with PSP (57.1 AE 3.1 vs. 65.5 AE 1.9 years of age at onset, respectively, and disease durations of 11.5 AE 1.6 vs. 7.6 AE 0.5 years, respectively), 2 and with early handwriting and gait abnormalities, such as freez- ing.

Falls, dysphagia, and vertical gaze palsy were late features of PSP-PNLA in contrast to PSP-Richardson syndrome in which these clinical findings are pathognomonic and occur early on.

Although cases of PSP-PNLA are extremely rare and there is limited clinical information available, uniformly all cases are clini- cally characterized by akinesia and parkinsonism.


We describe a case of PSP-PNLA confirmed pathologically with a clinical presentation of hemichorea/ballism, spasticity, progressive hemiparesis, and a frontal behavioral syndrome with relative cognitive sparing early on.

This clinical presentation in the setting of laboratory findings resulted in an antemortem diag- nosis of primary antiphospholipid syndrome (aPS).

Longitudinal clinical evaluation and laboratory, neuropsychological, and neu- roimaging investigations serve to document this rare presentation of PSP-PNLA.


A 79-year-old right-handed Palestinian man developed 1 year of right-hand clumsiness and involuntary movements.

He noted difficulties including writing and dropping objects from his right hand.

He experienced falls, tripping over his cane with the right leg.

Involuntary, low-amplitude movements of his right arm and leg presented insidiously.

There were word-finding difficulties, but he continued to work as an international consultant.


His previous medical history included coronary artery disease, hypertension, hypercholesterolemia, and lumbar degenerative disc disease.

He had no history of autoimmune conditions, can- cer, or nonatherosclerotic thrombotic events.

There was no exposure to antipsychotics or antiemetics.

There was no relevant family history.


Vital signs and general examination were normal.

Mini- Mental State Examination was 28/30.

He behaved inappropri- ately, talking loudly and seeming overly familiar; he touched the examiner's shoes because he "liked them." He was short- tempered with his wife.

This was out of character according to the family.


Extraocular movements were full with saccadic pur- suit.

There was occasional blinking with horizontal saccades.

His face was symmetric, and the tongue was midline with no motor impersistence or orobuccal dyskinesia.

There was a positive gla- bellar tap and bilateral palmomental reflexes.

Muscle bulk, tone, and power were normal.

Reflexes were exaggerated in the right arm and leg (3+) and normal on the left (2+), with bilateral flexor plantar responses.

There was no ataxia.

Primary and sec- ondary sensory modalities were intact.


Chorea was present at rest and increased with action in the right arm and leg, sometimes with a ballistic quality.

He easily stood up from a chair and had a narrow gait with chorea in his right hand and leg while walking.

The chorea in his leg sometimes impaired his walking and bal- ance (Video 1, segment S1).


The following year, chorea increased in amplitude but remained unilateral.

He became more irritable, impulsive, and unpredictable in his behavior, for example, tipping a musician excessively and increased interest in sexual activity with his wife discordant with her wishes.

There was no paranoia, hallucina- tions, or delusions.

Cognitively, his family noted distractibility and short-term memory problems.

However, he remained intact in his instrumental activities of daily living.

Efforts to reduce cho- rea were unsuccessful: tetrabenazine caused sedation, and risperi- done did not help.


He scored within normal limits on the Mattis Dementia Rat- ing Scale, although a formal neuropsychological assessment rev- ealed mild to moderate impairment in areas of speeded information processing, verbal working memory, complex visuo- spatial construction, effortful encoding, and retrieval of verbal information, suggesting frontosubcortical and mild left temporal dysfunction.

Frontal Behavioral Inventory score was 33 (cutoff score ≥ 30).


Initial investigations including brain magnetic resonance imag- ing revealed mild generalized atrophy and microangiopathic changes consistent with age, but with no strokes or other pathol- ogy in the subthalamic nuclei or basal ganglia to account for the hemichorea (Fig. 1).

Blood work, including electrolytes, ESR, AST/ALT, B12, ANA, CPK, C3/C4, INR/PTT, and thyroid functions, was normal.


Further investigations were pursued, including brain single photon emission computed tomography showing increased perfusion in the basal ganglia bilaterally com- pared with matched normal controls (patient vs. control average percentage increase: left, +12%; right, +18%; see Fig 1E).

Serum paraneoplastic antibody panel was negative.

Huntington's disease and SCA 1, 2, 3, 6, 7, 8, and 17 genetic testing were negative.

CBC was normal except for thrombocytopenia (platelets 120).

Lupus anticoagulant was negative; however, anticardiolipin immunoglobulin G (IgG) antibody level was 54 (repeated 3 months later = 66; normal, 0-18), with an immunoglobulin M (IgM) value of 33 (repeated 3 months later = 18; normal, 0-10).

Because of these results in conjunction with his clinical presentation, he was reviewed by a rheumatologist who suggested a diagnosis of aPS.


He received a 3-day course of methylprednisolone 1000 mg intravenously daily followed by longer term immunosuppression with methotrexate.

Despite improvement in his antibody param- eters, he continued to progress clinically.


He had motor imper- sistence of the right arm resulting in difficulty maintaining any posture in addition to choreic/ballistic movements exacerbated with action.

There was truncal chorea and subtle left leg chorea with action (Video 1, segment S1).

Reflexes were exaggerated bilaterally (right > left), and gait was unsteady with lunging and ballistic movements.


A neurodegenerative disorder was suspected.

Given the asymmetry at presentation, atypical cor- ticobasal syndrome was considered, but he did not meet the clin- ical criteria.

Apraxia was hard to assess given the chorea, but he lacked any cortical sensory loss, and his neuropsychological assessment was also not consistent with that diagnosis.


Symptom- atic treatment with flupenthixol depot at 10 mg intramuscularly every 3 weeks was added to both dampen movements and improve behavior.

This reduced the chorea; however, he contin- ued to have progressive loss of use of his right arm with associ- ated 4+ corticospinal distribution weakness and superimposed motor impersistence.


Treatment with intravenous immunoglob- ulin at a dose of 1 g/kg/day for 2 days was instituted without benefit.


During the next 3 years he became progressively disabled with spastic right hemiplegia and increasing chorea/ballism on his left.

He was admitted to a nursing home, had frequent falls, and became wheelchair bound.

He became dysarthric to the point of incomprehensibility, and in the last months of life he had vertical gaze palsy and severe pseudobulbar dysfunction.

He died of sepsis secondary to an infected sacral ulcer, approxi- mately 5 years after symptom onset.

His brain was submitted for postmortem examination after informed consent was obtained.


The brain was examined and sampled for microscopy according to a standard neurodegenerative neuropathology blocking proto- col.

Formalin-fixed, paraffin-embedded blocks were cut into 4-μm thick sections, mounted on glass slides, and stained with hematoxylin-eosin/luxol fast blue, Gallyas, and the immunohis- tochemistry antibodies listed in Table 1.


The brain weighed 1040 grams.

There was mild symmetrical atrophy of the frontal and temporal lobes bilaterally.

The mid- brain and pons were small in caliber.

There was mild dark discol- oration of the putamen and nucleus of Luys bilaterally, and the substantia nigra appeared pale (Fig. 2A).


Microscopic examination revealed a unique glioneuronal tauopathy with atrophy of the corticospinal tracts (left greater than right; Fig. 2F) and subthalamic nuclei and sparing of the inferior olivary and dentate nuclei.

The tau-positive inclusions strongly stained with Gallyas and were predominantly glial and cytoplasmic; the oligodendroglial inclusions were mostly coiled bodies, and the astrocytic inclusions often had a "bushy" mor- phology.

Neurofibrillary tangles were a relatively minor component.


Examination of the deep gray structures showed numerous oligodendroglial tau-positive inclusions, less frequent bushy astrocytic tau-positive inclusions, no tufted astrocytes, some neu- ronal cytoplasmic tau-positive inclusions in the putamen (Fig. 2D), a normal anterior striatum, neuronal dropout and gliosis of the pallidum, and severe neuronal loss, vacuolation, glial inclusions, axonal spheroids (Fig. 2C, red arrows) and iron deposition in the subthalamic nuclei (Fig. 2B,C).

The substantia nigra showed neuronal loss, gliosis, and coiled bodies on tau immunolabeling.

The remainder of the brainstem and cerebel- lum appeared normal without tau inclusions, including the olivary and dentate nuclei.


Neocortical pathology was mild and included slight cortical neuronal loss with scattered bushy tau- immunopositive astrocytic inclusions (Fig. 2E).

Only rare cortical ballooned neurons were present, and no glial plaques were noted.

In the amygdala and hippocampus, there were neuronal pretangles and glial tau-positive inclusions.

There was no evi- dence of hippocampal sclerosis.


Transactive response DNA bind- ing protein 43 kDa (TDP-43), α-synuclein, and β-amyloid staining were negative, and p62 highlighted the tau pathology only.

There was no significant cerebrovascular pathology includ- ing lacunes or thrombotic microangiopathy.


This case features a man with a frontal behavioral syndrome and hemichorea in the setting of positive antiphospholipid antibodies that resulted in an initial diagnosis of aPS, although subsequent clinical evolution led to the suspicion of an atypical neurodegenerative disorder, ultimately confirmed at postmortem study to be PSP-PNLA.


Chorea, particularly unilateral, without an obvious explana- tion is exceedingly rare.

3 The working diagnosis of aPS was rea- sonable considering the clinical presentation and laboratory findings.

aPS is a rare but well-established cause of chorea,


Postmortem neuropathological study revealed atrophy and tau immunopathology in the pallidum, substantia nigra, and most severely, the subthalamic nuclei, with unilateral corticospinal tract degeneration.

In contrast, the areas usually found to be affected in conventional PSP such as the inferior olives and den- tate nuclei were largely spared, and tufted astrocytes, a feature commonly found in PSP, were mostly absent.

PSP-PNLA has pathological findings with a similar distribution to our case.

However, of the published cases of PSP-PNLA, none presented with unilateral chorea to our knowledge.

Wong et al 5 describe the case of a 70-year-old man who presented with slurred speech and went on to develop gait difficulties and left-sided chorea.

At autopsy, this patient was found to have right-predominant degeneration in the globus pallidus and subthalamic nucleus.

TDP-43 staining was positive in the hippocampus, amygdala, cerebellum, and frontoparietal cortices.

Immunohistochemistry revealed p62-positive, tau-negative, thread-like profiles in glial cells.

There were no tau-positive glial inclusions, and the patient was diagnosed with PNLA.

Chorea is described in PNLA and can be seen in up to 20% of cases, but it is a relatively uncom- mon symptom in adult neurodegenerative diseases and when present should raise the question of PNLA or dentatorubral- pallidoluysian atrophy.


Despite the presence of nigral pathology, our case had no parkinso- nian features as described in PSP and PNLA.

Supranuclear gaze and pseudobulbar palsies, clues to the PSP phenotype, only appeared near the end when neurological functions were severely impaired.

The prominent degeneration and tauopathy affecting the subthalamic nucleus (STN) likely accounted for the chorea/ballism.

The relative sparing of the dentate and olives, the dominant "bushy" morphology to the astrocytic inclusions, and corticospinal tract degeneration are atypical of PSP.

7 This correlated poorly with the clinical phenotype marked by hemichorea, hemiparesis, and lack of parkinsonism.

Some of the observed pathologic findings are reminiscent of those described in globular glial tauopathies (GGT), 8 including the involvement of the corticospinal tract.

8,9 However, the present case contained more abundant coiled bodies then usually encountered in GGT, and some astrocytic inclusions stained positively with Gallyas.


The laboratory findings of aPS were a red herring.

Anti-immu- noglobulin-like cell adhesion molecule 5 (IgLON5) antibodies were not described at the time of this case and so are unavailable, but the neuropathology is not consistent with this condition.

10 Other diagnoses that have a similar tau burden and distribution to our case include some cases of frontotemporal dementia- parkinsonism linked to chromosome 17 associated with MAPT gene mutations (FTDP-17T).

Microtubule-associated protein tau (MAPT) mutation screening was negative in our patient, although he had the MAPT H1/H1 genotype, a common genetic risk factor for PSP and corticobasal degeneration.

11 This case broadens the phenotype of PSP-PNLA and is the only case to our knowledge presenting with unilateral chorea.


(1) Manuscript Preparation: A. Writing of the First Draft, B. Review and Critique.


This work was embedded within the Sunnybrook Dementia Study (SDS) (ClinicalTrials.

gov: NCT01800214) - a prospective observational study of patients with neurodegenerative dementia and movement disor- ders.

It is approved by the local Research Ethics Board at Sun- nybrook Health Sciences Centre and written informed consent was obtained from the participant and their surrogate caregivers according to the Declaration of Helsinki.

A separate written con- sent was also obtained from the patient for the purposes of case report preparation.

All authors have read and complied with the Journal's Ethical Publication Guidelines.

We confirm that we have read the Journal's position on issues involved in ethical pub- lication and affirm that this work is consistent with those guidelines.


No specific funding was received for this work.

The authors declare that there are no conflicts of interest relevant to this work.

Financial Disclosures for the Previous 12 Months: The authors declare that there are no additional disclosures to report.


I read with interest Galit Kleiner and colleagues’ report of a 79-year-old man suffering from progressive hemichorea, spasticity, and a frontal behavioral syndrome, with thrombocytopenia and positive antiphospholipid antibodies [1].

Rationally, antiphospholipid antibody syndrome (aPS) was the antemortem diagnosis.

However, brain autopsy revealed a pattern of tau aggregation diagnostic of the pallidonigroluysian atrophy (PNLA) variant of progressive supranuclear palsy (PSP).

The authors concluded that aPS must have been a red herring.


Is neuropathology always the final diagnosis? If so, was chorea also a red herring? Will any entity ever cease to expand if held to a pathology gold standard? Will the differential between hyper- and hypokinetic movement disorders remain negotiable pending a pathology report?


As a clinician, I struggle with these questions.

We recently evaluated a 60-year-old woman with a 7-year history of a progressive ataxic-dystonia syndrome, an unusual jerky head tremor, and supranuclear vertical gaze palsy we suspected to be Niemann-Pick C [2].

There were no mutations in NPC1 or NPC2.

To our surprise, the neuropathological evaluation revealed tau deposition in a pattern diagnostic of PSP.

As we considered reporting this case as an “expansion of PSP phenotype,” genetic studies demonstrated a pathogenic mutation in TGM6, which codes for transglutaminase 6 (TG6).

TG6 has a role in polyaminating proteins and fostering isopeptide bonds between them, supporting microtubular stability [3].

With low TG6, tau crosslinking is affected and axon microtubules become compromised.

Plausibly, this enzymatic deficiency affecting the structure of tau could have caused this patient’s disease; the 4-repeat tau deposition was its consequence.


A similar dilemma is at play in Kleiner et al. ’s case.

As anticardiolipin antibodies can be elevated among healthy elderly and microangiopathy was absent on autopsy, aCL may have been a red herring.

On the other hand, aCL was associated with thrombocytopenia, raising the odds for an underlying autoimmune disorder [4].

Advances in immunology have revealed disorders never before considered antibody-mediated, such as “atypical PSP” due to anti-immunoglobulin-like cell adhesion molecule 5 (IgLON5) disease.

Anti-IgLON5 antibodies disrupt the neuronal cytoskeleton and induce neurodegeneration.

The authors argue that IgLON5 antibodies were unlikely because of insufficiently supportive pathology.

Even then, would it not be reasonable to speculate that an autoantibody could have caused (or contributed to) the tau deposition in a man with aPS and thrombocytopenia?


Toward supporting the notion that pathology is a secondary expression of many diseases, Kleiner et al. ’s case exhibited nigral pathology without parkinsonism.

The authors helpfully referenced a previously reported case (Wong et al. [ 5]) reminiscent of theirs: progressive hemichorea and gait difficulties with autopsy-diagnosed PNLA, although with TDP-43 instead of tau pathology.

Hemichorea, then, could be thought of as a non-specific manifestation of a PNLA pattern of degeneration, regardless of the associated proteinopathy.


The value of clinicopathologic correlations notwithstanding, the weight of human observations suggests that protein aggregation is not the epicenter of neurotoxicity but a reactive phenomenon to a broad range of cellular pathobiologies [5].

If we continue to enshrine pathology as the final diagnosis, would not we continue to find paradoxes, red herrings, and “expansions of the phenotype”?


We read with interest Dr. Espay’s viewpoint regarding the place of pathology in defining disease [1].

He describes a fascinating case challenging how we ultimately classify disease—a patient with a progressive dystonia-ataxia syndrome and a supranuclear gaze palsy who fulfilled pathological diagnostic criteria for PSP, but was shown to harbor a variant in the TGM6 gene that they postulate ultimately led to the tau pathology [2].

Medicine, and the way in which we come to a medical diagnosis, is ever evolving.

As a neurologist one typically starts by localizing the clinical problem, and then considers pathologies known to affect this area.

The available paraclinical tests, antibodies and genetic analyses are ever growing.

Which of these gets the ultimate say on the diagnosis is subject to revision as knowledge expands.


Our case demonstrated clear clinical-pathological correlation over a decade of follow-up [3].

The right sided chorea correlated with pathological findings in the STN.

The Wallerian degeneration and atrophy of the left > right corticospinal tract correlated with the eventual development of right hemiplegia.

Both clinical presentation and pathology are relevant to nosology.

Dr Espay asks: could an autoantibody have caused or contributed to the visualized tau deposition in a man with aPL antibodies and thrombocytopenia?

Indeed, given the chorea and antiphospholipid (aPL) antibodies without an obvious alternate cause, we elected to treat empirically with pulsed IV steroids, IVIG and methotrexate.

The aPL antibodies and thrombocytopenia normalized but there was ongoing neurological progression, making a causal relationship unlikely.

Furthermore, at autopsy there was no microangiopathy that might have been expected had there been a causal relationship and we know that aPL antibodies are common in the general population [4].


Anti-IgLON5 related tauopathy is a fascinating disorder that makes us think about correlations between neuroinflammation, neurodegeneration and genetics.

This condition has another expanding clinical phenotype, and emerging pathological correlates and genetic associations.

As testing for this antibody was not available when treating our patient, we cannot refute that this as-yet-unknown antibody potentially existed.

There are different pathological distributions of tau between PSP-PNLA and anti-IgLON5 cases [5], both the pathology and its distribution are worthy of consideration.

Furthermore, sequencing of the MAPT gene did not reveal any pathological mutations.

However, he was found to have the MAPT H1/H1 genotype, a known and common genetic risk factor for PSP pathology.


Reporting novel clinical presentations with pathological findings on autopsy remains worthwhile.

Consider how we classify dementias, one could group dementias based on site of lesion, histopathology, clinical features, or etiology—reliance on one alone would cause error but an integrated nosology brings both clinical and experimental benefits [6].

As our understanding of disease pathways evolves, so our nosology will have to also.

We thank Dr Espay for his thoughtful comments.


Information processing is optimized by predicting not only the location or identity of upcom- ing events but also their timing and allocating resources accordingly [1-6].

Understanding the mechanisms of temporal prediction is thus central for models of cognitive and neural process- ing.

One ubiquitous source for temporal predictions is regularity in the stimulus stream, mainly when events appear rhythmically, as in speech, music, or biological motion [3,4,6-9].

It has been suggested that in such cases, a costly "vigilance mode" is replaced by a metabolically efficient "rhythmic mode" by entraining low-frequency neural oscillations to the external rhythm [10,11].

In this study, we tease apart the neural manifestations of oscillatory entrain- ment from rhythm-independent mechanisms of temporal predictions.

This is done by com- paring temporal prediction based on rhythms to predictions based on memory, a critical comparison that was so far missing.


Neural excitability fluctuates in low frequencies from sub-delta to alpha [12,13], with appar- ent optimal phases for performance in these frequencies, as indicated by findings of an associa- tion between phase and performance [3,7,14,15].

According to entrainment models, such endogenous oscillations can be entrained (i.e., undergo period correction and phase align- ment) to an external rhythmic stream such that optimal phases align with on-beat times of rhythmic stimuli.

Consistent with this idea, behavioral performance is facilitated for rhythm on-beat compared to off-beat times [9,16-20].

In addition, electrophysiological recordings found that forming temporal predictions based on rhythmic streams is associated with phase concentration (PC) of delta-band activity in task-relevant sensory regions [6,21,22], such that the optimal phase for performance is observed at the time of rhythmic stimuli [7] (for a review see [23]).

This pattern is consistent with entrainment and is often treated as a direct indicator for this mechanism (e.g., [3,4,7,21,22,24].


Arguably, entrainment should be most applicable and efficient when the sequence is periodic.

Conversely, entrainment should become less applicable and less efficient as the stimulus stream becomes less periodic (i.e., the stream inter- vals are more jittered), because stable alignment of endogenous oscillations with the stream is not possible.

However, temporal predictions can also be formed without periodicity, or any continuous stimulus stream, when the interval between an expected event and a preceding event is known based on previous experience [1,25,26] (for a review see [27]).

Such predictions presumably operate through dedicated interval-timing circuits [28,29], forming temporal memory traces to which new intervals are compared [30,31].

This mechanism can operate regardless of the periodicity of the stimulus stream as long as there is knowledge regarding the temporal contin- gency.


This raises the question whether temporal predictions in rhythmic scenarios are medi- ated by oscillatory entrainment, by "memory-based" mechanisms based on the repeated exposure to the interval [32], or by both.

Recent findings in a speeded discrimination task sug- gest that while any type of temporal prediction improves perceptual accuracy, only rhythms facilitate response speed, seemingly in line with the latter alternative [33].

However, other studies found that response speed can also be facilitated by memory-based predictions [25,26,34].


To date, no study has directly compared the neural expressions of these two prediction modes, namely, memory-based and rhythm\entrainment-based.

Electrophysiological studies have associated rhythm-based predictions with phase adjustments of delta-band activity [3,6,7,21,22] and of the envelope of alpha activity (amplitude reduction prior to on-beat times [34,35]).

However, as these were not tested in memory-based predictions, it is impossible to determine that they are unique to rhythm entrainment.

Rhythm-based predictions are also associated with the contingent negative variation (CNV) potential peaking at on-beat times and resolve immediately after [9,34] and with earlier latency of the P3 potential [8,36].

How- ever, separate studies found CNV and P3 effects for memory-based predictions as well [26,37,38], and in the absence of direct comparison, it is impossible to determine the unique contribution of rhythmicity to these effects.


In the current study, we identify the unique mechanisms of rhythm-based processing by directly comparing the behavioral and electroencephalogram (EEG) expressions of temporal predictions in a perfect periodic condition and in two less periodic conditions, one that enabled formation of memory-based temporal predictions and one that provided no temporal information for prediction.

We show that the patterns of delta-band PC, and also of alpha- band envelope and target-evoked event-related potentials (ERPs), are identical in the rhyth- mic- and memory-based conditions.

We also show that the delta PC pattern is inconsistent with a computational model of oscillatory entrainment, which predicts weaker entrainment for the less periodic stimulation of the memory-based condition.

However, we also find evi- dence for rhythm-unique neural patterns: violation of predictions results in immediate obliga- tory resource withdrawal and larger behavioral costs when rhythm-based, while memory- based predictions are more flexible, in line with the concept of resonance that is predicted by oscillatory entrainment.


To test the unique signatures of rhythm-based predictions, participants (n = 21) responded to targets that followed a sequence of visual stimuli and a warning signal (WS, Fig 1A) in three different conditions.

In the Rhythmic condition, the sequence was perfectly periodic (i.e., the sequence stimuli appeared with identical stimulus onset asynchrony [SOA]), and the target fol- lowing the WS had high probability to appear on-beat, making its timing predictable.

In the Repeated-Interval condition, every odd interval was identical to the SOA of the Rhythmic con- dition, whereas even intervals were jittered around a different interval duration and were never identical to the rhythm interval or an integer multiple of it.

To equate the information Mechanisms of rhythm- and memory-based temporal prediction PLOS Biology | DOI:10.1371/journal.pbio.2001665 February 10, 2017 3 / 30 PLOS Biology | DOI:10.1371/journal.pbio.2001665 February 10, 2017 4 / 30 provided by the Rhythmic and Repeated-Interval conditions, the number of repeated fixed SOA intervals in the Repeated-Interval was equal to the number of overall intervals in the Rhythmic condition.

The target had high probability to follow the WS at the interval of the fixed SOA, making its timing predictable.


In the Random condition, the sequence SOAs were jittered around the SOA of the Rhythmic condition, and the target SOA (interval between the WS onset and the target onset) was drawn from a wider distribution that was not centered on the rhythm SOA orthogonally to the sequence mean SOA.

Thus, the sequence provided no information regarding the timing of the upcoming target.

The Random and Repeated-Interval conditions were, therefore, much less periodic than the Rhythmic condition, as is indicated by (1) SOA variance: zero in the Rhythmic conditions, larger in the other conditions (Fig 1A); (2) frequency domain representation: strong amplitudes in the delta frequency in the Rhythmic condition (its base frequency and harmonics), lower amplitude in the other conditions (Fig 1B); and (3) autocorrelation function: high autocorrelation in the Rhythmic condition at equal lags, minimal autocorrelation in the other conditions (Fig 1C).

We surmised that because of this variability, low-frequency oscillations would be less efficiently entrained to the stimulus stream in the Random and Repeated-Interval conditions.

However, unlike the Random condi- tion, the Repeated-Interval condition enables accurate temporal prediction because of the repeated presentation of the WS-Target SOA.

Thus, any effects that are not different between the Rhythmic and Repeated-Interval conditions reflect temporal predictability, which is not driven by periodicity in the stream.


Our design allowed us to separately test two indicators of forming temporal predictions, namely, benefit and cost, using planned contrasts.

First, we could examine whether having a context affording temporal predictions leads to a benefit relative to an unpredictable context and whether the benefit is different between rhythm- and memory-based predictions.

For this purpose, we compared response times (RTs) of valid trials, in which the temporal predic- tion was fulfilled (i.e., targets appeared at the expected time), between the three experimental conditions (for analysis purposes, "valid" trials were defined in the Random condition as those in which cue and target SOAs were drawn from the same distributions; see Materials and methods for more details).

Second, we could examine whether violating a temporal pre- diction leads to a cost relative to an unpredictable context and whether the cost is different between rhythm- and memory-based predictions.

For this purpose, we compared RTs of invalid trials, in which targets appeared at an unexpected time, between the three experimen- tal conditions.


RTs to targets were first analyzed using an omnibus three-way ANOVA with factors condi- tion X target SOA X validity.

RTs were overall shorter to valid compared to invalid trials (F (1,20) = 76.37, p = 2.9 × 10 −8 , ω 2 p = 0.64, Fig 1D), but the validity effect differed across condi- tions (interaction F (2,40) = 18.69, p = 1.9 × 10 −6 , ω 2 p = 0.22).

For valid trials, RTs were faster rel- ative to the Random condition both in the Rhythmic (t (20) = 5.88, p = 9 × 10 −6 , Cohen's d = 1.28) and the Repeated-Interval (t (20) = 5.8, p = 1 × 10 −6 , d = 1.27) conditions with no differ- ence between the two (t (20) = 0.12, uncorrected p = 0.91, 95% confidence interval [CI] for the difference −6.23 to 6.99 ms).

Thus, while both rhythm-based and memory-based predictions lead to a benefit relative to no prediction, there is no advantage to one of them over the other.


Unlike for valid trials, responses to invalid trials were slower in the Rhythmic relative to the Repeated-Interval condition, (t (20) = 3.14, p = 0.005, d = 0.69).

Responses were also slower in weakest in the Repeated-Interval condition.

( C) Autocorrelation functions of the stimulus sequence in the three experimental conditions, calculated on the same modeled stimulus sequences that were used to create the spectral representations in B. The Rhythmic condition has high autocorrelation in fixed lags because of the perfect periodicity; the other conditions have relatively low autocorrelation at varying lags.

( D) Mean reaction times in each combination of SOA and cue validity in the three experimental conditions.

Error bars represent standard errors of the validity effect within each SOA and condition. * p < 0.05.

Mechanisms of rhythm- and memory-based temporal prediction PLOS Biology | DOI:10.1371/journal.pbio.2001665 February 10, 2017 5 / 30 the Rhythmic relative to the Random condition (t (20) = 1.88, p = 0.037, one-tailed, d = 0.41), while the Repeated-Interval condition did not differ from the Random condition (t (20) = 1.16, p = 0.26).

Thus, unlike their similar benefits, the cost of violation of rhythm-based prediction was larger than that of memory-based predictions.

Finally, there was a main effect of condition (F (2,40) = 13.01, p = 4.4 × 10 −5 , ω 2 p = 0.28) and a condition X SOA interaction (F (2,40) = 6.5, p = 0.0036, ω 2 p = 0.08).

This was because of faster responses to long than short SOA targets (foreperiod effect, [27]) only in the Random condition (F (1,20) = 8.04, p = 0.01, ω 2 p = 0.14).


The data of two participants were rejected from all EEG analyses because of excessive artifacts, leaving 19 datasets.

Analysis of ERPs during the presentation of the sequence was focused on the CNV, a negative deflection that builds up over fronto-central sites in the interval between a WS and an impending event and resolves (towards baseline) when the latter has occurred [39,40].

We first examined the predictive conditions to test whether a CNV is observed during the presentation of the stream prior to the WS, only after WS, or in both time ranges.


If the CNV reflects oscillatory entrainment, it could be expected to be elicited repeatedly in the Rhythmic condition during the entire stream.

We observed a typi- cal CNV after the WS in both the Rhythmic and Repeated-interval conditions (Fig 2A, top and middle rows, respectively) but no repeating CNV or any oscillatory pattern prior to the WS in the Rhythmic condition or the Repeated-Interval condition.

Furthermore, no CNV was observed when locking to the final pair prior to the WS in the Repeated-Interval condition (Fig 2A, bottom row), confirming that the lack of CNV was not due to averaging across the temporally jittered stimuli.


We repeated this analysis in an occipital electrode cluster to test for similar modulations or oscillatory pattern at the sensory level.

Notably, no oscillatory pattern or any repeating deflection was observed in occipital electrodes during any of the conditions (Fig 2B).

Instead, there were typical stimulus-evoked visual responses and a positive deflection following the WS that changed trajectory to a buildup of negativity till the time of the target.


Importantly, locking to the final pair prior to the WS in the Repeated-Interval condition revealed no trace of a slow oscillation elicited by the S1 or S2 stimuli of the sequence (Fig 2B, bottom row).

Thus, the CNV was not driven automatically by the rhythm when subjects were exposed to the stream and used it to form a temporal prediction but was only elicited when preparing for the target.


We compared two parameters of the CNV between the two sources of prediction: the rate of CNV buildup and its resolution after omission of a temporally expected event (Fig 2C).


First, the CNV characteristically builds up faster as the expected interval is shorter [9,34,38].

We tested whether rhythm-based temporal prediction is associated with a distinct modulation pattern of the CNV buildup compared to the modulation that is associated with memory- based temporal prediction.


To quantify this effect, we averaged the CNV amplitude in a time window preceding the short SOA target.

We expected to find more negative waveforms in this window when the target was expected in the short compared to long SOA both in the Rhyth- mic and Repeated-Interval conditions and compared this effect between conditions.


We found more negative amplitude of the CNV buildup when expecting the target at short compared to long SOAs in the Rhythmic (t (18) = 2.81, p = 0.012, d = 0.64) condition as well as in the Repeated-Interval (t (18) = 2.81, p = 0.012, d = 0.64) condition.


Importantly, there was no differ- ence between the effects of the expected SOA in the two conditions (interaction between the effects of SOA and condition: F (1,18) = 0.18, p = 0.68, 95% CI for the difference −1.46 to 2.19 μV, Fig 2D).


For illustration, we also present the Random condition, in which there was no temporal prediction, and, indeed, the SOA had no significant effect (t (18) = 0.36, p = 0.72).


Thus, the modulation of CNV buildup was affected by temporal predictions but did not show a rhythm-specific pattern.


Another characteristic of the CNV is that it resolves when an expected interval elapses even when the impending event is delayed [9,37,40].

In our data, this occurred in the Rhythmic and Repeated-Interval conditions when a target was expected at the short SOA but omitted (i.e., it unexpectedly appeared in the long SOA; Fig 3A).

We examined whether the CNV resolution is different, in magnitude or latency, when the temporal prediction is based on rhythm com- pared to when it is based on memory.

To quantify the CNV resolution, we fitted the CNV waveform (averaged across subjects) in the Rhythmic and Repeated-Interval conditions with a continuous model that included the latencies of buildup initiation, slope, and termination, and resolution initiation and slope as free parameters (Fig 3B).

This approach is superior to quanti- fying the CNV resolution in a predefined interval [9] or using fixed length bins [37], which could miss the actual latencies of different CNV stages and distort the estimated slope if the trajectory is not linear during the entire interval.


Comparison of the estimated parameters between the two predictive conditions revealed that the time of initiation (T 0 ), slope (S 1 ), and termination (T 1 ) of the CNV buildup and the slope of its resolution (S 2 ) were not different between the Rhythmic and Repeated-Interval Mechanisms of rhythm- and memory-based temporal prediction PLOS Biology | DOI:10.1371/journal.pbio.2001665 February 10, 2017 8 / 30 conditions (t (18) = 0.34, 0.37, 0.03, 1.62, respectively, all p > 0.05).

However, the start point of the CNV's return to baseline (T 2 ) occurred later in the Repeated-Interval (1,152 ms) than in the Rhythmic condition (767 ms, t (18) = 3.73, p = 0.0015, d = 0.86, Fig 3E and 3F).

Further- more, there was a significant gap between T 1 and T 2 in the Repeated-Interval condition (t (18) = 2.17, p = 0.043, d = 0.5) but not in the Rhythmic condition (t (18) = 0.09, p = 0.93).


To justify the use of this five-parameter model, we took a model-selection approach (using the Bayesian Information Criterion, BIC) to examine whether this model is preferred to an alter- native, less elaborate model in which the CNV resolution was immediate.

That is, this four- parameter model did not include a free parameter for a delay between the termination of the CNV buildup and the initiation of its resolution.

For each condition, the model with the lower BIC value was considered the preferred model.

In the Repeated-Interval condition, the five- parameter "resolution after delay" model turned out to be superior over an alternative four- parameter "immediate resolution" model (Fig 3D) with no delay (ΔBIC = 76).

However, in the Rhythmic condition, the four-parameter model was superior (ΔBIC = 7.3).

This indicates that the five-parameter model is indeed necessary, as it is preferred for the Repeated-Interval con- dition but also provides converging evidence to the difference between conditions, as it was not preferred for the Rhythmic condition.

Thus, in rhythmic streams, target omission trig- gered an immediate resolution of the CNV, while in memory-based temporal predictions, the initiation of the CNV resolution was delayed, presumably anticipating the upcoming event at the long SOA.


Previous studies found that the amplitude of alpha-band oscilla- tions (8-13 Hz) is reduced just prior to, or at the time of, an event that is temporally expected based on rhythms [9,34,35].

We tested whether this modulation of alpha-band amplitude is unique to rhythms or whether it is expressed to the same extent when the prediction is based on a memorized interval.

To dissociate the effect of expectation from response to the target we analyzed the alpha-band amplitude in the time window of the short SOA (700-900 ms after the WS) exclusively in trials in which the target appeared at the long SOA.

In this window, we expected to find in the Rhythmic condition lower alpha amplitude when expecting the target at the short compared to long SOA.

If this phenomenon is unique to rhythm-based predic- tions, the Repeated-Interval condition should have smaller, or even no modulation.


In our data, there were narrow-band amplitude modulations in the alpha range (Fig 4A).

The most pronounced amplitude modulation was a sharp decrease of amplitude of the alpha band following the WS, which then recovered, a typical response to visual stimuli (Fig 4B).

In the Rhythmic condition, when expecting a target at a short SOA, the recovery was blunted, resulting in lower amplitude in the short SOA window compared to when expecting a target at the long SOA (t (18) = 1.80, p = 0.044, d = 0.41).

This modulation was not explained by differ- ences in the average stream SOA, as it was not observed in the nonpredictable Random condi- tion, in which there was even a trend for higher alpha amplitude when expecting a target at the short SOA.

However, similar amplitude modulation of alpha-band activity was observed in the Repeated-Interval (t (18) = 1.82, p = 0.043, d = 0.42) condition, and an ANOVA conducted across the predictive conditions revealed no interaction between SOA and condition (F (1,18) = 0.14, p = 0.71) but only a main effect of SOA (F (1,18) = 5.23, p = 0.036).

There was also no main effect of condition (F (1,18) = 0.01, p = 0.92).

Thus, the reduction of alpha-band amplitude at the time of an expected event did not depend on whether the prediction was rhythm or memory based.


Previous studies reported increased PC in the delta band at the time of rhythm stimuli, such that the optimal phase for performance aligns with rhythmic stimuli times [3,6,7,21,22].

This effect was interpreted as reflecting entrainment of intrinsic oscillations to the rhythm.

Under this premise, PC should be stronger in the Rhythmic relative to the Random and Repeated-Interval conditions, since, in the latter two, the stream is less periodic and hence 9 / 30 less able to entrain oscillatory activity.

To test this, we extracted the phase of delta-band activity (0.5-3 Hz) from occipital electrodes at target time in the three experimental conditions and, as additional control, at the time of the WS in the Rhythmic condition.

The Random condition target was used as a control in which there was preparation for a target but no specific temporal prediction.

The WS in the Rhythmic condition was used as a control in which a task-relevant event (the WS) is temporally predictable, but there is no preparation to respond to it.


We first examined whether there is an optimal delta phase for performance, that is, a corre- lation between delta phase at target time and RTs.

Such correlation is essential for entrainment accounts because if performance is not associated with the phase of oscillations, there is no benefit from aligning the oscillation phase with expected times.


Plotting RTs as a function of delta phase across subjects revealed a circular-linear association in all conditions (sinusoidal instead of straight line, Fig 5A, compare with the much weaker correlation between RT and Mechanisms of rhythm- and memory-based temporal prediction PLOS Biology | DOI:10.1371/journal.pbio.2001665 February 10, 2017 11 / 30 delta phase at the time of the WS in the Rhythmic condition, S1 Fig and S1 Text).

Finding the phase-RT correlation in the Random condition is essential, as it indicates that this correlation is not merely driven by phase correction in the predictive conditions (Fig 5A, right panel).


To test this correlation across all subjects while allowing a different optimal angle for each subject, we used a linear mixed-effects model that predicted single trial RTs from delta phase (repre- sented as its sine and cosine) and target SOA in the Random condition (see S1 Text).

This analysis revealed a significant fixed effect for delta phase (w 2 ð2Þ ¼ 7:82, p = 0.02) but also a sig- nificant effect for the random slopes of delta phase (w 2 ð5Þ ¼ 15:16, p = 0.01), suggesting that delta phase was associated to RT with a different optimal phase for each subject.

Optimal phase did not differ between short and long SOAs (w 2 ð1Þ ¼ 0:06, p = 0.81).


Thus, the phase of delta activity at target time was associated with RT, in accordance with the idea that this activ- ity reflects fluctuations in efficiency of neural processing.

This also indicated that performance is optimal in selected portions of the delta cycle.


Next, we tested whether rhythm-based predictions lead to increased PC at the time of the tar- get relative to memory-based predictions or with no temporal information.

If delta PC reflects entrainment of low-frequency oscillations, the inter-trial phase coherence (ITPC) should be stronger in the Rhythmic condition compared to the other conditions.

For this purpose, we cal- culated the ITPC at target time (Fig 5B), and compared it between conditions using an omnibus permutation-based ANOVA that does not require the parametric assumptions of conventional ANOVA (see S1 Text).

This analysis revealed a main effect of condition (p < 0.01).

Planned contrasts revealed that the ITPC was increased in the Rhythmic condition compared to the Ran- dom condition (p < 0.01) and compared to the WS in the Rhythmic condition (p < 0.01).

How- ever, the ITPC was also increased in the Repeated-Interval condition compared to the Random condition (p < 0.01) and compared to the WS in the Rhythmic condition (p < 0.01).

Crucially, the ITPC did not differ between the Rhythmic and Repeated-Interval conditions (p > 0.5, CI for the ITPC difference: −0.061 to 0.035).

The ANOVA also revealed stronger ITPC for targets appearing at long compared to short SOA (p < 0.01) and a significant interaction between tar- get SOA and condition (p < 0.05).

Permutation-based t tests revealed that for short target SOA, there was a nonsignificant trend for the ITPC to be stronger for the Rhythmic compared to the Repeated-Interval condition (p = 0.1), while for long target SOA, the ITPC was in fact weaker for the Rhythmic compared to the Repeated-Interval condition (p < 0.01).

Thus, enhancement of delta PC was not unique to rhythm-based temporal predictions.


Finally, we examined whether the delta-band PC is around the optimal phase for perfor- mance.

It could be the case that memory-based predictions lead to increase in delta PC, but not towards the optimal angle, which would suggest that this PC reflects a different process than the one that takes place when predictions are based on rhythms.


First, we estimated the optimal phase for performance of each subject by averaging the phases of the fastest 33% of the trials in the Random condition (Fig 5C).

Next, we calculated for each subject in each condition the average phase at target time (Fig 5D).

If the phase at target time was shifted to the optimal angle, the difference between each subject's optimal phase and phase at target time should have an expected value of zero.


Fig 5E presents these differences for all subjects in each condi- tion, demonstrating a distribution around zero.

To quantify this, we used a circular statistical test, termed V test, which tests whether phases are unimodally distributed around an a priori specified angle (in contrast to uniformly distributed).

When using zero as the a priori angle, this test was significant in both short and long SOAs in the Rhythmic (short V = 14.04, long V = 15.54, all p < 0.01) and similarly in the Repeated-Interval (short V = 11.84, long V = 15.03, all p < 0.01) conditions.


Thus, the distribution of the circular distances between the phase angle at target time and the optimal phase for each subject was significantly concentrated 12 / 30 around zero, indicating alignment with the optimal phase in both predictive conditions (Fig 5C).

In summary, the delta PC and alignment with the optimal phase observed in the Rhythmic condition were observed at least to a similar extent in the Repeated-Interval condi- tion, implying that these effects were not unique to rhythms.


The P3 is a parietal-maximum ERP following target detection [41,42].

Its latency is shortened when target timing is expected compared to unexpected [26,34].

We examined whether this modulation differs between rhythm- and memory- based temporal pre- dictions, separately for short and long SOA targets (due to pretarget CNV-related effects, Fig 6A).

We used a jackknifing method (see S1 Text) to extract the latency in which the P3 reached 50% of its full amplitude and submitted them to a two-way ANOVA with factors Condition and Validity within each SOA.

As rhythm-based predictions lead to stronger behavioral valid- ity effect, it could have been expected that they will be reflected in a larger validity effect on the P3 latency.


Overall, the P3 latency was shorter for valid compared to invalid targets both for short SOA targets (F (1,18) = 24.57, p = 0.0001, Rhythmic: t (18) = 2.35, p = 0.03, Repeated-Inter- val: t (18) = 2.85, p = 0.01) and for long SOA targets (F (1,18) = 13.38, p = 0.0017, Rhythmic: t (18) = 2.57, p = 0.019, Repeated-Interval t (18) = 2.97, p = 0.008, Fig 6B).

Crucially, the validity effect did not differ between the Rhythmic and Repeated-Interval conditions (Validity X Condition interaction F (1,18) = 0.93, p = 0.35 and F (1,18) = 1.37, p = 0.26 for short and long SOA targets, Fig 6C).

There was also no main effect of condition in either target SOAs (both F's < 1, both p > 0.3).

Thus, the modulation of the P3 latency by temporal predictions did not differ between rhythm-based and memory-based predictions.


To examine whether the observed EEG results are consistent with an oscillatory entrainment mechanism, we used a computational oscillatory entrainment model [43,44] to estimate the ITPC magnitude and mean angles that entrained neural oscillators are expected to show in the three experimental conditions.

Following Large and colleagues [43,44], we used a model con- sisting of an array of neural oscillators with different characteristic frequencies in the delta range.

Each neural oscillator was modeled using a dynamical system characterized by two state variables, phase angle (ϕ) and amplitude (r), and was coupled to an external stream (s) that was modeled as a sequence of transients at the times of stimuli onsets:


In the absence of external stimulation (i.e., s(t) = 0), this system produces periodic dynam- ics, with the angle revolving steadily with time as a function of the F, the natural frequency of the oscillator.

When a stimulus is presented (s(t) = 1), an additional term corrects the angle towards zero, which is assumed to be the angle of the external stream.

The strength of correc- tion is determined by a coupling parameter c, which was adjusted such that the modeled ITPC levels in the Rhythmic condition would be similar to those observed in the actual data.

Given a set of initial conditions, this system can be numerically integrated to examine its convergence to a limit cycle, its phase consistency at times of stimulus onsets, and the angle at stimulus times to which it converges.


Note that (a) the focus was on the phase of the oscillator and not its amplitude; therefore, the system was simplified relative to a canonical coupled oscillator model (e.g., the one sug- gested by [43]) by fixing the amplitude; (b) to quantify the extent to which oscillators at differ- ent frequencies can be entrained to the stimulation streams, we used an array of oscillators with fixed periods instead of a single oscillator that can change its frequency.

Therefore, the system was simplified relative to a canonical coupled oscillator model by removing the period correction term; (c) the model was implemented such that instead of receiving only the period and phase of the external stream as input (which would have only allowed rhythmic external streams, [44]), our model received a time series of stimulus energy (0 = no stimulus, 1 = full stimulus energy), such that stimulus bursts can have any desired SOA.

To estimate the ITPC values and phase angles expected in the experimental conditions, we numerically integrated this dynamical system using the Runge-Kutta method (fourth order).

The frequency range of interest were 0.2 to 5 Hz (logarithmic scale), corresponding to interval lengths of 5,000 to 200 ms in 50 ms steps.

The coupling parameter c was adjusted such that ITPC values in the Rhythmic condition at the stimulation frequency would be similar to those observed in the empirical data.

For each participant and each experimental condition, we applied the following steps:


1. Generate the time series that represents a single trial in the current condition.

The stimula- tion stream in this trial was represented as a train of impulses at the times of stimulus onsets.

Specifically, we created a time series vector with a 1,000 Hz resolution (i.e., one value for every millisecond).

This time series contained one at the time of stimulus onset in the actual trial and zero otherwise.


2. Determine the initial phase angle for the current trial.

Phase angles were sampled without replacement from a uniform circular distribution of equally-spaced angles, the number of which was identical to the number of trials in the specific experimental condition (48 for the Rhythmic and Repeated-Interval conditions, 24 for the Random condition).

The initial phase angle was identical to all oscillators stimulated by this trial, which guaranteed match- ing phase dispersion between frequencies and conditions.


Numerically integrate the system given the trial time series and initial phase angle (from stages 1 and 2, respectively), separately for each oscillator in the oscillator array, and register the angle at the time of the last stimulus in the stream (the target) for that oscillator.


4. Repeat steps 1-3 for each trial, in each experimental condition, to obtain a series of angles for each natural frequency (48 trials in the Rhythmic and Repeated-Interval, 24 in the Random).


Calculate the ITPC and the mean angle across trials, identically to how it was calculated for the EEG angle data, separately for each natural frequency.


Files of raw behavioral and EEG data, model simulation data, and cover information are deposited at the Dryad data repository (http://dx.doi.org/10.5061/dryad.5vb8h) [84].


The current study compared predictions formed in a rhythmic context to predictions formed by interval memorization in streams that are less rhythmic, in order to dissociate these two prediction modes and put the putative manifestations of oscillatory entrainment to a critical test.

We found that PC and alignment of delta-band EEG activity did not differ between the Rhythmic and Repeated-Interval conditions.

Thus, these measures are not good indicators of entrainment mechanisms and could reflect memory-based predictions.

This was also the case for behavioral facilitation for expected targets, modulations of alpha amplitude and post-target P3 latency.

However, we also found that rhythm-based predictions resulted in slower responses to unexpected targets than memory-based predictions and in immediate resolution of the CNV when a temporal prediction was unexpectedly violated.

These possibly reflect a res- onance mechanism compatible with entrainment.

Our findings thus suggest that temporal predictions in rhythmic contexts is a combination of memory-based and entrainment-like mechanisms, affecting different manifestations of performance and electrophysiology.

Cru- cially, they imply that resonance after stream termination, and not PC during the stream, is the indicator of rhythm entrainment.


Delta-band activity at rhythmic stimuli times demonstrates enhanced PC around the opti- mal angle for performance relative, e.g., to aperiodic nonpredictive streams [3,6,7,21,22].

This phenomenon, also replicated in our data, is commonly interpreted as reflecting entrainment of intrinsic low-frequency neural oscillators to the external rhythm [10If similar delta PC is observed in streams that clearly promote oscillatory entrainment and streams that are more detrimental to entrainment, what could be the source of PC?


One plausi- ble alternative is the temporal predictability of the streams, which is inherent to rhythms [33,34] and was matched in our study between the Rhythmic and Repeated-Interval conditions.

Neurophysiologically, temporal predictability is known to drive buildup of neuronal activity to peak at the expected time point and then resolve [30,31,38], a phenomenon that is often referred to as climbing neuronal activity (CNA) [45,46].

This pattern produces a waveform with a wavelength matching the expected interval, which, as the rhythm interval is in the delta range, seems like an apparent delta cycle (see also [21], for acknowledging this possibility).

Since accurate temporal prediction means that the peak repeatedly coincides with the stimulus, apparent PC is observed around stimulus time.

Furthermore, PC is observed around the opti- mal phase because the CNA peak coincides with the time in which temporally driven prepara- tion is maximal, making it seem as an optimal phase.

It was recently argued that a CNA cannot account for entrainment effects, as PC did not increase in conditions in which the CNV ampli- tude was expected to increase [47].

However, phase consistency could in fact be independent of amplitude.

Regardless, the present findings directly show that rhythmicity is not needed for apparent delta PC and that it could be well explained by CNA.

Our study relies on scalp EEG but even with finer spatial resolution methods (e.g., electrocorticography), the critical compari- son we present here is needed to disentangle entrainment from repeating climbing activity.


The source of the CNA recorded in occipital channels is yet to be determined.

Several lines of evidence point to a key role of the motor system in timing and in the generation of CNA, intracranially [48-50] or on the scalp as the CNV [38].

Possibly, the observed occipital CNA reflects volume-conducted CNV originating in premotor regions.

Notably, this caveat applies to all EEG studies of visual and especially auditory rhythms, as auditory responses and the CNV are measured in the same sites.

However, the motor system has also been associated with predictions even when these are nonmotor [51] and was suggested to be involved in purely sensory temporal predictions [52-54].

Thus, another possibility is that the occipital CNA may result from top-down signals from the motor system that prepare the relevant sensory cortex at the right time [55,56].

A final possibility is that the occipital CNA originates from nonmotor duration representations, for example in parietal regions [57], or even localized timing mecha- nisms in the sensory cortex itself [58].


Further support for PC not being due to continuous rhythm entrainment comes from the finding that PC was low at the time of the WS, despite the WS being embedded in a rhythmic stream, anticipated, and task-relevant (as it signaled an upcoming target, see also Fig 2A and 2B for absence of CNV or any oscillatory trace prior to the WS).

We speculate that in para- digms in which there is no WS and all rhythmic stimuli are possible targets (e.g., [6,22]), CNA is elicited after each stimulus, resulting in apparent continuous oscillation.


Importantly, the current findings do not refute the existence of low-frequency oscillations, their role in sensory selection, or the possibility that they become entrained to external rhythms.

However, we argue that phase modulations, whether increased concentration or angle reversal, of filtered neural signal during the presentation of the stimulus stream cannot be taken as evidence for the existence or entrainment of such oscillations.

This argument is agnostic to the question of whether the climbing activity (and in particular the CNV) is a result of phase-resetting of ongoing low-frequency oscillations by the WS [11,21] (see also [59]).

However, in the Repeated-Interval condition such phase-resetting should be selective for the oscillator with the wavelength of the expected interval, based on memory, rather than on bot- tom-up entrainment.

Interval timing loses its efficiency for intervals outside the range of 200-2,000 ms (i.e., the delta range), [60].

Thus, it is less likely that the effects of rhythmic stimulation at faster rates, especially alpha in visual perception [18], can be explained by interval timing.

Given the prom- inence of alpha rhythms in visual cortex and its hypothesized relation to the timing of neural activation [61], it is possible that effects of alpha-rate stimulation are indeed mediated by entrainment of alpha oscillations, aligning stimuli with a preferred phase of the alpha cycle [14,15].

Future studies should examine the mechanism of alpha entrainment using appropriate control for temporal predictability as used here.


Previous studies separately found behavioral validity effects for rhythms and for memory- based predictions [16,25].

Rhythm processing is supported by the motor system, especially the supplementary motor area [62,63].

Given the manual nature of our task, it was plausible to expect an advantage of rhythm-based over memory-based predictions.

Indeed, rhythms exerted a stronger expectation, but this was expressed in larger cost of prediction violation, rather than in stronger facilitation for expected targets.

This suggests a dissociation between the processes involved in increasing preparation at expected times and relative inhibition at unexpected times instead of a tradeoff of the same resource between the expected and unex- pected times.

The pattern of CNV resolution when the anticipated event was unexpectedly omitted provides an apparent neural correlate for the extra cost of unexpected events (Fig 3).

When a short SOA target was expected but did not appear, the CNV resolved immediately towards baseline with rhythm-based predictions (compare with [9]) but only after a delay for memory-based predictions (compare with [37]).


The interpretation of this pattern depends on the process reflected by the CNV, an issue that is still under debate [64].

Under the model that the CNV reflect the accumulation of pulses of a timer mechanism [37,65], the CNV resolution reflects "emptying" of the accumulator once the memorized interval has elapsed and a decision has been made.

In this context, the delayed resolution of the CNV in the Repeated-Interval condition could result from reduced temporal accuracy of memory-based predictions, which rely on multiple factors like working memory, arousal, and attention [28,66].

This would lead to more jitter in the peak latency of the accu- mulation at the end of the expected interval and, as a byproduct, to "leakage" of preparation to times other than the expected time.


However, the timer model of CNV is challenged by recent arguments that the CNV resolu- tion is modulated according to optimal decision strategies, which suggests that the CNV is more likely to reflect active preparation than mere passive timing [64,67,68].

Thus, the differ- ence between our predictive conditions in the CNV resolution, as well as the RT results, imply that memory-based predictions are more flexible, allowing dynamic shifting of resources to unexpected times when a prediction is violated.

Rhythmic predictions on the other hand are obligating, with inability to maintain a high level of preparation in between rhythmic stimuli.

This obligatory nature of the rhythmic condition is in line with our previous finding, showing that rhythms affect the CNV even when they are not predictive and not intentionally used to form predictions [34].


Earlier P3 latency for temporally expected compared to unexpected events was previously observed in both rhythmic [34,36] and memory-based paradigms [26], although the two Mechanisms of rhythm- and memory-based temporal prediction PLOS Biology | DOI:10.1371/journal.pbio.2001665 February 10, 2017 18 / 30 modes were not directly compared until now.

Since the P3 has been related to stimulus eval- uation and response selection [41,42], the earlier P3 for temporally expected stimuli was interpreted as reflecting sharpening of such processes due to preparation.

Our results, showing similar effects of rhythmic and memory-based predictions, imply that this sharp- ening results from intentional usage of available temporal information, independent of its source.


Previous studies found amplitude reduction of occipital alpha-band oscillations before or at the expected time of a target embedded in a rhythmic stream [9,34,35].

Our findings suggest that this phenomenon (presumably indexing increased perceptual excitability) is a signature of temporally focused preparation in general.

Anticipatory reduction of occipital alpha-band amplitude was also observed in paradigms in which a nontemporal feature was anticipated, such as in reduction of alpha power in electrodes contralateral to an expected target location [69,70].

While spatial information allows biasing the cortical excitability to match the expected location (e.g., across hemifields), temporal information allows the increase of cortical excitabil- ity to focus at the expected time, possibly sparing resources.

This may underlie synergistic effects of temporal and spatial predictions [8,71].