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No evidence for critical slowing down prior to human epileptic seizures

T0 review · 4 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Using intracranial EEG from 28 people and 105 seizures, this paper finds no evidence that variance or lag-1 autocorrelation—the standard critical-slowing-down indicators—increase before seizure onset.

desk verdict A systematic, careful negative study: CSD indicators show no reliable increase before 105 seizures, though the contaminated ROC control baseline and unremoved circadian rhythm make the null less clean than the abstract implies. read the letter →

arxiv 1908.08973 v1 pith:2F4NDXJR submitted 2019-08-23 physics.med-ph physics.bio-phq-bio.NC

Theresa Wilkat , Thorsten Rings , Klaus Lehnertz This is my paper · ORCID
classification physics.med-phphysics.bio-phq-bio.NC
keywords criticalslowingdownearlywarningsignalsepilepticseizuresintracranialEEGlag-1autocorrelationvarianceseizure-timesurrogatesROCanalysis
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The authors set out to test whether variance and lag-1 autocorrelation—the two standard early-warning indicators for critical slowing down, the slower recovery and larger fluctuations expected near a tipping point—increase before human epileptic seizures. They analyzed 2056 hours of intracranial EEG from 28 people with epilepsy, covering 105 seizures, and compared pre-seizure windows with all remaining data using ROC analysis and seizure-time surrogates. They report no consistent, statistically significant increase in either indicator before seizures; at the minority of sites that passed the surrogate test, the indicators more often decreased than increased. If this negative result is correct, it undercuts a widely cited class of seizure-prediction claims based on generic critical-slowing-down signals and redirects attention to network-level and multi-day processes.

What carries the argument

The argument rests on a surrogate-based ROC evaluation. For every brain site, the two indicators are computed in sliding 20.48-s windows; the area under the receiver-operating-characteristic curve measures how well indicator values in assumed pre-seizure periods (4, 2, 1, or 0.5 h) separate from the remaining data, while 19 seizure-time surrogates per subject—random permutations of the intervals between seizures—define the null distribution for significance. This machinery is what lets the authors distinguish a real pre-seizure change from chance fluctuations and from the strong 24-h circadian modulation they observe in both indicators.

What would settle it

Look for whether the conclusion changes when the control set is cleaned: rerun the ROC analysis excluding every 4-h window immediately before any seizure from the "remaining data," rather than only the 60 min after each seizure. If variance or lag-1 autocorrelation then shows $A_{\mathrm{ROC}}>0$ beyond the seizure-time surrogate threshold at substantially more sites, the paper's negative result would be overturned by exactly the kind of pre-seizure signal it claims to rule out.

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Extended reading notes

Core claim

The central discovery is a null result stated on the paper's own terms: across 1647 intracranial brain sites in 28 subjects, neither variance nor lag-1 autocorrelation of the EEG shows a robust, statistically significant increase in the 4 h before seizure onset relative to the rest of the recording. Roughly one-seventh of sites passed a seizure-time surrogate test, but most of those significant sites showed decreases ("critical speeding up"), and the few sites with CSD-like increases could be traced to spatial oversampling in two to three subjects. The authors conclude that these data do not support the bifurcation-induced tipping picture of seizure generation and that univariate linear CSD indicators are not reliable generic early warning signals for human seizures.

Load-bearing premise

The control baseline still contains the 4-h pre-seizure windows of other seizures—only the 60 min after each seizure was removed—so a true pre-seizure signal would contaminate both distributions and bias the ROC area toward zero, making the null result easier to obtain.

Editorial extensions

If this is right

  • If the result holds, seizure-warning devices that rely solely on rising variance or lag-1 autocorrelation lack empirical support in this dataset.
  • The absence of spatial specificity to the seizure-onset zone argues against a local bifurcation driving the transition into a seizure and supports network-level accounts of seizure emergence.
  • Daily modulation of the indicators means that any future early-warning test must control for time-of-day and sleep-wake state, or risk false positives and missed detections.
  • The same surrogate-ROC framework can be applied to other candidate seizure-predictor indicators, not just the two CSD indicators tested here.
  • The negative result narrows but does not eliminate the possibility of seizure prediction; it redirects attention to noise-induced or rate-dependent tipping, for which generic early warnings are not expected.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct check the authors do not perform: repeating the analysis with all pre-seizure windows excluded from the control baseline could reveal whether the null result is partly an artifact of the contaminated reference set.
  • The strong circadian modulation they report suggests a testable alternative route to seizure prediction: use multiday rhythms of interictal activity or network measures as covariates, since those are the processes their spectral analysis shows dominate the variability of the CSD indicators.
  • If the result generalizes, the human epileptic brain becomes a counterexample in the wider early-warning-signal debate, showing that a system can undergo a major transition to an extreme event without leaving the statistical fingerprints of bifurcation-induced critical slowing down.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. This paper tests the hypothesis that critical slowing down (CSD), measured as increases in variance and lag-1 autocorrelation, precedes human epileptic seizures. The authors analyze 2056 h of multichannel intracranial EEG from 28 subjects with 105 seizures. For each recording site, they compute sliding-window estimates of variance and lag-1 autocorrelation, compare the distributions of these indicators in pre-seizure windows (T_pre = 4, 2, 1, 0.5 h) with the remaining data using the area under the ROC curve, and assess significance with a seizure-time surrogate test. They report that only about one-seventh of brain sites pass the surrogate test, that the majority of significant sites show a 'critical speeding up' rather than slowing down, and that there is no clear spatial clustering in the seizure onset zone. They conclude that there is no evidence for CSD prior to these seizures and that bifurcation-induced tipping may be too simplistic for the epileptic brain.

Significance. The manuscript addresses an important and controversial question with an unusually large clinical dataset (28 subjects, 105 seizures, 2056 h of iEEG). The use of seizure-time surrogates is a methodological strength, and a robust null result would be a valuable contribution to both the epilepsy and critical-transition literatures. However, the negative conclusion is conditional on several control assumptions that are not tested or not clearly described: the ROC baseline includes pre-seizure windows of other seizures, the strong 24 h modulation is identified but not removed, and the reported 'critical speeding up' result appears inconsistent with the one-sided surrogate test defined in the Methods. These issues require reanalysis before the central claim is fully supported.

major comments (4)
  1. [Section II, ROC analysis] The control distribution in the ROC analysis is contaminated by other pre-seizure periods. The manuscript states that only the 60 min interval after seizure onset is excluded, so for the 105 seizures, the 'remaining data' include the T_pre windows preceding every other seizure. If a genuine pre-seizure increase in ρ or σ² exists, those elevated values appear in both the signal distribution and the control distribution, compressing AROC toward zero and reducing the number of sites that pass the surrogate threshold. The seizure-time surrogate test does not repair this bias because it uses the same contaminated baseline. Please re-run the analysis after excluding all pre-seizure windows from the control distribution, or using a time-of-day/state-matched control, and report whether the distribution of AROC and the number of significant sites change.
  2. [Section III, Fig. 3 vs Section II] There is an internal inconsistency in the definition of the surrogate test. Section II defines a significant indication of CSD as 'AROC > 0 for original seizure times and AROC exceeds the maximum one obtained with 19 the seizure time surrogates', which is a one-sided test for positive AROC. Section III, however, reports for the sites that passed the surrogate test that 'in the majority of cases both ρ and σ2 rather point to a "critical speeding up"', which requires negative AROC values in that set. Please clarify whether the surrogate test was actually two-sided; if so, specify the two thresholds and report the counts of significant positive and negative AROC values separately. If not, the 'critical speeding up' statement is unsupported by the test as defined.
  3. [Section III, Fig. 2 and Conclusion] The 24 h rhythm is identified as a dominant contributor to the variability of both indicators, yet it is not removed or stratified in the ROC analysis. The power spectral densities in Fig. 2 show strong contributions at about 24 h for both ρ and σ², and the Conclusion names the sleep-wake cycle as a potential confounding variable. Because this slow modulation inflates the variance of both the pre-seizure and baseline distributions, it can reduce the sensitivity of the ROC comparison even if seizures are uniformly distributed over the day. Please repeat the analysis after removing the 24 h component (e.g., by subtracting a subject-specific diurnal average computed from interictal data or by stratifying the comparison by time of day) and report the resulting AROC distributions and surrogate-test outcomes.
  4. [Section II, variance preprocessing] The preprocessing that omits the upper 0.5% of all σ² values is applied to the very indicator under study. If a genuine pre-seizure elevation of variance falls in the upper tail, this step removes the signal of interest. The paper provides no robustness analysis for this threshold, and it does not report whether the excluded windows are enriched in the pre-seizure periods. Please either justify the threshold with a quantitative check or repeat the analysis for several thresholds (e.g., no exclusion, 0.1%, 1%) and show that the negative result is unchanged.
minor comments (5)
  1. [Throughout] There are small typographical errors; for example, 'There is a ongoing debate' in the abstract, 'An alternatively, data-driven approach' in the Introduction, and 'The absolute value of AROC is than confined' in Section II.
  2. [Data and code availability] No data or code availability statement is provided; for a quantitative null result of this kind, a statement about data availability and analysis code would improve reproducibility.
  3. [Section II, surrogate test] The surrogate test uses only 19 surrogates, yielding a minimum p-value of 0.05; the manuscript does not discuss how the granularity of this permutation test affects the interpretation of 'one-seventh of brain sites' passing.
  4. [Section III, seizure timing] The statement that seizures occurred equally distributed over the 24 h cycle is based on all 105 seizures pooled across subjects; a per-subject circadian distribution would be more informative given the subject-level analysis elsewhere in the paper.
  5. [Section III, prior results] The phrase 'similar to what has been described before [24,39]' would benefit from a brief description of how the previously reported fraction compares quantitatively with the 1-2% found here.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the negative result is derived from direct data estimates and a surrogate null; no fitted quantity is renamed as a prediction.

full rationale

The paper's central claim is an empirical null result: variance and lag-1 autocorrelation estimated from iEEG do not systematically increase before seizures. The derivation chain is data -> sliding-window estimates -> ROC separation of preseizure vs. remaining data -> seizure-time surrogate threshold. No parameter is fitted to the outcome and then reported as a prediction; the surrogate procedure is an explicit null built by permuting seizure onset intervals, so a significant AROC is defined as exceeding the surrogate maximum, not as a transformed fit. The indicators (variance, lag-1 autocorrelation) are standard operationalizations of CSD, not definitions that presuppose the conclusion. The self-citations (refs. 39-45, 49) concern data provenance, seizure-prediction context, and the seizure-time-surrogate concept, but the surrogate procedure is described in the text and applied to the authors' own data; the cited surrogate method is an independent statistical tool, not an unverified premise that forces the result. A reviewer's concern that the 'remaining data' baseline includes 4 h preseizure windows of other seizures is a legitimate statistical power/control issue that could obscure a real signal, but it is not a circular reduction: the AROC is still computed from the data, and the negative finding is conditional on that baseline choice rather than equivalent to an input by construction. No equation is reused as its own conclusion, and no known empirical pattern is simply renamed. Therefore the paper is self-contained against external benchmarks and merits a circularity score of 0.

Assumptions & free parameters 5 free parameters · 4 assumptions · 0 invented entities

The central claim rests on standard statistical choices rather than fitted model parameters. The main burden is the baseline definition and the validity of the surrogate null, which are listed as axioms above. No new particles, forces, dimensions, or conserved quantities are introduced.

free parameters (5)
  • Pre-seizure window T_pre = 4 h (2, 1, and 0.5 h also tested)
    Chosen by hand from the assumption that seizure generation unfolds over minutes to hours (Section II); the primary result is reported for 4 h. This choice controls the sensitivity of the ROC comparison.
  • Lag tau for lag-1 autocorrelation = 30 ms
    Chosen as the smallest delay for which the autocorrelation function is not dominated by low-pass filter artifacts (Section II). The indicator value depends on this delay.
  • Upper 0.5% variance values omitted = 0.5%
    Ad hoc exclusion of high-amplitude artifact values from the sigma^2 distribution (Section II); affects the variance indicator and can affect pre-seizure sensitivity.
  • Number of seizure-time surrogates = 19
    Defines the significance threshold: p < 0.05 requires exceeding the maximum of 19 surrogates (Section II). This is coarse but conservative.
  • Sliding window length = 20.48 s
    Chosen for time-resolved estimates (Section II); determines the temporal resolution of the indicators.
assumptions (4)
  • domain assumption Increased variance and lag-1 autocorrelation are valid indicators of critical slowing down for the observed iEEG observable
    The paper relies on the standard critical-transition framework (Section I) and interprets these indicators as CSD signatures; if the observable is not the right one, the negative result is uninformative.
  • domain assumption Each electrode's iEEG time series is an appropriate system observable whose fluctuations reflect perturbations
    Sliding-window estimates are computed directly on filtered iEEG (Section II); no state-space reconstruction or model is used.
  • domain assumption Randomly permuting intervals between seizures produces a valid null distribution for pre-seizure indicator changes
    The surrogate test (Section II, third step) assumes that randomized seizure onset times destroy the true pre-seizure relationship while preserving temporal statistics; the paper does not test whether circadian rhythms are preserved.
  • ad hoc to paper The remaining data, excluding only 60 min after each seizure, form a valid control baseline for the ROC comparison
    This baseline includes other pre-seizure periods, which can dilute AROC toward zero (Section II, second paragraph). This is an unstated and questionable modeling choice.

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Pith. "Pith review of No evidence for critical slowing down prior to human epileptic seizures." pith.science (2026). https://pith.science/paper/2F4NDXJR

@misc{pith2026190808973,
  author       = {Pith},
  title        = {Pith review of: No evidence for critical slowing down prior to human epileptic seizures},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/2F4NDXJR}},
  note         = {Machine review of arXiv:1908.08973}
}
read the original abstract

There is a ongoing debate whether generic early warning signals for critical transitions exist that can be applied across diverse systems. The human epileptic brain is often considered as a prototypical system, given the devastating and, at times, even life-threatening nature of the extreme event epileptic seizure. More than three decades of international effort has successfully identified predictors of imminent seizures. However, the suitability of typically applied early warning indicators for critical slowing down, namely variance and lag-1 autocorrelation, for indexing seizure susceptibility is still controversially discussed. Here, we investigated long-term, multichannel recordings of brain dynamics from 28 subjects with epilepsy. Using a surrogate-based evaluation procedure of sensitivity and specificity of time-resolved estimates of early warning indicators, we found no evidence for critical slowing down prior to 105 epileptic seizures.

Figures

Figures reproduced from arXiv: 1908.08973 by the authors.

Figure 1
Figure 1. FIG. 1. Left: exemplary time series of indicators of critical slowing down (top: [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Top and middle: mean power spectral density esti [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Distribution of values of [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.