Pith. sign in

REVIEW 3 major objections 4 minor 67 references

Toward a Dynamical Taxonomy of Insomnia: A Multiaxial Framework for Sleep-State Transitions and Architectural Failure

T0 review · 3 major / 4 minor · reviewed 2026-08-08 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Insomnia is not one syndrome but a family of separable dynamical failures, classified along three axes.

desk verdict A carefully hedged taxonomy paper with real organizational value and an honest limitations section; its central Axis I assignments are untested and may not be identifiable from routine PSG, but it deserves a serious referee. read the letter →

arxiv 2608.05462 v1 pith:3VAABMHI submitted 2026-08-05 physics.bio-ph q-bio.NC

Alexander Poltorak This is my paper · ORCID
classification physics.bio-phq-bio.NC
keywords insomniasleeparchitecturesleep-stagetransitionsdynamicalsystemsLandau-Ginzburgbistabilityonsettwo-processmodel
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

Insomnia disorder is defined by symptoms, but the same complaint—trouble falling or staying asleep—can arise through different physiological routes. This paper proposes that the field would do better to classify insomnia profiles by three separate axes: the dynamical operation that fails, the sleep stage or boundary at which it fails, and whether that failure is primary, secondary, maintaining, or just a biomarker. The claim is organizational rather than a new diagnosis: a Landau-Ginzburg field model supplies a common vocabulary for nested mechanistic hypotheses, and the taxonomy should be judged by whether it improves communication, stratification, and prediction. The authors are explicit that only sleep onset currently has strong boundary-level evidence, and that even there a driven homeostatic-circadian ramp remains an alternative to genuine bifurcation or bistability.

What carries the argument

The machinery is a three-axis taxonomic grammar—operation, stage or boundary, causal status—layered on an effective field theory. The central object is a dimensionless latent NREM-ordering coordinate $\phi(r,t)$ governed by a stochastic time-dependent Ginzburg-Landau equation, a statistical-physics description of a scalar field relaxing in a potential landscape, with an effective quasipotential functional whose local curvature, barrier height, bias, noise, and relaxation coefficient are kept distinct. A gradient approximation with additive noise permits Kramers-type escape language, while non-gradient regimes require a Freidlin-Wentzell quasipotential or minimum action; whole-night REM-NREM sequencing requires reactive or oscillatory dynamics beyond the relaxational form. The taxonomy is the organizing contribution; the field formalism supplies a falsifiable way to turn each operation-location cell into a candidate mechanistic hypothesis.

What would settle it

Run the paper's near-term crux analysis on a large sleep-onset cohort with continuous presleep EEG: fit each subject's onset trajectory with a driven Process-S/Process-C sigmoid, a rounded bifurcation model, stochastic bistable/escape models, and hidden-state baselines, holding out subjects rather than epochs. If the driven sigmoid predicts held-out trajectories as well as or better than the dynamical alternatives, the sleep-onset mechanistic claims must be downgraded to descriptive state change.

Watch

Extended reading notes

Core claim

The central claim is that a candidate insomnia profile is specified by the dynamical operation that fails, the sleep stage or boundary at which it fails, and its causal status. Six operation classes are proposed: control-path progression, boundary crossing, post-transition stabilization, spatial recruitment, architectural sequencing, and observation mapping. Objective sleep duration, age, circadian phase, comorbidity, medication exposure, and night-to-night variability are treated as modifier or covariate dimensions rather than additional mechanistic classes. Within this organization, the paper adapts a stochastic Landau-Ginzburg description of a latent NREM-ordering coordinate, and insists that local curvature, gradient barrier or non-gradient escape action, bias, noise, and relaxation are distinct quantities that routine polysomnography usually identifies only in combination. It makes two affirmative predictions: local recovery and escape will dissociate reproducibly across patients, and rounded-bifurcation or bistable/escape models will predict subject-held-out sleep-onset trajectories better than a Process-S/Process-C driven smooth sigmoid.

Load-bearing premise

The load-bearing premise is that the six operation classes are separable in real patients, so trained raters or algorithms can assign operation-location-causal labels reliably and those labels add predictive value beyond conventional insomnia subtypes.

Editorial extensions

If this is right

  • If recovery and escape dissociate reproducibly, patients with similar symptom labels can be separated into profiles that predict how they respond to graded perturbations.
  • If rounded-bifurcation or bistable models beat a driven smooth sigmoid on held-out sleep-onset data, sleep onset should be described as a transition near a loss of stability rather than a smooth consequence of homeostatic and circadian drive.
  • The framework implies that two treatments producing the same total sleep time can alter different dynamical domains, so mechanism studies should measure more than sleep duration.
  • The paper's own utility criterion means the taxonomy stands or falls on inter-rater reliability and incremental held-out prediction; otherwise it should be simplified or retired.
  • Cross-night reproducibility of fitted dynamical parameters, if it exceeds that of conventional stage percentages, would be evidence that these profiles are genuine endotypes.

Reading between the lines

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

  • A natural extension the author leaves implicit is that the same three-axis grammar could be applied beyond insomnia, to other sleep-wake disorders or to daytime vigilance transitions, where the same operations may fail in different locations.
  • The near-term crux experiment can be run on already-collected polysomnography cohorts, so the strongest claim can be tested without new data collection; if the driven sigmoid wins, the dynamical mechanism claims lose their strongest empirical anchor.
  • The recovery-escape dissociation could be tested in animal models or with closed-loop stimulation protocols, where graded perturbation amplitudes are easier to deliver than in human PSG.
  • One could also fit the competing models to individual subjects rather than group trajectories; if bistable parameters are not stable within a person across nights, the endotype claim weakens even if group-level fits look similar.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. This paper proposes a multiaxial research ontology for insomnia, in which a patient profile is specified by (Axis I) the dynamical operation that fails—control-path progression, boundary crossing, post-transition stabilization, spatial recruitment, architectural sequencing, or observation mapping—(Axis II) the sleep stage or boundary at which it fails, and (Axis III) the causal status of the abnormality, with objective sleep duration and other clinical variables treated as modifiers. A local Landau-Ginzburg quasipotential model is introduced for boundary-local transitions, with careful distinctions between gradient and non-gradient regimes, curvature vs. barrier vs. escape action, and effective parameter combinations vs. individual coefficients. The paper makes two affirmative predictions (local recovery/escape dissociation; rounded-bifurcation or bistable models outperforming a driven Process-S/C smooth sigmoid for sleep-onset trajectories) and proposes a near-term crux reanalysis plus simulation-based identifiability and taxonomy-utility studies. The authors explicitly state that the framework is phenomenological, not a validated diagnostic or treatment-selection system.

Significance. If the framework survives operationalization, it would provide a common language for separating mechanism-level hypotheses in insomnia heterogeneity and generate tests that conventional onset/maintenance/terminal-awakening subtyping does not. The paper's strengths are its discipline: it guards against circularity (§3.7), identifies identifiability limits and effective combinations (§3.9, Table 3), preregisters falsifying conditions for the model and the ontology (§6.4–6.6), and gives a concrete near-term experiment. Its significance is conditional on the yet-unmet empirical assumption that the six operation classes can be reliably assigned and add predictive value; the paper itself acknowledges this, but the referee's assessment should reflect that the central utility claim is not yet demonstrated.

major comments (3)
  1. [§3.9, §3.10, Table 3] The manuscript's central three-axis claim requires that the failed dynamical operation and its location be assignable, yet Section 3.9 and Table 3 show that passive PSG identifies only effective combinations (Γμ, ΔV/Deff, ΔW/ε) and that the coarsest sleep-onset split (control-path progression vs. boundary crossing vs. post-transition stabilization) requires separating an unobserved homeostatic/circadian ramp from an unobserved boundary-loss process. These latent quantities can compensate for each other, so the paper should demonstrate—at minimum on synthetic data with known ground truth, as listed in Table 5—that the coarse Axis I labels are recoverable from the proposed PSG-plus-covariate protocol. Without such an identifiability check, the utility tests proposed in Section 6.4 would validate labels that may already be non-identifiable from the planned measurements.
  2. [§4.1, §4.5, Table 4] The six Axis I operation classes and the candidate profiles in Table 4 are not accompanied by an operational coding rubric specifying which measurements, thresholds, or hierarchical decision rules assign a given recording to a given operation. Section 6.4 makes inter-rater reliability a utility criterion, but the manuscript does not provide the coding manual that would allow trained raters to apply the labels; for example, the rule for assigning primary N2 organization deficit versus failure of early-NREM consolidation is stated qualitatively. A minimal scoring/classification protocol should be specified before the taxonomy can be tested.
  3. [§6.6] The near-term crux experiment compares trajectory-prediction performance of driven sigmoid vs. rounded/bistable/escape models, but the central three-axis claim is about taxonomy-level labels and their incremental predictive utility. The paper should state precisely how a positive crux result advances the three-axis ontology (rather than only the sleep-onset mechanical model), and which prespecified outcome would lead to simplification or retirement of the ontology despite a positive model-comparison result; otherwise the mapping between model falsification and taxonomy validation remains ambiguous.
minor comments (4)
  1. [§3.1–§3.8] Equations (1)–(8) are referenced throughout the text but are not displayed in the manuscript; the formulas should be included in the published version or in an appendix.
  2. [Figures 1 and 2] Figure 1 and Figure 2 are described in the text but are not present in the manuscript; they should be provided or explicitly marked as schematic placeholders in the submission.
  3. [Declarations] Reference 59 is a preprint from the author's own group; Section 1.2 states that preprints carry no essential evidentiary burden, but the declarations should explicitly flag this as a self-citation for transparency.
  4. [§1.2] The evidence-selection paragraph is thorough, but it would be easier to verify if the references classified as peer-reviewed, accepted, or preprint were listed in a table or footnote rather than only described in prose.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the taxonomy is explicitly judged by utility, the two predictions are unfitted held-out comparisons, and the only self-citations are non-essential speculative extensions.

full rationale

The paper's central claim is an organizational taxonomy, and it explicitly disclaims any derivation of mechanisms from the adopted Landau-Ginzburg scaffold. The two affirmative predictions in Section 1.1 - dissociation of local recovery from escape, and superior held-out prediction by rounded/bifurcation/bistable/escape models over a Process-S/Process-C-driven sigmoid - are not fitted to data and are accompanied by explicit falsifying conditions (Sections 6.2, 6.5, and 6.6). The model's parameters (mu, Delta V, Delta W, D, Gamma, h) are presented as effective combinations whose separate identification requires specific protocols (Table 3), and the paper repeatedly states that passive PSG identifies only combinations such as Gamma*mu or Delta V/Deff, not individual quantities. The main potential circularity - using a feature to define a boundary and then proving a discontinuity at that boundary - is explicitly guarded against in Section 3.7, where transition time and class are to be inferred from held-out continuous features and scored stages only identify candidate windows. The taxonomy's utility is judged independently: Section 6.4 states that if inter-rater agreement is poor or no incremental prediction is found, 'the ontology should be simplified or retired even when an individual dynamical model remains mathematically adequate.' The only self-citations (refs 42 and 59) are preprints, declared in Section 1.2 to carry no essential evidentiary burden; ref 42 appears only in Box 1, labeled speculative and nonessential, and ref 59 is not load-bearing in the derivation chain. No fitted parameter is renamed as a prediction, and no step in the paper reduces by construction to its own inputs. No significant circularity was found.

Assumptions & free parameters 8 free parameters · 5 assumptions · 3 invented entities

The framework introduces model parameters and a latent coordinate that would be estimated from future data; none are fitted in this paper. The core axioms are the scalar-field boundary-local approximation and the separability of the six operation classes. The invented entities (phi, profiles, quasipotential) all have proposed independent tests, so they are not unfalsifiable.

free parameters (8)
  • a (local curvature) = not fitted in this paper
    Proposed to be estimated from transition-window data; controls the continuous-to-bistable character.
  • b (nonlinear transition character) = not fitted in this paper
    Sets the leading nonlinearity; b>0, c=0 is the default sector.
  • c (high-amplitude stabilization) = not fitted in this paper
    Retained for mixed or first-order-like regimes when b<0.
  • h (bias or tilt) = not fitted in this paper
    Circadian phase, arousal, and sensory context are proposed covariates.
  • Gamma (relaxation coefficient) = not fitted in this paper
    Only identifiable with mu as Gamma-mu; separate estimation requires calibrated perturbations.
  • kappa (spatial coupling) = not fitted in this paper
    Sets the latent correlation length xi; requires high-density EEG or MEG.
  • D (effective fluctuation intensity) = not fitted in this paper
    Noise scale in the SDE; only appears in combinations such as Delta-V/D.
  • phi0 (reference level) = prespecified, not estimated
    Centering reference for the latent coordinate; chosen by hand.
assumptions (5)
  • standard math Standard Landau-Ginzburg free energy and stochastic Ginzburg-Landau dynamics (Eqs 1-2)
    The mathematical scaffold is adopted from prior cortex and sleep-dynamics work (refs 43,45,61).
  • domain assumption The sleeping brain near a stage boundary can be represented by a scalar order parameter phi with approximate gradient dynamics
    Section 3.1 states this is a boundary-local approximation and is not valid for whole-night sequencing.
  • ad hoc to paper The six operation classes capture the relevant axes of insomnia heterogeneity
    Section 4.1 defines these classes; their separability and utility are the empirical claim to be tested.
  • domain assumption The default c=0, b>0 sector is sufficient for most boundaries
    Section 3.1 says the simpler sector suffices; the sixth-order term is retained only for special regimes.
  • ad hoc to paper A latent composite NREM-ordering coordinate phi can be operationalized via slow-oscillation density, complexity, and spatial coordination
    Section 3.6 gives a candidate estimator; this is a heuristic starting point that must pass invariance and discriminant validity tests.
invented entities (3)
  • latent NREM-ordering coordinate phi independent evidence
    purpose: Common scalar measure of slow-wave organization to describe and compare stage transitions
    Not directly observed, but operationalized through EEG indicators with specified invariance and prediction tests; a falsifiable handle exists.
  • three-axis operation-location-causal profile independent evidence
    purpose: Patient-level classification of insomnia complaints
    Proposed inter-rater reliability and incremental prediction tests give an external falsifiable handle.
  • low-dimensional quasipotential V (or action W) independent evidence
    purpose: Phenomenological landscape to organize barrier, bias, and escape language
    Perturbation-recovery and graded escape experiments can test the predicted dissociation between stiffness and barrier.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Toward a Dynamical Taxonomy of Insomnia: A Multiaxial Framework for Sleep-State Transitions and Architectural Failure." pith.science (2026). https://pith.science/paper/3VAABMHI

@misc{pith2026260805462,
  author       = {Pith},
  title        = {Pith review of: Toward a Dynamical Taxonomy of Insomnia: A Multiaxial Framework for Sleep-State Transitions and Architectural Failure},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3VAABMHI}},
  note         = {Machine review of arXiv:2608.05462}
}
read the original abstract

Insomnia disorder is defined at the syndrome level, yet similar complaints can arise from different abnormalities in sleep regulation, state transition, state stabilization, spatial recruitment, architectural sequencing, and state perception. We propose a multiaxial dynamical framework whose principal contribution is organizational: a candidate profile is specified by the dynamical operation that fails, the sleep stage or boundary at which it fails, and its causal status. Objective sleep duration, age, circadian phase, comorbidity, medication exposure, and night-to-night variability are modifier/covariate dimensions rather than additional mechanistic classes. A local Landau-Ginzburg formalism, adopted from prior cortical and sleep-dynamics work, supplies a phenomenological language for nested hypotheses. Its relaxational form applies only to boundary-local dynamics under an approximate gradient description; non-gradient escape requires an action or quasipotential treatment, and whole-night REM-NREM sequencing requires reactive or oscillatory dynamics. Routine polysomnography usually identifies effective combinations rather than curvature, escape action, bias, noise, and relaxation separately. The strongest boundary-level evidence concerns sleep onset, where published results are consistent with bifurcation-like or bistable dynamics but do not yet exclude a driven smooth transition produced by the homeostatic-circadian ramp. The remaining operation classes are hypothesis-generating extensions. The taxonomy is judged by pragmatic utility, including improved communication, stratification, and prediction; the scalar-field implementation and specific dynamical profiles are separately falsifiable. The framework is a phenomenological organizing model rather than a new diagnosis, validated biomarker, or treatment-selection system.

Figures

Figures reproduced from arXiv: 2608.05462 by the authors.

Figure 1
Figure 1. Multiaxial architecture of the proposed framework. The arrows show analytical combination of dimensions [PITH_FULL_IMAGE:figures/full_fig_p014_1.png] view at source ↗
Figure 2
Figure 2. Compositional operation-by-location matrix. Filled cells are worked examples generated by crossing an operation with a stage or boundary; they are not an exhaustive list, prevalence claim, or assertion that all cells are independent biological entities. Blank cells are neither proposed nor ruled out. Causal status and modifier/covariate dimensions are assigned separately. 4.6Candidate profiles generated by the axes … view at source ↗
Figure 3
Figure 3. Gradient-idealized dissociation between local stiffness and escape. With at least two free shape parameters, μ and ΔV can vary independently: a stiff/low-barrier profile recovers quickly but escapes readily, whereas a soft/high￾barrier profile recovers slowly but rarely escapes. In non-gradient dynamics, ΔW or empirical hazard replaces ΔV. Fitted parameters must outperform model-free recovery and escape measures. Sc… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

67 extracted references · 64 canonical work pages

  1. [1]

    International Classification of Sleep Disorders

    American Academy of Sleep Medicine. International Classification of Sleep Disorders. 3rd ed, text revision. Darien, IL: American Academy of Sleep Medicine; 2023

    work page 2023

  2. [2]

    A two process model of sleep regulation

    Borbély AA. A two process model of sleep regulation. Hum Neurobiol. 1982;1:195-204

    work page 1982

  3. [3]

    The two-process model of sleep regulation: a reappraisal

    Borbély AA, Daan S, Wirz-Justice A, Deboer T. The two-process model of sleep regulation: a reappraisal. J Sleep Res. 2016;25:131-143. doi:10.1111/jsr.12371

    work page doi:10.1111/jsr.12371 2016

  4. [4]

    The two-process model of sleep regulation: beginnings and outlook

    Borbély AA. The two-process model of sleep regulation: beginnings and outlook. J Sleep Res. 2022;31:e13598. doi:10.1111/jsr.13598

    work page doi:10.1111/jsr.13598 2022

  5. [5]

    The sleep switch: hypothalamic control of sleep and wakefulness

    Saper CB, Chou TC, Scammell TE. The sleep switch: hypothalamic control of sleep and wakefulness. Trends Neurosci. 2001;24:726-731

    work page 2001

  6. [6]

    Hypothalamic regulation of sleep and circadian rhythms

    Saper CB, Scammell TE, Lu J. Hypothalamic regulation of sleep and circadian rhythms. Nature. 2005;437:1257-1263

    work page 2005

  7. [7]

    Neural circuitry of wakefulness and sleep

    Scammell TE, Arrigoni E, Lipton JO. Neural circuitry of wakefulness and sleep. Neuron. 2017;93:747-765

    work page 2017

  8. [8]

    A behavioral perspective on insomnia treatment

    Spielman AJ, Caruso LS, Glovinsky PB. A behavioral perspective on insomnia treatment. Psychiatr Clin North Am. 1987;10:541-553. Toward a Dynamical Taxonomy of Insomnia 25

    work page 1987

Show all 67 references
  1. [9]

    Insomnia

    Buysse DJ. Insomnia. JAMA. 2013;309:706-716

    2013

  2. [10]

    The hyperarousal model of insomnia: a review of the concept and its evidence

    Riemann D, Spiegelhalder K, Feige B, et al. The hyperarousal model of insomnia: a review of the concept and its evidence. Sleep Med Rev. 2010;14:19-31

    2010

  3. [11]

    24-hour metabolic rate in insomniacs and matched normal sleepers

    Bonnet MH, Arand DL. 24-hour metabolic rate in insomniacs and matched normal sleepers. Sleep. 1995;18:581-588. doi:10.1093/sleep/18.7.581

    1995 doi

  4. [12]

    Heart rate variability in insomniacs and matched normal sleepers

    Bonnet MH, Arand DL. Heart rate variability in insomniacs and matched normal sleepers. Psychosom Med. 1998;60:610-615. doi:10.1097/00006842-199809000-00017

    1998 doi

  5. [13]

    Chronic insomnia is associated with nyctohemeral activation of the hypothalamic-pituitary-adrenal axis: clinical implications

    Vgontzas AN, Bixler EO, Lin HM, et al. Chronic insomnia is associated with nyctohemeral activation of the hypothalamic-pituitary-adrenal axis: clinical implications. J Clin Endocrinol Metab. 2001;86:3787-3794

    2001

  6. [14]

    Functional neuroimaging evidence for hyperarousal in insomnia

    Nofzinger EA, Buysse DJ, Germain A, Price JC, Miewald JM, Kupfer DJ. Functional neuroimaging evidence for hyperarousal in insomnia. Am J Psychiatry. 2004;161:2126-2128. doi:10.1176/appi.ajp.161.11.2126

    2004 doi

  7. [15]

    Spectral characteristics of sleep EEG in chronic insomnia

    Merica H, Blois R, Gaillard JM. Spectral characteristics of sleep EEG in chronic insomnia. Eur J Neurosci. 1998;10:1826-1834. doi:10.1046/j.1460-9568.1998.00189.x

    1998

  8. [16]

    Beta/gamma EEG activity in patients with primary and secondary insomnia and good sleeper controls

    Perlis ML, Smith MT, Andrews PJ, Orff H, Giles DE. Beta/gamma EEG activity in patients with primary and secondary insomnia and good sleeper controls. Sleep. 2001;24:110-117

    2001

  9. [17]

    EEG spectral analysis in primary insomnia: NREM period effects and sex differences

    Buysse DJ, Germain A, Hall ML, et al. EEG spectral analysis in primary insomnia: NREM period effects and sex differences. Sleep. 2008;31:1673-1682

    2008

  10. [18]

    Increased EEG sigma and beta power during NREM sleep in primary insomnia

    Spiegelhalder K, Regen W, Feige B, et al. Increased EEG sigma and beta power during NREM sleep in primary insomnia. Biol Psychol. 2012;91:329-333. doi:10.1016/j.biopsycho.2012.08.009

    2012 doi

  11. [19]

    EEG power during waking and NREM sleep in primary insomnia

    Wu YM, Pietrone R, Cashmere JD, et al. EEG power during waking and NREM sleep in primary insomnia. J Clin Sleep Med. 2013;9:1031-1037. doi:10.5664/jcsm.3076

    2013 doi

  12. [20]

    On the relationship between EEG spectral analysis and pre-sleep cognitive arousal in insomnia disorder: towards an integrated model of cognitive and cortical arousal

    Dressle RJ, Riemann D, Spiegelhalder K, Frase L, Perlis ML, Feige B. On the relationship between EEG spectral analysis and pre-sleep cognitive arousal in insomnia disorder: towards an integrated model of cognitive and cortical arousal. J Sleep Res. 2023;32:e13861. doi:10.1111/...

    2023 doi

  13. [21]

    Slow-oscillation activity is reduced and high frequency activity is elevated in older adults with insomnia

    Hogan SE, Delgado GM, Hall MH, et al. Slow-oscillation activity is reduced and high frequency activity is elevated in older adults with insomnia. J Clin Sleep Med. 2020;16:1445-1454. doi:10.5664/jcsm.8568

    2020 doi

  14. [22]

    Regional patterns of elevated alpha and high- frequency electroencephalographic activity during nonrapid eye movement sleep in chronic insomnia: a pilot study

    Riedner BA, Goldstein MR, Plante DT, et al. Regional patterns of elevated alpha and high- frequency electroencephalographic activity during nonrapid eye movement sleep in chronic insomnia: a pilot study. Sleep. 2016;39:801-812. doi:10.5665/sleep.5632

    2016 doi

  15. [23]

    NREM sleep EEG frequency spectral correlates of sleep complaints in primary insomnia

    Marsh GR, Erwin CW, Nair AK, et al. NREM sleep EEG frequency spectral correlates of sleep complaints in primary insomnia. Sleep. 2002;25:626-636. doi:10.1093/sleep/25.6.626. Toward a Dynamical Taxonomy of Insomnia 26

    2002 doi

  16. [24]

    Information processing varies between insomnia types: measures of N1 and P2 during the night

    Bastien CH, St-Jean G, Morin CM, Turcotte I, Carrier J. Information processing varies between insomnia types: measures of N1 and P2 during the night. Behav Sleep Med. 2013;11:56-72. doi:10.1080/15402002.2012.660896

    2013

  17. [25]

    Actigraphic multi-night home-recorded sleep estimates reveal three types of sleep misperception in insomnia disorder and good sleepers

    te Lindert BHW, Blanken TF, van der Meijden WP, et al. Actigraphic multi-night home-recorded sleep estimates reveal three types of sleep misperception in insomnia disorder and good sleepers. J Sleep Res. 2020;29:e12937. doi:10.1111/jsr.12937

    2020 doi

  18. [26]

    Negative and positive sleep state misperception in patients with insomnia: factors associated with sleep perception

    Yoon G, Lee MH, Oh SM, Choi JW, Yoon SY, Lee YJ. Negative and positive sleep state misperception in patients with insomnia: factors associated with sleep perception. J Clin Sleep Med. 2022;18:1789-1795. doi:10.5664/jcsm.9974

    2022 doi

  19. [27]

    Neurophysiological parameters influencing sleep-wake discrepancy in insomnia disorder: a preliminary analysis on alpha rhythm during sleep onset

    Berra F, Fasiello E, Zucconi M, et al. Neurophysiological parameters influencing sleep-wake discrepancy in insomnia disorder: a preliminary analysis on alpha rhythm during sleep onset. Brain Sci. 2024;14:97. doi:10.3390/brainsci14010097

    2024 doi

  20. [28]

    Sleep state misperception in insomnia: the role of sleep instability and emotional dysregulation

    Cini E, Bolengo F, Fasiello E, et al. Sleep state misperception in insomnia: the role of sleep instability and emotional dysregulation. Brain Sci. 2025;15:1078. doi:10.3390/brainsci15101078

    2025 doi

  21. [29]

    Sleep changes in the disorder of insomnia: a meta-analysis of polysomnographic studies

    Baglioni C, Regen W, Teghen A, Spiegelhalder K, Feige B, Nissen C, Riemann D. Sleep changes in the disorder of insomnia: a meta-analysis of polysomnographic studies. Sleep Med Rev. 2014;18:195-213. doi:10.1016/j.smrv.2013.04.001

    2014 doi

  22. [30]

    The effectiveness of cognitive behavioral therapy for insomnia on sleep EEG hyperarousal: a multicentric polysomnographic study

    Sforza M, Morin CM, Dang-Vu TT, et al. The effectiveness of cognitive behavioral therapy for insomnia on sleep EEG hyperarousal: a multicentric polysomnographic study. Transl Psychiatry. 2026;16:88. doi:10.1038/s41398-026-03882-1

    2026 doi

  23. [31]

    Impaired sleep resilience underlies transient neural instability in insomnia disorder

    Yang C, Gu H, Chen H, et al. Impaired sleep resilience underlies transient neural instability in insomnia disorder. iScience. 2026;29(6):116151. doi:10.1016/j.isci.2026.116151

    2026

  24. [32]

    Beyond aging, sex and insomnia disorder shape NREM brain oscillations

    Walsh NA, Perrault AA, Cross NE, et al. Beyond aging, sex and insomnia disorder shape NREM brain oscillations. Sleep. Accepted manuscript published online July 15, 2026:zsag192. doi:10.1093/sleep/zsag192

    2026 doi

  25. [33]

    REM sleep fragmentation and arousability distinguish chronic insomnia subtypes and reflect subjective-objective sleep discrepancy

    Hanif U, Crosbie F, Aloulou A, Romano F, Sauvet F, Jennum P, Solelhac G, Leger D. REM sleep fragmentation and arousability distinguish chronic insomnia subtypes and reflect subjective-objective sleep discrepancy. Sleep Med. 2026;147:109165. doi:10.1016/j.sleep.2026.109165

    2026

  26. [34]

    Restless REM sleep impedes overnight amygdala adaptation

    Wassing R, Lakbila-Kamal O, Ramautar JR, Stoffers D, Schalkwijk F, Van Someren EJW. Restless REM sleep impedes overnight amygdala adaptation. Curr Biol. 2019;29:2351-2358.e4. doi:10.1016/j.cub.2019.06.034

    2019 doi

  27. [35]

    The neurobiology, investigation, and treatment of chronic insomnia

    Riemann D, Nissen C, Palagini L, Otte A, Perlis ML, Spiegelhalder K. The neurobiology, investigation, and treatment of chronic insomnia. Lancet Neurol. 2015;14:547-558

    2015

  28. [36]

    Insomnia with objective short sleep duration and incident hypertension: the Penn State Cohort

    Fernandez-Mendoza J, Vgontzas AN, Liao D, et al. Insomnia with objective short sleep duration and incident hypertension: the Penn State Cohort. Hypertension. 2012;60:929-935. doi:10.1161/HYPERTENSIONAHA.112.193268. Toward a Dynamical Taxonomy of Insomnia 27

    2012 doi

  29. [37]

    Insomnia with objective short sleep duration is associated with deficits in neuropsychological performance: a general population study

    Fernandez-Mendoza J, Calhoun S, Bixler EO, et al. Insomnia with objective short sleep duration is associated with deficits in neuropsychological performance: a general population study. Sleep. 2010;33:459-465. doi:10.1093/sleep/33.4.459

    2010 doi

  30. [38]

    Insomnia with objective short sleep duration: the most biologically severe phenotype of the disorder

    Vgontzas AN, Fernandez-Mendoza J, Liao D, Bixler EO. Insomnia with objective short sleep duration: the most biologically severe phenotype of the disorder. Sleep Med Rev. 2013;17:241-

    2013

  31. [39]

    Persistent insomnia: the role of objective short sleep duration and mental health

    Fernandez-Mendoza J, Vgontzas AN, Bixler EO, et al. Persistent insomnia: the role of objective short sleep duration and mental health. Sleep. 2012;35:689-697. doi:10.5665/sleep.1586

    2012 doi

  32. [40]

    Falling asleep follows a predictable bifurcation dynamic

    Li J, Ilina A, Peach R, et al. Falling asleep follows a predictable bifurcation dynamic. Nat Neurosci. 2025;28:2515-2525. doi:10.1038/s41593-025-02091-1

    2025 doi

  33. [41]

    Learning the bistable cortical dynamics of the sleep-onset period

    Hu Z, Aravind M, Lei X, Kutz JN, Aucouturier JJ. Learning the bistable cortical dynamics of the sleep-onset period. PLoS Comput Biol. 2026;22:e1014246. doi:10.1371/journal.pcbi.1014246

    2026 doi

  34. [42]

    Landau-Ginzburg Phenomenology of Sleep Architecture

    Poltorak A. Landau-Ginzburg Phenomenology of Sleep Architecture. arXiv:2608.03000 [physics.bio-ph]. 2026. doi:10.48550/arXiv.2608.03000

    2026 doi arXiv

  35. [43]

    The sleep cycle modelled as a cortical phase transition

    Steyn-Ross ML, Steyn-Ross DA, Sleigh JW, Wilson MT, Gillies IP, Wright JJ. The sleep cycle modelled as a cortical phase transition. J Biol Phys. 2005;31:547-569

    2005

  36. [44]

    Quantitative modelling of sleep dynamics

    Robinson PA, Phillips AJK, Fulcher BD, Puckeridge M, Roberts JA. Quantitative modelling of sleep dynamics. Philos Trans A Math Phys Eng Sci. 2011;369:3840-3854

    2011

  37. [45]

    Landau-Ginzburg theory of cortex dynamics: scale-free avalanches emerge at the edge of synchronization

    Di Santo S, Villegas P, Burioni R, Muñoz MA. Landau-Ginzburg theory of cortex dynamics: scale-free avalanches emerge at the edge of synchronization. Proc Natl Acad Sci U S A. 2018;115:E1356-E1365

    2018

  38. [46]

    Early-warning signals for critical transitions

    Scheffer M, Bascompte J, Brock WA, et al. Early-warning signals for critical transitions. Nature. 2009;461:53-59

    2009

  39. [47]

    Dynamics of sleep onset: early-warning signals in EEG

    de Mooij SMM, Blanken TF, Grasman RPPP, Ramautar JR, Van Someren EJW, van der Maas HLJ. Dynamics of sleep onset: early-warning signals in EEG. Comput Methods Programs Biomed. 2020;193:105448

    2020

  40. [48]

    Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep

    Stevner ABA, Vidaurre D, Cabral J, et al. Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep. Nat Commun. 2019;10:1035

    2019

  41. [49]

    Generative embeddings of brain collective dynamics using variational autoencoders

    Perl YS, Bocaccio H, Pérez-Ipiña I, et al. Generative embeddings of brain collective dynamics using variational autoencoders. Phys Rev Lett. 2020;125:238101

    2020

  42. [50]

    The human K-complex represents an isolated cortical down-state

    Cash SS, Halgren E, Dehghani N, et al. The human K-complex represents an isolated cortical down-state. Science. 2009;324:1084-1087

    2009

  43. [51]

    Sleep spindles: mechanisms and functions

    Fernandez LMJ, Lüthi A. Sleep spindles: mechanisms and functions. Physiol Rev. 2020;100:805-868

    2020

  44. [52]

    The sleep slow oscillation as a traveling wave

    Massimini M, Huber R, Ferrarelli F, Hill S, Tononi G. The sleep slow oscillation as a traveling wave. J Neurosci. 2004;24:6862-6870. Toward a Dynamical Taxonomy of Insomnia 28

    2004

  45. [53]

    Regional slow waves and spindles in human sleep

    Nir Y, Staba RJ, Andrillon T, et al. Regional slow waves and spindles in human sleep. Neuron. 2011;70:153-169

    2011

  46. [54]

    Local sleep in awake rats

    Vyazovskiy VV, Olcese U, Hanlon EC, Nir Y, Cirelli C, Tononi G. Local sleep in awake rats. Nature. 2011;472:443-447

    2011

  47. [55]

    Low-frequency (<1 Hz) oscillations in the human sleep electroencephalogram

    Achermann P, Borbély AA. Low-frequency (<1 Hz) oscillations in the human sleep electroencephalogram. Neuroscience. 1997;81:213-222

    1997

  48. [56]

    Parameterizing neural power spectra into periodic and aperiodic components

    Donoghue T, Haller M, Peterson EJ, et al. Parameterizing neural power spectra into periodic and aperiodic components. Nat Neurosci. 2020;23:1655-1665

    2020

  49. [57]

    An electrophysiological marker of arousal level in humans

    Lendner JD, Helfrich RF, Mander BA, et al. An electrophysiological marker of arousal level in humans. eLife. 2020;9:e55092

    2020

  50. [58]

    Analysis of a sleep- dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG

    Kemp B, Zwinderman AH, Tuk B, Kamphuisen HAC, Oberye JJL. Analysis of a sleep- dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE Trans Biomed Eng. 2000;47:1185-1194

    2000

  51. [59]

    EEG signatures of sleep-stage transitions in a large polysomnography cohort

    Passaro AD, Poltorak A. EEG signatures of sleep-stage transitions in a large polysomnography cohort. bioRxiv. 2025. doi:10.1101/2024.12.16.628645

    2025 doi

  52. [60]

    Sleep and human aging

    Mander BA, Winer JR, Walker MP. Sleep and human aging. Neuron. 2017;94:19-36

    2017

  53. [61]

    Theory of dynamic critical phenomena

    Hohenberg PC, Halperin BI. Theory of dynamic critical phenomena. Rev Mod Phys. 1977;49:435-479

    1977

  54. [62]

    Random Perturbations of Dynamical Systems

    Freidlin MI, Wentzell AD. Random Perturbations of Dynamical Systems. 3rd ed. Berlin: Springer; 2012. doi:10.1007/978-3-642-25847-3

    2012 doi

  55. [63]

    Existence of a potential for dissipative dynamical systems

    Graham R, Tél T. Existence of a potential for dissipative dynamical systems. Phys Rev Lett. 1984;52:9-12. doi:10.1103/PhysRevLett.52.9

    1984 doi

  56. [64]

    Testing for the equivalence of factor covariance and mean structures: the issue of partial measurement invariance

    Byrne BM, Shavelson RJ, Muthén B. Testing for the equivalence of factor covariance and mean structures: the issue of partial measurement invariance. Psychol Bull. 1989;105:456-466. doi:10.1037/0033-2909.105.3.456

    1989 doi

  57. [65]

    Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders

    Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, Sanislow C, Wang P. Research Domain Criteria (RDoC): Toward a New Classification Framework for Research on Mental Disorders. Am J Psychiatry. 2010;167:748-751. doi:10.1176/appi.ajp.2010.09091379

    2010 arXiv

  58. [66]

    Diagnostic and Statistical Manual of Mental Disorders: DSM- IV

    American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM- IV. 4th ed. Washington, DC: American Psychiatric Association; 1994

    1994

  59. [254]

    doi:10.1016/j.smrv.2012.09.005

    2012 doi

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.