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Reduced Gibbs free energy supply hinders brain information processing during mental fatigue

T0 review · 3 major / 5 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read Mental fatigue may be a drop in the brain's ATP free energy

desk verdict A clear, well-written hypothesis linking ATP free energy to fatigue, but the load-bearing 0.6 eV threshold is asserted rather than derived, and the neuronal half rests on hand-picked ion shifts. read the letter →

arxiv 2608.10211 v1 pith:7SLTLQSW submitted 2026-08-10 q-bio.NC

Danko D. Georgiev , Oskan B. Tasinov , Danail V. Pavlov , Deyan S. Hrusafov This is my paper · ORCID
classification q-bio.NC
keywords mentalfatigueATPhydrolysisGibbsfreeenergyamideIexcitonmolecularsolitonNernstreversalpotentialneuronalexcitabilitydepolarizationblock
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

This theoretical paper argues that mental fatigue is not just a subjective feeling but a measurable thermodynamic state: as neurons work, the reaction quotient of ATP hydrolysis rises, so the free energy released per ATP molecule falls. The paper claims that when this free energy drops below about 0.6 eV, protein machinery that relies on three coupled amide I excitons loses its thermal stability, and the solitons that carry energy through protein alpha-helices survive for only about 20 ps instead of 50 ps, cutting their reliable transmission range to less than half. Simultaneously, the paper models how degraded sodium and potassium gradients shift reversal potentials, making pyramidal neurons hyperexcitable and prone to depolarization block. If correct, fatigue is a reversible continuum between a rested state and a paralyzed one, and cognitive errors can occur before any subjective tiredness sets in.

What carries the argument

The central mechanism is the coupling between ATP hydrolysis free energy and the number of amide I exciton quanta created in protein alpha-helices. The paper uses a stochastic three-spine exciton-lattice model for energy transport in the presence of thermal noise, and a morphologically detailed CA1 pyramidal neuron model for action-potential firing. The identity that carries the argument is the integer quantization: one amide I quantum costs $0.2$ eV, so a rested brain's $0.62$ eV excites three quanta but a fatigued brain's sub-$0.6$ eV excites at most two, flipping soliton lifetime and range. The second half of the mechanism is the Nernst equation, which converts weakened ion gradients into shifted reversal potentials that raise excitability and lower the stimulation threshold for depolarization block.

What would settle it

A direct measurement of amide I exciton lifetimes in a protein alpha-helix while titrating the ATP hydrolysis free energy across the 0.6 eV threshold would settle the question: if the reliable transmission range does not sharply drop to below 42.3% of rested range, or if lifetimes glide smoothly instead of stepping from 50 ps to 20 ps, the central claim fails.

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Extended reading notes

Core claim

The paper's central claim is that mental fatigue is the brain's macroscopic expression of a reduced thermodynamic driving force: $|\Delta G_{\mathrm{ATP}}|$ falls as reaction products accumulate, and this single drop produces two cascading failures. At the nanoscale, the free energy from one ATP hydrolysis becomes an integer number of amide I exciton quanta of $0.2$ eV each; the paper holds that $0.62$ eV supports three quanta and a stable molecular soliton with a lifetime of at least 50 ps, whereas below $0.6$ eV only two quanta are possible and the soliton disperses in about 20 ps, limiting reliable energy delivery to less than $42.3\%$ of the rested range. At the cellular scale, weaker ATP supply degrades the Na$^+$/K$^+$ gradients, shifting reversal potentials from $E_{\mathrm{Na}}=71$ mV, $E_{\mathrm{K}}=-89$ mV toward $E_{\mathrm{Na}}=60$ mV, $E_{\mathrm{K}}=-70$ mV; in a CA1 pyramidal neuron model this raises excitability and causes depolarization block at lower injected currents, degrading signal-to-noise ratio and accumulating extracellular glutamate. The paper concludes that fatigue is a reversible continuum of functional states bounded by full rest and depolarization block, with a phase of subtle incapacitation before subjective tiredness.

Load-bearing premise

The argument depends on the premise that the energy from splitting one ATP molecule lands as whole packets of 0.2 eV in the protein's vibrating bonds, so that a rested cell's 0.62 eV creates three packets but a fatigued cell's below-0.6 eV creates at most two; if that conversion has losses or produces fractions, the claimed large drop in soliton lifetime does not follow.

Editorial extensions

If this is right

  • If free energy drops below 0.6 eV, reliable molecular-soliton transmission falls to less than 42.3% of the rested range, so ATP supply directly sets a protein-level information transport limit.
  • Neurons with shifted reversal potentials enter depolarization block at lower stimulation, so fatigue lowers the ceiling on cognitive throughput before subjective tiredness appears.
  • Because the rested state is the high free-energy state, intermittent short rest periods should outperform less frequent long breaks in restoring ATP reaction quotients.
  • Fatigue is a continuous, reversible spectrum; subtle incapacitation can occur with no felt tiredness, which matters for safety-critical jobs like piloting or air traffic control.
  • Extracellular glutamate accumulates as a downstream consequence, linking the thermodynamic model to metabolite measurements in the prefrontal cortex.

Reading between the lines

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

  • If the integer-quantum mapping is right, the same threshold logic should appear in other ATP-hungry tissues, so cardiac muscle soliton transport should degrade at the same 0.6 eV boundary; that could be tested in isolated cardiomyocyte energy-transport models.
  • The model predicts a sharp, not gradual, change in neuronal information reliability as $|\Delta G_{\mathrm{ATP}}|$ crosses 0.6 eV; a careful dose-response study of cognitive performance against measured ATP/ADP/Pi ratios could look for such a discontinuity.
  • The hyperexcitability step suggests that early fatigue could present as distractibility or attention lapses, and that interventions restoring reversal potentials might work before subjective energy is affected.
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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

3 major / 5 minor

Summary. The manuscript proposes a thermodynamic mechanism for mental fatigue: reduced Gibbs free energy from ATP hydrolysis (below 0.6 eV) restricts the number of amide I exciton quanta available to stabilize protein solitons in α-helices, shortening their reliable transmission range, while concurrently shifted Na+ and K+ Nernst reversal potentials increase neuronal excitability and lead to depolarization block. The authors compute organ-specific |ΔG_ATP| values using the Gibbs free energy equation (Eq. 1) and literature concentrations (Table 1), obtain 0.62 eV for brain, and then invoke Davydov soliton simulations from prior work to assert that three 0.2 eV quanta give a soliton lifetime of at least 50 ps and range 45 nm, versus 20 ps and 19 nm for two quanta. They also simulate a CA1 pyramidal neuron with NEURON under rested (ENa = 71 mV, EK = -89 mV) and fatigued (ENa = 60 mV, EK = -70 mV) reversal potentials, finding hyperexcitability and depolarization block. The conclusion is that fatigue is a reversible continuum between rested and exhausted states, with practical recommendations for intermittent rest.

Significance. If the central mapping from chemical free energy to integer numbers of amide I quanta were justified, the paper would offer a concrete, quantitative physical mechanism for mental fatigue, with a specific energy threshold (0.6 eV) linking molecular energy transport to cellular excitability. The authors use standard thermodynamic equations and cite reproducible computational infrastructure, including the NEURON model (ModelDB 143719) and explicit equation sets for the Davydov model. However, the significance depends critically on an asserted, not derived, quantization of ATP free energy into amide I excitons and on hand-picked ion concentration shifts; as written, the paper demonstrates a plausible narrative rather than an established mechanism.

major comments (3)
  1. [Section 3.1, Eqs. (1)-(6) and text after Fig. 2] The central claim that 0.62 eV of Gibbs free energy from ATP hydrolysis excites exactly three 0.2 eV amide I quanta, and that a drop below 0.6 eV leaves at most two, is asserted without derivation. No coupling Hamiltonian between the ATP hydrolysis reaction coordinate and the amide I C=O stretching modes is given, and no partitioning of the released free energy into phonons, solvation, or other intramolecular degrees of freedom is analyzed. The 0.6 eV threshold is therefore not an independent prediction but a restatement of the assumed 0.2 eV quantum energy multiplied by three; all subsequent quantitative claims (efficiency >96.7%, safety margin <3.3%, range restriction <42.3%) inherit this assumption. The sensitivity is consequential: the amide I frequency near 1650 cm^-1 corresponds to about 0.205 eV per quantum, which shifts the three-quantum threshold to 0.615 eV and reduces the rested-brain safety margin from the claimed 0.02 eV to about 0.005 eV. Please either derive or explicitly justify the conversion efficiency and the quantization, or reframe these claims as conditional scenarios with a sensitivity analysis.
  2. [Section 2.3, Eq. (10) and Fig. 4] The fatigued-state reversal potentials (ENa = 60 mV, EK = -70 mV) are introduced as a 'conservative' example, but no sensitivity analysis is provided and the corresponding ionic concentrations (Figure 4E) are not justified as representative of mental fatigue. The text cites an experiment with 10 s stimulation and [K+]out = 10 mM [49], which is consistent with EK = -70 mV given [K+]in = 135 mM, but that experiment does not establish ENa = 60 mV, and the 5 mM exchange of intracellular K+ for Na+ is a specific assumption rather than a measured change. Because the increased excitability and depolarization block constitute the cellular-level pillar of the fatigue continuum, the results should be shown over a range of reversal potentials or anchored to measurements obtained during mental fatigue.
  3. [Section 3.1, Eqs. (7)-(8) and Fig. 3] The simulations that produce the central 50 ps versus 20 ps soliton lifetime comparison are not reported or reproduced in this manuscript; they are taken from the authors' own prior papers [23, 29]. As a result, the paper does not provide an independent check that the difference is attributable to Λ=3 versus Λ=2 rather than to the specific initial conditions or noise realizations used previously. Moreover, the claim that 'for Λ=3, the maximal distance is up to 45 nm' assumes a constant propagation speed of 900 m/s over the full 50 ps lifetime, and neither 'lifetime' nor 'reliable transmission' is defined by a quantitative dispersal criterion. Please include an ensemble statistic with error bars, a definition of lifetime, and a clear statement that the curves are reproduced from the cited papers.
minor comments (5)
  1. [Section 1] The daily ATP consumption estimates (25.6 mol for brain, 9.86 mol for heart) are presented without showing the arithmetic; please add a sentence explaining the conversion from glucose consumption and ATP yield per glucose.
  2. [Section 2.1, Eq. (1)] Equation (1) reports Gibbs free energy in eV but the expression kBT/qe is not defined; please state explicitly that qe converts joules to electronvolts and define all symbols in one place.
  3. [Section 3.1] The statement that a 2 μm photon is '400 times wider than the average protein diameter' compares wavelength to diameter and does not by itself establish low absorption probability; please rephrase to describe the physical mismatch (e.g., diffraction limit or vanishing overlap of the photon mode with the molecular transition).
  4. [Section 4.1] The four-step mechanism is clearly stated, but the quantitative link between reduced |ΔG_ATP| and the specific Nernst shifts in Section 2.3 is missing; a brief estimate of how much ATP deficit corresponds to a 1 mM change in intracellular [Na+] or extracellular [K+] would strengthen the narrative.
  5. [Figure 3] The color scale in Figure 3 is not labeled; please add a color bar or state the peak value (0.15) of the exciton probability in the caption.

Circularity Check

2 steps flagged · score 6.0 of 10

The 0.6 eV fatigue threshold is 3 multiplied by the assumed 0.2 eV amide I quantum, so the <42.3% range collapse is a restatement of the model's input mapping; the soliton lifetimes are imported from the authors' own prior simulations.

  1. self definitional [Section 3.1, 'Importance of ATP energy for protein function', eq. (3), Figure 3 and threshold discussion]
    "It is noteworthy that individual amide I excitons have an energy of 0.2 eV ... Depending on the available amount of free ATP energy, two or three amide I excitons can be generated ... In the rested state, 0.62 eV of ATP energy is sufficient to excite 3 amide I exciton quanta, however if |∆G ATP|<0.6 it will be able to excite at most 2 amide I exciton quanta."

    The paper presents the 0.6 eV cutoff as a quantitative result, but 0.6 eV is definitionally 3 × 0.2 eV, the assumed energy of one amide I quantum. No coupling or efficiency calculation connects the Gibbs free energy of ATP hydrolysis to the creation of amide I excitons; the paper simply asserts that 'depending on the available amount' two or three quanta can be generated. Consequently, the 'prediction' that fatigue below 0.6 eV leaves at most two quanta and restricts the reliable soliton range to <42.3% is the input mapping restated, not independently derived.

  2. self citation load bearing [Section 3.1, Figure 3, refs [23] and [29]]
    "We performed detailed computer simulations based on Davydov's model of energy transport inside a protein α-helix immersed in a thermal bath with physiological temperature T=310 K [23], which showed that a protein soliton comprised of Λ=3 amide I exciton quanta has a lifetime of at least 50 ps (Figure 3A), whereas a soliton comprised of only Λ=2 amide I exciton quanta persists for approximately 20 ps (Figure 3B)."

    The quantitative core of the nanoscale arm, namely the 50 ps versus 20 ps lifetimes and the corresponding 45 nm versus 19 nm distances, is taken directly from prior papers by the paper's own first author, refs [23] and [29], without reproduction of the simulations, release of code, or comparison against independent experimental data. The paper's conclusion of 'decreased thermal stability' of molecular solitons is therefore a restatement of those self-cited outputs. If those prior simulations are not accepted, the claimed fatigue-induced reduction of the reliable range loses its quantitative anchor, so the argument is load-bearing self-citation rather than an independent derivation.

full rationale

The paper's neuronal arm is not circular: it takes a previously published CA1 pyramidal neuron model (ModelDB ID 143719), inserts shifted Nernst reversal potentials corresponding to assumed fatigue conditions, and then reports the computed f-I curves and depolarization block. Those outputs are genuine model consequences, not fits of the conclusion. The circularity is concentrated in the soliton arm. The key thermodynamic threshold is constructed from the assumed 0.2 eV amide I quantum energy: 0.6 eV is exactly three quanta, so the statement that fatigue 'below 0.6 eV' causes a transition from 3-quantum to 2-quantum soliton behavior is a definitional consequence of the integer-quantization ansatz, not a derived result. The dramatic range ratio (<42.3%) then follows from the authors' own prior simulations of Λ=3 versus Λ=2 solitons, which are cited without independent reproduction. For these reasons the central nanoscale 'prediction' partially reduces to its inputs: the threshold is definitional and the simulation outputs are self-cited. Because the NEURON results are independent and the soliton model is at least a concrete computational model, the overall score is 6 rather than 8.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new entities. Its free parameters are the hand-chosen fatigued Nernst potentials and the integer exciton number mapping. The axioms include standard thermodynamics and two domain-specific modeling assumptions that are central to the claimed results.

free parameters (2)
  • Fatigued-state Nernst reversal potentials (ENa=+60 mV, EK=-70 mV) = ENa=60 mV, EK=-70 mV
    Hand-assigned in Section 2.3 to represent mental fatigue; based on an exchange of 5 mM ions, no sensitivity analysis, and all f-I curve conclusions depend on this choice.
  • Amide I exciton quantum number per ATP hydrolysis (Lambda=2 vs 3) = 2 or 3
    Chosen from the 0.2 eV per quantum energy and the computed |DeltaG_ATP|; the mapping is asserted rather than derived.
assumptions (4)
  • standard math Gibbs free energy equation and Nernst equation are applicable to intracellular conditions with activities approximated by concentrations.
    Equations (1), (6), (10) use textbook formulas with concentration ratios.
  • ad hoc to paper The free energy from ATP hydrolysis is converted into integer amide I exciton quanta of 0.2 eV each.
    Section 3.1: 'Depending on the available amount of free ATP energy, two or three amide I excitons can be generated'; no derivation for this energy partition.
  • ad hoc to paper The chosen fatigued-state ion concentrations (ENa=60, EK=-70) represent mental fatigue in humans.
    Section 2.3: 'To simulate physical conditions of mental fatigue, we have investigated neuronal firing with degraded Nernst reversal potentials...' based on a conservative 5 mM exchange.
  • domain assumption Davydov soliton model with the specified parameters is a valid description of protein energy transport.
    Relies on prior model [23] by the same first author; the model remains debated in the literature.

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Cite this review

Pith. "Pith review of Reduced Gibbs free energy supply hinders brain information processing during mental fatigue." pith.science (2026). https://pith.science/paper/7SLTLQSW

@misc{pith2026260810211,
  author       = {Pith},
  title        = {Pith review of: Reduced Gibbs free energy supply hinders brain information processing during mental fatigue},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7SLTLQSW}},
  note         = {Machine review of arXiv:2608.10211}
}
abstract

Background: Brain information processing deteriorates as cortical neurons gradually transition from a rested state into fatigue. Subjectively, fatigue is experienced as a state of weariness, tiredness, or lack of energy that reduces the ability to work safely and effectively. Objective: In this theoretical paper, we pinpoint the physical origin of brain fatigue in the gradual deterioration of biochemical reaction quotients and transmembrane ion concentration gradients, which increase neuronal excitability and decrease the signal-to-noise ratio in the brain cortex. Methods: Brain performance in a rested state versus fatigue is examined by well-established, data-driven computer models for energy transport inside protein $\alpha$-helices in the presence of thermal noise for different ATP energy states or for pyramidal neuron firing of action potentials under electric stimulation in a rested membrane state versus fatigue. Results: We found that reduced Gibbs free energy supply from ATP hydrolysis impairs the cooperative effect between amide I excitons propagating inside protein $\alpha$-helices, with resulting decreased thermal stability of molecular solitons. Concurrent changes in Nernst reversal potentials for Na+ or K+ ions further led to neuronal hyperexcitability and a higher risk of neuronal depolarization block during mental fatigue. Conclusions: Detailed computational modeling showed that inefficient protein function due to diminished ATP energy status, alters the electrophysiological properties of individual neurons, thereby impairing their information processing capacity for the proper execution of cognitive tasks. Scheduling practices aimed at intermittent recovery of the rested brain state during intellectually challenging work could protect physical and mental wellbeing, prevent burnout, and enhance long-term productivity.

Figures

Figures reproduced from arXiv: 2608.10211 by the authors.

Figure 1
Figure 1. ATP utilization in the brain. (A) The morphology of a pyramidal neuron (NeuroMor [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Atomic structure of poly-alanine protein [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Thermal stability of protein solitons with different numbers of amide I exciton quanta [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: CA1 pyramidal neuron (NeuroMorpho ID: NMO_00123) [31] firing under different elec [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]

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Pith tools

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