A Conductance Based Amygdala Model of Threat Processing in Anxiety and Depression

Indrakshi Dey; Malik Faizan; P.J.White

REVIEW 4 major objections 4 minor 36 references

A Conductance Based Amygdala Model of Threat Processing in Anxiety and Depression

T0 review · 4 major / 4 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A nine-equation amygdala–hypothalamus loop reproduces three stress-regulatory regimes and links psychometric scores to cardiovascular output.

desk verdict A promising compact model with a real kernel, but the reported strong-regime numbers contradict the paper's own Eq. (9), and the external validation is partly calibration; worth refereeing, not accepting as-is. read the letter →

arxiv 2608.03712 v1 pith:DZL34DCG submitted 2026-08-04 eess.SY cs.SY

Malik Faizan , P.J.White , Indrakshi Dey This is my paper

classification eess.SYcs.SY

keywords amygdalaanxietydepressioncomputationalmodelingcardiovascularresponsebaroreflexHodgkin-Huxleydigitalphenotyping

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 paper tries to show that a single, compact circuit model—nine equations describing an amygdala neuron, a hypothalamic integrator, and a baroreflex feedback loop—can account for how anxiety and depression shape the body's stress response. The authors extend the standard Hodgkin-Huxley neuron formalism with three clinically meaningful knobs: coping capacity, perceived stress load, and prefrontal regulatory strength. These knobs move an adaptive threshold and a slow internal state so that healthy, subclinical, and clinical individuals become three operating regimes of the same closed-loop system, not separate mechanisms. The model generates heart-rate and blood-pressure trajectories that stay inside published stress-physiology ranges and, the authors report, match three independent real-world datasets. The central insight is that an adaptive, state-dependent threshold, not the slow sensitization variable, is the load-bearing mechanism that separates the regimes.

What carries the argument

The adaptive threshold θ(t) = θ0 + Kc·C − Kp·P + Kr·RPFC, read out through a graded sigmoid A = σ(u(Vm−θ)), is the load-bearing device. It converts shifting psychometric state into a decision variable—membrane voltage minus threshold—whose sign and magnitude determine threat acknowledgment, hypothalamic drive, and ultimately heart-rate and blood-pressure output. The slow gating variable h(t), a reinterpreted Hodgkin-Huxley inactivation variable, multiplies inhibitory conductance and is described as accumulating sensitization across pulses, but the paper's ablation shows it is mainly an amplifier rather than the source of regime separation.

What would settle it

Record an amygdala neuron's membrane potential during repeated depolarizing pulses with inter-pulse intervals shorter than the 220 ms time constant of the weak regime: if successive pulses produce progressively smaller responses, the h(t) dynamics act as inhibition, contradicting the model's claimed cross-pulse sensitization. Alternatively, run the model with h(t) clamped to a constant and check whether regime separation persists; the paper's own ablation suggests it does, which any reader can verify from the nine equations.

Watch

Extended reading notes

Core claim

On the paper's terms, the discovery is that a single amygdala–hypothalamus–cardiovascular loop, with no architecture switching, produces the full spectrum of threat responses by shifting two quantities: an adaptive threshold θ(t) that rises with coping and prefrontal control and falls with perceived stress, and a graded threat-acknowledgment signal A = σ(u(Vm−θ)). In the strong regime the membrane voltage never crosses threshold, in the moderate regime it crosses transiently, and in the weak regime it stays above threshold for extended periods; the resulting hypothalamic drive and baroreflex feedback yield heart-rate and blood-pressure excursions that increase and recover more slowly as regu

Load-bearing premise

The load-bearing premise is the model's interpretation of its slow internal state h(t) (Section II-B) as a substrate for stress sensitization: h multiplies inhibitory conductance and rises with membrane voltage, so if cumulative activity actually inhibits rather than sensitizes the amygdala, the paper's sensitization claim collapses even though the adaptive threshold may still separate regimes.

Editorial extensions

If this is right

A single closed-loop architecture, without switching, produces healthy, subclinical, and clinical cardiovascular stress profiles from the same sensory input.
The adaptive threshold is the principal mechanism for quantitative regime separation; the slow internal state mostly amplifies differences already set by the threshold.
Simulated heart-rate and blood-pressure peaks and recovery times stay inside published acute-stress ranges, so the model can serve as a mechanistic map from psychometric scores to wearable-observed autonomic trajectories.
The model reproduces real-world cardiovascular magnitudes (about 3.7% mean absolute percentage error on the truck-driver dataset), monotonic heart-rate increases with stress burden, and state-dependent hemodynamic shifts in the stroke-rehabilitation cohort.
Variability and parameter-perturbation analyses preserve regime ordering, supporting the claim that the three regimes are stable operating states rather than artifacts of a single parameter choice.

Reading between the lines

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

Editorial extension: because the threshold is linear in the three modulators, the model predicts a composite score (resilience minus perceived stress plus prefrontal index) should track cardiovascular reactivity; this is testable in the same datasets without collecting new physiology.
Editorial extension: the threshold-ablation result implies that interventions which raise the adaptive threshold—such as strengthening prefrontal regulation—should shift a weak-regime profile back toward moderate or strong, a prediction that can be checked in silico before any clinical trial.
Editorial extension: cohort-level validation supports population-level stratification and digital phenotyping more than person-specific prediction; moving to digital twins will require inverse estimation of the modulators from wearable data and adding the HPA axis and inflammatory signalling that the paper explicitly excludes.
Editorial extension: the slow internal state's sign is an empirical handle—if stressors spaced closer than its roughly 220 ms time constant do not produce cross-pulse accumulation under the stated equations, the sensitization interpretation should be revised.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Quick take: this is a paper with a real kernel—a compact closed-loop amygdala–hypothalamus–baroreflex model with an adaptive threshold—but the strong-regime results in Section IV-C contradict the paper's own Eq. (9), and the external validation is partly calibration wearing validation clothes. I would send it to referees, but I would expect them to send it back for major revision.

What's genuinely good: the model is small and interpretable, the idea of mapping CD-RISC/PSS-10-style modulators onto threshold dynamics is sensible, and the ablation in Section V-B is the strongest part. It cleanly shows that the adaptive threshold, not the slow state h(t), does most of the work separating regimes. The robustness sweeps over noise and ±20–25% parameter perturbations are a reasonable check, and the continuous-regulation-axis framing is consistent with the transdiagnostic literature.

Now the problems, in order of severity.

First, the arithmetic in Section IV-C does not close. The text says strong-regulated peaks sit near Vm ≈ −50 mV with threshold θ ≈ −50.25 mV. That puts Vm − θ at +0.25 mV. With u = 0.20 mV⁻¹, Eq. (9) gives A ≈ σ(0.05) ≈ 0.51, not the reported mean A = 0.060 or the claim that A stays below 0.2 and never crosses threshold. To get A = 0.060 you would need Vm − θ ≈ −13.8 mV. This is not rounding; the sign is backward. Since the mean A values (0.060 / 0.241 / 0.475) are the headline evidence for regime separation, the paper's central quantitative claim is currently unsupported.

Second, the slow state h(t) is described as the substrate for "sustained sensitization," but it multiplies inhibitory synaptic conductance and its steady state h∞(Vm) increases with voltage. That is an activity-dependent inhibition enhancer, which should reduce responsiveness, not sensitize it. The ablation shows h is not the main driver, so the model may survive without this interpretation, but as written the claimed mechanism is wrong.

Third, the external validation in Section V-D is not as independent as claimed. Dataset quartiles are fed in as baseline inputs, and the output gains g_hr and g_sbp are never reported. With hand-set gains and no code, the 2–5% errors are consistency checks, not predictions.

What is here is publishable after serious revision: fix the strong-regime numbers and report the actual Vm−θ traces, either fix the h(t) interpretation or drop the sensitization language, report all gains and baselines, and re-frame the dataset comparisons as plausibility checks. The topic matters and the ablation is a legitimate contribution. Send it to peer review, but flag the Section IV-C contradiction for referees.

Referee Report

4 major / 4 minor

Summary. The paper presents a nine-equation conductance-based model of an amygdala–hypothalamus–cardiovascular loop. A single amygdala neuron receives sensory drive gated by prefrontal regulatory strength; an HH-style gating variable h is reinterpreted as a slow, history-dependent internal state; an adaptive threshold θ depends linearly on coping capacity, perceived stress, and prefrontal strength; a graded threat readout A feeds a hypothalamic integrator with baroreflex feedback; and the resulting state drives HR/SBP/DBP excursions. The authors report three regulatory regimes (strong, moderate, weak), robustness to noise and parameter perturbation, an ablation study identifying the adaptive threshold as the principal mechanism, and external validation against three datasets.

Significance. If the numerical and conceptual issues were resolved, this compact model could provide a useful, interpretable bridge between psychometric constructs and autonomic signals, with potential applications in digital phenotyping. The paper's strengths are that it states all equations explicitly, gives parameter tables, includes robustness and ablation analyses, and benchmarks against published stress-physiology ranges and external datasets. However, the strong-regime description is internally inconsistent with the stated readout equation, the slow-state h(t) has the wrong sign for the claimed sensitization mechanism, the psychometric-to-parameter mapping is not actually specified, and the external validation is in part circular. These are load-bearing problems for the paper's central claims.

major comments (4)

[Section IV-C and Eq. (9)] The strong-regime numerical description is internally inconsistent. The text states peak Vm ≈ −50 mV and θ ≈ −50.25 mV, so Vm−θ ≈ +0.25 mV, while also stating that Vm−θ remains negative and A stays below approximately 0.2, with mean A = 0.060. With u = 0.20 mV⁻¹, Eq. (9) gives A = σ(0.20·0.25) ≈ 0.512, not 0.060; obtaining A = 0.060 would require Vm−θ ≈ −13.8 mV. This sign error propagates to y(t) in Eq. (10), the hypothalamic state in Eq. (11), and the reported cardiovascular excursions in Fig. 4. Since no code or complete parameter set is provided, the results cannot be independently checked.
[Section II-B, Eqs. (3) and (7)] The slow state h(t) has the wrong sign/interpretation for the claimed sensitization mechanism. In Eq. (3), h multiplies the inhibitory synaptic conductance, so for Vm > Esyn = −70 mV, larger h increases a hyperpolarizing current. Equation (7) makes h_inf increase with Vm, so activation builds up inhibition. This is a negative-feedback adaptation variable, not a substrate for the stated 'sustained sensitization' or 'cross-pulse accumulation' that heightens threat responses (Sections II-B and IV-B). If h is instead intended to encode disinhibition, either Eq. (3) or the voltage dependence of h_inf must be reversed. The ablation in Section V-B shows h is not the principal regime separator, but Contribution 1 still assigns it a central mechanistic role.
[Section II-B and Table II] The claimed psychometric-to-parameter mapping is not specified. The paper states that C, P, and RPFC are operationalized through CD-RISC, PSS-10, and a neuroimaging-derived prefrontal index, but it never provides the transformation from raw scale scores to the bounded [0,1] modulator values. Table II simply assigns C = 0.75/0.50/0.25, P = 0.25/0.50/0.75, and RPFC = 0.75/0.50/0.25 by regime. Without this mapping, the model cannot be driven by clinical questionnaires, and the 'clinically grounded modulators' claim is untestable.
[Section V-D and Eq. (17)] The external validation is not genuinely independent. For the first dataset, the model is given 'quartile statistics giving baseline inputs per regime' from the same dataset before simulated peaks are compared with observed peaks. This means resting cardiovascular values (HR_rest, SBP_rest, DBP_rest) and possibly gains are informed by the data being predicted. The reported 3.7% MAPE is therefore at least partly an in-sample calibration result, not an out-of-sample validation. The paper does not report which parameters were set from the data or how g_hr and g_sbp were chosen. Dataset 2 is a single-subject monotonic trend and dataset 3 provides directional LME results; these support qualitative ordering but not quantitative model predictions.

minor comments (4)

[Section III-A / Table I] Table I omits several parameters used in the equations: resting cardiovascular values (HR_rest, SBP_rest, DBP_rest), baroreflex parameters (η_b0, φ_0, δ), and the initial-condition constants hy0 and n(0) are only given in the text. A reader cannot reproduce the simulations from the paper alone.
[Section III-B vs Section IV-D] Section III-B sets the simulation window to T = 5000 ms, but Section IV-D refers to an '8000 ms observation window'. This inconsistency should be reconciled.
[Section IV-C] The claim that the fraction of time with A > 0.5 increases by 'roughly two orders of magnitude' is not accompanied by the underlying fractions; numerical values would make the comparison more transparent.
[References] Reference [28] contains the author name 'LaR. Lindsey', which appears to be a typographical error for 'R. Lindsey'.

Circularity Check

1 steps flagged · score 5.0 of 10

External validation on the first dataset is partly by construction: dataset quartiles enter as baseline inputs and the hemodynamic gains g_hr/g_sbp are unreported; the core model equations are not otherwise circular.

fitted input called prediction [Section V-D (External Empirical Validation), with Eq. (17) in Section II-C]
"Records were stratified into resilience-based groups approximating moderate and severe stress, with quartile statistics giving baseline inputs per regime. Simulated peak HR, SBP, and DBP (Table III) matched observed peaks to a mean absolute percentage error of about 3.7% (typically 2-5% per variable). [Eq. 17:] HR(t)=HR_rest+ΔHR(t), ΔHR(t)=g_hr·(h_y(t)-h_y0); SBP(t)=SBP_rest+ΔSBP(t), ΔSBP(t)=g_sbp·(h_y(t)-h_y0)."

The simulated peaks are computed from Eq. (17) as baseline values plus an unstated scaling of the hypothalamic state. In the first external-validation dataset, the baseline values HR_rest/SBP_rest/DBP_rest are taken from the same dataset's quartile statistics, i.e. from the very records whose peaks are then declared 'matched' to 2-5% error. Moreover, the gains g_hr and g_sbp that determine the peak excursions are not reported in Table I or elsewhere in the paper, so they are free to absorb the remaining mismatch. The 'prediction' therefore reduces to target-cohort baselines plus unreported slopes; the quantitative agreement in Table III is partly by construction and cannot be verified as an independent model prediction.

full rationale

The paper's central biophysical derivation—Eqs. (1)-(17)—is not fitted to the validation datasets, and there are no load-bearing self-citations, imported uniqueness theorems, or ansatz-smuggling citations by the authors. The main circularity concern is confined to the first external-validation component: Section V-D explicitly feeds the target dataset's quartile statistics into the model as baseline inputs, and Eq. (17)'s output gains g_hr/g_sbp are never given numerical values. That makes the reported 2-5% agreement partly a reconstruction rather than a free prediction. The other two datasets provide more independent directional support (monotonic HR shifts and LME-based state effects), so the overall central claim retains some independent content. Separately, the strong-regime numbers in Section IV-C are internally inconsistent with Eq. (9): at the quoted Vm≈−50 mV and θ≈−50.25 mV, A=σ(0.2·0.25)≈0.51, not the stated A<0.2 with mean 0.060; and the h(t) sign/interpretation issue (larger h increases inhibition while being described as sensitization) is a correctness/biological-plausibility problem. These are not circularity per se, but they compound the reliability concerns. Overall score 5: one prediction component reduces substantially to its inputs/unreported fit parameters, but the derivation is not wholly self-referential.

Assumptions & free parameters 7 free parameters · 6 assumptions · 1 invented entities

The model depends on a large set of hand-set parameters and domain assumptions. The most problematic is the reinterpretation of the HH gating variable h as a sensitization state: the equations give it an inhibitory, activity-damping role, the opposite of the claimed mechanism. The external validation also introduces free baseline values from the target datasets, further increasing the parameter burden and reducing the independence of the validation.

free parameters (7)

Threshold scaling coefficients Kc, Kp, Kr = 4, 5, 4 mV
Hand-tuned to keep the sigmoid readout in the graded range (Section II-C, Table I).
Sigmoidal readout gain u = 0.2 mV^-1
Calibrated to produce graded threat acknowledgment (Section II-C, Table I).
Baroreflex/NTS coefficients Kb, Kbs, Kn = 0.30, 0.075, 0.045
Hand-picked coupling constants; no empirical fit cited (Table I).
Output gains g_hr, g_sbp = unreported
Gains mapping hypothalamic state to HR and SBP changes (Eq. 17) are not listed in Table I, which prevents independent reproduction of absolute cardiovascular peaks.
Regime-specific parameters alpha, beta, gamma, delta, tau_h = Table II values
Chosen by hand to represent strong, moderate, and weak regulation; these settings define the regimes, so qualitative regime separation is partly built in.
Modulator values C, P, RPFC per regime = e.g., 0.75/0.25/0.75 strong
Arbitrary scalings used for the three regimes; no formula maps real CD-RISC or PSS-10 scores to these values.
Resting cardiovascular values HR_rest, SBP_rest, DBP_rest = unreported in Table I, taken from dataset quartiles in V-D
Needed to convert deviations to absolute peaks; in validation these are read from the target dataset, making the fit partly circular.

assumptions (6)

domain assumption A single isopotential compartment with implicit spiking represents the amygdala's role in threat processing
Section II-A and V-G state this as a deliberate simplification.
domain assumption Psychometric instruments (CD-RISC, PSS-10) and a prefrontal index map linearly to model modulators C, P, RPFC
Section II-B asserts this mapping but provides no calibration formula.
ad hoc to paper The HH gating variable h can be reinterpreted as a slow internal state whose steady state increases with Vm and which multiplies inhibitory conductance
Eq. (7) in Section II-B; the sign convention is not standard for sensitization and is never justified physiologically.
domain assumption Baroreflex feedback can be represented by a first-order NTS leaky integrator with sigmoidal baroreceptor input
Section II-C, Eqs. (12)-(15).
domain assumption Short-term cardiovascular stress responses are driven mainly by the neurocircuit path; HPA axis and inflammation can be neglected
Section V-G explicitly excludes HPA and inflammatory signaling.
standard math Forward Euler with dt=0.1 ms is a numerically stable integration scheme for the coupled equations
Section III-B; no stability analysis is provided, but the time constants make it plausible.

invented entities (1)

Reinterpreted slow state h(t) as a cumulative stress-sensitization variable
purpose: To model history-dependent amygdala responsiveness across stress timescales
This is a new modeling construct with no direct experimental counterpart cited. Moreover, its implementation as a multiplier on inhibitory conductance with h_inf increasing with Vm makes it an activity-dependent inhibition enhancer, which conflicts with the 'sensitization' narrative in the text.

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Cite this review

Pith. "Pith review of A Conductance Based Amygdala Model of Threat Processing in Anxiety and Depression." pith.science (2026). https://pith.science/paper/DZL34DCG

@misc{pith2026260803712,
  author       = {Pith},
  title        = {Pith review of: A Conductance Based Amygdala Model of Threat Processing in Anxiety and Depression},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DZL34DCG}},
  note         = {Machine review of arXiv:2608.03712}
}

read the original abstract

Anxiety and depressive disorders are increasingly viewed as dysregulations along continuous stress-regulatory dimensions. However, existing computational approaches seldom connect interpretable circuit level mechanisms to autonomic physiology. Methods: This study develops a mechanistic framework that links amygdala dysregulation to cardiovascular stress responses for digital phenotyping and clinical interpretation. We formulated a compact, nine equation, conductance based model of the amygdala hypothalamus cardiovascular pathway. The framework extends Hodgkin Huxley formalism with three clinically grounded modulators: coping capacity, perceived stress load, and prefrontal regulatory strength. A slow, history-dependent internal state, adaptive thresholding, graded threat acknowledgement, and baroreflex coupled hypothalamic integration were used to generate heart rate and blood pressure trajectories. Results: Distinct strong, moderate, and weak regulatory regimes emerged as stable operating states of a single closed-loop system. Robust analyses showed that stochastic variability and parameter perturbation preserved regime separation, while ablation studies identified the adaptive threshold as the principal mechanism driving quantitative regime separation. Simulated cardiovascular responses remained within reported stress physiology ranges. Furthermore, external evaluation across three independent datasets supported robust agreement with real world, stress related autonomic patterns. Conclusion: A compact, mechanistic model can jointly link psychometric modulators, amygdala excitability, and downstream cardiovascular output within a single, interpretable framework. Significance: This work provides a computationally tractable basis for mechanism informed digital phenotyping, patient specific stress monitoring, and future digital twin approaches for mental health decision support.

Figures

Figures reproduced from arXiv: 2608.03712 by the authors.

**Figure 2.** Computational schematic of amygdala threat processing with cognitive [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Closed-loop baroreflex feedback linking hypothalamic regulation, [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (10 more)

**Figure 4.** Figure 4: Overview of regime behavior across the anxiety/depression spectrum. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: Slow internal state dynamics. (a) Stimulus [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 7.** Figure 7: Membrane Voltage Vm(t) and threshold θ(t) [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]

**Figure 6.** Figure 6: (a) Family of h∞(Vm) curves. (b) Time course of h(t) for the three regimes. Figure 6a shows h∞(Vm) for different slope factors, defining a slow manifold in the (Vm, h) plane, and Figure 6b the time courses of h(t), contrasting tight tracking and rapid reset under stron…

**Figure 11.** Figure 11: HR/SBP/DBP response and recovery. slowing monotonically as regulation weakens (fig. 11). The normal (healthy) regime shows very small excursions and a rapid return toward baseline due to healthy homeostatic regulation. The moderate (sub-clinical) regime produces large…

**Figure 10.** Figure 10: (a) Output y(t) from amygdala to hypothalamus.(b) Hypothalamic hy(t) accumulation under different regime. the hypothalamic state hy(t). In the strong regime hy(t) stays near its low baseline; in the moderate regime it rises during pulses and decays between them; in th…

**Figure 12.** Figure 12: plots mean threat acknowledgment, log dispersion, 0.8 0.6 0.4 0.2 RPFC 0.1 0.2 0.3 0.4 0.5 Mea n A (a) Mean readout 0.8 0.6 0.4 0.2 RPFC 10 2 10 1 Std. dev. A (lo g) (b) Dispersion (log scale) 0.8 0.6 0.4 0.2 RPFC 0.00 0.01 0.02 0.03 0.04 Mea n hy (a uto n o mic) (c) …

**Figure 14.** Figure 14: (a) Variance ratios under ±20% perturbations of core. (b) Regime means/variance ratios under ±25% perturbations of linking coefficients. and 4. Joint ablation with both slow gating and adaptive threshold removed. The full model exhibited a large variance ratio of A be…

**Figure 13.** Figure 13: (a) MeanA with 95% confidence intervals over 50 seeds for each regime. (b) Variance with confidence intervals. Structural robustness was further assessed by perturbing key parameters by ±20% (Fig. 14a). For six core parameters (Ks , gin, Kp, u, Cm, gL), the weak/stron…

**Figure 16.** Figure 16: Comprehensive heart rate alterations across advancing stress loads [PITH_FULL_IMAGE:figures/full_fig_p010_16.png]

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Works this paper leans on

36 extracted references · 30 canonical work pages

[1]

Global, regional, and na- tional burden of 12 mental disorders in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019,

GBD 2019 Mental Disorders Collaborators, “Global, regional, and na- tional burden of 12 mental disorders in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019,”The Lancet Psychiatry, vol. 9, no. 2, pp. 137-150, Jan. 2022, doi: 10.1016/S2215-0366(21)00395-3

work page doi:10.1016/s2215-0366(21)00395-3 2019
[2]

Stress and cardiovascular disease: an update,

V . Vaccarino and J. D. Bremner, “Stress and cardiovascular disease: an update,”Nature Reviews Cardiology, vol. 21, no. 9, pp. 1-14, May 2024, doi: 10.1038/s41569-024-01024-y

work page doi:10.1038/s41569-024-01024-y 2024
[3]

Heart rate variability in patients with anxiety disorders: a systematic review and meta-analysis,

Y . Cheng, M. Su, C. Liu, Y . Huang, and W. Huang, “Heart rate variability in patients with anxiety disorders: a systematic review and meta-analysis,” Psychiatry and Clinical Neurosciences, vol. 76, no. 7, Mar. 2022, doi: 10.1111/pcn.13356

work page doi:10.1111/pcn.13356 2022
[4]

Investigating the association of anxiety disorders with heart rate variability measured using a wearable device,

J. Tomasiet al., “Investigating the association of anxiety disorders with heart rate variability measured using a wearable device,”Jour- nal of Affective Disorders, vol. 351, pp. 569-578, Apr. 2024, doi: 10.1016/j.jad.2024.01.137

work page doi:10.1016/j.jad.2024.01.137 2024
[5]

Advances in the computational understanding of mental illness,

Q. J. M. Huys, M. Browning, M. P. Paulus, and M. J. Frank, “Advances in the computational understanding of mental illness,”Neuropsychophar- macology, vol. 46, no. 1, pp. 3-19, Jan. 2021, doi: 10.1038/s41386-020- 0746-4

work page doi:10.1038/s41386-020- 2021
[6]

A Dynamical Systems View of Psychiatric Disorders Theory,

M. Schefferet al., “A Dynamical Systems View of Psychiatric Disorders Theory,”JAMA Psychiatry, vol. 81, no. 6, pp. 618-630, Jun. 2024, doi: 10.1001/jamapsychiatry.2024.0215

work page arXiv 2024
[7]

Neuronal circuits for fear and anxiety,

P. Tovote, J. P. Fadok, and A. L ¨uthi, “Neuronal circuits for fear and anxiety,”Nature Reviews Neuroscience, vol. 16, no. 6, pp. 317-331, Jun. 2015, doi: 10.1038/nrn3945

work page doi:10.1038/nrn3945 2015
[8]

From circuits to behaviour in the amygdala,

P. H. Janak and K. M. Tye, “From circuits to behaviour in the amygdala,” Nature, vol. 517, no. 7534, pp. 284-292, Jan. 2015, doi: 10.1038/na- ture14188

doi:10.1038/na- 2015

Show all 36 references

[9]

Subcortico-amygdala pathway processes innate and learned threats,

V . Khalil, I. Faress, N. Mermet-Joret, P. Kerwin, K. Yonehara, and S. Nabavi, “Subcortico-amygdala pathway processes innate and learned threats,”eLife, vol. 12, p. e85459, Aug. 2023,doi: 10.7554/eLife.85459

2023 doi
[10]

Cortical–Hypothalamic Integration of Autonomic and Endocrine Stress Responses,

D. Schaeuble and B. Myers, “Cortical–Hypothalamic Integration of Autonomic and Endocrine Stress Responses,”Frontiers in Physiology, vol. 13, Feb. 2022,doi: 10.3389/fphys.2022.820398

2022
[11]

Amygdala-prefrontal connectivity dur- ing emotion regulation: A meta-analysis of psychophysiological in- teractions,

S. Berboth and C. Morawetz, “Amygdala-prefrontal connectivity dur- ing emotion regulation: A meta-analysis of psychophysiological in- teractions,”Neuropsychologia, vol. 153, p. 107767, Jan. 2021, doi: 10.1016/j.neuropsychologia.2021.107767

2021
[12]

The neural bases of emotion regulation,

A. Etkin, C. B ¨uchel, and J. J. Gross, “The neural bases of emotion regulation,”Nature Reviews Neuroscience, vol. 16, no. 11, pp. 693-700, Oct. 2015, doi: 10.1038/nrn4044

2015 doi
[13]

Digital Phenotyping for Stress, Anxiety and Mild Depression: A Systematic Literature Review,

A. Choi, A. Ooi, and D. Lottridge, “Digital Phenotyping for Stress, Anxiety and Mild Depression: A Systematic Literature Review,”JMIR mHealth and uHealth, vol. 12, p. e40689, May 2024,doi: 10.2196/40689

2024 doi
[14]

Toward Robust Stress Prediction in the Age of Wearables: Modeling Perceived Stress in a Longitudinal Study With Information Workers,

B. M. Booth, H. Vrzakova, S. M. Mattingly, G. J. Martinez, L. Faust, and S. K. D’Mello, “Toward Robust Stress Prediction in the Age of Wearables: Modeling Perceived Stress in a Longitudinal Study With Information Workers,”IEEE Transactions on Affective Computing, vol. 13, no. ...

2022
[15]

Wearable Sensors and Machine Learn- ing: Insights into Depression, Anxiety, and Emotional States and Changes,

W. Zhao and K. Go, “Wearable Sensors and Machine Learn- ing: Insights into Depression, Anxiety, and Emotional States and Changes,” in2025 International Conference on Computing, Net- working and Communications (ICNC), Feb. 2025, pp. 145-150, doi: 10.1109/ICNC64010.2025.10993871

2025
[16]

The Growing Field of Digital Psychiatry: Current Evidence and the Future of Apps, Social Media, Chatbots, and Virtual Reality,

J. Torouset al., “The Growing Field of Digital Psychiatry: Current Evidence and the Future of Apps, Social Media, Chatbots, and Virtual Reality,”World Psychiatry, vol. 20, no. 3, pp. 318-335, Sep. 2021,doi: 10.1002/wps.20883

2021 doi
[17]

Digital mental health care: five lessons from Act 1 and a preview of Acts 2-5,

T. Insel, “Digital mental health care: five lessons from Act 1 and a preview of Acts 2-5,”npj Digital Medicine, vol. 6, no. 1, Jan. 2023,doi: 10.1038/s41746-023-00760-8

2023 doi
[18]

Digital twins to personalize medicine,

B. Bj ¨ornssonet al., “Digital twins to personalize medicine,”Genome Medicine, vol. 12, no. 1, Art. no. 4, Dec. 2019,doi: 10.1186/s13073- 019-0701-3

2019 doi
[19]

Building digital twins of the human immune system: toward a roadmap,

R. Laubenbacheret al., “Building digital twins of the human immune system: toward a roadmap,”npj Digital Medicine, vol. 5, no. 1, May 2022, doi: 10.1038/s41746-022-00610-z

2022 doi
[20]

The effect of sodium ions on the electrical activity of the giant axon of the squid,

A. L. Hodgkin and B. Katz, “The effect of sodium ions on the electrical activity of the giant axon of the squid,”The Journal of Physiology, vol. 108, no. 1, pp. 37-77, Mar. 1949, doi: 10.1113/jphysiol.1949.sp004310

1949 doi
[21]

The Hodgkin-Huxley theory of the action potential,

M. H ¨ausser, “The Hodgkin-Huxley theory of the action potential,”Na- ture Neuroscience, vol. 3, no. 11, p. 1165, Nov. 2000, doi: 10.1038/81426

doi
[22]

Development of a New Resilience Scale: The Connor-Davidson Resilience Scale (CD-RISC),

K. M. Connor and J. R. T. Davidson, “Development of a New Resilience Scale: The Connor-Davidson Resilience Scale (CD-RISC),”Depression and Anxiety, vol. 18, no. 2, pp. 76-82, 2003, doi: 10.1002/da.10113

2003 doi
[23]

A global measure of per- ceived stress,

S. Cohen, T. Kamarck, and R. Mermelstein, “A global measure of per- ceived stress,”Journal of Health and Social Behavior, vol. 24, no. 4, pp. 385-396, Dec. 1983. Available: doi: pubmed.ncbi.nlm.nih.gov/6668417

1983
[24]

Factor Structure of the 10-Item Perceived Stress Scale and Measurement Invariance Across Genders Among Chinese Adolescents,

X. Liu, Y . Zhao, J. Li, J. Dai, X. Wang, and S. Wang, “Factor Structure of the 10-Item Perceived Stress Scale and Measurement Invariance Across Genders Among Chinese Adolescents,”Frontiers in Psychology, vol. 11, Apr. 2020,doi: 10.3389/fpsyg.2020.00537

2020
[25]

Evaluation of reliability generalization of Conner-Davison Resilience Scale (CD-RISC- 10 and CD-RISC-25): A Meta-analysis,

A. K. Wojujutari, E. S. Idemudia, and L. E. Ugwu, “Evaluation of reliability generalization of Conner-Davison Resilience Scale (CD-RISC- 10 and CD-RISC-25): A Meta-analysis,”PLOS ONE, vol. 19, no. 11, Nov. 2024,doi: 10.1371/journal.pone.0297913

2024 doi
[26]

Deep Generative Model of Individual Variability in fMRI Images of Psychiatric Patients,

T. Matsubara, K. Kusano, T. Tashiro, Ken’ya Ukai, and K. Uehara, “Deep Generative Model of Individual Variability in fMRI Images of Psychiatric Patients,”IEEE Transactions on Biomedical Engineering, vol. 68, no. 2, pp. 592-605, Feb. 2021,doi: 10.1109/TBME.2020.3008707

2021
[27]

Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis,

T. Matsubara, T. Tashiro, and K. Uehara, “Deep Neural Generative Model of Functional MRI Images for Psychiatric Disorder Diagnosis,” IEEE Transactions on Biomedical Engineering, vol. 66, no. 10, pp. 2768- 2779, Oct. 2019,doi: 10.1109/TBME.2019.2895663

2019
[28]

To- ward functional neurobehavioral assessment of mood and anxiety,

LaR. Lindsey, B. King-Casas, J. Brovko, and P. H. Chiu, “To- ward functional neurobehavioral assessment of mood and anxiety,” in2009 Annual International Conference of the IEEE Engineer- ing in Medicine and Biology Society, Sep. 2009, pp. 5393-5396,doi: 10.1109/IEMBS.2009.5332809

2009
[29]

Modeling obsessive compulsive disorder,

C. H. Cline, S. S. Nair, D. Xu, J. Nair, and B. Beitman, “Modeling obsessive compulsive disorder,” in26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 3, pp. 890–893, doi: 10.1109/IEMBS.2004.1403302

2004 arXiv
[30]

A multimodal sensor dataset for continuous stress detection of nurses in a hospital,

S. Hosseiniet al., “A multimodal sensor dataset for continuous stress detection of nurses in a hospital,”Scientific Data, vol. 9, no. 1, Jun. 2022,doi: 10.1038/s41597-022-01361-y

2022 doi
[31]

Cerebral Flow Velocities During Daily Ac- tivities Depend on Blood Pressure in Patients With Chronic Is- chemic Infarctions,

V . Novak, K. Hu, L. Desrochers, P. Novak, L. Caplan, L. Lip- sitz, and M. Selim, “Cerebral Flow Velocities During Daily Ac- tivities Depend on Blood Pressure in Patients With Chronic Is- chemic Infarctions,”Stroke, vol. 41, no. 1, pp. 61-66, Jan. 2010,doi: 10.1161/STROKEAHA.1...

2010 doi
[32]

Nervous-System-Wise Functional Es- timation of Directed Brain–Heart Interplay Through Microstate Occur- rences,

V . Catrambone and G. Valenza, “Nervous-System-Wise Functional Es- timation of Directed Brain–Heart Interplay Through Microstate Occur- rences,”IEEE Transactions on Biomedical Engineering, vol. 70, no. 8, pp. 2270-2278, Aug. 2023, doi: 10.1109/TBME.2023.3240593

2023
[33]

Modeling Brain-Heart Interaction: A Review of Mechanistic Dynamical Mod- els,

S. N. Sadoun, A. Boutin, F. Cottin, and T.-M. Laleg-Kirati, “Modeling Brain-Heart Interaction: A Review of Mechanistic Dynamical Mod- els,”IEEE Reviews in Biomedical Engineering, pp. 1-17, 2025, doi: 10.1109/RBME.2025.3641959

2025
[34]

Autonomic Cardiovascular Control Following Transient Arousal From Sleep: A Time-Varying Closed-Loop Model,

A. Blasi, J. A. Jo, E. Valladares, R. Juarez, A. Baydur, and M. C. K. Khoo, “Autonomic Cardiovascular Control Following Transient Arousal From Sleep: A Time-Varying Closed-Loop Model,”IEEE Transactions on Biomedical Engineering, vol. 53, no. 1, pp. 74-82, Dec. 2005,doi: 10.110...

2005
[35]

Noninvasive measurement of baroreflex sensitivity. A better indicator of cardiac vagal tone than heart rate variability?,

B. W. Hyndman, C. A. Swenne, M. Bootsma, J. V oogd, and A. V . G. Bruschke, “Noninvasive measurement of baroreflex sensitivity. A better indicator of cardiac vagal tone than heart rate variability?,” in Proceedings of the 18th Annual International Conference of the IEEE Engine...

1996
[36]

Factors Associated with Common Mental Disorders in Truck Drivers - figshare - NLM Dataset Catalog,

“Factors Associated with Common Mental Disorders in Truck Drivers - figshare - NLM Dataset Catalog,”NIH.gov, 2021. doi: datasetcata- log.nlm.nih.gov/dataset?q=0000884952. Accessed: Jun. 21, 2026

2021

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [1]

Global, regional, and na- tional burden of 12 mental disorders in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019,

GBD 2019 Mental Disorders Collaborators, “Global, regional, and na- tional burden of 12 mental disorders in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019,”The Lancet Psychiatry, vol. 9, no. 2, pp. 137-150, Jan. 2022, doi: 10.1016/S2215-0366(21)00395-3

work page doi:10.1016/s2215-0366(21)00395-3 2019

[2] [2]

Stress and cardiovascular disease: an update,

V . Vaccarino and J. D. Bremner, “Stress and cardiovascular disease: an update,”Nature Reviews Cardiology, vol. 21, no. 9, pp. 1-14, May 2024, doi: 10.1038/s41569-024-01024-y

work page doi:10.1038/s41569-024-01024-y 2024

[3] [3]

Heart rate variability in patients with anxiety disorders: a systematic review and meta-analysis,

Y . Cheng, M. Su, C. Liu, Y . Huang, and W. Huang, “Heart rate variability in patients with anxiety disorders: a systematic review and meta-analysis,” Psychiatry and Clinical Neurosciences, vol. 76, no. 7, Mar. 2022, doi: 10.1111/pcn.13356

work page doi:10.1111/pcn.13356 2022

[4] [4]

Investigating the association of anxiety disorders with heart rate variability measured using a wearable device,

J. Tomasiet al., “Investigating the association of anxiety disorders with heart rate variability measured using a wearable device,”Jour- nal of Affective Disorders, vol. 351, pp. 569-578, Apr. 2024, doi: 10.1016/j.jad.2024.01.137

work page doi:10.1016/j.jad.2024.01.137 2024

[5] [5]

Advances in the computational understanding of mental illness,

Q. J. M. Huys, M. Browning, M. P. Paulus, and M. J. Frank, “Advances in the computational understanding of mental illness,”Neuropsychophar- macology, vol. 46, no. 1, pp. 3-19, Jan. 2021, doi: 10.1038/s41386-020- 0746-4

work page doi:10.1038/s41386-020- 2021

[6] [6]

A Dynamical Systems View of Psychiatric Disorders Theory,

M. Schefferet al., “A Dynamical Systems View of Psychiatric Disorders Theory,”JAMA Psychiatry, vol. 81, no. 6, pp. 618-630, Jun. 2024, doi: 10.1001/jamapsychiatry.2024.0215

work page arXiv 2024

[7] [7]

Neuronal circuits for fear and anxiety,

P. Tovote, J. P. Fadok, and A. L ¨uthi, “Neuronal circuits for fear and anxiety,”Nature Reviews Neuroscience, vol. 16, no. 6, pp. 317-331, Jun. 2015, doi: 10.1038/nrn3945

work page doi:10.1038/nrn3945 2015

[8] [8]

From circuits to behaviour in the amygdala,

P. H. Janak and K. M. Tye, “From circuits to behaviour in the amygdala,” Nature, vol. 517, no. 7534, pp. 284-292, Jan. 2015, doi: 10.1038/na- ture14188

doi:10.1038/na- 2015