# Detecting mood disorder state transitions through dynamical systems analysis of EEG-derived neural mass model parameters - URL: https://alisanburak.com/en/wcp-2026-posterler/so385 - Language: en - Last updated: not specified - Page title: Detecting mood disorder state transitions through dynamical systems analysis of EEG-derived neural mass model parameters | WCP 2026 Summary > The Freeman KII neural mass model makes it possible to characterize a mood episode transition as a locatable bifurcation rather than an opaque score threshold.… This page was translated automatically from Turkish. [Read the original](https://alisanburak.com/wcp-2026-posterler/so385) This page is an AI-assisted Turkish summary of the study presented at the 26th World Congress of Psychiatry. The study belongs to the presenting authors; please refer to the original source when citing it. SO385 Brief Oral Poster PresentationNeuroscience and ImagingComputational modeling study (fitting to EEG data) # Detecting mood disorder state transitions through dynamical systems analysis of EEG-derived neural mass model parameters DETECTING MOOD DISORDER STATE TRANSITIONS THROUGH DYNAMICAL SYSTEMS ANALYSIS OF EEG-DERIVED NEURAL MASS MODEL PARAMETERS Presenter: Murat R. Demirer Country: Turkey Session: 3950 - PARALLEL SHORT ORAL POSTER SESSIONS: NEUROSCIENCE & NEUROIMAGING - Part IV Date: Fri, 25.09.2026 The Freeman KII neural mass model makes it possible to characterize a mood episode transition as a locatable bifurcation rather than an opaque score threshold. However, because the four-population model is structurally unable to reach a T³ torus, it supports a quasiperiodic torus breakdown route rather than the strict Newhouse–Ruelle–Takens route. The aim was to establish a mechanistically interpretable dynamical systems framework that, in response to the failure of EEG biomarkers to enter clinical use, treats mood episode onset not as a threshold on a static feature but as a bifurcation in the space of synaptic coupling gains. In this approach, the Freeman KII model couples four populations in an eight-dimensional state space (a = 0.22, b = 0.72 ms⁻¹), four gains (KEE, KEI, KIE, KII) are scanned, the Lyapunov spectrum is calculated using an analytical Jacobian with periodic QR re-orthonormalization, and attractors are classified by the number of vanishing exponents (tolerance ε = 3/T_measurement); all panels in the poster depict properties of the differential equations, with only one panel overlaying between-session trajectories from 123 recordings of 34 individuals onto the map (group labels only, with no clinical state data). In the gain plane, a sequence of fixed points, limit cycles, quasiperiodic tori, and strange attractors appears within a narrow band, but the sequence stops at T², and no participant reaches the torus or chaotic band. Two structural limits were demonstrated: with uniform rate ratios, KII supports at most two independent frequencies; when distinct AMPA and GABA_A kinetics are introduced, 48% of points on a 16 x 16 x 10 gain grid exhibit three or more distinct frequencies, compared with 0% under uniform rates. The ceiling on unstable pairs, however, remained unchanged across 1.4 x 10⁴ gain points, eight excitatory/inhibitory kinetic ratios, and six ways of breaking W symmetry (at most 2 unstable pairs for four populations and dim J = 8, 3 for six populations, and 4 for eight populations). This led to the conclusion that, regardless of kinetics, the four-population model cannot reach T³ and thus cannot realize the strict Newhouse–Ruelle–Takens route; the observed sequence instead follows the Afraimovich–Shilnikov torus breakdown route, which requires only T². Scanning the inhibitory gain KIE yielded narrow periodic windows embedded within the chaotic regime, and a KEE scan at KIE = 5.0 revealed a similar window around KEE ≈ 2.30. To illustrate the unreliability of estimating λ₁ from raw EEG, the Rosenstein estimator applied to the Lorenz x component produced slopes ranging from 0.813 to 1.650 solely depending on the location of the scaling region (true value: 0.906). Limitations include holding KEI and KII fixed; the finding that, when KEI ∈ [0.5, 4.0] was scanned, 19 of the 34 individuals with multiple sessions (56%) crossed a genuine regime boundary, but this proportion did not differ by diagnosis (Fisher’s exact test p = 1.00); the reclassification of 29% of the cohort by an equally defensible alternative value of KEI; and the convergence of joint estimation of all four gains from the resting-state spectrum of a single recording to three mutually inconsistent solutions. In conclusion, the framework has been numerically validated and has delineated the limits of its own model class. Although its ability to recast state transitions as locatable bifurcations and its reliance on universal neurodynamic principles rather than population-specific training data are promising for its clinical value, the poster presents correspondences between the model and clinical states as hypotheses to be tested rather than established findings; testing them requires state labels and paired pre-/post-treatment sessions. [View the original poster](https://cslide.ctimeetingtech.com/wcp26/attendee/eposter/file/1438) How to cite this study APA 7: Demirer, M. R., et al. (2026, September 25). Detecting mood disorder state transitions through dynamical systems analysis of eeg-derived neural mass model parameters [Poster presentation]. 26th WPA World Congress of Psychiatry (WCP 2026), Stockholm, Sweden. https://cslide.ctimeetingtech.com/wcp26/attendee/eposter/file/1438 Vancouver: Demirer MR, et al. Detecting mood disorder state transitions through dynamical systems analysis of eeg-derived neural mass model parameters [poster]. 26th WPA World Congress of Psychiatry (WCP 2026); 2026 Sep 23-26; Stockholm, Sweden. 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