Computational modeling of the temporal influences between cues, craving and use in addiction: a dynamical system analysis based on ecological momentary assessment data

Christophe Gauld, Damien Depannemaecker, Marc Auriacombe, Fuschia Serre
Translational Psychiatry. 2025-10-20; 15(1):
DOI: 10.1038/s41398-025-03531-z

Gauld C(#)(1)(2)(3), Depannemaecker D(#)(4), Auriacombe M(5)(6)(7), Serre
F(8)(9)(10).

Author information:
(1)Department of Child Psychiatry, CHU de Lyon, F-69000, Lyon, France.
.
(2)Institut des Sciences Cognitives Marc Jeannerod, UMR 5229 CNRS & Université
Claude Bernard Lyon 1, F-69000, Lyon, France. .
(3)Sleep, Addiction and Neuropsychiatry (SANPSY), Centre National de la
Recherche Scientifique, (CNRS), UMR 6033, F-33076, Bordeaux, France.
.
(4)Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst, F-13005, Marseille,
France.
(5)Sleep, Addiction and Neuropsychiatry (SANPSY), Centre National de la
Recherche Scientifique, (CNRS), UMR 6033, F-33076, Bordeaux, France.
.
(6)University of Bordeaux, F-33076, Bordeaux, France.
.
(7)Pôle interétablissement d’addictologie, CHU de Bordeaux et CH Charles
Perrens, F-33076, Bordeaux, France. .
(8)Sleep, Addiction and Neuropsychiatry (SANPSY), Centre National de la
Recherche Scientifique, (CNRS), UMR 6033, F-33076, Bordeaux, France.
(9)University of Bordeaux, F-33076, Bordeaux, France.
(10)Pôle interétablissement d’addictologie, CHU de Bordeaux et CH Charles
Perrens, F-33076, Bordeaux, France.
(#)Contributed equally

Substance Use Disorders (SUD) can be conceptualized as a prospective link from
cues to craving and use. To explore the nonlinear relationships between craving
and cues, this study applied dynamical systems theory (DST) to ecological
momentary assessment (EMA) data. Optimized linear Seasonal Auto-Regressive
Integrated Moving Average with eXogenous variable (SARIMAX) models were used to
phenotype patients with SUD (alcohol, tobacco, cannabis, opiates, and cocaine),
considering the potential for complex interactions between cue exposure and
craving intensity in daily life. These phenotypic profiles were replicated in
computational DST models to analyze the nonlinear interactions between cues,
craving, and use. The study involved 211 individuals and 8260 observations, with
154 patients fitting the SARIMAX model for the influence of cues on craving, and
57 patients fitting the SARIMAX model for a possible influence of craving on
cues. Two DST models were adjusted to replicate the complex temporal dynamics of
SUD based on these two directions of influence. The first DST model (adjusted to
the influence of cues on craving) showed that an increase in cues leads to a
rise in craving, which then diminishes both cues and craving itself, with use
patterns following craving’s trajectory. This patient profile is driven by a
phenomenon of « maximum cue saturation ». The second DST model (adjusted to the
influence of craving on cues) demonstrated that an increase in craving was
followed by an increase in cue reporting, leading to use, with use peaking and
then craving droping. This patient profile is characterized by a phenomenon of
« maximum use saturation ». Both models highlight craving as an essential
modulator between cues and use, opening new therapeutic avenues.