Cusp catastrophe model of intermittent explosive disorder and road rage

8^th International Conference on Neuroscience and Neurological Disorders

September 28, 2022 | Webinar

P J Riley

Deakin University, Australia

Posters & Accepted Abstracts: J Neurol Clin Neurosci

Abstract :

Recent studies in Intermittent Explosive Disorder (IED) have addressed the aggression faciltatory role of an underlying complex neurochemistry (NC) detected by inflammatory markers. These studies confirm that there are non-personality mechanisms which drive aggression in an agonist/antagonist modulating mechanism. Whilst these models are successful in identifying NC processes, they do not address the "explosive" nature of aggressive behavior observed in both IED and average subjects. Lorentz/ Zeeman successfully modeled explosive aggression with Rage and Fear as competing co-existent drivers leading to behavioral hysteresis. We previously demonstrated a cusp catastrophe model for abnormal sleep/wake cycles based upon a general principle in Logistic catastrophes where there are two competing processes, the sleep & wake NCs mediated by a scavenging function. We now propose a Lorentz/Zeeman type Logistic Cusp Catastrophe model with competing NCs and scavenging, promoting both Rage and Fear, applied to road rage behaviors. Overall, the model explains a variety of behaviours observed in road rage incidents that are not readily explicable in 2D linear models. Recent Publications 1. A Neurochemistry Cusp Catastrophe Model of Abnormal Sleep-Wake Cycles, Riley.P, EC Psychology and Psychiatry 8(1):50-52 01 Jan 2019. 2. An evaluation of the effect of tube potential on clinical image quality using direct digital detectors for pelvis and lumbar spine radiographs, Peacock, Steward, Riley.P, Journal of medical radiation sciences 67(4):260-268 Dec 2020. 3. Cusp Catastrophe Models in Neurochemistry & Behaviour, Riley.P, Neuroscience Summit 2021, virtual, 05 Oct 2021-05 Oct 2021.

Biography :

Peter Riley is a Consultant in Medical Physics teaching into the Medical Imaging course at Deakin University. He has previously undertaken non-linear modeling of tumor growth and abnormal sleep/wake cycles. He is developing Deep Neural Networks for the detection and staging of disease from medical images, including covid-19 and prostate cancer.