The Continuum from Temperament to Mental Illness: Dynamical Perspectives

Abstract

Temperament in healthy individuals and mental illness have been conjectured to lie along a continuum of neurobehavioral regulation. This continuum is frequently regarded in dimensional terms, with temperament and mental illness lying at opposite poles along various dimensional descriptors. However, temperament and mental illness are quintessentially dynamical phenomena, and as such there is value in examining what insights can be arrived at through the lens of our current understanding of dynamical systems. The formal study of dynamical systems has led to the development of a host of markers which serve to characterize and classify dynamical systems and which could be used to study temperament and mental illness. The most useful markers for temperament and mental illness apply to time series data and include geometrical markers such as (strange) attractors and repellors and analytical markers such as fluctuation spectroscopy, scaling, entropy, recurrence time. Temperament and mental illness, however, possess fundamental characteristics that present considerable challenges for current dynamical systems approaches: transience, contextuality and emergence. This review discusses the need for time series data and the implications of these three characteristics on the formal study of the continuum and presents a dynamical systems model based upon Whitehead’s Process Theory and the neurochemical Functional Ensemble of Temperament model. The continuum can be understood as second or higher order dynamical phases in a multiscale landscape of superposed dynamical systems. Markers are sought to distinguish the order parameters associated with these phases and the control parameters which describe transitions among these dynamics.

The Continuum Between Temperament and Mental Illness as Dynamical Phases and Transitions

“…On the other hand, the FET has shown differential effects in the presence of several categories of mental illness and in particular, differentiates between major depression, generalized anxiety disorder, comorbid depression and anxiety, and several personality and psychotic disorders ( 12 , 21 – 24 ). This result showing differential effects in the presence of illness makes the FET model the most promising model currently for exploring the temperament-illness continuum and the value of basing a model on neurophysiological evidence rather than lexical descriptors ( 7 , 8 , 13 , 14 , 21 – 24 ).…”

“…Several authors have suggested that temperament and mental illness can be viewed as lying along some form of continuum ( 7 – 14 ). This idea can be traced back to Hippocrates who suggested that temperament and mental illness can be understood as being caused by different levels of four humors within the body.…”

“…Second, many DSM symptoms of illness bear a similarity to certain temperament traits, particularly those comprising the Functional Ensemble of Temperament (FET) model ( 12 , 14 , 21 – 26 ). Most models of temperament have been based upon the lexical approach, whose source material consists of verbal descriptors of behaviors ( 27 , 28 ).…”

“…There have been several attempts at formal modeling relevant to psychiatry [see also ( 14 ) for a more detailed survey], particularly related to serotonin and dopamine dynamics ( 68 – 75 ). The earliest work on the continuum between temperament and mental illness is that of Mandell and Selz ( 76 ), who proposed perhaps the first formal model of the temperament-mental illness continuum, though strictly speaking they focused on personality disorders (borderline and obsessive compulsive) which straddle the gray middle ground.…”

“…Acute mental illness, which consists of short to medium duration patterns of maladaptive, dysfunctional, pathological patterns of behavior can be associated to frontal systems and storms, which are short to medium duration patterns of dysfunctional, pathological weather. Chronic mental illness, which consists of long duration patterns of altered or dysfunctional, pathological behavior can be associated to climate change ( 13 , 14 ). The purpose for exploiting this metaphor is not to draw on the specifics of the formal models, the differential equations and such used to describe these meteorological phenomena, which are clearly inappropriate, but rather to draw on the qualitative dynamical constructs and in particular various time series methodologies for identifying various dynamical phases and their transitions, and in particular, identifying markers of critical states.…”