Detecting simulated versus bona fide traumatic brain injury using pupillometry.

Abstract

Objective: Pupil dilation patterns are outside of conscious control and provide information regarding neuropsychological processes related to deception, cognitive effort, and familiarity. This study examined the incremental utility of pupillometry on the Test of Memory Malingering (TOMM) in classifying individuals with verified traumatic brain injury (TBI), individuals simulating TBI, and healthy comparisons. Method: Participants were 177 adults across three groups: verified TBI (n = 53), feigned cognitive impairment due to TBI (SIM, n = 52), and heathy comparisons (HC, n = 72). Results: Logistic regression and ROC curve analyses identified several pupil indices that discriminated the groups. Pupillometry discriminated best for the comparison of greatest clinical interest, verified TBI versus simulators, adding information beyond traditional accuracy scores. Simulators showed evidence of greater cognitive load than both groups instructed to perform at their best ability (HC and TBI). Additionally, the typically robust phenomenon of dilating to familiar stimuli was relatively diminished among TBI simulators compared to TBI and HC. This finding may reflect competing, interfering effects of cognitive effort that are frequently observed in pupillary reactivity during deception. However, the familiarity effect appeared on nearly half the trials for SIM participants. Among those trials evidencing the familiarity response, selection of the unfamiliar stimulus (i.e., dilation-response inconsistency) was associated with a sizeable increase in likelihood of being a simulator. Conclusions: Taken together, these findings provide strong support for multimethod assessment: adding unique performance assessments such as biometrics to standard accuracy scores. Continued study of pupillometry will enhance the identification of simulators who are not detected by traditional performance validity test scoring metrics.

Feigned ADHD Associated Cognitive Impairment: Utility of Integrating an Eye-tracker and the MOXO-dCPT

“…First, researchers are encouraged to further explore the integration of traditional CPT measures and eye-movement-based validity indicators (as long as the validity indicators do not overlap excessively, this may enhance their sensitivity; Sherman et al, 2020, p. 748). Second, additional eye-movement measures can be explored, including pupillometry (i.e., pupil size analyses) which showed potential when assessed during the performance of the test of memory malingering, TOMM, an established forced-choice memory-based PVT (Patrick et al, 2021). Third, sources of eye-movement patterns (i.e., intentional vs. unintentional effects) can be further explored through post-experimental debriefing surveys and other psychophysiological measures (e.g., use of pupil dilation as a marker of cognitive load; Patrick et al, 2021).…”

“…Second, additional eye-movement measures can be explored, including pupillometry (i.e., pupil size analyses) which showed potential when assessed during the performance of the test of memory malingering, TOMM, an established forced-choice memory-based PVT (Patrick et al, 2021). Third, sources of eye-movement patterns (i.e., intentional vs. unintentional effects) can be further explored through post-experimental debriefing surveys and other psychophysiological measures (e.g., use of pupil dilation as a marker of cognitive load; Patrick et al, 2021). Finally, there is room to investigate the utility of the MOXO-dCPT in detecting feigned cognitive impairment in other neuropsychiatric disorders.…”

“…Moreover, eye-movements are associated with deception (Gamer & Pertzov, 2018) and, importantly, the feigning of cognitive impairment. For example, simulators in Mahoney et al (2018) had lower overall accuracy compared to controls in a forced-choice memory-recognition task, while gazing disproportionately at stimuli that they were presented with earlier (see also, Kanser et al, 2020;Patrick et al, 2021;Tomer et al, 2020). These studies, however, are numbered and we are aware of only one study that investigated the potential of a CPT integrated eye-tracker (Barry & Ettenhofer, 2016).…”

The Role of Automated Infrared Pupillometry in Traumatic Brain Injury: A Narrative Review

“…These tools extend beyond ophthalmology cabinets, finding applications in clinical and research settings. Key areas include emer gency departments for brain injury assessment [9][10][11][12], sports settings [13][14][15], for early detection of neuro-worsening [16][17][18], non-invasive monitoring of intracranial hyperten sion in critically ill brain trauma patients [19][20][21][22], post-cardiac-arrest evaluations o Traditionally, assessing pupils involved a pupil gauge for estimating size and a penlight or flashlight for manual evaluation of reactivity, categorized as "normal", "brisk", "slow", or "sluggish". In a recent study by Couret and colleagues, they found limited consistency between measurements of the pupil size obtained through an automated device compared to traditional clinical evaluation, revealing an overall disagreement rate of 18%.…”

“…These tools extend beyond ophthalmology cabinets, finding applications in clinical and research settings. Key areas include emergency departments for brain injury assessment [9][10][11][12], sports settings [13][14][15], for early detection of neuro-worsening [16][17][18], non-invasive monitoring of intracranial hypertension in critically ill brain trauma patients [19][20][21][22], post-cardiac-arrest evaluations of brainstem function [5,[23][24][25], and even in cases involving extracorporeal cardiopulmonary resuscitation [26]. Notably, during cardiopulmonary resuscitation (CPR), PLR serves as a surrogate method for monitoring CPR effectiveness and may predict the likelihood of returning to spontaneous circulation as evidenced in a cardiac arrest swine model [27].…”