Interactions between decision-making and emotion in behavioral-variant frontotemporal dementia and Alzheimer’s disease

Abstract

<jats:title>Abstract</jats:title> <jats:p>Negative and positive emotions are known to shape decision-making towards more or less impulsive responses respectively. Decision-making and emotion processing are underpinned by shared brain regions including the ventromedio-prefrontal cortex (vmPFC) and the amygdala. How these processes interact at the behavioural and brain levels is still unclear. We used a lesion model to address this question. Study participants included individuals diagnosed with behavioural-variant frontotemporal dementia (bvFTD, n=18), who typically present deficits in decision-making/emotion processing and atrophy of the vmPFC, individuals with Alzheimer’s disease (AD, n=12) who present with atrophy in limbic structures, and age-matched healthy controls (CTRL, n=15). Prior to each choice on the delay discounting task participants were cued with a positive, negative or neutral picture and asked to vividly imagine witnessing the event. As hypothesised, our findings showed that, bvFTD patients were more impulsive than AD and CTRL and did not show any emotion-related modulation of delay discounting rate. In contrast, AD patients showed increased impulsivity when primed by negative emotion. This increased impulsivity was associated with reduced integrity of bilateral amygdala in AD but not in bvFTD. Altogether, our results indicate that decision-making and emotion interact at the level of the amygdala supporting findings from animal studies.</jats:p>

Subjective Time in Dementia: A Critical Review

“…Across AD and the prodromal stage of MCI, there is evidence for a modest elevation in delay discounting, leading to a greater prioritization of immediate rewards relative to delayed ones e.g., [ 163 , 164 , 165 , 166 , 167 ], although mixed findings exist in the literature (e.g., [ 168 , 169 , 170 , 171 , 172 , 173 ]). In bvFTD, the evidence is sparser but more consistent, with patients demonstrating substantially steeper delay discounting relative to healthy controls, and in some cases relative to AD [ 168 , 174 , 175 ] (though see [ 170 , 173 ]).…”

“…The use of personalized, real-life cues may overcome this deficit, allowing individuals with impairments to mental time travel to modify their discounting in response to future event cuing [ 198 , 199 ]. While there is evidence to suggest that neurodegenerative disorders are associated with reduced flexibility in response to related imagery-based cueing protocols [ 175 ], it remains to be seen whether alterations to the future event cuing procedure to make it more self-relevant, personalized, and congruent with planned activities will enable reductions to delay discounting in dementia (see [ 174 ]).…”

Decision-Making Support for People With Alzheimer’s Disease: A Narrative Review

“…These findings are probably related to impaired autobiographical memory in patients with AD ( El Haj et al, 2020 ) and executive function deficits in patients with MCI ( Geng et al, 2020 ). Patients with AD may be more prone to immediate rewards due to a shrinking hippocampus ( Lebreton et al, 2013 ) and amygdala ( Manuel et al, 2020 ). However, Bertoux et al (2015) found opposite results of no difference in impulsivity between AD patients and controls.…”

“…The different types of reward may affect the response, since AD patients have been shown to have faster reaction times for monetary loss than monetary gain, and for social wins compared to social losses, which is opposite to findings in bvFTD ( Perry et al, 2015 ). The condition might be also associated with the response because patients with AD were significantly more impulsive than controls only in the situations that caused negative emotions ( Manuel et al, 2020 ). In general, executive function and DD reflects the ability of AD patients to make decisions.…”

Challenges in Data Capturing and Collection for Physiological Detection of Dementia-Related Difficulties and Proposed Solutions

“…Once all the data is collected and labelled, and a synthetic dataset has been generated, it shall be used to train several classification models with the MATLAB software [47]. This will allow for the identification of the best model for identifying and predicting the difficulties from the physiological dataset.…”