Translational neuropsychology
and Aging lab


Life expectancy is gradually increasing, giving rise to new essential questions in the study of aging. Whereas past research has focused on understanding the predictors and causes of pathological aging, recent focus has shifted towards studying optimal or successful aging. A main characteristic of aging is age-related cognitive decline, which results from an interaction between biological, physiological, and emotional factors. In the current line of work we aim to uncover the factors that decrease age-related cognitive decline, and enable maintaining higher levels of cognitive function, independence, and quality of life. To achieve this we combine the study of cognitive abilities, psychiatric symptoms, and resting-state brain networks in healthy older adults and those at high-risk for developing cognitive decline.  

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Psychiatric symptoms are assessed using self-reports that are traditionally thought to explain sets of latent variables. Computational symptom-networks offer an alternative by describing symptoms as nodes in an interacting network. Within these complex networks, it is possible to examine how symptoms interact and reinforce one another and what are factors that affect these interactions. Since neurocognitive (dys)functions and psychiatric symptoms are tightly associated, adding cognitive nodes to such symptom networks, reveals connections that are otherwise be overlooked. We are interested in studying the effect of cognitive abilities over psychiatric symptoms using networks, and comparing these over time.

Tracking changes in mental and cognitive states has long been a goal of neuropsychological assessments. In recent years, this has also become an interest for understanding psychiatric disorders. Currently, the measures of cognitive abilities standardly employed by the field, are limited and outdated. Many of these have limited norms, are susceptible to retest effects, and require trained clinicians, thus restricting how many older adults can be assessed as well as the frequency with which they can be assessed. Additionally, they are designed to describe abilities in total scores ignoring possible sub-processes and complex interactions in performance. To address these caveats, we are interested in developing technology-based tools that would enable to collect more specific high-resolution data from neuropsychological assessments.

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