(K-405) Tool Use and Healthy Brain Aging

Friday, October 13, 2023

9:30 AM – 10:30 AM PDT

Location: Exhibit Hall - Row K - Poster # 405

Presenting Author(s)

CC

Camille Carter

Undergraduate Student
University of North Carolina Asheville
Chapel Hill, North Carolina, United States

Co-Author(s)

AS

Alex Shaver

Graduate Student
University of Maryland, United States

Primary Investigator(s)

CM

Chris Mizelle

Professor
East Carolina University, United States

Introduction:: Every day, humans perform skilled or learned movements to complete goals, this is referred to as praxis [1]. Completing many of these goals requires the use of tools. Tool use is fundamental to human life. These tools include everyday items like silverware or more specific items like a hammer or a screwdriver. Unfortunately, deficits relating to praxis and tool use can manifest. Often times, it can be the result of a medical problem such as apraxia. Apraxia occurs after a stroke and results in either the loss of the ability to use a tool (ideomotor apraxia) or the loss of the ability to select the appropriate tool for a task (conceptual apraxia) [2]. However, it is suggested that aging causes similar deficits in tool use knowledge. In studies done examining healthy older adults and tool use, it was found that the participants exhibited deficits similar to ideomotor apraxia. They would be asked to pantomime an action (e.g., brushing their teeth) and would often make tool use errors. It is unclear as to why healthy older individuals are experiencing these deficits [2]. To better understand why this is happening, a baseline must be created to explore the neural connections in healthy older individuals. The baseline will help model the cortical dynamics that older individuals engage in. To do this, we further analyzed EEG data from older and younger healthy adults in relation to normal and plausible tool use.

Materials and Methods:: For the study, 21 younger (22 ± 4 years of age) and 12 older (75 ± 9 years of age) righthanded healthy adults were recruited. Each participant was fitted with a 64-channel EEG cap (Compumedics Neuroscan, USA) which was used to collect brain activity. To collect data and present stimuli SuperLab (Cedrus, TX, USA) and Curry 7 (Compumedics, Neuroscan, NC, USA) were used. The stimuli consisted of black and white photos with normal tool use (e.g., a spoon being used to stir coffee) and plausible tool use (e.g., a screwdriver being used to stir coffee). At the start of each trial, subjects were presented with written instructions on the screen for what to do which was followed by a screen prompting them to begin. A blank white screen was presented for a random amount of time which was then followed by the stimulus. The stimulus remained on the screen until the participants made their selection on a response pad. The data was recorded in Curry 7 and analyzed using a MATLAB script (MathWorks, USA). The data from each participant was filtered using a Wavelet Band Filter with the frequency range of 8-12 Hz and a sampling rate of 1000 Hz. Functional Connectivity Analysis (FCA), Graph Measures Analysis, and Directed Phase Transfer Entropy (dPTE) were then performed on the data. The specific FCA that was used was Debiased Squared Weighted Phase Lag Index (dswPLI) and the specific graph measure analyzed was clustering coefficient.

Results, Conclusions, and Discussions::

Both participants performed equally during the study. There was an increase in neural activity seen in both participants between conditions 1 and 2. The older participants also exhibited hemispheric asymmetry reduction (HAROLD) and overactivation in both conditions.

During healthy brain aging, neuronal alterations are bound to happen which may be why these compensatory mechanisms can be seen in older individuals. This results in the overactivation seen in other neural areas to keep up the same level of processing. Older adults who exhibit this pattern are more likely to have enhanced cognitive function or perform better compared to adults who do not engage in these compensatory mechanisms [3]. The increased activity seen in both participants between condition 1 and condition 2 can be attributed to analysis of a more complex tool use concept [4]. Because the image shown in condition 2 is not typically how the goal is completed, the brain activates other regions, distributing the workload to understand how the unusual tool can be used to complete the action goal. Other conditions were also tested during the study but have yet to be analyzed. The other conditions that were tested were incorrect context (e.g., using a hammer to stir coffee) and incorrect manipulation (e.g., holding a hammer by the head instead of the handle). Moving forward, analyzing these other conditions would be beneficial to see if there are any differences that could affect the development of the baseline.

Acknowledgements (Optional): :

This material is based upon work supported by the National Science Foundation under Grant No. 1950507. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. Special thanks to Dr. Stacey Meardon, Dr. Nick Murray, and Dr. Chris Venters who are the directors of the Biomedical Engineering Simulations Imaging and Modeling program.

References (Optional): :

[1] Park, J. E. J Clin Neurol (2017). 13(3), 317-324

[2] Mizelle, J. C. et al. Frontiers in Psych (2010). 1, 1-9

[3] Cabeza, R. Psychol Aging (2002). 17(1), 85-100

[4] Mizelle, J. C. et al. Brain and Cognition (2013). 82(3), 274-281