Aprinda Indahlastari1, Alejandro Albizu2, Jessica N Kraft2, Andrew O'Shea3, Nicole R Nissim2, Ayden L Dunn4, Daniela Carballo3, Michael P Gordon3, Shreya Taank4, Alex T Kahn4, Cindy Hernandez3, William M Zucker4, Adam J Woods5. 1. Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA. Electronic address: aprinda.indahlas@phhp.ufl.edu. 2. Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Department of Neuroscience, University of Florida, Gainesville, FL, USA. 3. Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA. 4. Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA. 5. Center for Cognitive Aging and Memory, McKnight Brain Institute, University of Florida, Gainesville, FL, USA; Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, USA; Department of Neuroscience, University of Florida, Gainesville, FL, USA.
Abstract
BACKGROUND: Working memory decline has been associated with normal aging. The frontal brain structure responsible for this decline is primarily located in the prefrontal cortex (PFC). Our previous neuroimaging study demonstrated a significant change in functional connectivity between the left dorsolateral PFC (DLPFC) and left ventrolateral PFC (VLPFC) when applying 2 mA tDCS in MRI scanner during an N-Back task. These regions were part of the working memory network. The present study is the first study that utilizes individualized finite element models derived from older adults' MRI to predict significant changes of functional connectivity observed from an acute tDCS application. METHODS: Individualized head models from 15 healthy older adults (mean age = 71.3 years) were constructed to create current density maps. Each head model was segmented into 11 tissue types: white matter, gray matter, CSF, muscle, blood vessels, fat, eyes, air, skin, cancellous, and cortical bone. Electrodes were segmented from T1-weighted images and added to the models. Computed median and maximum current density values in the left DLPFC and left VLPFC regions of interest (ROIs) were correlated with beta values as functional connectivity metrics measured in different timepoint (baseline, during stimulation) and stimulation condition (active and sham). MAIN RESULTS: Positive significant correlations (R2 = 0.523 for max J, R2 = 0.367 for median J, p < 0.05) were found between the beta values and computed current densities in the left DLPFC ROIs for active stimulation, but no significant correlation was found during sham stimulation. We found no significant correlation between connectivity and current densities computed in the left VLPFC for both active and sham stimulation. CONCLUSIONS: The amount of current within the left DLPFC ROIs was found positively correlated with changes in functional connectivity between left DLPFC and left VLPFC during active 2 mA stimulation. Future work may include expansion of number of participants to further test the accuracy of tDCS models used to predict tDCS-induced functional connectivity changes within the working memory network.
BACKGROUND: Working memory decline has been associated with normal aging. The frontal brain structure responsible for this decline is primarily located in the prefrontal cortex (PFC). Our previous neuroimaging study demonstrated a significant change in functional connectivity between the left dorsolateral PFC (DLPFC) and left ventrolateral PFC (VLPFC) when applying 2 mA tDCS in MRI scanner during an N-Back task. These regions were part of the working memory network. The present study is the first study that utilizes individualized finite element models derived from older adults' MRI to predict significant changes of functional connectivity observed from an acute tDCS application. METHODS: Individualized head models from 15 healthy older adults (mean age = 71.3 years) were constructed to create current density maps. Each head model was segmented into 11 tissue types: white matter, gray matter, CSF, muscle, blood vessels, fat, eyes, air, skin, cancellous, and cortical bone. Electrodes were segmented from T1-weighted images and added to the models. Computed median and maximum current density values in the left DLPFC and left VLPFC regions of interest (ROIs) were correlated with beta values as functional connectivity metrics measured in different timepoint (baseline, during stimulation) and stimulation condition (active and sham). MAIN RESULTS: Positive significant correlations (R2 = 0.523 for max J, R2 = 0.367 for median J, p < 0.05) were found between the beta values and computed current densities in the left DLPFC ROIs for active stimulation, but no significant correlation was found during sham stimulation. We found no significant correlation between connectivity and current densities computed in the left VLPFC for both active and sham stimulation. CONCLUSIONS: The amount of current within the left DLPFC ROIs was found positively correlated with changes in functional connectivity between left DLPFC and left VLPFC during active 2 mA stimulation. Future work may include expansion of number of participants to further test the accuracy of tDCS models used to predict tDCS-induced functional connectivity changes within the working memory network.
Authors: Tim Wagner; Felipe Fregni; Shirley Fecteau; Alan Grodzinsky; Markus Zahn; Alvaro Pascual-Leone Journal: Neuroimage Date: 2007-02-04 Impact factor: 6.556
Authors: Nicole R Nissim; Andrew O'Shea; Aprinda Indahlastari; Rachel Telles; Lindsey Richards; Eric Porges; Ronald Cohen; Adam J Woods Journal: Front Aging Neurosci Date: 2019-03-15 Impact factor: 5.750
Authors: Sudeshna A Chatterjee; Rachael D Seidler; Jared W Skinner; Paige E Lysne; Chanoan Sumonthee; Samuel S Wu; Ronald A Cohen; Dorian K Rose; Adam J Woods; David J Clark Journal: Neuromodulation Date: 2022-04-08