ABSTRACTBy event-related potentials (ERP) during a counting Stroop task it was shown that the elderly with excess in theta activity in their electroencephalogram (EEG) are at risk of cognitive decline and have a higher neuronal activity during stimulus categorization than the elderly with a normal EEG. It was suggested that this increased neuronal activity could have a compensatory function. However, the quantification of energy associated with the enhanced neuronal activity was not investigated in this group. By wavelet analysis, we measured total and relative energy in ERP during the execution of a counting Stroop task in two groups of elderly: one with excess in theta activity (Theta-EEG, n = 23) and the other with normal EEG (Normal-EEG, n = 23). In delta, theta, and alpha bands, the Theta-EEG group used a higher amount of total energy as compared to the Normal-EEG group for both types of stimuli, interference and no interference. In theta and alpha bands, the total energy was higher in the Theta-EEG group, specifically in the window of 258-516 ms, coinciding with stimulus categorization. Given that no major behavioral differences were observed between EEG groups, we suggest that a higher energy in delta, theta, and alpha bands is one of the neurobiological mechanisms that allows the Theta-EEG group to cope with the cognitive demands of the task. However, this increased energy might not be an effective mechanism in the long term as it could promote a metabolic and cellular dysregulation that would trigger the transition to cognitive impairment.SIGNIFICANCE STATEMENTBy using wavelet transform analysis we report that the elderly with excess in theta activity show a higher energy in delta, theta, and alpha bands during the categorization of stimuli in a counting Stroop task. Our findings imply that this increase neuronal activity might be related to a dysregulated energy metabolism in the elderly with theta excess that could explain the progress to cognitive impairment in this group. The analysis of energy by wavelet transform in data obtained by ERP complements other techniques that evaluate the risk of cognitive impairment.
Anomalous brain energy in old age by wavelet analysis of ERP during a Stroop task
