The Relationship of Cognitive Performance and the Theta-Alpha Power Ratio Is Age-Dependent: An EEG Study of Short Term Memory and Reasoning during Task and Resting-State in Healthy Young and Old Adults.
ABSTRACT: Objective: The Theta-Alpha ratio (TAR) is known to differ based upon age and cognitive ability, with pathological electroencephalography (EEG) patterns routinely found within neurodegenerative disorders of older adults. We hypothesized that cognitive ability would predict EEG metrics differently within healthy young and old adults, and that healthy old adults not showing age-expected EEG activity may be more likely to demonstrate cognitive deficits relative to old adults showing these expected changes. Methods: In 216 EEG blocks collected in 16 young and 20 old adults during rest (eyes open, eyes closed) and cognitive tasks (short-term memory [STM]; matrix reasoning [RM; Raven's matrices]), models assessed the contributing roles of cognitive ability, age, and task in predicting the TAR. A general linear mixed-effects regression model was used to model this relationship, including interaction effects to test whether increased cognitive ability predicted TAR differently for young and old adults at rest and during cognitive tasks. Results: The relationship between cognitive ability and the TAR across all blocks showed age-dependency, and cognitive performance at the CZ midline location predicted the TAR measure when accounting for the effect of age (p < 0.05, chi-square test of nested models). Age significantly interacted with STM performance in predicting the TAR (p < 0.05); increases in STM were associated with increased TAR in young adults, but not in old adults. RM showed similar interaction effects with aging and TAR (p < 0.10). Conclusion: EEG correlates of cognitive ability are age-dependent. Adults who did not show age-related EEG changes were more likely to exhibit cognitive deficits than those who showed age-related changes. This suggests that healthy aging should produce moderate changes in Alpha and TAR measures, and the absence of such changes signals impaired cognitive functioning.
Project description:Neocortical gamma activity is crucial for sensory perception and cognition. This study examines the value of using non-task stimulation-induced EEG oscillations to predict cognitive status in a birth cohort of healthy Danish males (Metropolit) with varying cognitive ability. In particular, we examine the steady-state VEP power response (SSVEP-PR) in the alpha (8Hz) and gamma (36Hz) bands in 54 males (avg. age: 62.0 years) and compare these with 10 young healthy participants (avg. age 27.6 years). Furthermore, we correlate the individual alpha-to-gamma difference in relative visual-area power (?RV) with cognitive scores for the older adults. We find that ?RV decrease with age by just over one standard deviation when comparing young with old participants (p<0.01). Furthermore, intelligence is significantly negatively correlated with ?RV in the older adult cohort, even when processing speed, global cognition, executive function, memory, and education (p<0.05). In our preferred specification, an increase in ?RV of one standard deviation is associated with a reduction in intelligence of 48% of a standard deviation (p<0.01). Finally, we conclude that the difference in cerebral rhythmic activity between the alpha and gamma bands is associated with age and cognitive status, and that ?RV therefore provide a non-subjective clinical tool with which to examine cognitive status in old age.
Project description:<h4>Background</h4>The lack of robust and reliable clinical biomarkers in Fragile X Syndrome (FXS), the most common inherited form of intellectual disability, has limited the successful translation of bench-to-bedside therapeutics. While numerous drugs have shown promise in reversing synaptic and behavioral phenotypes in mouse models of FXS, none have demonstrated clinical efficacy in humans. Electroencephalographic (EEG) measures have been identified as candidate biomarkers as EEG recordings of both adults with FXS and mouse models of FXS consistently exhibit alterations in resting state and task-related activity. However, the developmental timing of these EEG differences is not known as thus far EEG studies have not focused on young children with FXS. Further, understanding how EEG differences are associated with core symptoms of FXS is crucial to successful use of EEG as a biomarker, and may improve our understanding of the disorder.<h4>Methods</h4>Resting-state EEG was collected from FXS boys with full mutation of Fmr1 (2.5-7 years old, n?=?11) and compared with both age-matched (n?=?12) and cognitive-matched (n?=?12) typically developing boys. Power spectra (including aperiodic and periodic components) were compared using non-parametric cluster-based permutation testing. Associations between 30 and 50 Hz gamma power and cognitive, language, and behavioral measures were evaluated using Pearson correlation and linear regression with age as a covariate.<h4>Results</h4>FXS participants showed increased power in the beta/gamma range (~?25-50 Hz) across multiple brain regions. Both a reduction in the aperiodic (1/f) slope and increase in beta/gamma periodic activity contributed to the significant increase in high-frequency power. Increased gamma power, driven by the aperiodic component, was associated with better language ability in the FXS group. No association was observed between gamma power and parent report measures of behavioral challenges, sensory hypersensitivities, or adaptive behaviors.<h4>Limitations</h4>The study sample size was small, although comparable to other human studies in rare-genetic disorders. Findings are also limited to males in the age range studied.<h4>Conclusions</h4>Resting-state EEG measures from this study in young boys with FXS identified similar increases in gamma power previously reported in adults and mouse models. The observed positive association between resting state aperiodic gamma power and language development supports hypotheses that alterations in some EEG measures may reflect ongoing compensatory mechanisms.
Project description:Previous research suggests declines in emotion perception in older as compared to younger adults, but the underlying neural mechanisms remain unclear. Here, we address this by investigating how "face-age" and "face emotion intensity" affect both younger and older participants' behavioural and neural responses using event-related potentials (ERPs). Sixteen young and fifteen older adults viewed and judged the emotion type of facial images with old or young face-age and with high- or low- emotion intensities while EEG was recorded. The ERP results revealed that young and older participants exhibited significant ERP differences in two neural clusters: the left frontal and centromedial regions (100-200 ms stimulus onset) and frontal region (250-900 ms) when perceiving neutral faces. Older participants also exhibited significantly higher ERPs within these two neural clusters during anger and happiness emotion perceptual tasks. However, while this pattern of activity supported neutral emotion processing, it was not sufficient to support the effective processing of facial expressions of anger and happiness as older adults showed reductions in performance when perceiving these emotions. These age-related changes are consistent with theoretical models of age-related changes in neurocognitive abilities and may reflect a general age-related cognitive neural compensation in older adults, rather than a specific emotion-processing neural compensation.
Project description:Age-related memory decline is associated with changes in neural functioning, but little is known about how aging affects the quality of information representation in the brain. Whereas a long-standing hypothesis of the aging literature links cognitive impairments to less distinct neural representations in old age ("neural dedifferentiation"), memory studies have shown that overlapping neural representations of different studied items are beneficial for memory performance. In an electroencephalography (EEG) study, we addressed the question whether distinctiveness or similarity between patterns of neural activity supports memory differentially in younger and older adults. We analyzed between-item neural pattern similarity in 50 younger (19-27 years old) and 63 older (63-75 years old) male and female human adults who repeatedly studied and recalled scene-word associations using a mnemonic imagery strategy. We compared the similarity of spatiotemporal EEG frequency patterns during initial encoding in relation to subsequent recall performance. The within-person association between memory success and pattern similarity differed between age groups: For older adults, better memory performance was linked to higher similarity early in the encoding trials, whereas young adults benefited from lower similarity between earlier and later periods during encoding, which might reflect their better success in forming unique memorable mental images of the joint picture-word pairs. Our results advance the understanding of the representational properties that give rise to subsequent memory, as well as how these properties may change in the course of aging.SIGNIFICANCE STATEMENT Declining memory abilities are one of the most evident limitations for humans when growing older. Despite recent advances of our understanding of how the brain represents and stores information in distributed activation patterns, little is known about how the quality of information representation changes during aging and thus affects memory performance. We investigated how the similarity between neural representations relates to subsequent memory in younger and older adults. We present novel evidence that the interaction of pattern similarity and memory performance differs between age groups: Older adults benefited from higher similarity during early encoding, whereas young adults benefited from lower similarity between early and later encoding. These results provide insights into the nature of memory and age-related memory deficits.
Project description:Alterations of resting-state EEG microstates have been associated with various neurological disorders and behavioral states. Interestingly, age-related differences in EEG microstate organization have also been reported, and it has been suggested that resting-state EEG activity may predict cognitive capacities in healthy individuals across the lifespan. In this exploratory study, we performed a microstate analysis of resting-state brain activity and tested allocentric spatial working memory performance in healthy adult individuals: twenty 25-30-year-olds and twenty-five 64-75-year-olds. We found a lower spatial working memory performance in older adults, as well as age-related differences in the five EEG microstate maps A, B, C, C' and D, but especially in microstate maps C and C'. These two maps have been linked to neuronal activity in the frontal and parietal brain regions which are associated with working memory and attention, cognitive functions that have been shown to be sensitive to aging. Older adults exhibited lower global explained variance and occurrence of maps C and C'. Moreover, although there was a higher probability to transition from any map towards maps C, C' and D in young and older adults, this probability was lower in older adults. Finally, although age-related differences in resting-state EEG microstates paralleled differences in allocentric spatial working memory performance, we found no evidence that any individual or combination of resting-state EEG microstate parameter(s) could reliably predict individual spatial working memory performance. Whether the temporal dynamics of EEG microstates may be used to assess healthy cognitive aging from resting-state brain activity requires further investigation.
Project description:Several findings underlined that the electrophysiological (EEG) background of the last segment of sleep before awakenings may predict the presence/absence of dream recall (DR) in young subjects. However, little is known about the EEG correlates of DR in elderly people. Only an investigation found differences between recall and non-recall conditions during NREM sleep EEG in older adults, while-surprisingly-no EEG predictor of DR was found for what concerns REM sleep. Considering REM sleep as a privileged scenario to produce mental sleep activity related to cognitive processes, our study aimed to investigate whether specific EEG topography and frequency changes during REM sleep in elderly people may predict a subsequent recall of mental sleep activity. Twenty-one healthy older volunteers (mean age 69.2 ± 6.07 SD) and 20 young adults (mean age 23.4 ± 2.76 SD) were recorded for one night from 19 scalp derivations. Dreams were collected upon morning awakenings from REM sleep. EEG signals of the last 5 min were analyzed by the Better OSCillation algorithm to detect the peaks of oscillatory activity in both groups. Statistical comparisons revealed that older as well as young individuals recall their dream experience when the last segment of REM sleep is characterized by frontal theta oscillations. No Recall (Recall vs. Non-Recall) × Age (Young vs. Older) interaction was found. This result replicated the previous evidence in healthy young subjects, as shown in within- and between-subjects design. The findings are completely original for older individuals, demonstrating that theta oscillations are crucial for the retrieval of dreaming also in this population. Furthermore, our results did not confirm a greater presence of the theta activity in healthy aging. Conversely, we found a greater amount of rhythmic theta and alpha activity in young than older participants. It is worth noting that the theta oscillations detected are related to cognitive functioning. We emphasize the notion that the oscillatory theta activity should be distinguished from the non-rhythmic theta activity identified in relation to other phenomena such as (a) sleepiness and hypoarousal conditions during the waking state and (b) cortical slowing, considered as an EEG alteration in clinical samples.
Project description:Whether there are age-related changes in slow wave activity (SWA) rise time, a marker of homeostatic sleep drive, is unknown. Additionally, although sleep medication use is highest among older adults, the quantitative electroencephalographic (EEG) profile of the most commonly prescribed sleep medication, zolpidem, in older adults is also unknown. We therefore quantified age-related and regional brain differences in sleep EEG with and without zolpidem.Thirteen healthy young adults aged 21.9?±?2.2?years and 12 healthy older adults aged 67.4?±?4.2?years participated in a randomized, double-blind, within-subject study that compared placebo to 5?mg zolpidem.Older adults showed a smaller rise in SWA and zolpidem increased age-related differences in SWA rise time such that age differences were observed earlier after latency to persistent sleep. Age-related differences in EEG power differed by brain region. Older, but not young, adults showed zolpidem-dependent reductions in theta and alpha frequencies. Zolpidem decreased stage 1 in older adults and did not alter other age-related sleep architecture parameters.SWA findings provide additional support for reduced homeostatic sleep drive or reduced ability to respond to sleep drive with age. Consequences of reduced power in theta and alpha frequencies in older adults remain to be elucidated.
Project description:In this paper, we explore the utility of resting-state EEG measures as potential biomarkers for the detection and assessment of cognitive decline in mild cognitive impairment (MCI) and Alzheimer's disease (AD). Neurophysiological biomarkers of AD derived from EEG and FDG-PET, once characterized and validated, would expand the set of existing diagnostic molecular biomarkers of AD pathology with associated biomarkers of disease progression and neural dysfunction. Since symptoms of AD often begin to appear later in life, successful identification of EEG-based biomarkers must account for age-related neurophysiological changes that occur even in healthy individuals. To this end, we collected EEG data from individuals with AD (n = 26), MCI (n = 53), and cognitively normal healthy controls stratified by age into three groups: 18-40 (n = 129), 40-60 (n = 62) and 60-90 (= 55) years old. For each participant, we computed power spectral density at each channel and spectral coherence between pairs of channels. Compared to age matched controls, in the AD group, we found increases in both spectral power and coherence at the slower frequencies (Delta, Theta). A smaller but significant increase in power of slow frequencies was observed for the MCI group, localized to temporal areas. These effects on slow frequency spectral power opposed that of normal aging observed by a decrease in the power of slow frequencies in our control groups. The AD group showed a significant decrease in the spectral power and coherence in the Alpha band consistent with the same effect in normal aging. However, the MCI group did not show any significant change in the Alpha band. Overall, Theta to Alpha ratio (TAR) provided the largest and most significant differences between the AD group and controls. However, differences in the MCI group remained small and localized. We proposed a novel method to quantify these small differences between Theta and Alpha bands' power using empirically derived distributions of spectral power across the time domain as opposed to averaging power across time. We defined Power Distribution Distance Measure (PDDM) as a distance measure between probability distribution functions (pdf) of Theta and Alpha power. Compared to average TAR, using PDDF enhanced the statistical significance, the effect size, and the spatial distribution of significant effects in the MCI group. We designed classifiers for differentiating individual MCI and AD participants from age-matched controls. The classification performance measured by the area under ROC curve after cross-validation were AUC = 0.85 and AUC = 0.6, for AD and MCI classifiers, respectively. Posterior probability of AD, TAR, and the proposed PDDM measure were all significantly correlated with MMSE score and neuropsychological tests in the AD group.
Project description:The authors evaluated the reliability and validity of a tool for measuring older adults' decision-making competence (DMC). A sample of 205 younger adults (25-45 years), 208 young-older adults (65-74 years), and 198 old-older adults (75-97 years) made judgments and decisions related to health, finance, and nutrition. Reliable indices of comprehension, dimension weighting, and cognitive reflection were developed. Comparison of the performance of old-older and young-older adults was possible in this study, unlike previous research. As hypothesized, old-older adults performed more poorly than young-older adults; both groups of older adults performed more poorly than younger adults. Hierarchical regression analyses showed that a large amount of variance in decision performance across age groups (including mean trends) could be accounted for by social variables, health measures, basic cognitive skills, attitudinal measures, and numeracy. Structural equation modeling revealed significant pathways from 3 exogenous latent factors (crystallized intelligence, other cognitive abilities, and age) to the endogenous DMC latent factor. Further research is needed to validate the meaning of performance on these tasks for real-life decision making.
Project description:Recent work has demonstrated that aging modulates the resting brain. However, the study of these modulations after cognitive practice, resulting from a memory task, has been scarce. This work aims at examining age-related changes in the functional reorganization of the resting brain after cognitive training, namely, neuroplasticity, by means of the most innovative tools for data analysis. To this end, electroencephalographic activity was recorded in 34 young and 38 older participants. Different methods for data analyses, including frequency, time-frequency and machine learning-based prediction models were conducted. Results showed reductions in Alpha power in old compared to young adults in electrodes placed over posterior and anterior areas of the brain. Moreover, young participants showed Alpha power increases after task performance, while their older counterparts exhibited a more invariant pattern of results. These results were significant in the 140-160 s time window in electrodes placed over anterior regions of the brain. Machine learning analyses were able to accurately classify participants by age, but failed to predict whether resting state scans took place before or after the memory task. These findings greatly contribute to the development of multivariate tools for electroencephalogram (EEG) data analysis and improve our understanding of age-related changes in the functional reorganization of the resting brain.