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Older adults experience difficulties in daily situations that require flexible information selection in the presence of multiple competing sensory inputs, like for instance multi-talker situations. Modulations of rhythmic neural activity in the alpha–beta (8–30 Hz) frequency range in posterior brain areas have been established as a cross-modal neural correlate of selective attention. However, research linking compromised auditory selective attention to changes in rhythmic neural activity in aging is sparse.We tested younger (n = 25; 22–35 years) and older adults (n = 26; 63–76 years) in an attention modulated dichotic listening task. In this, two streams of highly similar auditory input were simultaneously presented to participants′ both ears (i.e., dichotically) while attention had to be focused on the input to only one ear (i.e. target) and the other, distracting information had to be ignored.We here demonstrate a link between severely compromised auditory selective attention in aging and a partial reorganization of attention-related rhythmic neural responses. In particular, in old age we observed a shift from a self-initiated, preparatory modulation of alpha rhythmic activity to an externally driven, response in the alpha–beta range. Critically, moment-to-moment fluctuations in the age-specific patterns of self-initiated and externally driven lateralized rhythmic activity were tied to selective attention. We conclude that adult age difference in spatial selective attention likely derive from a functional reorganization of rhythmic neural activity within the aging brain.

The average power of rhythmic neural responses as captured by MEG/EEG/LFP recordings is a prevalent index of human brain function. Increasing evidence questions the utility of trial-/group averaged power estimates, as seemingly sustained activity patterns may be brought about by time-varying transient signals in each single trial. Hence, it is crucial to accurately describe the duration and power of rhythmic and arrhythmic neural responses on the single trial-level. However, it is less clear how well this can be achieved in empirical MEG/EEG/LFP recordings. Here, we extend an existing rhythm detection algorithm (extended Better OSCillation detection: “eBOSC”; cf. Whitten et al., 2011) to systematically investigate boundary conditions for estimating neural rhythms at the single-trial level. Using simulations as well as resting and task-based EEG recordings from a micro-longitudinal assessment, we show that alpha rhythms can be successfully captured in single trials with high specificity, but that the quality of single-trial estimates varies greatly between subjects. Importantly, our analyses suggest that rhythmic estimates are reliable within-subject markers, but may not be consistently valid descriptors of the individual rhythmic process. Finally, we highlight the utility and potential of rhythm detection with multiple proof-of-concept examples, and discuss various implications for single-trial analyses of neural rhythms in electrophysiological recordings.HighlightsTraditional narrow-band rhythm metrics conflate the power and duration of rhythmic and arrhythmic periods.We extend a state-of-the-art rhythm detection method (eBOSC) to derive rhythmic episodes in single trials that can disambiguate rhythmic and arrhythmic periods.Simulations indicate that this can be done with high specificity given sufficient rhythmic power, but with strongly impaired sensitivity when rhythmic power is low.Empirically, surface EEG recordings exhibit stable inter-individual differences in α-rhythmicity in ranges where simulations suggest a gradual bias, leading to high collinearity between narrow-band and rhythm-specific estimates.Beyond these limitations, we highlight multiple empirical proof-of-concept benefits of characterizing rhythmic episodes in single trials.

Experience can affect human gray matter volume. The behavioral correlates of individual differences in such brain changes are not well understood. In a group of Swedish individuals studying Italian as a foreign language, we investigated associations among time spent studying, acquired vocabulary, baseline performance on memory tasks, and gray matter changes. As a way of studying episodic memory training, the language learning focused on acquiring foreign vocabulary and lasted for 10weeks. T1-weighted structural magnetic resonance imaging and cognitive testing were performed before and after the studies. Learning behavior was monitored via participants' use of a smartphone application dedicated to the study of vocabulary. A whole-brain analysis showed larger changes in gray matter structure of the right hippocampus in the experimental group (N=33) compared to an active control group (N=23). A first path analyses revealed that time spent studying rather than acquired knowledge significantly predicted change in gray matter structure. However, this association was not significant when adding performance on baseline memory measures into the model, instead only the participants' performance on a short-term memory task with highly similar distractors predicted the change. This measure may tap similar individual difference factors as those involved in gray matter plasticity of the hippocampus.

article i nfo Article history: Accepted 15 April 2013 Available online 25 April 2013 Some eighty years after the discovery of the human electroencephalogram (EEG) and its dominant rhythm, alpha (~10 Hz), the neurophysiological functions and behavioral correlates of alpha oscillations are still under debate. Similarly, the biological mechanisms contributing to the general factor of intelligence, or g, have been under scrutiny for decades. Individual alpha frequency (IAF), a trait-like parameter of the EEG, has been found to correlate with individual differences in cognitive performance and cognitive abilities. In- formed by large-scale theories of neural organization emphasizing the general functional significance of oscil- latory activity, the present study replicates and extends these findings by testing the hypothesis that IAF is related to intelligence at the level of g, rather than at the level of specific cognitive abilities. Structural equation modeling allowed us to statistically control for measurement error when estimating the association between IAF and intellectual functioning. In line with our hypothesis, we found a statistically reliable and substantial correlation between IAF and g (r = .40). The magnitude of this correlation did not differ significantly between younger and older adults, and captured all of the covariation between IAF and the cognitive abilities of reason- ing, memory, and perceptual speed. The observed association between IAF and g provides a parsimonious ex- planation for the commonly observed diffuse pattern of correlations between IAF and cognitive performance. We conclude that IAF is a marker of global architectural and functional properties of the human brain.