Biology subjects

Garrett, D. D.

Publications and source records attributed to Garrett, D. D..

3 recordsLinked to original sources

bioRxiv2018

Single-trial characterization of neural rhythms: potentials and challenges

AbstractThe 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 however, 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. Despite those signal-to-noise-based limitations, we highlight the utility and potential of rhythm detection with multiple proof-of-concept examples, and discuss implications for single-trial analyses of neural rhythms in electrophysiological recordings. Using an applied example of working memory retention, rhythm detection indicated load-related increases in the duration of frontal theta and posterior alpha rhythms, in addition to a frequency decrease of frontal theta rhythms that was observed exclusively through amplification of rhythmic amplitudes. HighlightsO_LITraditional 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. C_LIO_LISimulations indicate that this can be done with high specificity given sufficient rhythmic power, but with strongly impaired sensitivity when rhythmic SNR is low. Empirically, surface EEG recordings exhibit stable inter-individual differences in -rhythmicity in ranges where simulations suggest a gradual bias, leading to high collinearity between narrow-band and rhythm-specific estimates. C_LIO_LIBeyond these limitations, we highlight multiple empirical benefits of characterizing rhythmic episodes in single trials, such as (a) a principled separation of rhythmic and arrhythmic content, (b) an amplification of rhythmic amplitudes, and (c) a specific characterization of sustained and transient events. C_LIO_LIIn an exemplary application, rhythm-specific estimates increase sensitivity to working memory load effects, in addition to indicating a frequency modulation of frontal theta rhythms through the amplification of rhythmic power. C_LI

neuroscience↗

bioRxiv2018

Higher performing older adults upregulate brain signal variability in response to feature-rich sensory input

The extent to which brain responses differ across varying cognitive demands is referred to as \"neural differentiation,\" and greater neural differentiation has been associated with better cognitive performance in older adults. An emerging approach has examined within-person neural differentiation using moment-to-moment brain signal variability. A number of studies have found that brain signal variability differs by cognitive state; however, the factors that cause signal variability to rise or fall on a given task remain understudied. We hypothesized that top performers would modulate signal variability according to the complexity of sensory input, upregulating variability when processing more feature-rich stimuli. In the current study, 46 older adults passively viewed face stimuli and house stimuli during fMRI. Low-level analyses of our stimuli showed that house images were more feature-rich than faces, and subsequent computational modelling of ventral visual stream responses (HMAX) revealed that houses were more feature-rich especially in V1/V2-like model layers. Notably, we then found that participants exhibiting greater face-to-house upregulation of brain signal variability in V1/V2 (higher for house relative to face stimuli) also exhibited more accurate, faster, and more consistent behavioral performance on a battery of offline visuo-cognitive tasks. Further, control models revealed that face-house modulation of mean brain signal was relatively insensitive to offline cognition, providing further evidence for the importance of brain signal variability for understanding human behavior. We conclude that the ability to align brain signal variability to the complexity of perceptual input may mark heightened trait-level behavioral performance in older adults.

neuroscience↗

bioRxiv2017

Criterion Setting Modulates Neural Excitability of Human Visual Cortex

Decision bias is traditionally conceptualized as an internal reference against which sensory evidence is compared. Instead, we show that individuals implement decision bias by shifting the rate of sensory evidence accumulation towards a decision bound. Participants performed a target detection task while we recorded EEG. We experimentally manipulated participants decision criterion for reporting targets using different stimulus-response reward contingencies, inducing either a liberal or a conservative bias. Drift diffusion modeling revealed that a liberal strategy biased sensory evidence accumulation towards target-present choices. Moreover, a liberal bias resulted in stronger midfrontal pre-stimulus 2-6 Hz (theta) power and suppression of pre-stimulus 8--12 Hz (alpha) power in posterior cortex. The alpha suppression in turn mediated the output activity of visual cortex, as expressed in 59--100 Hz (gamma) power. These findings show that observers can intentionally control cortical excitability to strategically bias evidence accumulation towards the decision bound that maximizes their reward.

neuroscience↗