Biology subjects

Engel, S. A.

Publications and source records attributed to Engel, S. A..

6 recordsLinked to original sources

bioRxiv2022

Visual Mode Switching: Improved General Compensation to Environmental Color Change Requires Only One Exposure Per Day

When the visual environment changes, vision adapts in order to maintain accurate perception. For repeatedly encountered environmental changes, the visual system may learn to adjust immediately, a process called "visual mode switching". For example, following experience with red glasses, participants report the glasses redness fades instantly when they put the glasses on. Here we tested: 1) whether once-daily experience suffices for learning to switch visual modes, and 2) whether effects of mode-switching apply to most stimuli affected by the environmental change. In Experiment 1, twelve participants wore bright red glasses for a single 5-hr period each day, for 5 days; and we tested for changes in the perception of unique yellow, which contains neither red nor green. In Experiment 2, we tested how mode switching affects larger parts of the color space. Thirteen participants donned and removed the glasses multiple times a day, for 5 days; and we used a dissimilarity rating task to measure and track perception of many different colors. Across days, immediately upon donning the glasses, the world appeared less and less reddish (Experiment 1), and colors across the whole color space appeared more and more normal (Experiment 2). These results indicate that mode switching can be learned from a once-daily experience, and it applies to most stimuli in a given environment. These findings may help to predict when and how mode switching occurs outside the laboratory.

neuroscience↗

bioRxiv2021

Variability of the Surface Area of the V1, V2, and V3 Maps in a Large Sample of Human Observers

How variable is the functionally-defined structure of early visual areas in human cortex and how much variability is shared between twins? Here we quantify individual differences in the best understood functionally-defined regions of cortex: V1, V2, V3. The Human Connectome Project 7T Retinotopy Dataset includes retinotopic measurements from 181 subjects, including many twins. We trained four "anatomists" to manually define V1-V3 using retinotopic features. These definitions were more accurate than automated anatomical templates and showed that surface areas for these maps varied more than three-fold across individuals. This three-fold variation was little changed when normalizing visual area size by the surface area of the entire cerebral cortex. In addition to varying in size, we find that visual areas vary in how they sample the visual field. Specifically, the cortical magnification function differed substantially among individuals, with the relative amount of cortex devoted to central vision varying by more than a factor of 2. To complement the variability analysis, we examined the similarity of visual area size and structure across twins. Whereas the twin sample sizes are too small to make precise heritability estimates (50 monozygotic pairs, 34 dizygotic pairs), they nonetheless reveal high correlations, consistent with strong effects of the combination of shared genes and environment on visual area size. Collectively, these results provide the most comprehensive account of individual variability in visual area structure to date, and provide a robust population benchmark against which new individuals and developmental and clinical populations can be compared. Significance StatementAreas V1, V2, and V3 are among the best studied functionally-defined regions in human cortex. Using the largest retinotopy dataset to date, we characterized the variability of these regions across individuals and the similarity between twin pairs. We find that the size of visual areas varies dramatically (up to 3.5x) across healthy young adults, far more than the variability of the cerebral cortex size as a whole. Much of this variability appears to arise from inherited factors, as we find very high correlations in visual area size between monozygotic twin-pairs, and lower but still substantial correlations between dizygotic twin pairs. These results provide the most comprehensive assessment of how functionally defined visual cortex varies across the population to date.

neuroscience↗

bioRxiv2020

Benefits of Deep Learning Classification of Continuous Noninvasive Brain Computer Interface Control

Noninvasive brain-computer interfaces (BCIs) assist paralyzed patients by providing access to the world without requiring surgical intervention. While the performance of noninvasive BCI is hindered by long training times and variable user proficiency, it may be improved by deep learning methods, such as convolutional neural networks (CovNets). Prior work has suggested that the application of deep learning to EEG signals collected over the motor cortex during motor imagery based BCI increases classification accuracy in standard sensorimotor rhythm (SMR) BCI datasets. It remains to be seen whether these improvements can generalize to practical scenarios such as continuous control tasks (as opposed to prior work reporting one classification per trial), or whether valuable information remains latent outside of the motor cortex (as no prior work has compared full scalp coverage to motor only electrode montages). Here we report that deep learning methods significantly increase offline classification accuracy on an independent, large, and longitudinal online motor imagery BCI dataset with up to 4-classes and continuous 2-dimensional feedback. Improvements in classification accuracy were found to negatively correlate with initial online BCI performance, suggesting deep learning methods preferentially benefit BCI participants who need it most. The CovNets also significantly increased the information transfer rate (ITR) of the BCI system: They produced a two-fold increase in ITR without loss in classification accuracy when comparing CovNet models trained with full scalp EEG coverage to the traditional motor cortex specific decoding. Our results suggest that a variety of neural biomarkers useful for BCI, including those outside the motor cortex, can be detected through deep learning methods.

neuroscience↗

bioRxiv2020

Color compensation in anomalous trichromats assessed with fMRI

Anomalous trichromacy is a common form of congenital color-deficiency resulting from a genetic alteration in the photopigments of the eyes light receptors. The changes reduce sensitivity to reddish and greenish hues, yet previous work suggests that these observers may experience the world to be more colorful than their altered receptor sensitivities would predict, potentially indicating an amplification of post-receptoral signals. However, past evidence suggesting such a gain adjustment rests on subjective measures of color appearance or salience. We directly tested for neural amplification by using fMRI to measure cortical responses in color-anomalous and normal control observers. Color contrast response functions were measured in two experiments with different tasks to control for attentional factors. Both experiments showed a predictable reduction in chromatic responses for anomalous trichromats in primary visual cortex. However, in later areas V2v and V3v, chromatic responses in the two groups were indistinguishable. Our results provide direct evidence for neural plasticity that compensates for the deficiency in the initial receptor color signals and suggest that the site of this compensation is in early visual cortex.

neuroscience↗

bioRxiv2020

Visual mode switching learned through experience

When the environment changes, vision adapts to maintain accurate perception. For repeatedly encountered environments, learning to switch immediately to prior adaptive states would be beneficial, but past work remains inconclusive. We tested if the visual system can learn such visual mode switching for a strongly tinted environment, where adaptation causes the dominant hue to fade over time. Eleven observers wore red glasses for five one-hour periods per day, for five days. Color adaptation was measured by asking observers to identify "unique yellow", appearing neither reddish nor greenish. As expected, the world appeared less and less reddish during the one-hour periods of glasses wear. Critically, across days the world also appeared significantly less reddish immediately after donning the glasses. This indicates that the visual system learned to shift rapidly to a partially adapted state, switching modes to stabilize color vision. Mode switching likely provides a general strategy to optimize perceptual processes.

neuroscience↗

bioRxiv2020

Mindfulness Improves Brain Computer Interface Performance by Increasing Control over Neural Activity in the Alpha Band

Brain-computer interfaces (BCIs) are promising tools for assisting patients with paralysis, but suffer from long training times and variable user proficiency. Mind-body awareness training (MBAT) can improve BCI learning, but how it does so remains unknown. Here we show that MBAT allows participants to learn to volitionally increase alpha band neural activity during BCI tasks that incorporate intentional rest. We trained individuals in mindfulness-based stress reduction (MBSR; a standardized MBAT intervention) and compared performance and brain activity before and after training between randomly assigned trained and untrained control groups. The MBAT group showed reliably faster learning of BCI than the control group throughout training. Alpha-band activity in EEG signals, recorded in the volitional resting state during task performance, showed a parallel increase over sessions, and predicted final BCI performance. The level of alpha-band activity during the intentional resting state correlated reliably with individuals mindfulness practice as well as performance on a sustained attention task. Collectively, these results show that MBAT modifies a specific neural signal used by BCI. MBAT, by increasing patients control over their brain activity during rest, may increase the effectiveness of BCI in the large population who could benefit from alternatives to direct motor control.

neuroscience↗