Biology subjects

Charest, I.

Publications and source records attributed to Charest, I..

4 recordsLinked to original sources

bioRxiv2018

Individuals with clinically relevant autistic traits tend to have an eye for detail

Individuals with an autism spectrum disorder (ASD) diagnosis are often described as having an eye for detail1. This observation, and the finding that individuals with ASD tend to see the trees before the forest when performing the Navon task2, has led to the proposal that ASD is characterized by a bias towards processing local image details3. However, it remains to be shown that natural image recognition in individuals with autism depends more on fine image detail. Here, we resolve this issue by showing that natural image recognition relies more on details in individuals with an above-median number of autistic traits. Furthermore, we found that reliance on details was best predicted by the presence of the most clinically relevant autistic traits. Therefore, our findings raise the possibility that a wide range of real-life abilities and difficulties associated with ASD are related to an enhanced reliance on visual details.

neuroscience↗

bioRxiv2018

GLMdenoise improves multivariate pattern analysis of fMRI data

GLMdenoise is a denoising technique for task-based fMRI. In GLMdenoise, estimates of spatially correlated noise (which may be physiological, instrumental, motion-related, or neural in origin) are derived from the data and incorporated as nuisance regressors in a general linear model (GLM) analysis. We previously showed that GLMdenoise outperforms a variety of other denoising techniques in terms of cross-validation accuracy of GLM estimates (Kay et al., 2013a). However, the practical impact of denoising for experimental studies remains unclear. Here we examine whether and to what extent GLMdenoise improves sensitivity in the context of multivariate pattern analysis of fMRI data. On a large number of participants (31 participants across 4 experiments; 3 T, gradient-echo, spatial resolution 2-3.75 mm, temporal resolution 1.3-2 s, number of conditions 32-75), we perform representational similarity analysis (Kriegeskorte et al., 2008a) as well as pattern classification (Haxby et al., 2001). We find that GLMdenoise substantially improves replicability of representational dissimilarity matrices (RDMs) across independent splits of each participants dataset (average RDM replicability increases from r = 0.46 to r = 0.61). Additionally, we find that GLMdenoise substantially improves pairwise classification accuracy (average classification accuracy increases from 79% correct to 84% correct). We show that GLMdenoise often improves and never degrades performance for individual participants and that GLMdenoise also improves across-participant consistency. We conclude that GLMdenoise is a useful tool that can be routinely used to maximize the amount of information extracted from fMRI activity patterns.

neuroscience↗

bioRxiv2017

Alpha/beta power compresses time in sub-second temporal judgments

While the perception of time plays a crucial role in our day-to-day functioning, the underlying neural mechanism of time processing on short time scales (~1s) remains to be elucidated. Recently, the power of beta oscillations (~20 Hz) has been suggested to play an important role in temporal processing. However, the paradigms supporting this view have often had confounds of working memory, as well as motor preparation. In the current EEG study, we set out to investigate if power of oscillatory activity would be involved in time perception without an explicit working memory component or confound of a motor response. Participants indicated through a button press whether the time between a tone and a visual stimulus was 1 or 1.5s.Critically, we focused on the differences in oscillatory activity in the alpha (~10 Hz) and beta (~20 Hz) ranges preceding correct versus incorrect temporal judgments. Behaviourally, we found participants made more errors on the long (1.5s) than on the short (1s) interval. In addition, we found that participants were fastest to correctly respond to a long interval. The onset of the tone induced a suppression of alpha and beta activity over occipital and parietal electrodes. In the long estimation intervals, this suppression was greater for correct than incorrect estimations. Interestingly, alpha and beta suppression allowed us to predict whether participants would judge the long interval correctly. For the short interval trials we did not find a significant difference in alpha or beta band activity for the correct and incorrect judgments. Taken together, our behavioural and EEG results suggest a multifaceted role of alpha and beta activity in the temporal estimation of sub- and supra-second intervals, where power increases seem to lead to temporal compression. Higher alpha and beta power resulted in shorter temporal judgments for sub-second intervals.\n\nHighlightsO_LITemporal judgments without motor confounds were studied with EEG.\nC_LIO_LIAlpha/beta activity differences for correct and incorrect temporal judgments.\nC_LIO_LISub-second intervals were judged as short when alpha/beta power was higher.\nC_LI

neuroscience↗

bioRxiv2017

Neural dynamics of real-world object vision that guide behaviour

1Vision involves complex neuronal dynamics that link the sensory stream to behaviour. To capture the richness and complexity of the visual world and the behaviour it entails, we used an ecologically valid task with a rich set of real-world object images. We investigated how human brain activity, resolved in space with functional MRI and in time with magnetoencephalography, links the sensory stream to behavioural responses. We found that behaviour-related brain activity emerged rapidly in the ventral visual pathway within 200ms of stimulus onset. The link between stimuli, brain activity, and behaviour could not be accounted for by either category membership or visual features (as provided by an artificial deep neural network model). Our results identify behaviourally-relevant brain activity during object vision, and suggest that object representations guiding behaviour are complex and can neither be explained by visual features or semantic categories alone. Our findings support the view that visual representations in the ventral visual stream need to be understood in terms of their relevance to behaviour, and highlight the importance of complex behavioural assessment for human brain mapping.

neuroscience↗