Biology subjects

Charest, I.

Publications and source records attributed to Charest, I..

At least 19 recordsLinked to original sources

bioRxiv2025

A Python Toolbox for Representational Similarity Analysis

Representational similarity analysis (RSA) is a method to characterize neural representations and evaluate computational models based on neural representational geometries. Here we present a wave of recent methodological advances, including improved measures of representational distances, evaluators for representational models, and statistical inference methods, which are available to the community in a new open-source toolbox in Python. The rsatoolbox enables neuroscientists to explore neural representational geometries and to evaluate neural network models, connecting theory to experiment in the new era of big models and big data.

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bioRxiv2022

Reconstructing Spatio-Temporal Trajectories of Visual Object Memories in the Human Brain

Our understanding of how information unfolds when we recall events from memory remains limited. In this study, we investigate whether the reconstruction of visual object memories follows a backward trajectory along the ventral visual stream with respect to perception, such that their neural feature representations are gradually reinstated from late areas close to the hippocampus backwards to lower-level sensory areas. We use multivariate analyses of fMRI activation patterns to map high-and low-level features of the object memories onto the brain during retrieval, and EEG-fMRI fusion to track the temporal evolution of the reactivated patterns. Participants studied new associations between verbs and randomly paired object images in an encoding phase, and subsequently recalled the objects when presented with the corresponding verb cue. Decoding reactivated memory features from fMRI activity revealed that retrieval patterns were dominated by conceptual features, represented in comparatively late visual and parietal areas. Representational-similarity-based fusion then allowed us to map the EEG patterns that emerged at each given time point of a trial onto the spatially resolved fMRI patterns. This fusion suggests that memory reconstruction proceeds from anterior fronto-temporal to posterior occipital and parietal regions, partly in line with a semantic-to-perceptual gradient. A linear regression on the peak time points of reactivated brain regions statistically confirms that the temporal progression is reversed with respect to encoding in ventral stream areas. Together, the results shed light onto the spatio-temporal trajectories along which memories and their constituent features are reconstructed during associative retrieval. Significance statementIn our work, we combined EEG and fMRI to investigate which features of a visual object are reactivated when recalled from episodic memory, and how the memory reconstruction stream unfolds over time and across the brain. Our findings suggest that with respect to perception, memory retrieval follows a backwards information trajectory along a conceptual-to-perceptual gradient, and additionally relays retrieved information to multimodal fronto-parietal brain regions. These findings address the fundamental question of whether memories are more or less complete and truthful reconstructions of past events, or instead are subject to systematic biases that prioritise some types of features over others. Our data suggests that episodic memory retrieval is a dynamic and highly reconstructive process with clear prioritisation of abstract-conceptual over detailed-perceptual information.

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bioRxiv2022

Neural computations in prosopagnosia

We aimed to identify neural computations underlying the loss of face identification ability by modelling the brain activity of brain-lesioned patient PS, a well-documented case of acquired pure prosopagnosia. We collected a large dataset of high-density electrophysiological (EEG) recordings from PS and neurotypicals while they completed a one-back task on a stream of face, object, animal and scene images. We found reduced neural decoding of face identity around the N170 window in PS, and conjointly revealed normal non-face identification in this patient. We used Representational Similarity Analysis (RSA) to correlate human EEG representations with those of deep neural network (DNN) models of vision and caption-level semantics, offering a window into the neural computations at play in patient PSs deficits. Brain representational dissimilarity matrices (RDMs) were computed for each participant at 4 ms steps using cross-validated classifiers. PSs brain RDMs showed significant reliability across sessions, indicating meaningful measurements of brain representations with RSA even in the presence of significant lesions. Crucially, computational analyses were able to reveal PSs representational deficits in high-level visual and semantic brain computations. Such multi-modal data-driven characterisations of prosopagnosia highlight the complex nature of processes contributing to face recognition in the human brain. HighlightsO_LIWe assess the neural computations in the prosopagnosic patient PS using EEG, RSA, and deep neural networks C_LIO_LINeural dynamics of brain-lesioned PS are reliably captured using RSA C_LIO_LINeural decoding shows normal evidence for non-face individuation in PS C_LIO_LINeural decoding shows abnormal neural evidence for face individuation in PS C_LIO_LIPS shows impaired high-level visual and semantic neural computations C_LI

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bioRxiv2022

Re-expression of CA1 and entorhinal activity patterns preserves temporal context memory at long timescales

Converging, cross-species evidence indicates that memory for time is supported by hippocampal area CA1 and entorhinal cortex. However, limited evidence characterizes how these regions preserve temporal memories over long timescales (e.g., months). At long timescales, memoranda may be encountered in multiple temporal contexts, potentially creating interference. Here, using 7T fMRI, we measured CA1 and entorhinal activity patterns as human participants viewed thousands of natural scene images distributed, and repeated, across many months. We show that memory for an images original temporal context was predicted by the degree to which CA1/entorhinal activity patterns from the first encounter with an image were re-expressed during re-encounters occurring minutes to months later. Critically, temporal memory signals were dissociable from predictors of recognition confidence, which were carried by distinct medial temporal lobe expressions. These findings suggest that CA1 and entorhinal cortex preserve temporal memories across long timescales by coding for and reinstating temporal context information.

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bioRxiv2022

The neural code behind real-world recognition abilities

Why are some individuals better at recognising faces? Uncovering the neural mechanisms supporting face recognition ability has proven elusive. To tackle this challenge, we used a multi-modal data-driven approach combining neuroimaging, computational modelling, and behavioural tests. We recorded the high-density electroencephalographic brain activity of individuals with extraordinary face recognition abilities--super-recognisers--and typical recognisers in response to diverse visual stimuli. Using multivariate pattern analyses, we decoded face recognition abilities from 1 second of brain activity with up to 80% accuracy. To better understand the mechanisms subtending this decoding, we compared computations in the brains of our participants with those in artificial neural network models of vision and semantics, as well as with those involved in human judgments of shape and meaning similarity. Compared to typical recognisers, we found stronger associations between early brain computations of super-recognisers and mid-level computations of vision models as well as shape similarity judgments. Moreover, we found stronger associations between late brain representations of super-recognisers and computations of the artificial semantic model as well as meaning similarity judgments. Overall, these results indicate that important individual variations in brain processing, including neural computations extending beyond purely visual processes, support differences in face recognition abilities. They provide the first empirical evidence for an association between semantic computations and face recognition abilities. We believe that such multi-modal data-driven approaches will likely play a critical role in further revealing the complex nature of idiosyncratic face recognition in the human brain. SignificanceThe ability to robustly recognise faces is crucial to our success as social beings. Yet, we still know little about the brain mechanisms allowing some individuals to excel at face recognition. This study builds on a sizeable neural dataset measuring the brain activity of individuals with extraordinary face recognition abilities--super-recognisers--to tackle this challenge. Using state-of-the-art computational methods, we show robust prediction of face recognition abilities in single individuals from a mere second of brain activity, and revealed specific brain computations supporting individual differences in face recognition ability. Doing so, we provide direct empirical evidence for an association between semantic computations and face recognition abilities in the human brain--a key component of prominent face recognition models.

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bioRxiv2022

GLMsingle: a toolbox for improving single-trial fMRI response estimates

Advances in modern artificial intelligence (AI) have inspired a paradigm shift in human neuroscience, yielding large-scale functional magnetic resonance imaging (fMRI) datasets that provide high-resolution brain responses to tens of thousands of naturalistic visual stimuli. Because such experiments necessarily involve brief stimulus durations and few repetitions of each stimulus, achieving sufficient signal-to-noise ratio can be a major challenge. We address this challenge by introducing GLMsingle, a scalable, user-friendly toolbox available in MATLAB and Python that enables accurate estimation of single-trial fMRI responses (glmsingle.org). Requiring only fMRI time-series data and a design matrix as inputs, GLMsingle integrates three techniques for improving the accuracy of trial-wise general linear model (GLM) beta estimates. First, for each voxel, a custom hemodynamic response function (HRF) is identified from a library of candidate functions. Second, cross-validation is used to derive a set of noise regressors from voxels unrelated to the experimental paradigm. Third, to improve the stability of beta estimates for closely spaced trials, betas are regularized on a voxel-wise basis using ridge regression. Applying GLMsingle to the Natural Scenes Dataset and BOLD5000, we find that GLMsingle substantially improves the reliability of beta estimates across visually-responsive cortex in all subjects. Furthermore, these improvements translate into tangible benefits for higher-level analyses relevant to systems and cognitive neuroscience. Specifically, we demonstrate that GLMsingle: (i) improves the decorrelation of response estimates between trials that are nearby in time; (ii) enhances representational similarity between subjects both within and across datasets; and (iii) boosts one-versus-many decoding of visual stimuli. GLMsingle is a publicly available tool that can significantly improve the quality of past, present, and future neuroimaging datasets that sample brain activity across many experimental conditions.

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bioRxiv2021

Disentangling dimensions of animacy in human brain and behaviour

Distinguishing animate from inanimate things is of great behavioural importance. Despite distinct brain and behavioural responses to animate and inanimate things, it remains unclear which object properties drive these responses. Here, we investigate the importance of five object dimensions related to animacy ("being alive", "looking like an animal", "having agency", "having mobility", and "being unpredictable") in brain (fMRI, EEG) and behaviour (property and similarity judgements) of 19 participants. We used a stimulus set of 128 images, optimized by a genetic algorithm to disentangle these five dimensions. The five dimensions explained much variance in the similarity judgments. Each dimension also explained a modest but significant amount of variance in the brain representations, except, surprisingly, "being alive". Different brain regions sensitive to animacy may represent distinct dimensions, either as accessible perceptual stepping stones toward detecting whether something is alive or because they are of behavioural importance in their own right.

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bioRxiv2021

Sleep spindles track cortical learning patterns for memory consolidation

Memory consolidation, the transformation of labile memory traces into stable long-term representations, is facilitated by post-learning sleep. Computational and biophysical models suggest that sleep spindles may play a key mechanistic role for consolidation, igniting structural changes at cortical sites involved in prior learning. Here we tested the resulting prediction that spindles are most pronounced over learning-related cortical areas and that the extent of this learning-spindle overlap predicts behavioural measures of memory consolidation. Using high-density scalp Electroencephalography (EEG) and Polysomnography (PSG) in healthy volunteers, we first identified cortical areas engaged during a temporospatial associative memory task (power decreases in the alpha/beta frequency range, 6-20 Hz). Critically, we found that participant-specific topographies (i.e., spatial distributions) of post-learning sleep spindle amplitude correlated with participant-specific learning topographies. Importantly, the extent to which spindles tracked learning patterns further predicted memory consolidation across participants. Our results provide empirical evidence for a role of post-learning sleep spindles in tracking learning networks, thereby facilitating memory consolidation.

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bioRxiv2021

A massive 7T fMRI dataset to bridge cognitive and computational neuroscience

Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. We present the Natural Scenes Dataset (NSD), in which high-resolution fMRI responses to tens of thousands of richly annotated natural scenes are measured while participants perform a continuous recognition task. To optimize data quality, we develop and apply novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we use NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality, and breadth, NSD opens new avenues of inquiry in cognitive and computational neuroscience.

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bioRxiv2020

Revealing the structure of affective schematic representations in the medial prefrontal cortex

Adaptive cognition is fostered by knowledge about the structure and value of our environment. Here, we hypothesize that these two kinds of information are inherently intertwined as value-weighted schemas in the medial prefrontal cortex (mPFC). Schemas (e.g., of a social network) emerge by extracting commonalities across experiences and can be understood as graphs comprising nodes (e.g., people) and edges (e.g., their relationships). We sampled information about unique real-life environments (i.e., about personally familiar people and places) and probed the neural representations of their schemas with fMRI. Using representational similarity analysis, we show that the mPFC encodes indeed both, the nodes and edges of the schemas. Critically, as hypothesized, the strength of the edges is not only determined by experience and centrality of a node but also by value. We thus account for the involvement of the mPFC in disparate functions and suggest that valuation emerges naturally from encoded memory representations.

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bioRxiv2020

Spatiotemporal Dynamics of Sound Representations Reveal a Hierarchical Progression of Category Selectivity

As the human brain transforms incoming sounds, it remains unclear whether semantic meaning is assigned via distributed, domain-general architectures or specialized hierarchical streams. Here we show that the spatiotemporal progression from acoustic to semantically dominated representations is consistent with a hierarchical processing scheme. Combining magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) patterns, we found superior temporal responses beginning ~80 ms post-stimulus onset, spreading to extratemporal cortices by ~130 ms. Early acoustically-dominated representations trended systematically toward semantic category dominance over time (after ~200 ms) and space (beyond primary cortex). Semantic category representation was spatially specific: vocalizations were preferentially distinguished in temporal and frontal voice-selective regions and the fusiform face area; scene and object sounds were distinguished in parahippocampal and medial place areas. Our results are consistent with an extended auditory processing hierarchy in which acoustic representations give rise to multiple streams specialized by category, including areas typically considered visual cortex.

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bioRxiv2020

The hippocampus as the switchboard between perception and memory

Adaptive memory recall requires a rapid and flexible switch from external perceptual reminders to internal mnemonic representations. However, owing to the limited temporal or spatial resolution of brain imaging modalities used in isolation, the hippocampal-cortical dynamics supporting this process remain unknown. We thus employed an object-scene cued recall paradigm across two studies, including intracranial Electroencephalography (iEEG) and high-density scalp EEG. First, a sustained increase in hippocampal high gamma power (60-110 Hz) emerged 500 ms after cue onset and distinguished successful vs. unsuccessful recall. This increase in gamma power for successful recall was followed by a decrease in hippocampal alpha power (8-12 Hz). Intriguingly, the hippocampal gamma power increase marked the moment at which extrahippocampal activation patterns shifted from perceptual cue towards mnemonic target representations. In parallel, source-localised EEG alpha power revealed that the recall signal progresses from hippocampus to posterior parietal cortex and then to medial prefrontal cortex. Together, these results identify the hippocampus as the switchboard between perception and memory and elucidate the ensuing hippocampal-cortical dynamics supporting the recall process. SignificanceHow do we adaptively switch from perceiving the external world to retrieving goal-relevant internal memories? To tackle this question, we used - in a cued-recall paradigm - direct intracranial recordings from the human hippocampus complemented by high-density scalp Electroencephalography (EEG). We found that a hippocampal signal ~500 ms after a perceptual cue marks the conversion from external (perceptual) to internal (mnemonic) representations. This sets in motion a recall cascade involving posterior parietal and medial prefrontal cortex, revealed via source-localised and time-resolved EEG alpha power. Together, these results unveil the hippocampal-cortical dynamics supporting rapid and flexible memory recall.

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bioRxiv2019

Alpha/beta power decreases track the fidelity of stimulus-specific information

Massed synchronised neuronal firing is detrimental to information processing. When networks of task-irrelevant neurons fire in unison, they mask the signal generated by task-critical neurons. On a macroscopic level, mass synchronisation of these neurons can contribute to the ubiquitous alpha/beta (8-30Hz) oscillations. Reductions in the amplitude of these oscillations, therefore, may reflect a boost in the processing of high-fidelity information within the cortex. Here, we test this hypothesis. Twenty-one participants completed an associative memory task while undergoing simultaneous EEG-fMRI recordings. Using representational similarity analysis, we quantified the amount of stimulus-specific information represented within the BOLD signal on every trial. When correlating this metric with concurrently-recorded alpha/beta power, we found a significant negative correlation which indicated that as alpha/beta power decreased, our metric of stimulus-specific information increased. This effect generalised across cognitive tasks, as the negative relationship could be observed during visual perception and episodic memory retrieval. Further analysis revealed that this effect could be better explained by alpha/beta power decreases providing favourable conditions for information processing, rather than directly representing stimulus-specific information. Together, these results indicate that alpha/beta power decreases parametrically track the fidelity of both externally-presented and internally-generated stimulus-specific information represented within the cortex.

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bioRxiv2019

Conscious perception of natural images is constrained by category-related visual features

Conscious perception is crucial for adaptive behaviour yet access to consciousness varies for different types of objects. The visual system comprises regions with widely distributed category information and exemplar-level representations that cluster according to category. Does this categorical organisation in the brain provide insight into object-specific access to consciousness? We address this question using the Attentional Blink (AB) approach with visual objects as targets. We find large differences across categories in the AB then employ activation patterns extracted from a deep convolutional neural network (DCNN) to reveal that these differences depend on mid- to high-level, rather than low-level, visual features. We further show that these visual features can be used to explain variance in performance across trials. Taken together, our results suggest that the specific organisation of the higher-tier visual system underlies important functions relevant for conscious perception of differing natural images.

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bioRxiv2018

Testing the fast consolidation hypothesis of retrieval-mediated learning

The testing-effect, or retrieval-mediated learning, is one of the most robust effects in memory research. It shows that actively and repeatedly retrieving information, compared to merely restudying it, improves long-term retention. Surprisingly, little is known about the neurocognitive mechanisms underlying this phenomenon. Attempting to fill this gap, a recent framework proposed that retrieval acts as a fast memory consolidation mechanism, stabilizing memories through online reactivation, similar to memory replay during offline (e.g. sleep) consolidation. In this fMRI study, we empirically tested the predictions derived from this framework.\n\nWe predicted that reactivation during retrieval allows memories to become embedded in neocortex, creating an additional route to access the memory trace and rendering it less hippocampus-dependent. Participants encoded scene-object pairs and either retrieved or restudied the objects over two sessions, two days apart. We analysed univariate and multivariate changes in brain activity specific to retrieval but not restudy, and tested whether the predicted changes occur rapidly within a session, or evolve slowly, across the two days.\n\nResults showed that medial prefrontal cortex activation increased across retrieval trials within one session, consistent with a fast consolidation account. Hippocampal activity decreased across sessions, suggesting a slower mechanism. Moreover, Representational Similarity Analyses (RSA) showed that consecutive retrieval attempts strengthen both higher-level semantic and episode-specific information in parietal areas, both across but not within sessions. Our findings suggest that retrieval supports the online creation of a neocortical trace, which becomes increasingly relevant at long delays when hippocampus-dependent episodic details would otherwise have faded.\n\nSignificance statementRepeated remembering strengthens memories much more so than repeated learning. The aim of this study was to shed light onto the poorly understood neurocognitive underpinnings of retrieval-mediated learning. We tested a novel framework proposing that a memorys stabilization via retrieval relies on mechanisms akin to those involved in offline systems consolidation. Observing the retrieval-induced neural pattern changes across different timescales, we find that retrieval stabilizes semantic and episodic aspects of the original memories, and produces increases in prefrontal activity and decreases in hippocampal activity that are consistent with the consolidation view, but not necessarily with a fast acting mechanism. Our findings inform cognitive theories of the testing effect, suggesting that retrieval produces its benefits by interacting with hippocampal-neocortical consolidation mechanisms.

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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.

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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.

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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

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