Biology subjects

Gosselin, F.

Publications and source records attributed to Gosselin, F..

7 recordsLinked to original sources

bioRxiv2022

Microscale geometrical features in the plant leaf epidermis confer enhanced resistance to mechanical failure

In many plant species, epidermal tissues of leaves and petals feature irregular wavy cell geometries forming jigsaw puzzle patterns. At the origin of plant tissues are simple polyhedral progenitor cells that divide and grow into a kaleidoscopic array of morphologies that underpin plant organ functionality. The wide prevalence and great diversity of the wavy cell shape in the plant kingdom point to the significance of this trait and its tunability by environmental pressures. Despite multiple attempts to explain the advent of this complex cell geometry by evolutionary relevant functionality, our understanding of this peculiar tissue patterning preserved through evolution remains lacking. Here, by combining microscopic and macroscopic fracture experiments with computational fracture mechanics, we show that wavy epidermal cells toughen the plants protective skin. Based on a multi-scale approach, we demonstrate that, biological and synthetic materials alike can be toughened through an energy-efficient patterning process. Our data reveal a ubiquitous and tunable structural-mechanical mechanism employed in the macro-scale design of plants to protect them from the detrimental effects of surface fissures and to enable and guide the direction of beneficial fractures. We expect these data to inform selective plant breeding for traits enhancing plant survival under changing environmental conditions. From a materials engineering perspective, this work exemplifies that plants hold sophisticated design principles to inspire human-made materials.

plant biology↗

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

neuroscience↗

bioRxiv2022

Rhythmic information sampling in the brain during visual recognition

When we fixate an object, visual information is continuously received on the retina. Several studies observed behavioral oscillations in perceptual sensitivity across such stimulus time, and these fluctuations have been linked to brain oscillations. However, whether specific brain areas show oscillations across stimulus presentation time (i.e., different time points of the stimulus being more or less processed, in a rhythmic fashion) has not been investigated. Here, we revealed random areas of face images at random moments across time and recorded the brain activity of human participants (both male and female) using magnetoencephalography (MEG) while they performed two recognition tasks. This allowed us to quantify how each snapshot of visual information coming from the stimulus is processed across time and across the brain. Oscillations across stimulus time (rhythmic sampling) were mostly visible in early visual areas, at theta, alpha and low beta frequencies. We also found that they contributed to brain activity more than previously investigated rhythmic processing (oscillations in the processing of a single snapshot of visual information). Non-rhythmic sampling was also visible at later latencies across the visual cortex, either in the form of a transient processing of early stimulus time points or of a sustained processing of the whole stimulus. Our results suggest that successive cycles of ongoing brain oscillations process stimulus information incoming at successive moments. Together, these results advance our understanding of the oscillatory neural dynamics associated with visual processing and show the importance of considering the temporal dimension of stimuli when studying visual recognition. Significance StatementSeveral behavioral studies have observed oscillations in perceptual sensitivity over the duration of stimulus presentation, and these fluctuations have been linked to brain oscillations. However, oscillations across stimulus time in the brain have not been studied. Here, we developed a MEG paradigm to quantify how visual information received at each moment during fixation is processed through time and across the brain. We showed that different snapshots of a stimulus are distinctly processed in many brain areas and that these fluctuations are oscillatory in early visual areas. Oscillations across stimulus time were more prevalent than previously studied oscillations across processing time. These results increase our understanding of how neural oscillations interact with the visual processing of temporal stimuli.

neuroscience↗

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.

neuroscience↗

bioRxiv2019

Mechanical stress initiates and sustains the morphogenesis of wavy leaf epidermal cells

Plant cell morphogenesis is governed by the mechanical properties of the cell wall and the resulting cell shape is intimately related to the respective specific function. Pavement cells covering the surface of plant leaves form wavy interlocking patterns in many plants. We use computational mechanics to simulate the morphogenetic process based on experimentally assessed cell shapes, growth dynamics, and cell wall chemistry. The simulations and experimental evidence suggest a multistep process underlying the morphogenesis of pavement cells during tissue differentiation. The mechanical shaping process relies on spatially confined, feedback-augmented stiffening of the cell wall in the periclinal walls, an effect that correlates with experimentally observed deposition patterns of cellulose and de-esterified pectin. We provide evidence for mechanical buckling of the pavement cell walls that can robustly initiate patterns de novo and may precede chemical and geometrical anisotropy.\n\nHighlightsO_LIA multistep mechano-chemical morphogenetic process underlies the wavy pattern of epidermal pavement cells.\nC_LIO_LIMicrotubule polarization is preceded by an event that breaks mechanical isotropy in the cell wall.\nC_LIO_LIMechanical models simulate the formation of wavy cell shapes, predict buckling of the cell walls and spatially confined variations in the mechanical properties of leaf epidermal cells.\nC_LIO_LIStress/strain stiffening following the buckling of the cell walls constitutes a crucial element in a positive feedback loop forming interlocking pavement cells.\nC_LIO_LIPolarization of cortical microtubules, cellulose microfibrils, and de-esterified pectin occur at the necks of wavy pavement cells, matching the in silico prediction of cell wall stiffening.\nC_LI

plant biology↗

bioRxiv2019

The processing of continuously received visual information

Visual object recognition seems to occur almost instantaneously. However, not only does it require hundreds of milliseconds of processing, but our eyes also typically fixate the object for hundreds of milliseconds. Consequently, information reaching our eyes at different moments is processed in the brain together. Moreover, information received at different moments during fixation is likely to be processed differently, notably because different features might be selectively attended at different moments. Here, we introduce a novel reverse correlation paradigm that allows us to uncover with millisecond precision the processing time course of specific information received on the retina at specific moments. Using faces as stimuli, we observed that processing at several electrodes and latencies was different depending on the moment at which information was received. Some of these variations were caused by a disruption occurring 160-200 ms after the face onset, suggesting a role of the N170 ERP component in gating information processing; others hinted at temporal compression and integration mechanisms. Importantly, the observed differences were not explained by simple adaptation or repetition priming, they were modulated by the task, and they were correlated with differences in behavior. These results suggest that top-down routines of information sampling are applied to the continuous visual input, even within a single eye fixation.

neuroscience↗

bioRxiv2018

Real-world expectations and their affective value modulate object processing

It is well known that expectations influence how we perceive the world. Yet the neural mechanisms underlying this process remain unclear. Studies about the effects of prior expectations have focused so far on artificial contingencies between simple neutral cues and events. Real-world expectations are however often generated from complex associations between contexts and objects learned over a lifetime. Additionally, these expectations may contain some affective value and recent proposals present conflicting hypotheses about the mechanisms underlying affect in predictions. In this study, we used fMRI to investigate how object processing is influenced by realistic context-based expectations, and how affect impacts these expectations. First, we show that the precuneus, the inferotemporal cortex and the frontal cortex are more active during object recognition when expectations have been elicited a priori, irrespectively of their validity or their affective intensity. This result supports previous hypotheses according to which these brain areas integrate contextual expectations with object sensory information. Notably, these brain areas are different from those responsible for simultaneous context-object interactions, dissociating the two processes. Then, we show that early visual areas, on the contrary, are more active during object recognition when no prior expectation has been elicited by a context. Lastly, BOLD activity was shown to be enhanced in early visual areas when objects are less expected, but only when contexts are neutral; the reverse effect is observed when contexts are affective. This result supports the proposal that affect modulates the weighting of sensory information during predictions. Together, our results help elucidate the neural mechanisms of real-world expectations.

neuroscience↗