Biology subjects

Brochard, J.

Publications and source records attributed to Brochard, J..

5 recordsLinked to original sources

bioRxiv2024

Predicting individual variations in mental effort-based decision-making using machine learning: Neurometabolic signature in the dorsomedial prefrontal cortex/dorsal anterior cingulate cortex

Motivation drives individuals to overcome costs to achieve desired outcomes, such as rewards or avoidance of punishment, with significant variability across individuals. The dorsomedial prefrontal cortex/dorsal anterior cingulate cortex (dmPFC/dACC) and anterior insula are key brain regions implicated in effort-based decision-making. Here, we utilized proton magnetic resonance spectroscopy (1H-MRS) at 7 Tesla on 69 healthy participants in these brain regions to uncover the neurometabolic factors that influence these differences. We designed and applied an effort-based decision-making task requiring mental and physical effort to probe motivated behavior, complemented by computational modeling to extract key behavioral parameters. Gradient boosting machine learning was applied to explore the predictive role of specific metabolites in motivated behavior. Our results reveal that a model established on dmPFC/dACC metabolites explains decisions to exert high mental effort and sensitivity to mental effort. In particular, glutamate, aspartate, and lactate in dmPFC/dACC, three metabolites linked through the tricarboxylic acid cycle and glycolysis, were identified as key discriminative metabolites in the dmPFC/dACC, predictive of mental effort choices, underpinning energy supply and cognitive processes. Anterior insula metabolites did not significantly relate to effort-related decisions. Notably, glutamine and lactate levels between the periphery (plasma) and the dmPFC/dACC were correlated, suggesting a metabolic link between peripheral and central biomarkers of effort. Our findings provide novel insights into the neurometabolic underpinnings of motivated behavior and propose novel biomarkers for mental effort-based decision-making. Importantly, our study highlights the relevance of multivariable approaches in elucidating complex cognitive functions.

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bioRxiv2023

Neural shape mediation analysis

Neural signal shapes convey significant information about their generating processes. In this study, we introduce a data-driven methodology to identify sensory and behaviourally-relevant traces within neural responses. We present a phenomenological model that characterises temporal variations in intracranial EEG using eight interpretable parameters: peak time, peak intensity, initial and final baselines, accumulation and depletion period, and their respective concavities. This model effectively captures subtle signal variations, especially in sensory decision-making tasks. By decomposing the signals in this manner, we then conduct a comprehensive brain mediation analysis on iEEG datas shape, pinpointing regions that mediate behavioural processes. Importantly, we can determine which signal dynamics specifically reflect underlying behavioural processes, enhancing the depth of analysis and critique of their role in behaviour. Preliminary applications on a cohort of epileptic patients reveal that our model explains over a third of the signal variance at the trial level across all brain regions. We identified four key regions--encompassing sensory, associative, frontal, and premotor areas--that mediate the impact of task difficulty on reaction time. Notably, in these regions, it was the depletion period, rather than signal amplitude, that correlated with behavioural speed. This study highlights the potential of our approach in providing detailed insights into the neural mechanisms linking stimuli to behaviour.

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bioRxiv2022

How subjective idea valuation energizes and guides creative idea generation

What drives us to search for creative ideas, and why does it feel good to find one? While previous studies demonstrated the positive influence of motivation on creative abilities, how reward and subjective values play a role in creativity remains unknown. This study proposes to characterize the role of individual preferences (how people value ideas) in creative ideation via behavioral experiments and computational modeling. Using the Free Generation of Associates Task coupled with rating tasks, we demonstrate the involvement of valuation processes during idea generation: preferred ideas are provided faster. We found that valuation depends on the adequacy and originality of ideas and guides response selection and creativity. Finally, our computational model correctly predicts the speed and quality of human creative responses, as well as interindividual differences in creative abilities. Altogether, this model introduces the mechanistic role of valuation in creativity. It paves the way for a neurocomputational account of creativity mechanisms. Public Significance StatementThis study addresses the role of individual preferences in creativity. It demonstrates that preferences for ideas energize creative idea production: the more participants like their ideas, the faster they provide them. Moreover, preferences rely on an equilibrium between the adequacy and originality of ideas and vary across individuals. This study introduces a computational model which incorporates individual preferences and that correctly predicts the speed and quality of responses in a creative idea generation task, as well as inter-individual differences in creative abilities. Comparison of several versions of this model demonstrated that preferences guide the selection of creative responses.

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bioRxiv2020

Meet me in the middle: brain-behavior mediation analysis for fMRI experiments

Functional outcomes (e.g., subjective percepts, emotions, memory retrievals, decisions, etc...) are partly determined by external stimuli and/or cues. But they may also be strongly influenced by (trial-by-trial) uncontrolled variations in brain responses to incoming information. In turn, this variability provides information regarding how stimuli and/or cues are processed by the brain to shape behavioral responses. This can be exploited by brain-behavior mediation analysis to make specific claims regarding the contribution of brain regions to functionally-relevant input-output transformations. In this work, we address four challenges of this type of approach, when applied in the context of mass-univariate fMRI data analysis: (i) we quantify the specificity and sensitivity profiles of different variants of mediation statistical tests, (ii) we evaluate their robustness to hemo-dynamic and other confounds, (iii) we identify the sorts of brain mediators that one can expect to detect, and (iv) we disclose possible interpretational issues and address them using complementary information-theoretic approaches. En passant, we propose a computationally efficient algorithmic implementation of the approach that is amenable to whole-brain exploratory analysis. We also demonstrate the strengths and weaknesses of brain-behavior mediation analysis in the context of an fMRI study of decision under risk. Finally, we discuss the limitations and possible extensions of the approach.

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bioRxiv2020

Blaming blunders on the brain: can indifferent choices be driven by range adaptation or synaptic plasticity?

Many deleterious behaviors, such as procrastinating on urgent matters or sustaining a toxic relationship, are irrational. But is irrational behavior the incidental outcome of biological constraints imposed on neural information processing? In particular, can those constraints alter decisions, even when people know the consequences of alternative actions? Recent studies indicate that orbitofrontal neurons encode decision value in relative terms, i.e. value signals in OFC neurons are normalized with respect to the context. Value-based decisions may thus exhibit irrational context-dependence effects. A candidate explanation is "efficient coding": OFC neurons may mitigate information loss by adapting their (bounded) output firing properties to the recent value range. This is seducing, because it suggests that relative value coding is the brains best attempt to produce rational behavior, given its own hard-wired biological constraints. However, whether the behavioral implications of this scenario are met, how it generalizes to realistic situations in which OFC neurons construct value from multiple decision-relevant attributes, and what its neurophysiological bases are, is unclear. Here, we address these issues by re-analyzing two open fMRI datasets from the OpenNeuro.org initiative, where people have to integrate prospective gains and losses to decide whether to gamble or not. First, we show that peoples risk attitudes critically depend on the range of gain prospects they are exposed to. Importantly, counter to simple efficient coding scenarios, differences in gain ranges induce progressive changes in peoples sensitivity to both gains and losses. Second, we use artificial neural network models to show that hebbian rewiring processes between attribute-specific and attribute-integration neurons predict (out-of-sample) both context-dependence effects in peoples risk attitude and multivariate patterns of fMRI activity in the OFC. Under mild conditions on distributed population codes for decision attributes, hebbian plasticity eventually translates the distribution of reweighted attribute signals towards the responsive range of integration neurons. In turn, integration units exhibit the known features of range adaptation, including (but not limited to), relative value coding. Our results demonstrate how hebbian plasticity within brain networks may result in range adaptation, eventually yielding complex though predictable irrational behavior.

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