Biology subjects

Michiels, M.

Publications and source records attributed to Michiels, M..

7 recordsLinked to original sources

bioRxiv2026

Novel devaluation methods to explore habits in humans

Habits in humans are commonly studied through outcome devaluation paradigms, but most existing tasks fail to capture the robustness of habitual behavior seen in animal models. I introduce two novel behavioral tasks designed to overcome these limitations. In the first task, ("shooting aliens task", n = 45), I simplified an existing instrumental learning task and implemented a novel intra-block reversal method in which stimulus positions changed unexpectedly within blocks while maintaining the same stimulus-action mappings. Participants also completed a classical devaluation phase with explicit reward changes. In the second task ("hands-attack task", n = 44), which relied on real-life avoidance behavior, devaluation was achieved by reversing reward contingencies and allowing participants to inhibit the dominant avoidance response in favor of a more effortful counterattack. Across both tasks, overtrained conditions led to more errors and longer response times after devaluation, confirming increased insensitivity to outcome change. Intra-block reversals in the shooting aliens task produced stronger habitual signatures than standard whole-block devaluation, revealing a greater cost of overriding automatic responses. In the hands-attack task, even without prior training, participants showed clear markers of habitual behavior, suggesting that real-world action patterns can replicate key features of laboratory habits. Interestingly, participants were more accurate in overriding overtrained responses when attacks were highly familiar, possibly due to enhanced perceptual processing, although this came at the cost of longer response times. These findings introduce two complementary tools that address key limitations in current paradigms: the intra-block reversal increases habit sensitivity without inflating working memory demands, while the hands-attack task captures naturalistic habit expression without artificial training, using a single, ecologically valid session. Both are suited for clinical applications, particularly where time constraints or cognitive load limit the feasibility of traditional approaches.

neuroscience↗

bioRxiv2025

The Neural Basis of Habit Formation Measured in Goal-Directed Response Switching

To override ongoing habitual responses requires switching well-learned actions with new goal-directed processing. However, the neural circuits responsible for these processes remain unclear. This study infers habit strength by introducing a novel task capturing the increased cost associated with switching a habitual response. We employed neuroimaging and brain stimulation to examine the dynamic interactions between human brain regions involved in habits and their interference with ongoing incompatible goal-directed behavior. Training S-R links in overtrained stimuli (compared to less trained ones, termed standard-trained stimuli) increased RT switch costs, explained by drift diffusion computations governing both the training and outcome devaluation phases. Training engaged sensorimotor areas and the posterior putamen, whereas standard trained behaviors, recruited the posterior caudate, insula, and prefrontal regions. A cortical network orchestrated habit expression (right S1 with the left anterior insula/prefrontal areas) while also implicating basal ganglia when overriding habits (left premotor with the putamen). Importantly, stimulation of the left premotor played a causal role in habit control, enhancing performance across both the training and devaluation phases. Our findings reveal an interaction between habitual and goal-directed brain regions, highlighting shared neural dynamics when overriding habitual behaviors.

neuroscience↗

bioRxiv2022

BET-independent MLV integration is retargeted in vivo and selects distinct genomic elements for lymphomagenesis

Moloney murine leukemia virus (MLV) infects BALB/c mice and induces T-cell lymphoma in mice. Retroviral integration is mediated by the interaction of the MLV integrase (IN) with members of the bromodomain and extra-terminal motif (BET) protein family (BRD2, BRD3 and BRD4). Introduction of the W390A mutation in MLV IN abolishes BET interaction. Here we compared the replication of W390A MLV and WT MLV in adult BALB/c mice to study the role of BET proteins in replication, integration and tumorigenesis in vivo. Comparing WT and W390A MLV infection revealed similar viral loads in blood, thymus and spleen cells. Interestingly, W390A MLV integration was retargeted away from GC-enriched genomic regions. However, both WT MLV and W390A MLV developed T cell lymphoma after a similar latency represented by an enlarged thymus and spleen and multi-organ tumor infiltration. Integration site sequencing from splenic tumor cells revealed clonal expansion in all WT MLV- and W390A MLV-infected mice. However, the integration profile of W390A MLV and WT MLV differed significantly. Integrations were enriched in enhancers and promoters but compared to WT, W390A MLV integrated less frequently into enhancers and more into oncogene bodies, such as Notch1 and Ppp1r16b. We conclude that host factors direct MLV in vivo integration site selection. Although, BET proteins target WT MLV integration preferentially towards enhancers and promoters, insertional lymphomagenesis can occur independently from BET, likely due to the intrinsically strong enhancer/promoter of the MLV LTR.

microbiology↗

bioRxiv2021

Striatal role in everyday life and laboratory developed habits

The dorsolateral striatum plays a critical role in the acquisition and expression of stimulus-response habits that are learned in experimental laboratories. Here, we use meta-analytic procedures to contrast the neural circuits activated by laboratory-acquired habits with those activated by stimulus-response behaviours acquired in everyday-life. We confirmed that newly learned habits rely more on the anterior putamen with activation extending into caudate and nucleus accumbens. Motor and associative components of everyday-life habits were identified. We found that motor-dominant stimulus-response associations developed outside the laboratory primarily engaged posterior dorsal putamen, supplementary motor area (SMA) and cerebellum. Importantly, associative components were also represented in the posterior putamen. Thus, common neural representations for both naturalistic and laboratory-based habits were found in the left posterior and right anterior putamen. These findings suggest a partial common striatal substrate for habitual actions that are performed predominantly by stimulus-response associations represented in the posterior striatum. The overlapping neural substrates for laboratory and everyday-life habits supports the use of both methods for the analysis of habitual behaviour.

neuroscience↗

bioRxiv2020

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian Networks

We present the Fast Greedy Equivalence Search (FGES)-Merge, a new method for learning the structure of gene regulatory networks via merging locally learned Bayesian networks, based on the fast greedy equivalent search algorithm. The method is competitive with the state of the art in terms of the Matthews correlation coefficient, which takes into account both precision and recall, while also improving upon it in terms of speed, scaling up to tens of thousands of variables and being able to use empirical knowledge about the topological structure of gene regulatory networks. We apply this method to learning the gene regulatory network for the full human genome using data from samples of different brain structures (from the Allen Human Brain Atlas). Furthermore, this Bayesian network model should predict interactions between genes in a way that is clear to experts, following the current trends in explainable artificial intelligence. To achieve this, we also present a new open-access visualization tool that facilitates the exploration of massive networks and can aid in finding nodes of interest for experimental tests.

bioinformatics↗

bioRxiv2020

Electrophysiology prediction of single neurons based on their morphology

Electrophysiology data acquisition of single neurons represents a key factor for the understanding of neuronal dynamics. However, the traditional method to acquire this data is through patch-clamp technology, which presents serious scalability flaws due to its slowness and complexity to record at fine-grained spatial precision (dendrites and axon). In silico biophysical models are therefore created for simulating hundreds of experiments that would be impractical to recreate in vitro. The optimal way to create these models is based on the knowledge of the morphological and electrical features for each neuron. Since large-scale data acquisition is often unfeasible for electrical data, previous expert knowledge can be used but it must have an acceptable degree of similarity with the type of neurons that we are trying to model. Here, we present a data-driven machine learning approach to predict the electrophysiological features of single neurons in case of only having their morphology available. To solve this multi-output regression problem, we use an artificial neural network that has the particularity of providing a probability distribution for every output feature, to incorporate uncertainty. Input data to train the model is obtained from from the Allen Cell Types database. The electrical properties can depend on the morphology, whose acquisition technology is highly automated and scalable so there exist large data sets of them. We also provide integrations with the BluePyOpt library to create a biophysical model using the original morphology and the predicted electrical features. Finally, we connect the resulting biophysical model with the Geppetto UI software to run all the simulations in a sophisticated user interface.

neuroscience↗

bioRxiv2020

NeuroSuites-BNs: An open web framework for massive Bayesian networks focused on neuroscience

BayeSuites is the first web framework for learning, visualizing, and interpreting Bayesian networks (BNs) that can scale to tens of thousands of nodes while providing fast and friendly user experience. All the necessary features that enable this are reviewed in this paper; these features include scalability, extensibility, interoperability, ease of use, and interpretability. Scalability is the key factor in learning and processing massive networks within reasonable time; for a maintainable software open to new functionalities, extensibility and interoperability are necessary. Ease of use and interpretability are fundamental aspects of model interpretation, fairly similar to the case of the recent explainable artificial intelligence trend. We present the capabilities of our proposed framework by highlighting a real example of a BN learned from genomic data obtained from Allen Institute for Brain Science. The extensibility properties of the software are also demonstrated with the help of our BN-based probabilistic clustering implementation, together with another genomic-data example.

bioinformatics↗