Biology subjects

Ruz, M.

Publications and source records attributed to Ruz, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Cracking the predictive stability code: dual trial-by-trial spatio-temporo-spectral dynamics of task representation directional tuning by Bayesian conflict expectations

Cognitive stability, or task focus, enables goal-directed behavior through representational dynamics: by prioritizing task-relevant representations over competing task-irrelevant ones. Beyond corrective reactive mechanisms operating after conflict arises, stability can be deployed predictively through the statistical learning of conflict regularities, either proactively, guided by global conflict expectations, or through a predictive reactive mode guided by item-specific ones. Yet, the representational mechanisms enabling such predictive adjustments remain underspecified. We addressed this by re-analyzing EEG data from a spatial Stroop task concurrently manipulating list-wide (LWPC) and item-specific (ISPC) proportion congruency. Moving beyond existing analytical approaches, we developed a framework combining trial-by-trial Bayesian conflict-expectancy modeling with a novel spatio-temporo-spectral searchlight Representational Similarity Analysis, and leveraged directional representational modeling complemented by ridge-regression decoding. We first identified the necessary computational substrate for stability predictive reconfigurations: the spatio-temporo-spectral patterns encoding task (Target, Distractor) and conflict-expectancy (LWPC, ISPC) representations, the latter representations encoded already in the pre-stimulus windows. Crucially, conflict expectations modulated Target and Distractor encoding strength with distinct spatio-temporal and spectral dynamics, revealing a functional dissociation. LWPC-induced proactive stability follows a "Target-first" policy, primarily strengthening the Target early post-stimulus via Alpha and lower-Beta patterns, weakening Distractor encoding only later on; conversely, ISPC-induced predictive reactive stability follows a "Distractor-first" policy, rapidly weakening Distractor encoding via response-locked posterior Beta patterns, strengthening the Target only downstream. Our study refines theoretical cognitive control models, incorporating a mechanistic explanation for predictive stability, while also establishing a multidimensional methodological template for interrogating the underlying substrates of adaptive behavior and beyond.

neuroscience↗

bioRxiv2026

Flexible predictive processing during face perception

Perceptual expectations facilitate perception of expected information by shaping neural coding. However, how the cognitive demands of different tasks may modulate this effect remains unclear. Here, we investigated whether and how expectations derived from sex/gender stereotypes impact behavioral and neural face processing as a function of task goals. Fifty-one participants categorized either the emotional expression (happy vs. angry) or the sex (male vs. female) of faces while mouse trajectories and electroencephalography (EEG) were recorded in separate sessions. Expectations were manipulated through the congruency between facial sex and emotional expression (congruent: angry male, happy female; incongruent: happy male, angry female). Behavioral analyses revealed that incongruent faces elicited larger trajectory deviations, longer movement dynamics and slower reaction times than congruent faces. Representational Similarity Analyses (RSA) showed that face congruency modulated coding geometries of both mouse trajectories and EEG, whereas multivariate classifiers revealed progressive impact of congruency from early stages of face neural coding. Importantly, all measurements revealed stronger congruency effects during emotion than during sex judgments, indicating that the influence of expectations is flexibly tailored to task demands. Furthermore, congruent faces exhibited enhanced decoding performance and reached peak neural discriminability earlier than incongruent ones. Finally, EEG-mouse fusion analyses revealed shared representational dynamics primarily explained by task demands. Together, these findings indicate that expectations facilitate face processing in a task-dependent manner and suggest that predictive facilitation operates, rather than by increasing the rate of evidence accumulation, by accelerating the emergence of neural coding patterns. Significance StatementPerception is shaped by the combination of sensory input and prior expectations, including socially acquired sex/gender face stereotypes. Learning how these expectations influence perception and how they are flexibly adjusted to current behavioral goals is essential for predictive coding models. Combining EEG and mouse tracking, we show that emotion judgments are more susceptible to expectations than sex categorization, and that expectations accelerate the emergence of neural coding patterns rather than simply increasing the rate of evidence accumulation. These findings characterize the neural mechanism through which predictive processing adapts to task demands and demonstrate that the influence of social stereotypes on face perception is dynamically regulated rather than automatically imposed, providing new insights into when social biases are most likely to shape human perception.

neuroscience↗

bioRxiv2026

Selection vs. integration task demands shape the similarity of information neural coding

Attention is a function that enables selection and integration of multiple sources of information. However, how these demands influence neural coding of information is not well understood. In this study we used EEG to examine how the selection vs. integration of stimuli shapes the content and geometry reflected on neural patterns, during both preparation and target processing. Participants performed a size judgement task in a cue-target paradigm that, depending on the block, required judging either the size of a selected item and ignoring the additional stimulus or integrating both items to respond. Decoding analyses showed that under selection demands, categorical templates of the cued stimulus were activated during preparation and target coding, contrasting with integration, where the cued category was active only during preparation. Notably, RSA suggested a specific exemplar encoding during its processing, that was sustained also across the post-stimulus window during selection, yet not under integration contexts. Our results also suggest that attentional demands shape the similarity between stimulus categories, by increasing the distance between selected stimuli and distractors or by increasing the similarity between to-be-integrated stimuli. Overall, this study uncovers the dynamics of stimulus encoding under selection and integration demands, offering crucial advances to understand how top-down processes shape information representation in the human brain.

neuroscience↗

bioRxiv2026

Multiple-Demand Network encoding geometry balances generalization and dimensionality during novel task assembly.

On the basis of verbal instructions, humans can accomplish novel and diverse demands on the very first try. This complex phenomenon recruits structured brain activity across the frontoparietal Multiple Demand Network (MDN), which is thought to encode upcoming task parameters and guide behavior. However, it is uncertain how novel instructions are translated into efficient neural task representations. To address this, we collected functional magnetic resonance imaging (fMRI) data while participants followed a rich set of novel verbal instructions. These varied along three core dimensions: the overarching task demand (to select or to integrate stimuli information), the relevant target category (animate or inanimate items), and the visual feature that participants responded to (color or shape). We used multivariate pattern analysis (MVPA) to examine whether and how each of these dimensions was reflected in MDN activity. We contrasted two alternative representational geometries that may underpin novel task coding: low-dimensional spaces based on abstract and generalizable representations, and high-dimensional architectures hosting context-unique, conjunctive neural codes. Our results showed that anticipatory MDN activity was sensitive to the content of instructions. While selection vs. integration task demands were broadly encoded across the MDN, coding of the relevant categories and features was restricted to lateral MDN regions, namely, the intraparietal sulcus and the inferior frontal junction. Critically, the representational spaces across the MDN displayed a mixture of geometrical motifs, partially supporting our two alternative hypotheses. On the one hand, Cross-Condition Generalization Performance revealed the presence of abstract and transferable neural codes for task demand information. On the other hand, Shattering Dimensionality showed complex, high dimensional coding spaces across the MDN, structured around both task-informative and non-informative axes. Despite this, no evidence of conjunctive neural codes was observed. Overall, these findings highlight that novel instructed behavior may recruit both abstraction and high dimensionality to promote generalization while maximizing the expressivity of MDN coding spaces. More broadly, they emphasize the importance of considering encoding geometry for a computational understanding of cognitive control processes.

neuroscience↗

bioRxiv2025

Rapid Computation of High-Level Visual Surprise

Predictive processing theories propose that the brain continuously generates expectations about incoming sensory information. Discrepancies between these predictions and actual inputs, sensory prediction errors, guide perceptual inference. A fundamental yet largely unresolved question is which stimulus features the brain predicts, and therefore, what kind of surprise drives neural responses. Here, we investigated this question using EEG and computational modelling based on deep neural networks (DNNs). Participants viewed object images whose identity was probabilistically predicted by preceding cues. We then quantified trial-by-trial surprise at both low-level (early DNN layers) and high-level (late DNN layers) visual feature representations. Results showed that stimulus-evoked responses around 200ms post-stimulus onset over parieto-occipital electrodes were increased by high-level, but not by low-level visual surprise. These findings demonstrate that high-level visual predictions are rapidly integrated into perceptual inference, suggesting that the brains predictive machinery is finely tuned to utilize expectations abstracted away from low-level sensory details to facilitate perception.

neuroscience↗

bioRxiv2024

Novel verbal instructions recruit abstract neural patterns of time-variable information dimensionality

Human performance is endowed by neural representations of information that is relevant for behavior, some of which are also activated in a preparatory fashion to optimize later execution. Most studies to date have focused on highly practiced actions, leaving largely unaddressed the novel re-configuration of information to generate unique whole task-sets. Using electroencephalography (EEG), this study investigated the dynamics of the content and geometry reflected on the neural patterns of control representations during re-configuration of information. We designed a verbal instruction paradigm where each trial involved novel combinations of multi-component task information. By manipulating three task-relevant factors in a sample of 40 participants (26 females, 14 males), we observed complex coding schemes throughout the trial, during both preparation and implementation stages. The temporal profiles were consistent with a hierarchical structure: whereas task information was active in a sustained manner, the coding of more concrete stimulus features was more transient. Data showed both high dimensionality and abstraction, particularly during instruction encoding and target processing. Our results suggest that whenever task content could be recovered from neural patterns of activity, there was evidence of abstract coding, with an underlying geometry that favored generalization. During target processing, where potential interference across stimulus and response factors increased, orthogonal configurations also appeared. Overall, our findings uncover the dynamic manner with which control representations operate during novel recombination unique scenarios, with changes in dimensionality and abstraction adjusting along processing stages. Significance StatementThe neural mechanisms that support task performance in novel contexts have been largely overlooked. Cognitive control is thought to enable complex behavior through the active maintenance of task sets, containing essential information for execution. However, how novel whole combinations of information are organized in neural patterns and their temporal dependencies remain unknown. Here, using a novel complex instruction paradigm, we observed that coding of informational content and its underlying geometry followed a dynamic temporal pattern. Our results reveal varying dimensionality and abstraction throughout the trial, with neural codes generally structured in a geometry favoring generalization of relevant information across task demands. These findings provide a first glimpse into the temporal computations engaged by the brain when encountering novel recombination settings.

neuroscience↗

bioRxiv2024

Context-dependent neural preparation for information relevance vs. probability

Preparation is a top-down phenomenon known to improve performance across different situations. In light of recent electrophysiological findings that suggest that anticipatory neural preactivations linked to preparation are context-specific and do not generalize across domains, in the current study we used fMRI to investigate the brain regions involved in these differential patterns. We applied multivariate decoding to data obtained in a paradigm where, in different blocks, cues provided information about the relevance or probability of incoming target stimuli. Results showed that the anticipated stimulus category was pre-activated in both conditions, mostly in different brain regions within the ventral visual cortex and with differential overlap with actual target perception. Crucially, there was scarce cross-classification across attention and expectation contexts except on a patch of the fusiform gyrus, indicating mostly differential neural coding of anticipated contents in relevance and probability scenarios. Finally, a model-based fMRI-EEG fusion showed that these regions differentially code for specific conditions during preparation, as well as specifically preparing for category anticipation in a ramping-up manner. Overall, our results stress the specificity of anticipatory neural processing depending on its informative role while highlighting a key hub of commonality in the fusiform gyrus.

neuroscience↗

bioRxiv2024

Proactive selective attention across competition contexts

Selective attention is a cognitive function that helps filter out unwanted information. Theories such as the biased competition model (Desimone & Duncan, 1995) explain how attentional templates bias processing towards targets in contexts where multiple stimuli compete for resources. However, it is unclear how the anticipation of different levels of competition influences the nature of attentional templates, in a proactive fashion. In this study, we used EEG to investigate how the anticipated demands of attentional selection (either high or low stimuli competition contexts) modulate target-specific preparatory brain activity and its relationship with task performance. To do so, participants performed a sex judgement task in a cue-target paradigm where, depending on the block, target and distractor stimuli appeared simultaneously (high competition) or sequentially (low competition). Multivariate Pattern Analysis (MVPA) showed that, in both competition contexts, there was a preactivation of the target category to select with a ramping-up profile at the end of the preparatory interval. However, cross-classification showed no generalization across competition conditions, suggesting different preparatory formats. Notably, time-frequency analyses showed differences between anticipated competition demands, reflecting higher theta band power for high than low competition, which mediated the impact of subsequent stimuli competition on behavioral performance. Overall, our results show that, whereas preactivation of the internal templates associated with the category to select are engaged in advance in both competition contexts, their underlying neural patterns differ. In addition, these codes could not be associated with theta power, suggesting different preparatory processes. The implications of these findings are crucial to increase our understanding of the nature of top-down processes across different contexts.

neuroscience↗

bioRxiv2023

The Temporal Dynamics of Metacognitive Experiences Track Rational Adaptations in Task Performance

Human task performance elicits diverse subjective metacognitive experiences, such as boredom, effort, fatigue and frustration, which are thought to play important roles in the monitoring and regulation of cognitive processes. Yet, their specific contributions to task performance remain poorly understood. Therefore, we investigated the temporal dynamics underlying these metacognitive experiences and the latent cognitive processes supporting task performance. We used a time-on-task design using a conflict Flanker task, and analyzed the data using a comprehensive approach encompassing behavioral, model-based, subjective, and neural measures. Our results show that the temporal dynamics in cognitive processes can be understood as a rational attempt to optimize task performance and that distinct metacognitive experiences track different aspects of this rational endeavor. These findings suggest that metacognitive experiences act as tools for humans to gain insights into the optimality of their cognitive performance.

neuroscience↗

bioRxiv2022

Preparatory neural activity differs between Selective Attention and Perceptual Expectations

SummaryProactive cognition brain models are mainstream nowadays. Within these, preparation is understood as an endogenous, top-down function that takes place prior to the actual perception of a stimulus and improves subsequent behavior. Neuroimaging has shown the existence of such preparatory activity separately in different cognitive domains, however no research to date has sought to uncover their potential similarities and differences. Two of these, often confounded in the literature, are Selective Attention (information relevance) and Perceptual Expectation (information probability). We used EEG to characterize the mechanisms that pre-activate specific contents in Attention and Expectation. In different blocks, participants were cued to the relevance or to the probability of target categories, faces vs. names, in a gender discrimination task. Multivariate Pattern (MVPA) and Representational Similarity Analyses (RSA) during the preparation window showed that both manipulations led to a significant, ramping-up prediction of the relevant or expected target category. However, classifiers trained on data from one condition did not generalize to the other, indicating the existence of unique anticipatory neural patterns. In addition, a Canonical Template Tracking procedure showed that there was stronger anticipatory perceptual reinstatement for relevance than for expectation blocks. Overall, results indicate that preparation during attention and expectation acts through distinguishable neural mechanisms. These findings have important implications for current models of brain functioning, as they are a first step towards characterizing and dissociating the neural mechanisms involved in top-down anticipatory processing.

neuroscience↗

bioRxiv2021

MVPAlab: A Machine Learning decoding toolbox for multidimensional electroencephalography data

Background and ObjectiveThe study of brain function has recently expanded from classical univariate to multivariate analyses. These multivariate, machine learning-based algorithms afford neuroscientists extracting more detailed and richer information from the data. However, the implementation of these procedures is usually challenging, especially for researchers with no coding experience. To address this problem, we have developed MVPAlab, a MATLAB-based, flexible decoding toolbox for multidimensional electroencephalography and magnetoencephalography data. MethodsThe MVPAlab Toolbox implements several machine learning algorithms to compute multivariate pattern analyses, cross-classification, temporal generalization matrices and feature and frequency contribution analyses. It also provides access to an extensive set of preprocessing routines for, among others, data normalization, data smoothing, dimensionality reduction and supertrial generation. To draw statistical inferences at the group level, MVPAlab includes a non-parametric cluster-based permutation approach. ResultsA sample electroencephalography dataset was compiled to test all the MVPAlab main functionalities. Significant clusters (p<0.01) were found for the proposed decoding analyses and different configurations, proving the software capability for discriminating between different experimental conditions. ConclusionsThis toolbox has been designed to include an easy-to-use and intuitive graphic user interface and data representation software, which makes MVPAlab a very convenient tool for users with few or no previous coding experience. In addition, MVPAlab is not for beginners only, as it implements several high and low-level routines allowing more experienced users to design their own projects in a highly flexible manner.

neuroscience↗

bioRxiv2021

The effect of task demands on the neural patterns generated by novel instruction encoding

Verbal instructions allow fast and optimal implementation of novel behaviors. Previous research has shown that different control-related variables structure neural activity in frontoparietal regions during the encoding of novel instructed tasks. However, it is uncertain whether different task goals modulate the organizing effect of these variables. In this study, we investigated whether the neural encoding of three task-relevant variables (dimension integration, response set complexity and target category) is modulated by implementation and memorization demands. To do so, we combined functional Magnetic Resonance Imaging (fMRI), an instruction-following paradigm and multivariate analyses. We addressed how and where distributed activity patterns encoded the instructions variables and the impact of the implementation and memorization demands on the fidelity of these representations. We further explored the nature of the neural code underpinning this process. Our results reveal, first, that the content of to-be-implemented and to-be-memorized instructions is represented in overlapping brain regions, flexibly using a common neural code across tasks. Importantly, they also suggest that preparing to implement the instructions increases the decodability of task-relevant information in frontoparietal areas, in comparison with memorization demands. Overall, our work emphasizes both similarities and differences in task coding under the two contextual demands. These findings qualify the previous understanding of novel instruction processing, suggesting that representing task attributes in a generalizable code, together with the increase in encoding fidelity induced by the implementation goals, could be key mechanisms for proactive control in novel scenarios.

neuroscience↗

bioRxiv2019

Multivariate pattern analysis techniques for electroencephalography data to study interference effects

A central challenge in cognitive neuroscience is to understand the neural mechanisms that underlie the capacity to control our behavior according to internal goals. Flanker tasks, which require responding to stimuli surrounded by distracters that trigger incompatible action tendencies, are frequently used to measure this conflict. Even though the interference generated in these situations has been broadly studied, multivariate analysis techniques can shed new light into the underlying neural mechanisms. The current study is an initial approximation to adapt an interference Flanker paradigm embedded in a Demand-Selection Task to a format that allows measuring concurrent high-density electroencephalography. We used multivariate pattern analysis (MVPA) to decode conflictrelated neural processes associated with congruent or incongruent target events in a time-frequency resolved way. Our results replicate findings obtained with other analysis approaches and offer new information regarding the dynamics of the underlying mechanisms, which show signs of reinstantiation. Our findings, some of which could not had been obtained with classic analytical strategies, open novel avenues of research.

neuroscience↗

bioRxiv2019

Neural representation of current and intended task sets during sequential judgements on human faces

Engaging in a demanding activity while holding in mind another task to be performed in the near future requires the maintenance of information about both the currently-active task set and the intended one. However, little is known about how the human brain implements such action plans. While some previous studies have examined the neural representation of current task sets and others have investigated delayed intentions, to date none has examined the representation of current and intended task sets within a single experimental paradigm. In this fMRI study, we examined the neural representation of current and intended task sets, employing sequential classification tasks on human faces. Multivariate decoding analyses showed that current task sets were represented in the orbitofrontal cortex (OFC) and fusiform gyrus (FG), while intended tasks could be decoded from lateral prefrontal cortex (lPFC). Importantly, a ventromedial region in PFC/OFC contained information about both current and delayed tasks, although cross-classification between the two types of information was not possible. These results help delineate the neural representations of current and intended task sets, and highlight the importance of ventromedial PFC/OFC for maintaining task-relevant information regardless of when it is needed.

neuroscience↗

bioRxiv2019

Representational organization of novel task sets during proactive encoding.

Recent multivariate analyses of brain data have boosted our understanding of the organizational principles that shape neural coding. However, most of this progress has focused on perceptual visual regions (Connolly et al., 2012), whereas far less is known about the organization of more abstract, action-oriented representations. In this study, we focused on humans remarkable ability to turn novel instructions into actions. While previous research shows that instruction encoding is tightly linked to proactive activations in fronto-parietal brain regions, little is known about the structure that orchestrates such anticipatory representation. We collected fMRI data while participants (both males and females) followed novel complex verbal rules that varied across control-related variables (integrating within/across stimuli dimensions, response complexity, target category) and reward expectations. Using Representational Similarity Analysis (Kriegeskorte et al., 2008) we explored where in the brain these variables explained the organization of novel task encoding, and whether motivation modulated these representational spaces. Instruction representations in the lateral prefrontal cortex were structured by the three control-related variables, while intraparietal sulcus encoded response complexity and the fusiform gyrus and precuneus organized its activity according to the relevant stimulus category. Reward exerted a general effect, increasing the representational similarity among different instructions, which was robustly correlated with behavioral improvements. Overall, our results highlight the flexibility of proactive task encoding, governed by distinct representational organizations in specific brain regions. They also stress the variability of motivation-control interactions, which appear to be highly dependent on task attributes such as complexity or novelty.\n\nSignificance StatementIn comparison with other primates, humans display a remarkable success in novel task contexts thanks to our ability to transform instructions into effective actions. This skill is associated with proactive task-set reconfigurations in fronto-parietal cortices. It remains yet unknown, however, how the brain encodes in anticipation the flexible, rich repertoire of novel tasks that we can achieve. Here we explored cognitive control and motivation-related variables that might orchestrate the representational space for novel instructions. Our results showed that different dimensions become relevant for task prospective encoding depending on the brain region, and that the lateral prefrontal cortex simultaneously organized task representations following different control-related variables. Motivation exerted a general modulation upon this process, diminishing rather than increasing distances among instruction representations.

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bioRxiv2018

Atlas-based classification algorithms for identification of informative brain regions in fMRI data

Multi-voxel pattern analysis (MVPA) has been successfully applied to neuroimaging data due to its larger sensitivity compared to univariate traditional techniques. Searchlight is the most widely employed approach to assign functional value to different regions of the brain. However, its performance depends on the size of the sphere, which can overestimate the region of activation when a large sphere size is employed\n\nIn the current study, we examined the validity of two different alternatives to Searchlight: an atlas-based local averaging method (ABLA, Schrouff et al., 2013a) and a Multi-Kernel Learning (MKL, Rakotomamonjy et al., 2008) approach, in a scenario where the goal is to find the informative brain regions that support certain mental operations. These methods employ weights to measure the informativeness of a brain region and highly reduce the large computational cost that Searchlight entails. We evaluated their performance in two different scenarios where the differential BOLD activation between experimental conditions was large vs. small, and employed nine different atlases to assess the influence of diverse brain parcellations.\n\nResults show that both methods were able to localize informative regions when differences between conditions were large, demonstrating a large sensitivity and stability in the identification of regions across atlases. Moreover, the sign of the weights reported by these methods provided the directionality of univariate approaches. However, when differences were small, only ABLA localized informative regions. Thus, our results show that atlas-based methods are useful alternatives to Searchlight, but that the nature of the classification to perform should be taken into account when choosing the specific method to implement.

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bioRxiv2018

Neural representation of social expectations during interpersonal decisions

Prior personal information is highly relevant during social interactions. Such knowledge aids in the prediction of others, and it affects choices even when it is unrelated to actual behaviour. In this investigation, we aimed to study the neural representation of positive and negative personal expectations, how these impact subsequent choices, and the effect of mismatches between expectations and encountered behaviour. We employed functional Magnetic Resonance Imaging in combination with a version of the Ultimatum Game (UG) where participants were provided with information about their partners moral traits previous to their fair or unfair offers. Univariate and multivariate analyses revealed the implication of the supplementary motor area (SMA) and inferior frontal gyrus (IFG) in the representation of expectations about the partners in the game. Further, these regions also represented the valence of expectations, together with the ventromedial prefrontal cortex (vmPFC). Importantly, the performance of multivariate classifiers in these clusters correlated with a behavioural choice bias to accept more offers following positive descriptions, highlighting the impact of the valence on the expectations on participants economic decisions. Altogether, our results suggest that expectations based on social information guide future interpersonal decisions and that the neural representation of such expectations in the vmPFC is related to their influence on behaviour.

neuroscience↗

bioRxiv2018

Influence of activation pattern estimates and statistical significance tests in fMRI decoding analysis

The use of Multi-Voxel Pattern Analysis (MVPA) has increased considerably in recent functional magnetic resonance imaging studies. A crucial step consists in the choice of methods for the estimation of responses and their statistical significance. However, a systematic comparison of these and their adequacy to predominant experimental design is missing.\n\nIn the current study, we compared three pattern estimation methods: Least-Squares Unitary (LSU), based on run-wise estimation, Least-Squares All (LSA) and Least-Squares Separate (LSS), which rely on trial-wise estimation. We compared the efficiency of these methods in an experiment where sustained activity had to be isolated from zero-duration events as well as in a block-design approach and in an event-related design. We evaluated the sensitivity of the t-test in comparison with two non-parametric methods based on permutation testing: one proposed in Stelzer et al. (2013), equivalent to performing a permutation in each voxel separately and the Threshold-Free Cluster Enhancement (Smith and Nichols, 2009).\n\nLSS resulted the most accurate approach to address the large overlap of signal among close events in the event-related designs. We found a larger sensitivity of Stelzers method in all settings, especially in the event-related designs, where voxels close to surpass the statistical threshold with the other approaches were now marked as informative regions.\n\nOur results provide evidence that LSS is the most accurate approach for unmixing events with different duration and large overlap of signal, consistent with previous studies showing better handling of collinearity in LSS. Moreover, Stelzers potentiates this better estimation with its larger sensitivity.

bioinformatics↗