Biology subjects

Mur, M.

Publications and source records attributed to Mur, M..

10 recordsLinked to original sources

bioRxiv2026

Individualized parcellation reveals functional boundaries in human prefrontal cortex

Human prefrontal cortex (PFC) supports diverse higher-order cognitive functions during task execution. Increasing evidence suggests that these functions are organized along large-scale gradients, such as a rostro-caudal axis supporting progressively abstract-to-concrete information processing. At the same time, regional specialization within PFC, including focal patches selective for specific stimulus categories, suggests the presence of discrete functional boundaries. Whether PFC organization is best characterized by continuous gradients or discrete subdivisions remains unresolved. Here we show that task-evoked functional organization in PFC exhibits substantial inter-individual variability, limiting the usefulness of conventional group atlases in testing for functional boundaries. To address this challenge, we estimated individualized functional parcellations by combining a group atlas with task-evoked fMRI data spanning diverse cognitive tasks. The group atlas revealed large-scale functional gradients across PFC, whereas individualized parcellations additionally uncovered sharp functional boundaries obscured by group averaging. Moreover, functional organization in PFC was substantially more fine-grained than in other association cortices, consistent with its integrative role in cognitive control. Together, these findings suggest that PFC organization reflects an individualized mosaic of fine-grained functional subdivisions embedded within broader large-scale gradients, with important implications for defining core constructs such as the multiple-demand system.

neuroscience↗

bioRxiv2025

Multiplexed BOLD oscillations reveal the interplay of normalization and attention

Monkey electrophysiology has linked attention to divisive normalization, yet noninvasive evidence in humans remains limited. We use frequency-tagged fMRI to isolate visual cortical populations that simultaneously encode multiple competing inputs. We show that responses of these sites are suppressed during inattention and enhanced during attention - consistent with the normalization model, which predicts that attention selectively disinhibits competing inputs - offering a noninvasive translational bridge to study fine-grained computations underlying attentional selection.

neuroscience↗

bioRxiv2025

Stimulus effects dwarf task effects in human visual cortex

Task context affects stimulus representations in human visual cortex, suggesting that visual representations are flexible. However, this interpretation is at odds with a major computational goal of the human visual system: creating a perceptually stable representation of the external visual environment. How does the visual system balance stability and flexibility? Here, human participants (71 percent females) categorized object images and written words according to different task rules, while brain responses were measured with fMRI. Using an ANOVA-based modeling strategy, we precisely quantified the relative contributions of stimulus, task, and their interaction in explaining representational variance across the cortical hierarchy. Our results show that stimulus effects account for the overwhelming majority of explainable representational variance across the ventral visual system: > 95 percent in V1 and V2, and > 90 percent in higher-level visual cortex. In prefrontal cortex, the relative contributions reverse: task effects dominate stimulus effects, accounting for 80 percent of explainable representational variance. In parietal cortex, contributions of stimulus and task are approximately equal. Our findings suggest that population coding in sensory cortex is optimized for representational stability to allow a consistent interpretation of the external environment. Population coding in parietal and frontal multiple-demand cortex, by contrast, is optimized for representational flexibility to accommodate changing behavioral goals and support flexible cognition and action. Significance statementStimulus representations in human visual cortex are affected by behavioral goals and are therefore thought to be flexible. However, this view is inconsistent with a major computational goal of the human visual system: creating a perceptually stable representation of the external environment. Here, we show that modulatory effects of behavioral goals on stimulus representations in visual cortex are surprisingly small. In contrast, behavioral goals strongly affect representations in parietal and frontal multiple-demand cortex. Our findings suggest that population coding in sensory cortex is optimized for stable perception, while population coding in parietal and frontal multiple-demand cortex is optimized for flexible cognition.

neuroscience↗

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.

neuroscience↗

bioRxiv2025

Attentional enhancement and suppression of stimulus-synchronized BOLD oscillations

Visual cortical neurons synchronize their firing rates to periodic visual stimuli. EEG is commonly used to study directed attention by frequency-tagging brain responses to multiple stimuli oscillating at different frequencies, but is limited by its coarse spatial resolution. Here we leverage frequency-tagging fMRI (ft-fMRI) to study the influence of directed attention on the fine-grained spatiotemporal dynamics of competing stimulus-driven visual cortical oscillations. Our analysis reveals that distinct populations of visual cortical neurons exhibit in-phase (enhancing) or anti-phase (suppressive) synchronization with the oscillating stimuli. Directed attention homogeneously increases the amplitude of anti-phase BOLD oscillations across the visual hierarchy, consistent with a distributed suppressive field. In contrast, attentional modulation of in-phase BOLD oscillations increases hierarchically from V1 to hV4. The strength of anti-phase, but not in-phase, modulation predicted psychophysical correlates of attentional performance. Our results strongly corroborate the biased competition model of attention and unveil a novel BOLD correlate of attentional suppression.

neuroscience↗

bioRxiv2024

Frequency-tagged fMRI: A platform for fine-grained spatiotemporal analysis of cortical function

Frequency tagging with functional MRI (ft-fMRI) enables precise mapping of neural dynamics by synchronizing oscillatory stimuli to stimulus-driven blood-oxygen-level-dependent (BOLD) responses. We developed and validated a dual-frequency tagging protocol to dissociate fundamental, multiplexed, and nonlinear intermodulation frequency responses across the human visual cortex at high spatial resolution. Using 3T and 7T fMRI, we reliably detected frequency-tagged BOLD responses at the level of individual vertices, revealing fine-grained cortical topographies and robust temporal synchronization to driving frequencies. Multiplexed responses, encoding multiple frequencies simultaneously, and nonlinear intermodulation components, were spatially dissociable and exhibited reproducible dynamics within and across experimental sessions. These findings establish ft-fMRI as a powerful tool for investigating fine-grained cortical computations, previously inaccessible to traditional fMRI. By bridging the spatiotemporal resolution gap between electrophysiology and fMRI, ft-fMRI provides a versatile platform for studying perception, attention, and multisensory integration in health and disease.

neuroscience↗

bioRxiv2024

Task-specific topographical maps of neural activity in the primate lateral prefrontal cortex

Neurons in the primate lateral prefrontal cortex (LPFC) flexibly adapt their activity to support a wide range of cognitive tasks. Whether and how the topography of LPFC neural activity changes as a function of task is unclear. In the present study, we address this issue by characterizing the functional topography of LPFC neural activity in awake behaving macaques performing three distinct cognitive tasks. We recorded from chronically implanted multi-electrode arrays and show that the topography of LPFC activity is stable within a task, but adaptive across tasks. The topography of neural activity exhibits a spatial scale compatible with prior anatomical tracing work on a!erent LPFC inputs. Our findings show that LPFC maps of neural population activity are stable for a specific task, providing robust neural codes that support task specialization. Moreover, the variability in functional topographies across tasks indicates activity landscapes can adapt, providing flexibility to LPFC neural codes.

neuroscience↗

bioRxiv2021

Deep neural networks and visuo-semantic models explain complementary components of human ventral-stream representational dynamics

Deep neural networks (DNNs) are promising models of the cortical computations supporting human object recognition. However, despite their ability to explain a significant portion of variance in neural data, the agreement between models and brain representational dynamics is far from perfect. We address this issue by asking which representational features are currently unaccounted for in neural timeseries data, estimated for multiple areas of the ventral stream via source-reconstructed magnetoencephalography (MEG) data acquired in human participants (9 females, 6 males) during object viewing. We focus on the ability of visuo-semantic models, consisting of human-generated labels of object features and categories, to explain variance beyond the explanatory power of DNNs alone. We report a gradual reversal in the relative importance of DNN versus visuo-semantic features as ventral-stream object rep-resentations unfold over space and time. While lower-level visual areas are better explained by DNN features, especially during the early phase of the response (< 128 ms after stimulus onset), higher-level cortical dynamics are best accounted for by visuo-semantic features during a later time window (starting 146 ms after stimulus onset). Among the visuo-semantic features, object parts and basic categories drive the advantage over DNNs. These results show that a significant component of the variance unexplained by DNNs in higher-level cortical dynamics is structured, and can be explained by readily nameable aspects of the objects. We conclude that current DNNs fail to fully capture dynamic representations in higher-level human visual cortex and suggest a path toward more accurate models of ventral stream computations. SIGNIFICANCE STATEMENTWhen we view objects such as faces and cars in our visual environment, their neural representations dynamically unfold over time at a millisecond scale. These dynamics reflect the cortical computations that support fast and robust object recognition. Deep neural networks (DNNs) have emerged as a promising framework for modeling these computations but cannot yet fully account for the neural dynamics. Using magnetoencephalography data acquired in human observers during object viewing, we show that readily nameable aspects of objects, such as "eye", "wheel", and "face", can account for variance in the neural dynamics over and above DNNs. These findings suggest that DNNs and humans may in part rely on different object features for visual recognition and provide guidelines for model improvement.

neuroscience↗

bioRxiv2019

Rapid invariant encoding of scene layout in human OPA

Successful visual navigation requires a sense of the geometry of the local environment. How do our brains extract this information from retinal images? Here we visually presented scenes with all possible combinations of five scene-bounding elements (left, right and back wall, ceiling, floor) to human subjects during functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). The fMRI response patterns in the scene-responsive occipital place area (OPA) reflected scene layout with invariance to changes in surface texture. This result contrasted sharply with the primary visual cortex (V1), which reflected low-level image features of the stimuli, and parahippocampal place area (PPA), which showed better texture than layout decoding. MEG indicated that the texture-invariant scene-layout representation is computed from visual input within ~100 ms, suggesting a rapid computational mechanism. Taken together, these results suggest that the cortical representation underlying our instant sense of the environmental geometry is located in OPA.

neuroscience↗

bioRxiv2018

Analysing linear multivariate pattern transformations in neuroimaging data

Most connectivity metrics in neuroimaging research reduce multivariate activity patterns in regions-of-interests (ROIs) to one dimension, which leads to a loss of information. Importantly, it prevents us from investigating the transformations between patterns in different ROIs. Here, we applied linear estimation theory in order to robustly estimate the linear transformations between multivariate fMRI patterns with a cross-validated Tikhonov regularisation approach. We derived three novel metrics that describe different features of these voxel-by-voxel mappings: goodness-of-fit, sparsity and pattern deformation. The goodness-of-fit describes the degree to which the patterns in an input region can be described as a linear transformation of patterns in an output region. The sparsity metric, which relies on a Monte Carlo procedure, was introduced in order to test whether the transformation mostly consists of one-to-one mappings between voxels in different regions. Furthermore, we defined a metric for pattern deformation, i.e. the degree to which the transformation rotates or rescales the input patterns. As a proof of concept, we applied these metrics to an event-related fMRI data set consisting of four subjects that has been used in previous studies. We focused on the transformations from early visual cortex (EVC) to inferior temporal cortex (ITC), fusiform face area (FFA) and parahippocampal place area (PPA). Our results suggest that the estimated linear mappings are able to explain a significant amount of variance of the three output ROIs. The transformation from EVC to ITC shows the highest goodness-of-fit, and those from EVC to FFA and PPA show the expected preference for faces and places as well as animate and inanimate objects, respectively. The pattern transformations are sparse, but sparsity is lower than would have been expected for one-to-one mappings, thus suggesting the presence of one-to-few voxel mappings. ITC, FFA and PPA patterns are not simple rotations of an EVC pattern, indicating that the corresponding transformations amplify or dampen certain dimensions of the input patterns. While our results are only based on a small number of subjects, they show that our pattern transformation metrics can describe novel aspects of multivariate functional connectivity in neuroimaging data.

neuroscience↗