Biology subjects

Visconti di Oleggio Castello, M.

Publications and source records attributed to Visconti di Oleggio Castello, M..

10 recordsLinked to original sources

bioRxiv2026

Attention warps visual-semantic representations across the human cerebellum

Humans effortlessly shift their attention between objects and events in the environment. These attentional shifts modulate the representation of information in many regions of the cerebral cortex, including those that represent visual-semantic information. Based on recent evidence that the cerebellum also represents semantic information, we hypothesized that attention would also shift semantic representations in the cerebellum. To test this hypothesis, we used fMRI to measure brain activity while participants watched naturalistic movie clips and were instructed to attend to either people or vehicles. We used a voxelwise encoding model framework to recover visual-semantic representations in the cerebral cortex and cerebellum under the two conditions, and quantified the direction and magnitude of visual-semantic tuning shifts between conditions. Attention to different semantic categories had a large effect on visual-semantic tuning in the cerebellum. Representations in some regions shifted toward the attended category, and representations in other regions shifted away from the attended category. Within cerebellar regions that best represent visual-semantic information, the tuning shifts were most pronounced in Crus I and II and in lobule VI. Further, the magnitude of attentional tuning shifts was greater in the cerebellum than in the cerebral cortex, particularly for tuning shifts towards the attended category. These differences suggest that attentional modulation in the cerebellum is different than in the cerebral cortex and may not solely be driven by cerebral inputs.

neuroscience↗

bioRxiv2026

Hyperface: a naturalistic fMRI dataset for investigating human face processing

Faces convey information that guides social behavior, yet neuroimaging studies investigating human face processing typically use static images with small sets of identities under artificial conditions. Controlled designs limit our ability to characterize human face processing under naturalistic conditions or test whether computational models generalize beyond the laboratory. To address this gap, here we release hyperface, a naturalistic face viewing fMRI dataset designed to investigate human face processing in response to faces portrayed in videos mimicking more ecologically valid conditions. Twenty-one participants watched 707 unique face video clips that vary systematically in identity, gender, age, ethnicity, expression, and head orientation. Each clip was rated by independent observers, and pairwise similarity judgments were collected through a behavioral arrangement task. Technical validation confirms high data quality with low motion, high tSNR, and high inter-subject correlation in visual and face-processing regions. The hyperface dataset is part of a comprehensive experimental framework to investigate human face processing: all 21 participants also watched "The Grand Budapest Hotel," performed a dynamic face localizer task, and 10 participants completed an additional face identity task with personally familiar and visually familiarized faces. These datasets are publicly available and enable within-subject comparisons across paradigms. Together they provide a unique resource for characterizing human face processing under naturalistic conditions and for benchmarking computational models against human brain responses.

neuroscience↗

bioRxiv2025

Individual differences shape conceptual representation in the brain

Each person experiences the world through a unique conceptual lens, shaped by personal experiences, natural variations, or disease. These individual differences have remained largely inaccessible to cognitive neuroscience and clinical neurology, limiting the development of precision medicine approaches to cognitive disorders. To overcome this limitation, here we develop a new statistical framework to measure and interpret individual differences in functional brain representations. We apply this framework to characterize how different individuals represent the same concepts. Twenty-four participants listened to narrative stories while their brain activity was measured with functional MRI (fMRI). Encoding models were used to recover how hundreds of concepts were represented in each persons brain. Despite listening to identical stories, participants showed systematic individual differences in conceptual representations. These differences reveal person-specific biases in how concepts are represented in the brain. Individual variability was highest in regions that represent social information. Because these regions are thought to integrate sensory information with personal beliefs and experiences, the observed individual differences may reflect cognitive traits unique to each person. Our work reveals that individual differences are a systematic, measurable principle of conceptual representations in the human brain. By enabling researchers to measure and interpret differences in person-specific functional brain representations, our work establishes a new paradigm for precision neuroscience. This paradigm provides a rigorous foundation for developing fMRI applications in precision medicine to diagnose and monitor cognitive disorders.

neuroscience↗

bioRxiv2023

Model connectivity: leveraging the power of encoding models to overcome the limitations of functional connectivity

Functional connectivity (FC) is the most popular method for recovering functional networks of brain areas with fMRI. However, because FC is defined as temporal correlations in brain activity, FC networks are confounded by noise and lack a precise functional role. To overcome these limitations, we developed model connectivity (MC). MC is defined as similarities in encoding model weights, which quantify reliable functional activity in terms of interpretable stimulus- or task-related features. To compare FC and MC, both methods were applied to a naturalistic story listening dataset. FC recovered spatially broad networks that are confounded by noise, and that lack a clear role during natural language comprehension. By contrast, MC recovered spatially localized networks that are robust to noise, and that represent distinct categories of semantic concepts. Thus, MC is a powerful data-driven approach for recovering and interpreting the functional networks that support complex cognitive processes.

neuroscience↗

bioRxiv2021

Not so fast: Limited validity of deep convolutional neural networks as in silico models for human naturalistic face processing

Deep convolutional neural networks (DCNNs) trained for face identification can rival and even exceed human-level performance. The ways in which the internal face representations in DCNNs relate to human cognitive representations and brain activity are not well understood. Nearly all previous studies focused on static face image processing with rapid display times and ignored the processing of naturalistic, dynamic information. To address this gap, we developed the largest naturalistic dynamic face stimulus set in human neuroimaging research (700+ naturalistic video clips of unfamiliar faces). We used this novel naturalistic dataset to compare representational geometries estimated from DCNNs, behavioral responses, and brain responses. We found that DCNN representational geometries were consistent across architectures, cognitive representational geometries were consistent across raters in a behavioral arrangement task, and neural representational geometries in face areas were consistent across brains. Representational geometries in late, fully-connected DCNN layers, which are optimized for individuation, were much more weakly correlated with cognitive and neural geometries than were geometries in late-intermediate layers. The late-intermediate face-DCNN layers successfully matched cognitive representational geometries, as measured with a behavioral arrangement task that primarily reflected categorical attributes, and correlated with neural representational geometries in known face-selective topographies. Our study suggests that current DCNNs successfully capture neural cognitive processes for categorical attributes of faces, but less accurately capture individuation and dynamic features.

neuroscience↗

bioRxiv2020

Hybrid Hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity

Shared information content is represented across brains in idiosyncratic functional topographies. Hyperalignment addresses these idiosyncrasies by using neural responses to project individuals brain data into a common model space while maintaining the geometric relationships between distinct activity patterns. The dimensions of this common model can encode any kind of functional profiles shared across individuals, such as cortical response profiles collected during a common time-locked stimulus presentation (e.g. movie viewing) or functional connectivity profiles. Performing hyperalignment with either response-based or connectivity-based input data derives transformations to project individuals neural data from anatomical space into the common model such that functional information is optimally aligned across brains. Previously, only response or connectivity profiles were used in the derivation of these transformations. In this study, we used three separate data sets collected while participants watched feature films to derive transformations representing both response-based and connectivity-based information with a single algorithm. Our new method, hybrid hyperalignment, aligns response-based information as well as or better than response hyperalignment while simultaneously aligning connectivity-based information better than connectivity hyperalignment, all in one information space. These results suggest that a single common information space could encode both shared cortical response and functional connectivity profiles across individuals.

neuroscience↗

bioRxiv2020

The Grand Budapest Hotel: an fMRI dataset in response to a socially-rich, naturalistic movie

Naturalistic stimuli evoke strong, consistent, and information-rich patterns of brain activity, and engage large extents of the human brain. They allow researchers to compare highly similar brain responses across subjects, and to study how complex representations are encoded in brain activity. Here, we describe and share a dataset where 25 subjects watched part of the feature film "The Grand Budapest Hotel" by Wes Anderson. The movie has a large cast with many famous actors. Throughout the story, the camera shots highlight faces and expressions, which are fundamental to understand the complex narrative of the movie. This movie was chosen to sample brain activity specifically related to social interactions and face processing. This dataset provides researchers with fMRI data that can be used to explore social cognitive processes and face processing, adding to the existing neuroimaging datasets that sample brain activity with naturalistic movies.

neuroscience↗

bioRxiv2018

Familiarity facilitates detection of angry expressions

Personal familiarity facilitates rapid and optimized detection of faces. In this study, we investigated whether familiarity associated with faces can also facilitate the detection of facial expressions. Models of face processing propose that face identity and face expression detection are mediated by distinct pathways. We used a visual search paradigm to assess if facial expressions of emotion (anger and happiness) were detected more rapidly when produced by familiar as compared to unfamiliar faces. We found that participants detected an angry expression 11% more accurately and 135 ms faster when produced by familiar as compared to unfamiliar faces while happy expressions were detected with equivalent accuracies and at equivalent speeds for familiar and unfamiliar faces. These results suggest that detectors in the visual system dedicated to processing features of angry expressions are optimized for familiar faces.

neuroscience↗

bioRxiv2018

Idiosyncratic, retinotopic bias in face identification modulated by familiarity

The perception of gender and age of unfamiliar faces is reported to vary idiosyncratically across retinal locations such that, for example, the same androgynous face may appear to be male at one location but female at another. Here we test spatial heterogeneity for the recognition of the identity of personally familiar faces in human participants. We found idiosyncratic biases that were stable within participants and that varied more across locations for low as compared to high familiar faces. These data suggest that like face gender and age, face identity is processed, in part, by independent populations of neurons monitoring restricted spatial regions and that the recognition responses vary for the same face across these different locations. Moreover, repeated and varied social interactions appear to lead to adjustments of these independent face recognition neurons so that the same familiar face is eventually more likely to elicit the same recognition response across widely separated visual field locations. We provide a mechanistic account of this reduced retinotopic bias based on computational simulations.\n\nSignificance statementIn this work we tested spatial heterogeneity for the recognition of personally familiar faces. We found retinotopic biases that varied more across locations for low as compared to highly familiar faces. The retinotopic biases were idiosyncratic and stable within participants. Our data suggest that, like face gender and age, face identity is processed by independent populations of neurons monitoring restricted spatial regions and that recognition may vary for the same face at these different locations. Unlike previous findings, our data and computational simulation address the effects of learning and show how increased familiarity modifies the representation of face identity in face-responsive cortical areas. This new perspective has broader implications for understanding how learning optimizes visual processes for socially salient stimuli.

neuroscience↗

bioRxiv2017

The Neural Representation Of Personally Familiar And Unfamiliar Faces In The Distributed System For Face Perception

Personally familiar faces are processed more robustly and efficiently than unfamiliar faces. The human face processing system comprises a core system that analyzes the visual appearance of faces and an extended system for the retrieval of person-knowledge and other nonvisual information. We applied multivariate pattern analysis to fMRI data to investigate aspects of familiarity that are shared by all familiar identities and information that distinguishes specific face identities from each other. Both identity-independent familiarity information and face identity could be decoded in an overlapping set of areas in the core and extended systems. Representational similarity analysis revealed a clear distinction between the two systems and a subdivision of the core system into ventral, dorsal and anterior components. This study provides evidence that activity in the extended system carries information about both individual identities and personal familiarity, while clarifying and extending the organization of the core system for face perception.

neuroscience↗