Biology subjects

Baldassano, C.

Publications and source records attributed to Baldassano, C..

23 records · Page 2Linked to original sources

bioRxiv2019

Facilitating open-science with realistic fMRI simulation: validation and application

BackgroundWith advances in methods for collecting and analyzing fMRI data, there is a concurrent need to understand how to reliably evaluate and optimally use these methods. Simulations of fMRI data can aid in both the evaluation of complex designs and the analysis of data. New MethodWe present fmrisim, a new Python package for standardized, realistic simulation of fMRI data. This package is part of BrainIAK: a recently released open-source Python toolbox for advanced neuroimaging analyses. We describe how to use fmrisim to extract noise properties from real fMRI data and then create a synthetic dataset with matched noise properties and a user-specified signal. ResultsWe validate the noise generated by fmrisim to show that it can approximate the noise properties of real data. We further show how fmrisim can help researchers find the optimal design in terms of power. Comparison with other methodsfmrisim ports the functionality of other packages to the Python platform while extending what is available in order to make it seamless to simulate realistic fMRI data. ConclusionsThe fmrisim package holds promise for improving the design of fMRI experiments, which may facilitate both the pre-registration of such experiments as well as the analysis of fMRI data. HighlightsO_LIfmrisim can simulate fMRI data matched to the noise properties of real fMRI. C_LIO_LIThis can help researchers investigate the power of their fMRI designs. C_LIO_LIThis also facilitates open science by making it easy to pre-register analysis pipelines. C_LI

neuroscience↗

bioRxiv2019

Rapid memory reactivation at movie event boundaries promotes episodic encoding

Segmentation of continuous experience into discrete events is driven by rapid fluctuations in encoding stability at context shifts (i.e., event boundaries), yet the mechanisms underlying the online formation of event memories are poorly understood. We investigated the neural spatiotemporal similarity patterns of the scalp electrophysiological (EEG) activity of 30 participants watching a 50 min movie and found that event boundaries triggered rapid reinstatement of the just-encoded movie event EEG patterns. We also found that the onset of memory reinstatement at boundary onset (around 1500ms) was preceded by an N400-like ERP component, which likely reflects the detection of a context switch between the current and just-encoded event. A data-driven approach based on Hidden Markov Modeling allowed us to detect event boundaries as shifts between stable patterns of brain EEG activity during encoding and identify their reactivation during a free recall task. These results provide the first neurophysiological underpinnings for how the memory system segments a continuous stream of experience into episodic events.

neuroscience↗

bioRxiv2018

Representation of real-world event schemas during narrative perception

Understanding movies and stories requires maintaining a high-level situation model that abstracts away from perceptual details to describe the location, characters, actions, and causal relationships of the currently unfolding event. These models are built not only from information present in the current narrative, but also from prior knowledge about schematic event scripts, which describe typical event sequences encountered throughout a lifetime. We analyzed fMRI data from 44 human subjects presented with sixteen three-minute stories, consisting of four schematic events drawn from two different scripts (eating at a restaurant or going through the airport). Aside from this shared script structure, the stories varied widely in terms of their characters and storylines, and were presented in two highly dissimilar formats (audiovisual clips or spoken narration). One group was presented with the stories in an intact temporal sequence, while a separate control group was presented with the same events in scrambled order. Regions including the posterior medial cortex, medial prefrontal cortex (mPFC), and superior frontal gyrus exhibited schematic event patterns that generalized across stories, subjects, and modalities. Patterns in mPFC were also sensitive to overall script structure, with temporally scrambled events evoking weaker schematic representations. Using a Hidden Markov Model, patterns in these regions can predict the script (restaurant vs. airport) of unlabeled data with high accuracy, and can be used to temporally align multiple stories with a shared script. These results extend work on the perception of controlled, artificial schemas in human and animal experiments to naturalistic perception of complex narrative stimuli.\n\nSignificance StatementIn almost all situations we encounter in our daily lives, we are able to draw on our schematic knowledge about what typically happens in the world to better perceive and mentally represent our ongoing experiences. In contrast to previous studies that investigated schematic cognition using simple, artificial associations, we measured brain activity from subjects watching movies and listening to stories depicting restaurant or airport experiences. Our results reveal a network of brain regions that is sensitive to the shared temporal structure of these naturalistic situations. These regions abstract away from the particular details of each story, activating a representation of the general type of situation being perceived.

neuroscience↗

bioRxiv2017

Distinct contributions of functional and deep neural network features to scene representation in brain and behavior

Inherent correlations between visual and semantic features in real-world scenes make it difficult to determine how different scene properties contribute to neural representations. Here, we assessed the contributions of multiple properties to scene representation by partitioning the variance explained in human behavioral and brain measurements by three feature models whose inter-correlations were minimized a priori through stimulus preselection. Behavioral assessments of scene similarity reflected unique contributions from a functional feature model indicating potential actions in scenes as well as high-level visual features from a deep neural network (DNN). In contrast, similarity of cortical responses in scene-selective areas was uniquely explained by mid- and high-level DNN features only, while an object label model did not contribute uniquely to either domain. The striking dissociation between functional and DNN features in their contribution to behavioral and brain representations of scenes indicates that scene-selective cortex represents only a subset of behaviorally relevant scene information.

neuroscience↗

bioRxiv2016

Discovering event structure in continuous narrative perception and memory

SummaryDuring realistic, continuous perception, humans automatically segment experiences into discrete events. Using a novel model of neural event dynamics, we investigate how cortical structures generate event representations during continuous narratives, and how these events are stored and retrieved from long-term memory. Our data-driven approach enables identification of event boundaries and event correspondences across datasets without human-generated stimulus annotations, and reveals that different regions segment narratives at different timescales. We also provide the first direct evidence that narrative event boundaries in high-order areas (overlapping the default mode network) trigger encoding processes in the hippocampus, and that this encoding activity predicts pattern reinstatement during recall. Finally, we demonstrate that these areas represent abstract, multimodal situation models, and show anticipatory event reinstatement as subjects listen to a familiar narrative. Our results provide strong evidence that brain activity is naturally structured into semantically meaningful events, which are stored in and retrieved from long-term memory.

neuroscience↗