Brianna Pritchett | Georgia Institute of Technology (original) (raw)

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Papers by Brianna Pritchett

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Two analytic traditions characterize fMRI language research. One relies on averaging activations ... more Two analytic traditions characterize fMRI language research. One relies on averaging activations across individuals. This approach has limitations: because of inter-individual variability in the locations of language areas, any given voxel/vertex in a common brain space is part of the language network in some individuals but in others, may belong to a distinct network. An alternative approach relies on identifying language areas in each individual using a functional ‘localizer’. Because of its greater sensitivity, functional resolution, and interpretability, functional localization is gaining popularity, but it is not always feasible, and cannot be applied retroactively to past studies. To bridge these disjoint approaches, we created a probabilistic functional atlas using fMRI data for an extensively validated language localizer in 806 individuals. This atlas enables estimating the probability that any given location in a common space belongs to the language network, and thus can he...

Proceedings of the 2018 ACM Conference on International Computing Education Research, 2018

Journal of Neurophysiology, 2018

A set of left frontal, temporal, and parietal brain regions respond robustly during language comp... more A set of left frontal, temporal, and parietal brain regions respond robustly during language comprehension and production (e.g., Fedorenko E, Hsieh PJ, Nieto-Castañón A, Whitfield-Gabrieli S, Kanwisher N. J Neurophysiol 104: 1177–1194, 2010; Menenti L, Gierhan SM, Segaert K, Hagoort P. Psychol Sci 22: 1173–1182, 2011). These regions have been further shown to be selective for language relative to other cognitive processes, including arithmetic, aspects of executive function, and music perception (e.g., Fedorenko E, Behr MK, Kanwisher N. Proc Natl Acad Sci USA 108: 16428–16433, 2011; Monti MM, Osherson DN. Brain Res 1428: 33–42, 2012). However, one claim about overlap between language and nonlinguistic cognition remains prominent. In particular, some have argued that language processing shares computational demands with action observation and/or execution (e.g., Rizzolatti G, Arbib MA. Trends Neurosci 21: 188–194, 1998; Koechlin E, Jubault T. Neuron 50: 963–974, 2006; Tettamanti M, W...

Nature communications, Mar 6, 2018

Prior work decoding linguistic meaning from imaging data has been largely limited to concrete nou... more Prior work decoding linguistic meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space constructed from massive text corpora. By efficiently sampling this space to select training stimuli shown to subjects, we maximize the ability to generalize to new meanings from limited imaging data. To validate this approach, we train the system on imaging data of individual concepts, and show it can decode semantic vector representations from imaging data of sentences about a wide variety of both concrete and abstract topics from two separate datasets. These decoded representations are sufficiently detailed to distinguish even semantically similar sentences, and to capture the similarity structure of meaning relationships between sentences.

Proceedings of the National Academy of Sciences of the United States of America, Oct 11, 2016

The neural processes that underlie your ability to read and understand this sentence are unknown.... more The neural processes that underlie your ability to read and understand this sentence are unknown. Sentence comprehension occurs very rapidly, and can only be understood at a mechanistic level by discovering the precise sequence of underlying computational and neural events. However, we have no continuous and online neural measure of sentence processing with high spatial and temporal resolution. Here we report just such a measure: intracranial recordings from the surface of the human brain show that neural activity, indexed by γ-power, increases monotonically over the course of a sentence as people read it. This steady increase in activity is absent when people read and remember nonword-lists, despite the higher cognitive demand entailed, ruling out accounts in terms of generic attention, working memory, and cognitive load. Response increases are lower for sentence structure without meaning ("Jabberwocky" sentences) and word meaning without sentence structure (word-lists), ...

Several different groups have demonstrated the feasibility of building forward models of function... more Several different groups have demonstrated the feasibility of building forward models of functional MRI data in response to concrete stimuli such as pictures or video, and of using these models to decode or reconstruct stimuli shown while acquiring test fMRI data. In this paper, we introduce an approach for building forward models of conceptual stimuli, concrete or abstract, and for using these models to carry out decoding of semantic information from new imaging data. We show that this approach generalizes to topics not seen in training, and provides a straightforward path to decoding from more complex stimuli such as sentences or paragraphs.

Two analytic traditions characterize fMRI language research. One relies on averaging activations ... more Two analytic traditions characterize fMRI language research. One relies on averaging activations voxel-wise across individuals. This approach has limitations: because of inter-individual variability in the locations of language areas, a location in a common brain space cannot be meaningfully linked to function. An alternative approach relies on identifying language areas in each individual using a functional ‘localizer’. Because of its greater sensitivity, functional resolution, and interpretability, functional localization is gaining popularity, but it is not always feasible, and cannot be applied retroactively to past studies. We provide a solution for bridging these currently disjoint approaches in the form of a probabilistic functional atlas created from fMRI data for an extensively validated language localizer in 806 individuals. This atlas enables estimating the probability that any given location in a common brain space belongs to the language network, and thus can help inter...

Scientific Data

Two analytic traditions characterize fMRI language research. One relies on averaging activations ... more Two analytic traditions characterize fMRI language research. One relies on averaging activations across individuals. This approach has limitations: because of inter-individual variability in the locations of language areas, any given voxel/vertex in a common brain space is part of the language network in some individuals but in others, may belong to a distinct network. An alternative approach relies on identifying language areas in each individual using a functional ‘localizer’. Because of its greater sensitivity, functional resolution, and interpretability, functional localization is gaining popularity, but it is not always feasible, and cannot be applied retroactively to past studies. To bridge these disjoint approaches, we created a probabilistic functional atlas using fMRI data for an extensively validated language localizer in 806 individuals. This atlas enables estimating the probability that any given location in a common space belongs to the language network, and thus can he...

Proceedings of the 2018 ACM Conference on International Computing Education Research, 2018

Journal of Neurophysiology, 2018

A set of left frontal, temporal, and parietal brain regions respond robustly during language comp... more A set of left frontal, temporal, and parietal brain regions respond robustly during language comprehension and production (e.g., Fedorenko E, Hsieh PJ, Nieto-Castañón A, Whitfield-Gabrieli S, Kanwisher N. J Neurophysiol 104: 1177–1194, 2010; Menenti L, Gierhan SM, Segaert K, Hagoort P. Psychol Sci 22: 1173–1182, 2011). These regions have been further shown to be selective for language relative to other cognitive processes, including arithmetic, aspects of executive function, and music perception (e.g., Fedorenko E, Behr MK, Kanwisher N. Proc Natl Acad Sci USA 108: 16428–16433, 2011; Monti MM, Osherson DN. Brain Res 1428: 33–42, 2012). However, one claim about overlap between language and nonlinguistic cognition remains prominent. In particular, some have argued that language processing shares computational demands with action observation and/or execution (e.g., Rizzolatti G, Arbib MA. Trends Neurosci 21: 188–194, 1998; Koechlin E, Jubault T. Neuron 50: 963–974, 2006; Tettamanti M, W...

Nature communications, Mar 6, 2018

Prior work decoding linguistic meaning from imaging data has been largely limited to concrete nou... more Prior work decoding linguistic meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space constructed from massive text corpora. By efficiently sampling this space to select training stimuli shown to subjects, we maximize the ability to generalize to new meanings from limited imaging data. To validate this approach, we train the system on imaging data of individual concepts, and show it can decode semantic vector representations from imaging data of sentences about a wide variety of both concrete and abstract topics from two separate datasets. These decoded representations are sufficiently detailed to distinguish even semantically similar sentences, and to capture the similarity structure of meaning relationships between sentences.

Proceedings of the National Academy of Sciences of the United States of America, Oct 11, 2016

The neural processes that underlie your ability to read and understand this sentence are unknown.... more The neural processes that underlie your ability to read and understand this sentence are unknown. Sentence comprehension occurs very rapidly, and can only be understood at a mechanistic level by discovering the precise sequence of underlying computational and neural events. However, we have no continuous and online neural measure of sentence processing with high spatial and temporal resolution. Here we report just such a measure: intracranial recordings from the surface of the human brain show that neural activity, indexed by γ-power, increases monotonically over the course of a sentence as people read it. This steady increase in activity is absent when people read and remember nonword-lists, despite the higher cognitive demand entailed, ruling out accounts in terms of generic attention, working memory, and cognitive load. Response increases are lower for sentence structure without meaning ("Jabberwocky" sentences) and word meaning without sentence structure (word-lists), ...

Several different groups have demonstrated the feasibility of building forward models of function... more Several different groups have demonstrated the feasibility of building forward models of functional MRI data in response to concrete stimuli such as pictures or video, and of using these models to decode or reconstruct stimuli shown while acquiring test fMRI data. In this paper, we introduce an approach for building forward models of conceptual stimuli, concrete or abstract, and for using these models to carry out decoding of semantic information from new imaging data. We show that this approach generalizes to topics not seen in training, and provides a straightforward path to decoding from more complex stimuli such as sentences or paragraphs.

Two analytic traditions characterize fMRI language research. One relies on averaging activations ... more Two analytic traditions characterize fMRI language research. One relies on averaging activations voxel-wise across individuals. This approach has limitations: because of inter-individual variability in the locations of language areas, a location in a common brain space cannot be meaningfully linked to function. An alternative approach relies on identifying language areas in each individual using a functional ‘localizer’. Because of its greater sensitivity, functional resolution, and interpretability, functional localization is gaining popularity, but it is not always feasible, and cannot be applied retroactively to past studies. We provide a solution for bridging these currently disjoint approaches in the form of a probabilistic functional atlas created from fMRI data for an extensively validated language localizer in 806 individuals. This atlas enables estimating the probability that any given location in a common brain space belongs to the language network, and thus can help inter...