This project proposes to discover the neural representation of some simple words and concepts. It uses newly developed machine learning techniques and dimension reduction methods applied to fMRI brain activation data that will be acquired in new experimental paradigms. This research approach has for the first time succeeded in identifying the content of individual human thoughts based on the pattern of brain activity. The initial published studies have demonstrated this capability in the case of concrete nouns and physical objects. This project proposes to expand the approach to a much larger set of different types of concepts and to examine the effect of the way the concept is presented (e.g. a written or spoken word, or a picture). The goal is to develop a comprehensive theory of how neural representations of meaning arise from the various brain systems that are used in interacting or considering the concept. An important secondary goal is to determine the degree of commonality of the neural representations across people. The studies propose to examine the neural dimensions of meaning in three domains: (a) physical objects;(b) human traits, emotions, and interpersonal interactions;and (c) small numerical quantities. This set of semantic domains is expected to provide sufficient breadth to reveal some of the principle neural bases of semantic representation. In contrast to the field of """"""""semantics"""""""" (the study of the relation between words and their meanings), this project will help establish a new research area, neurosemantics, which is the study of the relation between words, thoughts, and their neural representations. The key assumption is that the underlying dimensions of meaning representation in the human brain are derived from basic neural systems. For example, one of the dimensions of representation of a physical object is how one physically interacts with or handles it. This dimension of representation is underpinned by a network of cortical areas that co-activate when one thinks about a physical object, and also when one actually handles the physical object. Other dimensions of neural representation similarly emerge when the concept is encountered. The studies will collectively identify the major dimensions of concept representation and relate them to networks of co-activating brain areas. The cumulative knowledge from the completed project will provide the framework of a theory of how brain systems map onto the representation of the meaning of concepts. The theory will be applicable to understanding and designing therapies for neurological conditions in which the meanings of concepts are distorted, such as Alzheimer's Disease, Pick's Disease, semantic dementia, and autism. The resulting theory will be foundational in relating the representation of meaning to brain function.

Public Health Relevance

The research has application to a number of neurological disorders, where the new approach is capable of (1) determining whether and how the neural representation of a particular set of concepts is distorted;(2) tracking the progressive loss of neurosemantic components in disorders affecting the representation of meaning (including Alzheimer's Disease, Pick's Disease, semantic dementia, and anomic aphasia);(3) identifying the particular neurosemantic dimensions that are affected by the disorder;and hence (4) pointing the way to the design of a therapy. More generally, as a recent review (Bray et al., 2009) indicates, the new approach provides a good match to the brain characteristics of several brain disorders, and it is beginning to be applied for health-related purposes in many domains.

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Project (R01)
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Study Section
Language and Communication Study Section (LCOM)
Program Officer
Rossi, Andrew
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Carnegie-Mellon University
Schools of Arts and Sciences
United States
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Just, Marcel Adam; Pan, Lisa; Cherkassky, Vladimir L et al. (2017) Machine learning of neural representations of suicide and emotion concepts identifies suicidal youth. Nat Hum Behav 1:911-919
Just, Marcel Adam; Wang, Jing; Cherkassky, Vladimir L (2017) Neural representations of the concepts in simple sentences: Concept activation prediction and context effects. Neuroimage 157:511-520
Bauer, Andrew James; Just, Marcel Adam (2017) A brain-based account of ""basic-level"" concepts. Neuroimage 161:196-205
Chase, Henry W; Segreti, Anna Maria; Keller, Timothy A et al. (2017) Alterations of functional connectivity and intrinsic activity within the cingulate cortex of suicidal ideators. J Affect Disord 212:78-85
Schipul, Sarah E; Just, Marcel Adam (2016) Diminished neural adaptation during implicit learning in autism. Neuroimage 125:332-341
Damarla, Saudamini Roy; Cherkassky, Vladimir L; Just, Marcel Adam (2016) Modality-independent representations of small quantities based on brain activation patterns. Hum Brain Mapp 37:1296-307
Kana, Rajesh K; Maximo, Jose O; Williams, Diane L et al. (2015) Aberrant functioning of the theory-of-mind network in children and adolescents with autism. Mol Autism 6:59
Mason, Robert A; Prat, Chantel S; Just, Marcel Adam (2014) Neurocognitive brain response to transient impairment of Wernicke's area. Cereb Cortex 24:1474-84
Buchweitz, Augusto; Mason, Robert A; Meschyan, Gayane et al. (2014) Modulation of cortical activity during comprehension of familiar and unfamiliar text topics in speed reading and speed listening. Brain Lang 139:49-57
Just, Marcel Adam; Cherkassky, Vladimir L; Buchweitz, Augusto et al. (2014) Identifying autism from neural representations of social interactions: neurocognitive markers of autism. PLoS One 9:e113879

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