Decomposing Neural Representational Patterns of Discriminatory and Hedonic Information during Somatosensory Stimulation

TY - JOUR

T1 - Decomposing Neural Representational Patterns of Discriminatory and Hedonic Information during Somatosensory Stimulation

AU - Kryklywy, James H

AU - Ehlers, Mana R

AU - Beukers, Andre O

AU - Moore, Sarah R

AU - Todd, Rebecca M

AU - Anderson, Adam K

N1 - Copyright © 2023 Kryklywy et al.

PY - 2022/12

Y1 - 2022/12

N2 - The ability to interrogate specific representations in the brain, determining how, and where, difference sources of information are instantiated can provide invaluable insight into neural functioning. Pattern component modeling (PCM) is a recent analytic technique for human neuroimaging that allows the decomposition of representational patterns in brain into contributing subcomponents. In the current study, we present a novel PCM variant that tracks the contribution of prespecified representational patterns to brain representation across areas, thus allowing hypothesis-guided employment of the technique. We apply this technique to investigate the contributions of hedonic and nonhedonic information to the neural representation of tactile experience. We applied aversive pressure (AP) and appetitive brush (AB) to stimulate distinct peripheral nerve pathways for tactile information (C-/CT-fibers, respectively) while patients underwent functional magnetic resonance imaging (fMRI) scanning. We performed representational similarity analyses (RSAs) with pattern component modeling to dissociate how discriminatory versus hedonic tactile information contributes to population code representations in the human brain. Results demonstrated that information about appetitive and aversive tactile sensation is represented separately from nonhedonic tactile information across cortical structures. This also demonstrates the potential of new hypothesis-guided PCM variants to help delineate how information is instantiated in the brain.

AB - The ability to interrogate specific representations in the brain, determining how, and where, difference sources of information are instantiated can provide invaluable insight into neural functioning. Pattern component modeling (PCM) is a recent analytic technique for human neuroimaging that allows the decomposition of representational patterns in brain into contributing subcomponents. In the current study, we present a novel PCM variant that tracks the contribution of prespecified representational patterns to brain representation across areas, thus allowing hypothesis-guided employment of the technique. We apply this technique to investigate the contributions of hedonic and nonhedonic information to the neural representation of tactile experience. We applied aversive pressure (AP) and appetitive brush (AB) to stimulate distinct peripheral nerve pathways for tactile information (C-/CT-fibers, respectively) while patients underwent functional magnetic resonance imaging (fMRI) scanning. We performed representational similarity analyses (RSAs) with pattern component modeling to dissociate how discriminatory versus hedonic tactile information contributes to population code representations in the human brain. Results demonstrated that information about appetitive and aversive tactile sensation is represented separately from nonhedonic tactile information across cortical structures. This also demonstrates the potential of new hypothesis-guided PCM variants to help delineate how information is instantiated in the brain.

KW - Humans

KW - Brain Mapping/methods

KW - Brain/diagnostic imaging

KW - Touch

KW - Magnetic Resonance Imaging/methods

KW - Neuroimaging

U2 - 10.1523/ENEURO.0274-22.2022

DO - 10.1523/ENEURO.0274-22.2022

M3 - SCORING: Journal article

C2 - 36549914

VL - 10

JO - ENEURO

JF - ENEURO

SN - 2373-2822

IS - 1

M1 - ENEURO.0274-22.2022

ER -