HomePublications

Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach

Research output: Contribution to journalArticle

Standard

Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach. / Wijeakumar, Sobanawartiny; Ambrose, Joseph P.; Spencer, John P.; Curtu, Rodica.

In: Journal of Mathematical Psychology, Vol. 76, No. Part B, 02.2017, p. 212–235.

Research output: Contribution to journalArticle

Harvard

APA

Vancouver

Author

Wijeakumar, Sobanawartiny ; Ambrose, Joseph P. ; Spencer, John P. ; Curtu, Rodica. / Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach. In: Journal of Mathematical Psychology. 2017 ; Vol. 76, No. Part B. pp. 212–235.

Bibtex- Download

@article{3a96ed9bb8e44a5b8d55093f8a91242c,
title = "Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach",
abstract = "A fundamental challenge in cognitive neuroscience is to develop theoretical frameworks that effectively span the gap between brain and behavior, between neuroscience and psychology. Here, we attempt to bridge this divide by formalizing an integrative cognitive neuroscience approach using dynamic field theory (DFT). We begin by providing an overview of how DFT seeks to understand the neural population dynamics that underlie cognitive processes through previous applications and comparisons to other modeling approaches. We then use previously published behavioral and neural data from a response selection Go/Nogo task as a case study for model simulations. Results from this study served as the {\textquoteleft}standard{\textquoteright} for comparisons with a model-based fMRI approach using dynamic neural fields (DNF). The tutorial explains the rationale and hypotheses involved in the process of creating the DNF architecture and fitting model parameters. Two DNF models, with similar structure and parameter sets, are then compared. Both models effectively simulated reaction times from the task as we varied the number of stimulus–response mappings and the proportion of Go trials. Next, we directly simulated hemodynamic predictions from the neural activation patterns from each model. These predictions were tested using general linear models (GLMs). Results showed that the DNF model that was created by tuning parameters to capture simultaneously trends in neural activation and behavioral data quantitatively outperformed a Standard GLM analysis of the same dataset. Further, by using the GLM results to assign functional roles to particular clusters in the brain, we illustrate how DNF models shed new light on the neural populations{\textquoteright} dynamics within particular brain regions. Thus, the present study illustrates how an interactive cognitive neuroscience model can be used in practice to bridge the gap between brain and behavior.",
keywords = "Dynamic field theory modeling, Integrative cognitive neuroscience, Response selection, Functional magnetic resonance imaging",
author = "Sobanawartiny Wijeakumar and Ambrose, {Joseph P.} and Spencer, {John P.} and Rodica Curtu",
year = "2017",
month = feb,
doi = "10.1016/j.jmp.2016.11.002",
language = "English",
volume = "76",
pages = "212–235",
journal = "Journal of Mathematical Psychology",
issn = "0022-2496",
publisher = "Academic Press Inc.",
number = "Part B",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Model-based functional neuroimaging using dynamic neural fields: An integrative cognitive neuroscience approach

AU - Wijeakumar, Sobanawartiny

AU - Ambrose, Joseph P.

AU - Spencer, John P.

AU - Curtu, Rodica

PY - 2017/2

Y1 - 2017/2

N2 - A fundamental challenge in cognitive neuroscience is to develop theoretical frameworks that effectively span the gap between brain and behavior, between neuroscience and psychology. Here, we attempt to bridge this divide by formalizing an integrative cognitive neuroscience approach using dynamic field theory (DFT). We begin by providing an overview of how DFT seeks to understand the neural population dynamics that underlie cognitive processes through previous applications and comparisons to other modeling approaches. We then use previously published behavioral and neural data from a response selection Go/Nogo task as a case study for model simulations. Results from this study served as the ‘standard’ for comparisons with a model-based fMRI approach using dynamic neural fields (DNF). The tutorial explains the rationale and hypotheses involved in the process of creating the DNF architecture and fitting model parameters. Two DNF models, with similar structure and parameter sets, are then compared. Both models effectively simulated reaction times from the task as we varied the number of stimulus–response mappings and the proportion of Go trials. Next, we directly simulated hemodynamic predictions from the neural activation patterns from each model. These predictions were tested using general linear models (GLMs). Results showed that the DNF model that was created by tuning parameters to capture simultaneously trends in neural activation and behavioral data quantitatively outperformed a Standard GLM analysis of the same dataset. Further, by using the GLM results to assign functional roles to particular clusters in the brain, we illustrate how DNF models shed new light on the neural populations’ dynamics within particular brain regions. Thus, the present study illustrates how an interactive cognitive neuroscience model can be used in practice to bridge the gap between brain and behavior.

AB - A fundamental challenge in cognitive neuroscience is to develop theoretical frameworks that effectively span the gap between brain and behavior, between neuroscience and psychology. Here, we attempt to bridge this divide by formalizing an integrative cognitive neuroscience approach using dynamic field theory (DFT). We begin by providing an overview of how DFT seeks to understand the neural population dynamics that underlie cognitive processes through previous applications and comparisons to other modeling approaches. We then use previously published behavioral and neural data from a response selection Go/Nogo task as a case study for model simulations. Results from this study served as the ‘standard’ for comparisons with a model-based fMRI approach using dynamic neural fields (DNF). The tutorial explains the rationale and hypotheses involved in the process of creating the DNF architecture and fitting model parameters. Two DNF models, with similar structure and parameter sets, are then compared. Both models effectively simulated reaction times from the task as we varied the number of stimulus–response mappings and the proportion of Go trials. Next, we directly simulated hemodynamic predictions from the neural activation patterns from each model. These predictions were tested using general linear models (GLMs). Results showed that the DNF model that was created by tuning parameters to capture simultaneously trends in neural activation and behavioral data quantitatively outperformed a Standard GLM analysis of the same dataset. Further, by using the GLM results to assign functional roles to particular clusters in the brain, we illustrate how DNF models shed new light on the neural populations’ dynamics within particular brain regions. Thus, the present study illustrates how an interactive cognitive neuroscience model can be used in practice to bridge the gap between brain and behavior.

KW - Dynamic field theory modeling

KW - Integrative cognitive neuroscience

KW - Response selection

KW - Functional magnetic resonance imaging

U2 - 10.1016/j.jmp.2016.11.002

DO - 10.1016/j.jmp.2016.11.002

M3 - Article

VL - 76

SP - 212

EP - 235

JO - Journal of Mathematical Psychology

JF - Journal of Mathematical Psychology

SN - 0022-2496

IS - Part B

ER -

ID: 100490045