Tags

Related Posts

Share This

Multitasking

Summary: How to best utilize a large (N~170) and comprehensive dataset to identify the neural, behavioural and trait correlates of emotional control РDeveloping analysis approaches for naturalistic and multivariate task-based fMRI.

With the advent of statistically powerful research projects, it has become feasible to improve on the ecological validity of task-based fMRI by allowing participants more freedom in the ways by which they solve the tasks. This allows for both better characterisation of the function in question itself, while also assessing the validity of the construct itself by assessing spontaneous, as opposed to rigidly constrained, task resolution. By combining this with a comprehensive multimodal assessment, such unconstrained approaches potentially allows one to use task-based fMRI investigation of naturalistic human behaviour while allowing for the complexity involved, thus allowing one to make more robust and generalizable statements about the topics of interest. However, allowing for complexity necessarily results in complicated data, which is not readily amenable to standard means of analysis.

The proposed project will investigate the possibility of utilizing advanced methods to analyse a relatively unconstrained and highly multivariate fMRI task with the ultimate aim of optimally characterizing the neural correlates of emotional self-control. A data set will be provided from the ReSource Project dataset (N~170) of the Regulation and Generation of Emotion (RAGE) task. In this task subjects were asked to control their affective states using a selection of strategies. Importantly, subjects were free to choose any combination of strategies, according to their own preferences, meaning the paradigm is both relatively unconstrained and highly multivariate. In addition to the task-based fMRI, data from questionnaire assessment (50+), cognitive and affective behavioural paradigms (10+) and real-life experience sampling is available. Data will be available in both raw and preprocessed forms via remote access.

The ultimate goal of this project is to identify the best approach to characterize naturalistic, self-generated affective states within the framework of the RAGE paradigm and the ReSource Project dataset as such. In service of this the project will have two main goals, the first of which is to develop analysis approaches that can be employed for these types of paradigm, identifying methodological approaches to optimally utilize the data to characterise the neural signatures of emotional self-control. A second, more theoretically focussed goal will be to identify the types of questions that can conceivably be asked from such high-dimensional, multimodal data, and explore how Big Data approaches can be best utilized to give an insight into highly subjective and variable psychological states like the present.

What can I do?

We’re going for a wide-net approach here, so anyone interested to join with questions or interests motivated by theory, to methods, to sheer curiosity is very welcome to join.

How can I join?

Drop me a line on engen@cbs.mpg.de if you already know you’ll want to join, with info on your skills and interests. If you’re still uncertain, feel free to look me up in Paris.

Who are the members?

Submitted by: Haakon Engen
Google group: