The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository

Abstract

This data descriptor describes a repository of openly shared data from an experiment to assess inter-individual differences in default mode network (DMN) activity. This repository includes cross-sectional functional magnetic resonance imaging (fMRI) data from the Multi Source Interference Task, to assess DMN deactivation, the Moral Dilemma Task, to assess DMN activation, a resting state fMRI scan, and a DMN neurofeedback paradigm, to assess DMN modulation, along with accompanying behavioral and cognitive measures. We report technical validation from n=125 participants of the final targeted sample of 180 participants. Each session includes acquisition of one whole-brain anatomical scan and whole-brain echo-planar imaging (EPI) scans, acquired during the aforementioned tasks and resting state. The data includes several self-report measures related to perseverative thinking, emotion regulation, and imaginative processes, along with a behavioral measure of rapid visual information processing. Technical validation of the data confirms that the tasks deactivate and activate the DMN as expected. Group level analysis of the neurofeedback data indicates that the participants are able to modulate their DMN with considerable inter-subject variability. Preliminary analysis of behavioral responses and specifically self-reported sleep indicate that as many as 73 participants may need to be excluded from an analysis depending on the hypothesis being tested. The present data are linked to the enhanced Nathan Kline Institute, Rockland Sample and builds on the comprehensive neuroimaging and deep phenotyping available therein. As limited information is presently available about individual differences in the capacity to directly modulate the default mode network, these data provide a unique opportunity to examine DMN modulation ability in relation to numerous phenotypic characteristics.

A Comparison of the Effectiveness of Neurofeedback (NFB) Training Method and Fernald’s Multisensory Approach on Dictation Performance among Students Suffering from Dictation Disorder (Dysgraphia)

“…Therefore, in general, it can be said that with an increase in the SMR wave in the CZ area, some improvements in working memory will be seen. Also, one other part of the protocol used in this study was to suppress theta; in other words, our findings showed neurofeedback training had positive effects on individual's mental functioning and cognitive processing; this finding is congruent with findings of Hans and Mayer [41], Oh [42] and McDonald [43]. Moreover, neurofeedback therapy helped students to develop skills required for reading comprehension, while students treated by Fernald's multisensory approach showed better indicators of written expression; however a combination therapy that integrated both groups (Fernald and neurofeedback), could have significant effect on both variables mentioned above in each isolated group [10].…”

A Comparison of the Effectiveness of Neurofeedback (NFB) Training Method and Fernald’s Multisensory Approach on Dictation Performance among Students Suffering from Dictation Disorder (Dysgraphia)

“…Therefore, in general, it can be said that with an increase in the SMR wave in the CZ area, some improvements in working memory will be seen. Also, one other part of the protocol used in this study was to suppress theta; in other words, our findings showed neurofeedback training had positive effects on individual's mental functioning and cognitive processing; this finding is congruent with findings of Hanslmayr [41], Oh [42] and McDonald [43]. Moreover, neurofeedback therapy helped students to develop skills required for reading comprehension, while students treated by Fernald's multisensory approach showed better indicators of written expression; however a combination therapy that integrated both groups (Fernald and neurofeedback), could have significant effect on both variables mentioned above in each isolated group [10].…”

Can we predict real‐time fMRI neurofeedback learning success from pretraining brain activity?

“…Following our request, we received data from 24 independent neurofeedback studies with data from 261healthy participants [studies 1, 2, 4, 5, 7, 9-15, 18-21, 24] Table 1 provides an overview over all studies (Auer et al, 2015;Blefari et al, 2015;Emmert, Kopel, et al, 2017;Kim et al, 2015;Kirschner et al, 2018;Koush et al, 2015, Koush et al, 2013MacInnes et al, 2016;Marins et al, 2015;McDonald et al, 2017;Megumi et al, 2015;Papoutsi et al, 2020, Papoutsi et al, 2018Scharnowski et al, 2012Scharnowski et al, , 2015Skouras & Scharnowski, 2019;Sorger et al, 2018;Spetter et al, 2017;Yao et al, 2016;Young et al, 2017;Zich et al, 2020).…”