TY - JOUR
T1 - Direct modulation of aberrant brain network connectivity through real-time NeuroFeedback
AU - Ramot, Michal
AU - Kimmich, Sara
AU - Gonzalez-Castillo, Javier
AU - Roopchansingh, Vinai
AU - Popal, Haroon
AU - White, Emily
AU - Gotts, Stephen J.
AU - Martin, Alex
N1 - We thank Lauren Kenworthy for insights into behavioral testing methods in ASD, and Miriam Menken for help with data pre-processing. This work was supported by the Revson Foundation Women in Science award through the Weizmann Institute of Science (to MR) and by the Intramural Research Program, National Institute of Mental Health (ZIAMH002920 and ZIAMH002783). Michal Ramot: Contribution: Conceptualization, Data curation, Software, Formal analysis, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing
PY - 2017/9/16
Y1 - 2017/9/16
N2 - The existence of abnormal connectivity patterns between resting state networks in neuropsychiatric disorders, including Autism Spectrum Disorder (ASD), has been well established. Traditional treatment methods in ASD are limited, and do not address the aberrant network structure. Using real-time fMRI neurofeedback, we directly trained three brain nodes in participants with ASD, in which the aberrant connectivity has been shown to correlate with symptom severity. Desired network connectivity patterns were reinforced in real-time, without participants’ awareness of the training taking place. This training regimen produced large, significant long-term changes in correlations at the network level, and whole brain analysis revealed that the greatest changes were focused on the areas being trained. These changes were not found in the control group. Moreover, changes in ASD resting state connectivity following the training were correlated to changes in behavior, suggesting that neurofeedback can be used to directly alter complex, clinically relevant network connectivity patterns.
AB - The existence of abnormal connectivity patterns between resting state networks in neuropsychiatric disorders, including Autism Spectrum Disorder (ASD), has been well established. Traditional treatment methods in ASD are limited, and do not address the aberrant network structure. Using real-time fMRI neurofeedback, we directly trained three brain nodes in participants with ASD, in which the aberrant connectivity has been shown to correlate with symptom severity. Desired network connectivity patterns were reinforced in real-time, without participants’ awareness of the training taking place. This training regimen produced large, significant long-term changes in correlations at the network level, and whole brain analysis revealed that the greatest changes were focused on the areas being trained. These changes were not found in the control group. Moreover, changes in ASD resting state connectivity following the training were correlated to changes in behavior, suggesting that neurofeedback can be used to directly alter complex, clinically relevant network connectivity patterns.
U2 - 10.7554/eLife.28974
DO - 10.7554/eLife.28974
M3 - مقالة
C2 - 28917059
SN - 2050-084X
VL - 6
JO - eLife
JF - eLife
M1 - e28974
ER -