Combining Real-Time fMRI Neurofeedback Training of the DLPFC with N-Back Practice Results in Neuroplastic Effects Confined to the Neurofeedback Target Region

Abstract

In traditional fMRI, individuals respond to exogenous stimuli and are naïve to the effects of the stimuli on their neural activity patterns. Changes arising in the fMRI signal are analyzed post-hoc to elucidate the spatial and temporal activation of brain regions associated with the tasks performed. The advent of real-time fMRI has enabled a new method to systematically alter brain activity across space and time using neurofeedback training (NFT), providing a new tool to study internally-driven processes such as neuroplasticity. In this work, we combined n-back practice with fMRI-NFT of the left dorsolateral prefrontal cortex (DLPFC) to better understand the relationship between open-and closed-loop neuromodulation. FMRI data were acquired during both traditional n-back and NFT across five imaging sessions. Region-of-interest (ROI) and voxel-wise 2 × 2 within subjects ANOVAs were carried out to determine the effects of, and interaction between, training session and neuromodulation type. A main effect of training session was identified for only a single, highly focused cluster that shared spatial properties with the fMRI-NFT target region (left DLPFC). This finding indicates that combined open-and closed-loop neuroplastic enhancement techniques result in focal changes that are confined to the target area of NFT, and do not affect up-or down-stream network components that are normally engaged during working memory. Additionally, we identified a main effect of neuromodulation type for 15 clusters with significantly different activation between open-and closed-loop neuromodulation during training, 12 of which demonstrated higher activity during the open-loop neuromodulation. Our results, taken together with previous reports, indicate that fMRI-NFT combined with n-back practice leads to a highly focal volume exhibiting neuroplasticity without additional network effects.

Control freaks: Towards optimal selection of control conditions for fMRI neurofeedback studies

“…The mental-rehearsal control condition can be implemented both inside (57,59,60,(62)(63)(64)(65)(66)(67)(68)(69)(70)(71)(72)(73)(74)(75)(76)(77)(78)(79)(80) and outside the MRI scanner (e.g., 41,81,[82][83][84]. The latter option is much easier to realize and considerably decreases associated (personnel and scanning) costs.…”

Existence of Initial Dip for BCI: An Illusion or Reality

“…Several studies successfully demonstrated that the participants using real-time feedback in fMRI-BCI were able to perform voluntary self-regulation on a focused brain region, see details in Ruiz et al (2014) and Sorger et al (2018) . The applications of real-time neuro-feedback in fMRI-BCI for healthy subjects include the voluntary self-regulation of motor areas ( Yoo and Jolesz, 2002 ; Yoo et al, 2004 , 2008 ; Berman et al, 2012 ; Johnson et al, 2012 ), sensory areas ( Scharnowski et al, 2012 ; Robineau et al, 2014 ; Auer et al, 2015 ), auditory cortex ( Yoo et al, 2006 ), language areas ( Rota et al, 2009 ), emotion areas ( Weiskopf et al, 2003 ; Caria et al, 2007 , 2010 ; Zotev et al, 2011 ), and working memory areas ( Zhang et al, 2013 ; Sherwood et al, 2016 ). In the case of patients, the real-time neuro-feedback helps to remediate the pathological brain activation associated with different disorders including neurological disorders ( Subramanian et al, 2011 ; Sitaram et al, 2012 ; Guan et al, 2015 ) and psychiatric disorders ( Ruiz et al, 2013 ; Hartwell et al, 2016 ; Kirsch et al, 2016 ; Zotev et al, 2016 ).…”

Causally informed activity flow models provide mechanistic insight into network-generated cognitive activations

“…(The full list of 95 contributing regions is included as Supplementary Table 1.) This analysis goes beyond previous studies that have determined that DLPFC is especially active during n-back tasks (Evangelista et al, 2021;Kumar et al, 2017;Sherwood et al, 2016;Woodcock et al, 2018) and studies that have characterized DLPFC connectivity (Cole et al, 2012;Panikratova et al, 2020;Reineberg & Banich, 2016), revealing likely direct causal mechanisms contributing to DLPFC's involvement in n-back task cognitive processes. As mentioned before, this single-causal-step activity flow model represents a starting point for a more comprehensive multiple-causal-step neurocognitive explanation of working memory.…”