scholarly journals Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

2020 ◽  
Author(s):  
Amelie Haugg ◽  
Ronald Sladky ◽  
Stavros Skouras ◽  
Amalia McDonald ◽  
Cameron Craddock ◽  
...  
Keyword(s):  
Real Time ◽  
Clinical Symptoms ◽  
Brain Activity ◽  
Treatment Success ◽  
Self Regulation ◽  
Brain Regions ◽  
Activity Levels ◽  
Success Predictors ◽  
Learning Success ◽  
Study Database

AbstractNeurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large interindividual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pre-training functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pre-training activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

scholarly journals Putamen volume predicts real-time fMRI neurofeedback learning success across paradigms and neurofeedback target regions

2020 ◽  
Author(s):  
Zhiying Zhao ◽  
Shuxia Yao ◽  
Jana Zweerings ◽  
Xinqi Zhou ◽  
Feng Zhou ◽  
...  
Keyword(s):  
Real Time ◽  
Gray Matter Volume ◽  
Self Regulation ◽  
Instrumental Learning ◽  
Neural Substrates ◽  
Brain Regions ◽  
Structural Variations ◽  
Learning Success ◽  
The Right

AbstractReal-time fMRI guided neurofeedback training has gained increasing interest as a non-invasive brain regulation technique with the potential to normalize functional brain alterations in therapeutic contexts. Individual variations in learning success and treatment response have been observed, yet the neural substrates underlying the learning of self-regulation remain unclear. Against this background, we explored potential brain structural predictors for learning success with pooled data from three real-time fMRI datasets. Our analysis revealed that gray matter volume of the right putamen could predict neurofeedback learning success across the three datasets (n = 66 in total). Importantly, the original studies employed different neurofeedback paradigms during which different brain regions were trained pointing to a general association with learning success independent of specific aspects of the experimental design. Given the role of the putamen in associative learning the finding may reflect an important role of instrumental learning processes and brain structural variations in associated brain regions for successful acquisition of fMRI neurofeedback-guided self-regulation.

Data-Driven Approach to the Analysis of Real-Time FMRI Neurofeedback Data: Disorder-Specific Brain Synchrony in PTSD

2021 ◽  
pp. 2150043
Author(s):  
Jana Zweerings ◽  
Kiira Sarasjärvi ◽  
Krystyna Anna Mathiak ◽  
Jorge Iglesias-Fuster ◽  
Fengyu Cong ◽  
...  
Keyword(s):  
Real Time ◽  
Brain Activity ◽  
Inferior Frontal Gyrus ◽  
Self Regulation ◽  
Blood Oxygen Level Dependent ◽  
Brain Regions ◽  
Anterior Cingulate ◽  
Data Driven ◽  
Post Traumatic Stress ◽  
Data Driven Approach

Brain–computer interfaces (BCIs) can be used in real-time fMRI neurofeedback (rtfMRI NF) investigations to provide feedback on brain activity to enable voluntary regulation of the blood-oxygen-level dependent (BOLD) signal from localized brain regions. However, the temporal pattern of successful self-regulation is dynamic and complex. In particular, the general linear model (GLM) assumes fixed temporal model functions and misses other dynamics. We propose a novel data-driven analyses approach for rtfMRI NF using intersubject covariance (ISC) analysis. The potential of ISC was examined in a reanalysis of data from 21 healthy individuals and nine patients with post-traumatic stress-disorder (PTSD) performing up-regulation of the anterior cingulate cortex (ACC). ISC in the PTSD group differed from healthy controls in a network including the right inferior frontal gyrus (IFG). In both cohorts, ISC decreased throughout the experiment indicating the development of individual regulation strategies. ISC analyses are a promising approach to reveal novel information on the mechanisms involved in voluntary self-regulation of brain signals and thus extend the results from GLM-based methods. ISC enables a novel set of research questions that can guide future neurofeedback and neuroimaging investigations.

scholarly journals A functional MRI study of presurgical cognitive deficits in glioma patients

2020 ◽  
Author(s):  
Irena T Schouwenaars ◽  
Miek J de Dreu ◽  
Geert-Jan M Rutten ◽  
Nick F Ramsey ◽  
Johan M Jansma
Keyword(s):  
Functional Mri ◽  
Cognitive Deficits ◽  
Brain Activity ◽  
Neuropsychological Testing ◽  
Region Of Interest ◽  
High Grade Glioma ◽  
Brain Regions ◽  
Brain State ◽  
Low Grade ◽  
Activity Levels

Abstract Background The main goal of this functional MRI (fMRI) study was to examine whether cognitive deficits in glioma patients prior to treatment are associated with abnormal brain activity in either the central executive network (CEN) or default mode network (DMN). Methods Forty-six glioma patients, and 23 group-matched healthy controls (HCs) participated in this fMRI experiment, performing an N-back task. Additionally, cognitive profiles of patients were evaluated outside the scanner. A region of interest–based analysis was used to compare brain activity in CEN and DMN between groups. Post hoc analyses were performed to evaluate differences between low-grade glioma (LGG) and high-grade glioma (HGG) patients. Results In-scanner performance was lower in glioma patients compared to HCs. Neuropsychological testing indicated cognitive impairment in LGG as well as HGG patients. fMRI results revealed normal CEN activation in glioma patients, whereas patients showed reduced DMN deactivation compared to HCs. Brain activity levels did not differ between LGG and HGG patients. Conclusions Our study suggests that cognitive deficits in glioma patients prior to treatment are associated with reduced responsiveness of the DMN, but not with abnormal CEN activation. These results suggest that cognitive deficits in glioma patients reflect a reduced capacity to achieve a brain state necessary for normal cognitive performance, rather than abnormal functioning of executive brain regions. Solely focusing on increases in brain activity may well be insufficient if we want to understand the underlying brain mechanism of cognitive impairments in patients, as our results indicate the importance of assessing deactivation.

scholarly journals Improving Emotion Regulation Through Real-Time Neurofeedback Training on the Right Dorsolateral Prefrontal Cortex: Evidence From Behavioral and Brain Network Analyses

2021 ◽  
Vol 15 ◽  
Author(s):  
Linlin Yu ◽  
Quanshan Long ◽  
Yancheng Tang ◽  
Shouhang Yin ◽  
Zijun Chen ◽  
...  
Keyword(s):  
Emotion Regulation ◽  
Prefrontal Cortex ◽  
Real Time ◽  
Dorsolateral Prefrontal Cortex ◽  
Near Infrared ◽  
Self Regulation ◽  
Brain Regions ◽  
Functional Near Infrared Spectroscopy ◽  
Network Analyses ◽  
Dorsolateral Prefrontal

We investigated if emotion regulation can be improved through self-regulation training on non-emotional brain regions, as well as how to change the brain networks implicated in this process. During the training period, the participants were instructed to up-regulate their right dorsolateral prefrontal cortex (rDLPFC) activity according to real-time functional near-infrared spectroscopy (fNIRS) neurofeedback signals, and there was no emotional element. The results showed that the training significantly increased emotion regulation, resting-state functional connectivity (rsFC) within the emotion regulation network (ERN) and frontoparietal network (FPN), and rsFC between the ERN and amygdala; however, training did not influence the rsFC between the FPN and the amygdala. However, self-regulation training on rDLPFC significantly improved emotion regulation and generally increased the rsFCs within the networks; the rsFC between the ERN and amygdala was also selectively increased. The present study also described a safe approach that may improve emotion regulation through self-regulation training on non-emotional brain regions.

Real-time fMRI brain computer interfaces: Self-regulation of single brain regions to networks

2014 ◽  
Vol 95 ◽  
pp. 4-20 ◽  
Author(s):  
Sergio Ruiz ◽  
Korhan Buyukturkoglu ◽  
Mohit Rana ◽  
Niels Birbaumer ◽  
Ranganatha Sitaram
Keyword(s):  
Real Time ◽  
Self Regulation ◽  
Brain Regions ◽  
Brain Computer Interfaces ◽  
Computer Interfaces

scholarly journals Human Brain Activity Predicts Individual Differences in Prior Knowledge Use during Decisions

2012 ◽  
Vol 24 (6) ◽  
pp. 1462-1475 ◽  
Author(s):  
Kathleen A. Hansen ◽  
Sarah F. Hillenbrand ◽  
Leslie G. Ungerleider
Keyword(s):  
Prior Knowledge ◽  
Brain Activity ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Anterior Insula ◽  
Activity Levels ◽  
Knowledge Use ◽  
Ample Evidence ◽  
Tuning Curves ◽  
Abstract Shapes

Studies by cognitive psychologists, psychophysicists, neuroscientists, and economists provide ample evidence that humans use prior knowledge to bias decisions adaptively. In this study, we sought to locate and investigate the brain areas mediating this behavior. Participants viewed ambiguous abstract shapes and decided whether a shape was of Category A (smoother) or B (bumpier). The decision was made in the context of one of two prior knowledge cues, 80/20 and 50/50. The 80/20 cue indicated that upcoming shapes had an 80% probability of being of one category, for example, B, and a 20% probability of being of the other. The 50/50 cue indicated that upcoming shapes had an equal probability of being of either category. The shift in bias produced by the 80/20 cue relative to the 50/50 cue was of the predicted sign for every subject but varied in magnitude. We searched for brain regions in which activity changes correlated with the extent of the bias shift; these were dorsolateral pFC (middle frontal gyrus), inferior frontal junction, anterior insula, inferior parietal lobule, intraparietal sulcus, head of the caudate, posterior cingulate cortex, and fusiform gyrus. The findings indicate that an individual's brain activity in these regions reflects the extent to which that individual makes use of prior knowledge to bias decisions. We also created within-ROI tuning curves by binning the shape curvature levels and plotting brain activity levels at each of the nine bins. In the fronto-parietal and anterior insula ROIs, the tuning curves peaked at targets contraindicated by the prior knowledge cue (e.g., Category B targets if the 80/20 cue meant 20% probability B). The increased activity in these regions likely indicates a no-go response when sufficient perceptual evidence favored the alternative contraindicated by the 80/20 cue.

scholarly journals Connectome-wide search for functional connectivity locus associated with pathological rumination as a target for real-time fMRI neurofeedback intervention

2020 ◽  
Author(s):  
Masaya Misaki ◽  
Aki Tsuchiyagaito ◽  
Obada A Zoubi ◽  
Martin Paulus ◽  
Jerzy Bodurka ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Functional Connectivity ◽  
Real Time ◽  
Target Location ◽  
Simulation Analysis ◽  
Clinical Symptoms ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Precise Location ◽  
Online Computation

AbstractReal-time fMRI neurofeedback (rtfMRI-nf) enables noninvasive targeted intervention in brain activation with high spatial specificity. To achieve this promise of rtfMRI-nf, we introduced and demonstrated a data-driven framework to design a rtfMRI-nf intervention through the discovery of precise target location associated with clinical symptoms and neurofeedback signal optimization. Specifically, we identified the functional connectivity locus associated with rumination symptoms, utilizing a connectome-wide search in resting-state fMRI data from a large cohort of mood and anxiety disorder individuals (N=223) and healthy controls (N=45). Then, we performed a rtfMRI simulation analysis to optimize the online functional connectivity neurofeedback signal for the identified functional connectivity. The connectome-wide search was performed in the medial prefrontal cortex and the posterior cingulate cortex/precuneus brain regions to identify the precise location of the functional connectivity associated with rumination severity as measured by the ruminative response style (RRS) scale. The analysis found that the functional connectivity between the loci in the precuneus (−6, −54, 48 mm in MNI) and the right temporo-parietal junction (RTPJ; 49, −49, 23 mm) was positively correlated with RRS scores (depressive, p < 0.001; brooding, p < 0.001; reflective, p = 0.002) in the mood and anxiety disorder group. We then performed a rtfMRI processing simulation to optimize the online computation of the precuneus-RTPJ connectivity. We determined that the two-point method without a control region was appropriate as a functional connectivity neurofeedback signal with less dependence on signal history and its accommodation of head motion. The present study offers a discovery framework for the precise location of functional connectivity targets for rtfMRI-nf intervention, which could help directly translate neuroimaging findings into clinical rtfMRI-nf interventions.

scholarly journals The role of superstition of cognitive control during neurofeedback training

2021 ◽  
Author(s):  
Doris Groessinger ◽  
Florian Ph.S Fischmeister ◽  
Mathias Witte ◽  
Karl Koschutnig ◽  
Manuel Ninaus ◽  
...  
Keyword(s):  
Basal Ganglia ◽  
Associative Learning ◽  
Real Time ◽  
Ventral Striatum ◽  
Psychiatric Symptoms ◽  
Brain Activity ◽  
Training Session ◽  
Brain Regions ◽  
Neurofeedback Training ◽  
The Impact

Background: Real-time fMRI neurofeedback is growing in reputation as a means to alter brain activity patterns and alleviate psychiatric symptoms. Activity in ventral striatum structures is considered an index of training efficacy. fMRI response in these brain regions indicates neurofeedback-driven associative learning. Here we investigated the impact of mere superstition of control as observed during neurofeedback training on patterns of fMRI activation. Methods: We examined the brain activations of a large sample of young participants (n = 97, 50 female, age range 18-54yrs) in a simple fMRI task. Participants saw a display similar to that typically used for real-time fMRI. They were instructed to watch the bars' movements or to control them with their own brain activity. Bar movements were not connected with brain activity of participants in any way and perceptions of control were superstitious. After the pretended control condition, they rated how well they were able to control the bars' movements. Results: Strong activation in the basal ganglia and ventral striatum as well as in large portions of the anterior insula, supplementary motor area, and the middle frontal gyrus due to the superstition of brain control. Conclusions: The superstition of control over one's own brain activity in a pretended neurofeedback training session activates the same neural networks as neurofeedback-driven learning. Therefore, activity in the basal ganglia and ventral striatum cannot be taken as evidence for neurofeedback-driven associative learning unless its effects are proven to supersede those elicited by appropriate sham conditions.

scholarly journals Evaluation of Real Brain Activity Using an Ultracompact Brain Activity Sensor for the Assessment of Cognitive Function

2021 ◽  
Author(s):  
Tatsuo Yasumitsu
Keyword(s):  
Cognitive Function ◽  
Real Time ◽  
Brain Activity ◽  
Exercise Program ◽  
Difficulty Level ◽  
Activity Levels ◽  
Color Changes ◽  
Dementia Prevention ◽  
Dorsolateral Prefrontal ◽  
The Brain

Abstract Background: The present study purpose aims to improve cognitive function at the preclinical stage of dementia and measure real-time brain activity levels in participants wearing an ultracompact brain activity sensor called XB-01. Methods: Four healthy people (two males and two females) aged 20–50 years involved in the dementia prevention program participated in the study. During the experiment, the participants wore XB-01, which was connected to their iPhone by Bluetooth to collect data. XB-01 data indicating the brain activity (blood flow) during the program was demonstrated by real-time color changes on the connected iPhone to evaluate the results on a 100-point scale. We examined 21 programs in total, including those reported to increase brain activity. Results: We conducted an analysis of variance for each of the four programs in the upper and lower positions detected to compare brain activity, resulting in finding a main effect of the program, F(7,21) = 4.35, p <.05. Conclusions: An exercise program including a dual-task with large limb movements was highly effective in increasing brain activity in the dorsolateral prefrontal cortex (DLPFC). Moreover, slightly higher speed, pace, and difficulty level of the program most suitable for participants were more effective. Brain activity increased in the DLPFC during the program and several minutes after its completion. These findings can help develop programs that prevent and improve cognitive function.Trial Registration: The research ethics committee at PCY, Ltd. Trial Registry, approval number 20-2. Registered 04 January 2020. Retrospectively registered.

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