Functional MRI
Research Papers
Neurofeedback-guided kinesthetic motor imagery training in Parkinson's disease: Randomized trial
BACKGROUND: Parkinson's disease (PD) causes difficulty with maintaining the speed, size, and vigor of movements, especially when they are internally generated. We previously proposed that the insula is important in motivating intentional movement via its connections with the dorsomedial frontal cortex (dmFC). We demonstrated that subjects with PD can increase the right insula-dmFC functional connectivity using fMRI-based neurofeedback (NF) combined with kinesthetic motor imagery (MI). The current study is a randomized clinical trial testing whether NF-guided kinesthetic MI training can improve motor performance and increase task-based and resting-state right insula-dmFC functional connectivity in subjects with PD. METHODS: We assigned nondemented subjects with mild PD (Hoehn & Yahr stage ≤ 3) to the experimental kinesthetic MI with NF (MI-NF, n = 22) and active control visual imagery (VI, n = 22) groups. Only the MI-NF group received NF-guided MI training (10-12 runs). The NF signal was based on the right insula-dmFC functional connectivity strength. All subjects also practiced their respective imagery tasks at home daily for 4 weeks. Post-training changes in 1) task-based and resting-state right insula-dmFC functional connectivity were the primary imaging outcomes, and 2) MDS-UPDRS motor exam and motor function scores were the primary and secondary clinical outcomes, respectively. RESULTS: The MI-NF group was not significantly different from the VI group in any of the primary imaging or clinical outcome measures. The MI-NF group reported subjective improvement in kinesthetic body awareness. There was significant and comparable improvement only in motor function scores in both groups (secondary clinical outcome). This improvement correlated with NF regulation of the right insula-dmFC functional connectivity only in the MI-NF group. Both groups showed specific training effects in whole-brain functional connectivity with distinct neural circuits supporting kinesthetic motor and visual imagery (exploratory imaging outcome). CONCLUSIONS: The functional connectivity-based NF regulation was unsuccessful, however, both kinesthetic MI and VI practice improved motor function in our cohort with mild PD.
View Full Paper →Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis
Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in open science research practices and data sharing.
View Full Paper →The real-time fMRI neurofeedback based stratification of Default Network Regulation Neuroimaging data repository
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.
View Full Paper →Latest Developments in Live Z-Score Training: Symptom Check List, Phase Reset, and Loreta Z-Score Biofeedback
Advances in neuroscience are applied to the clinical applications of EEG neurofeedback by linking symptoms to functional networks in the brain. This is achieved by reviews of the last 20 years of functional neuroimaging studies of brain networks related to clinical disorders based on positron emission tomography, functional MRI, diffusion tensor imaging, and EEG/MEG inverse solutions. Considerable consistency exists between different imaging modalities because of the property of functional localization and the existence of large clusters of connections in the brain representing network modules and hubs. Reviewed here is new method of EEG neurofeedback called Z-Score Neurofeedback, and it is demonstrated how real-time comparison to an age-matched population of healthy subjects simplifies protocol generation and allows clinicians to target modules and hubs that indicate dysregulation and instability in networks related to symptoms. Z-score neurofeedback, by measuring the distance from the center of the healthy age-matched population, increases specificity in operant conditioning and provides a guide by which extreme Z-score outliers are linked to symptoms and then reinforced toward states of greater homeostasis and stability. The goal is increased efficiency of information processing in brain networks related to the patient's symptoms. The unique advantage of EEG over other neuroimaging methods is high temporal resolution in which the fine temporal details of phase lock and phase shift between large masses of neurons is quantified and can be modified by Z-score neurofeedback to address the patient's symptoms. The latest developments in Z-score neurofeedback are a harbinger of a bright future for clinicians and, most important, patients that suffer from a variety of brain dysfunctions.
View Full Paper →Ready to Optimize Your Brain?
Schedule a free consultation to discuss functional mri and how neurofeedback training can help
Or call us directly at 855-88-BRAIN
View Programs & Pricing →