30 research outputs found
Quality and denoising in real-time functional magnetic resonance imaging neurofeedback: A methods review
First published: 25 April 2020Neurofeedback training using real-time functional magnetic resonance imaging
(rtfMRI-NF) allows subjects voluntary control of localised and distributed brain activity.
It has sparked increased interest as a promising non-invasive treatment option in
neuropsychiatric and neurocognitive disorders, although its efficacy and clinical significance
are yet to be determined. In this work, we present the first extensive review
of acquisition, processing and quality control methods available to improve the quality
of the neurofeedback signal. Furthermore, we investigate the state of denoising
and quality control practices in 128 recently published rtfMRI-NF studies. We found:
(a) that less than a third of the studies reported implementing standard real-time
fMRI denoising steps, (b) significant room for improvement with regards to methods
reporting and (c) the need for methodological studies quantifying and comparing the
contribution of denoising steps to the neurofeedback signal quality. Advances in
rtfMRI-NF research depend on reproducibility of methods and results. Notably, a systematic
effort is needed to build up evidence that disentangles the various mechanisms
influencing neurofeedback effects. To this end, we recommend that future
rtfMRI-NF studies: (a) report implementation of a set of standard real-time fMRI denoising
steps according to a proposed COBIDAS-style checklist (https://osf.io/kjwhf/),
(b) ensure the quality of the neurofeedback signal by calculating and reporting
community-informed quality metrics and applying offline control checks and (c) strive
to adopt transparent principles in the form of methods and data sharing and support
of open-source rtfMRI-NF software. Code and data for reproducibility, as well as an
interactive environment to explore the study data, can be accessed at https://github.
com/jsheunis/quality-and-denoising-in-rtfmri-nf.LSHâTKI, Grant/Award Number: LSHM16053âSGF; Philips Researc
Beyond fingerprinting: Choosing predictive connectomes over reliable connectomes
Recent years have seen a surge of research on variability in functional brain connectivity within and between individuals, with encouraging progress toward understanding the consequences of this variability for cognition and behavior. At the same time, well-founded concerns over rigor and reproducibility in psychology and neuroscience have led many to question whether functional connectivity is sufficiently reliable, and call for methods to improve its reliability. The thesis of this opinion piece is that when studying variability in functional connectivityâboth across individuals and within individuals over timeâwe should use behavior prediction as our benchmark rather than optimize reliability for its own sake. We discuss theoretical and empirical evidence to compel this perspective, both when the goal is to study stable, trait-level differences between people, as well as when the goal is to study state-related changes within individuals. We hope that this piece will be useful to the neuroimaging community as we continue efforts to characterize inter- and intra-subject variability in brain function and build predictive models with an eye toward eventual real-world applications
Clinical applications of real-time FMRI neurofeedback training â premises, promises, and pitfalls
Neurofeedback training represents a form of biofeedback training with a history of over 50 years. During neurofeedback training participants aim to gain control over a feedback signal that represents the activity of a brain region or network of interest. As such, it holds promise for clinical translation as an add-on treatment for psychiatric and neurological conditions. Yet, currently available evidence for its therapeutic efficacy remains limited.
Originally provided based on cortical signals measured with electroencephalography (EEG), methodological developments have allowed providing neurofeedback based on (cortical and subcortical) brain signals acquired from functional magnetic resonance imaging (fMRI). The aim of this thesis was to test the feasibility and clinical efficacy of fMRI neurofeedback (fMRI-NF) training in a psychiatric population and to develop protocols that allow translating the technique to motor rehabilitation. Specifically, this thesis summarises the clinical and neuroimaging results from a randomised controlled trial conducted in patients suffering from depression. Depression represents a leading cause of disability in adults and epidemiological data indicates that up to one third of patients remain depressed after treatment.
Another focus was the development of a motor imagery-based fMRI-NF protocol in healthy participants. This work has informed a proof-of-concept study for motor rehabilitation in stroke survivors, for which the methodology was preregistered on a public platform before data collection started to increase transparency. The thesis aims to address problematic research practices that have been attributed to the replication crisis in many areas of science, including a clear separation of planned and exploratory hypotheses and the use and adaptation of alternative statistical methods. A review chapter discusses potential electrophysiological target signatures for EEG-NF to improve motor symptoms in Parkinsonâs disease patients. The thesis concludes with a discussion of current premises, promises, and pitfalls in clinical applications of neurofeedback training and considerations for clinical trials development
Applications of multi-way analysis for characterizing paediatric electroencephalogram (EEG) recordings
This doctoral thesis outlines advances in multi-way analysis for characterizing
electroencephalogram (EEG) recordings from a paediatric population, with the aim to
describe new links between EEG data and changes in the brain. This entails establishing the
validity of multi-way analysis as a framework for identifying developmental information at the
individual and collective level. Multi-way analysis broadens matrix analysis to a multi-linear
algebraic architecture to identify latent structural relationships in naturally occurring higher
order (n-way) data, like EEG. We use the canonical polyadic decomposition (CPD) as a
multi-way model to efficiently express the complex structures present in paediatric EEG
recordings as unique combinations of low-rank matrices, offering new insights into child
development. This multi-way CPD framework is explored for both typically developing (TD)
children and children with potential developmental delays (DD), e.g. children who suffer from
epilepsy or paediatric stroke.
Resting-state EEG (rEEG) data serves as an intuitive starting point in analyzing paediatric
EEG via multi-way analysis. Here, the CPD model probes the underlying relationships
between the spatial, spectral and subject modes of several rEEG datasets. We demonstrate the
CPD can reveal distinct population-level features in rEEG that reflect unique developmental
traits in varying child populations. These development-affiliated profiles are evaluated with
respect to capturing structures well-established in childhood EEG. The identified features are
also interrogated for their predictive abilities in anticipating new subjectsâ ages. Assessing
simulations and real rEEG datasets of TD and DD children establishes the multi-way analysis
framework as well suited for identifying developmental profiles from paediatric rEEG.
We extend the multi-way analysis scheme to more complex EEG scenarios common in
EEG rehabilitation technology, like brain-computer interfaces. We explore the feasibility of
multi-way modelling for interventions where developmental changes often pose as barriers.
The multi-way CPD model is expanded to include four modes- task, spatial, spectral and
subject data, with non-negativity and orthogonality constraints imposed. We analyze a visual
attention task that elucidates a steady-state visual evoked potential and present the advantages
gained from the extended CPD model. Through direct multi-linear projection, we demonstrate
that linear profiles of the CPD can be capitalized upon for rapid task classification sans
individual subject classifier calibration.
Incorporating concepts from the multi-way analysis scheme with child development measured
by psychometric tests, we propose the Joint EEG Development Inference (JEDI) model for
inferring development from paediatric EEG. We utilize a common EEG task (button-press) to
establish a 4-way CPD model of paediatric EEG data. Structured data fusion of the CPD model
and cognitive scores from psychometric evaluations then permits joint decomposition of the
two datasets to identify common features associated with each representation of development.
Use of grid search optimization and a fully cross-validated design supports the JEDI model as
another technique for rapidly discerning the developmental status of a child via EEG.
We then briefly turn our attention to associating child development as measured by
psychometric tests to markers in the EEG using graph network properties. Using graph
networks, we show how the functional connectivity can inform on potential developmental
delays in very young epileptic children using routine, clinical rEEG measures. This establishes
a potential tool complementary to the JEDI model for identifying and inferring links between
the established psychometric evaluation of developing children and functional analysis of the
EEG.
Multi-way analysis of paediatric EEG data offers a new approach for handling the
developmental status and profiles of children. The CPD model offers flexibility in terms of
identifying development-related features, and can be integrated into EEG tasks common in
rehabilitation paradigms. We aim for the multi-way framework and associated techniques
pursued in this thesis to be integrated and adopted as a useful tool clinicians can use for
characterizing paediatric development
Wearable brain computer interfaces with near infrared spectroscopy
Brain computer interfaces (BCIs) are devices capable of relaying information directly from the brain to a digital device. BCIs have been proposed for a diverse range of clinical and commercial applications; for example, to allow paralyzed subjects to communicate, or to improve machine human interactions. At their core, BCIs need to predict the current state of the brain from variables measuring functional physiology. Functional near infrared spectroscopy (fNIRS) is a non-invasive optical technology able to measure hemodynamic changes in the brain. Along with electroencephalography (EEG), fNIRS is the only technique that allows non-invasive and portable sensing of brain signals. Portability and wearability are very desirable characteristics for BCIs, as they allow them to be used in contexts beyond the laboratory, extending their usability for clinical and commercial applications, as well as for ecologically valid research. Unfortunately, due to limited access to the brain, non-invasive BCIs tend to suffer from low accuracy in their estimation of the brain state. It has been suggested that feedback could increase BCI accuracy as the brain normally relies on sensory feedback to adjust its strategies. Despite this, presenting relevant and accurate feedback in a timely manner can be challenging when processing fNIRS signals, as they tend to be contaminated by physiological and motion artifacts.
In this dissertation, I present the hardware and software solutions we proposed and developed to deal with these challenges. First, I will talk about ninjaNIRS, the wearable open source fNIRS device we developed in our laboratory, which could help fNIRS neuroscience and BCIs to become more accessible. Next, I will present an adaptive filter strategy to recover the neural responses from fNIRS signals in real-time, which could be used for feedback and classification in a BCI paradigm.
We showed that our wearable fNIRS device can operate autonomously for up to three hours and can be easily carried in a backpack, while offering noise equivalent power comparable to commercial devices. Our adaptive multimodal Kalman filter strategy provided a six-fold increase in contrast to noise ratio of the brain signals compared to standard filtering while being able to process at least 24 channels at 400 samples per second using a standard computer. This filtering strategy, along with visual feedback during a left vs right motion imagery task, showed a relative increase of accuracy of 37.5% compared to not using feedback. With this, we show that it is possible to present relevant feedback for fNIRS BCI in real-time. The findings on this dissertation might help improve the design of future fNIRS BCIs, and thus increase the usability and reliability of this technology
Eye quietness and quiet eye in expert and novice golf performance: an electrooculographic analysis
Quiet eye (QE) is the final ocular fixation on the target of an action (e.g., the ball in golf putting). Camerabased eye-tracking studies have consistently found longer QE durations in experts than novices; however, mechanisms underlying QE are not known. To offer a new perspective we examined the feasibility of measuring the QE using electrooculography (EOG) and developed an index to assess ocular activity across time: eye quietness (EQ). Ten expert and ten novice golfers putted 60 balls to a 2.4 m distant hole. Horizontal EOG (2ms resolution) was recorded from two electrodes placed on the outer sides of the eyes. QE duration was measured using a EOG voltage threshold and comprised the sum of the pre-movement and post-movement initiation components. EQ was computed as the standard deviation of the EOG in 0.5 s bins from â4 to +2 s, relative to backswing initiation: lower values indicate less movement of the eyes, hence greater quietness. Finally, we measured club-ball address and swing durations. T-tests showed that total QE did not differ between groups (p = .31); however, experts had marginally shorter pre-movement QE (p = .08) and longer post-movement QE (p < .001) than novices. A group Ă time ANOVA revealed that experts had less EQ before
backswing initiation and greater EQ after backswing initiation (p = .002). QE durations were inversely correlated with EQ from â1.5 to 1 s (rs = â.48 - â.90, ps = .03 - .001). Experts had longer swing durations than novices (p = .01) and, importantly, swing durations correlated positively with post-movement QE (r = .52, p = .02) and negatively with EQ from 0.5 to 1s (r = â.63, p = .003). This study demonstrates the feasibility of measuring ocular activity using EOG and validates EQ as an index of ocular activity. Its findings challenge the dominant perspective on QE and provide new evidence that expert-novice differences in ocular activity may reflect differences in the kinematics of how experts and novices execute skills