Automated Real-Time Behavioral and Physiological Data Acquisition and Display Integrated with Stimulus Presentation for fMRI

Functional magnetic resonance imaging (fMRI) is based on correlating blood oxygen-level dependent (BOLD) signal fluctuations in the brain with other time-varying signals. Although the most common reference for correlation is the timing of a behavioral task performed during the scan, many other behavioral and physiological variables can also influence fMRI signals. Variations in cardiac and respiratory functions in particular are known to contribute significant BOLD signal fluctuations. Variables such as skin conduction, eye movements, and other measures that may be relevant to task performance can also be correlated with BOLD signals and can therefore be used in image analysis to differentiate multiple components in complex brain activity signals. Combining real-time recording and data management of multiple behavioral and physiological signals in a way that can be routinely used with any task stimulus paradigm is a non-trivial software design problem. Here we discuss software methods that allow users control of paradigm-specific audio–visual or other task stimuli combined with automated simultaneous recording of multi-channel behavioral and physiological response variables, all synchronized with sub-millisecond temporal accuracy. We also discuss the implementation and importance of real-time display feedback to ensure data quality of all recorded variables. Finally, we discuss standards and formats for storage of temporal covariate data and its integration into fMRI image analysis. These neuroinformatics methods have been adopted for behavioral task control at all sites in the Functional Biomedical Informatics Research Network (FBIRN) multi-center fMRI study

Flexible Adaptive Paradigms for fMRI Using a Novel Software Package ‘Brain Analysis in Real-Time’ (BART)

In this work we present a new open source software package offering a unified framework for the real-time adaptation of fMRI stimulation procedures. The software provides a straightforward setup and highly flexible approach to adapt fMRI paradigms while the experiment is running. The general framework comprises the inclusion of parameters from subject’s compliance, such as directing gaze to visually presented stimuli and physiological fluctuations, like blood pressure or pulse. Additionally, this approach yields possibilities to investigate complex scientific questions, for example the influence of EEG rhythms or fMRI signals results themselves. To prove the concept of this approach, we used our software in a usability example for an fMRI experiment where the presentation of emotional pictures was dependent on the subject’s gaze position. This can have a significant impact on the results. So far, if this is taken into account during fMRI data analysis, it is commonly done by the post-hoc removal of erroneous trials. Here, we propose an a priori adaptation of the paradigm during the experiment’s runtime. Our fMRI findings clearly show the benefits of an adapted paradigm in terms of statistical power and higher effect sizes in emotion-related brain regions. This can be of special interest for all experiments with low statistical power due to a limited number of subjects, a limited amount of time, costs or available data to analyze, as is the case with real-time fMRI

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.LSH‐TKI, Grant/Award Number: LSHM16053‐SGF; Philips Researc

Fear from the heart: sensitivity to fear stimuli depends on individual heartbeats

Cognitions and emotions can be influenced by bodily physiology. Here, we investigated whether the processing of brief fear stimuli is selectively gated by their timing in relation to individual heartbeats. Emotional and neutral faces were presented to human volunteers at cardiac systole, when ejection of blood from the heart causes arterial baroreceptors to signal centrally the strength and timing of each heartbeat, and at diastole, the period between heartbeats when baroreceptors are quiescent. Participants performed behavioral and neuroimaging tasks to determine whether these interoceptive signals influence the detection of emotional stimuli at the threshold of conscious awareness and alter judgments of emotionality of fearful and neutral faces. Our results show that fearful faces were detected more easily and were rated as more intense at systole than at diastole. Correspondingly, amygdala responses were greater to fearful faces presented at systole relative to diastole. These novel findings highlight a major channel by which short-term interoceptive fluctuations enhance perceptual and evaluative processes specifically related to the processing of fear and threat and counter the view that baroreceptor afferent signaling is always inhibitory to sensory perception

Functional MRI Data Analysis Techniques and Strategies to Map the Olfactory System of a Rat Brain.

Understanding mysteries of a brain represents one of the great challenges for modern science. Functional magnetic resonance imaging (fMRI) has two features that make it unique amongst other imaging modalities used in behavioral neuroscience. First, it can be entirely non-invasive and second, fMRI has the spatial and temporal resolution to resolve patterns of neuronal activity across the entire brain in less than a minute. fMRI indirectly detects neural activity in different parts of the brain by comparing contrast in MR signal intensity prior to and following stimulation. Areas of the brain with increased synaptic and neuronal activity require increased levels of oxygen to sustain this activity. Enhanced brain activity is accompanied by an increase in metabolism followed by increases in blood flow and blood volume. The enhanced blood flow usually exceeds the metabolic demand exposing the active brain area to high level of oxygenated hemoglobin. Oxygenated hemoglobin increases the MR signal intensity that can be detected in MR scanner. This relatively straight forward scenario is, unfortunately, oversimplified. The fMRI signal change to noise ratio is extremely small. In this work a quantitative analysis strategy to analyze fMRI data was successfully developed, implemented and optimized for the rat brain. Therein, each subject is registered or aligned to a complete volume-segmented rat atlas. The matrices that transformed the subject\u27s anatomy to the atlas space are used to embed each slice within the atlas. All transformed pixel locations of the anatomy images are tagged with the segmented atlas major and minor regions creating a fully segmented representation of each subject. This task required the development of a full 3D surface atlas based upon 2D non-uniformly spaced 2D slices from an existing atlas. A multiple materials marching cube (M3C) algorithm was used to generate these 1277 subvolumes. After this process, they were coalesced into a dozen major zones of the brain (amygdaloid complex, cerebrum, cerebellum, hypothalamus, etc.). Each major brain category was subdivided into approximately 10 sub-major zones. Many scientists are interested in behavior and reactions to pain, pleasure, smell, for example. Consequently, the 3D volume atlas was segmented into functional zones as well as the anatomical regions. A utility (program) called Tree Browser was developed to interactively display and choose different anatomical and/or functional areas. Statistical t-tests are performed to determine activation on each subject within their original coordinate system. Due to the multiple t-test analyses performed, a false-positive detection controlling mechanism was introduced. A statistical composite of five components was created for each group. The individual analyses were summed within groups. The strategy developed in this work is unique as it registers segments and analyzes multiple subjects and presents a composite response of the whole group. This strategy is robust, incredibly fast and statistically powerful. The power of this system was demonstrated by mapping the olfactory system of a rat brain. Synchronized changes in neuronal activity across multiple subjects and brain areas can be viewed as functional neuro-anatomical circuits coordinating the thoughts, memories and emotions for particular behaviors using this fMRI module

The Function Biomedical Informatics Research Network Data Repository

The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical datasets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 dataset consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4 Tesla scanners. The FBIRN Phase 2 and Phase 3 datasets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN’s multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data

High-Density Diffuse Optical Tomography During Passive Movie Viewing: A Platform for Naturalistic Functional Brain Mapping

Human neuroimaging techniques enable researchers and clinicians to non-invasively study brain function across the lifespan in both healthy and clinical populations. However, functional brain imaging methods such as functional magnetic resonance imaging (fMRI) are expensive, resource-intensive, and require dedicated facilities, making these powerful imaging tools generally unavailable for assessing brain function in settings demanding open, unconstrained, and portable neuroimaging assessments. Tools such as functional near-infrared spectroscopy (fNIRS) afford greater portability and wearability, but at the expense of cortical field-of-view and spatial resolution. High-Density Diffuse Optical Tomography (HD-DOT) is an optical neuroimaging modality directly addresses the image quality limitations associated with traditional fNIRS techniques through densely overlapping optical measurements. This thesis aims to establish the feasibility of using HD-DOT in a novel application demanding exceptional portability and flexibility: mapping disrupted cortical activity in chronically malnourished children. I first motivate the need for dense optical measurements of brain tissue to achieve fMRI-comparable localization of brain function (Chapter 2). Then, I present imaging work completed in Cali, Colombia, where a cohort of chronically malnourished children were imaged using a custom HD-DOT instrument to establish feasibility of performing field-based neuroimaging in this population (Chapter 3). Finally, in order to meet the need for age appropriate imaging paradigms in this population, I develop passive movie viewing paradigms for use in optical neuroimaging, a flexible and rich stimulation paradigm that is suitable for both adults and children (Chapter 4)