Decoding visual information from high-density diffuse optical tomography neuroimaging data

BACKGROUND: Neural decoding could be useful in many ways, from serving as a neuroscience research tool to providing a means of augmented communication for patients with neurological conditions. However, applications of decoding are currently constrained by the limitations of traditional neuroimaging modalities. Electrocorticography requires invasive neurosurgery, magnetic resonance imaging (MRI) is too cumbersome for uses like daily communication, and alternatives like functional near-infrared spectroscopy (fNIRS) offer poor image quality. High-density diffuse optical tomography (HD-DOT) is an emerging modality that uses denser optode arrays than fNIRS to combine logistical advantages of optical neuroimaging with enhanced image quality. Despite the resulting promise of HD-DOT for facilitating field applications of neuroimaging, decoding of brain activity as measured by HD-DOT has yet to be evaluated. OBJECTIVE: To assess the feasibility and performance of decoding with HD-DOT in visual cortex. METHODS AND RESULTS: To establish the feasibility of decoding at the single-trial level with HD-DOT, a template matching strategy was used to decode visual stimulus position. A receiver operating characteristic (ROC) analysis was used to quantify the sensitivity, specificity, and reproducibility of binary visual decoding. Mean areas under the curve (AUCs) greater than 0.97 across 10 imaging sessions in a highly sampled participant were observed. ROC analyses of decoding across 5 participants established both reproducibility in multiple individuals and the feasibility of inter-individual decoding (mean AUCs \u3e 0.7), although decoding performance varied between individuals. Phase-encoded checkerboard stimuli were used to assess more complex, non-binary decoding with HD-DOT. Across 3 highly sampled participants, the phase of a 60° wide checkerboard wedge rotating 10° per second through 360° was decoded with a within-participant error of 25.8±24.7°. Decoding between participants was also feasible based on permutation-based significance testing. CONCLUSIONS: Visual stimulus information can be decoded accurately, reproducibly, and across a range of detail (for both binary and non-binary outcomes) at the single-trial level (without needing to block-average test data) using HD-DOT data. These results lay the foundation for future studies of more complex decoding with HD-DOT and applications in clinical populations

Identifying Respiration-Related Aliasing Artifacts in the Rodent Resting-State fMRI

Resting-state functional magnetic resonance imaging (rs-fMRI) combined with optogenetics and electrophysiological/calcium recordings in animal models is becoming a popular platform to investigate brain dynamics under specific neurological states. Physiological noise originating from the cardiac and respiration signal is the dominant interference in human rs-fMRI and extensive efforts have been made to reduce these artifacts from the human data. In animal fMRI studies, physiological noise sources including the respiratory and cardiorespiratory artifacts to the rs-fMRI signal fluctuation have typically been less investigated. In this article, we demonstrate evidence of aliasing effects into the low-frequency rs-fMRI signal fluctuation mainly due to respiration-induced B0 offsets in anesthetized rats. This aliased signal was examined by systematically altering the fMRI sampling rate, i.e., the time of repetition (TR), in free-breathing conditions and by adjusting the rate of ventilation. Anesthetized rats under ventilation showed a significantly narrower frequency bandwidth of the aliasing effect than free-breathing animals. It was found that the aliasing effect could be further reduced in ventilated animals with a muscle relaxant. This work elucidates the respiration-related aliasing effects on the rs-fMRI signal fluctuation from anesthetized rats, indicating non-negligible physiological noise needed to be taken care of in both awake and anesthetized animal rs-fMRI studies

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

Working Memory Network Connectivity and Inhibitory Control in Cocaine Use

Support exists for inhibitory control and working memory deficits among cocaine users. Existing literature suggests that working memory is central in successful inhibitory control, and that working memory processes may be best captured by examining network connectivity. This study examined whether working memory network connectivity mediates the relationship between group (cocaine users versus controls) and working memory performance, and group and inhibitory control performance. Participants completed working memory and inhibitory control tasks during functional magnetic resonance imaging. Cocaine users demonstrated poorer inhibitory control performance and reduced activation during the working memory task compared to controls. Working memory network connectivity did not account for group differences in working memory or inhibitory control performance. Specific connectivity between the right insula and inferior frontal gyrus and the right precuneus and inferior parietal lobule were significantly related to working memory and inhibitory performance, respectively, suggesting the role of attention and default mode network regulation.Master of Art

Suppression des artefacts de mouvement dans les signaux de spectroscopie proche-infrarouge acquis pendant la marche

RÉSUMÉ Ce mémoire de maîtrise présente un algorithme de suppression des artefacts de mouvement présents dans les signaux de spectroscopie proche-infrarouge (NIRS pour Near-infrared spectroscopy). Cette technique d'imagerie cérébrale est peu encombrante, peu coûteuse et offre une bonne résolution temporelle et spatiale. Il existe aujourd'hui des systèmes NIRS entièrement portables. Pour toutes ces raisons, elle est de plus en plus utilisée dans le domaine médical ainsi qu'en recherche neuroscientifique. Cependant, la NIRS est une technique encore jeune et elle doit surmonter plusieurs obstacles pour être utilisée de façon optimale. Les artefacts de mouvement sont l'un des principaux problèmes rencontrés lors de son utilisation. Ces-derniers se caractérisent par une modification plus ou moins brutale du signal NIRS, sans raison physiologique possible, due aux mouvements du sujet.L'algorithme présenté dans ce mémoire utilise l'analyse par composantes indépendantes (ACI) et des accéléromètres pour supprimer les artefacts de mouvement présents dans des signaux NIRS acquis sur des sujets marchant librement. Ce travail a été réalisé dans le cadre d'une étude clinique, dirigée par l'Institut Universitaire de Gériatrie de Montréal, visant à analyser l'influence de l'activité physique sur les capacités cognitives des aînés. Le système utilisé est un prototype développé par le groupe de recherche multidisciplinaire, Imaginc. Ce prototype est un système entièrement portable permettant l'acquisition de signaux NIRS et EEG (électroencéphalographie), et qui offre la possibilité d'imager tout le cortex cérébral sur de longues périodes de temps. L'étude présentée dans ce projet est l'une des premières applications cliniques du prototype Imaginc.La finalisation du prototype Imaginc et l'acquisition des données ont été des étapes à part entière de ce projet de maîtrise. Néanmoins, ce mémoire se concentre sur les algorithmes de suppression des artefacts de mouvement développés. Ces-derniers ont été testés sur les 24 premiers sujets de l'étude et montrent des résultats prometteurs. Le traitement des données a permis de supprimer les artefacts de mouvement tout en préservant les données utiles. Ainsi, plus d'activations cérébrales sont visibles grâce aux algorithmes présentés.----------ABSTRACT This work presents an algorithm dedicated to the removal of motion artifacts from near-infrared spectroscopy signals (NIRS). This cerebral imaging technique is low cost, not cumbersome and offers a high temporal and spatial resolution. Nowadays, some NIRS systems tend to be totally portable. For all these reasons, NIRS is very well adapted and used in the biomedical field as well as in the neuroscientific research. However, NIRS is still a relatively new technology and should undergo several developments before being optimally used. Motion artifacts are one of the principal problems when using this technology. These artifacts are characterized by a modification of the NIRS signal, without any physiological reason, due to the subject's movement. The algorithm presented in this work combines Independent component Analysis and accelerometers to remove motion artifacts from NIRS signals acquired during free walking. This work was realized as part of a clinical study directed by the “Institut Universitaire de Gériatrie de Montréal”. The purpose of this study is to analyze the impact of physical activity on the cognitive abilities of seniors. The acquisition system used is a prototype developed by the multidisciplinary research group, Imaginc. This prototype is a fully portable system that can acquire both EEG and NIRS signals. It offers the possibility of imaging the whole cerebral cortex for a relatively long time. This work is one of the first clinical applications of the Imaginc prototype. The improvement of the Imaginc prototype as well as its clinical validation by acquiring signals from patients are two parts of this master project. These measurements are used for the implementation of motion artifact removal algorithms, which were tested on 24 subjects and gave promising results. Signal processing techniques showed the removal of motion artifacts while preserving data of interest. Therefore, more cerebral activations were visible after filtering

Mapping Brain Development and Decoding Brain Activity with Diffuse Optical Tomography

Functional neuroimaging has been used to map brain function as well as decode information from brain activity. However, applications like studying early brain development or enabling augmentative communication in patients with severe motor disabilities have been constrained by extant imaging modalities, which can be challenging to use in young children and entail major tradeoffs between logistics and image quality. Diffuse optical tomography (DOT) is an emerging method combining logistical advantages of optical imaging with enhanced image quality. Here, we developed one of the world’s largest DOT systems for high-performance optical brain imaging in children. From visual cortex activity in adults, we decoded the locations of checkerboard visual stimuli, e.g. localizing a 60 degree wedge rotating through 36 positions with an error of 25.8±24.7 degrees. Using animated movies as more child-friendly stimuli, we mapped reproducible responses to speech and faces with DOT in awake, typically developing 1-7 year-old children and adults. We then decoded with accuracy significantly above chance which movie a participant was watching or listening to from DOT data. This work lays a valuable foundation for ongoing research with wearable imaging systems and increasingly complex algorithms to map atypical brain development and decode covert semantic information in clinical populations