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Showing 6 of 14EEG-LLAMAS: A low-latency neurofeedback platform for artifact reduction in EEG-fMRI
Simultaneous EEG-fMRI is a powerful multimodal technique for imaging the brain, but its use in neurofeedback experiments has been limited by EEG noise caused by the MRI environment. Neurofeedback studies typically require analysis of EEG in real time, but EEG acquired inside the scanner is heavily contaminated with ballistocardiogram (BCG) artifact, a high-amplitude artifact locked to the cardiac cycle. Although techniques for removing BCG artifacts do exist, they are either not suited to real-time, low-latency applications, such as neurofeedback, or have limited efficacy. We propose and validate a new open-source artifact removal software called EEG-LLAMAS (Low Latency Artifact Mitigation Acquisition Software), which adapts and advances existing artifact removal techniques for low-latency experiments. We first used simulations to validate LLAMAS in data with known ground truth. We found that LLAMAS performed better than the best publicly-available real-time BCG removal technique, optimal basis sets (OBS), in terms of its ability to recover EEG waveforms, power spectra, and slow wave phase. To determine whether LLAMAS would be effective in practice, we then used it to conduct real-time EEG-fMRI recordings in healthy adults, using a steady state visual evoked potential (SSVEP) task. We found that LLAMAS was able to recover the SSVEP in real time, and recovered the power spectra collected outside the scanner better than OBS. We also measured the latency of LLAMAS during live recordings, and found that it introduced a lag of less than 50 ms on average. The low latency of LLAMAS, coupled with its improved artifact reduction, can thus be effectively used for EEG-fMRI neurofeedback. A limitation of the method is its use of a reference layer, a piece of EEG equipment which is not commercially available, but can be assembled in-house. This platform enables closed-loop experiments which previously would have been prohibitively difficult, such as those that target short-duration EEG events, and is shared openly with the neuroscience community.
View Full Paper →Comparison of diffusion MRI and CLARITY fiber orientation estimates in both gray and white matter regions of human and primate brain
Diffusion MRI (dMRI) represents one of the few methods for mapping brain fiber orientations non-invasively. Unfortunately, dMRI fiber mapping is an indirect method that relies on inference from measured diffusion patterns. Comparing dMRI results with other modalities is a way to improve the interpretation of dMRI data and help advance dMRI technologies. Here, we present methods for comparing dMRI fiber orientation estimates with optical imaging of fluorescently labeled neurofilaments and vasculature in 3D human and primate brain tissue cuboids cleared using CLARITY. The recent advancements in tissue clearing provide a new opportunity to histologically map fibers projecting in 3D, which represents a captivating complement to dMRI measurements. In this work, we demonstrate the capability to directly compare dMRI and CLARITY in the same human brain tissue and assess multiple approaches for extracting fiber orientation estimates from CLARITY data. We estimate the three-dimensional neuronal fiber and vasculature orientations from neurofilament and vasculature stained CLARITY images by calculating the tertiary eigenvector of structure tensors. We then extend CLARITY orientation estimates to an orientation distribution function (ODF) formalism by summing multiple sub-voxel structure tensor orientation estimates. In a sample containing part of the human thalamus, there is a mean angular difference of 19o±15o between the primary eigenvectors of the dMRI tensors and the tertiary eigenvectors from the CLARITY neurofilament stain. We also demonstrate evidence that vascular compartments do not affect the dMRI orientation estimates by showing an apparent lack of correspondence (mean angular difference = 49o±23o) between the orientation of the dMRI tensors and the structure tensors in the vasculature stained CLARITY images. In a macaque brain dataset, we examine how the CLARITY feature extraction depends on the chosen feature extraction parameters. By varying the volume of tissue over which the structure tensor estimates are derived, we show that orientation estimates are noisier with more spurious ODF peaks for sub-voxels below 30 µm3 and that, for our data, the optimal gray matter sub-voxel size is between 62.5 µm3 and 125 µm3. The example experiments presented here represent an important advancement towards robust multi-modal MRI-CLARITY comparisons.
View Full Paper →Mechanisms of Stem Cell Therapy in Spinal Cord Injuries
Every year, 0.93 million people worldwide suffer from spinal cord injury (SCI) with irretrievable sequelae. Rehabilitation, currently the only available treatment, does not restore damaged tissues; therefore, the functional recovery of patients remains limited. The pathophysiology of spinal cord injuries is heterogeneous, implying that potential therapeutic targets differ depending on the time of injury onset, the degree of injury, or the spinal level of injury. In recent years, despite a significant number of clinical trials based on various types of stem cells, these aspects of injury have not been effectively considered, resulting in difficult outcomes of trials. In a specialty such as cancerology, precision medicine based on a patient's characteristics has brought indisputable therapeutic advances. The objective of the present review is to promote the development of precision medicine in the field of SCI. Here, we first describe the multifaceted pathophysiology of SCI, with the temporal changes after injury, the characteristics of the chronic phase, and the subtypes of complete injury. We then detail the appropriate targets and related mechanisms of the different types of stem cell therapy for each pathological condition. Finally, we highlight the great potential of stem cell therapy in cervical SCI.
View Full Paper →Dominance of layer-specific microvessel dilation in contrast-enhanced high-resolution fMRI: Comparison between hemodynamic spread and vascular architecture with CLARITY
Contrast-enhanced cerebral blood volume-weighted (CBVw) fMRI response peaks are specific to the layer of evoked synaptic activity (Poplawsky et al., 2015), but the spatial resolution limit of CBVw fMRI is unknown. In this study, we measured the laminar spread of the CBVw fMRI evoked response in the external plexiform layer (EPL, 265 ± 65 μm anatomical thickness, mean ± SD, n = 30 locations from 5 rats) of the rat olfactory bulb during electrical stimulation of the lateral olfactory tract and examined its potential vascular source. First, we obtained the evoked CBVw fMRI responses with a 55 × 55 μm2 in-plane resolution and a 500-μm thickness at 9.4 T, and found that the fMRI signal peaked predominantly in the inner half of EPL (136 ± 54 μm anatomical thickness). The mean full-width at half-maximum of these fMRI peaks was 347 ± 102 μm and the functional spread was approximately 100 or 200 μm when the effects of the laminar thicknesses of EPL or inner EPL were removed, respectively. Second, we visualized the vascular architecture of EPL from a different rat using a Clear Lipid-exchanged Anatomically Rigid Imaging/immunostaining-compatible Tissue hYdrogel (CLARITY)-based tissue preparation method and confocal microscopy. Microvascular segments with an outer diameter of <11 μm accounted for 64.3% of the total vascular volume within EPL and had a mean segment length of 55 ± 40 μm (n = 472). Additionally, vessels that crossed the EPL border had a mean segment length outside of EPL equal to 73 ± 61 μm (n = 28), which is comparable to half of the functional spread (50-100 μm). Therefore, we conclude that dilation of these microvessels, including capillaries, likely dominate the CBVw fMRI response and that the biological limit of the fMRI spatial resolution is approximately the average length of 1-2 microvessel segments, which may be sufficient for examining sublaminar circuits.
View Full Paper →Involvement of the Red Nucleus in the Compensation of Parkinsonism may Explain why Primates can develop Stable Parkinson's Disease
Neurological compensatory mechanisms help our brain to adjust to neurodegeneration as in Parkinson's disease. It is suggested that the compensation of the damaged striato-thalamo-cortical circuit is focused on the intact thalamo-rubro-cerebellar pathway as seen during presymptomatic Parkinson, paradoxical movement and sensorimotor rhythm (SMR). Indeed, the size of the red nucleus, connecting the cerebellum with the cerebral cortex, is larger in Parkinson's disease patients suggesting an increased activation of this brain area. Therefore, the red nucleus was examined in MPTP-induced parkinsonian marmoset monkeys during the presymptomatic stage and after SMR activation by neurofeedback training. We found a reverse significant correlation between the early expression of parkinsonian signs and the size of the parvocellular part of the red nucleus, which is predominantly present in human and non-human primates. In quadrupedal animals it consists mainly of the magnocellular part. Furthermore, SMR activation, that mitigated parkinsonian signs, further increased the size of the red nucleus in the marmoset monkey. This plasticity of the brain helps to compensate for dysfunctional movement control and can be a promising target for compensatory treatment with neurofeedback technology, vibrotactile stimulation or DBS in order to improve the quality of life for Parkinson's disease patients.
View Full Paper →Resting-State Functional Connectivity-Based Biomarkers and Functional MRI-Based Neurofeedback for Psychiatric Disorders: A Challenge for Developing Theranostic Biomarkers
Psychiatric research has been hampered by an explanatory gap between psychiatric symptoms and their neural underpinnings, which has resulted in poor treatment outcomes. This situation has prompted us to shift from symptom-based diagnosis to data-driven diagnosis, aiming to redefine psychiatric disorders as disorders of neural circuitry. Promising candidates for data-driven diagnosis include resting-state functional connectivity MRI (rs-fcMRI)-based biomarkers. Although biomarkers have been developed with the aim of diagnosing patients and predicting the efficacy of therapy, the focus has shifted to the identification of biomarkers that represent therapeutic targets, which would allow for more personalized treatment approaches. This type of biomarker (i.e., "theranostic biomarker") is expected to elucidate the disease mechanism of psychiatric conditions and to offer an individualized neural circuit-based therapeutic target based on the neural cause of a condition. To this end, researchers have developed rs-fcMRI-based biomarkers and investigated a causal relationship between potential biomarkers and disease-specific behavior using functional MRI (fMRI)-based neurofeedback on functional connectivity. In this review, we introduce a recent approach for creating a theranostic biomarker, which consists mainly of 2 parts: (1) developing an rs-fcMRI-based biomarker that can predict diagnosis and/or symptoms with high accuracy, and (2) the introduction of a proof-of-concept study investigating the relationship between normalizing the biomarker and symptom changes using fMRI-based neurofeedback. In parallel with the introduction of recent studies, we review rs-fcMRI-based biomarker and fMRI-based neurofeedback, focusing on the technological improvements and limitations associated with clinical use.
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