The alpha power increase that occurs when the eyes are closed is one of the most well-known effects in human electrophysiology. In particular, previous psychological studies have investigated whether ...eye closure can boost memory performance under certain circumstances, providing contradictory evidence across sensory input modalities. Although alpha power is modulated during different phases of memory and these modulations are correlated with performance, few studies have reported on the relationship between eye closure, memory, and alpha-band power. The present study investigates the influence of eye closure while participants (n = 21) performed an auditory recognition memory task with spoken words during the recording of magnetoencephalography (MEG) data. Our results showed no evidence for a behavioural effect of eye closure in the performance of the task. In addition, electrophysiological responses to the stimuli showed the expected alpha event-related desynchronization (ERD) 0.5–1 s and a high-alpha/beta event-related synchronization (ERS) 1–2 s after word onset. The data showed the expected memory effect, i.e. remembered words elicited greater 10 Hz ERD than forgotten words in the brain regions typically associated with the language network, suggesting a modulation of tau rhythm. Eye closure modulated alpha rhythm only in posterior-parietal and occipital regions. The lack of interaction and the different localizations found for modulations of tau and classical alpha rhythms suggests that these rhythms play distinct functional roles in memory performance.
•We studied the effect of eye-closure alpha on memory retrieval.•We found no evidence of a behavioural effect of eye closure.•Eye closure modulated posterior-parietal and occipital rhythms.•Memory effects modulated the tau rhythm in language-related areas and right parietal-frontal lobes.•Tau and alpha rhythms seem to play distinct functional roles in auditory memory performance.
Multi-slab MRI overcomes some of the hardware limitations of today's clinical scanners (e.g., memory size), enabling the acquisition of ultra-high resolution ex vivo MRI of the whole human brain with ...high SNR efficiency. However, multi-slab MRI suffers from slab boundary artifacts (SBA) that can greatly bias subsequent analyses. Since SBA heavily interplays with the bias field (BF) present in MRI, we propose a Bayesian method that corrects for SBA and BF simultaneously. The method, which combines a probabilistic brain atlas with an Expectation Maximization inference algorithm, is shown to outperform state-of-the-art SBA and BF correction techniques - even when used in combination.
Paradigm-free mapping enables to map the hæmodynamic response in space and time without prior knowledge of the timing of the underlying neuronal events (i.e., no stimulation paradigm). Such ...deconvolution approach can take advantage of modern sparsity-promoting regularization. Here we extend this concept using structured sparsity approaches in order to gain robustnesss against model mismatch. Specifically, we extend the hæmodynamic dictionary with the informed basis set (i.e., canonical HRF, and its temporal and dispersion derivatives) and we deploy state-of-the art structured sparsity functionals. In addition, we propose the group-weighted fusion penalty. We demonstrate the feasibility of the proposed approach for both synthetic and experimental data, showing superior abilities to characterize the single-trial BOLD response with no timing information.
Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well ...as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.
•Numerous techniques are available for denoising the BOLD fMRI signal.•Motion-related artifacts and physiological noise fluctuations are the main targets.•Phase-based and multi-echo fMRI can help to improve the performance of denoising.•There exist multiple equally-efficient alternatives to global signal regression.•There is no “best” method for preprocessing, but there are incorrect methods.
•We compared breath-hold CVR maps made using CO2, RVT, and the global BOLD signal.•All show good agreement in CVR amplitude and delay provided CO2 data are “sufficient”.•We define “sufficient” CO2 ...data quality as relative power >50% in the task frequency.•RVT or the global signal can map relative CVR parameters if CO2 data are insufficient.•Offers solution to map breath-hold CVR when CO2 data are unavailable or unreliable.
Cerebrovascular reactivity (CVR), defined as the cerebral blood flow response to a vasoactive stimulus, is an imaging biomarker with demonstrated utility in a range of diseases and in typical development and aging processes. A robust and widely implemented method to map CVR involves using a breath-hold task during a BOLD fMRI scan. Recording end-tidal CO2 (PETCO2) changes during the breath-hold task is recommended to be used as a reference signal for modeling CVR amplitude in standard units (%BOLD/mmHg) and CVR delay in seconds. However, obtaining reliable PETCO2 recordings requires equipment and task compliance that may not be achievable in all settings. To address this challenge, we investigated two alternative reference signals to map CVR amplitude and delay in a lagged general linear model (lagged-GLM) framework: respiration volume per time (RVT) and average gray matter BOLD response (GM-BOLD). In 8 healthy adults with multiple scan sessions, we compare spatial agreement of CVR maps from RVT and GM-BOLD to those generated with PETCO2. We define a threshold to determine whether a PETCO2 recording has “sufficient” quality for CVR mapping and perform these comparisons in 16 datasets with sufficient PETCO2 and 6 datasets with insufficient PETCO2. When PETCO2 quality is sufficient, both RVT and GM-BOLD produce CVR amplitude maps that are nearly identical to those from PETCO2 (after accounting for differences in scale), with the caveat they are not in standard units to facilitate between-group comparisons. CVR delays are comparable to PETCO2 with an RVT regressor but may be underestimated with the average GM-BOLD regressor. Importantly, when PETCO2 quality is insufficient, RVT and GM-BOLD CVR recover reasonable CVR amplitude and delay maps, provided the participant attempted the breath-hold task. Therefore, our framework offers a solution for achieving high quality CVR maps in both retrospective and prospective studies where sufficient PETCO2 recordings are not available and especially in populations where obtaining reliable measurements is a known challenge (e.g., children). Our results have the potential to improve the accessibility of CVR mapping and to increase the prevalence of this promising metric of vascular health.
Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and ...breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.
•We propose adding a short breathing task to the start of a resting state fMRI scan•This protocol facilitates mapping of cerebrovascular reactivity and hemodynamic lag•This practical modification ...adds vascular insight to typical resting state fMRI•Protocol is sensitive to pathology, and modeling lag is crucial for interpretation
Cerebrovascular reactivity (CVR), defined here as the Blood Oxygenation Level Dependent (BOLD) response to a CO2 pressure change, is a useful metric of cerebrovascular function. Both the amplitude and the timing (hemodynamic lag) of the CVR response can bring insight into the nature of a cerebrovascular pathology and aid in understanding noise confounds when using functional Magnetic Resonance Imaging (fMRI) to study neural activity. This research assessed a practical modification to a typical resting-state fMRI protocol, to improve the characterization of cerebrovascular function. In 9 healthy subjects, we modelled CVR and lag in three resting-state data segments, and in data segments which added a 2–3 minute breathing task to the start of a resting-state segment. Two different breathing tasks were used to induce fluctuations in arterial CO2 pressure: a breath-hold task to induce hypercapnia (CO2 increase) and a cued deep breathing task to induce hypocapnia (CO2 decrease). Our analysis produced voxel-wise estimates of the amplitude (CVR) and timing (lag) of the BOLD-fMRI response to CO2 by systematically shifting the CO2 regressor in time to optimize the model fit. This optimization inherently increases gray matter CVR values and fit statistics. The inclusion of a simple breathing task, compared to a resting-state scan only, increases the number of voxels in the brain that have a significant relationship between CO2 and BOLD-fMRI signals, and improves our confidence in the plausibility of voxel-wise CVR and hemodynamic lag estimates. We demonstrate the clinical utility and feasibility of this protocol in an incidental finding of Moyamoya disease, and explore the possibilities and challenges of using this protocol in younger populations. This hybrid protocol has direct applications for CVR mapping in both research and clinical settings and wider applications for fMRI denoising and interpretation.
The study of mild cognitive impairment (MCI) is critical to understand the underlying processes of cognitive decline in Parkinson's disease (PD). Functional connectivity (FC) disruptions in PD-MCI ...patients have been observed in several networks. However, the functional and cognitive changes associated with the disruptions observed in these networks are still unclear. Using a data-driven methodology based on independent component analysis, we examined differences in FC RSNs among PD-MCI, PD cognitively normal patients (PD-CN) and healthy controls (HC) and studied their associations with cognitive and motor variables. A significant difference was found between PD-MCI vs PD-CN and HC in a FC-trait comprising sensorimotor (SMN), dorsal attention (DAN), ventral attention (VAN) and frontoparietal (FPN) networks. This FC-trait was associated with working memory, memory and the UPDRS motor scale. SMN involvement in verbal memory recall may be related with the FC-trait correlation with memory deficits. Meanwhile, working memory impairment may be reflected in the DAN, VAN and FPN interconnectivity disruptions with the SMN. Furthermore, interactions between the SMN and the DAN, VAN and FPN network reflect the intertwined decline of motor and cognitive abilities in PD-MCI. Our findings suggest that the memory impairments observed in PD-MCI are associated with reduced FC within the SMN and between SMN and attention networks.
The design of frequency synthesizers is especially challenging for wireless applications due to the requirements for high spectral purity, high frequency range, and fast tuning together with ...reasonable power consumption. The idea of combining digital and analog synthesis techniques for achieving these goals is discussed and analyzed. The proposed architecture uses I/Q modulation to translate a digitally synthesized tuneable low frequency tone to the final frequency range. In practical implementations, however, unavoidable mismatches between the amplitudes and phases of the I and Q branches result in imperfect sideband rejection degrading the spectral purity of the synthesized signal. A compensation structure based on digital pre-distortion of the low frequency tone is presented to enhance the signal quality. Furthermore, practical algorithms for updating the compensator parameters are proposed based on minimizing the envelope variation of the synthesizer output signal. Simulation results are also presented to illustrate the efficiency of the proposed synthesizer concept.
•We add diffusion MRI to Bayesian thalamic nuclei segmentation with structural MRI.•Adding fiber tracts to probabilistic atlases enables orientation modelling.•Thalamus segmentation from joint ...structural and diffusion MRI improves accuracy.•Atlas and companion segmentation code are freely distributed with FreeSurfer.
The human thalamus is a highly connected brain structure, which is key for the control of numerous functions and is involved in several neurological disorders. Recently, neuroimaging studies have increasingly focused on the volume and connectivity of the specific nuclei comprising this structure, rather than looking at the thalamus as a whole. However, accurate identification of cytoarchitectonically designed histological nuclei on standard in vivo structural MRI is hampered by the lack of image contrast that can be used to distinguish nuclei from each other and from surrounding white matter tracts. While diffusion MRI may offer such contrast, it has lower resolution and lacks some boundaries visible in structural imaging. In this work, we present a Bayesian segmentation algorithm for the thalamus. This algorithm combines prior information from a probabilistic atlas with likelihood models for both structural and diffusion MRI, allowing segmentation of 25 thalamic labels per hemisphere informed by both modalities. We present an improved probabilistic atlas, incorporating thalamic nuclei identified from histology and 45 white matter tracts surrounding the thalamus identified in ultra-high gradient strength diffusion imaging. We present a family of likelihood models for diffusion tensor imaging, ensuring compatibility with the vast majority of neuroimaging datasets that include diffusion MRI data. The use of these diffusion likelihood models greatly improves identification of nuclear groups versus segmentation based solely on structural MRI. Dice comparison of 5 manually identifiable groups of nuclei to ground truth segmentations show improvements of up to 10 percentage points. Additionally, our chosen model shows a high degree of reliability, with median test-retest Dice scores above 0.85 for four out of five nuclei groups, whilst also offering improved detection of differential thalamic involvement in Alzheimer’s disease (AUROC 81.98%). The probabilistic atlas and segmentation tool will be made publicly available as part of the neuroimaging package FreeSurfer (https://freesurfer.net/fswiki/ThalamicNucleiDTI).
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