Caffeine-Induced Global Reductions in Resting-State BOLD Connectivity Reflect Widespread Decreases in MEG Connectivity.

In resting-state functional magnetic resonance imaging (fMRI), the temporal correlation between spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal from different brain regions is used to assess functional connectivity. However, because the BOLD signal is an indirect measure of neuronal activity, its complex hemodynamic nature can complicate the interpretation of differences in connectivity that are observed across conditions or subjects. For example, prior studies have shown that caffeine leads to widespread reductions in BOLD connectivity but were not able to determine if neural or vascular factors were primarily responsible for the observed decrease. In this study, we used source-localized magnetoencephalography (MEG) in conjunction with fMRI to further examine the origins of the caffeine-induced changes in BOLD connectivity. We observed widespread and significant (p < 0.01) reductions in both MEG and fMRI connectivity measures, suggesting that decreases in the connectivity of resting-state neuro-electric power fluctuations were primarily responsible for the observed BOLD connectivity changes. The MEG connectivity decreases were most pronounced in the beta band. By demonstrating the similarity in MEG and fMRI based connectivity changes, these results provide evidence for the neural basis of resting-state fMRI networks and further support the potential of MEG as a tool to characterize resting-state connectivity

Abnormal white matter blood-oxygen-level-dependent signals in chronic mild traumatic brain injury

Concussion, or mild traumatic brain injury (mTBI), can cause persistent behavioral symptoms and cognitive impairment, but it is unclear if this condition is associated with detectable structural or functional brain changes. At two sites, chronic mTBI human subjects with persistent post-concussive symptoms (three months to five years after injury) and age- and education-matched healthy human control subjects underwent extensive neuropsychological and visual tracking eye movement tests. At one site, patients and controls also performed the visual tracking tasks while blood-oxygen-level-dependent (BOLD) signals were measured with functional magnetic resonance imaging. Although neither neuropsychological nor visual tracking measures distinguished patients from controls at the level of individual subjects, abnormal BOLD signals were reliably detected in patients. The most consistent changes were localized in white matter regions: anterior internal capsule and superior longitudinal fasciculus. In contrast, BOLD signals were normal in cortical regions, such as the frontal eye field and intraparietal sulcus, that mediate oculomotor and attention functions necessary for visual tracking. The abnormal BOLD signals accurately differentiated chronic mTBI patients from healthy controls at the single-subject level, although they did not correlate with symptoms or neuropsychological performance. We conclude that subjects with persistent post-concussive symptoms can be identified years after their TBI using fMRI and an eye movement task despite showing normal structural MRI and DTI

Multi-core beamformer for spatio-temporal MEG source activity reconstruction

Beamformer adaptive spatial filters have been used extensively in the field of magnetoencephalography (MEG) as tools to reconstruct functional activation of the brain. Conventional single beamformer techniques suffer from distortion in the presence of coherent activation of the cortex or are difficult to use due to the need of a priori information. These qualities present a major disadvantage to analyzing human brain responses, as coordinated functional responses require a degree of synchronous activation in different parts of the active cortex. In this dissertation, a novel beamformer technique, the multi-core beamformer, is developed that is robust to source correlation and does not require the use of a priori information. This novel approach is tested in both simulated and real experiments, including auditory and median-nerve stimulation, which provide well-studied systems to gauge the effectiveness of our new technique. Simulations show that the multi-core beamformer can successfully determine source time-courses, source powers, and source locations while minimizing or eliminating the distortion present in other methods. Results from real- life experiments show that the multi-core beamformer produces physiologically meaningful solutions that agree with previous functional imaging and neurophysiology studies. The use of the multi-core beamformer is expected to greatly contribute to the analysis of MEG recordings and, in general, improve our understanding of functional brain activit

Robust Empirical Bayesian Reconstruction of Distributed Sources for Electromagnetic Brain Imaging.

Electromagnetic brain imaging is the reconstruction of brain activity from non-invasive recordings of the magnetic fields and electric potentials. An enduring challenge in this imaging modality is estimating the number, location, and time course of sources, especially for the reconstruction of distributed brain sources with complex spatial extent. Here, we introduce a novel robust empirical Bayesian algorithm that enables better reconstruction of distributed brain source activity with two key ideas: kernel smoothing and hyperparameter tiling. Since the proposed algorithm builds upon many of the performance features of the sparse source reconstruction algorithm - Champagne and we refer to this algorithm as Smooth Champagne. Smooth Champagne is robust to the effects of high levels of noise, interference, and highly correlated brain source activity. Simulations demonstrate excellent performance of Smooth Champagne when compared to benchmark algorithms in accurately determining the spatial extent of distributed source activity. Smooth Champagne also accurately reconstructs real MEG and EEG data

Robust estimation of noise for electromagnetic brain imaging with the champagne algorithm.

Robust estimation of the number, location, and activity of multiple correlated brain sources has long been a challenging task in electromagnetic brain imaging from M/EEG data, one that is significantly impacted by interference from spontaneous brain activity, sensor noise, and other sources of artifacts. Recently, we introduced the Champagne algorithm, a novel Bayesian inference algorithm that has shown tremendous success in M/EEG source reconstruction. Inherent to Champagne and most other related Bayesian reconstruction algorithms is the assumption that the noise covariance in sensor data can be estimated from "baseline" or "control" measurements. However, in many scenarios, such baseline data is not available, or is unreliable, and it is unclear how best to estimate the noise covariance. In this technical note, we propose several robust methods to estimate the contributions to sensors from noise arising from outside the brain without the need for additional baseline measurements. The incorporation of these methods for diagonal noise covariance estimation improves the robust reconstruction of complex brain source activity under high levels of noise and interference, while maintaining the performance features of Champagne. Specifically, we show that the resulting algorithm, Champagne with noise learning, is quite robust to initialization and is computationally efficient. In simulations, performance of the proposed noise learning algorithm is consistently superior to Champagne without noise learning. We also demonstrate that, even without the use of any baseline data, Champagne with noise learning is able to reconstruct complex brain activity with just a few trials or even a single trial, demonstrating significant improvements in source reconstruction for electromagnetic brain imaging

Rate of head ultrasound abnormalities at one month in very premature and extremely premature infants with normal initial screening ultrasound

BackgroundPremature infants are at risk for multiple types of intracranial injury with potentially significant long-term neurological impact. The number of screening head ultrasounds needed to detect such injuries remains controversial.ObjectiveTo determine the rate of abnormal findings on routine follow-up head ultrasound (US) performed in infants born at ≤ 32 weeks' gestational age (GA) after initial normal screening US.Materials and methodsA retrospective study was performed on infants born at ≤ 32 weeks' GA with a head US at 3-5 weeks following a normal US at 3-10 days at a tertiary care pediatric hospital from 2014 to 2020. Exclusion criteria included significant congenital anomalies, such as congenital cardiac defects necessitating surgery, congenital diaphragmatic hernia or spinal dysraphism, and clinical indications for US other than routine screening, such as sepsis, other risk factors for intracranial injury besides prematurity, or clinical neurological abnormalities. Ultrasounds were classified as normal or abnormal based on original radiology reports. Images from initial examinations with abnormal follow-up were reviewed.ResultsThirty-three (14.2%) of 233 infants had 34 total abnormal findings on follow-up head US after normal initial US. Twenty-seven infants had grade 1 germinal matrix hemorrhage, and four had grade 2 intraventricular hemorrhage. Two had periventricular echogenicity and one had a focus of cerebellar echogenicity that resolved and was determined to be artifactual.ConclusionWhen initial screening head ultrasounds in premature infants are normal, follow-up screening ultrasounds are typically also normal. Abnormal findings are usually limited to grade 1 germinal matrix hemorrhage

Caffeine-Induced Global Reductions in Resting-State BOLD Connectivity Reflect Widespread Decreases in MEG Connectivity.

In resting-state functional magnetic resonance imaging (fMRI), the temporal correlation between spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal from different brain regions is used to assess functional connectivity. However, because the BOLD signal is an indirect measure of neuronal activity, its complex hemodynamic nature can complicate the interpretation of differences in connectivity that are observed across conditions or subjects. For example, prior studies have shown that caffeine leads to widespread reductions in BOLD connectivity but were not able to determine if neural or vascular factors were primarily responsible for the observed decrease. In this study, we used source-localized magnetoencephalography (MEG) in conjunction with fMRI to further examine the origins of the caffeine-induced changes in BOLD connectivity. We observed widespread and significant (p < 0.01) reductions in both MEG and fMRI connectivity measures, suggesting that decreases in the connectivity of resting-state neuro-electric power fluctuations were primarily responsible for the observed BOLD connectivity changes. The MEG connectivity decreases were most pronounced in the beta band. By demonstrating the similarity in MEG and fMRI based connectivity changes, these results provide evidence for the neural basis of resting-state fMRI networks and further support the potential of MEG as a tool to characterize resting-state connectivity