Data acquired with functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) are often interpreted in terms of the underlying neuronal activity, despite mounting evidence that these signals do not always correlate with electrophysiological recordings. Therefore, considering the increasing popularity of functional neuroimaging, it is clear that a more comprehensive theory is needed to reconcile these apparent disparities and more accurately explain the mechanisms through which various PET and fMRI signals arise. In the present article, we have turned our attention to astrocytes, which vastly outnumber neurons and are known to serve a number of functions throughout the central nervous system (CNS). For example, astrocytes are known to be critically involved in neurotransmitter uptake and recycling, and empirical data suggests that brain activation increases both oxidative and glycolytic astrocyte metabolism. Furthermore, a number of recent studies imply that astrocytes are likely to play a key role in regulating cerebral blood delivery. Therefore, we propose that, by mediating neurometabolic and neurovascular processes throughout the CNS, astrocytes could provide a common physiological basis for fMRI and PET signals. Such a theory has significant implications for the interpretation of functional neuroimaging signals, because astrocytic changes reflect subthreshold neuronal activity, simultaneous excitatory/inhibitory synaptic inputs, and other transient metabolic demands that may not elicit electrophysiological changes. It also suggests that fMRI and PET signals may have inherently less sensitivity to decreases in synaptic input (i.e. 'negative activity') and/or inhibitory (GABAergic) neurotransmission.

Various MRI techniques, including myelin water imaging, T1w/T2w ratio mapping and diffusion-based imaging can be used to characterize tissue microstructure. However, surprisingly few studies have examined the degree to which these MRI measures are related within and between various brain regions. Therefore, whole-brain MRI scans were acquired from 31 neurologically-healthy participants to empirically measure and compare myelin water fraction (MWF), T1w/T2w ratio, fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD) in 25 bilateral (10 grey matter; 15 white matter) regions-of-interest (ROIs). Except for RD vs. T1w/T2w, MD vs. T1w/T2w, moderately significant to highly significant correlations (p < 0.001) were found between each of the other measures across all 25 brain structures [T1w/T2w vs. MWF (Pearson r = 0.33, Spearman ρ = 0.31), FA vs. MWF (r = 0.73, ρ = 0.75), FA vs. T1w/T2w (r = 0.25, ρ = 0.22), MD vs. AD (r = 0.57, ρ = 0.58), MD vs. RD (r = 0.64, ρ = 0.61), AD vs. MWF (r = 0.43, ρ = 0.36), RD vs. MWF (r = −0.49, ρ = −0.62), MD vs. MWF (r = −0.22, ρ = −0.29), RD vs. FA (r = −0.62, ρ = −0.75) and MD vs. FA (r = −0.22, ρ = −0.18)]. However, while all six MRI measures were correlated with each other across all structures, there were large intra-ROI and inter-ROI differences (i.e., with no one measure consistently producing the highest or lowest values). This suggests that each quantitative MRI measure provides unique, and potentially complimentary, information about underlying brain tissues – with each metric offering unique sensitivity/specificity tradeoffs to different microstructural properties (e.g., myelin content, tissue density, etc.).

Spinal cord (SC) motion is thought to be the dominant source of error in current diffusion and spinal functional MRI (fMRI) methods. However, until now, such motion has not been well characterized in three dimensions. While previous studies have predominantly examined motion in the superior/inferior (S/I) direction, the foci of the present study were the anterior/posterior (A/P) and right/left (R/L) components of human cervical and upper thoracic SC motion. Cardiac-gated, turbofast low-angle shot (turbo-FLASH) cinematic MRI was employed at 3T to acquire images of the cord at 24 phases throughout the cardiac cycle. Time-dependent signal fluctuations within voxels adjacent to the cord/cerebrospinal fluid (CSF) interface were then used to measure SC motion, which was found to occur predictably as a function of cardiac activity. Functional magnetic resonance imaging (fMRI) and diffusion-tensor imaging (DTI) have made significant contributions to our knowledge of human brain function, organization, and pathology (1,2), and have more recently been implemented in studies of the human spinal cord (SC) (3-8). Spinal fMRI was initially adapted from brain fMRI methods, but has since evolved to deal with the length and small cross-sectional area of the cord, as well as magnetic susceptibility effects resulting from its proximity to vertebrae (9 -11). These improvements have permitted more detailed studies of SC activity in healthy and SC injured (SCI) populations (12)(13)(14), demonstrating the potential utility of spinal fMRI for both research and clinical applications. DTI has also been applied to the human SC in a small number of studies, and holds promise for noninvasively analyzing white matter connectivity and integrity (15). However, the sensitivity of these methods is compromised due to errors arising from SC motion (11,16 -18), which has been observed in a number of studies (19 -25) but has not yet been adequately characterized in terms of its timing, direction, and magnitude in three dimensions. The objective of this study is to more fully characterize cervical and upper thoracic SC motion by measuring it in the anterior/posterior (A/P) and right/left (R/L) directions throughout the cardiac cycle.Early imaging studies of SC motion employed intraoperative ultrasonography, whereby motion was observed through saline-filled laminectomies. In this manner, oscillations of the human SC were found to occur consistently at the cardiac rate (19), and cord motion in a canine model was found to cease completely upon transection of the nerve roots and supporting vasculature (20). These experiments suggest that motion of the cord results, at least in part, from pulsatile arterial blood flow. In contrast, pioneering MRI investigations described motion of the human brain, cerebrospinal fluid (CSF), and SC under normal physiological conditions (21,22). Complete characterization of SC motion was not the focus of these studies, and even though motion was only measured in the superior/ inferior (S/I) direction, the results suggest th...

PurposePostmortem MRI can be used to reveal important pathologies and establish radiology–pathology correlations. However, quantitative MRI values are altered by tissue fixation. Therefore, the purpose of this study was to investigate time-dependent effects of formalin fixation on MRI relaxometry (T1 and T2), diffusion tensor imaging (fractional anisotropy, FA; and mean diffusivity, MD), and myelin water fraction (MWF) measurements throughout intact human brain specimens.MethodsTwo whole, neurologically-healthy human brains were immersed in 10% formalin solution and scanned at 13 time points between 0 and 1,032 h. Whole-brain maps of longitudinal (T1) and transverse (T2) relaxation times, FA, MD, and MWF were generated at each time point to illustrate spatiotemporal changes, and region-of-interest analyses were then performed in eight brain structures to quantify temporal changes with progressive fixation.ResultsAlthough neither of the diffusion measures (FA nor MD) showed significant changes as a function of formalin fixation time, both T1 and T2-relaxation times significantly decreased, and MWF estimates significantly increased with progressive fixation until (and likely beyond) our final measurements were taken at 1,032 h.ConclusionThese results suggest that T1-relaxation, T2-relaxation and MWF estimates must be performed quite early in the fixation process to avoid formalin-induced changes compared to in vivo values; and furthermore, that different ex vivo scans within an experiment must be acquired at consistent (albeit still early) fixation intervals to avoid fixative-related differences between samples. Conversely, ex vivo diffusion measures (FA and MD) appear to depend more on other factors (e.g., pulse sequence optimization, sample temperature, etc.).

Diffusion tensor imaging (DTI) is a powerful MRI technique that can be used to estimate both the microstructural integrity and the trajectories of white matter pathways throughout the central nervous system. This fiber tracking (aka, “tractography”) approach is often carried out using anatomically-defined seed points to identify white matter tracts that pass through one or more structures, but can also be performed using functionally-defined regions of interest (ROIs) that have been determined using functional MRI (fMRI) or other methods. In this study, we performed fMRI-guided DTI tractography between all of the previously defined nodes within each of six common resting-state brain networks, including the: dorsal Default Mode Network (dDMN), ventral Default Mode Network (vDMN), left Executive Control Network (lECN), right Executive Control Network (rECN), anterior Salience Network (aSN), and posterior Salience Network (pSN). By normalizing the data from 32 healthy control subjects to a standard template—using high-dimensional, non-linear warping methods—we were able to create probabilistic white matter atlases for each tract in stereotaxic coordinates. By investigating all 198 ROI-to-ROI combinations within the aforementioned resting-state networks (for a total of 6336 independent DTI tractography analyses), the resulting probabilistic atlases represent a comprehensive cohort of functionally-defined white matter regions that can be used in future brain imaging studies to: (1) ascribe DTI or other white matter changes to particular functional brain networks, and (2) compliment resting state fMRI or other functional connectivity analyses.

Background and Purpose— Physiological effects of stroke are best assessed over entire brain networks rather than just focally at the site of structural damage. Resting-state functional magnetic resonance imaging can map functional-anatomic networks by analyzing spontaneously correlated low-frequency activity fluctuations across the brain, but its potential usefulness in predicting functional outcome after acute stroke remains unknown. We assessed the ability of resting-state functional magnetic resonance imaging to predict functional outcome after acute stroke. Methods— We scanned 37 consecutive reperfused stroke patients (age, 69±14 years; 14 females; 3-day National Institutes of Health Stroke Scale score, 6±5) on day 3 after symptom onset. After imaging preprocessing, we used a whole-brain mask to calculate the correlation coefficient matrices for every paired region using the Harvard-Oxford probabilistic atlas. To evaluate functional outcome, we applied the modified Rankin Scale at 90 days. We used region of interest analyses to explore the functional connectivity between regions and graph-computation analysis to detect differences in functional connectivity between patients with good functional outcome (modified Rankin Scale score ≤2) and those with poor outcome (modified Rankin Scale score >2). Results— Patients with good outcome had greater functional connectivity than patients with poor outcome. Although 3-day National Institutes of Health Stroke Scale score was the most accurate independent predictor of 90-day modified Rankin Scale (84.2%), adding functional connectivity increased accuracy to 94.7%. Preserved bilateral interhemispheric connectivity between the anterior inferior temporal gyrus and superior frontal gyrus and decreased connectivity between the caudate and anterior inferior temporal gyrus in the left hemisphere had the greatest impact in favoring good prognosis. Conclusions— These data suggest that information about functional connectivity from resting-state functional magnetic resonance imaging may help predict 90-day stroke outcome.

Fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) are commonly used as MRI biomarkers of white matter microstructure in diffusion MRI studies of neurodevelopment, brain aging, and neurologic injury/disease. Some of the more frequent practices include performing voxel-wise or region-based analyses of these measures to cross-sectionally compare individuals or groups, longitudinally assess individuals or groups, and/or correlate with demographic, behavioral or clinical variables. However, it is now widely recognized that the majority of cerebral white matter voxels contain multiple fiber populations with different trajectories, which renders these metrics highly sensitive to the relative volume fractions of the various fiber populations, the microstructural integrity of each constituent fiber population, and the interaction between these factors. Many diffusion imaging experts are aware of these limitations and now generally avoid using FA, AD or RD (at least in isolation) to draw strong reverse inferences about white matter microstructure, but based on the continued application and interpretation of these metrics in the broader biomedical/neuroscience literature, it appears that this has perhaps not yet become common knowledge among diffusion imaging end-users. Therefore, this paper will briefly discuss the complex biophysical underpinnings of these measures in the context of crossing fibers, provide some intuitive “thought experiments” to highlight how conventional interpretations can lead to incorrect conclusions, and suggest that future studies refrain from using (over-interpreting) FA, AD, and RD values as standalone biomarkers of cerebral white matter microstructure.

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