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Neuroscientists have long sought to study the dynamic activity of the human brain-what's happening in the brain, that is, while people are thinking, feeling, and acting. Ideally, an inside look at brain function would simultaneously and continuously measure the biochemical state of every cell in the central nervous system. While such a miraculous method is science fiction, a century of progress in neuroimaging technologies has made such simultaneous and continuous measurement a plausible fiction. Despite this progress, practitioners of modern neuroimaging struggle with two kinds of limitations: those that attend the particular neuroimaging methods we have today and those that would limit any method of imaging neural activity, no matter how powerful. In this essay, I consider the liabilities and potential of techniques that measure human brain activity. I am concerned here only with methods that measure relevant physiologic states of the central nervous system and relate those measures to particular mental states. I will consider in particular the preeminent method of functional neuroimaging: BOLD fMRI. While there are several practical limits on the biological information that current technologies can measure, these limits-as important as they are-are minor in comparison to the fundamental logical restraints on the conclusions that can be drawn from brain imaging studies., (© 2014 by The Hastings Center.)

Objective: To compare the prevalence of an incomplete circle of Willis in patients with migraine with aura, migraine without aura, and control subjects, and correlate circle of Willis variations with alterations in cerebral perfusion., Methods: Migraine with aura, migraine without aura, and control subjects were prospectively enrolled in a 1∶1∶1 ratio. Magnetic resonance angiography was performed to examine circle of Willis anatomy and arterial spin labeled perfusion magnetic resonance imaging to measure cerebral blood flow. A standardized template rating system was used to categorize circle of Willis variants. The primary pre-specified outcome measure was the frequency of an incomplete circle of Willis. The association between circle of Willis variations and cerebral blood flow was also analyzed., Results: 170 subjects were enrolled (56 migraine with aura, 61 migraine without aura, 53 controls). An incomplete circle of Willis was significantly more common in the migraine with aura compared to control group (73% vs. 51%, p = 0.02), with a similar trend for the migraine without aura group (67% vs. 51%, p = 0.08). Using a quantitative score of the burden of circle of Willis variants, migraine with aura subjects had a higher burden of variants than controls (p = 0.02). Compared to those with a complete circle, subjects with an incomplete circle had greater asymmetry in hemispheric cerebral blood flow (p = 0.05). Specific posterior cerebral artery variants were associated with greater asymmetries of blood flow in the posterior cerebral artery territory., Conclusions: An incomplete circle of Willis is more common in migraine with aura subjects than controls, and is associated with alterations in cerebral blood flow.

Stimulus counterbalance is critical for studies of neural habituation, bias, anticipation, and (more generally) the effect of stimulus history and context. We introduce de Bruijn cycles, a class of combinatorial objects, as the ideal source of pseudo-random stimulus sequences with arbitrary levels of counterbalance. Neuro-vascular imaging studies (such as BOLD fMRI) have an additional requirement imposed by the filtering and noise properties of the method: only some temporal frequencies of neural modulation are detectable. Extant methods of generating counterbalanced stimulus sequences yield neural modulations that are weakly (or not at all) detected by BOLD fMRI. We solve this limitation using a novel "path-guided" approach for the generation of de Bruijn cycles. The algorithm encodes a hypothesized neural modulation of specific temporal frequency within the seemingly random order of events. By positioning the modulation between the signal and noise bands of the neuro-vascular imaging method, the resulting sequence markedly improves detection power. These sequences may be used to study stimulus context and history effects in a manner not previously possible., (Copyright © 2011 Elsevier Inc. All rights reserved.)

We investigated the biophysical mechanism of low-frequency drift in blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) (0.00-0.01 Hz), by exploring its spatial distribution, dependence on imaging parameters, and relationship with task-induced brain activation. Cardiac and respiratory signals were concurrently recorded during MRI scanning and subsequently removed from MRI data. It was found that the spatial distribution of low-frequency drifts in human brain followed a tissue-specific pattern, with greater drift magnitude in the gray matter than in white matter. In gray matter, the dependence of drift magnitudes on TE was similar to that of task-induced BOLD signal changes, i.e., the absolute drift magnitude reached the maximum when TE approached T(2)* whereas relative drift magnitude increased linearly with TE. By systematically varying the flip angle, it was found that drift magnitudes possessed a positive dependence on image intensity. In phantom experiments, the observed drift was not only much smaller than that of human brain, but also showed different dependence on TE and flip angle. In fMRI studies with visual stimulation, a strong positive correlation between drift effects at baseline and task-induced BOLD signal changes was observed both across subjects and across activated pixels within individual participants. We further demonstrated that intrinsic, physiological drift effects are a major component of the spontaneous fluctuations of BOLD fMRI signal within the frequency range of 0.0-0.1 Hz. Our study supports brain physiology, as opposed to scanner instabilities or cardiac/respiratory pulsations, as the main source of low-frequency drifts in BOLD fMRI.

Arterial spin labeling (ASL) perfusion fMRI data differ in important respects from the more familiar blood oxygen level-dependent (BOLD) fMRI data and require specific processing strategies. In this paper, we examined several factors that may influence ASL data analysis, including data storage bit resolution, motion correction, preprocessing for cerebral blood flow (CBF) calculations and nuisance covariate modeling. Continuous ASL data were collected at 3 T from 10 subjects while they performed a simple sensorimotor task with an epoch length of 48 s. These data were then analyzed using systematic variations of the factors listed above to identify the approach that yielded optimal signal detection for task activation. Improvements in statistical power were found for use of at least 10 bits for data storage at 3 T. No significant difference was found in motor cortex regarding using simple subtraction or sinc subtraction, but the former presented minor but significantly (P<.024) larger peak t value in visual cortex. While artifactual head motion patterns were observed in synthetic data and background-suppressed ASL data when label/control images were realigned to a common target, independent realignment of label and control images did not yield significant improvements in activation in the sensorimotor data. It was also found that CBF calculations should be performed prior to spatial normalization and that modeling of global fluctuations yielded significantly increased peak t value in motor cortex. The implementation of all ASL data processing approaches is easily accomplished within an open-source toolbox, ASLtbx, and is advocated for most perfusion fMRI data sets.

Blood oxygenation level dependent (BOLD) contrast has been widely used for visualizing regional neural activation. Temporal filtering and parameter estimation algorithms are generally used to account for the intrinsic temporal autocorrelation present in BOLD data. Arterial spin labeling perfusion imaging is an emerging methodology for visualizing regional brain function both at rest and during activation. Perfusion contrast manifests different noise properties compared with BOLD contrast, represented by the even distribution of noise power and spatial coherence across the frequency spectrum. Consequently, different strategies are expected to be employed in the statistical analysis of functional magnetic resonance imaging (fMRI) data based on perfusion contrast. In this study, the effect of different analysis methods upon signal detection efficacy, as assessed by receiver operator characteristic (ROC) measures, was examined for perfusion fMRI data. Simulated foci of neural activity of varying amplitude and spatial extent were added to resting perfusion data, and the accuracy of each analysis was evaluated by comparing the results with the known distribution of pseudo-activation. In contrast to the BOLD fMRI, temporal smoothing or filtering reduces the power of perfusion fMRI data analyses whereas spatial smoothing is beneficial to the efficacy of analyses.

Cerebral hemodynamic responses to brief periods of neural activity are delayed and dispersed in time. The specific shape of these responses is of some importance to the design and analysis of blood oxygenation level-dependent (BOLD), functional magnetic resonance imaging (fMRI) experiments. Using fMRI scanning, we examine here the characteristics and variability of hemodynamic responses from the central sulcus in human subjects during an event-related, simple reaction time task. Specifically, we determine the contribution of subject, day, and scanning session (within a day) to variability in the shape of evoked hemodynamic response. We find that while there is significant and substantial variability in the shape of responses collected across subjects, responses collected during multiple scans within a single subject are less variable. The results are discussed in terms of the impact of response variability upon sensitivity and specificity of analyses of event-related fMRI designs., (Copyright 1998 Academic Press.)