Kimberly P. Lindsey

Partitioning of physiological noise signals in the brain with concurrent near-infrared spectroscopy and fMRI

The blood–oxygen level dependent (BOLD) signals measured by functional magnetic resonance imaging (fMRI) are contaminated with noise from various physiological processes, such as spontaneous low-frequency oscillations (LFOs), respiration, and cardiac pulsation. These processes are coupled to the BOLD signal by different mechanisms, and represent variations with very different frequency content; however, because of the low sampling rate of fMRI, these signals are generally not separable by frequency, as the cardiac and respiratory waveforms alias into the LFO band. In this study, we investigated the spatial and temporal characteristics of the individual noise processes by conducting concurrent near-infrared spectroscopy (NIRS) and fMRI studies on six subjects during a resting state acquisition. Three time series corresponding to LFO, respiration, and cardiac pulsation were extracted by frequency from the NIRS signal (which has sufficient temporal resolution to critically sample the cardiac waveform) and used as regressors in a BOLD fMRI analysis. Our results suggest that LFO and cardiac signals modulate the BOLD signal independently through the circulatory system. The spatiotemporal evolution of the LFO signal in the BOLD data correlates with the global cerebral blood flow. Near-infrared spectroscopy can be used to partition these contributing factors and independently determine their contribution to the BOLD signal.

Evaluating the effects of systemic low frequency oscillations measured in the periphery on the independent component analysis results of resting state networks

Independent component analysis (ICA) is widely used in resting state functional connectivity studies. ICA is a data-driven method, which uses no a priori anatomical or functional assumptions. However, as a result, it still relies on the user to distinguish the independent components (ICs) corresponding to neuronal activation, peripherally originating signals (without directly attributable neuronal origin, such as respiration, cardiac pulsation and Mayer wave), and acquisition artifacts. In this concurrent near infrared spectroscopy (NIRS)/functional MRI (fMRI) resting state study, we developed a method to systematically and quantitatively identify the ICs that show strong contributions from signals originating in the periphery. We applied group ICA (MELODIC from FSL) to the resting state data of 10 healthy participants. The systemic low frequency oscillation (LFO) detected simultaneously at each participants fingertip by NIRS was used as a regressor to correlate with every subject-specific IC time course. The ICs that had high correlation with the systemic LFO were those closely associated with previously described sensorimotor, visual, and auditory networks. The ICs associated with the default mode and frontoparietal networks were less affected by the peripheral signals. The consistency and reproducibility of the results were evaluated using bootstrapping. This result demonstrates that systemic, low frequency oscillations in hemodynamic properties overlay the time courses of many spatial patterns identified in ICA analyses, which complicates the detection and interpretation of connectivity in these regions of the brain.

Cortical activation during cocaine use and extinction in rhesus monkeys

RationaleAcute re-exposure to cocaine or drug cues associated with cocaine use can elicit drug craving and relapse. Neuroimaging studies have begun to define neurobiological substrates underlying the acute effects of cocaine or cocaine cues in cocaine-dependent subjects.ObjectiveThe present study was the first to use functional brain imaging to document acute cocaine-induced changes in brain activity during active drug use in nonhuman primates.Materials and methodsPositron emission tomography imaging with O15-labeled water was used to measure drug-induced changes in cerebral blood flow. The acute effects of cocaine administered noncontingently were characterized in four drug-naïve rhesus monkeys. The same subjects were trained to self-administer cocaine under a fixed ratio schedule during image acquisition. Subsequently, three subjects with an extensive history of cocaine use were trained to self-administer cocaine under a second-order schedule. The same subjects also underwent extinction sessions during which saline was substituted for cocaine under the second-order schedule.ResultsNoncontingent administration of cocaine in drug-naïve subjects induced robust activation of prefrontal cortex localized primarily to the dorsolateral regions. In contrast, the pattern of brain activation induced by self-administered cocaine differed qualitatively and included anterior cingulate cortex. Moreover, drug-associated stimuli during extinction also induced robust activation of prefrontal cortex.ConclusionsThe effects of cocaine and associated cues extend beyond the limbic system to engage brain areas involved in cognitive processes. The identification of neural circuits underlying the direct pharmacological and conditioned stimulus effects of cocaine may be highly relevant toward efforts to develop treatments for cocaine addiction.

Perfusion information extracted from resting state functional magnetic resonance imaging

It is widely known that blood oxygenation level dependent (BOLD) contrast in functional magnetic resonance imaging (fMRI) is an indirect measure for neuronal activations through neurovascular coupling. The BOLD signal is also influenced by many non-neuronal physiological fluctuations. In previous resting state (RS) fMRI studies, we have identified a moving systemic low frequency oscillation (sLFO) in BOLD signal and were able to track its passage through the brain. We hypothesized that this seemingly intrinsic signal moves with the blood, and therefore, its dynamic patterns represent cerebral blood flow. In this study, we tested this hypothesis by performing Dynamic Susceptibility Contrast (DSC) MRI scans (i.e. bolus tracking) following the RS scans on eight healthy subjects. The dynamic patterns of sLFO derived from RS data were compared with the bolus flow visually and quantitatively. We found that the flow of sLFO derived from RS fMRI does to a large extent represent the blood flow measured with DSC. The small differences, we hypothesize, are largely due to the difference between the methods in their sensitivity to different vessel types. We conclude that the flow of sLFO in RS visualized by our time delay method represents the blood flow in the capillaries and veins in the brain.

Time delay processing of hypercapnic fMRI allows quantitative parameterization of cerebrovascular reactivity and blood flow delays

Blood oxygenation level-dependent fMRI contrast depends on the volume and oxygenation of blood flowing through the circulatory system. The effects on image intensity depend temporally on the arrival of blood within a voxel, and signal can be monitored during the time course of such blood flow. It has been previously shown that the passage of global endogenous variations in blood volume and oxygenation can be tracked as blood passes through the brain by determining the strength and peak time lag of their cross-correlation with blood oxygenation level-dependent data. By manipulating blood composition using transient hypercarbia and hyperoxia, we can induce much larger oxygenation and volume changes in the blood oxygenation level-dependent signal than result from natural endogenous fluctuations. This technique was used to examine cerebrovascular parameters in healthy subjects (n = 8) and subjects with intracranial stenosis (n = 22), with a subgroup of intracranial stenosis subjects scanned before and after surgical revascularization (n = 6). The halfwidth of cross-correlation lag times in the brain was larger in IC stenosis subjects (21.21 ± 14.22 s) than in healthy control subjects (8.03 ± 3.67), p < 0.001, and was subsequently reduced in regions that co-localized with surgical revascularization. These data show that blood circulatory timing can be measured robustly and longitudinally throughout the brain using simple respiratory challenges.

Correcting for Blood Arrival Time in Global Mean Regression Enhances Functional Connectivity Analysis of Resting State fMRI-BOLD Signals

Resting state functional connectivity analysis is a widely used method for mapping intrinsic functional organization of the brain. Global signal regression (GSR) is commonly employed for removing systemic global variance from resting state BOLD-fMRI data; however, recent studies have demonstrated that GSR may introduce spurious negative correlations within and between functional networks, calling into question the meaning of anticorrelations reported between some networks. In the present study, we propose that global signal from resting state fMRI is composed primarily of systemic low frequency oscillations (sLFOs) that propagate with cerebral blood circulation throughout the brain. We introduce a novel systemic noise removal strategy for resting state fMRI data, “dynamic global signal regression” (dGSR), which applies a voxel-specific optimal time delay to the global signal prior to regression from voxel-wise time series. We test our hypothesis on two functional systems that are suggested to be intrinsically organized into anticorrelated networks: the default mode network (DMN) and task positive network (TPN). We evaluate the efficacy of dGSR and compare its performance with the conventional “static” global regression (sGSR) method in terms of (i) explaining systemic variance in the data and (ii) enhancing specificity and sensitivity of functional connectivity measures. dGSR increases the amount of BOLD signal variance being modeled and removed relative to sGSR while reducing spurious negative correlations introduced in reference regions by sGSR, and attenuating inflated positive connectivity measures. We conclude that incorporating time delay information for sLFOs into global noise removal strategies is of crucial importance for optimal noise removal from resting state functional connectivity maps.

Comparison of peripheral near‐infrared spectroscopy low‐frequency oscillations to other denoising methods in resting state functional MRI with ultrahigh temporal resolution

Functional MRI (fMRI) blood–oxygen level–dependent (BOLD) signals result not only from neuronal activation, but also from nonneuronal physiological processes. These changes, especially in the low‐frequency domain (0.01–0.2 Hz), can significantly confound inferences about neuronal processes. It is crucial to effectively identify these nuisance low‐frequency oscillations (LFOs).

Systemic Low-Frequency Oscillations in BOLD Signal Vary with Tissue Type.

Blood-oxygen-level dependent (BOLD) signals are widely used in functional magnetic resonance imaging (fMRI) as a proxy measure of brain activation. However, because these signals are blood-related, they are also influenced by other physiological processes. This is especially true in resting state fMRI, during which no experimental stimulation occurs. Previous studies have found that the amplitude of resting state BOLD is closely related to regional vascular density. In this study, we investigated how some of the temporal fluctuations of the BOLD signal also possibly relate to regional vascular density. We began by identifying the blood-bound systemic low-frequency oscillation (sLFO). We then assessed the distribution of all voxels based on their correlations with this sLFO. We found that sLFO signals are widely present in resting state BOLD signals and that the proportion of these sLFOs in each voxel correlates with different tissue types, which vary significantly in underlying vascular density. These results deepen our understanding of the BOLD signal and suggest new imaging biomarkers based on fMRI data, such as amplitude of low-frequency fluctuation (ALFF) and sLFO, a combination of both, for assessing vascular density.

An MR-compatible device for delivering smoked marijuana during functional imaging.

Smoking is the preferred method of administration for two of the most frequently abused drugs, marijuana and nicotine. The high temporal and spatial resolution of functional magnetic resonance imaging (fMRI) make it a natural choice for studying the neurobiological effects of smoked drugs if the challenges of smoking in a magnetic resonance (MR) scanner can be overcome. We report on a design for an MR-compatible smoking device that can be used for smoking marijuana (or tobacco) during fMRI examinations. Nine volunteers smoked marijuana cigarettes (3.51% Delta9-THC) on two occasions: with and without the device. The device allowed subjects to smoke while they lay in the scanner, while containing all smoke and odors. Plasma Delta9-THC, subjective reports of intoxication, and heart rate increases are reported, and were all similar in individuals smoking marijuana either with or without the device. The use of this device will help advance research studies on smoked drugs including marijuana, tobacco and crack cocaine.

Decreasing Nicotine Content Reduces Subjective and Physiological Effects of Smoking

Objective Assessment of the subjective and physiological effects of smoking cigarettes with different machine-smoked nicotine yields. Methods Eight volunteers rated the characteristics of cigarettes with varying levels of nicotine (Quest®). At 30 minute intervals, participants smoked one of three different Quest® brand cigarettes in a counterbalanced order (reported machine-smoked nicotine yield: 0.6 mg, 0.3 mg, or 0.05 mg). Smoking satisfaction and sensations were measured on a cigarette evaluation questionnaire. A mood questionnaire measured self-reported subjective changes in ‘happy’, ‘stimulated’, ‘anxious’, ‘desire to smoke’, and ‘desire not to smoke’. Heart rate and skin temperature were recorded continuously. Results As nicotine yield decreased, cigarettes produced smaller changes in subjective ratings on the evaluation questionnaire with the placebo nicotine cigarette always rated lower or less potent than the other two cigarettes evaluated. Heart rate was significantly increased by the reduced nicotine cigarettes, but was not affected by the nicotine-free cigarette. Conclusion These results indicate that machine-smoked yield is an important determinant of both the subjective and physiological effects of smoking. The use of reduced and nicotine free cigarettes in smoking cessation programs remains to be evaluated.