Lisa D. Nickerson

Cerebral blood flow measurement by dynamic contrast MRI using singular value decomposition with an adaptive threshold

Singular value decomposition (SVD) is a promising deconvolution technique for use in dynamic contrast agent magnetic resonance perfusion imaging. Computer simulations, however, show that the selection of the threshold for SVD affects the accuracy of the cerebral blood flow measurements and may distort the shape of the vascular residue function. In this report, a pixel‐by‐pixel thresholding method is proposed based on the signal‐to‐noise ratio of the concentration time curve at maximum concentration (SNRC). Monte Carlo simulations were used to determine the optimal threshold for different SNRC. This technique was used to analyze data from six healthy volunteers, resulting in a mean gray to white matter cerebral blood flow ratio of 2.67 ± 0.07. This value is in excellent agreement with values published in the literature. Magn Reson Med 42:167–172, 1999.

Prefrontal and limbic resting state brain network functional connectivity differs between nicotine-dependent smokers and non-smoking controls

BACKGROUND Brain dysfunction in prefrontal cortex (PFC) and dorsal striatum (DS) contributes to habitual drug use. These regions are constituents of brain networks thought to be involved in drug addiction. To investigate whether networks containing these regions differ between nicotine dependent female smokers and age-matched female non-smokers, we employed functional MRI (fMRI) at rest. METHODS Data were processed with independent component analysis (ICA) to identify resting state networks (RSNs). We identified a subcortical limbic network and three discrete PFC networks: a medial prefrontal cortex (mPFC) network and right and left lateralized fronto-parietal networks common to all subjects. We then compared these RSNs between smokers and non-smokers using a dual regression approach. RESULTS Smokers had greater coupling versus non-smokers between left fronto-parietal and mPFC networks. Smokers with the greatest mPFC-left fronto-parietal coupling had the most DS smoking cue reactivity as measured during an fMRI smoking cue reactivity paradigm. This may be important because the DS plays a critical role in maintaining drug-cue associations. Furthermore, subcortical limbic network amplitude was greater in smokers. CONCLUSIONS Our results suggest that prefrontal brain networks are more strongly coupled in smokers, which could facilitate drug-cue responding. Our data also are the first to document greater reward-related network fMRI amplitude in smokers. Our findings suggest that resting state PFC network interactions and limbic network amplitude can differentiate nicotine-dependent smokers from controls, and may serve as biomarkers for nicotine dependence severity and treatment efficacy.

Physiological denoising of BOLD fMRI data using Regressor Interpolation at Progressive Time Delays (RIPTiDe) processing of concurrent fMRI and near-infrared spectroscopy (NIRS)

Confounding noise in BOLD fMRI data arises primarily from fluctuations in blood flow and oxygenation due to cardiac and respiratory effects, spontaneous low frequency oscillations (LFO) in arterial pressure, and non-task related neural activity. Cardiac noise is particularly problematic, as the low sampling frequency of BOLD fMRI ensures that these effects are aliased in recorded data. Various methods have been proposed to estimate the noise signal through measurement and transformation of the cardiac and respiratory waveforms (e.g. RETROICOR and respiration volume per time (RVT)) and model-free estimation of noise variance through examination of spatial and temporal patterns. We have previously demonstrated that by applying a voxel-specific time delay to concurrently acquired near infrared spectroscopy (NIRS) data, we can generate regressors that reflect systemic blood flow and oxygenation fluctuations effects. Here, we apply this method to the task of removing physiological noise from BOLD data. We compare the efficacy of noise removal using various sets of noise regressors generated from NIRS data, and also compare the noise removal to RETROICOR+RVT. We compare the results of resting state analyses using the original and noise filtered data, and we evaluate the bias for the different noise filtration methods by computing null distributions from the resting data and comparing them with the expected theoretical distributions. Using the best set of processing choices, six NIRS-generated regressors with voxel-specific time delays explain a median of 10.5% of the variance throughout the brain, with the highest reductions being seen in gray matter. By comparison, the nine RETROICOR+RVT regressors together explain a median of 6.8% of the variance in the BOLD data. Detection of resting state networks was enhanced with NIRS denoising, and there were no appreciable differences in the bias of the different techniques. Physiological noise regressors generated using Regressor Interpolation at Progressive Time Delays (RIPTiDe) offer an effective method for efficiently removing hemodynamic noise from BOLD data.

Comparison of the temporal response in perfusion and BOLD-based event- related functional MRI

Event‐related functional MRI (ER‐fMRI) based on both blood oxygen level‐dependent (BOLD) contrast and perfusion contrast has been recently developed to study human brain activation due to brief stimulation. In this report, both BOLD‐ and perfusion‐based ER‐fMRI were directly compared using repeated single‐trial, short visual stimulation (1 sec) in six human volunteers. The results show that the cerebral blood flow change reached a maximum approximately 1 sec earlier than the BOLD signal change (4.2 ± 0.2 sec vs. 5.1 ± 0.2 sec after the stimulation, P < 0.05). The full width at half maximum of the hemodynamic response measured by perfusion was not significantly different from that measured with BOLD (5.1 ± 0.6 sec vs. 5.9 ± 0.6 sec). A positive linear correlation was found between the maximum perfusion and maximum BOLD signal changes (r = 0.77, P = 0.07). Magn Reson Med 43:768–772, 2000.

Quantitative PET with positron emitters that emit prompt gamma rays

The purpose of this work was to determine the feasibility of using positron emitting isotopes that emit prompt gammas to acquire quantitative positron emission tomography (PET) data using standard PET instrumentation. Prompt gammas can contaminate PET data by increasing dead time, converting singles into invalid coincidences, and producing multiple coincidences which can lead to the replacement of valid coincidences by invalid coincidences. The measurements in this work were made by scanning point sources containing F-18, Na-22, and Co-60 and studying the effects of the prompt gammas on the PET data, We found that for the Na-22 point source, the annihilation photon coincidence rate was about 25 times the prompt gamma-annihilation photon coincidence rate in the entire active volume of the scanner. With scatter, the Na-22 prompt gamma-annihilation photon coincidence rate was 1.3 times higher than the F-18 scatter coincidence rate. The most significant effect of the prompt gamma was to increase dead time; the dead time correction factor for Cu-60 was 2.4 times higher than the correction factor for N-13 for the same source activity. We conclude that, in many cases, quantitative PET data can be readily obtained with isotopes that emit prompt gammas, using standard PET 2-D instrumentation. However there are some cases, such as 3-D PET, where prompt gammas could significantly contaminate the PET data.

Age-related adaptations of brain function during a memory task are also present at rest.

Several studies have demonstrated age-related regional differences in the magnitude of the BOLD signal using task-based fMRI. It has been suggested that functional changes reflect either compensatory or de-differentiation mechanisms, both of which assume response to a specific stimulus. Here, we have tested whether ageing affects both task-based and resting brain function, and the extent to which functional changes are mediated by reductions in grey matter (GM) volume. Two groups, of 22 healthy younger and 22 older volunteers, underwent an imaging protocol involving structural and functional MRI, both during a memory task and at rest. The two groups had similar socio-demographical characteristics and cognitive performance. Image analysis revealed both structural and functional differences. Increased BOLD signal in older relative to younger volunteers was mainly observed in the frontal lobes, both during the task and at rest. Functional changes in the frontal lobes were largely located in brain regions spared from GM loss, and adding GM covariates to the fMRI analysis did not significantly alter the group differences. Our results are consistent with the suggestion that, during normal ageing, the brain responds to neuronal loss by fine-tuning connections between spared neurons. Longitudinal studies will be necessary to fully test this hypothesis.

Cue reactivity in cannabis-dependent adolescents.

The authors measured event-related potentials with a craving manipulation to investigate the neural correlates of drug cue reactivity in 13 adolescents who are cannabis dependent (CD; ages 14-17). The P300 responses to marijuana (MJ) pictures (MJ-P300) and control pictures (C-P300) were assessed after handling neutral objects and again after handling MJ paraphernalia (MJP). Self-reported drug craving and heart rates also were measured. MJ-P300 were larger than C-P300 (p < .001), and both the MJ-P300 and craving increased significantly after handling MJP (p = .002 and p = .003, respectively), with no association between the magnitude of craving and MJ-P300. Heart rates were not affected by handling MJP. The results showed that adolescents who are CD have an attentional bias to MJ stimuli that increases after handling marijuana paraphernalia. Generally, the results are consistent with what has been reported for adult heavy chronic cannabis smokers, although there were some differences that require further investigation.

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.

An Increase in Tobacco Craving Is Associated with Enhanced Medial Prefrontal Cortex Network Coupling

Craving is a key aspect of drug dependence that is thought to motivate continued drug use. Numerous brain regions have been associated with craving, suggesting that craving is mediated by a distributed brain network. Whether an increase in subjective craving is associated with enhanced interactions among brain regions was evaluated using resting state functional magnetic imaging (fMRI) in nicotine dependent participants. We focused on craving-related changes in the orbital and medial prefrontal cortex (OMPFC) network, which also included the subgenual anterior cingulate cortex (sgACC) extending into the ventral striatum. Brain regions in the OMPFC network are not only implicated in addiction and reward, but, due to their rich anatomic interconnections, may serve as the site of integration across craving-related brain regions. Subjective craving and resting state fMRI were evaluated twice with an ∼1 hour delay between the scans. Cigarette craving was significantly increased at the end, relative to the beginning of the scan session. Enhanced craving was associated with heightened coupling between the OMPFC network and other cortical, limbic, striatal, and visceromotor brain regions that are both anatomically interconnected with the OMPFC, and have been implicated in addiction and craving. This is the first demonstration confirming that an increase in craving is associated with enhanced brain region interactions, which may play a role in the experience of craving.

Dissociable cortico-striatal connectivity abnormalities in major depression in response to monetary gains and penalties

BACKGROUND Individuals with major depressive disorder (MDD) are characterized by maladaptive responses to both positive and negative outcomes, which have been linked to localized abnormal activations in cortical and striatal brain regions. However, the exact neural circuitry implicated in such abnormalities remains largely unexplored. METHOD In this study 26 unmedicated adults with MDD and 29 matched healthy controls (HCs) completed a monetary incentive delay task during functional magnetic resonance imaging (fMRI). Psychophysiological interaction (PPI) analyses probed group differences in connectivity separately in response to positive and negative outcomes (i.e. monetary gains and penalties). RESULTS Relative to HCs, MDD subjects displayed decreased connectivity between the caudate and dorsal anterior cingulate cortex (dACC) in response to monetary gains, yet increased connectivity between the caudate and a different, more rostral, dACC subregion in response to monetary penalties. Moreover, exploratory analyses of 14 MDD patients who completed a 12-week, double-blind, placebo-controlled clinical trial after the baseline fMRI scans indicated that a more normative pattern of cortico-striatal connectivity pre-treatment was associated with greater improvement in symptoms 12 weeks later. CONCLUSIONS These results identify the caudate as a region with dissociable incentive-dependent dACC connectivity abnormalities in MDD, and provide initial evidence that cortico-striatal circuitry may play a role in MDD treatment response. Given the role of cortico-striatal circuitry in encoding action-outcome contingencies, such dysregulated connectivity may relate to the prominent disruptions in goal-directed behavior that characterize MDD.