However, this trend was not significant in the larger subject population presented here, and was further decreased by the additional noise correction steps used in the present study, including removal of motion related and global signal confounds. While we did not find resting-state functional connectivity to be related to vascular differences, it did exhibit a dependence on the magnitude of spontaneous BOLD fluctuations. Previous work has shown that resting-state BOLD connectivity and amplitude also co-vary across behavioral and disease states . If spontaneous BOLD fluctuations are interpreted as faithful measures of intrinsic neural activity, these results may be interpreted as evidence for true variations in both the amplitude and coherence of the underlying neural sources. On the other hand, the results might also reflect variations in the BOLD signal’s sensitivity to spontaneous neural activity. As the BOLD signal becomes more sensitive to underlying neural activity, there can be a relative increase in the signal-to-noise ratio of the resting-state measures, i.e. the magnitude of BOLD signal fluctuations of neuronal origin divided by the magnitude of fluctuations of non-neuronal origin. Since the estimated correlation between two signals tends to increase with SNR, the relation between BOLD functional connectivity strength and fluctuation magnitude may partially reflect the change in SNR with BOLD signal sensitivity. Future experiments with simultaneous electroencephalography and fMRI would be helpful in elucidating the relationship between resting BOLD amplitude and the underlying coherence of intrinsic neural activity. In this study, subjects performed a 5-minute motor task before undergoing resting-state scans. Previous work has shown that spontaneous BOLD fluctuation amplitude and connectivity can be altered by a preceding motor task . However,cannabis drying racks these effects followed a strenuous task causing muscle fatigue, or extended periods of tapping inducing neural plasticity. We expect that these types of effects would be minimal for the relatively short and non-strenuous finger-tapping task used in this study.

Furthermore, as scan order was kept constant for all subjects, it is unlikely that task-related modulation of resting BOLD signals accounted for the inter-subject differences observed here. To conclude, the results presented here indicate that measures of resting-state BOLD fluctuation amplitude and connectivity are relatively insensitive to inter-subject differences in baseline CBF. This lack of dependence on baseline CBF, when compared to the strong inverse dependence exhibited by the task BOLD response, appears to be caused in part by a weakened inverse relationship between relative changes in CBF and baseline CBF during rest. In addition, flow-metabolism coupling is tighter during rest than during a robust motor task, further weakening the relationship between spontaneous BOLD fluctuations and CBF. As resting-state BOLD fluctuations are often used to examine functional connectivity changes in patients, where both disease and medication can alter the vasculature, our findings are very important. However, while we have demonstrated that measures of spontaneous BOLD fluctuations are not affected by vascular differences between subjects, further work is necessary to identify other non-neural confounds before these metrics can be used as reliable estimates of underlying spontaneous neural activity.In the first part of this work, we assessed the effect of a 200 mg dose of caffeine on resting-state BOLD connectivity in the motor cortex across a sample of 9 healthy subjects. As caffeine reduces baseline CBF through adenosine antagonism, and previous work has suggested that vasoconstriction increases the sensitivity of the BOLD signal to neural activity, we expected to find that BOLD fluctuations and functional connectivity were increased. Surprisingly, we found the opposite: that caffeine significantly reduced spontaneous BOLD fluctuations and connectivity. These results suggest that the primary mechanism of caffeine’s action on functional connectivity is probably not through vascular changes, but possibly through caffeine’s direct effect on neural activity. Preliminary work by our group supports this conclusion by directly demonstrating caffeine induced reductions in neural connectivity using MEG . However, the physiological mechanisms behind the caffeine-induced decrease in BOLD signal power remain unknown.

It is possible that a caffeine-induced decrease in the coupling between CBF and oxygen metabolism, which has been found during task , could be responsible for the smaller BOLD fluctuations, but this remains to be investigated. Even if vascular confounds are not responsible for the results presented here, caffeine should still be carefully considered in the design and interpretation of resting-state BOLD fMRI studies. In the second part of this work, we employed a non-stationary analysis approach to gain further insight into the mechanisms of caffeine’s effect on functional connectivity. Specifically, we used a sliding window correlation analysis to assess whether caffeine consistently weakens the correlation over time or if transient periods of strong correlation still exist, albeit less frequently. We found that BOLD correlation was significantly more variable over time following a caffeine dose, and that extended periods of strong correlation still existed between periods of lower correlation. Furthermore, the temporal variability of BOLD signal correlation was driven by phase differences between the BOLD signals in the left and right motor cortices. While a consistent decrease in correlation could be caused by an overall change in the vascular system induced by caffeine, it is unlikely that a shift in the state of the vascular system would give rise to the increase in the non-stationarity of the correlations that we found. Instead, the caffeine-induced increase in the temporal variability of functional connectivity tends to support the existence of greater temporal variability in the coherence of the underlying neural fluctuations. In the third part of this work, we investigated the BOLD signal dependence on inter-subject differences in baseline CBF using a sample of 17 healthy subjects. We acquired simultaneous BOLD and CBF measures during a motor task and resting state. Consistent with prior studies, we found a strong dependence of the task-evoked BOLD response on inter-subject variations in baseline CBF, but found a much weaker and not significant dependence of the resting-state BOLD response on baseline CBF. In addition, inter-hemispheric resting-state BOLD connectivity between motor cortex regions did not show a significant dependence on baseline CBF.

The strong inverse dependence of the task BOLD amplitude on baseline CBF appears to be caused by the direct dependence of %∆BOLD on %∆CBF, which is modeled in the Davis equation . This is because %∆CBF is inversely related to baseline CBF, as CBF0 is the denominator in calculating %∆CBF. We find that both of these relationships are weaker during rest than task. The reduced dependence of percent changes in CBF on baseline CBF during rest is caused by significantly smaller absolute CBF changes, which are independent of inter-subject differences in baseline CBF during both task and rest. The weakened relationship between relative changes in BOLD and CBF appears to be caused by tighter flow-metabolism coupling during rest than during a robust motor task. These two factors work together to produce an insignificant relationship between spontaneous BOLD fluctuations and baseline CBF. These findings suggest that differences in both the amplitude and correlation of spontaneous BOLD fluctuations between subjects are probably more reflective of neural activity differences than vascular differences.As the use of fMRI to estimate functional connectivity in the brain is a new and rapidly growing field, work that identifies potential limitations of the technique is very important. In this dissertation, differences in baseline blood flow have been investigated as confounds to interpreting BOLD connectivity changes as neural connectivity changes.While these findings are very encouraging for the field, more work remains to be done before BOLD connectivity measures can be relied on as robust measures of neural connectivity. In particular, future work is necessary to determine whether a caffeine-induced decrease in the ratio of flow-metabolism coupling is responsible for the reduced resting-state BOLD signal amplitude found in this study. If proven to be the case,vertical grow system it would suggest that flow-metabolism coupling changes can influence functional connectivity measures even if changes in CBF alone do not.Empirically determining the ratio of blood flow to oxygen metabolism changes during rest can be challenging because of the inherently low signal to noise ratio in ASL. While previous work has shown that caffeine reduces flow-metabolism coupling in response to a task , it remains to be seen whether this is the case during resting state. An important future study would therefore use the Davis model introduced in Chapter 4 to determine how caffeine affects flow-metabolism coupling during resting-state, and whether this change could be responsible for the diminished spontaneous BOLD fluctuations . This future experiment would also address some of the limitations in the methods applied in Chapter 4 to determine flow-metabolism coupling during rest. For example, in typical Davis model experiments, a model of the task paradigm is used to obtain estimates of BOLD and CBF amplitude changes that stem from neural activation, however this is not possible in resting-state studies. In Chapter 4 of this work we estimated BOLD and CBF amplitude changes using root mean square values, but these values are not necessarily reflections of neural activity. For example, physiological noise or motion may contribute significantly to the variance of the resting BOLD and CBF fluctuations and would confound Davis model estimates using RMS values. A new technique using independent component analysis has shown promise in identifying and removing signal components of non-neural origin from spontaneous BOLD signals . In this method, multi-echo data is acquired and ICA components are examined for a linear dependence on echo time , which indicates that they are truly representative of changes in deoxyhemoglobin content and thus likely to reflect neural activity rather than noise.

It is still unclear how this method can be used on CBF data, which has low sensitivity to deoxyhemoblobin content, but possibly it could be applied to the raw multi-echo ASL data before performing a sliding window difference on the cleaned first echo data set. Another approach that could improve Davis model estimates during resting-state is to acquire simultaneous EEG/fMRI data, which could provide a reference spontaneous neural signal for estimating BOLD and CBF amplitude changes that directly result from neural activity changes . Furthermore, the EEG measurements could provide information on how the amplitude of electrical power fluctuations are altered by caffeine. These findings would shed light on whether the mag-nitude of neural activity is truly reduced by caffeine and then reflected in the decreased power of spontaneous BOLD fluctuations.For each subject and run, the sliding window correlation coefficients between the measured motor cortex BOLD time courses were plotted in a histogram. The sliding window correlation coefficients between the simulated data-derived SNR signal + noise pairs were plotted in a histogram below. For several subjects, the data produced visibly multi-modal histograms in both the pre-dose and post-dose scan sessions, which are not predicted by noise. Representative examples are shown for the pre-dose scan section in Figure B.3 and post-dose scan section in Figure B.4. These findings suggest that true variations in correlation exist between the neural “signal” components of the BOLD signal.In addition to the multi-modal shape of the measured correlation histograms, it can be seen that the variance in measured r-values is visually larger than the variance of the simulated r-values, particularly for the pre-dose subject. To quantify the likelihood that noise is responsible for the measured variability in BOLD correlation present in our data, we simulated correlation variability values. We created a histogram of variability values, which were calculated as the standard deviation of the sliding window correlation time course between each pair of simulated signal + noise time courses. This resulted in 10,000 variability values. Then the actual measured variability was compared to the simulated data to determine a percentile and associated p-value . An example of this procedure is shown in Figure B.5.This was done for each subject and run. Plots of simulated data percentile versus measured variability values are shown for each subject and run during the pre-dose and post-dose scan sessions . Note there are two runs per subject and session. Data points above the dashed line correspond to p-values less than 0.05. These simulations suggest that it is highly unlikely that noise is primarily responsible for the temporal variability in BOLD correlation found in the present study. While noise may still contribute to correlation variability, it is probable that underlying variability in the coherence of neural activity is responsible as well.Since California’s Compassionate Use Act of 1996, cannabis has been legally available — under state but not federal law — to those with medical permission. Until 2018, however, no statewide regulations governed the production, manufacturing and sale of cannabis. Prior to development and enforcement of statewide regulations, there were no testing requirements for chemicals used during cannabis cultivation and processing, including pesticides, fertilizers or solvents .