Optimization and Application of Electrochemical Probes for Neurotransmitter Detection

The brain is a complex matrix that is difficult to study. Signaling molecules, neurotransmitters, are constantly being released and sequestered back into neurons within milliseconds to facilitate communication and normal function, a process called neurotransmission. There are few analytical techniques available to selectively probe such a dynamic system, and even fewer can detect these discrete changes in real-time. In order to make robust measurements in the brain you need speed, sensitivity, selectivity and small probe size, which are encompassed by fast-voltammetry with microelectrodes. Traditional fast-voltammetry at carbon fiber microelectrodes (fast-scan cyclic voltammetry (FSCV)) requires background subtraction to overcome the large capacitive currents generated from scanning at such a high rate, therefore crucial basal information is lost. To capture this previously lost information, fast-scan controlled adsorption (FSCAV) was developed for serotonin detection in vivo. Serotonin is tightly regulated in the brain and thought to mark affective disorders, therefore it is crucial to develop and cultivate new tools to better understand this biomarker. Recently, our lab has employed FSCAV to understand niche mechanisms of action and regulatory processes of the central nervous system regarding serotonin. Here, we discuss improvements towards basal serotonin detection with FSCAV by first modifying CFMs to improve sensitivity and stability over prolonged periods of time and then demonstrate their use in elucidating the effects of antidepressants on tonic serotonin levels. These efforts have indeed improved our own measurements of serotonin and have clearly shown that exploiting the in vivo environment can improve biomarker detection and thus shows promise as a methodology to be adopted across the fast-voltammetry community

Neurobiological Foundations Of Stability And Flexibility

In order to adapt to changing and uncertain environments, humans and other organisms must balance stability and flexibility in learning and behavior. Stability is necessary to learn environmental regularities and support ongoing behavior, while flexibility is necessary when beliefs need to be revised or behavioral strategies need to be changed. Adjusting the balance between stability and flexibility must often be based on endogenously generated decisions that are informed by information from the environment but not dictated explicitly. This dissertation examines the neurobiological bases of such endogenous flexibility, focusing in particular on the role of prefrontally-mediated cognitive control processes and the neuromodulatory actions of dopaminergic and noradrenergic systems. In the first study (Chapter 2), we examined the role of frontostriatal circuits in instructed reinforcement learning. In this paradigm, inaccurate instructions are given prior to trial-and-error learning, leading to bias in learning and choice. Abandoning the instructions thus necessitates flexibility. We utilized transcranial direct current stimulation over dorsolateral prefrontal cortex to try to establish a causal role for this area in this bias. We also assayed two genes, the COMT Val158Met genetic polymorphism and the DAT1/SLC6A3 variable number tandem repeat, which affect prefrontal and striatal dopamine, respectively. The results support the role of prefrontal cortex in biasing learning, and provide further evidence that individual differences in the balance between prefrontal and striatal dopamine may be particularly important in the tradeoff between stability and flexibility. In the second study (Chapter 3), we assess the neurobiological mechanisms of stability and flexibility in the context of exploration, utilizing fMRI to examine dynamic changes in functional brain networks associated with exploratory choices. We then relate those changes to changes in norepinephrine activity, as measured indirectly via pupil diameter. We find tentative support for the hypothesis that increased norepinephrine activity around exploration facilitates the reorganization of functional brain networks, potentially providing a substrate for flexible exploratory states. Together, this work provides further support for the framework that stability and flexibility entail both costs and benefits, and that optimizing the balance between the two involves interactions of learning and cognitive control systems under the influence of catecholamines

NEW APPROACHES FOR ASSESSING TIME-VARYING FUNCTIONAL BRAIN CONNECTIVITY USING FMRI DATA

It was long assumed that functional connectivity (FC) among brain regions did not vary substantially during a single resting-state functional magnetic resonance imaging (rs-fMRI) run. However, an increasing number of studies have reported on the existence of time-varying functional connectivity (TVC) in rs-fMRI data taking place in a considerably shorter time window than previously thought (i.e., on the order of seconds and minutes). However, the study of TVFC is a relatively new research area and there remain a number of unaddressed problems hindering its ability to fulfill its promise of increasing our knowledge of human brain function. First, while it has previously been shown that autocorrelation can negatively impact estimates of static functional connectivity, its impact on TVC estimates has not been established. Understanding the influence of autocorrelation on TVFC is of high importance, as we hypothesize the autocorrelation within a time series can inflate the sampling variability of TVC estimated using sliding window techniques, leading to the increase of risk of misinterpreting noise as true TVC and negatively impact subsequent estimation of whole-brain time varying functional connectivity. We thus study the impact of autocorrelation on TVC and how to mitigate it. Second, there is a need for new analytic approaches for estimating TVC. Most studies use a sliding window approach, where the correlation between region is computed locally within a specific time window that is moved across time. A shortcoming of this approach is the need to select an a priori window length for analysis. To circumvent this issue, we focus on the use of instantaneous phase synchronization (IPS), which offers single time-point resolution of time-resolved fMRI connectivity. The use of IPS necessitates bandpass filtering the data to obtain valid results. We seek to show how bandpass filtering affects the estimates of IPS metrics such as phase locking value (PLV) and phase coherence. Further, as current metrics discard the temporal transitions from positive to negative associations common in IPS analysis we introduce a new approach within IPS framework for circumventing this issue. Third, the choice of cut-off frequencies when bandpass filtering in IPS analysis is to some extend arbitrary. We seek to compare standard phase synchronization using the Hilbert transform with empirical mode decomposition (EMD) which eliminates the need for bandpass filtering in a data driven manner. While the use of EMD has a number of benefits compared to the Hilbert transform, it has a couple shortcomings: the susceptibility of the EMD to the SNR of the signal and untangling frequencies close to one another. To circumvent this issue and improve the assessment of IPS, we propose the use of an alternative decomposition approach, multivariate variational mode decomposition (MVMD) for phase synchronization analysis.