Browsing by Subject "fMRI"

The goal of this dissertation is to characterize goal directed proactive behavioral responses to threat as well as reactive responses to threat exposure, and to identify the neural and personality correlates of individual differences in these responses. Three specific studies are reported wherein participants completed a novel shock avoidance paradigm while concurrent measures of behavioral, muscular, and sympathetic autonomic activity were collected; self-report was used to measure mood and trait personality; and blood oxygen-level dependent functional magnetic resonance imaging (BOLD fMRI) was used to measure individual differences in threat-related amygdala reactivity and intrinsic connectivity within the corticolimbic circuit.

Results from Study 1 demonstrate that during threat exposure, participants exhibit increased avoidance behavior, faster reaction times, and increased muscular and sympathetic activity. Moreover, results demonstrate that two broad patterns characterize individual differences in how participants respond during avoidance: 1) a generalized tendency to exhibit magnified threat responses across domains; and 2) a tendency to respond either with proactive behavioral responses or reactive autonomic responses. Heightened state anxiety during the shock avoidance paradigm, and increased trait anxiety were both associated with the generalized tendency to exhibit magnified threat responses. However, gender moderated the relationship between trait anxiety and generalized increases in threat responses during avoidance, such that only male participants exhibited a positive relationship between these two factors. Study 2 demonstrates that intrinsic connectivity between the dorsomedial prefrontal cortex and centromedial region of the amygdala prospectively predicts whether participants will respond proactively or reactively during active avoidance. Finally, Study 3 provides evidence that responses to threat-related facial expressions within the centromedial region of the amygdala are associated with more reactive and less proactive responses during avoidance.

These results demonstrate that patterns observed in animal models of avoidance, specifically the competition between proactive and reactive responses to threat cues, extend to human participants. Moreover, our results suggest that while anxious mood during performance and heightened trait anxiety are associated with a generalized facilitation of threat responses across domains, measures of neural circuit function within the corticolimbic system predict whether individuals will exhibit increased proactive or reactive responses during active avoidance. In addition to facilitating the search for the neural processes underlying how the brain responds dynamically to threat, these results have the potential to aide researchers in characterizing the symptoms and neural processes underlying anxiety disorders.

Recent research into resting-state functional magnetic resonance imaging (fMRI) has shown that the brain is very active during rest. This thesis work utilizes blood oxygenation level dependent (BOLD) signals to investigate the spatial and temporal functional network information found within resting-state data, and aims to investigate the feasibility of extracting functional connectivity networks using different methods as well as the dynamic variability within some of the methods. Furthermore, this work looks into producing valid networks using a sparsely-sampled sub-set of the original data.

In this work we utilize four main methods: independent component analysis (ICA), principal component analysis (PCA), correlation, and a point-processing technique. Each method comes with unique assumptions, as well as strengths and limitations into exploring how the resting state components interact in space and time.

Correlation is perhaps the simplest technique. Using this technique, resting-state patterns can be identified based on how similar the time profile is to a seed region’s time profile. However, this method requires a seed region and can only identify one resting state network at a time. This simple correlation technique is able to reproduce the resting state network using subject data from one subject’s scan session as well as with 16 subjects.

Independent component analysis, the second technique, has established software programs that can be used to implement this technique. ICA can extract multiple components from a data set in a single analysis. The disadvantage is that the resting state networks it produces are all independent of each other, making the assumption that the spatial pattern of functional connectivity is the same across all the time points. ICA is successfully able to reproduce resting state connectivity patterns for both one subject and a 16 subject concatenated data set.

Using principal component analysis, the dimensionality of the data is compressed to find the directions in which the variance of the data is most significant. This method utilizes the same basic matrix math as ICA with a few important differences that will be outlined later in this text. Using this method, sometimes different functional connectivity patterns are identifiable but with a large amount of noise and variability.

To begin to investigate the dynamics of the functional connectivity, the correlation technique is used to compare the first and second halves of a scan session. Minor differences are discernable between the correlation results of the scan session halves. Further, a sliding window technique is implemented to study the correlation coefficients through different sizes of correlation windows throughout time. From this technique it is apparent that the correlation level with the seed region is not static throughout the scan length.

The last method introduced, a point processing method, is one of the more novel techniques because it does not require analysis of the continuous time points. Here, network information is extracted based on brief occurrences of high or low amplitude signals within a seed region. Because point processing utilizes less time points from the data, the statistical power of the results is lower. There are also larger variations in DMN patterns between subjects. In addition to boosted computational efficiency, the benefit of using a point-process method is that the patterns produced for different seed regions do not have to be independent of one another.

This work compares four unique methods of identifying functional connectivity patterns. ICA is a technique that is currently used by many scientists studying functional connectivity patterns. The PCA technique is not optimal for the level of noise and the distribution of the data sets. The correlation technique is simple and obtains good results, however a seed region is needed and the method assumes that the DMN regions is correlated throughout the entire scan. Looking at the more dynamic aspects of correlation changing patterns of correlation were evident. The last point-processing method produces a promising results of identifying functional connectivity networks using only low and high amplitude BOLD signals.

The brain’s dopaminergic system is critical to adaptive behaviors, and is centrally implicated in various pathologies. For decades, research has aimed at better characterizing what drives the mesolimbic dopamine system and the resulting influence on brain physiology and behavior in both humans and animals. To date, the dominant modes of research have relied on extrinsic approaches: pharmacological manipulations, direct brain stimulation, or delivering behavioral incentives in laboratory tasks. A critical open question concerns whether individuals can modulate activation within this system volitionally. That is, can individuals use self-generated thoughts and imagery to invigorate this system on their own? This process can be referred to as “cognitive neurostimulation” -- a precise and non-invasive stimulation of neural systems via cognitive and behavioral strategies. And if not, can they be taught to do so? Recent technological advances make it feasible to present human participants with information about ongoing neural activations in a fast and spatially precise manner. Such feedback signals might enable individuals to eventually learn to control neural systems via fine-tuning of behavioral strategies. The studies described herein investigate whether individuals can learn to volitionally invigorate activation within the mesolimbic reward network. We demonstrate that under the right training context, individuals can successfully learn to generate cognitive states that elicit and sustain activation in the ventral tegmental area (VTA), the source of dopamine production within the mesolimbic network. Although participants were explicitly trained to increase VTA activation, multiple mesolimbic regions exhibited increased connectivity during and after training. Together, these findings suggest new frameworks for aligning psychological and biological perspectives, and for understanding and harnessing the power of neuromodulatory systems.

Moral cognition represents a foundational faculty of the human species. Our sense of morality develops beginning at a very young age, and its dysfunction can lead to devastating mental disorders. Given its central importance, it has fittingly garnered the attention of thinkers throughout the ages. For millennia, philosophers have pondered what it is to be right or wrong, good or bad, virtuous or vicious. For centuries, psychologists have elucidated how people acquire and act upon a sense of morality. More recently in the last decade, neuroscientists have embarked on a project to study how morality arises from computations in the brain. However, this latest project has been fragmented: researchers have largely studied various neural components of morality - including emotion, value, and mentalizing - in isolation. This has resulted in an informal and disjointed model for the neural mechanisms of morality. This dissertation is concerned with more formally identifying neural components and their influences on each other in the context of moral cognition.

In Chapter 2, I study how the component neural networks of moral cognition may be involved in distinct aspects of a single decision by employing a complex clinical decision making task involving the disclosure of conflicts of interest. I show that for a given decision, the magnitude of conflict of interest is tracked by mentalizing networks, while the degree of disclosure-induced behavioral change exhibited by participants is predicted by value networks. In Chapter 3, I move beyond the informal model of morality used in Chapter 2 and previous literature by devising a methodology to identify hierarchical ontologies of neural circuits; such an approach can have implications on further discussions of morality, and more generally, on other aspects of cognitive neuroscience. From this, I present the 50 elemental neural circuits that are fundamental to human cognition and explore how these elements can differentially combine to form emergent neural circuits. In Chapter 4, I use these advances to address morality, uncovering its relevant component neural networks in a data-driven way. I show that neural circuits important in supporting higher-level moral computations include mentalizing and taste. In Chapter 5, I demonstrate an important complexity in a compositional model of morality. I show that one of the components of moral cognition, mentalizing, can paradoxically be influenced by moral judgments themselves. To conclude, I highlight the implications of both theoretical and methodological advances. The hierarchical ontologies of neural circuits may be a profitable framework for the future characterization and study of mental disorders; and to effectively study these circuits, the use of moral judgment and decision-making paradigms will be effective experimental tasks, considering the centrality of moral cognition to who we are, whether in health or illness.

Neuroimaging studies of episodic memory, or memory of events from our personal past, have predominantly focused their attention on medial temporal lobe (MTL). There is growing acknowledgement however, from the cognitive neuroscience of memory literature, that regions outside the MTL can support episodic memory processes. The medial prefrontal cortex is one such region garnering increasing interest from researchers. Using behavioral and functional magnetic resonance imaging measures, over two studies, this thesis provides evidence of a mnemonic role of the medial PFC. In the first study, participants were scanned while judging the extent to which they agreed or disagreed with the sociopolitical views of unfamiliar individuals. Behavioral tests of associative recognition revealed that participants remembered with high confidence viewpoints previously linked with judgments of strong agreement/disagreement. Neurally, the medial PFC mediated the interaction between high-confidence associative recognition memory and beliefs associated with strong agree/disagree judgments. In an effort to generalize this finding to well-established associative information, in the second study, we investigated associative recognition memory for real-world concepts. Object-scene pairs congruent or incongruent with a preexisting schema were presented to participants in a cued-recall paradigm. Behavioral tests of conceptual and perceptual recognition revealed memory enhancements arising from strong resonance between presented pairs and preexisting schemas. Neurally, the medial PFC tracked increases in visual recall of schema-congruent pairs whereas the MTL tracked increases in visual recall of schema-incongruent pairs. Additionally, ventral areas of the medial PFC tracked conceptual components of visual recall specifically for schema-congruent pairs. These findings are consistent with a recent theoretical proposal of medial PFC contributions to memory for schema-related content. Collectively, these studies provide evidence of a role for the medial PFC in associative recognition memory persisting for associative information deployed in our daily social interactions and for those associations formed over multiple learning episodes. Additionally, this set of findings advance our understanding of the cognitive contributions of the medial PFC beyond its canonical role in processes underlying social cognition.

Memories are not veridical representations of the environment. Rather, an individual's goals can influence how the surrounding environment is represented in long-term memory. The present dissertation aims to delineate the influence of reward and punishment motivation on human declarative memory encoding and its underlying neural circuitry. Chapter 1 provides a theoretical framework for investigating motivation's influence on declarative memory. This chapter will review the animal and human literatures on declarative memory encoding, reward and punishment motivation, and motivation's influence on learning and memory. Chapter 2 presents a study examining the behavioral effects of reward and punishment motivation on declarative memory encoding. Chapter 3 presents a study examining the neural circuitry underlying punishment-motivated declarative encoding using functional magnetic resonance imaging (fMRI), and compares these findings to previous studies of reward-motivated declarative encoding. Chapter 4 presents a study examining the influence of reward and punishment motivation on neural sensitivity to and declarative memory for unexpected events encountered during goal pursuit using fMRI. Finally, Chapter 5 synthesizes these results and proposes a model of how and why motivational valence has distinct influences on declarative memory encoding. Results indicated that behaviorally, reward motivation resulted in more enriched representations of the environment compared to punishment motivation. Neurally, these motivational states engaged distinct neuromodulatory systems and medial temporal lobe (MTL) targets during encoding. Specifically, results indicated that reward motivation supports interactions between the ventral tegmental area and the hippocampus, whereas, punishment motivation supports interactions between the amygdala and parahippocampal cortex. Together, these findings suggest that reward and punishment engage distinct systems of encoding and result in the storage of qualitatively different representations of the environment into long-term memory.

Everyday, animals make decisions that require balancing tradeoffs like time delays, uncertainty, and physical effort demands with the prospect of rewards like food or money. The tendency to devalue rewards according to these tradeoffs is also known as discounting and depends on how much subjective value an animal places on a reward. These discounting decisions are supported by different neural systems. The influence of dopamine signaling is well-characterized as a modulator of motivation and decision making. However, the role of dopamine as a marker of interindividual differences of reward sensitivity and valuation is less clearly understood. Using a combination of neuroimaging techniques (functional magnetic resonance imaging and positron emission tomography), behavioral experiments, and meta-analyses, this dissertation identifies how trait-like variation in dopamine function explains the way people differ in their preferences and neural computations of value. Overall, the findings indicate that while dopamine may exert acute influence over reward discounting behavior, these associations may not extend to trait-like differences. Specifically, individual differences in dopamine receptor availability are related to discounting behavior in clinical populations but not healthy adults. Nevertheless, individual differences in dopamine are related to functional brain activation associated with the subjective valuation of rewards—the input to choice behavior. These results highlight that interindividual variation in dopamine is more directly linked to neural computations than observed behaviors and that dopamine-mediated psychopathology does not precisely map on to acute pharmacodynamics.

The ability to re-experience the past is a defining feature of episodic memory. Yet we know that even the most detailed memories are distinct from the initial experiences to which they refer. This relationship between the initial encoding and subsequent retrieval of information is central to our understanding of memory and its capacity to connect us to the past. Past research has shown that neural signatures present during perception are reactivated during later memory, but the correspondence between this reactivation and various aspects of memory function remains unclear. This dissertation attempts to connect behavioral measures of memory to the reinstatement and modification of neural information that takes place when memories are retrieved. In the first two studies reported, functional magnetic resonance imaging (fMRI) is used to assess event-specific cortical patterns from encoding that are reinstated during retrieval (encoding-retrieval similarity, ERS). Increases in this fine-grained of reinstatement are found in occipitotemporal cortex (OTC) during detailed memory for scenes (Study 1), and in the medial temporal lobes (MTL) for the recovery of relational information (Study 2). In addition to reflecting encoding-related content, retrieval is also found to strengthen previously encoded information via hippocampally-mediated mechanisms in Study 3. Together, these studies demonstrate the detailed nature of information that is recovered across varying degrees of memory and show how retrieval can also alter stored representations, emphasizing the interactive nature of memory processes.

Anticipation is a state of expectancy for something that will happen, and it allows us to use past learning to prepare for and make predictions about the future. Studies have shown that anticipation influences behavioral performance, learning, and memory, and studies implicate reward-related brain circuitry. However, few studies have investigated the neural underpinnings of anticipation on a brain-wide network scale . In this set of experiments, I take an interdisciplinary cross-species approach, using in-vivo electrophysiology in mice and functional magnetic resonance imaging (fMRI) in humans, to investigate brain networks underlying anticipation in motivated behavior. Using a data-driven machine learning approach, I characterize the anticipatory network in mice running through a T-maze, and show how it is affected by behavioral perturbation in the form of a task reversal, and circuit perturbation in the form of a genetic mutant mouse line. I also validate this network in a separate cohort of mice in a variation of the T-maze task that varies in difficulty, and show how activity in this network is modulated by task difficulty and intermediate instrumental goals. Finally, I investigate this network using fMRI in human subjects performing a trivia-based task to show how this network links curiosity, a more intrinsic form of motivation, to memory. The findings from these studies provide evidence at multiple levels and across multiple species for an anticipatory network that links motivational state to cognitive performance.

In MRI, magnetic susceptibility differences at air/tissue interfaces result in main magnetic field (B0) inhomogeneities, which in turn cause artifacts such as distortions and signal loss, especially in gradient-echo echo-planar imaging (EPI). Because of the air cavities in the sinuses and ear canals, these artifacts are most prominent in the inferior frontal and temporal brain regions, and hinder our ability to accurately assess brain structure and function. Existing shimming techniques can correct for these artifacts by applying additional magnetic fields that compensate for the B0 inhomogeneities. However, passive shimming has limited flexibility and patient comfort, whereas spherical harmonic shimming cannot shim localized B0 inhomogeneities.

Integrated parallel reception, excitation, and shimming (iPRES) is a novel technology that allows radio-frequency and direct currents (DC) to flow in the same coil simultaneously, enabling image acquisition and localized B0 shimming, respectively. By using a single coil array placed close to the subject, iPRES saves valuable space in the magnet bore and maximizes both the signal-to-noise ratio and shimming performance. Two advancements to this technology are proposed in this thesis: (1) a method to effectively recover signal loss in functional MRI (fMRI) and (2) a plug-and-play battery- powered iPRES head coil array.

Previously, iPRES has been used to correct for distortions in EPI images. In this work, we propose to use it to recover signal loss in fMRI. Three types of approaches were simulated: modifications of the coil geometry, adjustments to the volume shimmed, and redefinition of the cost function used in the shim optimization. The simulation results from the modified cost function were the most promising and showed that there was a tradeoff between reduction in B0 inhomogeneity and signal loss, which could be adjusted by using a weight.

The shimming performance was further assessed in in vivo fMRI experiments by acquiring B0 maps and EPI images during a breathholding task, before and after shimming with the optimal weight. The results confirmed that the proposed method could effectively recover signal loss in fMRI, while also reducing the B0 inhomogeneity.

Existing iPRES coil arrays use a multi-channel DC power supply, which allows the DC currents to be optimized to shim individual subjects and/or slices, giving the best shimming performance. However, this implementation requires many cables and filters between the coil array and the machine room. Additionally, subject-specific shimming involves the acquisition of a B0 map and a DC current optimization for each subject, which requires additional time and technical expertise, and may not be practical for clinical applications.

In this work, an MR-compatible battery pack was developed to power an iPRES head coil array, eliminating the external DC power supply, cables, and filters, and reducing the cost and complexity of the system. The battery pack was designed to deliver fixed DC currents to a subset of iPRES coil elements, which were optimized in advance to shim an average subject’s brain, thus eliminating the subject-specific shim optimization.

The system was validated through bench-top measurements and MRI experiments to verify that the shim currents were accurate and remained stable within the operating time. B0 maps and EPI images acquired in vivo before and after shimming with the battery- powered iPRES head coil array showed that it could reduce the B0 inhomogeneity and geometric distortion. While it does not offer the flexibility to shim individual subjects or slices, this stand-alone, plug-and-play system is expected to enable a wider adoption of iPRES in clinical applications.

Biological drives such as hunger, thirst, and sexual reproduction are potent motivators of behavior. Extrinsic rewards in the environment (i.e. food, drink, money) are also important behavioral and cognitive motivators. In addition to the relevance of an extrinsic reward in meeting the needs of biological drives, an individual’s sensitivity to the physiological state of their body (interoceptive awareness) would also be expected to mediate motivation for these extrinsic primary rewards (i.e. food, drink). Importantly, a better characterization of the predicted behavioral and neural interactions between interoception, motivation, and memory systems can highlight novel targets for interventions to facilitate motivation and memory for adaptive behaviors and/or impede motivation and memory for maladaptive behaviors (i.e. addiction, relapse, overeating).

The present dissertation examines how individual differences in interoceptive awareness may modulate motivated memory formation via motivational and affective mechanisms. Specifically, interoceptive accuracy is associated with increased motivation for relevant primary rewards and enhanced encoding for these rewards. However, anxiety, negatively predicted by interoceptive accuracy, negatively predicts memory the next day. Furthermore, memory for relevant primary rewards was negatively predicted by insula-parahippocampal and ventral tegmental area-hippocampal background connectivity.