Cortico-hippocampal activations for high entropy visual stimulus: an fMRI perspective

We perceive the environment around us in order to act upon it. To gain the desirable outcome effectively, we not only need the incoming information to be processed efficiently but we also need to know how reliable this information is. How this uncertainty is extracted from the visual input and how is it represented in the brain are still open questions. The hippocampus reacts to different measures of uncertainty. Because it is strongly connected to different cortical and subcortical regions, the hippocampus has the resources to communicate such information to other brain regions involved in visual processing and other cognitive processes. In this thesis, we investigate the aspects of uncertainty to which the hippocampus reacts. Is it the uncertainty in the ongoing recognition attempt of a temporally unfolding stimulus or is it the low-level spatiotemporal entropy? To answer this question, we used a dynamic visual stimulus with varying spatial and spatiotemporal entropy. We used well-structured virtual tunnel videos and the corresponding phase-scrambled videos with matching local luminance and contrast per frame. We also included pixel scrambled videos with high spatial and spatiotemporal entropy in our stimulus set. Brain responses (fMRI images) from the participants were recorded while they watched these videos and performed an engaging but cognitively independent task. Using the General Linear Model (GLM), we modeled the brain responses corresponding to different video types and found that the early visual cortex and the hippocampus had a stronger response to videos with higher spatiotemporal entropy. Using independent component analysis, we further investigated which underlying networks were recruited in processing high entropy visual information. We also discovered how these networks might influence each other. We found two cortico-hippocampal networks involved in processing our stimulus videos. While one of them represented a general primary visual processing network, the other was activated strongly by the high entropy videos and deactivated by the well-structured virtual tunnel videos. We also found a hierarchy in the processing stream with information flowing from less stimulus-specific to more stimulus-specific networks

Cortico-hippocampal activations for high entropy visual stimulus: an fMRI perspective

We perceive the environment around us in order to act upon it. To gain the desirable outcome effectively, we not only need the incoming information to be processed efficiently but we also need to know how reliable this information is. How this uncertainty is extracted from the visual input and how is it represented in the brain are still open questions. The hippocampus reacts to different measures of uncertainty. Because it is strongly connected to different cortical and subcortical regions, the hippocampus has the resources to communicate such information to other brain regions involved in visual processing and other cognitive processes. In this thesis, we investigate the aspects of uncertainty to which the hippocampus reacts. Is it the uncertainty in the ongoing recognition attempt of a temporally unfolding stimulus or is it the low-level spatiotemporal entropy? To answer this question, we used a dynamic visual stimulus with varying spatial and spatiotemporal entropy. We used well-structured virtual tunnel videos and the corresponding phase-scrambled videos with matching local luminance and contrast per frame. We also included pixel scrambled videos with high spatial and spatiotemporal entropy in our stimulus set. Brain responses (fMRI images) from the participants were recorded while they watched these videos and performed an engaging but cognitively independent task. Using the General Linear Model (GLM), we modeled the brain responses corresponding to different video types and found that the early visual cortex and the hippocampus had a stronger response to videos with higher spatiotemporal entropy. Using independent component analysis, we further investigated which underlying networks were recruited in processing high entropy visual information. We also discovered how these networks might influence each other. We found two cortico-hippocampal networks involved in processing our stimulus videos. While one of them represented a general primary visual processing network, the other was activated strongly by the high entropy videos and deactivated by the well-structured virtual tunnel videos. We also found a hierarchy in the processing stream with information flowing from less stimulus-specific to more stimulus-specific networks

The fearful face and beyond: fMRI studies of the human amygdala

The amygdala has been labeled as a detector of threat , evidenced by its heightened response to fearful faces in human neuroimaging studies. A critical element of the fearful face is an increase in eye white area, hypothesized to be crucial for the rapid detection of fear in another\u27s face. Yet other facial expressions can also increase eye white area in a manner that is similar (a lateral shift in gaze) or identical (surprise) to fear. It is unknown if the amygdala can differentiate between these types of increases in eye white area and those that are specifically associated with fear when using only the eye region of the face. Furthermore, whether the fearful mouth can elicit an amygdala response when shown in isolation is unknown. Using functional magnetic resonance imaging, we found that the amygdala responded robustly to fearful eyes as well as eye stimuli that were ambiguous in nature. The fearful mouth, on the other hand, was unable to generate a significant response from the amygdala, however the happy condition elicited a slight response from the right amygdala, most likely due to the visual salience of the smile. We also observed a functional laterality between the two amygdalae in that the left amygdala responded only to fearful eyes while the right amygdala activated to any change in the eyes; the same laterality was also evident when eye stimuli were ambiguous in nature indicating that the left amygdala is more tuned to detect fear in the eyes while the right amygdala acts as a general detector of eye changes. This lends more evidence to the existence of parallel mechanisms for processing visual threat. Together, our results indicate that while the amygdala is primarily a detector of fearful faces, it has evolved to respond to other facial expressions that are also behaviorally relevant or potentially threatening to the viewer

The Role of the Hippocampus in Representations of Emotional Memory

Although the hippocampus has long been implicated in contextual fear learning, the exact function of this brain structure is unclear. It is generally thought to encode a spatial context with which a fear memory can be associated, but how it may accomplish this and whether it plays a role in emotional memory is largely unknown. It is also unclear whether the hippocampus acts as a single unitary structure, or whether the dorsal and ventral poles, which exhibit differential connectivity to other brain regions, function independently. This dissertation examines the involvement of the hippocampus in emotional learning. A contextual fear conditioning paradigm using a predator odor as an ethologically relevant fearful stimulus was developed and lesions and immunohistochemistry were used to examine differential involvement of the dorsal and ventral hippocampus in response to fear learning. Long-term physiological recordings of dorsal place cells were then conducted to determine the effects of fear conditioning and also fear extinction on contextual representations in the hippocampus. Additionally, cells in the ventral hippocampus were assessed for responses to visuospatial manipulations and changing odor cues of varying emotional valence. It was found that the dorsal and ventral hippocampal regions are both independently required for contextual fear conditioning, and neurons in each region are differentially activated in response to fear learning. Furthermore, place cells in the dorsal hippocampus remapped in response to fear conditioning and stabilized those new fields in the long term. Extinction training caused many place cells to remap once again, suggesting that the dorsal hippocampus encodes varying representations of `fearful\u27 and `safe\u27 contexts. Finally, cells in the ventral hippocampus exhibited stronger responses to anxiogenic contextual cues compared to dorsal cells. In conclusion, these data suggest that the hippocampus is involved in emotional learning and that its function may vary along its longitudinal axis

The structural neurobiology of social anxiety disorder : a clinical neuroimaging study

Includes bibliographical referencesWhile a number of studies have explored the functional neuroanatomy of social anxiety disorder (SAD), comparatively few studies have investigated the structural underpinnings in SAD. 18 psychopharmacologically and psychotherapeutically naïve adult patients with a primary Axis I diagnosis of generalized social anxiety disorder and 18 demographically (age, gender and education) matched healthy controls underwent 3T structural magnetic resonance imaging. A manual tracing protocol was specifically developed to compute the volume of the most prominent subcortical gray matter structures implicated in SAD by previous functional research. Cortical thickness was estimated using an automated algorithm and whole brain analyses of white matter structure were performed using FSL's tract - based spatial statistics comparing fractional anisotropy (FA), mean diffusivity (MD) in individuals with SAD. Manual tracing demonstrated that compared to controls, SAD patients showed an enlarged right globus pallidus. Cortical thickness analyses demonstrated significant cortical thinning in the left isthmus of the cingulate gyrus, the left temporal pole, and the left superior temporal gyrus. Analyses of white matter tractographic data demonstrated reduced FA in in the genu, splenium and tapetum of the corpus callosum. Additionally reduced FA was noticed in the fornix and the right cingulum. Reduced FA was also noted in bilateral corticospinal tracts and the right corona radiata. The results demonstrate structural alterations in limbic circuitry as well as involvement of the basal glanglia and their cortical projections and input pathways

Psychophysiological Concomitants of Levels of Cognitive Processing.

The peripheral electrophysiological manifestations of levels of cognitive processing and memory performance were investigated by recording heart rate, skin conductance, skin temperature and electromyogram measures during a three phase verbal task. Subjects processed words at three cognitive levels (phonetic, low semantic, high semantic), and physiological recordings were made during cue covert processing and verbalization phases. Three colored lights were used to cue subjects to the appropriate processing level for each word. An incidental memory task was given following the processing tasks. As expected, words processed at the higher cognitive levels were recalled better. There was greater physiological reactivity associated with the phonetic tasks during the cue phase, while the semantic tasks produced more reactivity during the covert processing and verbalization phases. The high and low semantic tasks were psychophysiologically differentiated, the more semantically complex task eliciting greater arousal. An analysis of recalled versus non-recalled trials indicated greater heart rate and skin conductance increases on trials that were later recalled. A multivariate regression of physiological reactivity on memory scores showed a moderate relationship, with heart rate contributing the most variance. The results were interpreted as demonstrating a definite relationship between the level of cognitive operation and the amount of physiological reactivity. The greater activation accompanying the higher processing levels seemed to reflect the degree of cognitive effort at these levels. The reactivity accompanying the cue was interpreted to reflect arousal associated with task expectancy

Amygdala volume in a cognitively impaired population at enhanced risk of schizophrenia

It is established that the mildly learning disabled population has a three fold elevated risk for schizophrenia. On the basis of findings from previous neuroimaging studies it has been proposed that in some individuals learning disability is a prepsychotic manifestation of schizophrenia. On this background, a cohort was selected from a nonpsychotic adolescent population in special education by employing tools previously shown to identify those at elevated risk of schizophrenia from within a high risk population. The risk of developing schizophrenia within this selected cohort was expected to be substantially greater than that for the learning disabled population as a whole. This population was then assessed by clinical interview, neuropsychological assessment and MRI scanning. Region of interest methodology was employed to ascertain amygdala volume in both the high risk and a matched control group. Two primary areas of interest were addressed; comparison of amygdala volume between the two groups and investigation of the relationship between symptomatology and amygdala volume within the high risk population. While no significant difference was found between amygdala volume in the high risk and control groups, a significant negative correlation was seen between left amygdala volume and weight of negative symptoms within the high risk group (p=0.009). This suggests that within this population reduced amygdala volume may be significant in the aetiology of negative-type symptoms and these symptoms may be present prior to clinical illness

Biologically inspired computational structures and processes for autonomous agents and robots

Recent years have seen a proliferation of intelligent agent applications: from robots for space exploration to software agents for information filtering and electronic commerce on the Internet. Although the scope of these agent applications have blossomed tremendously since the advent of compact, affordable computing (and the recent emergence of the World Wide Web), the design of such agents for specific applications remains a daunting engineering problem;Rather than approach the design of artificial agents from a purely engineering standpoint, this dissertation views animals as biological agents, and considers artificial analogs of biological structures and processes in the design of effective agent behaviors. In particular, it explores behaviors generated by artificial neural structures appropriately shaped by the processes of evolution and spatial learning;The first part of this dissertation deals with the evolution of artificial neural controllers for a box-pushing robot task. We show that evolution discovers high fitness structures using little domain-specific knowledge, even in feedback-impoverished environments. Through a careful analysis of the evolved designs we also show how evolution exploits the environmental constraints and properties to produce designs of superior adaptive value. By modifying the task constraints in controlled ways, we also show the ability of evolution to quickly adapt to these changes and exploit them to obtain significant performance gains. We also use evolution to design the sensory systems of the box-pushing robots, particularly the number, placement, and ranges of their sensors. We find that evolution automatically discards unnecessary sensors retaining only the ones that appear to significantly affect the performance of the robot. This optimization of design across multiple dimensions (performance, number of sensors, size of neural controller, etc.) is implicitly achieved by the evolutionary algorithm without any external pressure (e.g., penalty on the use of more sensors or neurocontroller units). When used in the design of robots with limited battery capacities , evolution produces energy-efficient robot designs that use minimal numbers of components and yet perform reasonably well. The performance as well as the complexity of robot designs increase when the robots have access to a spatial learning mechanism that allows them to learn, remember, and navigate to power sources in the environment;The second part of this dissertation develops a computational characterization of the hippocampal formation which is known to play a significant role in animal spatial learning. The model is based on neuroscientific and behavioral data, and learns place maps based on interactions of sensory and dead-reckoning information streams. Using an estimation mechanism known as Kalman filtering, the model explicitly deals with uncertainties in the two information streams, allowing the robot to effectively learn and localize even in the presence sensing and motion errors. Additionally, the model has mechanisms to handle perceptual aliasing problems (where multiple places in the environment appear sensorily identical), incrementally learn and integrate local place maps, and learn and remember multiple goal locations in the environment. We show a number of properties of this spatial learning model including computational replication of several behavioral experiments performed with rodents. Not only does this model make significant contributions to robot localization, but also offers a number of predictions and suggestions that can be validated (or refuted) through systematic neurobiological and behavioral experiments with animals