Automated hippocampal location and extraction

The hippocampus is a complex brain structure that has been studied extensively and is subject to abnormal structural change in various neuropsychiatric disorders. The highest definition in vivo method of visualizing the anatomy of this structure is structural Magnetic Resonance Imaging (MRI). Gross structure can be assessed by the naked eye inspection of MRI scans but measurement is required to compare scans from individuals within normal ranges, and to assess change over time in individuals. The gold standard of such measurement is manual tracing of the boundaries of the hippocampus on scans. This is known as a Region Of Interest (ROI) approach. ROI is laborious and there are difficulties with test-retest and inter-rater reliability. These difficulties are primarily due to uncertainty in designation of the hippocampus boundary. An improved, less labour intensive and more reliable method is clearly desirable. This thesis describes a fully automated hybrid methodology that is able to first locate and then extract hippocampal volumes from 3D 1.5T MRI T1 brain scans automatically. The hybrid algorithm uses brain atlas mappings and fuzzy inference to locate hippocampal areas and create initial hippocampal boundaries. This initial location is used to seed a deformable manifold algorithm. Rule based deformations are then applied to refine the estimate of the hippocampus locations. Finally, the hippocampus boundaries are corrected through an inference process that assures adherence to an expected hippocampus volume. The ICC values of this methodology when compared to the manual segmentation of the same hippocampi result in a 0.73 for the left and 0.81 for the right hippocampi. These values both fall within the range of reliability testing according to the manual ‘gold standard’ technique. Thus, this thesis describes the development and validation of a genuinely automated approach to hippocampal volume extraction of potential utility in studies of a range of neuropsychiatric disorders and could eventually find clinical applications

Representations of specific acoustic patterns in the auditory cortex and hippocampus

Previous behavioural studies have shown that repeated presentation of a randomly chosen acoustic pattern leads to the unsupervised learning of some of its specific acoustic features. The objective of our study was to determine the neural substrate for the representation of freshly learnt acoustic patterns. Subjects first performed a behavioural task that resulted in the incidental learning of three different noise-like acoustic patterns. During subsequent high-resolution functional magnetic resonance imaging scanning, subjects were then exposed again to these three learnt patterns and to others that had not been learned. Multi-voxel pattern analysis was used to test if the learnt acoustic patterns could be 'decoded' from the patterns of activity in the auditory cortex and medial temporal lobe. We found that activity in planum temporale and the hippocampus reliably distinguished between the learnt acoustic patterns. Our results demonstrate that these structures are involved in the neural representation of specific acoustic patterns after they have been learnt

Retrospective Inference as a Form of Bounded Rationality, and Its Beneficial Influence on Learning

Probabilistic models of cognition typically assume that agents make inferences about current states by combining new sensory information with fixed beliefs about the past, an approach known as Bayesian filtering. This is computationally parsimonious, but, in general, leads to suboptimal beliefs about past states, since it ignores the fact that new observations typically contain information about the past as well as the present. This is disadvantageous both because knowledge of past states may be intrinsically valuable, and because it impairs learning about fixed or slowly changing parameters of the environment. For these reasons, in offline data analysis it is usual to infer on every set of states using the entire time series of observations, an approach known as (fixed-interval) Bayesian smoothing. Unfortunately, however, this is impractical for real agents, since it requires the maintenance and updating of beliefs about an ever-growing set of states. We propose an intermediate approach, finite retrospective inference (FRI), in which agents perform update beliefs about a limited number of past states (Formally, this represents online fixed-lag smoothing with a sliding window). This can be seen as a form of bounded rationality in which agents seek to optimize the accuracy of their beliefs subject to computational and other resource costs. We show through simulation that this approach has the capacity to significantly increase the accuracy of both inference and learning, using a simple variational scheme applied to both randomly generated Hidden Markov models (HMMs), and a specific application of the HMM, in the form of the widely used probabilistic reversal task. Our proposal thus constitutes a theoretical contribution to normative accounts of bounded rationality, which makes testable empirical predictions that can be explored in future work

Multimodal Feature Integration in the Angular Gyrus during Episodic and Semantic Retrieval.

UNLABELLED: Much evidence from distinct lines of investigation indicates the involvement of angular gyrus (AnG) in the retrieval of both episodic and semantic information, but the region's precise function and whether that function differs across episodic and semantic retrieval have yet to be determined. We used univariate and multivariate fMRI analysis methods to examine the role of AnG in multimodal feature integration during episodic and semantic retrieval. Human participants completed episodic and semantic memory tasks involving unimodal (auditory or visual) and multimodal (audio-visual) stimuli. Univariate analyses revealed the recruitment of functionally distinct AnG subregions during the retrieval of episodic and semantic information. Consistent with a role in multimodal feature integration during episodic retrieval, significantly greater AnG activity was observed during retrieval of integrated multimodal episodic memories compared with unimodal episodic memories. Multivariate classification analyses revealed that individual multimodal episodic memories could be differentiated in AnG, with classification accuracy tracking the vividness of participants' reported recollections, whereas distinct unimodal memories were represented in sensory association areas only. In contrast to episodic retrieval, AnG was engaged to a statistically equivalent degree during retrieval of unimodal and multimodal semantic memories, suggesting a distinct role for AnG during semantic retrieval. Modality-specific sensory association areas exhibited corresponding activity during both episodic and semantic retrieval, which mirrored the functional specialization of these regions during perception. The results offer new insights into the integrative processes subserved by AnG and its contribution to our subjective experience of remembering. SIGNIFICANCE STATEMENT: Using univariate and multivariate fMRI analyses, we provide evidence that functionally distinct subregions of angular gyrus (AnG) contribute to the retrieval of episodic and semantic memories. Our multivariate pattern classifier could distinguish episodic memory representations in AnG according to whether they were multimodal (audio-visual) or unimodal (auditory or visual) in nature, whereas statistically equivalent AnG activity was observed during retrieval of unimodal and multimodal semantic memories. Classification accuracy during episodic retrieval scaled with the trial-by-trial vividness with which participants experienced their recollections. Therefore, the findings offer new insights into the integrative processes subserved by AnG and how its function may contribute to our subjective experience of remembering.This study was funded by a James S. McDonnell Foundation Scholar Award to JSS, and was carried out within the University of Cambridge Behavioural and Clinical Neuroscience Institute, funded by a joint award from the Medical Research Council and the Wellcome Trust. We would like to thank the staff of the MRC Cognition and Brain Sciences Unit MRI facility for scanning assistance.This is the final version of the article. It first appeared from the Society for Neuroscience via https://doi.org/10.1523/JNEUROSCI.4310-15.201

Goal-directed mechanisms that constrain retrieval predict subsequent memory for new "foil" information.

To remember a previous event, it is often helpful to use goal-directed control processes to constrain what comes to mind during retrieval. Behavioral studies have demonstrated that incidental learning of new "foil" words in a recognition test is superior if the participant is trying to remember studied items that were semantically encoded compared to items that were non-semantically encoded. Here, we applied subsequent memory analysis to fMRI data to understand the neural mechanisms underlying the "foil effect". Participants encoded information during deep semantic and shallow non-semantic tasks and were tested in a subsequent blocked memory task to examine how orienting retrieval towards different types of information influences the incidental encoding of new words presented as foils during the memory test phase. To assess memory for foils, participants performed a further surprise old/new recognition test involving foil words that were encountered during the previous memory test blocks as well as completely new words. Subsequent memory effects, distinguishing successful versus unsuccessful incidental encoding of foils, were observed in regions that included the left inferior frontal gyrus and posterior parietal cortex. The left inferior frontal gyrus exhibited disproportionately larger subsequent memory effects for semantic than non-semantic foils, and significant overlap in activity during semantic, but not non-semantic, initial encoding and foil encoding. The results suggest that orienting retrieval towards different types of foils involves re-implementing the neurocognitive processes that were involved during initial encoding.James S. McDonnell Foundation (Scholar Award), Medical Research Council, Wellcome TrustThis is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.neuropsychologia.2016.07.01

Two years later – Revisiting autobiographical memory representations in vmPFC and hippocampus

A long-standing question in memory neuroscience concerns how and where autobiographical memories of personal experiences are represented in the brain. In a previous high resolution multivoxel pattern analysis fMRI study, we examined two week old (recent) and ten year old (remote) autobiographical memories (Bonnici et al., 2012, J. Neurosci. 32:16982–16991). We found that remote memories were particularly well represented in ventromedial prefrontal cortex (vmPFC) compared to recent memories. Moreover, while both types of memory were represented within anterior and posterior hippocampus, remote memories were more easily distinguished in the posterior portion. These findings suggested that a change of some kind had occurred between two weeks and ten years in terms of where autobiographical memories were represented in the brain. In order to examine this further, here participants from the original study returned two years later and recalled the memories again. We found that there was no difference in the detectability of memory representations within vmPFC for the now 2 year old and 12 year old memories, and this was also the case for the posterior hippocampus. Direct comparison of the two week old memories (original study) with themselves two years later (present study) confirmed that their representation within vmPFC had become more evident. Overall, this within-subjects longitudinal fMRI study extends our understanding of autobiographical memory representations by allowing us to narrow the window within which their consolidation is likely to occur. We conclude that after a memory is initially encoded, its representation within vmPFC has stablised by, at most, two years later

Decoding information in the human hippocampus: a user's guide

Multi-voxel pattern analysis (MVPA), or 'decoding', of fMRI activity has gained popularity in the neuroimaging community in recent years. MVPA differs from standard fMRI analyses by focusing on whether information relating to specific stimuli is encoded in patterns of activity across multiple voxels. If a stimulus can be predicted, or decoded, solely from the pattern of fMRI activity, it must mean there is information about that stimulus represented in the brain region where the pattern across voxels was identified. This ability to examine the representation of information relating to specific stimuli (e.g., memories) in particular brain areas makes MVPA an especially suitable method for investigating memory representations in brain structures such as the hippocampus. This approach could open up new opportunities to examine hippocampal representations in terms of their content, and how they might change over time, with aging, and pathology. Here we consider published MVPA studies that specifically focused on the hippocampus, and use them to illustrate the kinds of novel questions that can be addressed using MVPA. We then discuss some of the conceptual and methodological challenges that can arise when implementing MVPA in this context. Overall, we hope to highlight the potential utility of MVPA, when appropriately deployed, and provide some initial guidance to those considering MVPA as a means to investigate the hippocampus

Assessing hippocampal functional reserve in temporal lobe epilepsy:A multi-voxel pattern analysis of fMRI data

Assessing the functional reserve of key memory structures in the medial temporal lobes (MTL) of pre-surgical patients with intractable temporal lobe epilepsy (TLE) remains a challenge. Conventional functional MRI (fMRI) memory paradigms have yet to fully convince of their ability to confidently assess the risk of a post-surgical amnesia. An alternative fMRI analysis method, multi-voxel pattern analysis (MVPA), focuses on the patterns of activity across voxels in specific brain regions that are associated with individual memory traces. This method makes it possible to investigate whether the hippocampus and related structures contralateral to any proposed surgery are capable of laying down and representing specific memories. Here we used MVPA-fMRI to assess the functional integrity of the hippocampi and MTL in patients with long-standing medically refractory TLE associated with unilateral hippocampal sclerosis (HS). Patients were exposed to movie clips of everyday events prior to scanning, which they subsequently recalled during high-resolution fMRI. MTL structures were delineated and pattern classifiers were trained to learn the patterns of brain activity across voxels associated with each memory. Predictable patterns of activity across voxels associated with specific memories could be detected in MTL structures, including the hippocampus, on the side contralateral to the HS, indicating their functional viability. By contrast, no discernible memory representations were apparent in the sclerotic hippocampus, but adjacent MTL regions contained detectable information about the memories. These findings suggest that MVPA in fMRI memory studies of TLE can indicate hippocampal functional reserve and may be useful to predict the effects of hippocampal resection in individual patients

The globalization of the iSchools movement

In 2005 a relatively small interdisciplinary group of LIS schools, all based in the U.S., announced its intention to form a new “iField.” The explicitly stated goal behind the formation and formalization of this group was the coming to grips with the “elusive identity [that] poses a challenge for the I-School movement” (King, 2006). Today 40% of the iSchools Caucus is non-U.S. based. This research examines the impact of the international member schools on what was once an exclusively American group. The internationalization phenomenon is examined from the perspective of the Information Outcome Space (Gross & Latham, 2011). Content analysis of school websites addressing vision and mission statements, “about the school” statements, and messages from the Deans/Directors were conducted to discern the philosophical approaches of iSchool as they relate to the concept of information. This research addresses whether information conception is the uniting, identifying, and defining identity for the iCaucus.published or submitted for publicationis peer reviewe

Automated hippocampal location and extraction

The hippocampus is a complex brain structure that has been studied extensively and is subject to abnormal structural change in various neuropsychiatric disorders. The highest definition in vivo method of visualizing the anatomy of this structure is structural Magnetic Resonance Imaging (MRI). Gross structure can be assessed by the naked eye inspection of MRI scans but measurement is required to compare scans from individuals within normal ranges, and to assess change over time in individuals. The gold standard of such measurement is manual tracing of the boundaries of the hippocampus on scans. This is known as a Region Of Interest (ROI) approach. ROI is laborious and there are difficulties with test-retest and inter-rater reliability. These difficulties are primarily due to uncertainty in designation of the hippocampus boundary. An improved, less labour intensive and more reliable method is clearly desirable. This thesis describes a fully automated hybrid methodology that is able to first locate and then extract hippocampal volumes from 3D 1.5T MRI T1 brain scans automatically. The hybrid algorithm uses brain atlas mappings and fuzzy inference to locate hippocampal areas and create initial hippocampal boundaries. This initial location is used to seed a deformable manifold algorithm. Rule based deformations are then applied to refine the estimate of the hippocampus locations. Finally, the hippocampus boundaries are corrected through an inference process that assures adherence to an expected hippocampus volume. The ICC values of this methodology when compared to the manual segmentation of the same hippocampi result in a 0.73 for the left and 0.81 for the right hippocampi. These values both fall within the range of reliability testing according to the manual ‘gold standard’ technique. Thus, this thesis describes the development and validation of a genuinely automated approach to hippocampal volume extraction of potential utility in studies of a range of neuropsychiatric disorders and could eventually find clinical applications.EThOS - Electronic Theses Online ServiceGBUnited Kingdo