Modeling Human Visual Detection Using Deep Networks

The work in this report describes the use of machine learning to model human visual detection. This is in contrast to typical machine learning models, which seek to optimize detection performance overall, e.g., precision versus recall or F1 scores. Instead the goal is to develop models that can accurately match humans\u27 abilities to detect objects in images. There are many AI algorithms that have far surpassed humans in, for example, object detection in large image databases or games such as Go. What is different about this work is that the objective is to accurately model humans\u27 performance in visual detection tasks, with the supporting task of knowledge discovery on how humans interpret complex images to detect objects. To accomplish this, deep learning architectures designed for image classification are adapted, extending these architectures to predict detection statistics of human observers

Unravelling The Subfields Of The Hippocampal Head Using 7-Tesla Structural MRI

Probing the functions of human hippocampal subfields is a promising area of research in cognitive neuroscience. However, defining subfield borders in Magnetic Resonance Imaging (MRI) is challenging. Here, we present a user-guided, semi-automated protocol for segmenting hippocampal subfields on T2-weighted images obtained with 7-Tesla MRI. The protocol takes advantage of extant knowledge about regularities in hippocampal morphology and ontogeny that have not been systematically considered in prior related work. An image feature known as the hippocampal ‘dark band’ facilitates tracking of subfield continuities, allowing for unfolding and segmentation of convoluted hippocampal tissue. Initial results suggest that this protocol offers sufficient precision and flexibility to accommodate inter-individual differences in morphology and produces segmentations that have improved accuracy and detail compared to other prominent protocols, with similar inter-rater reliability. We anticipate that this protocol will allow for improved anatomical precision in future research on hippocampal subfields in health and neurological disease

Results of High Velocity Tests at Tampa Electric Company`s Big Bend 4 FGD System.

Test were conducted at the Big Bend Station to determine the feasibility of scrubbing gas from an additional boiler in the existing FGD system. Testing was accomplished by increasing the gas flow from the D absorber tower and measuring the performance of this module. Key performance aspects evaluated during the testing include mist eliminator performance, SO2 removal efficiency, oxidation of absorbed SO2, and limestone utilization

Computational Unfolding of the Human Hippocampus

The hippocampal subfields are defined by their unique cytoarchitectures, which many recent studies have tried to map to human in-vivo MRI because of their promise to further our understanding of hippocampal function, or its dysfunction in disease. However, recent anatomical literature has highlighted broad inter-individual variability in hippocampal morphology and subfield locations, much of which can be attributed to different folding configurations within hippocampal (or archicortical) tissue. Inspired in part by analogous surface-based neocortical analysis methods, the current thesis aimed to develop a standardized coordinate framework, or surface-based method, that respects the topology of all hippocampal folding configurations. I developed such a coordinate framework in Chapter 2, which was initialized by detailed manual segmentations of hippocampal grey matter and high myelin laminae which are visible in 7-Tesla MRI and which separate different hippocampal folds. This framework was leveraged to i) computationally unfold the hippocampus which provided implicit topological inter-individual alignment, ii) delineate subfields with high reliability and validity, and iii) extract novel structural features of hippocampal grey matter. In Chapter 3, I applied this coordinate framework to the open source BigBrain 3D histology dataset. With this framework, I computationally extracted morphological and laminar features and showed that they are sufficient to derive hippocampal subfields in a data-driven manner. This underscores the sensitivity of these computational measures and the validity of the applied subfield definitions. Finally, the unfolding coordinate framework developed in Chapter 2 and extended in Chapter 3 requires manual detection of different tissue classes that separate folds in hippocampal grey matter. This is costly in the time and the expertise required. Thus, in Chapter 4, I applied state-of-the-art deep learning methods in the open source Human Connectome Project MRI dataset to automate this process. This allowed for scalable application of the methods described in Chapters 2, 3, and 4 to similar new datasets, with support for extensions to suit data of different modalities or resolutions. Overall, the projects presented here provide multifaceted evidence for the strengths of a surface-based approach to hippocampal analysis as developed in this thesis, and these methods are readily deployable in new neuroimaging work

Unfolding the hippocampus: An intrinsic coordinate system for subfield segmentations and quantitative mapping

The hippocampus, like the neocortex, has a morphological structure that is complex and variable in its folding pattern, especially in the hippocampal head. The current study presents a computational method to unfold hippocampal grey matter, with a particular focus on the hippocampal head where complexity is highest due to medial curving of the structure and the variable presence of digitations. This unfolding was performed on segmentations from high-resolution, T2-weighted 7T MRI data from 12 healthy participants and one surgical patient with epilepsy whose resected hippocampal tissue was used for histological validation. We traced a critical image feature composed of the hippocampal sulcus and stratum radiatum lacunosum-moleculare, (SRLM) in these images, then employed user-guided semi-automated techniques to detect and subsequently unfold the surrounding hippocampal grey matter. This unfolding was performed by solving Laplace\u27s equation in three dimensions of interest (long-axis, proximal-distal, and laminar). The resulting ‘unfolded coordinate space’ provides an intuitive way of mapping the hippocampal subfields in 2D space (long-axis and proximal-distal), such that similar borders can be applied in the head, body, and tail of the hippocampus independently of variability in folding. This unfolded coordinate space was employed to map intracortical myelin and thickness in relation to subfield borders, which revealed intracortical myelin differences that closely follow the subfield borders used here. Examination of a histological resected tissue sample from a patient with epilepsy reveals that our unfolded coordinate system has biological validity, and that subfield segmentations applied in this space are able to capture features not seen in manual tracing protocols

Hippocampal subfields revealed through unfolding and unsupervised clustering of laminar and morphological features in 3D BigBrain

© 2019 Elsevier Inc. The internal structure of the human hippocampus is challenging to map using histology or neuroimaging due to its complex archicortical folding. Here, we aimed to overcome this challenge using a unique combination of three methods. First, we leveraged a histological dataset with unprecedented 3D coverage, BigBrain. Second, we imposed a computational unfolding framework that respects the topological continuity of hippocampal subfields, which are traditionally defined by laminar composition. Third, we adapted neocortical parcellation techniques to map the hippocampus with respect to not only laminar but also morphological features. Unsupervised clustering of these features revealed subdivisions that closely resemble gold standard manual subfield segmentations. Critically, we also show that morphological features alone are sufficient to derive most hippocampal subfield boundaries. Moreover, some features showed differences within subfields along the hippocampal longitudinal axis. Our findings highlight new characteristics of internal hippocampal structure, and offer new avenues for its characterization with in-vivo neuroimaging

Hippocampal connectivity patterns echo macroscale cortical evolution in the primate brain

While the hippocampus is key for human cognitive abilities, it is also a phylogenetically old cortex and paradoxically considered evolutionarily preserved. Here, we introduce a comparative framework to quantify preservation and reconfiguration of hippocampal organisation in primate evolution, by analysing the hippocampus as an unfolded cortical surface that is geometrically matched across species. Our findings revealed an overall conservation of hippocampal macro- and micro-structure, which shows anterior-posterior and, perpendicularly, subfield-related organisational axes in both humans and macaques. However, while functional organisation in both species followed an anterior-posterior axis, we observed a marked reconfiguration in the latter across species, which mirrors a rudimentary integration of the default-mode-network in non-human primates. Here we show that microstructurally preserved regions like the hippocampus may still undergo functional reconfiguration in primate evolution, due to their embedding within heteromodal association networks

Convergence of cortical types and functional motifs in the human mesiotemporal lobe

The mesiotemporal lobe (MTL) is implicated in many cognitive processes, is compromised in numerous brain disorders, and exhibits a gradual cytoarchitectural transition from six-layered parahippocampal isocortex to three-layered hippocampal allocortex. Leveraging an ultra-high-resolution histological reconstruction of a human brain, our study showed that the dominant axis of MTL cytoarchitectural differentiation follows the iso-to-allocortical transition and depth-specific variations in neuronal density. Projecting the histology-derived MTL model to in-vivo functional MRI, we furthermore determined how its cytoarchitecture underpins its intrinsic effective connectivity and association to large-scale networks. Here, the cytoarchitectural gradient was found to underpin intrinsic effective connectivity of the MTL, but patterns differed along the anterior-posterior axis. Moreover, while the iso-to-allocortical gradient parametrically represented the multiple-demand relative to task-negative networks, anterior-posterior gradients represented transmodal versus unimodal networks. Our findings establish that the combination of micro- and macrostructural features allow the MTL to represent dominant motifs of whole-brain functional organisation

Progress update from the hippocampal subfields group

Introduction: Heterogeneity of segmentation protocols for medial temporal lobe regions and hippocampal subfields on in vivo magnetic resonance imaging hinders the ability to integrate findings across studies. We aim to develop a harmonized protocol based on expert consensus and histological evidence. Methods: Our international working group, funded by the EU Joint Programme–Neurodegenerative Disease Research (JPND), is working toward the production of a reliable, validated, harmonized protocol for segmentation of medial temporal lobe regions. The working group uses a novel postmortem data set and online consensus procedures to ensure validity and facilitate adoption. Results: This progress report describes the initial results and milestones that we have achieved to date, including the development of a draft protocol and results from the initial reliability tests and consensus procedures. Discussion: A harmonized protocol will enable the standardization of segmentation methods across laboratories interested in medial temporal lobe research worldwid