Measurement of allocentric processing in mild cognitive impairment and early Alzheimer’s disease using a virtual reality object location paradigm

Aim: Mild cognitive impairment (MCI) and Alzheimer’s Disease (AD) are major contributors to disability in old age and defined in the early stages by spatial memory deficits associated with hippocampal (HC) and entorhinal (EC) atrophy. Currently diagnosis occurs late in the process which limits efficacy of interventions. This study investigated the neural correlates of a novel object location task (OLT) in immersive virtual reality (iVR). Methods: Twenty amnestic MCI (aMCI) patients and twenty two healthy controls were tested on the iVR OLT, underwent neuropsychological testing and structural MRI scanning. OLT performance and HC, EC subfield volumetric data were compared between groups, and correlational analyses of HC/EC volumes and performance were conducted. Results: Participants with aMCI were significantly impaired in object location recall and object recognition compared to controls. They had significantly smaller total HC, subiculum, CA1, EC and perirhinal volumes. There was a significant interaction of group in analysis of neural correlates: OLT performance was strongly predicted by total HC and subiculum volumes in patients only. EC subfields were not significant predictors of performance. Conclusion: Performance on the novel OLT in immersive VR is a good indicator of HC integrity in older adults with amnestic MCI and can improve the diagnostic process for people with MCI and AD in the future

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

Advanced Magnetic Resonance Imaging and Quantitative Analysis Approaches in Patients with Refractory Focal Epilepsy

Background Epilepsy has a high prevalence of 1%, which makes it the most common serious neurological disorder. The most difficult to treat type of epilepsy is temporal lobe epilepsy (TLE) with its most commonly associated lesion being hippocampal sclerosis (HS). About 30-50% of all patients undergoing resective surgery of epileptogenic tissue continue to have seizures postoperatively. Indication for this type of surgery is only given when lesions are clearly visible on magnetic resonance images (MRI). About 30% of all patients with focal epilepsy do not show an underlying structural lesion upon qualitative neuroradiological MRI assessment (MRI-negative). Objectives The work presented in this thesis uses MRI data to quantitatively investigate structural differences between brains of patients with focal epilepsy and healthy controls using automated imaging preprocessing and analysis methods. Methods All patients studied in this thesis had electrophysiological evidence of focal epilepsy, and underwent routine clinical MRI prior to participation in this study. There were two datasets and both included a cohort of age-matched controls: (i) Patients with TLE and associated HS who later underwent selective amygdalahippocampectomy (cohort 1) and (ii) MRI-negative patients with medically refractory focal epilepsy (cohort 2). The participants received high- resolution routine clinical MRI as well as additional sequences for gray and white matter (GM/WM) structural imaging. A neuroradiologist reviewed all images prior to analysis. Hippocampal subfield volume and automated tractography analysis was performed in patients with TLE and HS and related to post-surgical outcomes, while images of MRI- negative patients were analyzed using voxel-based morphometry (VBM) and manual/automated tractography. All studies were designed to detect quantitative differences between patients and controls, except for the hippocampal subfield analysis as control data was not available and comparisons were limited to patients with persistent postoperative seizures and those without. Results 1. Automated hippocampal subfield analysis (cohort 1): The high-resolution hippocampal subfield segmentation technique cannot establish a link between hippocampal subfield volume loss and post-surgical outcome. Ipsilateral and contralateral hippocampal subfield volumes did not correlate with clinical variables such as duration of epilepsy and age of onset of epilepsy. 2. Automated WM diffusivity analysis (cohort 1): Along-the-tract analysis showed that ipsilateral tracts of patients with right/left TLE and HS were more extensively affected than contralateral tracts and the affected regions within tracts could be specified. The extent of hippocampal atrophy (HA) was not related to (i) the diffusion alterations of temporal lobe tracts or (ii) clinical characteristics of patients, whereas diffusion alterations of ipsilateral temporal lobe tracts were significantly related to age at onset of epilepsy, duration of epilepsy and epilepsy burden.Patients without any postoperative seizure symptoms (excellent outcomes) had more ipsilaterally distributed WM tract diffusion alterations than patients with persistent postoperative seizures (poorer outcomes), who were affected bilaterally. 3. Automated epileptogenic lesion detection (cohort 2): Comparison of individual patients against the controls revealed that focal cortical dysplasia (FCD) can be detected automatically using statistical thresholds. All sites of dysplasia reported at the start of the study were detected using this technique. Two additional sites in two different patients, which had previously escaped neuroradiological assessment, could be identified. When taking these statistical results into account during re-assessment of the dedicated epilepsy research MRI, the expert neuroradiologist was able to confirm these as lesions. 4. Manual and automated WM diffusion tensor imaging (DTI) analysis (cohort 2): The analysis of consistency across approaches revealed a moderate to good agreement between extracted tract shape, morphology and space and a strong correlation between diffusion values extracted with both methods. While whole-tract DTI-metrics determined using Automated Fiber Quantification (AFQ) revealed correlations with clinical variables such as age of onset and duration of epilepsy, these correlations were not found using the manual technique. The manual approach revealed more differences than AFQ in group comparisons of whole-tract DTI-metrics. Along-the-tract analysis provided within AFQ gave a more detailed description of localized diffusivity changes along tracts, which correlated with clinical variables such as age of onset and epilepsy duration. Conclusions While hippocampal subfield volume loss in patients with TLE and HS was not related with any clinical variables or to post-surgical outcomes, WM tract diffusion alterations were more bilaterally distributed in patients with persistent postoperative seizures, compared to patients with excellent outcomes. This may indicate that HS as an initial precipitating injury is not affected by clinical features of the disorder and automated hippocampal subfield mapping based on MRI is not sufficient to stratify patients according to outcome. Presence of persisting seizures may depend on other pathological processes such as seizure propagation through WM tracts and WM integrity. Automated and time-efficient three-dimensional voxel-based analysis may complement conventional visual assessments in patients with MRI-negative focal epilepsy and help to identify FCDs escaping routine neuroradiological assessment. Furthermore, automated along-the-tract analysis may identify widespread abnormal diffusivity and correlations between WM integrity loss and clinical variables in patients with MRI-negative epilepsy. However, automated WM tract analysis may differ from results obtained with manual methods and therefore caution should be exercised when using automated techniques

Ultra-high resolution imaging of the functional anatomy and plasticity of the human hippocampal-entorhinal circuitry

Magdeburg, Univ., Fak. für Naturwiss., Diss., 2015von Anne Maa

Two photon interrogation of hippocampal subregions CA1 and CA3 during spatial behaviour

The hippocampus is crucial for spatial navigation and episodic memory formation. Hippocampal place cells exhibit spatially selective activity within an environment and form the neural basis of a cognitive map of space which supports these mnemonic functions. Hebb’s (1949) postulate regarding the creation of cell assemblies is seen as the pre-eminent model of learning in neural systems. Investigating changes to the hippocampal representation of space during an animal’s exploration of its environment provides an opportunity to observe Hebbian learning at the population and single cell level. When exploring new environments animals form spatial memories that are updated with experience and retrieved upon re-exposure to the same environment, but how this is achieved by different subnetworks in hippocampal CA1 and CA3, and how these circuits encode distinct memories of similar objects and events remains unclear. To test these ideas, we developed an experimental strategy and detailed protocols for simultaneously recording from CA1 and CA3 populations with 2P imaging. We also developed a novel all-optical protocol to simultaneously activate and record from ensembles of CA3 neurons. We used these approaches to show that targeted activation of CA3 neurons results in an increasing excitatory amplification seen only in CA3 cells when stimulating other CA3 cells, and not in CA1, perhaps reflecting the greater number of recurrent connections in CA3. To probe hippocampal spatial representations, we titrated input to the network by morphing VR environments during spatial navigation to assess the local CA3 as well as downstream CA1 responses. To this end, we found CA1 and CA3 neural population responses behave nonlinearly, consistent with attractor dynamics associated with the two stored representations. We interpret our findings as supporting classic theories of Hebbian learning and as the beginning of uncovering the relationship between hippocampal neural circuit activity and the computations implemented by their dynamics. Establishing this relationship is paramount to demystifying the neural underpinnings of cognition

Bridging generative models and Convolutional Neural Networks for domain-agnostic segmentation of brain MRI

Segmentation of brain MRI scans is paramount in neuroimaging, as it is a prerequisite for many subsequent analyses. Although manual segmentation is considered the gold standard, it suffers from severe reproducibility issues, and is extremely tedious, which limits its application to large datasets. Therefore, there is a clear need for automated tools that enable fast and accurate segmentation of brain MRI scans. Recent methods rely on convolutional neural networks (CNNs). While CNNs obtain accurate results on their training domain, they are highly sensitive to changes in resolution and MRI contrast. Although data augmentation and domain adaptation techniques can increase the generalisability of CNNs, these methods still need to be retrained for every new domain, which requires costly labelling of images. Here, we present a learning strategy to make CNNs agnostic to MRI contrast, resolution, and numerous artefacts. Specifically, we train a network with synthetic data sampled from a generative model conditioned on segmentations. Crucially, we adopt a domain randomisation approach where all generation parameters are drawn for each example from uniform priors. As a result, the network is forced to learn domain-agnostic features, and can segment real test scans without retraining. The proposed method almost achieves the accuracy of supervised CNNs on their training domain, and substantially outperforms state-of-the-art domain adaptation methods. Finally, based on this learning strategy, we present a segmentation suite for robust analysis of heterogeneous clinical scans. Overall, our approach unlocks the development of morphometry on millions of clinical scans, which ultimately has the potential to improve the diagnosis and characterisation of neurological disorders

The relationship between individual variation in macroscale functional gradients and distinct aspects of ongoing thought

Contemporary accounts of ongoing thought recognise it as a heterogeneous and multidimensional construct, varying in both form and content. An emerging body of evidence demonstrates that distinct types of experience are associated with unique neurocognitive profiles, that can be described at the whole-brain level as interactions between multiple large scale networks. The current study sought to explore the possibility that whole-brain functional connectivity patterns at rest may be meaningfully related to patterns of ongoing thought that occurred over this period. Participants underwent resting-state functional magnetic resonance imaging (rs-fMRI) followed by a questionnaire retrospectively assessing the content and form of their ongoing thoughts during the scan. A non-linear dimension reduction algorithm was applied to the rs-fMRI data to identify components explaining the greatest variance in whole brain connectivity patterns, and ongoing thought patterns during the resting-state were measured retrospectively at the end of the scan. Multivariate analyses revealed that individuals for whom the connectivity of the sensorimotor system was maximally distinct from the visual system were most likely to report thoughts related to finding solutions to problems or goals and least likely to report thoughts related to the past. These results add to an emerging literature that suggests that unique patterns of experience are associated with distinct distributed neurocognitive profiles and highlight that unimodal systems may play an important role in this process

Developing a System to Investigate Age-Related Differences in the Real-Time Utilization of Dynamically Changing External Cues during Navigation

Successful navigation depends critically upon two broad categories of sensory information, environmental (allothetic) and self-motion (idiothetic). Both the hippocampus and the medial portion of the entorhinal cortex (MEC) are critical for spatial navigation and contain functionally distinct sub-networks of spatially-modulated cells. These cells are characterized by their tuning to different spatial sensory-perceptual features of the environment and all utilize both allothetic and idiothetic cues to anchor and update their spatial firing to generate a comprehensive and dynamic representation of space. As with older adults, aged rats show pronounced impairments on a number of different spatial navigation tasks and these impairments are accompanied by a bias toward relying on egocentric over allocentric navigation strategies. Similarly, the hippocampus and MEC are also highly susceptible to age-associated changes. The influence visual allothetic cues exert on hippocampal place cell spatial tuning is diminished and delayed in aged animals. Two plausible and non-exclusive explanations that could account for these age-related alterations in allothetic processing are 1) circuit disruptions caused by known age-related functional and anatomical changes in the entorhinal-hippocampal processing pathway or 2) degraded sensory-perceptual information resulting from well-established age-related deficits across multiple sensory domains. Either of these possibilities could have the effect of either slowing allothetic cue processing or weakening the ability of these cues to influence firing field alignment. Within this context, this thesis was conceived with the aim of investigating the degree and timing with which young and aged animals utilize allothetic and idiothetic feedback to update their internal representation of space and calibrate their behavioral output. A large focus of this thesis is given to the incremental design and piloting of a number of novel technologies. Foremost among the methodological contributions of this study is the development of an augmented reality behavioral apparatus, termed the Instantaneous Cue Rotation (ICR) arena, which utilizes projected visual cues to allow for rapid remote control of all symmetry breaking visual features in the environment as rats actively engage in a visual-cue based goal navigation task. The results of extensive behavioral piloting of old and young rats validate both the ICR rotation manipulation as well as the mobile reward delivery system. This system traverses the track in tandem with the rat, enabling food based spatial reinforcement while preventing food-related olfactory cues from becoming associated with any specific location. In parallel with this work, microdrive technology was developed to enable simultaneous recording from both MEC and CA1 which is discussed along with results from single-region hippocampal and MEC implanted rats assessed in the context of a cue rotation manipulation conceptually similar to the that of the ICR. Finally, the results of the behavioral study suggest that in young rats the cue rotation exerts reliable but incomplete control over running behavior. In aged rats, by comparison, the cues exert an overall less pronounced influence on running behavior, consistent with known age-related deficits in allothetic processing. When assessed on a lap-by-lap basis, it was found that the behavior of both young and aged rats became progressively more aligned with the cues over the first few laps following cue rotation. These findings suggest a progressive realignment of behavior from an egocentric to an allocentric reference frame which is reminiscent of the reported progressive realignment of place field firing in response to conflicting spatial feedback