Distortion and Signal Loss in Medial Temporal Lobe

Background: The medial temporal lobe (MTL) contains subregions that are subject to severe distortion and signal loss in functional MRI. Air/tissue and bone/tissue interfaces in the vicinity of the MTL distort the local magnetic field due to differences in magnetic susceptibility. Fast image acquisition and thin slices can reduce the amount of distortion and signal loss, but at the cost of image signal-to-noise ratio (SNR). Methodology/Principal Findings: In this paper, we quantify the severity of distortion and signal loss in MTL subregions for three different echo planar imaging (EPI) acquisitions at 3 Tesla: a conventional moderate-resolution EPI (36363 mm), a conventional high-resolution EPI (1.561.562 mm), and a zoomed high-resolution EPI. We also demonstrate the advantage of reversing the phase encode direction to control the direction of distortion and to maximize efficacy of distortion compensation during data post-processing. With the high-resolution zoomed acquisition, distortion is not significant and signal loss is present only in the most anterior regions of the parahippocampal gyrus. Furthermore, we find that the severity of signal loss is variable across subjects, with some subjects showing negligible loss and others showing more dramatic loss. Conclusions/Significance: Although both distortion and signal loss are minimized in a zoomed field of view acquisition with thin slices, this improvement in accuracy comes at the cost of reduced SNR. We quantify this trade-off between distortion and SNR in order to provide a decision tree for design of high-resolution experiments investigating the functio

Establishing the boundaries: the hippocampal contribution to imagining scenes

When we visualize scenes, either from our own past or invented, we impose a viewpoint for our “mind's eye” and we experience the resulting image as spatially coherent from that viewpoint. The hippocampus has been implicated in this process, but its precise contribution is unknown. We tested a specific hypothesis based on the spatial firing properties of neurons in the hippocampal formation of rats, that this region supports the construction of spatially coherent mental images by representing the locations of the environmental boundaries surrounding our viewpoint. Using functional magnetic resonance imaging, we show that hippocampal activation increases parametrically with the number of enclosing boundaries in the imagined scene. In contrast, hippocampal activity is not modulated by a nonspatial manipulation of scene complexity nor to increasing difficulty of imagining the scenes in general. Our findings identify a specific computational role for the hippocampus in mental imagery and episodic recollection

Functional MRI investigations of cortical mechanisms of auditory spatial attention

In everyday settings, spatial attention helps listeners isolate and understand individual sound sources. However, the neural mechanisms of auditory spatial attention (ASpA) are only partially understood. This thesis uses within-subject analysis of functional magnetic resonance imaging (fMRI) data to address fundamental questions regarding cortical mechanisms supporting ASpA by applying novel multi-voxel pattern analysis (MVPA) and resting-state functional connectivity (rsFC) approaches. A series of fMRI studies of ASpA were conducted in which subjects performed a one-back task in which they attended to one of two spatially separated streams. Attention modulated blood oxygenation level-dependent (BOLD) activity in multiple areas in the prefrontal, temporal, and parietal cortex, including non-visuotopic intraparietal sulcus (IPS), but not the visuotopic maps in IPS. No spatial bias was detected in any cortical area using standard univariate analysis; however, MVPA revealed that activation patterns in a number of areas, including the auditory cortex, predicted the attended direction. Furthermore, we explored how cognitive task demands and the sensory modality of the inputs influenced activity with a visual one-back task and a visual multiple object tracking (MOT) task. Activity from the visual and auditory one-back tasks overlapped along the fundus of IPS and lateral prefrontal cortex (lPFC). However, there was minimal overlap of activity in the lPFC between the visual MOT task and the two one-back tasks. Finally, we endeavored to identify visual and auditory networks using rsFC. We identified a dorsal visual attention network reliably within individual subjects using visuotopic seeds. Using auditory seeds, we found a prefrontal area nested between segments of the dorsal visual attention network. These findings mark fundamental progress towards elucidating the cortical network controlling ASpA. Our results suggest that similar lPFC structures support both ASpA and its visual counterpart during a spatial one-back task, but that ASpA does not drive visuotopic IPS in the parietal cortex. Furthermore, rsFC reveals that visual and auditory seed regions are functionally connected with non-overlapping lPFC regions, possibly reflecting spatial and temporal cognitive processing biases, respectively. While we find no evidence for a spatiotopic map, the auditory cortex is sensitive to direction of attention in its patterns of activation

MICROSTRUCTURE AND CONNECTIVITY OF THE CEREBELLUM WITH ADVANCED DIFFUSION MRI IN HEALTH AND PATHOLOGY

The cerebellum contains most of the central nervous system neurons and it is classically known to be a key region for sensorimotor coordination and learning. However, its role in higher cognitive functions has been increasingly recognised, thus raising the interest of neuroscience and neuroimaging communities. Despite this, knowledge of cerebellar structure and function is still incomplete and the interpretation of experimental results is often problematic. For these and also technical reasons the cerebellum is still frequently disregarded in magnetic resonance imaging (MRI) studies. Therefore, the principal aim of this work was to use MRI to investigate cerebellar microstructure and macrostructural connectivity in health and pathology, focusing also on technical aspects of image acquisition. The starting point of each project described in the present thesis were techniques, models and pipelines currently accepted in clinical practice. The meeting of inadequacies or problems of such techniques raised questions that pushed research to a more fundamental level. This thesis has three main contributions. The first part presents a clinical study of cerebellar involvement in processing speed deficits in multiple sclerosis, where combined tractography and network science highlighted the importance of the cerebellum in patients\u2019 cognitive performance. Then a deeper investigation conducted on high-quality diffusion MRI data with advanced diffusion signal models showed that subregions of the cerebellar cortex are characterised by different microstructural features: this represents one of the very first attempts to use diffusion MRI to face the widespread idea of cerebellar cortex uniformity, which has been recently challenged by findings from other research fields, thus providing new perspectives for the study of cerebellar information processing in health and pathology. Finally, the emerging technical problems that hamper the study of small structures within the cerebellum were tackled by developing dedicated acquisition protocols that exploit reduced field-of-view techniques for 3T and 7T MRI scanners

Famous Faces Activate Contextual Associations in the Parahippocampal Cortex

The parahippocampal cortex (PHC) has been traditionally implicated both in place processing and in episodic memory. How could the same cortical region mediate these cognitive functions that seem quite different? We have recently proposed that the PHC should be seen as more generally mediating contextual associative processing, which is required for both navigation and memory. We therefore predicted that any associative objects should activate the PHC. To test this generalization, we investigated the extent to which common stimuli that are nonspatial by nature, namely faces, activate the PHC, although their perception is typically associated with other cortical structures. Specifically, we compared the activation elicited by famous faces, which are highly associated with rich pictorial and contextual information (e.g., Tom Cruise) and are not associated with a specific place, with activation elicited by unfamiliar faces. Consistent with our prediction, contrasting famous with unfamiliar faces revealed significant activation within the PHC. Taken collectively, these findings indicate that the PHC should be regarded as mediating contextual associations in general and not necessarily spatial or episodic informatio

Motion robust acquisition and reconstruction of quantitative T2* maps in the developing brain

The goal of the research presented in this thesis was to develop methods for quantitative T2* mapping of the developing brain. Brain maturation in the early period of life involves complex structural and physiological changes caused by synaptogenesis, myelination and growth of cells. Molecular structures and biological processes give rise to varying levels of T2* relaxation time, which is an inherent contrast mechanism in magnetic resonance imaging. The knowledge of T2* relaxation times in the brain can thus help with evaluation of pathology by establishing its normative values in the key areas of the brain. T2* relaxation values are a valuable biomarker for myelin microstructure and iron concentration, as well as an important guide towards achievement of optimal fMRI contrast. However, fetal MR imaging is a significant step up from neonatal or adult MR imaging due to the complexity of the acquisition and reconstruction techniques that are required to provide high quality artifact-free images in the presence of maternal respiration and unpredictable fetal motion. The first contribution of this thesis, described in Chapter 4, presents a novel acquisition method for measurement of fetal brain T2* values. At the time of publication, this was the first study of fetal brain T2* values. Single shot multi-echo gradient echo EPI was proposed as a rapid method for measuring fetal T2* values by effectively freezing intra-slice motion. The study concluded that fetal T2* values are higher than those previously reported for pre-term neonates and decline with a consistent trend across gestational age. The data also suggested that longer than usual echo times or direct T2* measurement should be considered when performing fetal fMRI in order to reach optimal BOLD sensitivity. For the second contribution, described in Chapter 5, measurements were extended to a higher field strength of 3T and reported, for the first time, both for fetal and neonatal subjects at this field strength. The technical contribution of this work is a fully automatic segmentation framework that propagates brain tissue labels onto the acquired T2* maps without the need for manual intervention. The third contribution, described in Chapter 6, proposed a new method for performing 3D fetal brain reconstruction where the available data is sparse and is therefore limited in the use of current state of the art techniques for 3D brain reconstruction in the presence of motion. To enable a high resolution reconstruction, a generative adversarial network was trained to perform image to image translation between T2 weighted and T2* weighted data. Translated images could then be served as a prior for slice alignment and super resolution reconstruction of 3D brain image.Open Acces

Overnight consolidation aids the transfer of statistical knowledge from the medial temporal lobe to the striatum

Sleep is important for abstraction of the underlying principles (or gist) which bind together conceptually related stimuli, but little is known about the neural correlates of this process. Here, we investigate this issue using overnight sleep monitoring and functional magnetic resonance imaging (fMRI). Participants were exposed to a statistically structured sequence of auditory tones then tested immediately for recognition of short sequences which conformed to the learned statistical pattern. Subsequently, after consolidation over either 30min or 24h, they performed a delayed test session in which brain activity was monitored with fMRI. Behaviorally, there was greater improvement across 24h than across 30min, and this was predicted by the amount of slow wave sleep (SWS) obtained. Functionally, we observed weaker parahippocampal responses and stronger striatal responses after sleep. Like the behavioral result, these differences in functional response were predicted by the amount of SWS obtained. Furthermore, connectivity between striatum and parahippocampus was weaker after sleep, whereas connectivity between putamen and planum temporale was stronger. Taken together, these findings suggest that abstraction is associated with a gradual shift from the hippocampal to the striatal memory system and that this may be mediated by SWS

Better Memory and Neural Efficiency in Young Apolipoprotein E ε4 Carriers

The apolipoprotein E (APOE) ε4 allele is the major genetic risk factor for Alzheimer's disease, but an APOE effect on memory performance and memory-related neurophysiology in young, healthy subjects is unknown. We found an association of APOE ε4 with better episodic memory compared with APOE ε2 and ε3 in 340 young, healthy persons. Neuroimaging was performed in a subset of 34 memory-matched individuals to study genetic effects on memory-related brain activity independently of differential performance. E4 carriers decreased brain activity over 3 learning runs, whereas ε2 and ε3 carriers increased activity. This smaller neural investment of ε4 carriers into learning reappeared during retrieval: ε4 carriers exhibited reduced retrieval-related activity with equal retrieval performance. APOE isoforms had no differential effects on cognitive measures other than memory, brain volumes, and brain activity related to working memory. We suggest that APOE ε4 is associated with good episodic memory and an economic use of memory-related neural resources in young, healthy human

Normative model for the diagnosis of neuropsychiatric disorders using deep learning methods

Tese de mestrado integrado, Engenharia Biomédica e Biofísica (Engenharia Clínica e Instrumentação Médica) Universidade de Lisboa, Faculdade de Ciências, 2021The diagnosis of neuropsychiatric disorders (NPDs) is still exclusively dependent on the analysis of the signs and symptoms of the patients since there are no biomarkers useful for clinical practice. Considering that several signs and symptoms are shared among different NPDs, the diagnosis is sometimes incorrect. Therefore, therapeutic approaches do not always succeed, which has an impact on the quality of life of neuropsychiatric patients. Furthermore, NPDs have a global economic and demographic impact. For this reason, technological solutions, such as DL, have been researched for the optimization of diagnosis, in the non-technological field of neuropsychiatry. However, the most promising studies on the diagnosis of NPDs with deep learning (DL) are based on binary classification, which may not be the most adequate approach to deal with the continuous spectrum of NPDs. Here, a DL-based normative model was developed to investigate functional connectivity abnormalities, that may contribute to the development of a novel diagnostic procedure. This method is here used to evaluate how patients deviate from a normal pattern learned by a group of healthy people. To create and evaluate the normative model, resting-state functional magnetic resonance imaging (rs-fMRI) data from three different databases were used. In order to maximise the balance between the amount and the quality of the data, conditions were defined to restrict the variability of the scan parameters. Subsequently, rs-fMRI data were trimmed to the lowest number of time points presented in the sample (150). Then, standard preprocessing steps were performed, including removal of the first 4 volumes of functional data, motion correction, spatial smoothing, and high pass filtering. Single-session independent component analysis (ICA) was run, and the FSL-FIX tool was used to clean noise and artefacts. The functional images were then registered to the T1-weighted brain extracted structural images, and finally to the Montreal Neurosciences Institute 152 standard space. Dual regression was applied using fourteen resting-state functional brain networks (FBN) previously identified in the literature. The Pearson’s correlation coefficient between the extracted blood oxygen level-dependent (BOLD) time series of each FBN was calculated, and a 14x14 network connectivity matrix was generated for each subject. The second part of the project consisted of the creation and optimization of a normative model. The normative model consisted of an autoencoder (AE) with three hidden layers. The AE was trained only in healthy subjects and was tested in both healthy subjects and neuropsychiatric patients, including schizophrenia (SCZ), bipolar disorder (BD), and attention deficit hyperactivity disorder (ADHD) patients. The hypothesis was that the model would “fail” on reconstructing data from neuropsychiatric patients. To evaluate the model performance, graph theory metrics were applied. Besides, the mean squared error was calculated for each feature (correlation between pairs of FBN) to evaluate which regions were worse reconstructed for each group of subjects. The pipeline for NPDs was tested for a SCZ case study, with the addition of a clustering algorithm. The results of this dissertation revealed that the proposed pipeline was able to identify patterns of functional connectivity abnormality that characterize different NPDs. Moreover, the results found for the two SCZ groups of patients were similar, which demonstrated that the normative model here presented was also generalizable. However, the group-level differences did not withstand individual-level analysis, implying that NPDs are highly heterogeneous. These findings support the idea that a precision-based medical approach, focusing on the specific functional network changes of individual patients, may be more beneficial than the traditional group-based diagnostic classification. A personalised diagnosis would allow for personalised therapy, improving the quality of life of neuropsychiatric patients