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The role of HG in the analysis of temporal iteration and interaural correlation
Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application
Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most
Brain Microstructure: Impact of the Permeability on Diffusion MRI
Diffusion Magnetic Resonance Imaging (dMRI) enables a non invasive in-vivo characterization of the brain tissue. The disentanglement of each microstructural property reflected on the total dMRI signal is one of the hottest topics in the field. The dMRI reconstruction techniques ground on assumptions on the signal model and consider the neurons axons as impermeable cylinders. Nevertheless, interactions with the environment is characteristic of the biological life and diffusional water exchange takes place through cell membranes. Myelin wraps axons with multiple layers constitute a barrier modulating exchange between the axon and the extracellular tissue. Due to the short transverse relaxation time (T2) of water trapped between sheets, myelin contribution to the diffusion signal is often neglected. This thesis aims to explore how the exchange influences the dMRI signal and how this can be informative on myelin structure. We also aimed to explore how recent dMRI signal reconstruction techniques could be applied in clinics proposing a strategy for investigating the potential as biomarkers of the derived tissue descriptors. The first goal of the thesis was addressed performing Monte Carlo simulations of a system with three compartments: intra-axonal, spiraling myelin and extra-axonal. The experiments showed that the exchange time between intra- and extra-axonal compartments was on the sub-second level (and thus possibly observable) for geometries with small axon diameter and low number of wraps such as in the infant brain and in demyelinating diseases. The second goal of the thesis was reached by assessing the indices derived from three dimensional simple harmonics oscillator-based reconstruction and estimation (3D-SHORE) in stroke disease. The tract-based analysis involving motor networks and the region-based analysis in grey matter (GM) were performed. 3D-SHORE indices proved to be sensitive to plasticity in both white matter (WM) and GM, highlighting their viability as biomarkers in ischemic stroke. The overall study could be considered the starting point for a future investigation of the interdependence of different phenomena like exchange and relaxation related to the established dMRI indices. This is valuable for the accurate dMRI data interpretation in heterogeneous tissues and different physiological conditions
Recommendations and guidelines from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 1 -- In vivo small-animal imaging
The value of in vivo preclinical diffusion MRI (dMRI) is substantial.
Small-animal dMRI has been used for methodological development and validation,
characterizing the biological basis of diffusion phenomena, and comparative
anatomy. Many of the influential works in this field were first performed in
small animals or ex vivo samples. The steps from animal setup and monitoring,
to acquisition, analysis, and interpretation are complex, with many decisions
that may ultimately affect what questions can be answered using the data. This
work aims to serve as a reference, presenting selected recommendations and
guidelines from the diffusion community, on best practices for preclinical dMRI
of in vivo animals. In each section, we also highlight areas for which no
guidelines exist (and why), and where future work should focus. We first
describe the value that small animal imaging adds to the field of dMRI,
followed by general considerations and foundational knowledge that must be
considered when designing experiments. We briefly describe differences in
animal species and disease models and discuss how they are appropriate for
different studies. We then give guidelines for in vivo acquisition protocols,
including decisions on hardware, animal preparation, imaging sequences and data
processing, including pre-processing, model-fitting, and tractography. Finally,
we provide an online resource which lists publicly available preclinical dMRI
datasets and software packages, to promote responsible and reproducible
research. An overarching goal herein is to enhance the rigor and
reproducibility of small animal dMRI acquisitions and analyses, and thereby
advance biomedical knowledge.Comment: 69 pages, 6 figures, 1 tabl
Conceptual disorganization and redistribution of resting state cortical hubs in drug-naive first episode psychosis: A 7T functional magnetic resonance imaging study
Network level dysconnectivity has been studied in positive and negative symptoms of schizophrenia. Conceptual disorganization (CD) is a symptom which predicts impaired real-world functioning. Systematic reviews have reported aberrant connectivity in formal thought disorder, a construct related to CD. However, no studies have investigated whole-brain functional correlates of CD in psychosis. We sought to investigate brain regions explaining the severity of CD in patients with first-episode psychosis (FEPs) compared with healthy controls (HCs). We computed whole-brain binarized degree centrality maps of 31 FEPs, 25 HCs and characterized the patterns of network connectivity in the two groups. In FEPs, we related these findings to the severity of CD. We also studied the effect of positive and negative symptoms on altered network connectivity. Compared to HCs, the FEPs showed reduced hubness of a cluster located in the right superior temporal gyrus (rSTG). In patients exhibiting high CD, increased hubness of a medial superior parietal (mSPL) cluster was observed, compared to patients exhibiting low CD. These two regions were strongly correlated with CD scores but not with other symptom scores. Our observations are congruent with previous findings of reduced but not increased hubness. We observed increased hubness of mSPL suggesting that a cortical reorganization occurs in brain networks to provide alternate routes for information transfer. These findings provide insight into the underlying neural processes mediating the presentation of symptoms in untreated FEP. A longitudinal tracking of the symptom course will be useful to assess the mechanisms underlying these compensatory changes
Investigating microstructural variation in the human hippocampus using non-negative matrix factorization
In this work we use non-negative matrix factorization to identify patterns of microstructural variance in the human hippocampus. We utilize high-resolution structural and diffusion magnetic resonance imaging data from the Human Connectome Project to query hippocampus microstructure on a multivariate, voxelwise basis. Application of non-negative matrix factorization identifies spatial components (clusters of voxels sharing similar covariance patterns), as well as subject weightings (individual variance across hippocampus microstructure). By assessing the stability of spatial components as well as the accuracy of factorization, we identified 4 distinct microstructural components. Furthermore, we quantified the benefit of using multiple microstructural metrics by demonstrating that using three microstructural metrics (T1-weighted/T2-weighted signal, mean diffusivity and fractional anisotropy) produced more stable spatial components than when assessing metrics individually. Finally, we related individual subject weightings to demographic and behavioural measures using a partial least squares analysis. Through this approach we identified interpretable relationships between hippocampus microstructure and demographic and behavioural measures. Taken together, our work suggests non-negative matrix factorization as a spatially specific analytical approach for neuroimaging studies and advocates for the use of multiple metrics for data-driven component analyses
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