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Optimizing Functional MRI Acquisitio...

Kumar, Rajat.

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Optimizing Functional MRI Acquisition and Analysis for Personalized Neurodiagnostics.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Optimizing Functional MRI Acquisition and Analysis for Personalized Neurodiagnostics./
Author:	Kumar, Rajat.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
Description:	107 p.
Notes:	Source: Dissertations Abstracts International, Volume: 83-01, Section: B.
Contained By:	Dissertations Abstracts International83-01B.
Subject:	Medical imaging. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28541370
ISBN:	9798516078965

Optimizing Functional MRI Acquisition and Analysis for Personalized Neurodiagnostics.
Kumar, Rajat.

Optimizing Functional MRI Acquisition and Analysis for Personalized Neurodiagnostics. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 107 p.

Source: Dissertations Abstracts International, Volume: 83-01, Section: B.

Thesis (Ph.D.)--State University of New York at Stony Brook, 2021.

This item must not be sold to any third party vendors.

Functional magnetic resonance image (fMRI) is a non-invasive neuroimaging modality that has become a standard tool for mapping human brain function, both for basic science and clinical insights. Although most neuroimaging studies using fMRI rely on group analysis, clinical applications require analysis and inference at a single-subject level. Such analysis has so far been underutilized because of the limited sensitivity of fMRI at a single-subject level due to its poor signal-to-noise ratio. Yet even if the sensitivity issues are overcome, we also require new analytic approaches for diagnosing and quantifying dysregulated brain dynamics in an individual subject. This dissertation presents new methods for improving signal-to-noise ratio of fMRI data and provides quantitative measures for personalized neurodiagnostics. We introduce a novel phantom designed and built for generating ground-truth "resting-state" signal that permits data-driven estimation and correction for scanner-induced distortion of fMRI time-series dynamics. Specifically, we introduce data-quality metrics for quantifying signal-to-noise ratio, multiplicative noise/scanner instability, input-output fidelity, and scanner-induced non-linearity in fMRI time-series. To correct scanner-induced distortion of fMRI time-series dynamics, we provide a deeplearning based temporal denoising algorithm, application of which showed increased detection sensitivity of resting-state networks at the single-subject level. Secondly, we developed a control-theoretic framework with applications to fMRI data for extracting generative computational models of human brain circuits at a single-subject level. These generative models provide two quantitative measures of direct relevance for psychiatric disorders: a circuit's sensitivity to external perturbation and its dysregulation. Lastly, we developed an MRI-compatible nasal drug delivery method for probing nicotine addiction dynamics. Our MR-compatible nasal delivery method enables measurement of neural circuit responses to drug doses on a single-subject level, allowing reliable identification of neuromodulatory targets for pharmacotherapy or brain stimulation, and the development of data-driven predictive models for quantifying dysregulation of the reward circuit causing addiction.

ISBN: 9798516078965Subjects--Topical Terms:

3172799
Medical imaging.
Subjects--Index Terms:

Functional MRI

Optimizing Functional MRI Acquisition and Analysis for Personalized Neurodiagnostics.
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520 $a Functional magnetic resonance image (fMRI) is a non-invasive neuroimaging modality that has become a standard tool for mapping human brain function, both for basic science and clinical insights. Although most neuroimaging studies using fMRI rely on group analysis, clinical applications require analysis and inference at a single-subject level. Such analysis has so far been underutilized because of the limited sensitivity of fMRI at a single-subject level due to its poor signal-to-noise ratio. Yet even if the sensitivity issues are overcome, we also require new analytic approaches for diagnosing and quantifying dysregulated brain dynamics in an individual subject. This dissertation presents new methods for improving signal-to-noise ratio of fMRI data and provides quantitative measures for personalized neurodiagnostics. We introduce a novel phantom designed and built for generating ground-truth "resting-state" signal that permits data-driven estimation and correction for scanner-induced distortion of fMRI time-series dynamics. Specifically, we introduce data-quality metrics for quantifying signal-to-noise ratio, multiplicative noise/scanner instability, input-output fidelity, and scanner-induced non-linearity in fMRI time-series. To correct scanner-induced distortion of fMRI time-series dynamics, we provide a deeplearning based temporal denoising algorithm, application of which showed increased detection sensitivity of resting-state networks at the single-subject level. Secondly, we developed a control-theoretic framework with applications to fMRI data for extracting generative computational models of human brain circuits at a single-subject level. These generative models provide two quantitative measures of direct relevance for psychiatric disorders: a circuit's sensitivity to external perturbation and its dysregulation. Lastly, we developed an MRI-compatible nasal drug delivery method for probing nicotine addiction dynamics. Our MR-compatible nasal delivery method enables measurement of neural circuit responses to drug doses on a single-subject level, allowing reliable identification of neuromodulatory targets for pharmacotherapy or brain stimulation, and the development of data-driven predictive models for quantifying dysregulation of the reward circuit causing addiction.
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