(JHBI TS/第1回) 2021年2月18日(木)16:00

参加登録フォーム(〆2021/2/11 17:00)

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16:00 – 16:05

Opening Remarks and announcements 

16:05 – 16:20

Talk 1: Hiroki Oishi 

(Chair: Ryuta Aoki)

16:20 – 16:35

Talk 2: Shohei Fujita

(Chair: Ryuta Aoki) 

16:35 – 17:20

Lecture: Aviv Mezer

(Chair: Hiromasa Takemura) 

17:20 – 

Free discussion between speakers and attendees 



Talk 1: Hiroki Oishi 
Center for Information and Neural Networks [CiNet], National Institute of Information and Communications Technology, and Osaka University 

Quantitative structural mapping of lateral geniculate nucleus subdivisions in living human brains

The lateral geniculate nucleus (LGN) is a key thalamic nucleus in the visual system, which receives projections from retinal neurons and in turn projects to the cortex. The LGN is composed of two main subdivisions, magnocellular (M) and parvocellular (P) subdivisions. Psychophysical works have proposed a variety of hypotheses on functional significance of LGN subdivisions, including their relevance with dyslexia (Demb et al., 2001) and glaucoma (Joffe et al., 1997). However, these hypotheses remain speculative since these studies did not directly compare measurements on LGN subdivisions with psychophysical data. This is because in vivo structural neuroimaging method for quantifying the properties of LGN subdivisions has not yet been fully established.

In this work, we propose a method to identify LGN subdivisions, primarily using a macromolecular tissue volume (MTV) mapping, which is a quantitative structural MRI method sensitive to a fraction of non-water macromolecules (Mezer et al., 2013). We defined the M and P subdivisions based on the MTV fraction data. We confirmed the validity of the definition by (1) comparisons with human histological data (Amunts et al., 2013), (2) comparisons with fMRI measurements on stimulus selectivity (Denison et al., 2014), and (3) analyzing test-retest reliability. Since this study provides a robust method to non-invasively investigate the structural and functional properties of LGN subdivisions in living human brains, this work will open an avenue to directly compare properties of LGN subdivisions with behavioral or functional data.


Talk 2: Shohei Fujita 
Juntendo University

Repeatability and reproducibility of human brain morphometry using three-dimensional magnetic resonance fingerprinting

Three-dimensional (3D) Magnetic resonance fingerprinting (MRF) permits whole-brain volumetric quantification of T1 and T2 relaxation values, potentially replacing conventional T1-weighted structural imaging for common brain imaging analysis. Accurate volumetric segmentation of the brain is a prerequisite for the detection of subtle regional changes in quantitative relaxation times, which are caused by numerous diseases. However, studies focusing on the reliability of brain morphometry derived from MRF have been limited, despite its clinical relevance. Here, we evaluate the repeatability and reproducibility of 3D MRF in evaluating brain cortical thickness and subcortical volumetric analysis in healthy volunteers using conventional 3D T1-weighted images as a reference standard. Our results demonstrate that 3D MRF provides anatomical information that is as reliable as that provided by conventional 3D T1-weighted imaging. Along with the highly reliable T1 and T2 value quantification, 3D MRF can identify subtle regional changes that may have been obscured during observation of large regions. 3D MRF measurements of human brain cortical thickness and subcortical volumes are highly repeatable, and consistent with measurements taken on conventional 3D T1-weighted images. A slight, consistent bias was evident between the two, and thus careful attention is required when combining data from MRF and conventional acquisitions.


Lecture: Aviv Mezer
Edmond and Lily Safra Center for Brain Sciences [ELSC], The Hebrew University of Jerusalem

Characterizing multiple aging -related changes in the human brain using quantitative MRI

Aging-related changes throughout the brain are likely a result of several distinct molecular mechanisms. Therefore, combining different measurements of brain tissue is crucial in order to fully describe the state of the aging brain.  Quantitative MRI (qMRI) parameters provide physical parametric measurements for non-invasive mapping of the aging process¥. Indeed, qMRI measurements display sensitivity to several microstructural properties such as iron content, lipid composition, water content, and cellular organization. However, an important challenge in applying qMRI measurements for uncovering distinct aging mechanisms is their biological specificity. It is common to assume that qMRI parameters are sensitive to the myelin fraction. Y et any brain tissue including myelin is a complex mixture of various molecules, affecting the MRI signal in an intertwined manner. I will discuss our approach for the decoding of distinct molecular properties from the qMRI signal, both in lipid samples and in the human brain. I will show how we exploit our methods to reveal region-specific molecular changes in the aging human brain.

Last, I will focus on a new approach to characterize the human striatum organization. Changes in the striatum organization are associated with normal aging and disease. I will describe a method for detection and quantification of microstructural gradients along axes of the striatum in vivo, using qMRI. I will show distinct profiles of aging-related changes in the striatum, associated with different biophysical sources.

Altogether I will suggest that qMRI opens the door to a quantitative characterization of the biological sources of aging, that until now was possible only post-mortem.