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Kouhei Kamiya

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6 papers

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YNICL Journal 2025 Journal Article

Brain network alterations in anorexia Nervosa: A Multi-Center structural connectivity study

Jun Kanzawa
Ryo Kurokawa
Tsunehiko Takamura
Nobuhiro Nohara
Kouhei Kamiya
Yoshiya Moriguchi
Yasuhiro Sato
Yumi Hamamoto

Anorexia nervosa (AN) is a severe eating disorder characterized by intense fear of weight gain, distorted body image, and extreme food restriction. This research employed advanced diffusion MRI techniques including single-shell 3-tissue constrained spherical deconvolution, anatomically constrained tractography, and spherical deconvolution informed filtering of tractograms to analyze brain network alterations in AN. Diffusion MRI data from 81 AN patients and 98 healthy controls were obtained. The structural brain connectome was constructed based on nodes set in 84 brain regions, and graph theory analysis was conducted. Results showed that AN patients exhibited significantly higher clustering coefficient and local efficiency in several brain regions, including the left fusiform gyrus, bilateral orbitofrontal cortex, right entorhinal cortex, right lateral occipital gyrus, right superior temporal gyrus, and right insula. A trend towards higher global efficiency and small-worldness was also observed in AN patients, although not statistically significant. These findings suggest increased local connectivity and efficiency within regions associated with behavioral rigidity, emotional regulation, and disturbed body image among AN patients. This study contributes to the understanding of the neurological basis of AN by highlighting structural connectivity alterations in specific brain regions.

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YNIMG Journal 2021 Journal Article

Cross-scanner reproducibility and harmonization of a diffusion MRI structural brain network: A traveling subject study of multi-b acquisition

Ryo Kurokawa
Kouhei Kamiya
Shinsuke Koike
Moto Nakaya
Akiko Uematsu
Saori C. Tanaka
Koji Kamagata
Naohiro Okada

Characterization of brain networks by diffusion MRI (dMRI) has rapidly evolved, and there are ongoing movements toward data sharing and multi-center studies. To extract meaningful information from multi-center data, methods to correct for the bias caused by scanner differences, that is, harmonization, are urgently needed. In this work, we report the cross-scanner differences in structural network analyses using data from nine traveling subjects (four males and five females, 21-49 years-old) who underwent scanning using four 3T scanners (public database available from the Brain/MINDS Beyond Human Brain MRI project (http://mriportal.umin.jp/)). The reliability and reproducibility were compared to those of data from another set of four subjects (all males, 29-42 years-old) who underwent scan-rescan (interval, 105-147 days) with the same scanner as well as scan-rescan data from the Human Connectome Project database. The results demonstrated that the reliability of the edge weights and graph theory metrics was lower for data including different scanners, compared to the scan-rescan with the same scanner. Besides, systematic differences between scanners were observed, indicating the risk of bias in comparing networks obtained from different scanners directly. We further demonstrate that it is feasible to reduce inter-scanner variabilities while preserving the inter-subject differences among healthy individuals by modeling the scanner effects at the level of network matrices, when traveling-subject data are available for calibration between scanners. The present data and results are expected to serve as a basis for developing and evaluating novel harmonization methods.

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YNIMG Journal 2019 Journal Article

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks

Charley Gros
Benjamin De Leener
Atef Badji
Josefina Maranzano
Dominique Eden
Sara M. Dupont
Jason Talbott
Ren Zhuoquiong

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework — robust to variability in both image parameters and clinical condition — for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n = 30). Data spanned three contrasts (T1-, T2-, and T2 ∗-weighted) for a total of 1943 vol and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p ≤ 0. 05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of −15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.

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YNICL Journal 2018 Journal Article

Connectome analysis with diffusion MRI in idiopathic Parkinson's disease: Evaluation using multi-shell, multi-tissue, constrained spherical deconvolution

Koji Kamagata
Andrew Zalesky
Taku Hatano
Maria Angelique Di Biase
Omar El Samad
Shinji Saiki
Keigo Shimoji
Kanako K. Kumamaru

Parkinson's disease (PD) is a progressive neurodegenerative disorder that affects extensive regions of the central nervous system. In this work, we evaluated the structural connectome of patients with PD, as mapped by diffusion-weighted MRI tractography and a multi-shell, multi-tissue (MSMT) constrained spherical deconvolution (CSD) method to increase the precision of tractography at tissue interfaces. The connectome was mapped with probabilistic MSMT-CSD in 21 patients with PD and in 21 age- and gender-matched controls. Mapping was also performed by deterministic single-shell, single tissue (SSST)-CSD tracking and probabilistic SSST-CSD tracking for comparison. A support vector machine was trained to predict diagnosis based on a linear combination of graph metrics. We showed that probabilistic MSMT-CSD could detect significantly reduced global strength, efficiency, clustering, and small-worldness, and increased global path length in patients with PD relative to healthy controls; by contrast, probabilistic SSST-CSD only detected the difference in global strength and small-worldness. In patients with PD, probabilistic MSMT-CSD also detected a significant reduction in local efficiency and detected clustering in the motor, frontal temporoparietal associative, limbic, basal ganglia, and thalamic areas. The network-based statistic identified a subnetwork of reduced connectivity by MSMT-CSD and probabilistic SSST-CSD in patients with PD, involving key components of the cortico-basal ganglia-thalamocortical network. Finally, probabilistic MSMT-CSD had superior diagnostic accuracy compared with conventional probabilistic SSST-CSD and deterministic SSST-CSD tracking. In conclusion, probabilistic MSMT-CSD detected a greater extent of connectome pathology in patients with PD, including those with cortico-basal ganglia-thalamocortical network disruptions. Connectome analysis based on probabilistic MSMT-CSD may be useful when evaluating the extent of white matter connectivity disruptions in PD.

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YNICL Journal 2017 Journal Article

Diffusion imaging of reversible and irreversible microstructural changes within the corticospinal tract in idiopathic normal pressure hydrocephalus

Kouhei Kamiya
Masaaki Hori
Ryusuke Irie
Masakazu Miyajima
Madoka Nakajima
Koji Kamagata
Kouhei Tsuruta
Asami Saito

The symptoms of idiopathic normal pressure hydrocephalus (iNPH) can be improved by shunt surgery, but prediction of treatment outcome is not established. We investigated changes of the corticospinal tract (CST) in iNPH before and after shunt surgery by using diffusion microstructural imaging, which infers more specific tissue properties than conventional diffusion tensor imaging. Two biophysical models were used: neurite orientation dispersion and density imaging (NODDI) and white matter tract integrity (WMTI). In both methods, the orientational coherence within the CSTs was higher in patients than in controls, and some normalization occurred after the surgery in patients, indicating axon stretching and recovery. The estimated axon density was lower in patients than in controls but remained unchanged after the surgery, suggesting its potential as a marker for irreversible neuronal damage. In a Monte-Carlo simulation that represented model axons as undulating cylinders, both NODDI and WMTI separated the effects of axon density and undulation. Thus, diffusion MRI may distinguish between reversible and irreversible microstructural changes in iNPH. Our findings constitute a step towards a quantitative image biomarker that reflects pathological process and treatment outcomes of iNPH.

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YNICL Journal 2013 Journal Article

In vivo evaluation of gray and white matter volume loss in the parkinsonian variant of multiple system atrophy using SPM8 plus DARTEL for VBM

Yoko Shigemoto
Hiroshi Matsuda
Kouhei Kamiya
Norihide Maikusa
Yasuhiro Nakata
Kimiteru Ito
Miho Ota
Naofumi Matsunaga

In multiple system atrophy with predominant parkinsonism (MSA-P), several voxel-based morphometry (VBM) studies have revealed gray matter loss; however, the white matter volume changes have been rarely reported. We investigated the volume changes of white matter as well as gray matter by VBM. A retrospective MRI study was performed in 20 patients with MSA-P and 30 age-matched healthy controls. We applied VBM with statistical parametric mapping (SPM8) plus diffeomorphic anatomical registration through exponentiated Lie algebra (DARTEL) to explore the regional atrophy of gray and white matter in all of the MSA-P patients, 14 patients with left-side dominant and 6 patients with right-side dominant onset as compared to controls. In all of the MSA-P patients, VBM revealed a significant volume reduction of gray matter in the bilateral putamina, cerebellums and dorsal midbrain. White matter loss was located in bilateral globus pallidi, external capsules extending to the midbrain, right subcortical to precentral area through internal capsule, the pons, bilateral middle cerebellar peduncles and left cerebellum. In left-side dominant MSA-P patients, the gray and white matter volume loss was detected predominantly on the right side and vice versa in right-side dominant MSA-P patients. A correlation with disease duration and severity was not detected. VBM using SPM8 plus DARTEL detected significant volume loss not only in the gray but also in the white matter of the area affected by MSA-P.

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