Evidence map›Paper›PMID 28470878›Full record

ArticleHuman brain mapping2017

Gray Matter Abnormalities in Idiopathic Parkinson's Disease: Evaluation by Diffusional Kurtosis Imaging and Neurite Orientation Dispersion and Density Imaging.

Koji Kamagata, Andrew Zalesky, Taku Hatano, Ryo Ueda, Maria Angelique Di Biase, Ayami Okuzumi, Keigo Shimoji, Masaaki Hori, Karen Caeyenberghs, Christos Pantelis and 2 more

Abstract read
In one paragraph

Article in Human brain mapping, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
72citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

72 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Gray Matter Microstructure Measured Using Diffusion Imaging as a Biomarker of Severity in Lewy Body Diseases.Movement disorders : official journal of the Movement Disorder Society · 2026
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  9. Utilizing combined quantitative multiparametric MRI as potential biomarkers for improved early-stage parkinson's disease diagnosis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025
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12 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Koji KamagataDepartment of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.ORCID http://orcid.org/0000-0001-5028-218X
Andrew ZaleskyMelbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Parkville, VIC, Australia.
Taku HatanoDepartment of Neurology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Ryo UedaDepartment of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan.
Maria Angelique Di BiaseMelbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Parkville, VIC, Australia.
Ayami OkuzumiDepartment of Neurology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Keigo ShimojiDepartment of Diagnostic Radiology, Tokyo Metropolitan Geriatric Hospital, Tokyo, Japan.
Masaaki HoriDepartment of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Karen CaeyenberghsSchool of Psychology, Faculty of Health Sciences, Australian Catholic University, Fitzroy, VIC, Australia.
Christos PantelisMelbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Parkville, VIC, Australia.
Nobutaka HattoriDepartment of Neurology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Shigeki AokiDepartment of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan.

Funding

Hitachi, Ltd.JSPS KAKENHI JP16K19854the program for Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from the Japan Agency for Medical Research and Development (AMED)
6 · The paper itself

Abstract

Mapping gray matter (GM) pathology in Parkinson's disease (PD) with conventional MRI is challenging, and the need for more sensitive brain imaging techniques is essential to facilitate early diagnosis and assessment of disease severity. GM microstructure was assessed with GM-based spatial statistics applied to diffusion kurtosis imaging (DKI) and neurite orientation dispersion imaging (NODDI) in 30 participants with PD and 28 age- and gender-matched controls. These were compared with currently used assessment methods such as diffusion tensor imaging (DTI), voxel-based morphometry (VBM), and surface-based cortical thickness analysis. Linear discriminant analysis (LDA) was also used to test whether subject diagnosis could be predicted based on a linear combination of regional diffusion metrics. Significant differences in GM microstructure were observed in the striatum and the frontal, temporal, limbic, and paralimbic areas in PD patients using DKI and NODDI. Significant correlations between motor deficits and GM microstructure were also noted in these areas. Traditional VBM and surface-based cortical thickness analyses failed to detect any GM differences. LDA indicated that mean kurtosis (MK) and intra cellular volume fraction (ICVF) were the most accurate predictors of diagnostic status. In conclusion, DKI and NODDI can detect cerebral GM abnormalities in PD in a more sensitive manner when compared with conventional methods. Hence, these methods may be useful for the diagnosis of PD and assessment of motor deficits. Hum Brain Mapp 38:3704-3722, 2017. © 2017 Wiley Periodicals, Inc.

Indexed as

diffusion MRIdiffusion tensor imagingdiscriminant analysisLewy bodieslimbic systemneurodegenerative disorders

Identifiers

PMID28470878
PMCPMC6867088

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.