Differences in cortical structure between cognitively normal East Asian and Caucasian older adults: a surface-based morphometry study

Kang, Dong Woo; Wang, Sheng‐Min; Na, Hae-Ran; Park, Sonya Youngju; Kim, Nak Young; Lee, Chang Uk; Kim, Donghyeon; Son, Seong-Jin; Lim, Hyun Kook

doi:10.1038/s41598-020-77848-8

Cited by 29 publications

(17 citation statements)

References 57 publications

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“…Unexpectedly, in the J-ADNI cohort, the calculated MAE for the OPLS model was smaller than for the CNN model. A potential explanation could be the difference in ethnicity which has been discussed elsewhere (Choi et al, 2020; Kang et al, 2020; Tang et al, 2018). Different ethnical populations can present differences in brain morphology.…”

Section: Discussionmentioning

confidence: 99%

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A Deep Learning Model for Brain Age Prediction Using Minimally Pre-processed T1w-images as Input

Dartora

Marseglia

Mårtensson

et al. 2022

Preprint

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In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w), using multivariate methods and artificial intelligence. We aimed to develop and validate a convolutional neural network (CNN) model for brain age prediction (PBA) using minimally processed T1w MRIs. Our model only requires one preprocessing step (i.e., image registration to MNI space), which is an advantage in comparison with previous methods that require more preprocessing steps. We used a multi-cohort dataset of cognitively healthy individuals comprising 16734 MRIs for training and evaluation. To validate our model and its interpretability, we used a multivariate model, Orthogonal Projections to Latent Structures (OPLS), which uses brain segmented cortical thicknesses and volumes. We trained and evaluated the models with the same dataset, and systematically investigated how predictions of the CNN model differ from those of the OPLS model. The validation of our model was made by testing an external dataset. The CNN and the OPLS model achieved a mean absolute error (MAE) in the testing dataset of 3.04 and 4.81 years, respectively. The model's performance in the external dataset was in the typical range of MAE found in the literature for testing sets. The CNN model revealed similar image patterns when grouped by chronological age (CA) and CNN predicted age. No significant differences were found between the oldest and youngest quartiles of age predictions by the CNN in a validation cohort of individuals with CA of 70 years old. Sensitivity maps analysis revealed that the age prediction is based mainly on the ventricles and other CSF spaces, which have been shown in the literature to reflect aging and are in accordance with the most important regions for the prediction in the OPLS model. While both the CNN and the OPLS model demonstrated acceptable performance metrics on a hold-out test set, individual predictions differed substantially, with brain age patterns of the CNN model being more comparable to the chronological age. In conclusion, our CNN model showed results comparable to the literature, using minimally processed images, which may facilitate the future implementation of brain age prediction in research and clinical settings.

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Section: Discussionmentioning

confidence: 99%

“…A potential explanation could be the difference in ethnicity which has been discussed elsewhere (Choi et al, 2020;Kang et al, 2020;Tang et al, 2018). Different ethnical populations can present differences in brain morphology.…”

Section: Validation In External Datasetmentioning

confidence: 99%

A Deep Learning Model for Brain Age Prediction Using Minimally Pre-processed T1w-images as Input

Dartora

Marseglia

Mårtensson

et al. 2022

Preprint

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“…Different MR imaging procedures and measurement software were used for the NF1 group and the unaffected comparison control group. As well, the NF1 and comparison groups probably differed in ancestry, and some differences in brain morphology have been associated with ethnicity [ 48 – 50 ]. However, all of the statistically significant differences in brain morphological measurements that we observed between NF1 and control subjects were relatively large (> 0.05 cm), and the differences in white matter volume found in our companion paper were in comparison to a control group studied in Hamburg using the same MRI scanners as these NF1 patients [ 28 ].…”

Section: Discussionmentioning

confidence: 99%

Alterations in brain morphology by MRI in adults with neurofibromatosis 1

Wang

Mautner

Buchert

et al. 2021

Orphanet J Rare Dis

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Objective Neurofibromatosis 1 (NF1) is a rare autosomal dominant disease that causes the dysregulated growth of Schwann cells. Most reported studies of brain morphology in NF1 patients have included only children, and clinical implications of the observed changes later in life remain unclear. In this study, we used MRI to characterize brain morphology in adults with NF1. Methods Planar (2D) MRI measurements of 29 intracranial structures were compared in 389 adults with NF1 and 112 age- and sex-matched unaffected control subjects. The 2D measurements were correlated with volumetric (3D) brain measurements in 99 of the adults with NF1 to help interpret the 2D findings. A subset (n = 70) of these NF1 patients also received psychometric testing for attention deficits and IQ and was assessed for clinical severity of NF1 features and neurological problems. Correlation analysis was performed between the MRI measurements and clinical and psychometric features of these patients. Results Four of nine corpus callosum measurements were significantly greater in adults with NF1 than in sex- and age-matched controls. All seven brainstem measurements were significantly greater in adults with NF1 than in controls. Increased corpus callosum and brainstem 2D morphology were correlated with increased total white matter volume among the NF1 patients. No robust correlations were observed between the 2D size of these structures and clinical or neuropsychometric assessments. Conclusion Our findings are consistent with the hypothesis that dysregulation of brain myelin production is an important manifestation of NF1 in adults.

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“…Interestingly, for some of the brain areas in which we found differences between the Japanese and the German sample, differences between “Eastern” and “Western” cultures have been reported previously. For example, differences between healthy Koreans and (white) US-Americans have been reported in the postcentral gyrus, bilateral inferior temporal gyrus, transversetemporal cortex, lingual gyrus, and lateral occipital cortex 29 . Because our statistical analyses take into account the cortical measures of both healthy participants and patients across the two study sites, our results are unlikely to be affected by any baseline differences.…”

Section: Discussionmentioning

confidence: 99%

“…For example, compared to (white) US-American participants, Korean participants had greater cortical thickness in areas such as the postcentral gyrus and the bilateral inferior temporal gyrus. US-Americans, on the other hand, showed more cortical thickness in the left transversetemporal cortex, lingual gyrus, and right lateral occipital cortex 29 . Interestingly, there also seems to be evidence for the fact that differences between international samples are age-dependent 30 .…”

Section: Introductionmentioning

confidence: 90%

Cortical changes in patients with schizophrenia across two ethnic backgrounds

Langenbach

Kohl

Murai

et al. 2022

Sci Rep

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While it is known that cultural background influences the healthy brain, less is known about how it affects cortical changes in schizophrenia. Here, we tested whether schizophrenia differentially affected the brain in Japanese and German patients. In a sample of 155 patients with a diagnosis of schizophrenia and 191 healthy controls from Japan and Germany, we acquired 3 T-MRI of the brain. We subsequently compared cortical thickness and cortical surface area to identify whether differences between healthy controls and patients might be influenced by ethnicity. Additional analyses were performed to account for effects of duration of illness and medication. We found pronounced interactions between schizophrenia and cultural background in the cortical thickness of several areas, including the left inferior and middle temporal gyrus, as well as the right lateral occipital cortex. Regarding cortical surface area, interaction effects appeared in the insula and the occipital cortex, among others. Some of these brain areas are related to the expression of psychotic symptoms, which are known to differ across cultures. Our results indicate that cultural background impacts cortical structures in different ways, probably resulting in varying clinical manifestations, and call for the inclusion of more diverse samples in schizophrenia research.

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Differences in cortical structure between cognitively normal East Asian and Caucasian older adults: a surface-based morphometry study

Cited by 29 publications

References 57 publications

A Deep Learning Model for Brain Age Prediction Using Minimally Pre-processed T1w-images as Input

A Deep Learning Model for Brain Age Prediction Using Minimally Pre-processed T1w-images as Input

Alterations in brain morphology by MRI in adults with neurofibromatosis 1

Cortical changes in patients with schizophrenia across two ethnic backgrounds

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