Deep Learning-Based Segmentation to Establish East Asian Normative Volumes Using Multisite Structural MRI

Kim, Regina E. Y.; Kang, Dong Woo; Wang, Sheng‐Min; Kim, Nak-Young; Lee, Min Kyoung; Lim, Hyun Kook; Kim, Donghyeon

doi:10.3390/diagnostics11010013

Cited by 17 publications

(12 citation statements)

References 24 publications

(13 reference statements)

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“…Recent initiatives for morphometric normative data includes percentile fitting curves on subcortical regions [67], deep learning-based segmentation of subcortical regions and cortical lobes for east Asians [68], and yearly percentage of brain volume changes [69]. To our knowledge, there is no other automated calculator for normative morphometric values available to researchers except the one from our previous work using FreeSurfer 5.3 (https://github.com/medicslab/mNormsFS53).…”

Section: Discussionmentioning

confidence: 99%

NOMIS: Quantifying morphometric deviations from normality over the lifetime of the adult human brain

Potvin¹,

Dieumegarde²,

Duchesne

2021

Preprint

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We present NOMIS (https://github.com/medicslab/NOMIS), a comprehensive open MRI tool to assess morphometric deviation from normality in the adult human brain. Based on MR anatomical images from 6,909 cognitively healthy individuals aged 18-100 years, we modeled 1,344 measures computed using the open access FreeSurfer pipeline, taking into account personal characteristics (age, sex, head size), scanner characteristics (manufacturer and magnetic field strength), and image quality, providing expected values for any new individual. Then, for each measure, the NOMIS tool was built to generate Z-score effect sizes denoting the extent of deviation from the normative sample. Depending on the user need, NOMIS offers four versions of Z-score adjusted on different sets of variables. While all versions take into account head size, image quality and scanner characteristics, they can also incorporate age and/or sex, thereby facilitating multi-site neuromorphometric research across adulthood.

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Section: Discussionmentioning

confidence: 99%

NOMIS: Quantifying morphometric deviations from normality over the lifetime of the adult human brain

Potvin¹,

Dieumegarde²,

Duchesne

2021

Preprint

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“…We used AQUA 3.0 software (Neurophet Inc) to process the acquired T 1 -weighted MRIs, a method that aids in precise identification and outlining of the region of interest in the brain as depicted in Figure 4 [ 34 , 35 ]. The AQUA 3.0 software uses the Split-Attention U-Net deep learning architecture, which combines elements from ResNeSt and U-Net++.…”

Section: Methodsmentioning

confidence: 99%

Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study

Park,

Kim,

Park

et al. 2024

J Med Internet Res

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Background Early detection of mild cognitive impairment (MCI), a transitional stage between normal aging and Alzheimer disease, is crucial for preventing the progression of dementia. Virtual reality (VR) biomarkers have proven to be effective in capturing behaviors associated with subtle deficits in instrumental activities of daily living, such as challenges in using a food-ordering kiosk, for early detection of MCI. On the other hand, magnetic resonance imaging (MRI) biomarkers have demonstrated their efficacy in quantifying observable structural brain changes that can aid in early MCI detection. Nevertheless, the relationship between VR-derived and MRI biomarkers remains an open question. In this context, we explored the integration of VR-derived and MRI biomarkers to enhance early MCI detection through a multimodal learning approach. Objective We aimed to evaluate and compare the efficacy of VR-derived and MRI biomarkers in the classification of MCI while also examining the strengths and weaknesses of each approach. Furthermore, we focused on improving early MCI detection by leveraging multimodal learning to integrate VR-derived and MRI biomarkers. Methods The study encompassed a total of 54 participants, comprising 22 (41%) healthy controls and 32 (59%) patients with MCI. Participants completed a virtual kiosk test to collect 4 VR-derived biomarkers (hand movement speed, scanpath length, time to completion, and the number of errors), and T1-weighted MRI scans were performed to collect 22 MRI biomarkers from both hemispheres. Analyses of covariance were used to compare these biomarkers between healthy controls and patients with MCI, with age considered as a covariate. Subsequently, the biomarkers that exhibited significant differences between the 2 groups were used to train and validate a multimodal learning model aimed at early screening for patients with MCI among healthy controls. Results The support vector machine (SVM) using only VR-derived biomarkers achieved a sensitivity of 87.5% and specificity of 90%, whereas the MRI biomarkers showed a sensitivity of 90.9% and specificity of 71.4%. Moreover, a correlation analysis revealed a significant association between MRI-observed brain atrophy and impaired performance in instrumental activities of daily living in the VR environment. Notably, the integration of both VR-derived and MRI biomarkers into a multimodal SVM model yielded superior results compared to unimodal SVM models, achieving higher accuracy (94.4%), sensitivity (100%), specificity (90.9%), precision (87.5%), and F1-score (93.3%). Conclusions The results indicate that VR-derived biomarkers, characterized by their high specificity, can be valuable as a robust, early screening tool for MCI in a broader older adult population. On the other hand, MRI biomarkers, known for their high sensitivity, excel at confirming the presence of MCI. Moreover, the multimodal learning approach introduced in our study provides valuable insights into the improvement of early MCI detection by integrating a diverse set of biomarkers.

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“…AQUA 2.0 software (Neurophet Inc., Seoul, Korea) was employed for the MRI processing pipeline and normative data analysis. The detailed methods appeared in the previous publication [28, 29]. Obtained subregional gray matter volumes were adjusted according to the intracranial volume normalization approach as suggested elsewhere [30].…”

Section: Methodsmentioning

confidence: 99%

“…Obtained subregional gray matter volumes were adjusted according to the intracranial volume normalization approach as suggested elsewhere [30]. Then, the corresponding percentile of an individual was calculated by quantile regression on the adjusted subregional volume with covariates of age and sex based on the East-Asian dataset described previously [28]. Finally, the normative percentiles of subregional volumes were compared between the groups using the T test.…”

Section: Methodsmentioning

confidence: 99%

The Effect of Hearing Loss on Cognitive Function in Subjective Cognitive Decline

Park

Hong

et al. 2022

Dement Geriatr Cogn Disord

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Introduction: Subjective cognitive decline (SCD) is a self-reported cognitive decline without objective cognitive impairment. The relationship between audiometric hearing loss (HL) and cognitive function has not been reported in SCD. The purpose of this study was to investigate whether HL affects cognition-related indexes in SCD individuals. Methods: This is a cross-sectional study that used the baseline data of a multicenter cohort study that monitors clinical progression from SCD to dementia. Individuals aged ≥60 years who reported cognitive decline but had no objective cognitive impairment on comprehensive neuropsychological tests were recruited. Participants were grouped into the normal-hearing (NH) and bilateral HL groups. The demographics, clinical characteristics, dementia biomarkers, global cognition, questionnaire scores, neuropsychological test scores, and segmental brain volumes from MRI were compared between the groups. Results: Of a total of 120 participants, one hundred and two had NH (n = 57) or bilateral HL (n = 45). There were no group differences in the demographic and clinical data except the age. The biomarkers, global cognition, and questionnaire scores were not different between the groups. The HL group performed worse (the z-score of −0.06) in the Stroop Color Word Test than the NH group (0.27) (p = 0.025). Brain volumetric analysis revealed that the HL group had reduced gray matter volumes in four brain subregions: left temporal pole, left caudal middle frontal gyrus, left hippocampus, and right isthmus of the cingulate gyrus. Conclusion: In SCD, HL exerted an adverse effect on cognitive function, primarily frontal executive function tested in the Stroop task. HL was also related to gray matter volume reductions in brain subregions, although causality needs further investigation. This study may provide evidence for a potential link between hearing and cognition in SCD, an emerging clinical entity.

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Deep Learning-Based Segmentation to Establish East Asian Normative Volumes Using Multisite Structural MRI

Cited by 17 publications

References 24 publications

NOMIS: Quantifying morphometric deviations from normality over the lifetime of the adult human brain

NOMIS: Quantifying morphometric deviations from normality over the lifetime of the adult human brain

Integrating Biomarkers From Virtual Reality and Magnetic Resonance Imaging for the Early Detection of Mild Cognitive Impairment Using a Multimodal Learning Approach: Validation Study

The Effect of Hearing Loss on Cognitive Function in Subjective Cognitive Decline

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