Journal Article10.1038/s41598-024-54663-z
Deep learning-based, fully automated, pediatric brain segmentation
Min-Jee Kim,Eunpyeong Hong,Mi-Sun Yum,Y. Lee,Jinyoung Kim,Tae-Sung Ko +5 more
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TL;DR: The DLS method could perfectly detect the reduced volume identified by the Freesurfer software and manual correction in patients with SCN1A mutations, compared with healthy controls, and it can also well detect brain morphological changes in children with a neurodevelopmental disorder.
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Abstract: Abstract The purpose of this study was to demonstrate the performance of a fully automated, deep learning-based brain segmentation (DLS) method in healthy controls and in patients with neurodevelopmental disorders, SCN1A mutation, under eleven. The whole, cortical, and subcortical volumes of previously enrolled 21 participants, under 11 years of age, with a SCN1A mutation, and 42 healthy controls, were obtained using a DLS method, and compared to volumes measured by Freesurfer with manual correction. Additionally, the volumes which were calculated with the DLS method between the patients and the control group. The volumes of total brain gray and white matter using DLS method were consistent with that volume which were measured by Freesurfer with manual correction in healthy controls. Among 68 cortical parcellated volume analysis, the volumes of only 7 areas measured by DLS methods were significantly different from that measured by Freesurfer with manual correction, and the differences decreased with increasing age in the subgroup analysis. The subcortical volume measured by the DLS method was relatively smaller than that of the Freesurfer volume analysis. Further, the DLS method could perfectly detect the reduced volume identified by the Freesurfer software and manual correction in patients with SCN1A mutations, compared with healthy controls. In a pediatric population, this new, fully automated DLS method is compatible with the classic, volumetric analysis with Freesurfer software and manual correction, and it can also well detect brain morphological changes in children with a neurodevelopmental disorder.
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Citations
Discovering Subtypes with Imaging Signatures in the Motoric Cognitive Risk Syndrome Consortium using Weakly-Supervised Clustering
Bhargav Teja Nallapu,Ali Ezzati,Helena M. Blumen,Kellen K. Petersen,Richard B. Lipton,Emmeline Ayers,V G Pradeep Kumar,Velandai Srikanth,Richard Beare,Olivier Beauchet,Takehiko Doi,Hiroyuki Shimada,Sofiya Milman,Sandra Aleksić,Joe Verghese +14 more
- 13 Oct 2024
TL;DR: Researchers applied a weakly-supervised clustering algorithm to MRI data from 1987 individuals with Motoric Cognitive Risk Syndrome, identifying three subgroups with distinct brain structure and cognitive performance profiles, highlighting heterogeneity in the population.
Discovering subtypes with imaging signatures in the Motoric Cognitive Risk Syndrome Consortium using weakly supervised clustering
Bhargav Teja Nallapu,Ali Ezzati,Helena M Blumen,Kellen K. Petersen,Richard B Lipton,Emmeline Ayers,V G Pradeep Kumar,Srikanth Velandai,Richard Beare,Olivier Beauchet,Takehiko Doi,Hiroyuki Shimada,Michele Callisaya,Sofiya Milman,Sandra Aleksic,Joe Verghese,Bhargav Teja Nallapu,Ali Ezzati,Helena M Blumen,Kellen K. Petersen,Richard B Lipton,Emmeline Ayers,V G Pradeep Kumar,Srikanth Velandai,Richard Beare,Olivier Beauchet,Takehiko Doi,Hiroyuki Shimada,Michele Callisaya,Sofiya Milman,Sandra Aleksic,Joe Verghese +31 more
Abstract: ABSTRACT INTRODUCTION Understanding the heterogeneity of brain structure in individuals with the Motoric Cognitive Risk Syndrome (MCR) may improve the current risk assessments of dementia. METHODS We used data from six cohorts from the MCR consortium ( N = 1987). A weakly‐supervised clustering algorithm called HYDRA (Heterogeneity through Discriminative Analysis) was applied to volumetric magnetic resonance imaging (MRI) measures to identify distinct subgroups in the population with gait speeds lower than one standard deviation (1SD) above mean. RESULTS Three subgroups (Groups A, B, and C) were identified through MRI‐based clustering with significant differences in regional brain volumes, gait speeds, and performance on Trail Making (Part‐B) and Free and Cued Selective Reminding Tests. DISCUSSION Based on structural MRI, our results reflect heterogeneity in the population with moderate and slow gait, including those with MCR. Such a data‐driven approach could help pave new pathways toward dementia at‐risk stratification and have implications for precision health for patients. Highlights Different patterns of brain atrophy were observed among the people with moderate and slow gait speeds Slower gait speeds were associated with substantial cortical atrophy, higher rates of Motoric Cognitive Risk Syndrome (MCR), and worse cognitive performance This approach can aid patient stratification at early asymptomatic stages and have implications for precision health.
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