Automated Segmentation and Volumetric Analysis of the Subplate in Fetuses with Down Syndrome
Poster No:
881
Submission Type:
Abstract Submission
Authors:
Helena Sousa1, Abi Fukami - Gartner2, Alena Uus2, Vanessa Kyriakopoulou3, Jonathan O'Muircheartaigh1, Joseph Hajnal4, Megan Hall1, Jana Hutter1, Lisa Story1, Donald Tournier4, Alexander Hammers1, Mary Rutherford1, Maria Deprez5
Institutions:
1King's College London, London, London, 2King's College London, London, Other, 3King's College London, London, United Kingdom, 4King's College London, London, England, 5King's College London, London, N/A
First Author:
Co-Author(s):
Introduction:
Down syndrome (DS) is the most common cause of intellectual disability with a known genetic aetiology affecting approximately 1 in 1000 live births [1]. There is a gap in knowledge about structural brain development in utero in DS. In particular, the growth trajectory of the subplate (SP), a transient compartment of the fetal brain, has never been defined in fetuses with DS. Here, we performed automatic segmentation of the SP in T2-weighted (T2w) fetal brain MRI, using a novel deep learning solution [2], to assess any differences in SP volumes across gestational age (GA) in fetuses with DS compared to appropriate controls.
Methods:
T2w fetal MRI were acquired on a 3T Philips Achieva system for 376 control subjects (21 to 36 weeks GA) from 3 studies: 257 subjects from the developing Human Connectome Project (dHCP, REC 14/LO/1169, with TE=250ms); 78 subjects from the Placental Imaging Project (PiP, REC 16/LO/1573, TE=180ms); and 33 subjects from the individualised risk prediction of adverse neonatal outcome in pregnancies that deliver preterm study (PRESTO, REC 21/SS/0082, with TE=180ms). 25 fetuses from the early brain imaging in DS study (eBiDS, REC 19/LO/0667), (24 to 36 weeks GA) were scanned at either TE=180 ms (20 subjects) or 250ms (5 subjects). All T2w scans were motion-corrected and 3D SVR reconstructed to 0.5mm isotropic resolution, as per [3]. The SP (and total WM) were segmented using an automated Attention-Unet model trained as per [2]. Non-linear regressions of volumes against GA were fitted and compared using the extra-sum-of-squares F-test in Graphpad Prism v9.0.
Results:
Figure 2a illustrates the exponential growth of total WM volume across gestation in both DS and control groups, although DS had a significantly different fit (p value < 0.0001) and reduced WM volumes. The SP (a sub-segment of total WM) showed growth from approximately 21 to 30 GA, followed by a plateau until 36 GA. The DS group showed a similar trend although the non-linear fit was significantly different (p value < 0.0001) with markedly reduced SP volumes. The SP volumes relative to total WM (Fig 2c) showed a linear decrease as the SP gradually resolved across gestation. The DS group showed a similar trend although linear fit was significantly different (p value < 0.0001) with reduced relative SP volumes. Visual assessment of the morphology of SP in both populations show an initial thick and continuous layer at early GA (21-26 weeks) followed by a gradual dissolution in sulcal pits as cortical gyrification progresses along GA.
Conclusions:
To the best of our knowledge the evolution of SP volumes has never been assessed in utero in fetuses with DS. This analysis showed that absolute SP volumes were markedly reduced across gestation from 24 to 36 weeks in DS. SP volumes also represented a smaller proportion of total WM across gestation in DS. It has been shown that there is altered cortical folding in fetuses with DS [4]. Thus, in future, it would be interesting to associate SP volumes with metrics related to cortical folding. Finally, this finding is in line with volumetry in neonates with DS, whereby relative regional WM volumes were significantly reduced [5].
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism)
Genetics:
Neurogenetic Syndromes 1
Modeling and Analysis Methods:
Segmentation and Parcellation 2
Keywords:
Aging
Data analysis
Development
Neurological
Segmentation
STRUCTURAL MRI
White Matter
Other - Down Syndrome ; Fetal Imaging ; Early Brain Development
1|2Indicates the priority used for review
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2. Sousa, H.S. et al. (2023). A Deep Learning Approach for Segmenting the Subplate and Proliferative Zones in Fetal Brain MRI. In: Link-Sourani, D., Abaci Turk, E., Macgowan, C., Hutter, J., Melbourne, A., Licandro, R. (eds) Perinatal, Preterm and Paediatric Image Analysis. PIPPI 2023. Lecture Notes in Computer Science, vol 14246. Springer, Cham. https://doi.org/10.1007/978-3-031-45544-5_2
3. Uus, A. U., Kyriakopoulou, V., Makropoulos, A., Fukami-Gartner, A., Cromb, D., Davidson, A., Cordero-Grande, L., Price, A. N., Grigorescu, I., Williams, L. Z. J., Robinson, E. C., Lloyd, D., Pushparajah, K., Story, L., Hutter, J., Counsell, S. J., Edwards, A. D., Rutherford, M. A., Hajnal, J. V., & Deprez, M. (2023). BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI. bioRxiv : the preprint server for biology, 2023.04.18.537347. https://doi.org/10.1101/2023.04.18.537347
4. Yun, H. J., Perez, J. D. R., Sosa, P., Valdés, J. A., Madan, N., Kitano, R., Akiyama, S., Skotko, B. G., Feldman, H. A., Bianchi, D. W., Grant, P. E., Tarui, T., & Im, K. (2020). Regional Alterations in Cortical Sulcal Depth in Living Fetuses with Down Syndrome. Cerebral Cortex, 31(2), 757–767. https://doi.org/10.1093/cercor/bhaa255
5. Fukami-Gartner, A., Baburamani, A. A., Dimitrova, R., Patkee, P. A., Ojinaga-Alfageme, O., Bonthrone, A. F., Cromb, D., Uus, A. U., Counsell, S. J., Hajnal, J. V., O’Muircheartaigh, J., & Rutherford, M. A. (2023). Comprehensive volumetric phenotyping of the neonatal brain in Down syndrome. Cerebral Cortex, 33(14), 8921–8941. https://doi.org/10.1093/cercor/bhad171