Identifying a common cause of macrocephaly using brain growth charts
Poster No:
1287
Submission Type:
Abstract Submission
Authors:
Ayan Mandal1, Barbara Chaiyachati2, Jenna Schabdach2, Margaret Gardner1, Susan Sotardi2, Maria Henry2, Joanne Wood2, Aaron Alexander-Bloch1, Jakob Seidlitz1
Institutions:
1University of Pennsylvania, Philadelphia, PA, 2Children's Hospital of Philadelphia, Philadelphia, PA
First Author:
Co-Author(s):
Introduction:
Macrocephaly, defined as a head circumference measurement of greater than two standard deviations from the mean on typical growth charts, is among the most common clinical indications for an MRI scan in young children. The differential diagnosis for macrocephaly is broad, constituting potentially serious etiologies such as non-accidental head trauma or neurogenetic disorders as well as benign causes. A common cause of macrocephaly is benign enlargement of the subarachnoid space (termed "BESS"), a condition marked by elevated thickness of extra-axial CSF (eaCSF) which typically self-resolves after 1-2 years of age.1 The diagnosis of BESS is complicated by the dynamic nature of eaCSF in early infancy as well as a lack of supporting quantitative diagnostic criteria for this condition. Growth charts of eaCSF could be a tremendous aid to clinicians seeking to differentiate BESS from other more clinically concerning causes of macrocephaly.2
Methods:
We accessed 457 clinically-acquired T1w MRI scans from pediatric patients (ages 0 -22) at the Children's Hospital of Philadelphia (CHOP) to form a cohort of clinical controls, termed Scans with Limited Imaging Pathology (SLIP), described previously.3 In parallel, nine T1w MRI scans from subjects with a diagnosis of BESS from a board-certified radiologist were also accessed. SynthSeg, a segmentation algorithm based on convolutional neural networks, was used to segment each T1w scan into various tissue types, including eaCSF.4 To isolate the components of eaCSF relevant to BESS, we isolated the eaCSF superior to the anterior commissure for each patient, consistent with prior studies.5 Extra-axial CSF thickness was measured using the function "measure_bb_thick" in AFNI and then averaged to produce a mean thickness for each subject (Figure 1).6 Growth charts of eaCSF thickness were modeled in R using generalized additive models for location, scale, and shape (GAMLSS) within the total cohort.7
·Figure 1: Coronal T1 MRI with sequential method processing. From left to right: Raw, Total segmented, Supra-tentorial eaCSF restricted segmentation mask, Voxel-thickness estimation.
Results:
In the SLIP cohort, eaCSF thickness varied nonlinearly with age, increasing from birth to six months, then gradually declining until around eight years, when it rises again and trends upwards throughout adolescence (Figure 2). Based on these normative trajectories of eaCSF thickness, seven of the nine patients with a clinical diagnosis of BESS were above the 97.5% percentile for their age.
·Figure 2: Extra-axial CSF Thickness in Childhood and Adolescence. Thickness reflects voxel-based average of supratentorial eaCSF mask from clinically acquired images in normal (gray) and BESS (orange)
Conclusions:
We demonstrate that the thickness of the subarachnoid space changes in a dynamic but relatively predictable pattern throughout childhood and adolescence. The dynamic nature of these changes complicates the diagnosis of BESS, a common cause of macrocephaly. We show that patients with BESS can be reliably differentiated from clinical controls using computational measurements of eaCSF thickness paired with normative modeling. Our findings demonstrate the feasibility of updating diagnostic guidelines and aiding clinical decision-making for BESS and likely other macrocephalic conditions using brain growth charts.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism)
Lifespan Development:
Early life, Adolescence, Aging
Normal Brain Development: Fetus to Adolescence 1
Modeling and Analysis Methods:
Image Registration and Computational Anatomy 2
Segmentation and Parcellation
Keywords:
Cerebro Spinal Fluid (CSF)
Development
MRI
PEDIATRIC
STRUCTURAL MRI
1|2Indicates the priority used for review
Provide references using author date format
2. Bethlehem, R. A. I. et al. Brain charts for the human lifespan. Nature 604, 525–533 (2022).
3. Schabdach, J. M. et al. Brain Growth Charts for Quantitative Analysis of Pediatric Clinical Brain MRI Scans with Limited Imaging Pathology. Radiology 309, e230096 (2023).
4. Billot, B. et al. Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets. Proc. Natl. Acad. Sci. U. S. A. 120, e2216399120 (2023).
5. Shen, M. D. et al. Extra-axial cerebrospinal fluid in high-risk and normal-risk children with autism aged 2-4 years: a case-control study. Lancet Psychiatry 5, 895–904 (2018).
6. Glen D, Taylor PA, Seidlitz J, et al. Through Thick and Thin: Measuring Thickness in MRI with AFNI. presented at: 24th Annual Meeting of the Organization for Human Brain Mapping; 2018; Singapore. https://afni.nimh.nih.gov/pub/dist/HBM2018/OHBM_2018_Thickness.pdf
7. Rigby RA, Stasinopoulos MD, Heller GZ, De Bastiani F. Distributions for modeling location, scale, and shape: Using GAMLSS in R. CRC press (2019)