Structural properties of the human corpus callosum: Multimodal assessment and sex differences
Stand-By Time
Monday, June 26, 2017: 12:45 PM - 2:45 PMSubmission No:
2044
Submission Type:
Abstract Submission
On Display:
Monday, June 26 & Tuesday, June 27
Authors:
Lassi Björnholm1, Juha Nikkinen2, Vesa Kiviniemi3, Tanja Nordström4, Solja Niemelä5,6, Mark Drakesmith7, John Evans7, Bruce Pike8, Juha Veijola1, Tomas Paus9,10,11
Institutions:
1Department of Psychiatry, University of Oulu and Oulu University Hospital, Oulu, Finland, 2Department of Radiotherapy, Oulu University Hospital, Oulu, Finland, 3Institute of Diagnostics, Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland, 4Institute of Health Sciences, University of Oulu, Oulu, Finland, 5Department of Psychiatry, University of Oulu, Oulu, Finland, 6Department of Psychiatry, Lapland Hospital District, Rovaniemi, Finland, 7School of Psychology, Cardiff University, Cardiff, United Kingdom, 8Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Canada, 9Rotman Research Institute, Baycrest, Toronto, Canada, 10Departments of Psychology and Psychiatry, University of Toronto, Toronto, Canada, 11Child Mind Institute, New York, NY
First Author:
Lassi Björnholm
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Lecture Information
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Department of Psychiatry, University of Oulu and Oulu University Hospital
Oulu, Finland
Department of Psychiatry, University of Oulu and Oulu University Hospital
Oulu, Finland
Introduction:
Corpus callosum (CC) is the main interhemispheric fiber tract in the human brain, consisting of about 200 million axons (Aboitiz et al., 1992). Heterogeneity of its fiber composition suggests that cortical regions differ in the type of channels carrying information between their left-right homologues (Aboitiz et al., 2003, De Lacoste et al., 1985). The microstructure of CC, as of all white matter (WM), can be characterized using quantitative MRI (qMRI). A combination of qMRI data with the knowledge of the underlying histology and the biophysical description of the MR contrast-generating parameters enables reverse modeling of WM microstructure in vivo , (Weiskopf et al., 2015).
In Part 1:, we combine information from six MRI-based measures (fractional anisotropy [FA], mean diffusivity [MD], magnetization transfer ratio [MTR], R1 and R2 relaxation rates and myelin water fraction [MWF], i.e. parametric images) obtained in a sample of 402 young men (19.55 ± 0.84 years) recruited from the Avon Longitudinal Study of Parents and Children (ALSPAC) with earlier literature on CC histology including fiber diameter (d) (Aboitiz et al., 1992). In Part 2:, we use this knowledge to interpret sex differences observed in the MRI measures (FA, MD and MTR) of 433 men and women (26.50 ± 0.51 years) recruited from the Northern Finland Birth Cohort 1986 (NFBC86).
In Part 1:, we combine information from six MRI-based measures (fractional anisotropy [FA], mean diffusivity [MD], magnetization transfer ratio [MTR], R1 and R2 relaxation rates and myelin water fraction [MWF], i.e. parametric images) obtained in a sample of 402 young men (19.55 ± 0.84 years) recruited from the Avon Longitudinal Study of Parents and Children (ALSPAC) with earlier literature on CC histology including fiber diameter (d) (Aboitiz et al., 1992). In Part 2:, we use this knowledge to interpret sex differences observed in the MRI measures (FA, MD and MTR) of 433 men and women (26.50 ± 0.51 years) recruited from the Northern Finland Birth Cohort 1986 (NFBC86).
Methods:
Ten segments of the CC were drawn on three consecutive midsagittal slices on the MNI152 1-mm template (Aboitiz et al., 1992), Fig. 1A. Segments were transformed into native parametric images and individual segment-wise mean values were extracted.
Part 1: Similarity between profiles of the MRI and histology measures across the 10 segments were estimated using slopes, Fig. 1B, and correlation analysis. Part 2: Linear mixed effects analyses of each MRI measure were used to test for sex differences and Sex * Segment interactions. Post hoc analyses (t-test) were performed for segment-wise sex differences in modalities where above analyses showed significant results, Fig 1C. In both parts we also performed hierarchical clustering on normalized (z-scored) values of the MR measures.
Part 1: Similarity between profiles of the MRI and histology measures across the 10 segments were estimated using slopes, Fig. 1B, and correlation analysis. Part 2: Linear mixed effects analyses of each MRI measure were used to test for sex differences and Sex * Segment interactions. Post hoc analyses (t-test) were performed for segment-wise sex differences in modalities where above analyses showed significant results, Fig 1C. In both parts we also performed hierarchical clustering on normalized (z-scored) values of the MR measures.
Results:
Part 1: The profiles of R1, R2 and MWF were similar to "small-fiber" (d > 0.4 µm) profile, whereas FA and MD showed more intermediate profiles, Fig 1B. High positive correlations were found between mcDESPOT metrics and D04, Fig 2A. Clustering grouped R1, R2, MWF and FA in one and MD into another cluster, Fig. 2B. Due to persistent bias fields, we decided to exclude all ALSPAC sample MTR data from further analyses. Part 2: A Sex * Segment interaction was found for FA (χ2(9)= 29.27, p=0.0006) and a main effect of Sex (males>females) for MTR (χ2(1)= 15.41, p=0.00009). The Sex * Segment interactions were driven by G2/3, B1/2 and SPLN 2/3, as revealed by post hoc t-tests, Fig 1C. Clustering placed MD in one and FA and MTR into another cluster.
Conclusions:
Our observations suggest that mcDESPOT-derived measures (R1, R2 and MWF) are sensitive to the outer surface area of myelin, as indicated by the highly similar cross-CC profiles of the imaging modalities and that of small-diameter axons. We also found a notable sex difference in the values of MTR (across most of the CC segments), which we interpret as reflecting higher myelin content (per unit of WM tissue) in young men (vs. women). Given our previous observations of lower MTR values in male (vs. female) adolescents (Perrin et al., 2009), future studies should investigate the exact time of a possible switchover in the sex differences in MTR values (and myelin-to-axon ratio) during the transition between adolescence and young adulthood.
Imaging Methods:
Multi-Modal Imaging 2
Lifespan Development:
Lifespan Development Other
Modeling and Analysis Methods:
Image Registration and Computational Anatomy
Neuroanatomy:
White Matter Anatomy, Fiber Pathways and Connectivity 1
Keywords:
MRI
Myelin
NORMAL HUMAN
Sexual Dimorphism
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
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Provide references in author date format
De Lacoste, M.C. (1985), “Topography of the human corpus callosum”, Journal of Neuropathology & Experimental Neurology, vol. 44, no. 6, pp. 578-591
Perrin, J.S. (2009), “Sex differences in the growth of white matter during adolescence”, Neuroimage, vol. 45, no. 4, pp. 1055-1066
Weiskopf, N. (2015), “Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology”, Current Opinion in Neurology, vol. 28, no. 4, pp. 313-322