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Diffusion microstructure measurements across the brain and lifespan

Benjamin T Newman¹ and T. Jason Druzgal¹
¹Department of Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, United States

Synopsis

Understanding how the brain develops, matures, ages, and declines is one of the fundamental questions facing neuroscience^1–3. Recent advances in diffusion microstructure analysis have allowed for detailed descriptions of neuronal change. However, it is essential that findings from these studies are appropriately contextualized to general age-related changes in the brain^4,5. This study uses 3-tissue constrained spherical deconvolution (3T-CSD) to examine the relationship between brain diffusion microstructure and chronological age in a number of gross anatomical structures, subcortical gray matter, and cortex while additionally evaluating lateral differences in microstructural measurements. The results should serve as a benchmark for diffusion microstructure studies.

Introduction

Across the human lifespan, the structure of the brain changes dynamically in response to internal and external factors. The brain dramatically increases in size early in life and shows a remarkable decrease in volume in older individuals. It is likely these coarse changes are accompanied by changes in the underlying brain tissue microstructure. 3T-CSD is a promising measurement of diffusion microstructure that can describe 3 tissue signal fraction compartments in a voxel-wise manner: intracellular anisotropically diffusing white matter (ICA), intracellular isotropically diffusing gray matter (ICI), and extracellular isotropically diffusing free water (ECI). Recent studies have used 3T-CSD to explore a number of conditions in development and aging. However, a comprehensive study of how these 3 brain microstructure compartments change across the lifespan has been lacking. This study aims to provide a detailed description of 3T-CSD signal fraction changes throughout the lifespan and across the brain as a reference for future studies.

Methods

409 subjects from the Nathanial Kline Institute’s Rockland study, a cohort designed to demographically reflect the overall population of the United States, were analyzed with an age range 6-85 years-old (mean 42.67 ± 20.79 S.D.)⁶. There were 144 male and 265 female participants with average ages of 36.09 ± 21.22 S.D. and 46.25 ± 19.68 S.D., respectively. Brain diffusion MRI was acquired using a Siemens MAGNETOM TrioTim 3T scanner with an isotropic voxel size of 2.0⨉2.0⨉2.0mm³, TE=85ms and TR=2400ms; 9 b=0 images and 127 gradient directions at b=1500s/mm². Each subject’s diffusion image set was analyzed using SS3T-CSD^7,8 implemented in the open source software MRtrix and MRtrix3Tissue^7,9. Several preprocessing steps utilized FSL^10,11. Diffusion images were denoised¹², corrected for Gibbs ringing¹³, susceptibility distortions¹¹, subject motion¹⁴, and eddy currents¹⁵. All images were upsampled to of 1.3⨉1.3⨉1.3mm³, and skullstripping was performed using the Brain Extraction Tool¹⁰. Response functions were generated¹⁶ from each tissue type and used to generate fiber orientation distributions (FODs)⁸. Signal fractions were calculated directly from the FODs using 3-tissue constrained spherical deconvolution (3T-CSD) which output ICA, ICI, and ECI signal fractions¹⁷. White matter FODs were used to generate a cohort-specific FOD template image from 50 individuals between ages 32-38 years-old and each subject’s individual WM FOD image was registered to this template^18,19. Both the JHU-DTI based ICBM-DTI-81 white matter atlas including 48 ROIs^10,20–22, and the Destrieux atlas including 164 grey matter ROIs²³were warped into the study space. The cortical ROIs from the Destrieux atlas were summarized in a whole cortical ribbon ROI, as well as a cerebellum GM, and a total subcortical GM ROI. The cortical ribbon ROI was further divided into 8 generalized cortical regions. All ROIs from the JHU WM atlas were combined to provide a summary of the WM skeleton. Signal fraction values from each of the three tissue compartments (ICA, ICI, and ECI) were averaged within each of the individual ROIs. The relationship between each signal fraction and age was plotted across the whole lifespan with a locally weighted smoothing (LOESS) line used to summarize the relationship, as well as subdividing the age range into 4 life phases: approximate development (<20 years), early adulthood (20-40 years), late adulthood (40-60 years), and senescence (>60 years). A linear model directly comparing the relationship between age and signal fraction was calculated individually for each ROI within each of the 4 phases and the slope displayed.

Results

Results from gross anatomical regions (Fig. 1), subcortical GM structures (Fig. 2), and cerebral cortex (Fig. 3) are presented with both whole lifespan trends for each of the 3 signal fractions and linear slopes within each of the 4 lifespan phases for ease of reference to studies with age populations grouped exclusively within those ranges. The general trajectory of signal fraction measurements was a positive relationship with age and ECI signal fraction, a negative relationship between age and ICI signal fraction, and an inverted U-shaped trajectory for the ICA signal fraction. Looking at individual sub-areas these trends tended to still be present, with some notable exceptions such as the increase in ICA signal fraction in the putamen. Larger differences were present between structures and there was significant lateral differences between hemispheres for each of the subcortical GM structures (Table 1) and for each of the cortical regions (Table 2).

Discussion

The relationship between age and each of the signal fraction metrics across multiple brain regions demonstrate that there are clear trends within the gross anatomical regions but that several specific subregions show clear departures from this trend. There was an unambiguous trend in aging toward increased ECI/free water signal fraction which supports the findings of several other 3T-CSD papers regarding neuronal decline or injury^5,24,25. These results are also important to interpret findings such as the effect of pubertal hormones, which cause changes against the age-related trajectory, suggesting that additional features beyond age-related development may be occurring in adolescent populations.

Conclusion

These results describe the relationship between brain microstructure and age and illustrate how cellular microstructure changes throughout the lifespan. Studies analyzing diffusion in developing or aging cohorts should address shifts in anisotropic and isotropic signal in development and dramatically increased levels of extracellular water in aging

Acknowledgements

No acknowledgement found.

References

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Figures

Figure 1: Lifespan trajectories of 3T-CSD metrics in 4 large anatomical brain subareas. The relationship between age and signal fraction is displayed across the whole lifespan (A, C, & E for ECI, ICI, & ICA) and as the slope of the linear relationship during a limited age range (B, D, & F for ECI, ICI, & ICA). Signal fraction compartment trajectories were relatively consistent between subareas, with a positive relationship between ECI and age, a negative relationship between ICI and age, and an inverted U-shaped relationship between ICA and age.

Figure 2: Lifespan trajectories of 3T-CSD metrics in 6 subcortical gray matter structures, including both left and right structures. The relationship between age and signal fraction is displayed across the lifespan (A, C, & E for ECI, ICI, & ICA) and as the slope of the linear relationship during a limited age range (B, D, & F for ECI, ICI, & ICA). Trajectories largely follow the pattern established for the whole subcortical GM structure in Fig. 3, however deviations include the Putamen in ICA and the Hippocampus, Amygdala, and Nucleus Accumbens in ICI.

Table 1: Lateral differences between subcortical GM structures across the lifespan. Pairwise t-tests were used to compare each lateralized ROI within subjects and p-values were adjusted using a Benjamini & Hochberg correction. The ICI signal fraction was higher in each left hemisphere ROI. The putamen, thalamus, and amygdala were observed to have the greatest laterality. The Hippocampus displayed the smallest mean but still significant difference, recapitulating previous work that found a significantly increased ECI signal fraction in the right hippocampus¹⁷.

Figure 3: Lifespan trajectories of each 3T-CSD metric in 8 cortical ROIs. The relationship between age and signal fraction is displayed either across the whole lifespan (A, C, & E for ECI, ICI, & ICA) or as the slope of the linear relationship during a limited age range (B, D, & F for ECI, ICI, & ICA). Several deviations from the average trajectory occur in the early and late adulthood phases in the cortex in the insular and limbic cortices in the ICI signal fraction but otherwise declines across the lifespan. Insular and Limbic cortices show notable differences in absolute measurements.

Table 2: Lateral differences between cortical GM regions across the lifespan. Pairwise t-tests were used to compare each lateralized ROI within subjects and p-values were adjusted using a Benjamini & Hochberg. All cortical regions showed some significant degree of lateralization with the insular and temporal cortices each having a mean difference of greater than 2% ICA and ICI measurements between hemispheres. Conversely the limbic and motor cortices each had a mean difference of less than 0.5% excepting the ECI signal fraction in the motor cortex.

Proc. Intl. Soc. Mag. Reson. Med. 30 (2022)

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DOI: https://doi.org/10.58530/2022/2739