Brain structural changes in older adults over time can help identify and predict which individuals are at risk for neurodegenerative disorders and dementias. These trajectories are traditionally calculated by assessing localized rates of brain tissue atrophy from a longitudinal series of brain MRIs as compared to group averages. However, these methods do not preserve individual differences in brain structure, which may provide added information regarding risk. A map of how an individual’s brain may look at a given age - in the case of a normal, healthy, aging trajectory - may help to identify deviations and abnormalities when presented with a true scan at that age. Here, we consider estimating the age-related brain changes as a domain transfer problem. We develop a fully unsupervised generative adversarial network (GAN) with cycle consistency reconstruction losses, trained on cross-sectional brain MRI data from participants of the UK Biobank aged 45 to 81. We show that brain MRIs for males and females at a given age can be predicted by converting the content information encoded in a T1-weighted MRI (i.e., the individual’s identifying anatomical features), accompanied by adding the style (age/sex) information from a reference group. Results on the PREVENT-AD cohort demonstrated that our style-encoding domain transfer model can predict follow-up brain MRIs, successfully, without relying on longitudinal data from the subjects. We show how deviations from the predicted images are indicative of factors related to neurodegenerative disease risk.
This study used advanced diffusion-weighted MRI (dMRI) to examine the association between exogenous sex-hormone exposure and the brain’s white matter aging trajectories in a large population-based sample of women. To investigate the effect of pre- and post-menopausal sex hormones on brain aging, cross-sectional brain dMRI data from the UK Biobank was analyzed using 3 diffusion models: conventional diffusion tensor imaging (DTI), the tensor distribution function (TDF), and neurite orientation dispersion and density imaging (NODDI). Mean skeletonized diffusivity measures were extracted and averaged across the whole brain, including fractional anisotropy, isotropic volume fraction, intracellular volume fraction and orientation dispersion index. We used general linear models and fractional polynomial regressions to characterize age-related trajectories in white matter measures following hormone therapy (HT) and oral contraceptive (OC) use in women (HT analysis: N=8,301; OC analysis: N=8,913). Sex hormone treatment (HT and OC) was statistically associated with the aging trends in white matter measures. Estrogen therapy alone appeared to exert a neuroprotective effect on age-related white matter processes, compared to HT containing both estrogen and progestin therapy - which was associated with accelerated aging-related processes in women. These results support the hypothesis that exogenous sex hormone exposure may impact white matter aging; white matter metrics may also be sensitive to sex hormone levels in women. Furthermore, we discuss the necessity to test alternative models for lifespan trajectories beyond popular linear and quadratic models, especially when dealing with large samples. Fractional polynomial models may provide a more adaptive alternative to linear or quadratic models.
The brain’s white matter microstructure, as assessed using diffusion-weighted MRI (DWI), changes significantly with age and also exhibits significant sex differences. Here we examined the ability of a traditional diffusivity metric (fractional anisotropy derived from diffusion tensor imaging, DTI-FA) and advanced diffusivity metrics (fractional anisotropy derived from the tensor distribution function, TDF-FA; neurite orientation dispersion and density imaging measures of intracellular volume fraction, NODDI-ICVF; orientation dispersion index, NODDI-ODI; and isotropic volume fraction, NODDI-ISOVF) to detect sex differences in white matter aging. We also created normative aging reference curves based on sex. Diffusion tensor imaging (DTI) applies a single-tensor diffusion model to single-shell DWI data, while the tensor distribution function (TDF) fits a continuous distribution of tensors to single-shell DWI data. Neurite orientation dispersion and density imaging (NODDI) fits a multi-compartment model to multi-shell DWI data to distinguish intra- and extracellular contributions to diffusion. We analyzed these traditional and advanced diffusion measures in a large population sample available through the UK Biobank (15,394 participants; age-range: 45-80 years) by using linear regression and fractional polynomials. Advanced diffusivity metrics (NODDI-ODI, NODDI-ISOVF, TDF-FA) detected significant sex differences in aging, whereas a traditional metric (DTI-FA) did not. These findings suggest that future studies examining sex differences in white matter aging may benefit from including advanced diffusion measures.
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