R. Nick Bryan

As medical imaging enters its information era and presents rapidly increasing needs for big data analytics, robust pooling and harmonization of imaging data across diverse cohorts with varying acquisition protocols have become critical. We describe a comprehensive effort that merges and harmonizes a large-scale dataset of 10,477 structural brain MRI scans from participants without a known neurological or psychiatric disorder from 18 different studies that represent geographic diversity. We use this dataset and multi-atlas-based image processing methods to obtain a hierarchical partition of the brain from larger anatomical regions to individual cortical and deep structures and derive age trends of brain structure through the lifespan (3-96 years old). Critically, we present and validate a methodology for harmonizing this pooled dataset in the presence of nonlinear age trends. We provide a web-based visualization interface to generate and present the resulting age trends, enabling future studies of brain structure to compare their data with this reference of brain development and aging, and to examine deviations from ranges, potentially related to disease.

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological processes associated with brain diseases. In particular, MRI-derived brain age has been used as a comprehensive biomarker of brain health that can identify both advanced and resilient ageing individuals via deviations from typical brain ageing. Imaging signatures of various brain diseases, including schizophrenia and Alzheimer's disease, have also been identified using machine learning. Prior efforts to derive these indices have been hampered by the need for sophisticated and not easily reproducible processing steps, by insufficiently powered or diversified samples from which typical brain ageing trajectories were derived, and by limited reproducibility across populations and MRI scanners. Herein, we develop and test a sophisticated deep brain network (DeepBrainNet) using a large (n = 11 729) set of MRI scans from a highly diversified cohort spanning different studies, scanners, ages and geographic locations around the world. Tests using both cross-validation and a separate replication cohort of 2739 individuals indicate that DeepBrainNet obtains robust brain-age estimates from these diverse datasets without the need for specialized image data preparation and processing. Furthermore, we show evidence that moderately fit brain ageing models may provide brain age estimates that are most discriminant of individuals with pathologies. This is not unexpected as tightly-fitting brain age models naturally produce brain-age estimates that offer little information beyond age, and loosely fitting models may contain a lot of noise. Our results offer some experimental evidence against commonly pursued tightly-fitting models. We show that the moderately fitting brain age models obtain significantly higher differentiation compared to tightly-fitting models in two of the four disease groups tested. Critically, we demonstrate that leveraging DeepBrainNet, along with transfer learning, allows us to construct more accurate classifiers of several brain diseases, compared to directly training classifiers on patient versus healthy control datasets or using common imaging databases such as ImageNet. We, therefore, derive a domain-specific deep network likely to reduce the need for application-specific adaptation and tuning of generic deep learning networks. We made the DeepBrainNet model freely available to the community for MRI-based evaluation of brain health in the general population and over the lifespan.

OBJECTIVE: A new technique for analyzing the morphology of the corpus callosum is presented, and it is applied to a group of elderly subjects. MATERIALS AND METHODS: The proposed approach normalizes subject data into the Talairach space using an elastic deformation transformation. The properties of this transformation are used as a quantitative description of the callosal shape with respect to the Talairach atlas, which is treated as a standard. In particular, a deformation function measures the enlargement/shrinkage associated with this elastic deformation. Intersubject comparisons are made by comparing deformation functions. RESULTS: This technique was applied to eight male and eight female subjects. Based on the average deformation functions of each group, the posterior region of the female corpus callosum was found to be larger than its corresponding region in the males. The average callosal shape of each group was also found, demonstrating visually the callosal shape differences between the two groups in this sample. CONCLUSION: The proposed methodology utilizes the full resolution of the data, rather than relying on global descriptions such as area measurements. The application of this methodology to an elderly group indicated sex-related differences in the callosal shape and size.

Dopamine (DA) deficiency has been implicated in Lesch-Nyhan disease (LND), a genetic disorder that is characterized by hyperuricemia, choreoathetosis, dystonia, and compulsive self-injury. To establish that DA deficiency is present in LND, the ligand WIN-35,428, which binds to DA transporters, was used to estimate the density of DA-containing neurons in the caudate and putamen of six patients with classic LND. Comparisons were made with 10 control subjects and 3 patients with Rett syndrome. Three methods were used to quantify the binding of the DA transporter so that its density could be estimated by a single dynamic positron emission tomography study. These approaches included the caudate- or putamen-to-cerebellum ratio of ligand at 80-90 min postinjection, kinetic analysis of the binding potential [Bmax/(Kd x Vd)] using the assumption of equal partition coefficients in the striatum and the cerebellum, and graphical analysis of the binding potential. Depending on the method of analysis, a 50-63% reduction of the binding to DA transporters in the caudate, and a 64-75% reduction in the putamen of the LND patients was observed compared to the normal control group. When LND patients were compared to Rett syndrome patients, similar reductions were found in the caudate (53-61%) and putamen (67-72%) in LND patients. Transporter binding in Rett syndrome patients was not significantly different from the normal controls. Finally, volumetric magnetic resonance imaging studies detected a 30% reduction in the caudate volume of LND patients. To ensure that a reduction in the caudate volume would not confound the results, a rigorous partial volume correction of the caudate time activity curve was performed. This correction resulted in an even greater decrease in the caudate-cerebellar ratio in LND patients when contrasted to controls. To our knowledge, these findings provide the first in vivo documentation of a dopaminergic reduction in LND and illustrate the role of positron emission tomography imaging in investigating neurodevelopmental disorders.