Developing neuroimaging biomarkers of blast-induced traumatic brain injury

In the past two decades, the awareness of the physical and emotional effects and sequalae of traumatic brain injuries (TBI) has grown considerably, especially in the case of soldiers returning from their deployment in Iraq and Afghanistan, after sustaining blast-induced TBI (bTBI). While the understanding of bTBI and how it compares to civilian non-blast TBI is essential for proper prevention, diagnosis and treatment, it is currently limited, especially in human in-vivo studies. Developing neuroimaging biomarkers of bTBI is key in understanding primary blast injury mechanism. I therefore investigated the patterns of white matter and grey matter injuries that are specific to bTBI and aren¶t commonl\ seen in civilians Zho suffered from head trauma using advanced neuroimaging techniques. However, because of significant methodological issues and limitations, I developed and tested a new pipeline capable of running the analysis of white matter abnormalities in soldiers, called subject-specific diffusion segmentation (SSDS). I also used standard methodologies to investigate changes at the level of the grey matter structures, and more particularly the limbic system. Finally, I trained a machine learning algorithm that builds decision trees with the aim of classifying between patients with TBI and controls, and between different TBI mechanisms as an example of what could potentially be applied in the context of bTBI. I found three main neuroimaging biomarkers specific to bTBI. The first one is a microstructural white matter abnormality at the level of the middle cerebellar peduncle, characterized by a decrease of diffusivity measures. The second is also a decrease in diffusivity properties, at the level of the white matter boundary, and the third one is a loss of hippocampal volume, with no association to post-traumatic stress disorder. Finally, I demonstrated that SSDS can be used in tandem with a machine learning algorithm for potential diagnosis of TBI with high accuracy. These findings provide mechanistic insights into bTBI and the effect of primary blast injuries on the human brain. This work also identifies important neuroimaging biomarkers that might facilitate prevention and diagnosis in soldiers who suffered from bTBI.Open Acces

Increased brain age in adults with Prader-Willi syndrome.

Prader-Willi syndrome (PWS) is the most common genetic obesity syndrome, with associated learning difficulties, neuroendocrine deficits, and behavioural and psychiatric problems. As the life expectancy of individuals with PWS increases, there is concern that alterations in brain structure associated with the syndrome, as a direct result of absent expression of PWS genes, and its metabolic complications and hormonal deficits, might cause early onset of physiological and brain aging. In this study, a machine learning approach was used to predict brain age based on grey matter (GM) and white matter (WM) maps derived from structural neuroimaging data using T1-weighted magnetic resonance imaging (MRI) scans. Brain-predicted age difference (brain-PAD) scores, calculated as the difference between chronological age and brain-predicted age, are designed to reflect deviations from healthy brain aging, with higher brain-PAD scores indicating premature aging. Two separate adult cohorts underwent brain-predicted age calculation. The main cohort consisted of adults with PWS (n = 20; age mean 23.1 years, range 19.8-27.7; 70.0% male; body mass index (BMI) mean 30.1 kg/m2, 21.5-47.7; n = 19 paternal chromosome 15q11-13 deletion) and age- and sex-matched controls (n = 40; age 22.9 years, 19.6-29.0; 65.0% male; BMI 24.1 kg/m2, 19.2-34.2) adults (BMI PWS vs. control P = .002). Brain-PAD was significantly greater in PWS than controls (effect size mean ± SEM +7.24 ± 2.20 years [95% CI 2.83, 11.63], P = .002). Brain-PAD remained significantly greater in PWS than controls when restricting analysis to a sub-cohort matched for BMI consisting of n = 15 with PWS with BMI range 21.5-33.7 kg/m2, and n = 29 controls with BMI 21.7-34.2 kg/m2 (effect size +5.51 ± 2.56 years [95% CI 3.44, 10.38], P = .037). In the PWS group, brain-PAD scores were not associated with intelligence quotient (IQ), use of hormonal and psychotropic medications, nor severity of repetitive or disruptive behaviours. A 24.5 year old man (BMI 36.9 kg/m2) with PWS from a SNORD116 microdeletion also had increased brain PAD of 12.87 years, compared to 0.84 ± 6.52 years in a second control adult cohort (n = 95; age mean 34.0 years, range 19.9-55.5; 38.9% male; BMI 28.7 kg/m2, 19.1-43.1). This increase in brain-PAD in adults with PWS indicates abnormal brain structure that may reflect premature brain aging or abnormal brain development. The similar finding in a rare patient with a SNORD116 microdeletion implicates a potential causative role for this PWS region gene cluster in the structural brain abnormalities associated primarily with the syndrome and/or its complications. Further longitudinal neuroimaging studies are needed to clarify the natural history of this increase in brain age in PWS, its relationship with obesity, and whether similar findings are seen in those with PWS from maternal uniparental disomy

Automation and standardization of subject-specific region-of-interest segmentation for investigation of diffusion imaging in clinical populations

Diffusion weighted imaging (DWI) is key in clinical neuroimaging studies. In recent years, DWI has undergone rapid evolution and increasing applications. Diffusion magnetic resonance imaging (dMRI) is widely used to analyse group-level differences in white matter (WM), but suffers from limitations that can be particularly impactful in clinical groups where 1) structural abnormalities may increase erroneous inter-subject registration and 2) subtle differences in WM microstructure between individuals can be missed. It also lacks standardization protocols for analyses at the subject level. Region of Interest (ROI) analyses in native diffusion space can help overcome these challenges, with manual segmentation still used as the gold standard. However, robust automated approaches for the analysis of ROI-extracted native diffusion characteristics are limited. Subject-Specific Diffusion Segmentation (SSDS) is an automated pipeline that uses pre-existing imaging analysis methods to carry out WM investigations in native diffusion space, while overcoming the need to interpolate diffusion images and using an intermediate T1 image to limit registration errors and guide segmentation. SSDS is validated in a cohort of healthy subjects scanned three times to derive test-retest reliability measures and compared to other methods, namely manual segmentation and tract-based spatial statistics as an example of group-level method. The performance of the pipeline is further tested in a clinical population of patients with traumatic brain injury and structural abnormalities. Mean FA values obtained from SSDS showed high test-retest and were similar to FA values estimated from the manual segmentation of the same ROIs (p-value > 0.1). The average dice similarity coefficients (DSCs) comparing results from SSDS and manual segmentations was 0.8 ± 0.1. Case studies of TBI patients showed robustness to the presence of significant structural abnormalities, indicating its potential clinical application in the identification and diagnosis of WM abnormalities. Further recommendation is given regarding the tracts used with SSDS

Automation and standardization of subject-specific region-of-interest segmentation for investigation of diffusion imaging in clinical populations.

Diffusion weighted imaging (DWI) is key in clinical neuroimaging studies. In recent years, DWI has undergone rapid evolution and increasing applications. Diffusion magnetic resonance imaging (dMRI) is widely used to analyse group-level differences in white matter (WM), but suffers from limitations that can be particularly impactful in clinical groups where 1) structural abnormalities may increase erroneous inter-subject registration and 2) subtle differences in WM microstructure between individuals can be missed. It also lacks standardization protocols for analyses at the subject level. Region of Interest (ROI) analyses in native diffusion space can help overcome these challenges, with manual segmentation still used as the gold standard. However, robust automated approaches for the analysis of ROI-extracted native diffusion characteristics are limited. Subject-Specific Diffusion Segmentation (SSDS) is an automated pipeline that uses pre-existing imaging analysis methods to carry out WM investigations in native diffusion space, while overcoming the need to interpolate diffusion images and using an intermediate T1 image to limit registration errors and guide segmentation. SSDS is validated in a cohort of healthy subjects scanned three times to derive test-retest reliability measures and compared to other methods, namely manual segmentation and tract-based spatial statistics as an example of group-level method. The performance of the pipeline is further tested in a clinical population of patients with traumatic brain injury and structural abnormalities. Mean FA values obtained from SSDS showed high test-retest and were similar to FA values estimated from the manual segmentation of the same ROIs (p-value > 0.1). The average dice similarity coefficients (DSCs) comparing results from SSDS and manual segmentations was 0.8 ± 0.1. Case studies of TBI patients showed robustness to the presence of significant structural abnormalities, indicating its potential clinical application in the identification and diagnosis of WM abnormalities. Further recommendation is given regarding the tracts used with SSDS

Example of boundary segmentation.

A) Boundary of the WM. The blue line is the binary mask. The T1 image is in native diffusion space. B) Boundary of the WM/CSF and WM/GM. The red line is the mask of the WM/CSF boundary and the green line is the mask of the WM/GM boundary. The T1 image is in native diffusion space.</p

Repeatability of measures across visits for boundaries of the WM.

3D correlation plot of mean FA value and number of voxels for all three visits per tract, per subject. Each color represents a region of the boundary of the WM. (TIF)</p

Overview of the pipeline.

DWI images are pre-processed individually. 3D T1-weighted images are segmented, the mask of the WM boundary is estimated and used for a BBR of the diffusion image to the T1 image. Non-linear registration is estimated to move the T1 image to a pre-defined template space. The BBR matrix and the non-linear warp are then combined and reversed to estimate a transformation from the standard template to the individual diffusion image. The reverse BBR matrix is applied to the original T1-image, resulting in an inter-modality registration of the T1 to the DWI image. In the last steps, once all three images are in DWI space, the T1-imaged is segmented, the WM map is then used for cross-masking pre-defined ROIs which have been moved to the DWI image. ** image adapted from [19].</p

Examples of individual registration performance.

Registration of T1 to diffusion image and of standard template to diffusion image on 3 controls (C1, C2, C3) and 3 TBI patients (P1, P2, P3). The mask of the WM boundary (red) resulting from the segmentation of the T1 image in DWI space is used to indicate voxel-wise correspondence among all three images and the accurate structural overlay.</p

Cohort demographics.

Demographics of patients and healthy controls used in the validation and testing of the SDSS pipeline.</p

Overview of the validation steps and the results presented in the current study.

Analyses on the two cohorts were done separately. The first validations were carried out on the healthy controls (scanned three times for test-retest validation). We present results from the different registration steps, estimation of FA values and test-retest measures, as well as a comparison to other methods. The clinical population (scanned once on the same scanner as the control population) was analyzed first in a group-level study by comparing individually obtained ROI measures, then the results of SSDS were compared to other methods. Finally, three subjects were chosen for case study comparative analyses.</p