Cerebral atrophy in mild cognitive impairment and Alzheimer disease: rates and acceleration.

OBJECTIVE: To quantify the regional and global cerebral atrophy rates and assess acceleration rates in healthy controls, subjects with mild cognitive impairment (MCI), and subjects with mild Alzheimer disease (AD). METHODS: Using 0-, 6-, 12-, 18-, 24-, and 36-month MRI scans of controls and subjects with MCI and AD from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we calculated volume change of whole brain, hippocampus, and ventricles between all pairs of scans using the boundary shift integral. RESULTS: We found no evidence of acceleration in whole-brain atrophy rates in any group. There was evidence that hippocampal atrophy rates in MCI subjects accelerate by 0.22%/year2 on average (p = 0.037). There was evidence of acceleration in rates of ventricular enlargement in subjects with MCI (p = 0.001) and AD (p < 0.001), with rates estimated to increase by 0.27 mL/year2 (95% confidence interval 0.12, 0.43) and 0.88 mL/year2 (95% confidence interval 0.47, 1.29), respectively. A post hoc analysis suggested that the acceleration of hippocampal loss in MCI subjects was mainly driven by the MCI subjects that were observed to progress to clinical AD within 3 years of baseline, with this group showing hippocampal atrophy rate acceleration of 0.50%/year2 (p = 0.003). CONCLUSIONS: The small acceleration rates suggest a long period of transition to the pathologic losses seen in clinical AD. The acceleration in hippocampal atrophy rates in MCI subjects in the ADNI seems to be driven by those MCI subjects who concurrently progressed to a clinical diagnosis of AD

The investigation of hippocampal and hippocampal subfield volumetry, morphology and metabolites using 3T MRI

A detailed account of the hippocampal anatomy has been provided. This thesis will explore and exploit the use of 3T MRI and the latest developments in image processing techniques to measure hippocampal and hippocampal subfield volumes, hippocampal metabolites and morphology. In chapter two a protocol for segmenting the hippocampus was created. The protocol was assessed in two groups of subjects with differing socioeconomic status (SES). This was a novel, community based sample in which hippocampal volumes have yet to be assessed in the literature. Manual and automated hippocampal segmentation measurements were compared on the two distinct SES groups. The mean volumes and also the variance in these measurements were comparable between two methods. The Dice overlapping metric comparing the two methods was 0.81. In chapter three voxel based morphometry (VBM) was used to compare local volume differences in grey matter volume between the two SES groups. Two approaches to VBM were compared. DARTEL-VBM results were found to be superior to the earlier ’optimised’ VBM method. Following a small volume correction, DARTEL-VBM results were suggesitive of focal GM volumes reductions in both the right and left hippocampi of the lower SES group. In chapter four an MR spectroscopy protocol was implemented to assess hippocampal metabolites in the two differing SES groups. Interpretable spectra were obtained in 73% of the 42 subjects. The poorer socioeconomic group were considered to have been exposed to chronic stress and therefore via inflammatory processes it was anticipated that the NAA/Cr metabolite ratio would be reduced in this group when compared to the more affluent group. Both NAA/Cr and Cho/Cr hippocampal metabolite ratios were not significantly different between the two groups. The aim of chapter 5 was to implement the protocol and methodology developed in chapter 2 to determine a normal range for hippocampal volumes at 3T MRI. 3D T1-weighted IR-FSPGR images were acquired in 39 healthy, normal volunteers in the age range from 19 to 64. Following the automated procedure hippocampal volumes were manually inspected and edited. The mean and standard deviation of the left and right hippocampal volumes were determined to be: 3421mm3 ± 399mm3 and 3487mm3 ± 431mm3 respectively. After correcting for total ICV the volumes were: 0.22% ± 0.03% and 0.23% ± 0.03% for the left and right hippocampi respectively. Thus, a normative database of hippocampal volumes was established. The normative data here will in future act as a baseline on which other methods of determining hippocampal volumes may be compared. The utility of using the normative dataset to compare other groups of subjects will be limited as a result of the lack of a comprehensive assessment of IQ or education level of the normal volunteers which may affect the volume of the hippocampus. In chapter six Incomplete hippocampal inversion (IHI) was assessed. Few studies have assessed the normal incidence of IHI and of those studies the analysis of IHI extended only to a radiological assessment. Here we present a comprehensive and quantitative assessment of IHI. IHI was found on 31 of the 84 normal subjects assessed (37%). ICV corrected IHI left-sided hippocampal volumes were compared against ICV corrected normal left-sided hippocampal volumes (25 vs. 52 hippocampi). The IHI hippocampal volumes were determined to be smaller than the normal hippocampal volumes (p<< 0.05). However, on further inspection it was observed that the ICV of the IHI was significantly smaller than the ICV of the normal group, confounding the previous result. In chapter seven a pilot study was performed on patients with Rheumatoid Arthritis (RA). The aim was to exploit the improved image quality offered by the 3T MRI to create a protocol for assessing the CA4/ dentate volume and to compare the volume of this subfield of the hippocampus before and after treatment. Two methodologies were implemented. In the first method a protocol was produced to manually segment the CA4/dentate region of the hippocampus from coronal T2-weighted FSE images. Given that few studies have assessed hippocampal subfields, an assessment of study power and sample size was conducted to inform future work. In the second method, the data the DARTEL-VBM image processing pipeline was applied. Statistical nonparametric mapping was applied in the final statistical interpretation of the VBM data. Following an FDR correction, a single GM voxel in the hippocampus was deemed to be statistically significant, this was suggestive of small GM volume increase following antiinflammatory treatment. Finally, in chapter eight, the manual segmentation protocol for the CA4/dentate hippocampal subfield developed in chapter seven was extended to include a complete set of hippocampal subfields. This is one of the first attempts to segment the entire hippocampus into its subfields using 3T MRI and as such, it was important to assess the quality of the measurement procedure. Furthermore, given the subfield volumes and the variability in these measurements, power and sample size calculations were also estimated to inform further work. Seventeen healthy volunteers were scanned using 3T MRI. A detailed manual segmentation protocol was created to guide two independent operators to measure the hippocampal subfield volumes. Repeat measures were made by a single operator for intra-operator variability and inter-operator variability was also assessed. The results of the intra-operator comparison proved reasonably successful where values compared well but were typically slightly poorer than similar attempts in the literature. This was likely to be the result of the additional complication of trying to segment subfields in the head and tail of the hippocampus where previous studies have focused only on the body of the hippocampus. Inter-rater agreement measures for subfield volumes were generally poorer than would be acceptable if full exchangeability of the data between the raters was necessary. This would indicate that further refinements to the manual segmentation protocol are necessary. Future work should seek to improve the methodology to reduce the variability and improve the reproducibility in these measures

A model of brain morphological changes related to aging and Alzheimer's disease from cross-sectional assessments

In this study we propose a deformation-based framework to jointly model the influence of aging and Alzheimer's disease (AD) on the brain morphological evolution. Our approach combines a spatio-temporal description of both processes into a generative model. A reference morphology is deformed along specific trajectories to match subject specific morphologies. It is used to define two imaging progression markers: 1) a morphological age and 2) a disease score. These markers can be computed locally in any brain region. The approach is evaluated on brain structural magnetic resonance images (MRI) from the ADNI database. The generative model is first estimated on a control population, then, for each subject, the markers are computed for each acquisition. The longitudinal evolution of these markers is then studied in relation with the clinical diagnosis of the subjects and used to generate possible morphological evolution. In the model, the morphological changes associated with normal aging are mainly found around the ventricles, while the Alzheimer's disease specific changes are more located in the temporal lobe and the hippocampal area. The statistical analysis of these markers highlights differences between clinical conditions even though the inter-subject variability is quiet high. In this context, the model can be used to generate plausible morphological trajectories associated with the disease. Our method gives two interpretable scalar imaging biomarkers assessing the effects of aging and disease on brain morphology at the individual and population level. These markers confirm an acceleration of apparent aging for Alzheimer's subjects and can help discriminate clinical conditions even in prodromal stages. More generally, the joint modeling of normal and pathological evolutions shows promising results to describe age-related brain diseases over long time scales.Comment: NeuroImage, Elsevier, In pres

A Quantitative Imaging Biomarker Supporting Radiological Assessment of Hippocampal Sclerosis Derived From Deep Learning-Based Segmentation of T1w-MRI.

Purpose Hippocampal volumetry is an important biomarker to quantify atrophy in patients with mesial temporal lobe epilepsy. We investigate the sensitivity of automated segmentation methods to support radiological assessments of hippocampal sclerosis (HS). Results from FreeSurfer and FSL-FIRST are contrasted to a deep learning (DL)-based segmentation method. Materials and Methods We used T1-weighted MRI scans from 105 patients with epilepsy and 354 healthy controls. FreeSurfer, FSL, and a DL-based method were applied for brain anatomy segmentation. We calculated effect sizes (Cohen's d) between left/right HS and healthy controls based on the asymmetry of hippocampal volumes. Additionally, we derived 14 shape features from the segmentations and determined the most discriminating feature to identify patients with hippocampal sclerosis by a support vector machine (SVM). Results Deep learning-based segmentation of the hippocampus was the most sensitive to detecting HS. The effect sizes of the volume asymmetries were larger with the DL-based segmentations (HS left d= -4.2, right = 4.2) than with FreeSurfer (left= -3.1, right = 3.7) and FSL (left= -2.3, right = 2.5). For the classification based on the shape features, the surface-to-volume ratio was identified as the most important feature. Its absolute asymmetry yielded a higher area under the curve (AUC) for the deep learning-based segmentation (AUC = 0.87) than for FreeSurfer (0.85) and FSL (0.78) to dichotomize HS from other epilepsy cases. The robustness estimated from repeated scans was statistically significantly higher with DL than all other methods. Conclusion Our findings suggest that deep learning-based segmentation methods yield a higher sensitivity to quantify hippocampal sclerosis than atlas-based methods and derived shape features are more robust. We propose an increased asymmetry in the surface-to-volume ratio of the hippocampus as an easy-to-interpret quantitative imaging biomarker for HS

Convolutional neural networks for the segmentation of small rodent brain MRI

Image segmentation is a common step in the analysis of preclinical brain MRI, often performed manually. This is a time-consuming procedure subject to inter- and intra- rater variability. A possible alternative is the use of automated, registration-based segmentation, which suffers from a bias owed to the limited capacity of registration to adapt to pathological conditions such as Traumatic Brain Injury (TBI). In this work a novel method is developed for the segmentation of small rodent brain MRI based on Convolutional Neural Networks (CNNs). The experiments here presented show how CNNs provide a fast, robust and accurate alternative to both manual and registration-based methods. This is demonstrated by accurately segmenting three large datasets of MRI scans of healthy and Huntington disease model mice, as well as TBI rats. MU-Net and MU-Net-R, the CCNs here presented, achieve human-level accuracy while eliminating intra-rater variability, alleviating the biases of registration-based segmentation, and with an inference time of less than one second per scan. Using these segmentation masks I designed a geometric construction to extract 39 parameters describing the position and orientation of the hippocampus, and later used them to classify epileptic vs. non-epileptic rats with a balanced accuracy of 0.80, five months after TBI. This clinically transferable geometric approach detects subjects at high-risk of post-traumatic epilepsy, paving the way towards subject stratification for antiepileptogenesis studies

A Modality-Adaptive Method for Segmenting Brain Tumors and Organs-at-Risk in Radiation Therapy Planning

In this paper we present a method for simultaneously segmenting brain tumors and an extensive set of organs-at-risk for radiation therapy planning of glioblastomas. The method combines a contrast-adaptive generative model for whole-brain segmentation with a new spatial regularization model of tumor shape using convolutional restricted Boltzmann machines. We demonstrate experimentally that the method is able to adapt to image acquisitions that differ substantially from any available training data, ensuring its applicability across treatment sites; that its tumor segmentation accuracy is comparable to that of the current state of the art; and that it captures most organs-at-risk sufficiently well for radiation therapy planning purposes. The proposed method may be a valuable step towards automating the delineation of brain tumors and organs-at-risk in glioblastoma patients undergoing radiation therapy.Comment: corrected one referenc

Unsuspected Involvement of Spinal Cord in Alzheimer Disease

Objective: Brain atrophy is an established biomarker for dementia, yet spinal cord involvement has not been investigated to date. As the spinal cord is relaying sensorimotor control signals from the cortex to the peripheral nervous system and vice-versa, it is indeed a very interesting question to assess whether it is affected by atrophy due to a disease that is known for its involvement of cognitive domains first and foremost, with motor symptoms being clinically assessed too. We, therefore, hypothesize that in Alzheimer’s disease (AD), severe atrophy can affect the spinal cord too and that spinal cord atrophy is indeed an important in vivo imaging biomarker contributing to understanding neurodegeneration associated with dementia. Methods: 3DT1 images of 31 AD and 35 healthy control (HC) subjects were processed to calculate volume of brain structures and cross-sectional area (CSA) and volume (CSV) of the cervical cord [per vertebra as well as the C2-C3 pair (CSA23 and CSV23)]. Correlated features (ρ > 0.7) were removed, and the best subset identified for patients’ classification with the Random Forest algorithm. General linear model regression was used to find significant differences between groups (p ≤ 0.05). Linear regression was implemented to assess the explained variance of the Mini-Mental State Examination (MMSE) score as a dependent variable with the best features as predictors. Results: Spinal cord features were significantly reduced in AD, independently of brain volumes. Patients classification reached 76% accuracy when including CSA23 together with volumes of hippocampi, left amygdala, white and gray matter, with 74% sensitivity and 78% specificity. CSA23 alone explained 13% of MMSE variance. Discussion: Our findings reveal that C2-C3 spinal cord atrophy contributes to discriminate AD from HC, together with more established features. The results show that CSA23, calculated from the same 3DT1 scan as all other brain volumes (including right and left hippocampi), has a considerable weight in classification tasks warranting further investigations. Together with recent studies revealing that AD atrophy is spread beyond the temporal lobes, our result adds the spinal cord to a number of unsuspected regions involved in the disease. Interestingly, spinal cord atrophy explains also cognitive scores, which could significantly impact how we model sensorimotor control in degenerative diseases with a primary cognitive domain involvement. Prospective studies should be purposely designed to understand the mechanisms of atrophy and the role of the spinal cord in AD