Timothy Brown

Fornix integrity and hippocampal volume predict memory decline and progression to Alzheimer's disease

The fornix is the predominant outflow tract of the hippocampus, a brain region known to be affected early in the course of Alzheimers disease (AD). The aims of the present study were to: (1) examine the cross‐sectional relationship between fornix diffusion tensor imaging (DTI) measurements (fractional anisotropy [FA], mean diffusivity [MD], axial diffusivity, and radial diffusivity), hippocampal volume, and memory performance, and (2) compare fornix DTI measures with hippocampal volumes as predictors of progression and transition from amnestic mild cognitive impairment to AD dementia.

Plasma ceramides are altered in mild cognitive impairment and predict cognitive decline and hippocampal volume loss

A blood‐based biomarker of Alzheimers disease (AD) would be superior to cerebrospinal fluid (CSF) and neuroimaging measures in terms of cost, invasiveness, and feasibility for repeated measures. We previously reported that blood ceramides varied in relation to timing of memory impairment in a population‐based study. The present objective was to examine whether plasma ceramides varied by AD severity in a well‐characterized clinic sample and were associated with cognitive decline and hippocampal volume loss over 1 year.

Collaborative Computational Anatomy: An MRI Morphometry Study of the Human Brain Via Diffeomorphic Metric Mapping

This article describes a large multi‐institutional analysis of the shape and structure of the human hippocampus in the aging brain as measured via MRI. The study was conducted on a population of 101 subjects including nondemented control subjects (n = 57) and subjects clinically diagnosed with Alzheimers Disease (AD, n = 38) or semantic dementia (n = 6) with imaging data collected at Washington University in St. Louis, hippocampal structure annotated at the Massachusetts General Hospital, and anatomical shapes embedded into a metric shape space using large deformation diffeomorphic metric mapping (LDDMM) at the Johns Hopkins University. A global classifier was constructed for discriminating cohorts of nondemented and demented subjects based on linear discriminant analysis of dimensions derived from metric distances between anatomical shapes, demonstrating class conditional structure differences measured via LDDMM metric shape (P < 0.01). Localized analysis of the control and AD subjects only on the coordinates of the population template demonstrates shape changes in the subiculum and the CA1 subfield in AD (P < 0.05). Such large scale collaborative analysis of anatomical shapes has the potential to enhance the understanding of neurodevelopmental and neuropsychiatric disorders. Hum Brain Mapp, 2009.

The diffeomorphometry of temporal lobe structures in preclinical Alzheimer's disease

This paper examines morphometry of MRI biomarkers derived from the network of temporal lobe structures including the amygdala, entorhinal cortex and hippocampus in subjects with preclinical Alzheimers disease (AD). Based on template-centered population analysis, it is demonstrated that the structural markers of the amygdala, hippocampus and entorhinal cortex are statistically significantly different between controls and those with preclinical AD. Entorhinal cortex is the most strongly significant based on the linear effects model (p < .0001) for the high-dimensional vertex- and Laplacian-based markers corresponding to localized atrophy. The hippocampus also shows significant localized high-dimensional change (p < .0025) and the amygdala demonstrates more global change signaled by the strength of the low-dimensional volume markers. The analysis of the three structures also demonstrates that the volume measures are only weakly discriminating between preclinical and control groups, with the average atrophy rates of the volume of the entorhinal cortex higher than amygdala and hippocampus. The entorhinal cortex thickness also exhibits an atrophy rate nearly a factor of two higher in the ApoE4 positive group relative to the ApoE4 negative group providing weak discrimination between the two groups.

Relationship of medial temporal lobe atrophy, APOE genotype, and cognitive reserve in preclinical Alzheimer's disease

This study evaluated the utility of baseline and longitudinal magnetic resonance imaging (MRI) measures of medial temporal lobe brain regions collected when participants were cognitively normal and largely in middle age (mean age 57 years) to predict the time to onset of clinical symptoms associated with mild cognitive impairment (MCI). Furthermore, we examined whether the relationship between MRI measures and clinical symptom onset was modified by apolipoprotein E (ApoE) genotype and level of cognitive reserve (CR). MRI scans and measures of CR were obtained at baseline from 245 participants who had been followed for up to 18 years (mean follow‐up 11 years). A composite score based on reading, vocabulary, and years of education was used as an index of CR. Cox regression models showed that lower baseline volume of the right hippocampus and smaller baseline thickness of the right entorhinal cortex predicted the time to symptom onset independently of CR and ApoE‐ɛ4 genotype, which also predicted the onset of symptoms. The atrophy rates of bilateral entorhinal cortex and amygdala volumes were also associated with time to symptom onset, independent of CR, ApoE genotype, and baseline volume. Only one measure, the left entorhinal cortex baseline volume, interacted with CR, such that smaller volumes predicted symptom onset only in individuals with lower CR. These results suggest that MRI measures of medial temporal atrophy, ApoE‐ɛ4 genotype, and the protective effects of higher CR all predict the time to onset of symptoms associated with MCI in a largely independent, additive manner during the preclinical phase of Alzheimers disease. Hum Brain Mapp 36:2826–2841, 2015.

Amygdalar atrophy in symptomatic Alzheimer's disease based on diffeomorphometry: the BIOCARD cohort.

This article examines the diffeomorphometry of magnetic resonance imaging-derived structural markers for the amygdala, in subjects with symptomatic Alzheimers disease (AD). Using linear mixed-effects models we show differences between those with symptomatic AD and controls. Based on template centered population analysis, the distribution of statistically significant change is seen in both the volume and shape of the amygdala in subjects with symptomatic AD compared with controls. We find that high-dimensional vertex based markers are statistically more significantly discriminating (p < 0.00001) than lower-dimensional markers and volumes, consistent with comparable findings in presymptomatic AD. Using a high-field 7T atlas, significant atrophy was found to be centered in the basomedial and basolateral subregions, with no evidence of centromedial involvement.

Resource atlases for multi-atlas brain segmentations with multiple ontology levels based on T1-weighted MRI

Technologies for multi-atlas brain segmentation of T1-weighted MRI images have rapidly progressed in recent years, with highly promising results. This approach, however, relies on a large number of atlases with accurate and consistent structural identifications. Here, we introduce our atlas inventories (n=90), which cover ages 4-82years with unique hierarchical structural definitions (286 structures at the finest level). This multi-atlas library resource provides the flexibility to choose appropriate atlases for various studies with different age ranges and structure-definition criteria. In this paper, we describe the details of the atlas resources and demonstrate the improved accuracy achievable with a dynamic age-matching approach, in which atlases that most closely match the subjects age are dynamically selected. The advanced atlas creation strategy, together with atlas pre-selection principles, is expected to support the further development of multi-atlas image segmentation.

Network Neurodegeneration in Alzheimer's Disease via MRI Based Shape Diffeomorphometry and High-Field Atlasing.

This paper examines MRI analysis of neurodegeneration in Alzheimer’s Disease (AD) in a network of structures within the medial temporal lobe using diffeomorphometry methods coupled with high-field atlasing in which the entorhinal cortex is partitioned into eight subareas. The morphometry markers for three groups of subjects (controls, preclinical AD, and symptomatic AD) are indexed to template coordinates measured with respect to these eight subareas. The location and timing of changes are examined within the subareas as it pertains to the classic Braak and Braak staging by comparing the three groups. We demonstrate that the earliest preclinical changes in the population occur in the lateral most sulcal extent in the entorhinal cortex (alluded to as transentorhinal cortex by Braak and Braak), and then proceeds medially which is consistent with the Braak and Braak staging. We use high-field 11T atlasing to demonstrate that the network changes are occurring at the junctures of the substructures in this medial temporal lobe network. Temporal progression of the disease through the network is also examined via changepoint analysis, demonstrating earliest changes in entorhinal cortex. The differential expression of rate of atrophy with progression signaling the changepoint time across the network is demonstrated to be signaling in the intermediate caudal subarea of the entorhinal cortex, which has been noted to be proximal to the hippocampus. This coupled to the findings of the nearby basolateral involvement in amygdala demonstrates the selectivity of neurodegeneration in early AD.

Linking white matter and deep gray matter alterations in premanifest Huntington disease.

Huntington disease (HD) is a fatal progressive neurodegenerative disorder for which only symptomatic treatment is available. A better understanding of the pathology, and identification of biomarkers will facilitate the development of disease-modifying treatments. HD is potentially a good model of a neurodegenerative disease for development of biomarkers because it is an autosomal-dominant disease with complete penetrance, caused by a single gene mutation, in which the neurodegenerative process can be assessed many years before onset of signs and symptoms of manifest disease. Previous MRI studies have detected abnormalities in gray and white matter starting in premanifest stages. However, the understanding of how these abnormalities are related, both in time and space, is still incomplete. In this study, we combined deep gray matter shape diffeomorphometry and white matter DTI analysis in order to provide a better mapping of pathology in the deep gray matter and subcortical white matter in premanifest HD. We used 296 MRI scans from the PREDICT-HD database. Atrophy in the deep gray matter, thalamus, hippocampus, and nucleus accumbens was analyzed by surface based morphometry, and while white matter abnormalities were analyzed in (i) regions of interest surrounding these structures, using (ii) tractography-based analysis, and using (iii) whole brain atlas-based analysis. We detected atrophy in the deep gray matter, particularly in putamen, from early premanifest stages. The atrophy was greater both in extent and effect size in cases with longer exposure to the effects of the CAG expansion mutation (as assessed by greater CAP-scores), and preceded detectible abnormalities in the white matter. Near the predicted onset of manifest HD, the MD increase was widespread, with highest indices in the deep and posterior white matter. This type of in-vivo macroscopic mapping of HD brain abnormalities can potentially indicate when and where therapeutics could be targeted to delay the onset or slow the disease progression.

Cortical thickness in relation to clinical symptom onset in preclinical AD.

Mild cognitive impairment (MCI) and Alzheimers disease (AD) dementia are preceded by a phase of disease, referred to as ‘preclinical AD’, during which cognitively normal individuals have evidence of AD pathology in the absence of clinical impairment. This study examined whether a magnetic resonance imaging (MRI) measure of cortical thickness in brain regions, collectively known as ‘AD vulnerable’ regions, predicted the time to onset of clinical symptoms associated with MCI and whether cortical thickness was similarly predictive of clinical symptom onset within 7 years post baseline versus progression at a later point in time. These analyses included 240 participants from the BIOCARD study, a cohort of longitudinally followed individuals who were cognitively normal at the time of their MRI (mean age = 56 years). Participants have been followed for up to 18 years (M follow-up = 11.8 years) and 50 participants with MRIs at baseline have developed MCI or dementia over time (mean time to clinical symptom onset = 7 years). Cortical thickness in AD vulnerable regions was based on the mean thickness of eight cortical regions. Using Cox regression models, we found that lower mean cortical thickness was associated with an increased risk of progression from normal cognition to clinical symptom onset within 7 years of baseline (p = 0.03), but not with progression > 7 years from baseline (p = 0.30). Lower cortical thickness was also associated with higher levels of phosphorylated tau, measured in cerebrospinal fluid at baseline. These results suggest that cortical thinning in AD vulnerable regions is detectable in cognitively normal individuals several years prior to the onset of clinical symptoms that are a harbinger of a diagnosis of MCI, and that the changes are more likely to be evident in the years proximal to clinical symptom onset, consistent with hypothetical AD biomarker models.