524 research outputs found
Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials
INTRODUCTION:
The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015.
METHODS:
We used standard searches to find publications using ADNI data.
RESULTS:
(1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers.
DISCUSSION:
Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial desig
Imaging genetics : Methodological approaches to overcoming high dimensional barriers
Imaging genetics is still a quite novel area of research which attempts to discover how genetic factors affect brain structures and functions.
In this thesis, using a various methodological approaches I showed how it can contribute to our understanding of the complex genetic architecture of the human brain
Cerebrospinal fluid biomarkers of neurofibrillary tangles and synaptic dysfunction are associated with longitudinal decline in white matter connectivity: A multi-resolution graph analysis
In addition to the development of beta amyloid plaques and neurofibrillary tangles, Alzheimer's disease (AD) involves the loss of connecting structures including degeneration of myelinated axons and synaptic connections. However, the extent to which white matter tracts change longitudinally, particularly in the asymptomatic, preclinical stage of AD, remains poorly characterized. In this study we used a novel graph wavelet algorithm to determine the extent to which microstructural brain changes evolve in concert with the development of AD neuropathology as observed using CSF biomarkers. A total of 118 participants with at least two diffusion tensor imaging (DTI) scans and one lumbar puncture for CSF were selected from two observational and longitudinally followed cohorts. CSF was assayed for pathology specific to AD (Aβ42 and phosphorylated-tau), neurodegeneration (total-tau), axonal degeneration (neurofilament light chain protein; NFL), and synaptic degeneration (neurogranin). Tractography was performed on DTI scans to obtain structural connectivity networks with 160 nodes where the nodes correspond to specific brain regions of interest (ROIs) and their connections were defined by DTI metrics (i.e., fractional anisotropy (FA) and mean diffusivity (MD)). For the analysis, we adopted a multi-resolution graph wavelet technique called Wavelet Connectivity Signature (WaCS) which derives higher order representations from DTI metrics at each brain connection. Our statistical analysis showed interactions between the CSF measures and the MRI time interval, such that elevated CSF biomarkers and longer time were associated with greater longitudinal changes in white matter microstructure (decreasing FA and increasing MD). Specifically, we detected a total of 17 fiber tracts whose WaCS representations showed an association between longitudinal decline in white matter microstructure and both CSF p-tau and neurogranin. While development of neurofibrillary tangles and synaptic degeneration are cortical phenomena, the results show that they are also associated with degeneration of underlying white matter tracts, a process which may eventually play a role in the development of cognitive decline and dementia
Methods for the analysis and characterization of brain morphology from MRI images
Brain magnetic resonance imaging (MRI) is an imaging modality that produces
detailed images of the brain without using any ionizing radiation.
From a structural MRI scan, it is possible to extract morphological properties
of different brain regions, such as their volume and shape. These measures
can both allow a better understanding of how the brain changes due
to multiple factors (e.g., environmental and pathological) and contribute to
the identification of new imaging biomarkers of neurological and psychiatric
diseases. The overall goal of the present thesis is to advance the knowledge
on how brain MRI image processing can be effectively used to analyze and
characterize brain structure.
The first two works presented in this thesis are animal studies that primarily
aim to use MRI data for analyzing differences between groups of
interest. In Paper I, MRI scans from wild and domestic rabbits were processed
to identify structural brain differences between these two groups.
Domestication was found to significantly reshape brain structure in terms
of both regional gray matter volume and white matter integrity. In Paper II,
rat brain MRI scans were used to train a brain age prediction model. This
model was then tested on both controls and a group of rats that underwent
long-term environmental enrichment and dietary restriction. This healthy
lifestyle intervention was shown to significantly affect the predicted brain
age trajectories by slowing the ratsâ aging process compared to controls.
Furthermore, brain age predicted on young adult rats was found to have a
significant effect on survival.
Papers III to V are human studies that propose deep learning-based
methods for segmenting brain structures that can be severely affected by
neurodegeneration. In particular, Papers III and IV focus on U-Net-based
2D segmentation of the corpus callosum (CC) in multiple sclerosis (MS)
patients. In both studies, good segmentation accuracy was obtained and a
significant correlation was found between CC area and the patientâs level of
cognitive and physical disability. Additionally, in Paper IV, shape analysis
of the segmented CC revealed a significant association between disability
and both CC thickness and bending angle. Conversely, in Paper V, a novel
method for automatic segmentation of the hippocampus is proposed, which
consists of embedding a statistical shape prior as context information into
a U-Net-based framework. The inclusion of shape information was shown
to significantly improve segmentation accuracy when testing the method
on a new unseen cohort (i.e., different from the one used for training).
Furthermore, good performance was observed across three different diagnostic
groups (healthy controls, subjects with mild cognitive impairment
and Alzheimerâs patients) that were characterized by different levels of hippocampal
atrophy.
In summary, the studies presented in this thesis support the great value
of MRI image analysis for the advancement of neuroscientific knowledge,
and their contribution is mostly two-fold. First, by applying well-established
processing methods on datasets that had not yet been explored in the literature,
it was possible to characterize specific brain changes and disentangle
relevant problems of a clinical or biological nature. Second, a technical
contribution is provided by modifying and extending already-existing brain
image processing methods to achieve good performance on new datasets
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