[Assessment of axonal degeneration on Alzheimer's disease with diffusion tensor MRI]

Improvement of partial volume segmentation for brain tissue on diffusion tensor images using multiple-tensor estimation

To improve evaluations of cortical and subcortical diffusivity in neurological diseases, it is necessary to improve the accuracy of brain diffusion tensor imaging (DTI) data segmentation. The conventional partial volume segmentation method fails to classify voxels with multiple white matter (WM) fiber orientations such as fiber-crossing regions. Our purpose was to improve the performance of segmentation by taking into account the partial volume effects due to both multiple tissue types and multiple WM fiber orientations. We quantitatively evaluated the overall performance of the proposed method using digital DTI phantom data. Moreover, we applied our method to human DTI data, and compared our results with those of a conventional method. In the phantom experiments, the conventional method and proposed method yielded almost the same root mean square error (RMSE) for gray matter (GM) and cerebrospinal fluid (CSF), while the RMSE in the proposed method was smaller than that in the conventional method for WM. The volume overlap measures between our segmentation results and the ground truth of the digital phantom were more than 0.8 in all three tissue types, and were greater than those in the conventional method. In visual comparisons for human data, the WM/GM/CSF regions obtained using our method were in better agreement with the corresponding regions depicted in the structural image than those obtained using the conventional method. The results of the digital phantom experiment and human data demonstrated that our method improved accuracy in the segmentation of brain tissue data on DTI compared to the conventional method.

Longitudinal changes of fractional anisotropy in Alzheimer's disease patients treated with galantamine: a 12-month randomized, placebo-controlled, double-blinded study

Diffusion tensor imaging (DTI) demonstrates decline of fractional anisotropy (FA) as a marker of fiber tract integrity in Alzheimer's disease (AD). We aimed to assess the longitudinal course of white matter microstructural changes in AD and healthy elderly control (HC) subjects and to evaluate the effects of treatment with the cholinesterase inhibitor galantamine on white matter microstructure in AD patients. We enrolled 28 AD patients and 11 healthy elderly control subjects (HC). AD patients were randomly assigned to 6-month double-blind galantamine treatment or placebo, with a 6-month open-label extension phase. DTI was performed at baseline, as well as at 6 and 12-month follow-up in AD patients. The HC subjects underwent DTI at baseline and 12-month follow-up without treatment. We measured FA in regions of interest covering the posterior cingulate and corpus callosum. At 6-month follow-up, the AD group showed significant FA decline in the left posterior cingulate. FA decline was significantly preserved in the posterior body of the corpus callosum in AD group with treatment compared to placebo. At 12-month follow-up, the AD patients showed no differences in FA decline between initial treatment and placebo groups after the 6-month open-label extension phase. A significant FA decline occurred in the left posterior cingulate across the AD and HC groups without between-group differences. DTI demonstrated FA decline in intracortically projecting fiber tracts in aging and AD over 1 year. Galantamine had limited impact on regional FA decline, which was not preserved after additional 6-month open-label treatment.

The search for neuroimaging biomarkers of Alzheimer's disease with advanced MRI techniques

The aim of this review is to examine the recent literature on using advanced magnetic resonance imaging (MRI) techniques for finding neuroimaging biomarkers that are sensitive to the detection of risks for Alzheimer's disease (AD). Since structural MRI techniques, such as brain structural volumetry and voxel-based morphometry (VBM), have been widely used for AD studies and extensively reviewed, we will only briefly touch on the topics of volumetry and morphometry. The focus of the current review is about the more recent developments in the search for AD neuroimaging biomarkers with functional MRI (fMRI), resting-state functional connectivity MRI (fcMRI), diffusion tensor imaging (DTI), arterial spin-labeling (ASL), and magnetic resonance spectroscopy (MRS).

Neuronal networks in Alzheimer's disease

Alzheimer's disease (AD) is a progressive, neurodegenerative disease that can be clinically characterized by impaired memory and many other cognitive functions. Previous studies have demonstrated that the impairment is accompanied by not only regional brain abnormalities but also changes in neuronal connectivity between anatomically distinct brain regions. Specifically, using neurophysiological and neuroimaging techniques as well as advanced graph theory-based computational approaches, several recent studies have suggested that AD patients have disruptive neuronal integrity in large-scale structural and functional brain systems underlying high-level cognition, as demonstrated by a loss of small-world network characteristics. Small world is an attractive model for the description of complex brain networks because it can support both segregated and integrated information processing. The altered small-world organization thus reflects aberrant neuronal connectivity in the AD brain that is most likely to explain cognitive deficits caused by this disease. In this review, we will summarize recent advances in the brain network research on AD, focusing mainly on the large-scale structural and functional descriptions. The literature reviewed here suggests that AD patients are associated with integrative abnormalities in the distributed neuronal networks, which could provide new insights into the disease mechanism in AD and help us to uncover an imaging-based biomarker for the diagnosis and monitoring of the disease.

MRI-based automated computer classification of probable AD versus normal controls

Automated computer classification (ACC) techniques are needed to facilitate physician's diagnosis of complex diseases in individual patients. We provide an example of ACC using computational techniques within the context of cross-sectional analysis of magnetic resonance images (MRI) in neurodegenerative diseases, namely Alzheimer's dementia (AD). In this paper, the accuracy of our ACC methodology is assessed when presented with real life, imperfect data, i.e., cohorts of MRI with varying acquisition parameters and imaging quality. The comparative methodology uses the Jacobian determinants derived from dense deformation fields and scaled grey-level intensity from a selected volume of interest centered on the medial temporal lobe. The ACC performance is assessed in a series of leave-one-out experiments aimed at separating 75 probable AD and 75 age-matched normal controls. The resulting accuracy is 92% using a support vector machine classifier based on least squares optimization. Finally, it is shown in the Appendix that determinants and scaled grey-level intensity are appreciably more robust to varying parameters in validation studies using simulated data, when compared to raw intensities or grey/white matter volumes. The ability of cross-sectional MRI at detecting probable AD with high accuracy could have profound implications in the management of suspected AD candidates.

Diffusion tensor imaging of cingulum fibers in mild cognitive impairment and Alzheimer disease

BACKGROUND

Neuroimaging in mild cognitive impairment (MCI) and Alzheimer disease (AD) generally shows medial temporal lobe atrophy and diminished glucose metabolism and cerebral blood flow in the posterior cingulate gyrus. However, it is unclear whether these abnormalities also impact the cingulum fibers, which connect the medial temporal lobe and the posterior cingulate regions.

OBJECTIVE

To use diffusion tensor imaging (DTI), by measuring fractional anisotropy (FA), to test 1) if MCI and AD are associated with DTI abnormalities in the parahippocampal and posterior cingulate regions of the cingulum fibers; 2) if white matter abnormalities extend to the neocortical fiber connections in the corpus callosum (CC); 3) if DTI improves accuracy to separate AD and MCI from healthy aging vs structural MRI.

METHODS

DTI and structural MRI were preformed on 17 patients with AD, 17 with MCI, and 18 cognitively normal (CN) subjects.

RESULTS

FA of the cingulum fibers was significantly reduced in MCI, and even more in AD. FA was also significantly reduced in the splenium of the CC in AD, but not in MCI. Adding DTI to hippocampal volume significantly improved the accuracy to separate MCI and AD from CN.

CONCLUSION

Assessment of the cingulum fibers using diffusion tensor imaging may aid early diagnosis of Alzheimer disease.

Fiber connections between the cerebral cortex and the corpus callosum in Alzheimer's disease: a diffusion tensor imaging and voxel-based morphometry study

Regional cortical atrophy in Alzheimer's disease (AD) most likely reflects the loss of cortical neurons. Several diffusion tensor imaging studies reported reduced fractional anisotropy (FA) in the corpus callosum in AD. The aim of this study was to investigate the association between reduced FA in the corpus callosum and gray matter atrophy in AD. Thirteen patients with AD with a mean (+/-standard deviation) age of 68.3 years (+/-11.5) and mean Mini Mental State Examination (MMSE) score of 21.8 (+/-4.8) were recruited. There were 13 control subjects with a mean age of 66.7 years (+/-6.4) and MMSE of 29.1 (+/-0.7). We used voxel-based morphometry of gray matter maps and region of interest-based analysis of FA in the corpus callosum. FA values of the anterior corpus callosum in AD patients were significantly correlated with gray matter volume in the prefrontal cortex and left parietal lobes. FA values of the posterior corpus callosum were significantly correlated with gray matter volume in the bilateral frontal, temporal, right parietal, and occipital lobes. In control subjects, no correlations were detected. Our findings suggest that decline of FA in the corpus callosum may be related to neuronal degeneration in corresponding cortical areas.

Multivariate network analysis of fiber tract integrity in Alzheimer's disease

Axonal and dendritic integrity is affected early in Alzheimer's disease (AD). Studies using region of interest or voxel-based analysis of diffusion tensor imaging data found significant decline of fractional anisotropy, a marker of fiber tract integrity, in selected white matter areas. We applied a multivariate network analysis based on principal component analysis to fractional anisotropy maps derived from diffusion-weighted scans from 15 AD patients, and 14 elderly healthy controls. Fractional anisotropy maps were obtained from an EPI diffusion sequence using parallel imaging to reduce distortion artifacts. We used high-dimensional image warping to control for partial volume effects due to white matter atrophy in AD. We found a significant regional pattern of fiber changes (p < 0.01) indicating that the integrity of intracortical projecting fiber tracts (including corpus callosum, cingulum and fornix, and frontal, temporal and occipital lobe white matter areas) was reduced, whereas extracortical projecting fiber tracts, including the pyramidal and extrapyramidal systems and somatosensory projections, were relatively preserved in AD. Effects of a univariate analysis were almost entirely contained within the multivariate effect. Our findings illustrate the use of a multivariate approach to fractional anisotropy data that takes advantage of the highly organized structure of anisotropy maps, and is independent of multiple comparison correction and partial volume effects. In agreement with post-mortem evidence, our study demonstrates dissociation between intracortical and extracortical projecting fiber systems in AD in the living human brain.

Axonal Transport and Alzheimer's Disease

In contrast to most eukaryotic cells, neurons possess long, highly branched processes called axons and dendrites. In large mammals, such as humans, some axons reach lengths of over 1 m. These lengths pose a major challenge to the movement of proteins, vesicles, and organelles between presynaptic sites and cell bodies. To overcome this challenge axons and dendrites rely upon specialized transport machinery consisting of cytoskeletal motor proteins generating directed movements along cytoskeletal tracks. Not only are these transport systems crucial to maintain neuronal viability and differentiation, but considerable experimental evidence suggests that failure of axonal transport may play a role in the development or progression of neurological diseases such as Alzheimer's disease.

Functional connectivity of the fusiform gyrus during a face-matching task in subjects with mild cognitive impairment

Cognitive function requires a high level of functional interaction between regions of a network supporting cognition. Assuming that brain activation changes denote an advanced state of disease progression, changes in functional connectivity may precede changes in brain activation. The objective of this study was to investigate changes in functional connectivity of the right middle fusiform gyrus (FG) in subjects with mild cognitive impairment (MCI) during performance of a face-matching task. The right middle FG is a key area for processing face stimuli. Brain activity was measured using functional MRI. There were 16 MCI subjects and 19 age-matched healthy controls. The linear correlation coefficient was utilized as a measure of functional connectivity between the right middle FG and all other voxels in the brain. There were no statistical differences found in task performance or activation between groups. The right middle FG of the healthy control and MCI groups showed strong bilateral positive linear correlation with the visual cortex, inferior and superior parietal lobules, dorsolateral prefrontal cortex (DLPFC) and anterior cingulate. The healthy controls showed higher positive linear correlation of the right middle FG to the visual cortex, parietal lobes and right DLPFC than the MCI group, whereas the latter had higher positive linear correlation in the left cuneus. In the healthy controls, the right middle FG had negative linear correlation with right medial frontal gyrus and superior temporal gyrus and with left inferior parietal lobule (IPL), angular gyrus, superior frontal gyrus and anterior cingulate gyrus, but the MCI group had negative linear correlation with the left IPL, angular gyrus, precuneus, anterior cingulate, and to right middle temporal gyrus and posterior cingulate gyrus. In the negatively linearly correlated regions, the MCI group had reduced functional connectivity to the frontal areas, right superior temporal gyrus and left IPL. Different regions of the cuneus and IPL had increased functional connectivity in either group. The putative presence of Alzheimer's disease neuropathology in MCI affects functional connectivity from the right middle FG to the visual areas and medial frontal areas. In addition, higher linear correlation in the MCI group in the parietal lobe may indicate the initial appearance of compensatory processes. The results demonstrate that functional connectivity can be an effective marker for the detection of changes in brain function in MCI subjects.

76-space analysis of grey matter diffusivity: methods and applications

Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) allow in vivo investigation of molecular motion of tissue water at a microscopic level in cerebral gray matter (GM) and white matter (WM). DWI/DTI measure of water diffusion has been proven to be invaluable for the study of many neurodegenerative diseases (e.g., Alzheimer's disease and Creutzfeldt-Jakob disease) that predominantly involve GM. Thus, quantitative analysis of GM diffusivity is of scientific interest and is promised to have a clinical impact on the investigation of normal brain aging and neuropathology. In this paper, we propose an automated framework for analysis of GM diffusivity in 76 standard anatomic subdivisions of gray matter to facilitate studies of neurodegenerative and other gray matter neurological diseases. The computational framework includes three enabling technologies: (1) automatic parcellation of structural MRI GM into 76 precisely defined neuroanatomic subregions ("76-space"), (2) automated segmentation of GM, WM and CSF based on DTI data, and (3) automatic measurement of the average apparent diffusion coefficient (ADC) in each segmented GM subregion. We evaluate and validate this computational framework for 76-space GM diffusivity analysis using data from normal volunteers and from patients with Creutzfeldt-Jakob disease.

Pathways towards and away from Alzheimer's disease

Slowly but surely, Alzheimer's disease (AD) patients lose their memory and their cognitive abilities, and even their personalities may change dramatically. These changes are due to the progressive dysfunction and death of nerve cells that are responsible for the storage and processing of information. Although drugs can temporarily improve memory, at present there are no treatments that can stop or reverse the inexorable neurodegenerative process. But rapid progress towards understanding the cellular and molecular alterations that are responsible for the neuron's demise may soon help in developing effective preventative and therapeutic strategies.