Alvin H. Bachman

Quantitative comparison of algorithms for inter-subject registration of 3D volumetric brain MRI scans.

The objective of inter-subject registration of three-dimensional volumetric brain scans is to reduce the anatomical variability between the images scanned from different individuals. This is a necessary step in many different applications such as voxelwise group analysis of imaging data obtained from different individuals. In this paper, the ability of three different image registration algorithms in reducing inter-subject anatomical variability is quantitatively compared using a set of common high-resolution volumetric magnetic resonance imaging scans from 17 subjects. The algorithms are from the automatic image registration (AIR; version 5), the statistical parametric mapping (SPM99), and the automatic registration toolbox (ART) packages. The latter includes the implementation of a non-linear image registration algorithm, details of which are presented in this paper. The accuracy of registration is quantified in terms of two independent measures: (1) post-registration spatial dispersion of sets of homologous landmarks manually identified on images before or after registration; and (2) voxelwise image standard deviation maps computed within the set of images registered by each algorithm. Both measures showed that the ART algorithm is clearly superior to both AIR and SPM99 in reducing inter-subject anatomical variability. The spatial dispersion measure was found to be more sensitive when the landmarks were placed after image registration. The standard deviation measure was found sensitive to intensity normalization or the method of image interpolation.

A quantitative comparison of motion detection algorithms in fMRI

An important step in the analysis of fMRI time-series data is to detect, and as much as possible, correct for subject motion during the course of the scanning session. Several public domain algorithms are currently available for motion detection in fMRI. This paper compares the performance of four commonly used programs: AIR 3.08, SPM99, AFNI98, and the pyramid method of Thevenaz, Ruttimann, and Unser (TRU). The comparison is based on the performance of the algorithms in correcting a range of simulated known motions in the presence of various degrees of noise. SPM99 provided the most accurate motion detection amongst the algorithms studied. AFNI98 provided only slightly less accurate results than SPM99, however, it was several times faster than the other programs. This algorithm represents a good compromise between speed and accuracy. AFNI98 was also the most robust program in presence of noise. It yielded reasonable results for very low signal to noise levels. For small initial misalignments, TRUs performance was similar to SPM99 and AFNI98. However, its accuracy diminished rapidly for larger misalignments. AIR was found to be the least accurate program studied.

Analysis of the MIRIAD Data Shows Sex Differences in Hippocampal Atrophy Progression

BACKGROUND Hippocampus (HC) atrophy is a hallmark of early Alzheimers disease (AD). Atrophy rates can be measured by high-resolution structural MRI. Longitudinal studies have previously shown sex differences in the progression of functional and cognitive deficits and rates of brain atrophy in early AD dementia. It is important to corroborate these findings on independent datasets. OBJECTIVE To study temporal rates of HC atrophy over a one-year period in probable AD patients and cognitively normal (CN) subjects by longitudinal MRI scans obtained from the Minimal Interval Resonance Imaging in AD (MIRIAD) database. METHODS We used a novel algorithm to compute an index of hippocampal (volumetric) integrity (HI) at baseline and one-year follow-up in 43 mild-moderate probable AD patients and 22 CN subjects in MIRIAD. The diagnostic power of longitudinal HI measurement was assessed using a support vector machines (SVM) classifier. RESULTS The HI was significantly reduced in the AD group (p <  10(-20)). In addition, the annualized percentage rate of reduction in HI was significantly greater in the AD group (p <  10(-13)). Within the AD group, the annual reduction of HI in women was significantly greater than in men (p = 0.008). The accuracy of SVM classification between AD and CN subjects was estimated to be 97% by 10-fold cross-validation. CONCLUSION In the MIRIAD patients with probable AD, the HC atrophies at a significantly faster rate in women as compared to men. Female sex is a risk factor for faster descent into AD. The HI measure has potential for AD diagnosis, as a biomarker of AD progression and a therapeutic target in clinical trials.

Corpus callosum shape and size changes in early Alzheimer's disease: a longitudinal MRI study using the OASIS brain database.

BACKGROUND Alzheimers disease (AD) has been shown to be associated with shrinkage of the corpus callosum mid-sagittal cross-sectional area (CCA). OBJECTIVE To study temporal rates of corpus callosum atrophy not previously reported for early AD. METHODS We used longitudinal MRI scans to study the rates of change of CCA and circularity (CIR), a measure of its shape, in normal controls (NC, n = 75), patients with very mild AD (AD-VM, n = 51), and mild AD (AD-M, n = 21). RESULTS There were significant reduction rates in CCA and CIR in all three groups. While CCA reduction rates were not statistically different between groups, the CIR declined faster in AD-VM (p < 0.03) and AD-M (p < 0.0001) relative to NC, and in AD-M relative to AD-VM (p < 0.0004). CONCLUSION CIR declines at an accelerated rate with AD severity. Its rate of change is more closely associated with AD progression than CCA or any of its sub-regions. CIR may be a useful group biomarker for objective assessment of treatments that aim to slow AD progression.

Corpus callosum atrophy rate in mild cognitive impairment and prodromal Alzheimer's disease.

BACKGROUND Corpus callosum (CC) size and shape have been previously studied in Alzheimers disease (AD) with the majority of studies having been cross-sectional. Due to the large variance in normal CC morphology, cross-sectional studies are limited in statistical power. Determining individual rates of change requires longitudinal data. Physiological changes are particularly relevant in mild cognitive impairment (MCI), in which CC morphology has not been previously studied longitudinally. OBJECTIVE To study temporal rates of change in CC morphology in MCI patients over a one-year period, and to determine whether these rates differ between MCI subjects who converted to AD (MCI-C) and those who did not (MCI-NC) over an average (±SD) observation period of 5.4 (±1.6) years. METHODS We used a novel multi-atlas based algorithm to segment the mid-sagittal cross-sectional area of the CC in longitudinal MRI scans. Rates of change of CC circularity, total area, and five sub-areas were compared between 57 MCI-NC and 81 MCI-C subjects. RESULTS The CC became less circular (-0.89% per year in MCI-NC, -1.85% per year in MCI-C) with time, with faster decline in MCI-C (p = 0.0002). In females, atrophy rates were higher in MCI-C relative to MCI-NC in total CC area (p = 0.0006), genu/rostrum (p = 0.005), and splenium (0.002). In males, these rates did not differ between groups. CONCLUSION A greater than normal decline in CC circularity was shown to be an indicator of prodromal AD in MCI subjects. This measure is potentially useful as an imaging biomarker of disease and a therapeutic target in clinical trials.

Prediction of Incipient Alzheimer's Disease Dementia in Patients with Mild Cognitive Impairment.

BACKGROUND Mild cognitive impairment (MCI) is a transitional stage from normal aging to Alzheimers disease (AD) dementia. It is extremely important to develop criteria that can be used to separate the MCI subjects at imminent risk of conversion to Alzheimer-type dementia from those who would remain stable. We have developed an automatic algorithm for computing a novel measure of hippocampal volumetric integrity (HVI) from structural MRI scans that may be useful for this purpose. OBJECTIVE To determine the utility of HVI in classification between stable and progressive MCI patients using the Random Forest classification algorithm. METHODS We used a 16-dimensional feature space including bilateral HVI obtained from baseline and one-year follow-up structural MRI, cognitive tests, and genetic and demographic information to train a Random Forest classifier in a sample of 164 MCI subjects categorized into two groups [progressive (n = 86) or stable (n = 78)] based on future conversion (or lack thereof) of their diagnosis to probable AD. RESULTS The overall accuracy of classification was estimated to be 82.3% (86.0% sensitivity, 78.2% specificity). The accuracy in women (89.1%) was considerably higher than that in men (78.9%). The prediction accuracy achieved in women is the highest reported in any previous application of machine learning to AD diagnosis in MCI. CONCLUSION The method presented in this paper can be used to separate stable MCI patients from those who are at early stages of AD dementia with high accuracy. There may be stronger indicators of imminent AD dementia in women with MCI as compared to men.

Predicting progression from mild cognitive impairment to Alzheimer's disease using longitudinal callosal atrophy

We investigate whether longitudinal callosal atrophy could predict conversion from mild cognitive impairment (MCI) to Alzheimers disease (AD).

Application of fused lasso logistic regression to the study of corpus callosum thickness in early Alzheimer's disease.

We propose a fused lasso logistic regression to analyze callosal thickness profiles. The fused lasso regression imposes penalties on both the l1-norm of the model coefficients and their successive differences, and finds only a small number of non-zero coefficients which are locally constant. An iterative method of solving logistic regression with fused lasso regularization is proposed to make this a practical procedure. In this study we analyzed callosal thickness profiles sampled at 100 equal intervals between the rostrum and the splenium. The method was applied to corpora callosa of elderly normal controls (NCs) and patients with very mild or mild Alzheimers disease (AD) from the Open Access Series of Imaging Studies (OASIS) database. We found specific locations in the genu and splenium of AD patients that are proportionally thinner than those of NCs. Callosal thickness in these regions combined with the Mini Mental State Examination scores differentiated AD from NC with 84% accuracy.

Stimulation‐induced transient changes in neuronal activity, blood flow and N‐acetylaspartate content in rat prefrontal cortex: a chemogenetic fMRS‐BOLD study

Brain activation studies in humans have shown the dynamic nature of neuronal N‐acetylaspartate (NAA) and N‐acetylaspartylglutamate (NAAG) based on changes in their MRS signals in response to stimulation. These studies demonstrated that upon visual stimulation there was a focal increase in cerebral blood flow (CBF) and a decrease in NAA or in the total of NAA and NAAG signals in the visual cortex, and that these changes were reversed upon cessation of stimulation. In the present study we have developed an animal model in order to explore the relationships between brain stimulation, neuronal activity, CBF and NAA. We use “designer receptor exclusively activated by designer drugs” (DREADDs) technology for site‐specific neural activation, a local field potential electrophysiological method for measurement of changes in the rate of neuronal activity, functional MRS for measurement of changes in NAA and a blood oxygenation level‐dependent (BOLD) MR technique for evaluating changes in CBF. We show that stimulation of the rat prefrontal cortex using DREADDs results in the following: (i) an increase in level of neuronal activity; (ii) an increase in BOLD and (iii) a decrease in the NAA signal. These findings show for the first time the tightly coupled relationships between stimulation, neuron activity, CBF and NAA dynamics in brain, and also provide the first demonstration of the novel inverse stimulation–NAA phenomenon in an animal model.

Hippocampal volume and integrity as predictors of cognitive decline in intact elderly

The risk of Alzheimer’s disease can be predicted by volumetric analyses of MRI data in the medial temporal lobe. The present study compared a volumetric measurement of the hippocampus with a novel measure of hippocampal integrity (HI) derived from the ratio of parenchyma volume over total volume. Participants were cognitively intact and aged 60 years or older at baseline, and were tested twice, roughly 3 years apart. Participants had been recruited for a study on late-life major depression (LLMD) and were evenly split between depressed patients and controls. Linear regression models were applied to the data with a cognitive composite score as the outcome, and HI and volume, together or separately, as predictors. Subsequent cognitive performance was predicted well by models that included an interaction between HI and LLMD status, such that lower HI scores predicted more cognitive decline in depressed patients. More research is needed, but tentative results from this study appear to suggest that the newly introduced measure HI is an effective tool for the purpose of predicting future changes in general cognitive ability, and especially so in individuals with LLMD.