Alzheimer Disease: Fischl B

Golland P, Fischl B. · Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. · Inf Process Med Imaging. · Pubmed #15344469 No free full text.

Abstract: Estimating statistical significance of detected differences between two groups of medical scans is a challenging problem due to the high dimensionality of the data and the relatively small number of training examples. In this paper, we demonstrate a non-parametric technique for estimation of statistical significance in the context of discriminative analysis (i.e., training a classifier function to label new examples into one of two groups). Our approach adopts permutation tests, first developed in classical statistics for hypothesis testing, to estimate how likely we are to obtain the observed classification performance, as measured by testing on a hold-out set or cross-validation, by chance. We demonstrate the method on examples of both structural and functional neuroimaging studies.

Wang L, Khan A, Csernansky JG, Fischl B, Miller MI, Morris JC, Beg MF. · Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, USA. · Hippocampus. · Pubmed #19405129 No free full text.

Abstract: Landmark-based high-dimensional diffeomorphic maps of the hippocampus (although accurate) is highly-dependent on rater's anatomic knowledge of the hippocampus in the magnetic resonance images. It is therefore vulnerable to rater drift and errors if substantial amount of effort is not spent on quality assurance, training, and re-training. A fully-automated, FreeSurfer-initialized large-deformation diffeomorphic metric mapping procedure of small brain substructures, including the hippocampus, has been previously developed and validated in small samples. In this report, we demonstrate that this fully-automated pipeline can be used in place of the landmark-based procedure in a large-sample clinical study to produce similar statistical outcomes. Some direct comparisons of the two procedures are also presented.

Desikan RS, Cabral HJ, Fischl B, Guttmann CR, Blacker D, Hyman BT, Albert MS, Killiany RJ. · Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA. · AJNR Am J Neuroradiol. · Pubmed #19112067 links to  free full text

Abstract: BACKGROUND AND PURPOSE: Mild cognitive impairment (MCI) represents a transitional state between normal aging and Alzheimer disease (AD). Our goal was to determine if specific temporoparietal regions can predict the time to progress from MCI to AD. MATERIALS AND METHODS: MR images from 129 individuals with MCI were analyzed to identify the volume of 14 neocortical and 2 non-neocortical brain regions, comprising the temporal and parietal lobes. In addition, 3 neuropsychological test scores were included to determine whether they would provide independent information. After a mean follow-up time of 5 years, 44 of these individuals had progressed to a diagnosis of AD. RESULTS: Cox proportional hazards models demonstrated significant effects for 6 MR imaging regions with the greatest differences being the following: the entorhinal cortex (hazard ratio [HR] = 0.54, P < .001), inferior parietal lobule (hazard ratio [HR] = 0.64, P < .005), and middle temporal gyrus (HR = 0.64, P < .004), indicating decreased risk with larger volumes. A multivariable model showed that a combination of the entorhinal cortex (HR = 0.60, P < .001) and the inferior parietal lobule (HR = 0.62, P < .01) was the best predictor of time to progress to AD. A multivariable model reiterated the importance of including both MR imaging and neuropsychological variables in the final model. CONCLUSIONS: These findings reaffirm the importance of the entorhinal cortex and present evidence for the importance of the inferior parietal lobule as a predictor of time to progress from MCI to AD. The inclusion of neuropsychological performance in the final model continues to highlight the importance of using these measures in a complementary fashion.

Salat DH, Greve DN, Pacheco JL, Quinn BT, Helmer KG, Buckner RL, Fischl B. · Department of Radiology, Massachusetts General Hospital, Boston MA, USA. · Neuroimage. · Pubmed #19027860 No free full text.

Abstract: Accumulating evidence suggests that altered cerebral white matter (WM) influences normal aging, and further that WM degeneration may modulate the clinical expression of Alzheimer's disease (AD). Here we conducted a study of differences in WM volume across the adult age span and in AD employing a newly developed, automated method for regional parcellation of the subcortical WM that uses curvature landmarks and gray matter (GM)/WM surface boundary information. This procedure measures the volume of gyral WM, utilizing a distance constraint to limit the measurements from extending into the centrum semiovale. Regional estimates were first established to be reliable across two scan sessions in 20 young healthy individuals. Next, the method was applied to a large clinically-characterized sample of 299 individuals including 73 normal older adults and 91 age-matched participants with very mild to mild AD. The majority of measured regions showed a decline in volume with increasing age, with strong effects found in bilateral fusiform, lateral orbitofrontal, superior frontal, medial orbital frontal, inferior temporal, and middle temporal WM. The association between WM volume and age was quadratic in many regions suggesting that WM volume loss accelerates in advanced aging. A number of WM regions were further reduced in AD with parahippocampal, entorhinal, inferior parietal and rostral middle frontal WM showing the strongest AD-associated reductions. There were minimal sex effects after correction for intracranial volume, and there were associations between ventricular volume and regional WM volumes in the older adults and AD that were not apparent in the younger adults. Certain results, such as the loss of WM in the fusiform region with aging, were unexpected and provide novel insight into patterns of age associated neural and cognitive decline. Overall, these results demonstrate the utility of automated regional WM measures in revealing the distinct patterns of age and AD associated volume loss that may contribute to cognitive decline.

Desikan RS, Fischl B, Cabral HJ, Kemper TL, Guttmann CR, Blacker D, Hyman BT, Albert MS, Killiany RJ. · Dept. of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA. · Neurology. · Pubmed #18672473 links to  free full text

Abstract: BACKGROUND: MRI studies have demonstrated differential rates of atrophy in the entorhinal cortex and hippocampus during the prodromal phase of Alzheimer disease (AD). The current study was designed to determine whether a broader set of temporoparietal regions show differential rates of atrophy during the evolution of AD. METHODS: Sixteen regions of interest (ROIs) were analyzed on MRI scans obtained at baseline and follow-up in 66 subjects comprising three groups: controls = individuals who were cognitively normal at both baseline and follow-up; nonconverters = subjects with mild cognitive impairment (MCI) at both baseline and follow-up; converters had MCI at baseline but had progressed to AD at follow-up. RESULTS: Annualized percent change was analyzed with multivariate analysis of variance (MANOVA), covaried for age. The MANOVA demonstrated an effect of group (p = 0.004). Post hoc comparisons demonstrated greater rates of atrophy for converters vs nonconverters for six ROIs: hippocampus, entorhinal cortex, temporal pole, middle temporal gyrus, fusiform gyrus, and inferior temporal gyrus. Converters showed differentially greater rates of atrophy than controls in five of the same ROIs (and inferior parietal lobule). Rates of change in clinical status were correlated with the atrophy rates in these regions. Comparisons between controls and nonconverters demonstrated no differences. CONCLUSION: These results demonstrate that temporoparietal regions show differential rates of atrophy on MRI during prodromal Alzheimer disease (AD). MRI data correlate with measures of clinical severity and cognitive decline, suggesting the potential of these regions of interest as antemortem markers of prodromal AD.

Bischoff-Grethe A, Ozyurt IB, Busa E, Quinn BT, Fennema-Notestine C, Clark CP, Morris S, Bondi MW, Jernigan TL, Dale AM, Brown GG, Fischl B. · Laboratory of Cognitive Imaging, Department of Psychiatry, University of California, San Diego, La Jolla, USA. · Hum Brain Mapp. · Pubmed #17295313 links to  free full text

Abstract: Due to the increasing need for subject privacy, the ability to deidentify structural MR images so that they do not provide full facial detail is desirable. A program was developed that uses models of nonbrain structures for removing potentially identifying facial features. When a novel image is presented, the optimal linear transform is computed for the input volume (Fischl et al. [2002]: Neuron 33:341-355; Fischl et al. [2004]: Neuroimage 23 (Suppl 1):S69-S84). A brain mask is constructed by forming the union of all voxels with nonzero probability of being brain and then morphologically dilated. All voxels outside the mask with a nonzero probability of being a facial feature are set to 0. The algorithm was applied to 342 datasets that included two different T1-weighted pulse sequences and four different diagnoses (depressed, Alzheimer's, and elderly and young control groups). Visual inspection showed none had brain tissue removed. In a detailed analysis of the impact of defacing on skull-stripping, 16 datasets were bias corrected with N3 (Sled et al. [1998]: IEEE Trans Med Imaging 17:87-97), defaced, and then skull-stripped using either a hybrid watershed algorithm (Ségonne et al. [2004]: Neuroimage 22:1060-1075, in FreeSurfer) or Brain Surface Extractor (Sandor and Leahy [1997]: IEEE Trans Med Imaging 16:41-54; Shattuck et al. [2001]: Neuroimage 13:856-876); defacing did not appreciably influence the outcome of skull-stripping. Results suggested that the automatic defacing algorithm is robust, efficiently removes nonbrain tissue, and does not unduly influence the outcome of the processing methods utilized; in some cases, skull-stripping was improved. Analyses support this algorithm as a viable method to allow data sharing with minimal data alteration within large-scale multisite projects.

Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ. · Department of Anatomy and Neurobiology, Boston University School of Medicine, 715 Albany Street, W701, Boston, MA 02118, USA. · Neuroimage. · Pubmed #16530430 No free full text.

Abstract: In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.

Gold BT, Balota DA, Cortese MJ, Sergent-Marshall SD, Snyder AZ, Salat DH, Fischl B, Dale AM, Morris JC, Buckner RL. · Department of Anatomy and Neurobiology, University of Kentucky, MN214 Chandler Medical Center, Lexington, KY 40536-0298, USA. · Neuropsychologia. · Pubmed #15716156 No free full text.

Abstract: Individuals with semantic dementia (SD) were differentiated neuropsychologically from individuals with dementia of the Alzheimer type (DAT) at very mild-to-mild stages (clinical dementia rating 0.5 or 1). A picture naming and recognition memory experiment provided a particularly useful probe for early identification, with SD individuals showing preserved picture recognition memory and impaired naming, and DAT individuals tending to show the reverse dissociation. The identification of an early SD group provided the opportunity to inform models of reading by exploring the influence of isolated lexical semantic impairment on reading regular words. Results demonstrated prolonged latency in both SD and DAT group reading compared to a control group but exaggerated influence of frequency and length only for the SD group. The SD reading pattern was associated with focal atrophy of the left temporal pole. These cognitive-neuroanatomical findings suggest a role for the left temporal pole in lexical/semantic components of reading and demonstrate that cortical thickness differences in the left temporal pole correlate with prolonged latency associated with increased reliance on sublexical components of reading.

Fischl B, Salat DH, Busa E, Albert M, Dieterich M, Haselgrove C, van der Kouwe A, Killiany R, Kennedy D, Klaveness S, Montillo A, Makris N, Rosen B, Dale AM. · Massachusetts General Hospital, Nuclear Magnetic Resonance Center, Rm. 2328, Building 149, 13th Street, Charlestown, MA 02129, USA. · Neuron. · Pubmed #11832223 No free full text.

Abstract: We present a technique for automatically assigning a neuroanatomical label to each voxel in an MRI volume based on probabilistic information automatically estimated from a manually labeled training set. In contrast to existing segmentation procedures that only label a small number of tissue classes, the current method assigns one of 37 labels to each voxel, including left and right caudate, putamen, pallidum, thalamus, lateral ventricles, hippocampus, and amygdala. The classification technique employs a registration procedure that is robust to anatomical variability, including the ventricular enlargement typically associated with neurological diseases and aging. The technique is shown to be comparable in accuracy to manual labeling, and of sufficient sensitivity to robustly detect changes in the volume of noncortical structures that presage the onset of probable Alzheimer's disease.