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There is great value to use of structural neuroimaging in the assessment of Alzheimer's disease (AD). However, to date, predictive value of structural imaging tend to range between 80% and 90% in accuracy and it is unclear why this is the case given that structural imaging should parallel the pathologic processes of AD. There is a possibility that clinical misdiagnosis relative to the gold standard pathologic diagnosis and/or additional brain pathologies are confounding factors contributing to reduced structural imaging classification accuracy. We examined potential factors contributing to misclassification of individuals with clinically diagnosed AD purely from cortical thickness measures. Correctly classified and incorrectly classified groups were compared across a range of demographic, biological, and neuropsychological data including cerebrospinal fluid biomarkers, amyloid imaging, white matter hyperintensity (WMH) volume, cognitive, and genetic factors. Individual subject analyses suggested that at least a portion of the control individuals misclassified as AD from structural imaging additionally harbor substantial AD biomarker pathology and risk, yet are relatively resistant to cognitive symptoms, likely due to "cognitive reserve," and therefore clinically unimpaired. In contrast, certain clinical control individuals misclassified as AD from cortical thickness had increased WMH volume relative to other controls in the sample, suggesting that vascular conditions may contribute to classification accuracy from cortical thickness measures. These results provide examples of factors that contribute to the accuracy of structural imaging in predicting a clinical diagnosis of AD, and provide important information about considerations for future work aimed at optimizing structural based diagnostic classifiers for AD.

Registration performance can significantly deteriorate when image regions do not comply with model assumptions. Robust estimation improves registration accuracy by reducing or ignoring the contribution of voxels with large intensity differences, but existing approaches are limited to monomodal registration. In this work, we propose a robust and inverse-consistent technique for crossmodal, affine image registration. The algorithm is derived from a contextual framework of image registration. The key idea is to use a modality invariant representation of images based on local entropy estimation, and to incorporate a heteroskedastic noise model. This noise model allows us to draw the analogy to iteratively reweighted least squares estimation and to leverage existing weighting functions to account for differences in local information content in multimodal registration. Furthermore, we use the nonparametric windows density estimator to reliably calculate entropy of small image patches. Finally, we derive the Gauss–Newton update and show that it is equivalent to the efficient secondorder minimization for the fully symmetric registration approach. We illustrate excellent performance of the proposed methods on datasets containing outliers for alignment of brain tumor, full head, and histology images.

Examination of associations between specific disorders and physical properties of functionally relevant frontal lobe sub-regions is a fundamental goal in neuropsychiatry. Here, we present and evaluate automated methods of frontal lobe parcellation with the programs FreeSurfer(FS) and TOADS-CRUISE(T-C), based on the manual method described in Ranta et al. [2009]: Psychiatry Res 172:147-154 in which sulcal-gyral landmarks were used to manually delimit functionally relevant regions within the frontal lobe: i.e., primary motor cortex, anterior cingulate, deep white matter, premotor cortex regions (supplementary motor complex, frontal eye field, and lateral premotor cortex) and prefrontal cortex (PFC) regions (medial PFC, dorsolateral PFC, inferior PFC, lateral orbitofrontal cortex [OFC] and medial OFC). Dice's coefficient, a measure of overlap, and percent volume difference were used to measure the reliability between manual and automated delineations for each frontal lobe region. For FS, mean Dice's coefficient for all regions was 0.75 and percent volume difference was 21.2%. For T-C the mean Dice's coefficient was 0.77 and the mean percent volume difference for all regions was 20.2%. These results, along with a high degree of agreement between the two automated methods (mean Dice's coefficient = 0.81, percent volume difference = 12.4%) and a proof-of-principle group difference analysis that highlights the consistency and sensitivity of the automated methods, indicate that the automated methods are valid techniques for parcellation of the frontal lobe into functionally relevant sub-regions. Thus, the methodology has the potential to increase efficiency, statistical power and reproducibility for population analyses of neuropsychiatric disorders with hypothesized frontal lobe contributions.

Little is known about genetic influences on the volume of subcortical brain structures in adult humans, particularly whether there is regional specificity of genetic effects. Understanding patterns of genetic covariation among volumes of subcortical structures may provide insight into the development of individual differences that have consequences for cognitive and emotional behavior and neuropsychiatric disease liability. We measured the volume of 19 subcortical structures (including brain and ventricular regions) in 404 twins (110 monozygotic and 92 dizygotic pairs) from the Vietnam Era Twin Study of Aging and calculated the degree of genetic correlation among these volumes. We then examined the patterns of genetic correlation through hierarchical cluster analysis and by principal components analysis. We found that a model with four genetic factors best fit the data: a Basal Ganglia/Thalamus factor; a Ventricular factor; a Limbic factor; and a Nucleus Accumbens factor. Homologous regions from each hemisphere loaded on the same factors. The observed patterns of genetic correlation suggest the influence of multiple genetic influences. There is a genetic organization among structures which distinguishes between brain and cerebrospinal fluid spaces and between different subcortical regions. Further study is needed to understand this genetic patterning and whether it reflects influences on early development, functionally dependent patterns of growth or pruning, or regionally specific losses due to genes involved in aging, stress response, or disease.

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 popula- tion of 101 subjects including nondemented control subjects (n 5 57) and subjects clinically diagnosed with Alzheimer's Disease (AD, n 5 38) or semantic dementia (n 5 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 diffeo- morphic metric mapping (LDDMM) at the Johns Hopkins University. A global classifier was con- structed for discriminating cohorts of nondemented and demented subjects based on linear discrimi- nant analysis of dimensions derived from metric distances between anatomical shapes, demonstrating class conditional structure differences measured via LDDMM metric shape (P < 0.01). Localized analy- sis 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 30:2132-2141, 2009. V 2008 Wiley-Liss, Inc.

Performance of automated methods to isolate brain from nonbrain tissues in magnetic resonance (MR) structural images may be influenced by MR signal inhomogeneities, type of MR image set, regional anatomy, and age and diagnosis of subjects studied. The present study compared the performance of four methods: Brain Extraction Tool (BET; Smith [2002]: Hum Brain Mapp 17:143–155); 3dIntracranial (Ward [1999] Milwaukee: Biophysics Research Institute, Medical College of Wisconsin; in AFNI); a Hybrid Watershed algorithm (HWA, Segonne et al. [2004] Neuroimage 22:1060–1075; in FreeSurfer); and Brain Surface Extractor (BSE, Sandor and Leahy [1997] IEEE Trans Med Imag 16:41–54; Shattuck et al. [2001] Neuroimage 13:856 – 876) to manually stripped images. The methods were applied to uncorrected and bias-corrected datasets; Legacy and Contemporary T1-weighted image sets; and four diagnostic groups (depressed, Alzheimer’s, young and elderly control). To provide a criterion for outcome assessment, two experts manually stripped six sagittal sections for each dataset in locations where brain and nonbrain tissue are difficult to distinguish. Methods were compared on Jaccard similarity coefficients, Hausdorff distances, and an Expectation-Maximization algorithm. Methods tended to perform better on contemporary datasets; bias correction did not significantly improve method performance. Mesial sections were most difficult for all methods. Although AD image sets were most difficult to strip, HWA and BSE were more robust across diagnostic groups compared with 3dIntracranial and BET. With respect to specificity, BSE tended to perform best across all groups, whereas HWA was more sensitive than other methods. The results of this study may direct users towards a method appropriate to their T1-weighted datasets and improve the efficiency of processing for large, multisite neuroimaging studies.

Registration performance can significantly deteriorate when image regions do not comply with model assumptions. Robust estimation improves registration accuracy by reducing or ignoring the contribution of voxels with large intensity differences, but existing approaches are limited to monomodal registration. In this work, we propose a robust and inverse-consistent technique for cross-modal, affine image registration. The algorithm is derived from a contextual framework of image registration. The key idea is to use a modality invariant representation of images based on local entropy estimation, and to incorporate a heteroskedastic noise model. This noise model allows us to draw the analogy to iteratively reweighted least squares estimation and to leverage existing weighting functions to account for differences in local information content in multimodal registration. Furthermore, we use the nonparametric windows density estimator to reliably calculate entropy of small image patches. Finally, we derive the Gauss-Newton update and show that it is equivalent to the efficient second-order minimization for the fully symmetric registration approach. We illustrate excellent performance of the proposed methods on datasets containing outliers for alignment of brain tumor, full head, and histology images.

Examination of associations between specific disorders and physical properties of functionally relevant frontal lobe sub-regions is a fundamental goal in neuropsychiatry. Here, we present and evaluate automated methods of frontal lobe parcellation with the programs FreeSurfer(FS) and TOADS-CRUISE(T-C), based on the manual method described in Ranta et al. [2009]: Psychiatry Res 172:147-154 in which sulcal-gyral landmarks were used to manually delimit functionally relevant regions within the frontal lobe: i.e., primary motor cortex, anterior cingulate, deep white matter, premotor cortex regions (supplementary motor complex, frontal eye field, and lateral premotor cortex) and prefrontal cortex (PFC) regions (medial PFC, dorsolateral PFC, inferior PFC, lateral orbitofrontal cortex [OFC] and medial OFC). Dice's coefficient, a measure of overlap, and percent volume difference were used to measure the reliability between manual and automated delineations for each frontal lobe region. For FS, mean Dice's coefficient for all regions was 0.75 and percent volume difference was 21.2%. For T-C the mean Dice's coefficient was 0.77 and the mean percent volume difference for all regions was 20.2%. These results, along with a high degree of agreement between the two automated methods (mean Dice's coefficient = 0.81, percent volume difference = 12.4%) and a proof-of-principle group difference analysis that highlights the consistency and sensitivity of the automated methods, indicate that the automated methods are valid techniques for parcellation of the frontal lobe into functionally relevant sub-regions. Thus, the methodology has the potential to increase efficiency, statistical power and reproducibility for population analyses of neuropsychiatric disorders with hypothesized frontal lobe contributions.

Subjects were scanned in a single functional MRI (fMRI) experiment that enabled us to localize cortical regions in each subject in the occipital and temporal lobes that responded significantly in a variety of contrasts: faces objects, body parts objects, scenes objects, objects scrambled objects, and moving stationary stimuli. The resulting activation maps were coregistered across subjects using spherical surface coordinates (Fischl et al., Hum Brain Mapp 1999;8:272-284) to produce a "percentage overlap map" indicating the percentage of subjects who showed a significant response for each contrast at each point on the surface. Prominent among the overlapping activations in these contrasts were the fusiform face area (FFA), extrastriate body area (EBA), parahippocampal place area (PPA), lateral occipital complex (LOC), and MT/V5; only a few other areas responded consistently across subjects in these contrasts. Another analysis showed that the spatial profile of the selective response drops off quite sharply outside the standard borders of the FFA and PPA (less so for the EBA and MT/V5), indicating that these regions are not simply peaks of very broad selectivities spanning centimeters of cortex, but fairly discrete regions of cortex with distinctive functional profiles. The data also yielded a surprise that challenges our understanding of the function of area MT: a higher response to body parts than to objects. The anatomical consistency of each of our functionally defined regions across subjects and the spatial sharpness of their activation profiles within subjects highlight the fact that these regions constitute replicable and distinctive landmarks in the functional organization of the human brain. Hum Brain Mapp 27:77- 89, 2006. © 2006 Wiley-Liss, Inc.

The neurons of the human cerebral cortex are arranged in a highly folded sheet, with the majority of the cortical surface area buried in folds. Cortical maps are typically arranged with a topography oriented parallel to the cortical surface. Despite this unambiguous sheetlike geometry, the most commonly used coordinate systems for localizing cortical features are based on 3‐D stereotaxic coordinates rather than on position relative to the 2‐D cortical sheet. In order to address the need for a more natural surface‐based coordinate system for the cortex, we have developed a means for generating an average folding pattern across a large number of individual subjects as a function on the unit sphere and of nonrigidly aligning each individual with the average. This establishes a spherical surface‐based coordinate system that is adapted to the folding pattern of each individual subject, allowing for much higher localization accuracy of structural and functional features of the human brain. Hum. Brain Mapping 8:272–284, 1999. © 1999 Wiley‐Liss, Inc.