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Traditional approaches in mental health research apply General Linear Models (GLM) to describe the longitudinal dynamics of observed psycho-behavioral measurements (questionnaire summary scores). Similarly, GLMs are also applied to characterize relationships between neurobiological measurements (regional fMRI signals) and perceptual stimuli or other regional signals. While these methods are useful for exploring linear correlations among the isolated signals of those constructs (i.e., summary scores or fMRI signals), these classical frameworks fall short in providing insights into the comprehensive system-level dynamics underlying observable changes. Hidden Markov Models (HMM) are a statistical model that enable us to describe the sequential relations among multiple observable constructs, and when applied through the lens of Finite State Automata (FSA), can provide a more integrated and intuitive framework for modeling and understanding the underlying controller (the prescription for how to respond to inputs) that fundamentally defines any system, as opposed to linearly correlating output signals produced by the controller. We present a simple and intuitive HMM processing pipeline vcHMM (See Preliminary Data) that highlights FSA theory and is applicable for both behavioral analysis of questionnaire data and fMRI data. HMMs offer theoretic promise as they are computationally equivalent to the FSA, the control processor of a Turing Machine (TM) The dynamic programming Viterbi algorithm is used to leverage the HMM model. It efficiently identifies the most likely sequence of hidden states. The vcHMM pipeline leverages this grammar to understand how behavior and neural activity relate to depression.

White matter hyperintensity (WMH) remains the top imaging biomarker for neurodegenerative diseases. Robust and accurate segmentation of WMH holds paramount significance for neuroimaging studies. The growing shift from 3T to 7T MRI necessitates robust tools for harmonized segmentation across field strengths and artifacts. Recent deep learning models exhibit promise in WMH segmentation but still face challenges, including diverse training data representation and limited analysis of MRI artifacts' impact. To address these, we introduce wmh_seg, a novel deep learning model leveraging a transformer-based encoder from SegFormer. wmh_seg is trained on an unmatched dataset, including 1.5T, 3T, and 7T FLAIR images from various sources, alongside with artificially added MR artifacts. Our approach bridges gaps in training diversity and artifact analysis. Our model demonstrated stable performance across magnetic field strengths, scanner manufacturers, and common MR imaging artifacts. Despite the unique inhomogeneity artifacts on ultra-high field MR images, our model still offers robust and stable segmentation on 7T FLAIR images. Our model, to date, is the first that offers quality white matter lesion segmentation on 7T FLAIR images.

ObjectiveMild parkinsonian signs (MPS) are an underappreciated neurologic condition in older adults; we assessed associations of MPS with measures of dopaminergic (catechol-O-methyltransferase [COMT] genotype, an indicator of synaptic dopamine levels) and vascular (white matter hyperintensities [WMH], an indicator of cerebral small vessel disease) factors.MethodsIn a cohort of older adults (mean age 82.6 years [SD 2.6]; 58.0% female; 38.8% black), we assessed cross-sectional associations of WMH volume and COMT Val158Met (rs4680) genotype (n = 35 Met/Met, n = 180 Val carriers) with MPS by regression models adjusted for demographic and health characteristics. Interactions between WMH and COMT were assessed and analyses were repeated stratified by COMT genotype (Met/Met related to higher synaptic dopamine vs Val carriers related to lower synaptic dopamine).ResultsMPS was present in 42.3% of our sample. WMH (odds ratio [OR] 1.16, confidence interval [CI] 1.05-1.27) but not COMT (Met/Met compared to Val carrier: OR 0.62, CI 0.27-1.42) was related to MPS. There was a significant interaction between WMH and COMT (p = 0.03). Stratified analyses reveled a strong association between WMH and MPS among COMT Val carriers (OR 1.23, CI 1.09-1.38), but not for Met/Met (OR 0.68, CI 0.45-1.02), independent of covariates.ConclusionsWMH had a direct relation with MPS. In contrast, COMT was not associated with MPS, but it did modify the effect of WMH on MPS. The dopaminergic system may provide compensation for the effects of WMH on MPS. These findings suggest that MPS has a vascular rather than dopaminergic origin in older adults, but both factors are important in MPS manifestation.

Previous studies have shown that more active older adults have better cognition and brain health based on a variety of structural neuroimaging measures. Nevertheless, the effects of maintaining physical activity (PA) over an extended period of time on future changes in older adults' cognition and brain structure are unknown. Participants were 141 initially well-functioning community-dwelling older adults (aged 70-79 years at baseline; 60% female; 42% black) studied over a 13-year period. PA (self-reported time spent walking) was assessed annually from years 1 to 10. Magnetic resonance imaging with diffusion tensor was performed at years 10 and 13. Time spent walking decreased on average by 8.4% annually from year 1 to year 10. Independent of initial time spent walking, demographics, and APOE e4 status, better maintenance of time spent walking over the decade predicted less reduction in hippocampal volume (p = 0.03), smaller increases in global gray matter mean diffusivity and white matter axial diffusivity (p < 0.01), and maintenance of general cognitive performance (p < 0.01). Maintenance of cognitive performance was associated with smaller increases in white matter axial diffusivity (p < 0.01). PA at baseline and at year 10, as well as changes in PA over a 5-year period, was less predictive of future changes in brain structure and cognition. Thus, how PA levels change over longer periods of aging may be an important contributor to cognitive and neural protection.

The neuroprotective effects of physical activity (PA) are consistently shown in older adults, but the neural substrates, particularly in white matter (WM), are understudied, especially in very old adults with the fastest growth rate and the highest risk of dementia. This study quantified the association between PA and WM integrity in adults over 80. The moderating effects of cardiometabolic conditions, physical functional limitations and WM hyperintensities were also examined, as they can affect PA and brain integrity. Fractional anisotropy (FA) from normal-appearing WM via diffusion tensor imaging and WM hyperintensities were obtained in 90 participants (mean age = 87.4, 51.1% female, 55.6% white) with concurrent objective measures of steps, active energy expenditure (AEE in kcal), duration (min), and intensity (metabolic equivalents, METs) via SenseWear Armband. Clinical adjudication of cognitive status, prevalence of stroke and diabetes, systolic blood pressure, and gait speed were assessed at time of neuroimaging. Participants were on average sedentary (mean ± SD/day: 1766 ± 1345 steps, 202 ± 311 kcal, 211 ± 39 min, 1.8 ± 1.1 METs). Higher steps, AEE and duration, but not intensity, were significantly associated with higher FA. Associations were localized in frontal and temporal areas. Moderating effects of cardiometabolic conditions, physical functional limitations, and WM hyperintensities were not significant. Neither FA nor PA was related to cognitive status. Older adults with a sedentary lifestyle and a wide range of cardiometabolic conditions and physical functional limitations, displayed higher WM integrity in relation to higher PA. Studies of very old adults to quantify the role of PA in reducing dementia burden via WM integrity are warranted.

In older adults, depressive symptoms are associated with lower quality of life, high morbidity and mortality. This study aims to identify brain magnetic resonance imaging (MRI) features associated with late-life depressive symptoms in the population. Older community-dwelling adults (n=314) from the Health ABC study underwent brain MRI. Logistic regression was used to characterize the relationships between depressive symptoms (Center for Epidemiologic Studies of Depression scale, CES-D) and the following whole-brain variables: white matter hyperintensity (WMH) burden, fractional anisotropy (FA), and gray matter volume (GMV). Analyses examining possible regional differences between the CES-D groups controlled for Modified Mini-Mental State Examination score and diabetes status. The relative importance of localization of the markers was examined by comparing the distribution of significant peaks across the brain. Each whole-brain variable showed loss of integrity associated with high CES-D. For GMV, the odds ratio (OR)=0.84 (95% confidence interval (CI) 0.74, 0.96); for FA, OR=0.714 (95% CI 0.57, 0.88); for WMH, OR=1.89 (95%CI 1.33, 2.69). Voxel-wise analyses and patterns of peak significance showed non-specific patterns for white matter measures. Loss of GMV was most significant in the bilateral insula and anterior cingulate cortex. This study supports a cerebrovascular pattern for depressive symptoms in older adults. The localization of gray matter changes to the insula, a watershed area and a hub of affective circuits, suggests an etiological pathway from ischemia to increased depressive burden.

BackgroundMaintaining cognitive function protects older adults from developing functional decline. This study aims to identify the neuroimaging correlates of maintenance of higher global cognition as measured by the Modified Mini Mental State Test (3 MS) score.MethodsRepeated 3 MS measures from 1997-98 through 2006-07 and magnetic resonance imaging with diffusion tensor in 2006-07 were obtained in a biracial cohort of 258 adults free from dementia (mean age 82.9 years, 56% women, 42% blacks). Participants were classified as having shown either maintenance (3 MS slope>0) or decline (3 MS slope<1 SD below the mean) of cognition using linear mixed models. Measures of interest were white matter hyperintensity volume (WMHv) from total brain, volume of the gray matter (GMv) and microstructure (mean diffusivity, MD) for total brain and for brain areas known to be related to memory and executive control function: medial temporal area (hippocampus, parahippocampus and entorhinal cortex), cingulate cortex, dorsolateral prefrontal and posterior parietal cortex.ResultsDifferences between cognitive maintainers (n=153) and non-maintainers (n=107) were significant for GMv of the medial temporal area (35.8%, p=0.004) and lower MD of the cingulate cortex (37.9%, p=0.008), but not for other neuroimaging markers. In multivariable regression models adjusted for age, race, WMHv and GMV from the total brain and vascular conditions, each standard deviation of GMv of the medial temporal area and each standard deviation of MD of the cingulate cortex were associated with a nearly 4 times greater probability (odds ratio [standard deviation]: 3.80 [1.16, 12.44]) and a 34% lower probability (0.66, [0.46, 0.97]) of maintaining cognitive function, respectively. In these models neither WMHv nor GMv from total brain were significantly associated with probability of maintaining cognitive function.ConclusionsPreserving the volume of the medial temporal area and the microstructure of the cingula

There is need for a valid and reliable biomarker for HIV Associated Neurocognitive Disorder (HAND). The purpose of the present study was to provide preliminary evidence of the potential utility of neuronal functional connectivity measures obtained using magnetoencephalography (MEG) to identify HIV-associated changes in brain function. Resting state, eyes closed, MEG data from 10 HIV-infected individuals and 8 seronegative controls were analyzed using mutual information (MI) between all pairs of MEG sensors to ...

Over the next twenty-five years, the proportion of the population over age 65 will increase 76%; therefore understanding both the normal and pathological processes involved in the aging of the human brain is of the highest public health priority. We report here the use of a computational method that provides estimates of the “brain age” of individuals that is based solely on a high resolution Magnetic Resonance Image (MRI) of the brain of the individual, and is blinded to his or her true chronological age. The method proceeds in two phases: first, a statistical learning algorithm is used to determine the numerical MRI-based features that predict true age on a training set of 198 healthy elderly individuals; second, these features are used to predict the true age of previously-unseen individuals. In cross-validation experiments, the brain age estimates differed from true age by a mean absolute error of 5.35 years in an elderly cohort, reflecting the broad heterogeneity in structural integrity of the elderly brain. The “brain age” of female subjects was significantly lower than that of male subjects who had the same true age (3.0 years younger for 50-year-olds and 1.6 years younger for 79 year olds), reflecting the longer life expectancy of females. Across the elderly age spectrum, the “brain age” of individuals with Alzheimer's Disease (AD) was significantly higher than that of cognitively-healthy elderly subjects with equivalent true age; however, this was not the case for the subjects with mild cognitive impairment (MCI), a possible AD prodrome.

Structural MRI has been utilized in numerous ways to measure morphologic characteristics of subcortical brain regions. Volumetric analysis is frequently used to quantify the size of brain structures to ultimately compare size differences between individuals. In order to make such comparisons, inter-subject variability in brain and/or head size must be taken into consideration. A heterogeneous set of methods are commonly used to normalize regional volume by brain and/or head size yielding inconsistent findings making it diffcult to interpret and compare results from published volumetric studies. This study investigated the effect that various volume normalization methodologies might have on group analysis. Specifically, the amygdalae were the regions of interest in elderly, healthy and depressed individuals. Normalization methods investigated included spatial transformations, brain and head volume, and tissue volume techniques. Group analyses were conducted with independent t-tests by dividing amygdalar volumes by various volume measures, as well as with univariate analysis of covariance (ANCOVA) analyses by using amygdalar volumes as dependent variables and various volume measures as covariates. Repeated measures ANOVA was performed to assess the effect of each normalization procedure. Results indicate that volumetric differences between groups varied based on the normalization method utilized, which may explain, in part, the discrepancy found in amygdalar volumetric studies. We believe the findings of this study are extensible to other brain regions and demographics, and thus, investigators should carefully consider the normalization methods utilized in volumetric studies to properly interpret the results and conclusions.

BackgroundMaintaining cognitive function protects older adults from developing functional decline. This study aims to identify the neuroimaging correlates of maintenance of higher global cognition as measured by the Modified Mini Mental State Test (3 MS) score.MethodsRepeated 3 MS measures from 1997-98 through 2006-07 and magnetic resonance imaging with diffusion tensor in 2006-07 were obtained in a biracial cohort of 258 adults free from dementia (mean age 82.9 years, 56% women, 42% blacks). Participants were classified as having shown either maintenance (3 MS slope>0) or decline (3 MS slope<1 SD below the mean) of cognition using linear mixed models. Measures of interest were white matter hyperintensity volume (WMHv) from total brain, volume of the gray matter (GMv) and microstructure (mean diffusivity, MD) for total brain and for brain areas known to be related to memory and executive control function: medial temporal area (hippocampus, parahippocampus and entorhinal cortex), cingulate cortex, dorsolateral prefrontal and posterior parietal cortex.ResultsDifferences between cognitive maintainers (n=153) and non-maintainers (n=107) were significant for GMv of the medial temporal area (35.8%, p=0.004) and lower MD of the cingulate cortex (37.9%, p=0.008), but not for other neuroimaging markers. In multivariable regression models adjusted for age, race, WMHv and GMV from the total brain and vascular conditions, each standard deviation of GMv of the medial temporal area and each standard deviation of MD of the cingulate cortex were associated with a nearly 4 times greater probability (odds ratio [standard deviation]: 3.80 [1.16, 12.44]) and a 34% lower probability (0.66, [0.46, 0.97]) of maintaining cognitive function, respectively. In these models neither WMHv nor GMv from total brain were significantly associated with probability of maintaining cognitive function.ConclusionsPreserving the volume of the medial temporal area and the microstructure of the cingula

There is need for a valid and reliable biomarker for HIV Associated Neurocognitive Disorder (HAND). The purpose of the present study was to provide preliminary evidence of the potential utility of neuronal functional connectivity measures obtained using magnetoencephalography (MEG) to identify HIV-associated changes in brain function. Resting state, eyes closed, MEG data from 10 HIV-infected individuals and 8 seronegative controls were analyzed using mutual information (MI) between all pairs of MEG sensors to ...

Structural MRI has been utilized in numerous ways to measure morphologic characteristics of subcortical brain regions. Volumetric analysis is frequently used to quantify the size of brain structures to ultimately compare size differences between individuals. In order to make such comparisons, inter-subject variability in brain and/or head size must be taken into consideration. A heterogeneous set of methods are commonly used to normalize regional volume by brain and/or head size yielding inconsistent findings making it diffcult to interpret and compare results from published volumetric studies. This study investigated the effect that various volume normalization methodologies might have on group analysis. Specifically, the amygdalae were the regions of interest in elderly, healthy and depressed individuals. Normalization methods investigated included spatial transformations, brain and head volume, and tissue volume techniques. Group analyses were conducted with independent t-tests by dividing amygdalar volumes by various volume measures, as well as with univariate analysis of covariance (ANCOVA) analyses by using amygdalar volumes as dependent variables and various volume measures as covariates. Repeated measures ANOVA was performed to assess the effect of each normalization procedure. Results indicate that volumetric differences between groups varied based on the normalization method utilized, which may explain, in part, the discrepancy found in amygdalar volumetric studies. We believe the findings of this study are extensible to other brain regions and demographics, and thus, investigators should carefully consider the normalization methods utilized in volumetric studies to properly interpret the results and conclusions.

Over the next twenty-five years, the proportion of the population over age 65 will increase 76%; therefore understanding both the normal and pathological processes involved in the aging of the human brain is of the highest public health priority. We report here the use of a computational method that provides estimates of the “brain age” of individuals that is based solely on a high resolution Magnetic Resonance Image (MRI) of the brain of the individual, and is blinded to his or her true chronological age. The method proceeds in two phases: first, a statistical learning algorithm is used to determine the numerical MRI-based features that predict true age on a training set of 198 healthy elderly individuals; second, these features are used to predict the true age of previously-unseen individuals. In cross-validation experiments, the brain age estimates differed from true age by a mean absolute error of 5.35 years in an elderly cohort, reflecting the broad heterogeneity in structural integrity of the elderly brain. The “brain age” of female subjects was significantly lower than that of male subjects who had the same true age (3.0 years younger for 50-year-olds and 1.6 years younger for 79 year olds), reflecting the longer life expectancy of females. Across the elderly age spectrum, the “brain age” of individuals with Alzheimer's Disease (AD) was significantly higher than that of cognitively-healthy elderly subjects with equivalent true age; however, this was not the case for the subjects with mild cognitive impairment (MCI), a possible AD prodrome.

BackgroundSlowing information processing is common among community-dwelling elderly and it predicts greater mortality and disability risk. Slowing information processing is related to brain macro-structural abnormalities. Specifically, greater global atrophy and greater small vessel disease of the white matter (WM) have been associated with slower processing speed. However, community-dwelling elderly with such macro-structural abnormalities can maintain processing speed. The roles of brain micro-structure for slow processing in very old adults living in the community is uncertain, as epidemiological studies relating these brain markers to cognition and in the context of other health characteristics are sparse.HypothesisInformation processing is cross-sectionally associated with WM micro-structure independent of overt macro-structural abnormalities and also independent of health related characteristics.MethodsImaging indices of micro-structure diffusion tensor imaging (DTI) and magnetization transfer imaging (MTI), macro-structure white matter hyperintensities (WMH), gray matter (GM) volume, digit symbol substitution test (DSST), and health characteristics were measured in 272 elderly (mean age 83 years old, 43% men, 40% black) living in the community.ResultsThe DTI- and MTI-indices of micro-structure from the normal appearing WM and not from the normal appearing GM were associated with DSST score independent of WMH and GM volumes. Associations were also independent of age, race, gender, mini-mental score, systolic blood pressure, and prevalent myocardial infarction.InterpretationDTI and MTI-indices of normal appearing WM are indicators of information processing speed in this cohort of very old adults living in the community. Since processing slowing is a potent index of mortality and disability, these indices may serve as biomarkers in prevention or treatment trials of disability.

ObjectiveTo evaluate the cause of diagnostic errors in the visual interpretation of positron emission tomographic scans with fludeoxyglucose F 18 (FDG-PET) in patients with frontotemporal lobar degeneration (FTLD) and patients with Alzheimer disease (AD).DesignTwelve trained raters unaware of clinical and autopsy information independently reviewed FDG-PET scans and provided their diagnostic impression and confidence of either FTLD or AD. Six of these raters also recorded whether metabolism appeared normal or abnormal in 5 predefined brain regions in each hemisphere-frontal cortex, anterior cingulate cortex, anterior temporal cortex, temporoparietal cortex, and posterior cingulate cortex. Results were compared with neuropathological diagnoses.SettingAcademic medical centers.PatientsForty-five patients with pathologically confirmed FTLD (n=14) or AD (n=31).ResultsRaters had a high degree of diagnostic accuracy in the interpretation of FDG-PET scans; however, raters consistently found some scans more difficult to interpret than others. Unanimity of diagnosis among the raters was more frequent in patients with AD (27 of 31 patients [87%]) than in patients with FTLD (7 of 14 patients [50%]) (P=.02). Disagreements in interpretation of scans in patients with FTLD largely occurred when there was temporoparietal hypometabolism, which was present in 7 of the 14 FTLD scans and 6 of the 7 scans lacking unanimity. Hypometabolism of anterior cingulate and anterior temporal regions had higher specificities and positive likelihood ratios for FTLD than temporoparietal hypometabolism had for AD.ConclusionsTemporoparietal hypometabolism in FTLD is common and may cause inaccurate interpretation of FDG-PET scans. An interpretation paradigm that focuses on the absence of hypometabolism in regions typically affected in AD before considering FTLD is likely to misclassify a significant portion of FTLD scans. Anterior cingulate and/or anterior temporal hypometabolism indicates a high likelih

ObjectiveTo evaluate the cause of diagnostic errors in the visual interpretation of positron emission tomographic scans with fludeoxyglucose F 18 (FDG-PET) in patients with frontotemporal lobar degeneration (FTLD) and patients with Alzheimer disease (AD).DesignTwelve trained raters unaware of clinical and autopsy information independently reviewed FDG-PET scans and provided their diagnostic impression and confidence of either FTLD or AD. Six of these raters also recorded whether metabolism appeared normal or abnormal in 5 predefined brain regions in each hemisphere-frontal cortex, anterior cingulate cortex, anterior temporal cortex, temporoparietal cortex, and posterior cingulate cortex. Results were compared with neuropathological diagnoses.SettingAcademic medical centers.PatientsForty-five patients with pathologically confirmed FTLD (n=14) or AD (n=31).ResultsRaters had a high degree of diagnostic accuracy in the interpretation of FDG-PET scans; however, raters consistently found some scans more difficult to interpret than others. Unanimity of diagnosis among the raters was more frequent in patients with AD (27 of 31 patients [87%]) than in patients with FTLD (7 of 14 patients [50%]) (P=.02). Disagreements in interpretation of scans in patients with FTLD largely occurred when there was temporoparietal hypometabolism, which was present in 7 of the 14 FTLD scans and 6 of the 7 scans lacking unanimity. Hypometabolism of anterior cingulate and anterior temporal regions had higher specificities and positive likelihood ratios for FTLD than temporoparietal hypometabolism had for AD.ConclusionsTemporoparietal hypometabolism in FTLD is common and may cause inaccurate interpretation of FDG-PET scans. An interpretation paradigm that focuses on the absence of hypometabolism in regions typically affected in AD before considering FTLD is likely to misclassify a significant portion of FTLD scans. Anterior cingulate and/or anterior temporal hypometabolism indicates a high likelih

BackgroundSlowing information processing is common among community-dwelling elderly and it predicts greater mortality and disability risk. Slowing information processing is related to brain macro-structural abnormalities. Specifically, greater global atrophy and greater small vessel disease of the white matter (WM) have been associated with slower processing speed. However, community-dwelling elderly with such macro-structural abnormalities can maintain processing speed. The roles of brain micro-structure for slow processing in very old adults living in the community is uncertain, as epidemiological studies relating these brain markers to cognition and in the context of other health characteristics are sparse.HypothesisInformation processing is cross-sectionally associated with WM micro-structure independent of overt macro-structural abnormalities and also independent of health related characteristics.MethodsImaging indices of micro-structure diffusion tensor imaging (DTI) and magnetization transfer imaging (MTI), macro-structure white matter hyperintensities (WMH), gray matter (GM) volume, digit symbol substitution test (DSST), and health characteristics were measured in 272 elderly (mean age 83 years old, 43% men, 40% black) living in the community.ResultsThe DTI- and MTI-indices of micro-structure from the normal appearing WM and not from the normal appearing GM were associated with DSST score independent of WMH and GM volumes. Associations were also independent of age, race, gender, mini-mental score, systolic blood pressure, and prevalent myocardial infarction.InterpretationDTI and MTI-indices of normal appearing WM are indicators of information processing speed in this cohort of very old adults living in the community. Since processing slowing is a potent index of mortality and disability, these indices may serve as biomarkers in prevention or treatment trials of disability.

BackgroundThe clinical diagnosis of dementing diseases largely depends on the subjective interpretation of patient symptoms. Consensus panels are frequently used in research to determine diagnoses when definitive pathologic findings are unavailable. Nevertheless, research on group decision making indicates that many factors can adversely affect panel performance.ObjectiveTo determine conditions that improve consensus panel diagnosis.DesignComparison of neuropathologic diagnoses with individual and consensus panel diagnoses based on clinical scenarios only, fludeoxyglucose F 18 positron emission tomography images only, and scenarios plus images.SettingExpert and trainee individual and consensus panel deliberations using a modified Delphi method in a pilot research study of the diagnostic utility of fludeoxyglucose F 18 positron emission tomography.PatientsForty-five patients with pathologically confirmed Alzheimer disease or frontotemporal dementia.Main outcome measuresStatistical measures of diagnostic accuracy, agreement, and confidence for individual raters and panelists before and after consensus deliberations.ResultsThe consensus protocol using trainees and experts surpassed the accuracy of individual expert diagnoses when clinical information elicited diverse judgments. In these situations, consensus was 3.5 times more likely to produce positive rather than negative changes in the accuracy and diagnostic certainty of individual panelists. A rule that forced group consensus was at least as accurate as majority and unanimity rules.ConclusionsUsing a modified Delphi protocol to arrive at a consensus diagnosis is a reasonable substitute for pathologic information. This protocol improves diagnostic accuracy and certainty when panelist judgments differ and is easily adapted to other research and clinical settings while avoiding the potential pitfalls of group decision making.

Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics-these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain. © 2009 Elsevier Inc. All rights reserved.

Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics-these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain. © 2009 Elsevier Inc. All rights reserved.

BackgroundThe clinical diagnosis of dementing diseases largely depends on the subjective interpretation of patient symptoms. Consensus panels are frequently used in research to determine diagnoses when definitive pathologic findings are unavailable. Nevertheless, research on group decision making indicates that many factors can adversely affect panel performance.ObjectiveTo determine conditions that improve consensus panel diagnosis.DesignComparison of neuropathologic diagnoses with individual and consensus panel diagnoses based on clinical scenarios only, fludeoxyglucose F 18 positron emission tomography images only, and scenarios plus images.SettingExpert and trainee individual and consensus panel deliberations using a modified Delphi method in a pilot research study of the diagnostic utility of fludeoxyglucose F 18 positron emission tomography.PatientsForty-five patients with pathologically confirmed Alzheimer disease or frontotemporal dementia.Main outcome measuresStatistical measures of diagnostic accuracy, agreement, and confidence for individual raters and panelists before and after consensus deliberations.ResultsThe consensus protocol using trainees and experts surpassed the accuracy of individual expert diagnoses when clinical information elicited diverse judgments. In these situations, consensus was 3.5 times more likely to produce positive rather than negative changes in the accuracy and diagnostic certainty of individual panelists. A rule that forced group consensus was at least as accurate as majority and unanimity rules.ConclusionsUsing a modified Delphi protocol to arrive at a consensus diagnosis is a reasonable substitute for pathologic information. This protocol improves diagnostic accuracy and certainty when panelist judgments differ and is easily adapted to other research and clinical settings while avoiding the potential pitfalls of group decision making.

Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics-these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain. © 2009 Elsevier Inc. All rights reserved.

Expansion of the cerebral ventricles may occur at an accelerated rate in subjects with dementia, but the time course of expansion during transitions between normal cognitive function, mild cognitive impairment (MCI), and dementia is not well understood. Furthermore, the effects of cardiovascular risk factors on rate of ventricular expansion are unclear. We used a fully automated segmentation technique to measure change rate in lateral ventricle-to-brain ratio (VBR) on 145 longitudinal pairs of magnetic resonance images of subjects in the Cardiovascular Health Study Cognition Study from the Pittsburgh Center. A multivariate model analyzed VBR change rate, accounting for dementia statuses at both imaging times (normal, MCI, or dementia), age, sex, education, race, magnetic resonance-defined infarcts, Center for Epidemiology Studies Depression Scale, baseline ventricular volume, and cardiovascular risk factors. VBR change was faster in subjects who were demented or transitioned from MCI to dementia, compared with subjects normal at both images and subjects who transitioned from normal to MCI or dementia. Patients with diabetes had faster VBR change. Ventricular expansion may accelerate late in the progression from normal cognitive function to dementia, and may be modulated by diabetes.

Expansion of the cerebral ventricles may occur at an accelerated rate in subjects with dementia, but the time course of expansion during transitions between normal cognitive function, mild cognitive impairment (MCI), and dementia is not well understood. Furthermore, the effects of cardiovascular risk factors on rate of ventricular expansion are unclear. We used a fully automated segmentation technique to measure change rate in lateral ventricle-to-brain ratio (VBR) on 145 longitudinal pairs of magnetic resonance images of subjects in the Cardiovascular Health Study Cognition Study from the Pittsburgh Center. A multivariate model analyzed VBR change rate, accounting for dementia statuses at both imaging times (normal, MCI, or dementia), age, sex, education, race, magnetic resonance-defined infarcts, Center for Epidemiology Studies Depression Scale, baseline ventricular volume, and cardiovascular risk factors. VBR change was faster in subjects who were demented or transitioned from MCI to dementia, compared with subjects normal at both images and subjects who transitioned from normal to MCI or dementia. Patients with diabetes had faster VBR change. Ventricular expansion may accelerate late in the progression from normal cognitive function to dementia, and may be modulated by diabetes.