Identification of voxel-based texture abnormalities as new biomarkers for schizophrenia and major depressive patients using layer-wise relevance propagation on deep learning decisions.

Non-segmented MRI brain images are used for the identification of new Magnetic Resonance Imaging (MRI) biomarkers able to differentiate between schizophrenic patients (SCZ), major depressive patients (MD) and healthy controls (HC). Brain texture measures such as entropy and contrast, capturing the neighboring variation of MRI voxel intensities, were computed and fed into deep learning technique for group classification. Layer-wise relevance was applied for the localization of the classification results. Texture feature map of non-segmented brain MRI scans were extracted from 141 SCZ, 103 MD and 238 HC. The gray level co-occurrence matrix (GLCM) was calculated on a voxel-by-voxel basis in a cube of voxels. Deep learning tested if texture feature map could predict diagnostic group membership of three classes under a binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method was applied in a repeated nested cross-validation scheme and cross-validated feature selection. The regions with the highest relevance (positive/negative) are presented. The method was applied on non-segmented images reducing the computation complexity and the error associated with segmentation process.

White Matter Lesion Penumbra Shows Abnormalities on Structural and Physiologic MRIs in the Coronary Artery Risk Development in Young Adults Cohort.

BACKGROUND AND PURPOSE: White matter lesions are 1 age-related manifestation of cerebrovascular disease, but subthreshold abnormalities have been identified in nonlesional WM. We hypothesized that structural and physiologic MR imaging findings of early cerebrovascular disease can be measured in middle-aged subjects in tissue adjacent to WM lesions, termed "penumbra." MATERIALS AND METHODS: WM lesions were defined using automated segmentation in 463 subjects, 43-56 years of age, from the Coronary Artery Risk Development in Young Adults (CARDIA) longitudinal observational cohort study. We described 0- to 2-mm and 2- to 4-mm-thick spatially defined penumbral WM tissue ROIs as rings surrounding WM lesions. The remaining WM was defined as distant normal-appearing WM. Mean signal intensities were measured for FLAIR, T1-, and T2-weighted images, and from fractional anisotropy, mean diffusivity, CBF, and vascular reactivity maps. Group comparisons were made using Kruskal-Wallis and pair-wise t tests. RESULTS: Lesion volumes averaged 0.738 ± 0.842 cm3 (range, 0.005-7.27 cm3). Mean signal intensity for FLAIR, T2, and mean diffusivity was increased, while T1, fractional anisotropy, and CBF were decreased in white matter lesions versus distant normal-appearing WM, with penumbral tissues showing graded intermediate values (corrected P < .001 for all group/parameter comparisons). Vascular reactivity was significantly elevated in white matter lesions and penumbral tissue compared with distant normal-appearing white matter (corrected P ≤ .001). CONCLUSIONS: Even in relatively healthy 43- to 56-year-old subjects with small white matter lesion burden, structural and functional MR imaging in penumbral tissue reveals significant signal abnormalities versus white matter lesions and other normal WM. Findings suggest that the onset of WM injury starts by middle age and involves substantially more tissue than evident from focal white matter lesions visualized on structural imaging.

A prospective study of focal brain atrophy, mobility and fitness.

BACKGROUND: The parallel decline of mobility and cognition with ageing is explained in part by shared brain structural changes that are related to fitness. However, the temporal sequence between fitness, brain structural changes and mobility loss has not been fully evaluated. METHODS: Participants were from the Baltimore Longitudinal Study of Aging, aged 60 or older, initially free of cognitive and mobility impairments, with repeated measures of fitness (400-m time), mobility (6-m gait speed) and neuroimaging markers over 4 years (n = 332). Neuroimaging markers included volumes of total brain, ventricles, frontal, parietal, temporal and subcortical motor areas, and corpus callosum. Autoregressive models were used to examine the temporal sequence of each brain volume with mobility and fitness, adjusted for age, sex, race, body mass index, height, education, intracranial volume and APOE ɛ4 status. RESULTS: After adjustment, greater volumes of total brain and selected frontal, parietal and temporal areas, and corpus callosum were unidirectionally associated with future faster gait speed over and beyond cross-sectional and autoregressive associations. There were trends towards faster gait speed being associated with future greater hippocampus and precuneus. Higher fitness was unidirectionally associated with future greater parahippocampal gyrus and not with volumes in other areas. Smaller ventricle predicted future higher fitness. CONCLUSION: Specific regional brain volumes predict future mobility impairment. Impaired mobility is a risk factor for future atrophy of hippocampus and precuneus. Maintaining fitness preserves parahippocampal gyrus volume. Findings provide new insight into the complex and bidirectional relationship between the parallel decline of mobility and cognition often observed in older persons.

Modifiable vascular risk factors, white matter disease and cognition in early Parkinson's disease.

BACKGROUND AND PURPOSE: Dementia in Parkinson's disease (PD) is common and disabling. Identification of modifiable risk factors for it is essential. Vascular risk factors (VRFs) may be associated with cognitive decline in early PD. Biomarkers that serve as surrogates of the long-term effect of VRFs on PD are needed. To that end, we aimed to quantitate white matter hyperintensities (WMH) in early PD, measure associations with VRFs and examine relationships between WMH and longitudinal cognition. METHODS: Participants in the Parkinson's Progression Markers Initiative study (141 patients with PD, 63 healthy controls) with adequate baseline structural brain magnetic resonance imaging data were included. Hypertension and diabetes history, and body mass index were combined to create a vascular risk score. WMH were quantitated via automated methods. Cognition was assessed annually with a comprehensive test battery. RESULTS: In the PD group, vascular risk score was associated with WMH for total brain (ß = 0.210; P = 0.021), total white matter (ß = 0.214; P = 0.013), frontal (ß = 0.220; P = 0.002) and temporal (ß = 0.212; P = 0.002) regions. Annual rate of change in global cognition was greater in those with higher vascular risk score (ß = -0.040; P = 0.007) and greater WMH (ß = -0.029; P = 0.049). Higher temporal WMH burden was associated with great decline over time in verbal memory (ß = -0.034; P = 0.031). CONCLUSIONS: In early PD, modifiable VRFs are associated with WMH on brain magnetic resonance imaging. Temporal WMH burden predicts decline in verbal memory. WMH may serve as a surrogate marker for the effect of VRFs on cognitive abilities in PD.

Defining SNAP by cross-sectional and longitudinal definitions of neurodegeneration.

Introduction: Suspected non-Alzheimer's pathophysiology (SNAP) is a biomarker driven designation that represents a heterogeneous group in terms of etiology and prognosis. SNAP has only been identified by cross-sectional neurodegeneration measures, whereas longitudinal measures might better reflect "active" neurodegeneration and might be more tightly linked to prognosis. We compare neurodegeneration defined by cross-sectional 'hippocampal volume' only (SNAP/L-) versus both cross-sectional and longitudinal 'hippocampal atrophy rate' (SNAP/L+) and investigate how these definitions impact prevalence and the clinical and biomarker profile of SNAP in Mild Cognitive Impairment (MCI). Methods: 276 MCI patients from ADNI-GO/2 were designated amyloid "positive" (A+) or "negative" (A-) based on their florbetapir scan and neurodegeneration 'positive' or 'negative' based on cross-sectional hippocampal volume and longitudinal hippocampal atrophy rate. Results: 74.1% of all SNAP participants defined by the cross-sectional definition of neurodegeneration also met the longitudinal definition of neurodegeneration, whereas 25.9% did not. SNAP/L+ displayed larger white matter hyperintensity volume, a higher conversion rate to dementia over 5 years and a steeper decline on cognitive tasks compared to SNAP/L- and the A- CN group. SNAP/L- had more abnormal values on neuroimaging markers and worse performance on cognitive tasks than the A- CN group, but did not show a difference in dementia conversion rate or longitudinal cognition. Discussion: Using a longitudinal definition of neurodegeneration in addition to a cross-sectional one identifies SNAP participants with significant cognitive decline and a worse clinical prognosis for which cerebrovascular disease may be an important driver.

Neurobiological support to the diagnosis of ADHD in stimulant-naïve adults: pattern recognition analyses of MRI data.

OBJECTIVE: In adulthood, the diagnosis of attention-deficit/hyperactivity disorder (ADHD) has been subject of recent controversy. We searched for a neuroanatomical signature associated with ADHD spectrum symptoms in adults by applying, for the first time, machine learning-based pattern classification methods to structural MRI and diffusion tensor imaging (DTI) data obtained from stimulant-naïve adults with childhood-onset ADHD and healthy controls (HC). METHOD: Sixty-seven ADHD patients and 66 HC underwent high-resolution T1-weighted and DTI acquisitions. A support vector machine (SVM) classifier with a non-linear kernel was applied on multimodal image features extracted on regions of interest placed across the whole brain. RESULTS: The discrimination between a mixed-gender ADHD subgroup and individually matched HC (n = 58 each) yielded area-under-the-curve (AUC) and diagnostic accuracy (DA) values of up to 0.71% and 66% (P = 0.003) respectively. AUC and DA values increased to 0.74% and 74% (P = 0.0001) when analyses were restricted to males (52 ADHD vs. 44 HC). CONCLUSION: Although not at the level of clinically definitive DA, the neuroanatomical signature identified herein may provide additional, objective information that could influence treatment decisions in adults with ADHD spectrum symptoms.

State-dependent microstructural white matter changes in drug-naïve patients with first-episode psychosis.

BACKGROUND: Diffusion tensor imaging (DTI) studies have consistently shown white matter (WM) microstructural abnormalities in schizophrenia. Whether or not such alterations could vary depending on clinical status (i.e. acute psychosis v. remission) remains to be investigated. METHODS: Twenty-five treatment-naïve first-episode psychosis (FEP) patients and 51 healthy-controls (HC) underwent MRI scanning at baseline. Twenty-one patients were re-scanned as soon as they achieved sustained remission of symptoms; 36 HC were also scanned twice. Rate-of-change maps of longitudinal DTI changes were calculated for in order to examine WM alterations associated with changes in clinical status. We conducted voxelwise analyses of fractional anisotropy (FA) and trace (TR) maps. RESULTS: At baseline, FEP presented reductions of FA in comparison with HC [p < 0.05, false-discovery rate (FDR)-corrected] affecting fronto-limbic WM and associative, projective and commissural fasciculi. After symptom remission, patients showed FA increase over time (p < 0.001, uncorrected) in some of the above WM tracts, namely the right anterior thalamic radiation, right uncinate fasciculus/inferior fronto-occipital fasciculus, and left inferior fronto-occipital fasciculus/inferior longitudinal fasciculus. We also found significant correlations between reductions in PANSS scores and FA increases over time (p < 0.05, FDR-corrected). CONCLUSIONS: WM changes affecting brain tracts critical to the integration of perceptual information, cognition and emotions are detectable soon after the onset of FEP and may partially reverse in direct relation to the remission of acute psychotic symptoms. Our findings reinforce the view that WM abnormalities in brain tracts are a key neurobiological feature of acute psychotic disorders, and recovery from such WM pathology can lead to amelioration of symptoms.

Volumetric Analysis from a Harmonized Multisite Brain MRI Study of a Single Subject with Multiple Sclerosis.

BACKGROUND AND PURPOSE: MR imaging can be used to measure structural changes in the brains of individuals with multiple sclerosis and is essential for diagnosis, longitudinal monitoring, and therapy evaluation. The North American Imaging in Multiple Sclerosis Cooperative steering committee developed a uniform high-resolution 3T MR imaging protocol relevant to the quantification of cerebral lesions and atrophy and implemented it at 7 sites across the United States. To assess intersite variability in scan data, we imaged a volunteer with relapsing-remitting MS with a scan-rescan at each site. MATERIALS AND METHODS: All imaging was acquired on Siemens scanners (4 Skyra, 2 Tim Trio, and 1 Verio). Expert segmentations were manually obtained for T1-hypointense and T2 (FLAIR) hyperintense lesions. Several automated lesion-detection and whole-brain, cortical, and deep gray matter volumetric pipelines were applied. Statistical analyses were conducted to assess variability across sites, as well as systematic biases in the volumetric measurements that were site-related. RESULTS: Systematic biases due to site differences in expert-traced lesion measurements were significant (P < .01 for both T1 and T2 lesion volumes), with site explaining >90% of the variation (range, 13.0-16.4 mL in T1 and 15.9-20.1 mL in T2) in lesion volumes. Site also explained >80% of the variation in most automated volumetric measurements. Output measures clustered according to scanner models, with similar results from the Skyra versus the other 2 units. CONCLUSIONS: Even in multicenter studies with consistent scanner field strength and manufacturer after protocol harmonization, systematic differences can lead to severe biases in volumetric analyses.

sGraSP: A graph-based method for the derivation of subject-specific functional parcellations of the brain.

BACKGROUND: Resting-state fMRI (rs-fMRI) has emerged as a prominent tool for the study of functional connectivity. The identification of the regions associated with the different brain functions has received significant interest. However, most of the studies conducted so far have focused on the definition of a common set of regions, valid for an entire population. The variation of the functional regions within a population has rarely been accounted for. NEW METHOD: In this paper, we propose sGraSP, a graph-based approach for the derivation of subject-specific functional parcellations. Our method generates first a common parcellation for an entire population, which is then adapted to each subject individually. RESULTS: Several cortical parcellations were generated for 859 children being part of the Philadelphia Neurodevelopmental Cohort. The stability of the parcellations generated by sGraSP was tested by mixing population and subject rs-fMRI signals, to generate subject-specific parcels increasingly closer to the population parcellation. We also checked if the parcels generated by our method were better capturing a development trend underlying our data than the original parcels, defined for the entire population. COMPARISON WITH EXISTING METHODS: We compared sGraSP with a simpler and faster approach based on a Voronoi tessellation, by measuring their ability to produce functionally coherent parcels adapted to the subject data. CONCLUSIONS: Our parcellations outperformed the Voronoi tessellations. The parcels generated by sGraSP vary consistently with respect to signal mixing, the results are highly reproducible and the neurodevelopmental trend is better captured with the subject-specific parcellation, under all the signal mixing conditions.

Relationship between APOE Genotype and Structural MRI Measures throughout Adulthood in the Study of Health in Pomerania Population-Based Cohort.

BACKGROUND AND PURPOSE: The presence of the apolipoprotein E ε4 allele is the strongest sporadic Alzheimer disease genetic risk factor. We hypothesized that apolipoprotein E ε4 carriers and noncarriers may already differ in imaging patterns in midlife. We therefore sought to identify the effect of apolipoprotein E genotype on brain atrophy across almost the entire adult age span by using advanced MR imaging-based pattern analysis. MATERIALS AND METHODS: We analyzed MR imaging scans of 1472 participants from the Study of Health in Pomerania (22-90 years of age). We studied the association among age, apolipoprotein E ε4 carrier status, and brain atrophy, which was quantified by using 2 MR imaging-based indices: Spatial Pattern of Atrophy for Recognition of Brain Aging (summarizing age-related brain atrophy) and Spatial Pattern of Abnormality for Recognition of Early Alzheimer Disease (summarizing Alzheimer disease-like brain atrophy patterns), as well as the gray matter volumes in several Alzheimer disease- and apolipoprotein E-related ROIs (lateral frontal, lateral temporal, medial frontal, and hippocampus). RESULTS: No significant association was found between apolipoprotein E ε4 carrier status and the studied ROIs or the MR imaging-based indices in linear regression models adjusted for age, sex, and education, including an interaction term between apolipoprotein E and age. CONCLUSIONS: Our study indicates that measurable apolipoprotein E-related brain atrophy does not occur in early adulthood and midlife and suggests that such atrophy may only occur more proximal to the onset of clinical symptoms of dementia.

Advanced brain aging: relationship with epidemiologic and genetic risk factors, and overlap with Alzheimer disease atrophy patterns.

We systematically compared structural imaging patterns of advanced brain aging (ABA) in the general-population, herein defined as significant deviation from typical BA to those found in Alzheimer disease (AD). The hypothesis that ABA would show different patterns of structural change compared with those found in AD was tested via advanced pattern analysis methods. In particular, magnetic resonance images of 2705 participants from the Study of Health in Pomerania (aged 20-90 years) were analyzed using an index that captures aging atrophy patterns (Spatial Pattern of Atrophy for Recognition of BA (SPARE-BA)), and an index previously shown to capture atrophy patterns found in clinical AD (Spatial Patterns of Abnormality for Recognition of Early Alzheimer's Disease (SPARE-AD)). We studied the association between these indices and risk factors, including an AD polygenic risk score. Finally, we compared the ABA-associated atrophy with typical AD-like patterns. We observed that SPARE-BA had significant association with: smoking (P<0.05), anti-hypertensive (P<0.05), anti-diabetic drug use (men P<0.05, women P=0.06) and waist circumference for the male cohort (P<0.05), after adjusting for age. Subjects with ABA had spatially extensive gray matter loss in the frontal, parietal and temporal lobes (false-discovery-rate-corrected q<0.001). ABA patterns of atrophy were partially overlapping with, but notably deviating from those typically found in AD. Subjects with ABA had higher SPARE-AD values; largely due to the partial spatial overlap of associated patterns in temporal regions. The AD polygenic risk score was significantly associated with SPARE-AD but not with SPARE-BA. Our findings suggest that ABA is likely characterized by pathophysiologic mechanisms that are distinct from, or only partially overlapping with those of AD.

Diabetes, Gray Matter Loss, and Cognition in the Setting of Parkinson Disease.

RATIONALE AND OBJECTIVES: Parkinson disease (PD) is a progressive neurodegenerative disorder affecting motor and cognitive functions. Prior studies showed that patients with PD and diabetes (DM) demonstrate worse clinical outcomes compared to nondiabetic subjects with PD. Our study aimed at defining the relationship between DM, gray matter volume, and cognition in patients with PD. MATERIALS AND METHODS: This study included 36 subjects with PD (12 with DM, 24 without DM, mean age = 66). Subjects underwent high-resolution T1-weighted brain magnetic resonance imaging, [(11)C]dihydrotetrabenazine positron emission tomography imaging to quantify nigrostriatal dopaminergic denervation, clinical, and cognitive assessments. Magnetic resonance images were postprocessed to determine total and lobar cortical gray matter volumes. Cognitive testing scores were converted to z-scores for specific cognitive domains and a composite global cognitive z-score based on normative data computed. Analysis of covariance, accounting for effects of age, gender, intracranial volume, and striatal [(11)C]dihydrotetrabenazine binding, was used to test the relationship between DM and gray matter volumes. RESULTS: Impact of DM on total gray matter volume was significant (P = 0.02). Post hoc analyses of lobar cortical gray matter volumes revealed that DM was more selectively associated with lower gray matter volumes in the frontal regions (P = 0.01). Cognitive post hoc analyses showed that interaction of total gray matter volume and DM status was significantly associated with composite (P = 0.007), executive (P = 0.02), and visuospatial domain cognitive z-scores (P = 0.005). These associations were also significant for the frontal cortical gray matter. CONCLUSION: DM may exacerbate brain atrophy and cognitive functions in PD with greater vulnerability in the frontal lobes. Given the high prevalence of DM in the elderly, delineating its effects on patient outcomes in the PD population is of importance.

GraSP: geodesic Graph-based Segmentation with Shape Priors for the functional parcellation of the cortex.

Resting-state functional MRI is a powerful technique for mapping the functional organization of the human brain. However, for many types of connectivity analysis, high-resolution voxelwise analyses are computationally infeasible and dimensionality reduction is typically used to limit the number of network nodes. Most commonly, network nodes are defined using standard anatomic atlases that do not align well with functional neuroanatomy or regions of interest covering a small portion of the cortex. Data-driven parcellation methods seek to overcome such limitations, but existing approaches are highly dependent on initialization procedures and produce spatially fragmented parcels or overly isotropic parcels that are unlikely to be biologically grounded. In this paper, we propose a novel graph-based parcellation method that relies on a discrete Markov Random Field framework. The spatial connectedness of the parcels is explicitly enforced by shape priors. The shape of the parcels is adapted to underlying data through the use of functional geodesic distances. Our method is initialization-free and rapidly segments the cortex in a single optimization. The performance of the method was assessed using a large developmental cohort of more than 850 subjects. Compared to two prevalent parcellation methods, our approach provides superior reproducibility for a similar data fit. Furthermore, compared to other methods, it avoids incoherent parcels. Finally, the method's utility is demonstrated through its ability to detect strong brain developmental effects that are only weakly observed using other methods.

Association of hearing impairment with brain volume changes in older adults.

Hearing impairment in older adults is independently associated in longitudinal studies with accelerated cognitive decline and incident dementia, and in cross-sectional studies, with reduced volumes in the auditory cortex. Whether peripheral hearing impairment is associated with accelerated rates of brain atrophy is unclear. We analyzed brain volume measurements from magnetic resonance brain scans of individuals with normal hearing versus hearing impairment (speech-frequency pure tone average>25 dB) followed in the neuroimaging substudy of the Baltimore Longitudinal Study of Aging for a mean of 6.4 years after the baseline scan (n=126, age 56-86 years). Brain volume measurements were performed with semi-automated region-of-interest (ROI) algorithms, and brain volume trajectories were analyzed with mixed-effect regression models adjusted for demographic and cardiovascular factors. We found that individuals with hearing impairment (n=51) compared to those with normal hearing (n=75) had accelerated volume declines in whole brain and regional volumes in the right temporal lobe (superior, middle, and inferior temporal gyri, parahippocampus, p<.05). These results were robust to adjustment for multiple confounders and were consistent with voxel-based analyses, which also implicated right greater than left temporal regions. These findings demonstrate that peripheral hearing impairment is independently associated with accelerated brain atrophy in whole brain and regional volumes concentrated in the right temporal lobe. Further studies investigating the mechanistic basis of the observed associations are needed.

Survival analysis of patients with high-grade gliomas based on data mining of imaging variables.

BACKGROUND AND PURPOSE: The prediction of prognosis in HGGs is poor in the majority of patients. Our aim was to test whether multivariate prediction models constructed by machine-learning methods provide a more accurate predictor of prognosis in HGGs than histopathologic classification. The prediction of survival was based on DTI and rCBV measurements as an adjunct to conventional imaging. MATERIALS AND METHODS: The relationship of survival to 55 variables, including clinical parameters (age, sex), categoric or continuous tumor descriptors (eg, tumor location, extent of resection, multifocality, edema), and imaging characteristics in ROIs, was analyzed in a multivariate fashion by using data-mining techniques. A variable selection method was applied to identify the overall most important variables. The analysis was performed on 74 HGGs (18 anaplastic gliomas WHO grades III/IV and 56 GBMs or gliosarcomas WHO grades IV/IV). RESULTS: Five variables were identified as the most significant, including the extent of resection, mass effect, volume of enhancing tumor, maximum B0 intensity, and mean trace intensity in the nonenhancing/edematous region. These variables were used to construct a prediction model based on a J48 classification tree. The average classification accuracy, assessed by cross-validation, was 85.1%. Kaplan-Meier survival curves showed that the constructed prediction model classified malignant gliomas in a manner that better correlates with clinical outcome than standard histopathology. CONCLUSIONS: Prediction models based on data-mining algorithms can provide a more accurate predictor of prognosis in malignant gliomas than histopathologic classification alone.

Longitudinal pattern of regional brain volume change differentiates normal aging from MCI.

BACKGROUND: Neuroimaging measures have potential as surrogate markers of disease through identification of consistent features that occur prior to clinical symptoms. Despite numerous investigations, especially in relation to the transition to clinical impairment, the regional pattern of brain changes in clinically normal older adults has not been established. We predict that the regions that show early pathologic changes in association with Alzheimer disease will show accelerated volume loss in mild cognitive impairment (MCI) compared to normal aging. METHODS: Through the Baltimore Longitudinal Study of Aging, we prospectively evaluated 138 nondemented individuals (age 64-86 years) annually for up to 10 consecutive years. Eighteen participants were diagnosed with MCI over the course of the study. Mixed-effects regression was used to compare regional brain volume trajectories of clinically normal individuals to those with MCI based on a total of 1,017 observations. RESULTS: All investigated volumes declined with normal aging (p < 0.05). Accelerated change with age was observed for ventricular CSF (vCSF), frontal gray matter, superior, middle, and medial frontal, and superior parietal regions (p < or = 0.04). The MCI group showed accelerated changes compared to normal controls in whole brain volume, vCSF, temporal gray matter, and orbitofrontal and temporal association cortices, including the hippocampus (p < or = 0.04). CONCLUSION: Although age-related regional volume loss is apparent and widespread in nondemented individuals, mild cognitive impairment is associated with a unique pattern of structural vulnerability reflected in differential volume loss in specific regions. Early identification of patterns of abnormality is of fundamental importance for detecting disease onset and tracking progression.

Postmenopausal hormone therapy and regional brain volumes: the WHIMS-MRI Study.

OBJECTIVES: To determine whether menopausal hormone therapy (HT) affects regional brain volumes, including hippocampal and frontal regions. METHODS: Brain MRI scans were obtained in a subset of 1,403 women aged 71-89 years who participated in the Women's Health Initiative Memory Study (WHIMS). WHIMS was an ancillary study to the Women's Health Initiative, which consisted of two randomized, placebo-controlled trials: 0.625 mg conjugated equine estrogens (CEE) with or without 2.5 mg medroxyprogesterone acetate (MPA) in one daily tablet. Scans were performed, on average, 3.0 years post-trial for the CEE + MPA trial and 1.4 years post-trial for the CEE-Alone trial; average on-trial follow-up intervals were 4.0 years for CEE + MPA and 5.6 years for CEE-Alone. Total brain, ventricular, hippocampal, and frontal lobe volumes, adjusted for age, clinic site, estimated intracranial volume, and dementia risk factors, were the main outcome variables. RESULTS: Compared with placebo, covariate-adjusted mean frontal lobe volume was 2.37 cm(3) lower among women assigned to HT (p = 0.004), mean hippocampal volume was slightly (0.10 cm(3)) lower (p = 0.05), and differences in total brain volume approached significance (p = 0.07). Results were similar for CEE + MPA and CEE-Alone. HT-associated reductions in hippocampal volumes were greatest in women with the lowest baseline Modified Mini-Mental State Examination scores (scores <90). CONCLUSIONS: Conjugated equine estrogens with or without MPA are associated with greater brain atrophy among women aged 65 years and older; however, the adverse effects are most evident in women experiencing cognitive deficits before initiating hormone therapy.

Individual patient diagnosis of AD and FTD via high-dimensional pattern classification of MRI.

The purpose of this study is to determine the diagnostic accuracy of MRI-based high-dimensional pattern classification in differentiating between patients with Alzheimer's disease (AD), Frontotemporal Dementia (FTD), and healthy controls, on an individual patient basis. MRI scans of 37 patients with AD and 37 age-matched cognitively normal elderly individuals, as well as 12 patients with FTD and 12 age-matched cognitively normal elderly individuals, were analyzed using voxel-based analysis and high-dimensional pattern classification. Diagnostic sensitivity and specificity of spatial patterns of regional brain atrophy found to be characteristic of AD and FTD were determined via cross-validation and via split-sample methods. Complex spatial patterns of relatively reduced brain volumes were identified, including temporal, orbitofrontal, parietal and cingulate regions, which were predominantly characteristic of either AD or FTD. These patterns provided 100% diagnostic accuracy, when used to separate AD or FTD from healthy controls. The ability to correctly distinguish AD from FTD averaged 84.3%. All estimates of diagnostic accuracy were determined via cross-validation. In conclusion, AD- and FTD-specific patterns of brain atrophy can be detected with high accuracy using high-dimensional pattern classification of MRI scans obtained in a typical clinical setting.

Biomarkers for early detection of Alzheimer pathology.

The increasing prevalence of Alzheimer's disease and the devastating consequences of late-life dementia motivates the drive to develop diagnostic biomarkers to reliably identify the pathology associated with this disorder. Strategies to accomplish this include the detection of altered levels of tau and amyloid in cerebrospinal fluid, the use of structural MRI to identify disease-specific patterns of regional atrophy and MRI T(1)rho to detect disease-related macromolecular protein aggregation, and the direct imaging of amyloid deposits using positron emission tomography and single photon emission computerized tomography. Success will facilitate the ability to reliably diagnose Alzheimer's disease while the symptoms of brain failure are mild and may provide objective measures of disease-modifying treatment efficacy.

Past adult lead exposure is linked to neurodegeneration measured by brain MRI.

OBJECTIVE: To determine whether cumulative lead dose in former organolead workers was associated with MRI measures of white matter lesions (WML) and global and structure-specific brain volumes. METHODS: MRIs, tibia lead, and other measures were obtained from 532 former organolead workers with a mean age of 56 years and a mean of 18 years since last occupational exposure to lead. Cumulative lead dose was measured by tibia lead, obtained by X-ray fluorescence, and expressed as microg lead per gram of bone mineral (microg Pb/g). WML were evaluated using the Cardiovascular Health Study grading scale. A total of 21 global and specific brain regions were evaluated. RESULTS: A total of 36% of individuals had WML grade of 1 to 7 (0 to 9 scale). Increasing peak tibia lead was associated with increasing WML grade (p = 0.004). The adjusted OR for a 1 microg Pb/g increase in tibia lead was 1.042 (95% CI = 1.021, 1.063) for a CHS grade of 5+ (> or = 5 vs < 5). In linear regression, the coefficient for tibia lead was negative for associations with all structures. Higher tibia lead was significantly related to smaller total brain volume, frontal and total gray matter volume, and parietal white matter volume. Of nine smaller specific regions of interest, higher tibia lead was associated with smaller volumes for the cingulate gyrus and insula. CONCLUSIONS: These data suggest that cumulative lead dose is associated with persistent brain lesions, and may explain previous findings of a progressive decline in cognitive function.