BrainPrint: A discriminative characterization of brain morphology

We introduce BrainPrint, a compact and discriminative representation of brain morphology. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the eigenvalue problem of the 2D and 3D Laplace–Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. This discriminative characterization enables new ways to study the similarity between brains; the focus can either be on a specific brain structure of interest or on the overall brain similarity. We highlight four applications for BrainPrint in this article: (i) subject identification, (ii) age and sex prediction, (iii) brain asymmetry analysis, and (iv) potential genetic influences on brain morphology. The properties of BrainPrint require the derivation of new algorithms to account for the heterogeneous mix of brain structures with varying discriminative power. We conduct experiments on three datasets, including over 3000 MRI scans from the ADNI database, 436 MRI scans from the OASIS dataset, and 236 MRI scans from the VETSA twin study. All processing steps for obtaining the compact representation are fully automated, making this processing framework particularly attractive for handling large datasets.National Cancer Institute (U.S.) (1K25-CA181632-01)Athinoula A. Martinos Center for Biomedical Imaging (P41-RR014075)Athinoula A. Martinos Center for Biomedical Imaging (P41-EB015896)National Alliance for Medical Image Computing (U.S.) (U54-EB005149)Neuroimaging Analysis Center (U.S.) (P41-EB015902)National Center for Research Resources (U.S.) (U24 RR021382)National Institute of Biomedical Imaging and Bioengineering (U.S.) (5P41EB015896-15)National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01EB006758)National Institute on Aging (AG022381)National Institute on Aging (5R01AG008122-22)National Institute on Aging (AG018344)National Institute on Aging (AG018386)National Center for Complementary and Alternative Medicine (U.S.) (RC1 AT005728-01)National Institute of Neurological Diseases and Stroke (U.S.) (R01 NS052585-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R21NS072652-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R01NS070963)National Institute of Neurological Diseases and Stroke (U.S.) (R01NS083534)National Institutes of Health (U.S.) ((5U01-MH093765

BrainPrint: A discriminative characterization of brain morphology

We introduce BrainPrint, a compact and discriminative representation of brain morphology. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the eigenvalue problem of the 2D and 3D Laplace–Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. This discriminative characterization enables new ways to study the similarity between brains; the focus can either be on a specific brain structure of interest or on the overall brain similarity. We highlight four applications for BrainPrint in this article: (i) subject identification, (ii) age and sex prediction, (iii) brain asymmetry analysis, and (iv) potential genetic influences on brain morphology. The properties of BrainPrint require the derivation of new algorithms to account for the heterogeneous mix of brain structures with varying discriminative power. We conduct experiments on three datasets, including over 3000 MRI scans from the ADNI database, 436 MRI scans from the OASIS dataset, and 236 MRI scans from the VETSA twin study. All processing steps for obtaining the compact representation are fully automated, making this processing framework particularly attractive for handling large datasets.National Cancer Institute (U.S.) (1K25-CA181632-01)Athinoula A. Martinos Center for Biomedical Imaging (P41-RR014075)Athinoula A. Martinos Center for Biomedical Imaging (P41-EB015896)National Alliance for Medical Image Computing (U.S.) (U54-EB005149)Neuroimaging Analysis Center (U.S.) (P41-EB015902)National Center for Research Resources (U.S.) (U24 RR021382)National Institute of Biomedical Imaging and Bioengineering (U.S.) (5P41EB015896-15)National Institute of Biomedical Imaging and Bioengineering (U.S.) (R01EB006758)National Institute on Aging (AG022381)National Institute on Aging (5R01AG008122-22)National Institute on Aging (AG018344)National Institute on Aging (AG018386)National Center for Complementary and Alternative Medicine (U.S.) (RC1 AT005728-01)National Institute of Neurological Diseases and Stroke (U.S.) (R01 NS052585-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R21NS072652-01)National Institute of Neurological Diseases and Stroke (U.S.) (1R01NS070963)National Institute of Neurological Diseases and Stroke (U.S.) (R01NS083534)National Institutes of Health (U.S.) ((5U01-MH093765

Functional Magnetic Resonance Imaging of Semantic Memory as a Presymptomatic Biomarker of Alzheimer’s Disease Risk

Extensive research efforts have been directed toward strategies for predicting risk of developing Alzheimer\u27s disease (AD) prior to the appearance of observable symptoms. Existing approaches for early detection of AD vary in terms of their efficacy, invasiveness, and ease of implementation. Several non-invasive magnetic resonance imaging strategies have been developed for predicting decline in cognitively healthy older adults. This review will survey a number of studies, beginning with the development of a famous name discrimination task used to identify neural regions that participate in semantic memory retrieval and to test predictions of several key theories of the role of the hippocampus in memory. This task has revealed medial temporal and neocortical contributions to recent and remote memory retrieval, and it has been used to demonstrate compensatory neural recruitment in older adults, apolipoprotein E ε4 carriers, and amnestic mild cognitive impairment patients. Recently, we have also found that the famous name discrimination task provides predictive value for forecasting episodic memory decline among asymptomatic older adults. Other studies investigating the predictive value of semantic memory tasks will also be presented. We suggest several advantages associated with the use of semantic processing tasks, particularly those based on person identification, in comparison to episodic memory tasks to study AD risk. Future directions for research and potential clinical uses of semantic memory paradigms are also discussed. This article is part of a Special Issue entitled: Imaging Brain Aging and Neurodegenerative disease

Brain imaging evidence of early involvement of subcortical regions in familial and sporadic Alzheimer's disease

Recent brain imaging studies have found changes in subcortical regions in presymptomatic autosomal dominant Alzheimer's disease (ADAD). These regions are also affected in sporadic Alzheimer's disease (sAD), but whether such changes are seen in early-stage disease is still uncertain. In this review, we discuss imaging studies published in the past 12 years that have found evidence of subcortical involvement in early-stage ADAD and/or sAD. Several papers have reported amyloid deposition in the striatum of presymptomatic ADAD mutation carriers, prior to amyloid deposition elsewhere. Altered caudate volume has also been implicated in early-stage ADAD, but findings have been variable. Less is known about subcortical involvement in sAD: the thalamus and striatum have been found to be atrophied in symptomatic patients, but their involvement in the preclinical phase remains unclear, in part due to the difficulties of studying this stage in sporadic disease. Longitudinal imaging studies comparing ADAD mutation carriers with individuals at high-risk for sAD may be needed to elucidate the significance of subcortical involvement in different AD clinical stages

Multimodal analysis in normal aging, mild cognitive impairment, and Alzheimer's disease: group differentiation, baseline cognition, and prediction of future cognitive decline

Thesis (Ph.D.)--Boston UniversityAlzheimer's disease (AD) is a progressive neurodegenerative disease with an insidious onset that makes it difficult to distinguish from normal aging. It begins with an impairment of memory that develops into amnestic mild cognitive impairment (aMCI) and later to dementia as deficits become apparent in other cognitive domains. Effective biomarkers that differentiate normal aging, MCI, and AD and predict future cognitive decline are needed. Potential biomarkers have been studied in isolation, but their impact when combined is not understood. The goal of this project is to determine the optimal combination of CSF biomarkers, MRI morphometry, FDG PET metabolism, and neuropsychological test scores to differentiate between normal aging subjects and those with MCI and AD. This study addresses: 1) the optimal normalization region and partial volume correction method to quantify FDG PET analysis, 2) the effects of adjusting MRI-based cortical thickness measures for differences in gray/white matter tissue contrast in normal aging and disease, 3) whether multimodal multivariate stepwise logistic regression models can predict group membership, and 4) whether multimodal multivariate stepwise linear regression models can determine which imaging and CSF biomarker variables best predict future cognitive decline. The results indicate that normalizing FDG PET to the cerebellum along with using a gray matter mask for partial volume correction provides optimal prediction. In contrast, age-associated changes in gray/white matter intensity ratio did not differentiate between the groups and only slightly improved the efficacy of cortical thickness as a biomarker. MRI morphometry of the gray matter and neuropsychological test scores were better able to discriminate between the groups than FDG PET or CSF biomarker concentrations. Combining all modalities significantly improved the index of discrimination, especially at the earliest stages of the disease. MRI gray matter morphometry variables were more highly associated with baseline cognitive function and best predicted future cognitive decline compared to other variables. Overall these findings demonstrate that a multimodal approach using MRI morphometry, FDG PET metabolism, neuropsychological test scores, and CSF biomarkers provides significantly better discrimination than any modality alone. Hence, the variables important for discriminating between the groups may be candidates for biomarkers in human clinical interventional trials

Gray Matter Changes in Parkinson's and Alzheimer's Disease and Relation to Cognition

Purpose of Review We summarize structural (s)MRI findings of gray matter (GM) atrophy related to cognitive impairment in Alzheimer's disease (AD) and Parkinson's disease (PD) in light of new analytical approaches and recent longitudinal studies results. Recent Findings The hippocampus-to-cortex ratio seems to be the best sMRI biomarker to discriminate between various AD subtypes, following the spatial distribution of tau pathology, and predict rate of cognitive decline. PD is clinically far more variable than AD, with heterogeneous underlying brain pathology. Novel multivariate approaches have been used to describe patterns of early subcortical and cortical changes that relate to more malignant courses of PD. New emerging analytical approaches that combine structural MRI data with clinical and other biomarker outcomes hold promise for detecting specific GM changes in the early stages of PD and preclinical AD that may predict mild cognitive impairment and dementia conversion

Role of Cerebrovascular Abnormality in Neurodegenerative Disease and Subcortical Ischemic Disease: CT Perfusion and PET Imaging

Clinical studies indicate that about 30% ~ 50% of patients have cognitive impairment after the first or recurrent stroke. Ischemic injury, particularly subcortical lesions, caused by stroke has been demonstrated to further exacerbate cognitive impairment of Alzheimer’s disease (AD) and vascular dementia. However, the mechanisms whereby cerebrovascular abnormalities contribute to neurodegeneration at early stage of disease and eventually to cognitive decline remain unclear. CT perfusion and positron emission tomography (PET) were used to investigate early mechanisms in a rat comorbid model of cerebral ischemia (CI) and β-amyloid (Aβ, a pathological hallmark of AD) toxicity, and in patients with small subcortical ischemic lesions. Chapter 2 investigates the early hemodynamic disturbances within the first month after transient CI insult in the presence of Aβ toxicity in the comorbid rat model. CT perfusion revealed significantly lower cerebral blood flow (CBF) and blood volume (CBV) at acute phase due to the transient ischemia, and increased CBF and CBV in the ipsilateral striatum of CI+Aβ and CI groups at the first week post ischemia. These results suggest that CI is the primary driving factor of cerebrovascular abnormalities at early stage, and prolonged hyperperfusion and hypervolemia may imply reperfusion-related injury and downstream inflammation. Chapter 3 further addresses these questions with CT Perfusion-PET imaging. Chapter 3 describes the temporal profiles of blood-brain barrier (BBB) disruption and neuroinflammation over 3 months after CI with and without concurrent Aβ toxicity in the comorbid rat model. CT perfusion showed significantly higher BBB permeability surface product (BBB-PS) in the ipsilateral striatum of CI+Aβ group at day 7, month 2 and 3, as compared to CI and sham group. PET imaging revealed the highest level of neuroinflammation as reflected by the significantly increased 18F-FEPPA uptake due to microglial activation in the striatal lesion of CI+Aβ group at day 7 and 14. The temporal features of these cererbrovascular and cellular changes may serve as early imaging biomarkers for development of cognitive impairment in high-risk patients post ischemic insult. Chapter 4 investigates the temporal changes in BBB-PS and cerebral perfusion using CT perfusion over the first 3 months after small lacunar/subcortical stroke in patients. This longitudinal investigation suggests the chronic BBB leakage detected by CT perfusion may contribute to cognitive impairment and associated pathology in lacunar/subcortical stroke. Overall, the imaging results presented in this thesis have demonstrated that BBB-PS, CBF, CBV and activated microglia can be used as imaging biomarkers for delineating the early pathogenic pattern and underlying contribution of cerebral ischemia to the disease development in the animal comorbid model and subcortical stroke patients

Magnetic resonance spectroscopy and brain volumetry in mild cognitive impairment. A prospective study

Objective To assess the accuracy of magnetic resonance spectroscopy (1H-MRS) and brain volumetry in mild cognitive impairment (MCI) to predict conversion to probable Alzheimer''s disease (AD). Methods Forty-eight patients fulfilling the criteria of amnestic MCI who underwent a conventional magnetic resonance imaging (MRI) followed by MRS, and T1-3D on 1.5 Tesla MR unit. At baseline the patients underwent neuropsychological examination. 1H-MRS of the brain was carried out by exploring the left medial occipital lobe and ventral posterior cingulated cortex (vPCC) using the LCModel software. A high resolution T1-3D sequence was acquired to carry out the volumetric measurement. A cortical and subcortical parcellation strategy was used to obtain the volumes of each area within the brain. The patients were followed up to detect conversion to probable AD. Results After a 3-year follow-up, 15 (31.2%) patients converted to AD. The myo-inositol in the occipital cortex and glutamate + glutamine (Glx) in the posterior cingulate cortex predicted conversion to probable AD at 46.1% sensitivity and 90.6% specificity. The positive predictive value was 66.7%, and the negative predictive value was 80.6%, with an overall cross-validated classification accuracy of 77.8%. The volume of the third ventricle, the total white matter and entorhinal cortex predict conversion to probable AD at 46.7% sensitivity and 90.9% specificity. The positive predictive value was 70%, and the negative predictive value was 78.9%, with an overall cross-validated classification accuracy of 77.1%. Combining volumetric measures in addition to the MRS measures the prediction to probable AD has a 38.5% sensitivity and 87.5% specificity, with a positive predictive value of 55.6%, a negative predictive value of 77.8% and an overall accuracy of 73.3%. Conclusion Either MRS or brain volumetric measures are markers separately of cognitive decline and may serve as a noninvasive tool to monitor cognitive changes and progression to dementia in patients with amnestic MCI, but the results do not support the routine use in the clinical settings

Molecular pathology and synaptic loss in primary tauopathies: A [18F]AV-1451 and [11C]UCB-J PET study

The relationship between in vivo synaptic density and tau burden in primary tauopathies is key to understanding the impact of tauopathy on functional decline and in informing new early therapeutic strategies. In this cross-sectional observational study, we determine the in vivo relationship between synaptic density and molecular pathology, in the primary tauopathies of Progressive Supranuclear Palsy (PSP) and Corticobasal Degeneration (CBD), as a function of disease severity. Twenty three patients with PSP, and twelve patients with Corticobasal Syndrome (CBS) were recruited from a tertiary referral centre. Nineteen education, sex and gender-matched control participants were recruited from the National Institute for Health Research ‘Join Dementia Research’ platform. Cerebral synaptic density and molecular pathology, in all participants, were estimated using PET imaging with the radioligands [11C]UCB-J and [18F]AV-1451, respectively. Patients with CBS also underwent amyloid PET imaging with [11C]PiB to exclude those with likely Alzheimer’s pathology – we refer to the amyloid negative cohort as having CBD although acknowledge other pathologies exist. Disease severity was assessed with the PSP rating scale; regional non-displaceable binding potentials (BPND) of [11C]UCB-J and [18F]AV-1451 were estimated in regions of interest from the Hammersmith Atlas, excluding those with known off-target binding for [18F]AV-1451. As an exploratory analysis, we also investigated the relationship between molecular pathology in cortical brain regions, and synaptic density in subcortical areas. Across brain regions, there was a positive correlation between [11C]UCB-J and [18F]AV-1451 BPND (ß = 0.4, t = 3.6, p = 0.001), independent of age or time between PET scans. However, this correlation became less positive as a function of disease severity in patients (ß = - 0.02, t = -2.9, p = 0.007, R = -0.41). Between regions, cortical [18F]AV-1451 binding was negatively correlated with synaptic density in subcortical areas (caudate nucleus, putamen). Brain regions with higher synaptic density are associated with a higher [18F]AV-1451 binding in PSP/CBD, but this association diminishes with disease severity. Moreover, higher cortical [18F]AV-1451 binding correlates with lower subcortical synaptic density. Longitudinal imaging is required to confirm the mediation of synaptic loss by molecular pathology. However, the effect of disease severity suggests a biphasic relationship between synaptic density and molecular pathology with synapse rich regions vulnerable to accrual of pathological aggregates, followed by a loss of synapses in response to pathology. Given the importance of synaptic function for cognition, our study elucidates the pathophysiology of primary tauopathies and may inform the design of future clinical trials.Cambridge Centre for Parkinson-Plus (RG95450); the National Institute for Health Research Cambridge Biomedical Research Centre (BRC-1215-20014); the PSP Association (“MAPT-PSP” study), and the Association of British Neurologists, Patrick Berthoud Charitable Trust (RG99368)

Network connectivity and structural correlates of survival in progressive supranuclear palsy and corticobasal syndrome

There is a pressing need to understand the factors that predict prognosis in progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS), with high heterogeneity over the poor average survival. We test the hypothesis that the magnitude and distribution of connectivity changes in PSP and CBS predict the rate of progression and survival time, using datasets from the Cambridge Centre for Parkinson-plus and the UK National PSP Research Network (PROSPECT-MR). Resting-state functional MRI images were available from 146 participants with PSP, 82 participants with CBS, and 90 healthy controls. Large-scale networks were identified through independent component analyses, with correlations taken between component time series. Independent component analysis was also used to select between-network connectivity components to compare with baseline clinical severity, longitudinal rate of change in severity, and survival. Transdiagnostic survival predictors were identified using partial least squares regression for Cox models, with connectivity compared to patients' demographics, structural imaging, and clinical scores using five-fold cross-validation. In PSP and CBS, between-network connectivity components were identified that differed from controls, were associated with disease severity, and were related to survival and rate of change in clinical severity. A transdiagnostic component predicted survival beyond demographic and motion metrics but with lower accuracy than an optimal model that included the clinical and structural imaging measures. Cortical atrophy enhanced the connectivity changes that were most predictive of survival. Between-network connectivity is associated with variability in prognosis in PSP and CBS but does not improve predictive accuracy beyond clinical and structural imaging metrics