Cross-sectional and Longitudinal Analysis of the Relationship Between A beta Deposition, Cortical Thickness, and Memory in Cognitively Unimpaired Individuals and in Alzheimer Disease

IMPORTANCE beta-amyloid (A beta) deposition is one of the hallmarks of Alzheimer disease. A beta deposition accelerates gray matter atrophy at early stages of the disease even before objective cognitive impairment is manifested. Identification of at-risk individuals at the presymptomatic stage has become a major research interest because it will allow early therapeutic interventions before irreversible synaptic and neuronal loss occur. We aimed to further characterize the cross-sectional and longitudinal relationship between A beta deposition, gray matter atrophy, and cognitive impairment

Tau PET and multimodal brain imaging in patients at risk for chronic traumatic encephalopathy.

ObjectiveTo characterize individual and group-level neuroimaging findings in patients at risk for Chronic Traumatic Encephalopathy (CTE).MethodsEleven male patients meeting criteria for Traumatic Encephalopathy Syndrome (TES, median age: 64) underwent neurologic evaluation, 3-Tesla MRI, and PET with [18F]-Flortaucipir (FTP, tau-PET) and [11C]-Pittsburgh compound B (PIB, amyloid-PET). Six patients underwent [18F]-Fluorodeoxyglucose-PET (FDG, glucose metabolism). We assessed imaging findings at the individual patient level, and in group-level comparisons with modality-specific groups of cognitively normal older adults (CN). Tau-PET findings in patients with TES were also compared to a matched group of patients with mild cognitive impairment or dementia due to Alzheimer's disease (AD).ResultsAll patients with TES sustained repetitive head injury participating in impact sports, ten in American football. Three patients met criteria for dementia and eight had mild cognitive impairment. Two patients were amyloid-PET positive and harbored the most severe MRI atrophy, FDG hypometabolism, and FTP-tau PET binding. Among the nine amyloid-negative patients, tau-PET showed either mildly elevated frontotemporal binding, a "dot-like" pattern, or no elevated binding. Medial temporal FTP was mildly elevated in a subset of amyloid-negative patients, but values were considerably lower than in AD. Voxelwise analyses revealed a convergence of imaging abnormalities (higher FTP binding, lower FDG, lower gray matter volumes) in frontotemporal areas in TES compared to controls.ConclusionsMildly elevated tau-PET binding was observed in a subset of amyloid-negative patients at risk for CTE, in a distribution consistent with CTE pathology stages III-IV. FTP-PET may be useful as a biomarker of tau pathology in CTE but is unlikely to be sensitive to early disease stages

Tissue Damage Quantification in Alzheimer\u27s Disease Brain via Magnetic Resonance Gradient Echo Plural Contrast Imaging (GEPCI)

Alzheimer’s disease (AD) affected approximately 48 million people worldwide in 2015. Its devastating consequences have stimulated an intense search for AD prevention and treatment. Clinically, AD is characterized by memory deficits and progressive cognitive impairment, leading to dementia. Over the past two to three decades, researchers have found that amyloidbeta (Aβ) plaques and neurofibrillary tau tangles occur during a long pre-symptomatic period (preclinical stage) before the onset of clinical symptoms. As a result, identification of the preclinical stage is essential for the initiation of prevention trials in asymptomatic individuals. Currently, Positron Emission Tomography (PET) imaging with injected 11C or 18F containing radiotracers (e.g., Pittsburgh compound B, PiB or florbetapir-fluorine-18, 18F-AV-45) is widely used to detect amyloid deposition in vivo and to identify this preclinical stage. However, PET scans are time consuming (about 1 hour), require injection of a radiotracer, thus, exposing the patient to ionizing radiation. After the preclinical stage, AD patients begin to show clinical symptoms, referred as a very mild or mild AD group. Post-mortem studies show that neuronal damage is the most proximate pathological substrate of cognitive impairment in AD compared with amyloid and tau deposition. Thus, a diagnostic tool is needed for detection of neuronal loss in vivo. As a faster, non-invasive, and radiation free imaging technique, Magnetic Resonance Imaging (MRI) plays an important role in the diagnosis of cognitive diseases. Conventional MRI yields superb definition of brain anatomy and structure and provide important volumetric information (e.g., brain atrophy). However, conventional MRI cannot provide microstructural and functional insight into the pathology of AD. The approach developed in Yablonskiy’s lab is based on the Gradient Echo Plural Contrast Imaging (GEPCI) protocol, which provides quantitative in vivo measurements of transverse relaxation properties of the tissue water 1H spins as determined from the gradient echo MRI signal. The measurements are corrected for macroscopic magnetic field inhomogeneity effects and physiologic-motion-driven fluctuations in magnetic field as these are the major artifacts present with the gradient echo technique. The principal relaxation property used in this dissertation is the tissue-specific transverse relaxation rate constant, R2*. The R2* value reflects the microscopic and mesoscopic magnetic field inhomogeneities rising from the complex tissuewater-environment within the human brain. In turn, changes in R2* reflect changes in the tissue’s microscopic and mesoscopic tissue structure. However, because of the presence of the cerebral blood vessel network, the magneticsusceptibility-driven blood-oxygen-level dependent (BOLD) effect also makes a significant contribution to R2*. A previously developed approach, quantitative BOLD (qBOLD), allows the separation of R2* into a tissue specific R2t* without blood vessel effects and the BOLD component. Quantifying the BOLD component allows the calculation of cerebral hemodynamics parameters, such as oxygen extraction fraction (OEF) and deoxygenated cerebral blood volume (dCBV). These parameters (R2*, R2t*, OEF, dCBV) describe structural and functional properties of tissue at the microstructural level in the human brain. In the study of normal aging, quantitative GEPCI measurements showed that R2t* increases with age while hemodynamic parameters, i.e., relative OEF and dCBV remain constant in most cerebral cortical regions. The comparison between quantitative GEPCI measurements and literature information suggest that (a) age-related increases in the cortical R2t* mostly reflect the age-related increases in the cellular packing density (or neuronal density); (b) regions in a brain characterized by higher R2t* contain a higher concentration of neurons with less developed cellular processes (dendrites, spines, etc.); and (c) brain regions characterized by lower R2t* represent regions with lower concentration of neurons but more developed cellular processes. In the Alzheimer study, R2* and R2t* together demonstrated significant differences among the normal, preclinical and mild AD groups. First, the results uncovered strong correlations between R2* and Aβ deposition measured by the PiB PET-tracer in several cortical regions (e.g., medial temporal lobe and precuneus). This finding indicates that R2* may be a potential surrogate marker for Aβ deposition. The strongest correlation was found in the medial temporal lobe (MTL), particularly in the parahippocampal cortex, which can be used to distinguish the normal and preclinical groups. Second, R2t* in the hippocampus, which characterized the hippocampal cellular integrity demonstrated much stronger correlations with psychometric tests than volume quantification of hippocampal atrophy. Importantly, decreased R2t* characterizing cellular damage was detected even in the hippocampal areas not affected by atrophy. In addition, R2t* significantly decreased in the mild AD group but was preserved in the preclinical group compared with the normal group. These results indicate a significant cellular density decrease in the mild group but not in the preclinical group, which is consistent with previous histological studies. In summary, GEPCI provides a new approach for evaluation of AD-related tissue pathology in vivo in the preclinical and early symptomatic stages of AD. Since MRI is widely available worldwide and does not require radiation exposure, it provides the opportunity to obtain new information on the pathogenesis of AD and for pre-screening cohorts (stratification) for clinical drug trials

The Cortical Signature of Alzheimer's Disease: Regionally Specific Cortical Thinning Relates to Symptom Severity in Very Mild to Mild AD Dementia and is Detectable in Asymptomatic Amyloid-Positive Individuals

Alzheimer's disease (AD) is associated with neurodegeneration in vulnerable limbic and heteromodal regions of the cerebral cortex, detectable in vivo using magnetic resonance imaging. It is not clear whether abnormalities of cortical anatomy in AD can be reliably measured across different subject samples, how closely they track symptoms, and whether they are detectable prior to symptoms. An exploratory map of cortical thinning in mild AD was used to define regions of interest that were applied in a hypothesis-driven fashion to other subject samples. Results demonstrate a reliably quantifiable in vivo signature of abnormal cortical anatomy in AD, which parallels known regional vulnerability to AD neuropathology. Thinning in vulnerable cortical regions relates to symptom severity even in the earliest stages of clinical symptoms. Furthermore, subtle thinning is present in asymptomatic older controls with brain amyloid binding as detected with amyloid imaging. The reliability and clinical validity of AD-related cortical thinning suggests potential utility as an imaging biomarker. This “disease signature” approach to cortical morphometry, in which disease effects are mapped across the cortical mantle and then used to define ROIs for hypothesis-driven analyses, may provide a powerful methodological framework for studies of neuropsychiatric diseases

The effect of amyloid pathology and glucose metabolism on cortical volume loss over time in Alzheimer’s disease

Purpose: The present multimodal neuroimaging study examined whether amyloid pathology and glucose metabolism are related to cortical volume loss over time in Alzheimer’s disease (AD) patients and healthy elderly controls. Methods: Structural MRI scans of eleven AD patients and ten controls were available at baseline and follow-up (mean interval 2.5 years). Change in brain structure over time was defined as percent change of cortical volume within seven a-priori defined regions that typically show the strongest structural loss in AD. In addition, two PET scans were performed at baseline: [[superscript 11]C]PIB to assess amyloid-β plaque load and [[superscript 18]F]FDG to assess glucose metabolism. [[superscript 11]C]PIB binding and [[superscript 18]F]FDG uptake were measured in the precuneus, a region in which both amyloid deposition and glucose hypometabolism occur early in the course of AD. Results: While amyloid-β plaque load at baseline was not related to cortical volume loss over time in either group, glucose metabolism within the group of AD patients was significantly related to volume loss over time (rho=0.56, p<0.05). Conclusion:The present study shows that in a group of AD patients amyloid-β plaque load as measured by [[superscript 11]C]PIB behaves as a trait marker (i.e., all AD patients showed elevated levels of amyloid, not related to subsequent disease course), whilst hypometabolism as measured by [[superscript 18]F]FDG changed over time indicating that it could serve as a state marker that is predictive of neurodegeneration.Hersenstichting Nederland (KS2011(1)-24)Athinoula A. Martinos Center for Biomedical ImagingInternationale Stichting Alzheimer Onderzoek (Project Number 11539

The Cortical Signature of Alzheimer's Disease: Regionally Specific Cortical Thinning Relates to Symptom Severity in Very Mild to Mild AD Dementia and is Detectable in Asymptomatic Amyloid-Positive Individuals

Alzheimer's disease (AD) is associated with neurodegeneration in vulnerable limbic and heteromodal regions of the cerebral cortex, detectable in vivo using magnetic resonance imaging. It is not clear whether abnormalities of cortical anatomy in AD can be reliably measured across different subject samples, how closely they track symptoms, and whether they are detectable prior to symptoms. An exploratory map of cortical thinning in mild AD was used to define regions of interest that were applied in a hypothesis-driven fashion to other subject samples. Results demonstrate a reliably quantifiable in vivo signature of abnormal cortical anatomy in AD, which parallels known regional vulnerability to AD neuropathology. Thinning in vulnerable cortical regions relates to symptom severity even in the earliest stages of clinical symptoms. Furthermore, subtle thinning is present in asymptomatic older controls with brain amyloid binding as detected with amyloid imaging. The reliability and clinical validity of AD-related cortical thinning suggests potential utility as an imaging biomarker. This “disease signature” approach to cortical morphometry, in which disease effects are mapped across the cortical mantle and then used to define ROIs for hypothesis-driven analyses, may provide a powerful methodological framework for studies of neuropsychiatric diseases.Psycholog

Neuronal dysfunction and disconnection of cortical hubs in non-demented subjects with elevated amyloid burden

Disruption of functional connectivity between brain regions may represent an early functional consequence of β-amyloid pathology prior to clinical Alzheimer's disease. We aimed to investigate if non-demented older individuals with increased amyloid burden demonstrate disruptions of functional whole-brain connectivity in cortical hubs (brain regions typically highly connected to multiple other brain areas) and if these disruptions are associated with neuronal dysfunction as measured with fluorodeoxyglucose-positron emission tomography. In healthy subjects without cognitive symptoms and patients with mild cognitive impairment, we used positron emission tomography to assess amyloid burden and cerebral glucose metabolism, structural magnetic resonance imaging to quantify atrophy and novel resting state functional magnetic resonance imaging processing methods to calculate whole-brain connectivity. Significant disruptions of whole-brain connectivity were found in amyloid-positive patients with mild cognitive impairment in typical cortical hubs (posterior cingulate cortex/precuneus), strongly overlapping with regional hypometabolism. Subtle connectivity disruptions and hypometabolism were already present in amyloid-positive asymptomatic subjects. Voxel-based morphometry measures indicate that these findings were not solely a consequence of regional atrophy. Whole-brain connectivity values and metabolism showed a positive correlation with each other and a negative correlation with amyloid burden. These results indicate that disruption of functional connectivity and hypometabolism may represent early functional consequences of emerging molecular Alzheimer's disease pathology, evolving prior to clinical onset of dementia. The spatial overlap between hypometabolism and disruption of connectivity in cortical hubs points to a particular susceptibility of these regions to early Alzheimer's-type neurodegeneration and may reflect a link between synaptic dysfunction and functional disconnection

Multi-Kernel Learning with Dartel Improves Combined MRI-PET Classification of Alzheimer’s Disease in AIBL Data: Group and Individual Analyses

Magnetic resonance imaging (MRI) and positron emission tomography (PET) are neuroimaging modalities typically used for evaluating brain changes in Alzheimer’s disease (AD). Due to their complementary nature, their combination can provide more accurate AD diagnosis or prognosis. In this work, we apply a multi-modal imaging machine-learning framework to enhance AD classification and prediction of diagnosis of subject-matched gray matter MRI and Pittsburgh compound B (PiB)-PET data related to 58 AD, 108 mild cognitive impairment (MCI) and 120 healthy elderly (HE) subjects from the Australian imaging, biomarkers and lifestyle (AIBL) dataset. Specifically, we combined a Dartel algorithm to enhance anatomical registration with multi-kernel learning (MKL) technique, yielding an average of &gt;95% accuracy for three binary classification problems: AD-vs.-HE, MCI-vs.-HE and AD-vs.-MCI, a considerable improvement from individual modality approach. Consistent with t-contrasts, the MKL weight maps revealed known brain regions associated with AD, i.e., (para)hippocampus, posterior cingulate cortex and bilateral temporal gyrus. Importantly, MKL regression analysis provided excellent predictions of diagnosis of individuals by r2 = 0.86. In addition, we found significant correlations between the MKL classification and delayed memory recall scores with r2 = 0.62 (p &lt; 0.01). Interestingly, outliers in the regression model for diagnosis were mainly converter samples with a higher likelihood of converting to the inclined diagnostic category. Overall, our work demonstrates the successful application of MKL with Dartel on combined neuromarkers from different neuroimaging modalities in the AIBL data. This lends further support in favor of machine learning approach in improving the diagnosis and risk prediction of AD