Accurate discrimination of Alzheimer's disease from other dementia and/or normal subjects using SPECT specific volume analysis

2014 ◽  
Author(s):  
Hitoshi Iyatomi ◽  
Jun Hashimoto ◽  
Fumuhito Yoshii ◽  
Toshiki Kazama ◽  
Shuichi Kawada ◽  
...  
1994 ◽  
Vol 15 ◽  
pp. S10
Author(s):  
C.E. Rosenberg ◽  
J. Lee ◽  
D. Rowland ◽  
P.J. Whitehouse

2021 ◽  
Vol 15 ◽  
Author(s):  
Daniel A. Llano ◽  
Susanna S. Kwok ◽  
Viswanath Devanarayan ◽  

Multiple epidemiological studies have revealed an association between presbycusis and Alzheimer’s Disease (AD). Unfortunately, the neurobiological underpinnings of this relationship are not clear. It is possible that the two disorders share a common, as yet unidentified, risk factor, or that hearing loss may independently accelerate AD pathology. Here, we examined the relationship between reported hearing loss and brain volumes in normal, mild cognitive impairment (MCI) and AD subjects using a publicly available database. We found that among subjects with AD, individuals that reported hearing loss had smaller brainstem and cerebellar volumes in both hemispheres than individuals without hearing loss. In addition, we found that these brain volumes diminish in size more rapidly among normal subjects with reported hearing loss and that there was a significant interaction between cognitive diagnosis and the relationship between reported hearing loss and these brain volumes. These data suggest that hearing loss is linked to brainstem and cerebellar pathology, but only in the context of the pathological state of AD. We hypothesize that the presence of AD-related pathology in both the brainstem and cerebellum creates vulnerabilities in these brain regions to auditory deafferentation-related atrophy. These data have implications for our understanding of the potential neural substrates for interactions between hearing loss and AD.


2018 ◽  
Author(s):  
Priya Devanarayan ◽  
Viswanath Devanarayan ◽  
Daniel A. Llano ◽  

AbstractThe 2018 NIA-AA research framework proposes a classification system with beta-Amyloid deposition, pathologic Tau, and neurodegeneration (ATN) for the diagnosis and staging of Alzheimer’s Disease (AD). Data from the ADNI (AD neuroimaging initiative) database can be utilized to identify diagnostic signatures for predicting AD progression, and to determine the utility of this NIA-AA research framework. Profiles of 320 peptides from baseline cerebrospinal fluid (CSF) samples of 287 normal, mild cognitive impairment (MCI) and AD subjects followed over a 3-10 year period were measured via multiple reaction monitoring (MRM) mass spectrometry. CSF Aβ42, total-Tau (tTau), phosphorylated-Tau (pTau-181) and hippocampal volume were also measured. From these candidate markers, optimal diagnostic signatures with decision thresholds to separate AD and normal subjects were first identified via unbiased regression and tree-based algorithms. The best performing signature determined via cross-validation was then tested in an independent group of MCI subjects to predict future progression. This multivariate analysis yielded a simple diagnostic signature comprising CSF pTau-181 to Aβ42 ratio, MRI hippocampal volume and a novel PTPRN peptide, with a decision threshold on each marker. When applied to a separate MCI group at baseline, subjects meeting this signature criteria experience 4.3-fold faster progression to AD compared to a 2.2-fold faster progression using only conventional markers. This novel 4-marker signature represents an advance over the current diagnostics based on widely used marker, and is much easier to use in practice than recently published complex signatures. In addition, this signature reinforces the ATN construct from the 2018 NIA-AA research framework.DisclosuresViswanath Devanarayan is an employee of Charles River Laboratories, and as such owns equity in, receives salary and other compensation from Charles River Laboratories.Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.;Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.


2012 ◽  
Vol 33 (s1) ◽  
pp. S427-S438 ◽  
Author(s):  
Oscar L. Lopez ◽  
James T. Becker ◽  
Lewis H. Kuller

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Toshioki Matsuzawa ◽  
Toshihiro Takata ◽  
Koichi Yokono ◽  
Hiroo Ueda ◽  
Kensuke Moriwaki ◽  
...  

Background/Aims. Diabetes might increase the risk of Alzheimer’s disease (AD). For detecting dementia, it is typical to obtain informants’ perceptions of cognitive deficits, but such interviews are usually difficult in routine care. We aimed to develop a model for predicting mild to moderate AD using a self-reported questionnaire and by evaluating vascular risk factors for dementia in elderly subjects with diabetes.Methods. We recruited 286 diabetic and 155 nondiabetic elderly subjects. There were 25 patients with AD and 261 cognitively normal individuals versus 30 with AD and 125 normal subjects, respectively. Each participant answered subjective questions on memory deficits and daily functioning. Information on vascular risk factors was obtained from clinical charts, and multivariate logistic regression was used to develop a model for predicting AD.Results. The predicted probabilities used in screening for AD in diabetic subjects constituted age, education, lower diastolic blood pressure, subjective complaints of memory dysfunction noticeable by others, and impaired medication, shopping, and travel outside a familiar locality. Receiver operating characteristic analysis revealed a satisfactory discrimination for AD specific for diabetic elderly subjects, with 95.2% sensitivity and 90.6% specificity.Conclusion. This is the first useful index that can prescreen for AD in elderly subjects with diabetes.


1988 ◽  
Vol 36 (8) ◽  
pp. 681-686 ◽  
Author(s):  
Pierre N. Tariot ◽  
Michael Gross ◽  
Trey Sunderland ◽  
Martin R. Cohen ◽  
Herbert Weingartner ◽  
...  

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