IC–P–025: Regional network pattern of MRI gray matter volume in Alzheimer's dementia

Alexander, Gene E.; Chen, Kewei; Aschenbrenner, Melaney; Reiman, Eric M.; Caselli, Richard J.; Merkley, Tricia L.; Lewis, Diana J.; Pietrini, Pietro; Teipel, Stefan J.; Hampel, Harald; Rapoport, Stanley I.; Moeller, James R.

doi:10.1016/j.jalz.2006.05.2230

Cited by 3 publications

(6 citation statements)

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“…Not surprisingly, in the human cognitive aging literature there are reports of hippocampal atrophy across age (e.g., O'Brien et al ., ; Tisserand et al ., ; Raz et al ., , ), along with reports of stability of overall hippocampal volume during aging (e.g., Van Petten, ; Sullivan et al ., ). Studies examining aging in the nonhuman primate and rodent using structural MRI methods suggest that hippocampal volume in these animals is preserved across age (Shamy et al ., ; Alexander et al ., ), while frontal cortical gray matter volumes do show changes with age in both species (Alexander et al ., , ; Shamy et al ., ).…”

Section: Age‐related Anatomical Changes In the Hippocampusmentioning

confidence: 99%

Impact of aging brain circuits on cognition

Samson

Barnes

2013

Eur J of Neuroscience

154

131

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Brain networks that engage the hippocampus and prefrontal cortex are central for enabling effective interactions with our environment. Some of the cognitive processes that these structures mediate, such as encoding and retrieving episodic experience, wayfinding, working memory and attention are known to be altered across the lifespan. As illustrated by examples given below, there is remarkable consistency across species in the pattern of age-related neural and cognitive change observed in healthy humans and other animals. These include changes in cognitive operations that are known to be dependent on the hippocampus, as well as those requiring intact prefrontal cortical circuits. Certain cognitive constructs that reflect the function of these areas lend themselves to investigation across species allowing brain mechanisms at different levels of analysis to be studied in greater depth.

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Section: Age‐related Anatomical Changes In the Hippocampusmentioning

confidence: 99%

Impact of aging brain circuits on cognition

Samson

Barnes

2013

Eur J of Neuroscience

154

131

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“…As a complement to traditional univariate statistical analyses, which separately analyze each regional brain, inherited or expressed gene, or protein measurement, we and others have begun using multivariate statistical algorithms to extract information about the patterns within complex data sets. Led by Gene Alexander, we have been using the Scaled Subprofile Model (SSM) network analysis to characterize the MRI patterns of regional gray matter atrophy associated with aging, 64 AD, 65 and APOE ɛ4 gene dose 61 . We have suggested how this strategy may aid in the detection and tracking of AD, AD risk, and normal brain aging, and we have suggested how it could be used in the evaluation of putative AD disease‐modifying, AD prevention, and antiaging therapies 64 .…”

Section: The Push–pull Relationship Between Brain Imaging and Genomicmentioning

confidence: 99%

Linking Brain Imaging and Genomics in the Study of Alzheimer's Disease and Aging

Reiman

2007

Annals of the New York Academy of Sciences

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My colleagues and I have been using positron emission tomography (PET) and magnetic resonance imaging (MRI) to detect and track the brain changes associated with Alzheimer's disease (AD) and normal brain aging in cognitively normal persons with two copies, one copy, and no copies of the apolipoprotein E (APOE) epsilon4 allele, a common AD susceptibility gene. In this review article, I consider how brain imaging techniques could be used to evaluate putative AD prevention therapies in cognitively normal APOE epsilon4 carriers and putative age-modifying therapies in cognitively normal APOE epsilon4 noncarriers, how they could help investigate the individual and aggregate effects of putative AD risk modifiers, and how they could help guide the investigation of a molecular mechanism associated with AD vulnerability and normal neurological aging. I suggest how high-resolution genome-wide genetic and transcriptomic studies could further help in the scientific understanding of AD, aging, and other common and genetically complex phenotypes, such as variation in normal human memory performance, and in the discovery and evaluation of promising treatments for these phenotypes. Finally, I illustrate the push-pull relationship between brain imaging, genomics research, and other neuroscientific research in the study of AD and aging.

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“…Numerous resting state FDG PET imaging studies have demonstrated differences in cerebral metabolism between patients with Alzheimer's dementia and healthy controls (e.g., Minoshima et al, 1995;Silverman et al, 2001;Smith et al, 1992;Alexander et al, 2002). For example, using voxel-based univariate analysis to compare regional cerebral metabolism in a group of 14 AD patients and 34 healthy controls, prominent baseline reductions in the parietotemporal brain regions were observed (figure 2; Alexander et al, 2002).…”

Section: Applications: Aging and Alzheimer's Diseasementioning

confidence: 99%

“…The FDG tracer is most often used to assess cerebral glucose metabolism in a resting state (e.g., with eyes closed and ears covered) to evaluate regional patterns of reduced brain function. FDG has been used to aid diagnosis and to track changes in regional brain activity over time or in response to treatment (e.g., Silverman et al, 2001;Alexander et al, 2002). Figure 2 shows the regions of reduced glucose metabolism typically observed in FDG PET group studies of AD patients compared to healthy controls.…”

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

“…For example, using voxel-based univariate analysis to compare regional cerebral metabolism in a group of 14 AD patients and 34 healthy controls, prominent baseline reductions in the parietotemporal brain regions were observed (figure 2; Alexander et al, 2002). Additional declines in the parietotemporal and frontal areas were found when the AD patients were assessed with PET after a one-year interval.…”

Section: Applications: Aging and Alzheimer's Diseasementioning

confidence: 99%

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Neuroimaging: Overview of Methods and Applications

Ryan¹,

Alexander²

2008

Handbook of Physiological Research Methods in Health Psychology

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IntroductionThe past few decades have seen enormous advances in the ability to visualize and measure aspects of brain anatomy and physiology in the living human being. The recent developments in neuroimaging techniques that provide measures of brain structure and function, as well as cerebral activity during cognitive processes such as perception, attention, and memory have changed the face of cognitive neuroscience, and have allowed us to ask questions about the relation between the brain and behavior that were simply not possible only a decade or two ago. As these techniques have become more accessible and affordable for research use, they have been increasingly applied to a variety of research questions, having the potential to greatly enhance our understanding of the normal brain and its development throughout the lifespan. Even areas of research that have traditionally not been linked to neuroscience, such as social psychology, economics, and education are increasingly making use of neuroimaging methods.From the beginning, evaluating the effects of health-related factors, such as aging, neurological and psychiatric illness, and brain injury have been an important and rapidly growing part of the neuroimaging field. Health psychology provides a potentially unique perspective in the application of neuroimaging methods studying how brainbehavior relationships are altered by disease states and other health-related factors.Neuroimaging provides a set of tools that can address a wide variety of questions. For example, researchers might address how neural systems important for specific cognitive processes are altered by the presence, progression, and treatment of a disease; test the effect of health factors such as genetic risk on regional patterns of 3 brain structure and function; identify subgroups for which treatment or prevention therapies are most effective; or determine the effect of individual differences on the expression, severity, and progression of brain diseases. To utilize these techniques effectively, however, those new to the neuroimaging field and unfamiliar with its rapidly evolving technology are often faced with the daunting task of figuring out where and how to begin.The purpose of this chapter is to provide an overview of the major neuroimaging methods and their applications to health psychology research. Since a detailed and complete description of the methods and general applications for neuroimaging in health-related research is well beyond the scope of this chapter, we have focused on the description of methods and applications that we believe will provide a useful starting point for researchers who are thinking of applying such neuroimaging techiques to their own areas of health-related psychology.We begin with a brief review of the major structural and functional neuroimaging research methods that are commonly in use, including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and diffusion-weight...

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IC–P–025: Regional network pattern of MRI gray matter volume in Alzheimer's dementia

Cited by 3 publications

References 0 publications

Impact of aging brain circuits on cognition

Impact of aging brain circuits on cognition

Linking Brain Imaging and Genomics in the Study of Alzheimer's Disease and Aging

Neuroimaging: Overview of Methods and Applications

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