Satoshi Minoshima

Bio: Satoshi Minoshima is an academic researcher from University of Utah. The author has contributed to research in topics: Dementia & Cerebral blood flow. The author has an hindex of 80, co-authored 289 publications receiving 29284 citations. Previous affiliations of Satoshi Minoshima include Veterans Health Administration & University of Michigan.

Diagnosis and management of dementia with Lewy bodies: Third report of the DLB Consortium

Abstract: The dementia with Lewy bodies (DLB) Consortium has revised criteria for the clinical and pathologic diagnosis of DLB incorporating new information about the core clinical features and suggesting improved methods to assess them. REM sleep behavior disorder, severe neuroleptic sensitivity, and reduced striatal dopamine transporter activity on functional neuroimaging are given greater diagnostic weighting as features suggestive of a DLB diagnosis. The 1-year rule distinguishing between DLB and Parkinson disease with dementia may be difficult to apply in clinical settings and in such cases the term most appropriate to each individual patient should be used. Generic terms such as Lewy body (LB) disease are often helpful. The authors propose a new scheme for the pathologic assessment of LBs and Lewy neurites (LN) using alpha-synuclein immunohistochemistry and semiquantitative grading of lesion density, with the pattern of regional involvement being more important than total LB count. The new criteria take into account both Lewy-related and Alzheimer disease (AD)-type pathology to allocate a probability that these are associated with the clinical DLB syndrome. Finally, the authors suggest patient management guidelines including the need for accurate diagnosis, a target symptom approach, and use of appropriate outcome measures. There is limited evidence about specific interventions but available data suggest only a partial response of motor symptoms to levodopa: severe sensitivity to typical and atypical antipsychotics in ∼50%, and improvements in attention, visual hallucinations, and sleep disorders with cholinesterase inhibitors.

Preclinical evidence of Alzheimer's disease in persons homozygous for the epsilon 4 allele for apolipoprotein E.

Abstract: Background Variants of the apolipoprotein E allele appear to account for most cases of late-onset Alzheimer's disease, and persons with two copies of the e4 allele appear to have an especially high risk of dementia. Positron-emission tomography (PET) has identified specific regions of the brain in which the rate of glucose metabolism declines progressively in patients with probable Alzheimer's disease. We used PET to investigate whether these same regions of the brain are affected in subjects homozygous for the e4 allele before the onset of cognitive impairment. Methods Apolipoprotein E genotypes were established in 235 volunteers 50 to 65 years of age who reported a family history of probable Alzheimer's disease. Neurologic and psychiatric evaluations, a battery of neuropsychological tests, magnetic resonance imaging, and PET were performed in 11 e4 homozygotes and 22 controls without the e4 allele who were matched for sex, age, and level of education. An automated method was used to generate an aggregat...

Spatial working memory in humans as revealed by PET

Abstract: The concept of working memory is central to theories of human cognition because working memory is essential to such human skills as language comprehension and deductive reasoning. Working memory is thought to be composed of two parts, a set of buffers that temporarily store information in either a phonological or visuospatial form, and a central executive responsible for various computations such as mental arithmetic. Although most data on working memory come from behavioural studies of normal and brain-injured humans, there is evidence about its physiological basis from invasive studies of monkeys. Here we report positron emission tomography (PET) studies of regional cerebral blood flow in normal humans that reveal activation in right-hemisphere prefrontal, occipital, parietal and premotor cortices accompanying spatial working memory processes. These results begin to uncover the circuitry of a working memory system in humans.

Intranasal Insulin Therapy for Alzheimer Disease and Amnestic Mild Cognitive Impairment

Abstract: Objective To examine the effects of intranasal insulin administration on cognition, function, cerebral glucose metabolism, and cerebrospinal fluid biomarkers in adults with amnestic mild cognitive impairment or Alzheimer disease (AD). Design Randomized, double-blind, placebo-controlled trial. Setting Clinical research unit of a Veterans Affairs medical center. Participants The intent-to-treat sample consisted of 104 adults with amnestic mild cognitive impairment (n = 64) or mild to moderate AD (n = 40). Intervention Participants received placebo (n = 30), 20 IU of insulin (n = 36), or 40 IU of insulin (n = 38) for 4 months, administered with a nasal drug delivery device (Kurve Technology, Bothell, Washington). Main Outcome Measures Primary measures consisted of delayed story recall score and the Dementia Severity Rating Scale score, and secondary measures included the Alzheimer Disease's Assessment Scale–cognitive subscale (ADAS-cog) score and the Alzheimer's Disease Cooperative Study–activities of daily living (ADCS-ADL) scale. A subset of participants underwent lumbar puncture (n = 23) and positron emission tomography with fludeoxyglucose F 18 (n = 40) before and after treatment. Results Outcome measures were analyzed using repeated-measures analysis of covariance. Treatment with 20 IU of insulin improved delayed memory (P Conclusions These results support longer trials of intranasal insulin therapy for patients with amnestic mild cognitive impairment and patients with AD. Trial Registration clinicaltrials.gov Identifier: NCT00438568

A Diagnostic Approach in Alzheimer's Disease Using Three-Dimensional Stereotactic Surface Projections of Fluorine-18-FDG PET

Abstract: To improve the diagnostic performance of PET as an aid in evaluating patients suspected of having Alzheimer9s disease, we developed a fully automated method which generates comprehensive image presentations and objective diagnostic indices. Methods: Ruodne-18-fluorodeoxyglucose PET image sets were collected from 37 patients with probable Alzheimer9s disease (including questionable and mildde mentia), 22 normal subjects and 5 patients with cerebrovascular disease. Following stereotectic anatomic standardization, metabolic activity on an individual9s PET image set was extracted to a set of predefined surface pixels (three-dimensional stereotactic surface projection, 3D-SSP), which was used in the subsequent analysis. A normal database was created by averaging extracted data sets of the normal subjects. Patients9 data sets were compared individually with the normal database by calculatinga Z-scoreona pixel-by pixel basis and were displayed in 3D-SSP views for visual inspections. Diagnostic indices were then generated based on averaged Z-scores for the association cortices. Results: Patterns and seventies of metabolic reduction in patients with probable Alzheimer9s disease were seen in the standard 3D-SSP views of extracted raw data and statistical Z-scores. When dscriminating patients with probable Alzheimer9s disease from normal subjects, diagnostic indices of the parietal assodation cortex and unilaterally averaged parietal-temporal-frontal cortex showed sensitivities of 95% and 97%, respectively, with a specificity of 100%. Neither index yielded false-positive results for cerebrovascular disease. Conclusion: 3D-SSP enables quantitative data extraction and reliable localization of metabolic abnormalities by means of stereotactic coordinates. The proposed method is a promising approach for interpreting functional brain PET scans.

The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer's disease

Abstract: The National Institute on Aging and the Alzheimer's Association charged a workgroup with the task of revising the 1984 criteria for Alzheimer's disease (AD) dementia. The workgroup sought to ensure that the revised criteria would be flexible enough to be used by both general healthcare providers without access to neuropsychological testing, advanced imaging, and cerebrospinal fluid measures, and specialized investigators involved in research or in clinical trial studies who would have these tools available. We present criteria for all-cause dementia and for AD dementia. We retained the general framework of probable AD dementia from the 1984 criteria. On the basis of the past 27 years of experience, we made several changes in the clinical criteria for the diagnosis. We also retained the term possible AD dementia, but redefined it in a manner more focused than before. Biomarker evidence was also integrated into the diagnostic formulations for probable and possible AD dementia for use in research settings. The core clinical criteria for AD dementia will continue to be the cornerstone of the diagnosis in clinical practice, but biomarker evidence is expected to enhance the pathophysiological specificity of the diagnosis of AD dementia. Much work lies ahead for validating the biomarker diagnosis of AD dementia.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Chapter 11 Working memory

Abstract: Publisher Summary This chapter focuses on the modern notion of short-term memory, called working memory. Working memory refers to the temporary maintenance of information that was just experienced or just retrieved from long-term memory but no longer exists in the external environment. These internal representations are short-lived, but can be maintained for longer periods of time through active rehearsal strategies, and can be subjected to various operations that manipulate the information in such a way that makes it useful for goal-directed behavior. Working memory is a system that is critically important in cognition and seems necessary in the course of performing many other cognitive functions, such as reasoning, language comprehension, planning, and spatial processing. This chapter demonstrates the functional importance of dopamine to working memory function in several ways. Elucidation of the cognitive and neural mechanisms underlying human working memory is an important focus of cognitive neuroscience and neurology for much of the past decade. One conclusion that arises from research is that working memory, a faculty that enables temporary storage and manipulation of information in the service of behavioral goals, can be viewed as neither a unitary, nor a dedicated system. Data from numerous neuropsychological and neurophysiological studies in animals and humans demonstrates that a network of brain regions, including the prefrontal cortex, is critical for the active maintenance of internal representations.

The Brain's Default Network Anatomy, Function, and Relevance to Disease

Abstract: Thirty years of brain imaging research has converged to define the brain’s default network—a novel and only recently appreciated brain system that participates in internal modes of cognition Here we synthesize past observations to provide strong evidence that the default network is a specific, anatomically defined brain system preferentially active when individuals are not focused on the external environment Analysis of connectional anatomy in the monkey supports the presence of an interconnected brain system Providing insight into function, the default network is active when individuals are engaged in internally focused tasks including autobiographical memory retrieval, envisioning the future, and conceiving the perspectives of others Probing the functional anatomy of the network in detail reveals that it is best understood as multiple interacting subsystems The medial temporal lobe subsystem provides information from prior experiences in the form of memories and associations that are the building blocks of mental simulation The medial prefrontal subsystem facilitates the flexible use of this information during the construction of self-relevant mental simulations These two subsystems converge on important nodes of integration including the posterior cingulate cortex The implications of these functional and anatomical observations are discussed in relation to possible adaptive roles of the default network for using past experiences to plan for the future, navigate social interactions, and maximize the utility of moments when we are not otherwise engaged by the external world We conclude by discussing the relevance of the default network for understanding mental disorders including autism, schizophrenia, and Alzheimer’s disease