Abstract:
:Accurate diagnosis of attentional disorders such as attention-deficit hyperactivity disorder (ADHD) is imperative because there are multiple negative psychosocial sequelae related to undiagnosed and untreated ADHD. Early and accurate detection can lead to effective intervention and prevention of negative sequelae. Unfortunately, diagnosing ADHD presents a challenge to traditional assessment paradigms because there is no single test that definitively establishes its presence. Even though ADHD is a physiologically based disorder with a multifactorial etiology, the diagnosis has been traditionally based on a subjective history of symptoms. In this chapter we outline a stochastic method that utilizes a Bayesian interface for quantifying and assessing ADHD. It can be used to combine of a variety of psychometric tests and physiological markers into a single standardized instrument that, on each step, refines a probability for ADHD for each individual based on information provided by the individual assessments. The method is illustrated with data from a small study of six college female students with ADHD and six matched controls in which the method achieves correct classification for all participants, where none of the individual assessments was capable of achieving perfect classification. Further, we provide a framework for applying this Bayesian method for performing meta-analysis of data obtained from disparate studies and using disparate tests for ADHD based on calibration of the data into a unified probability scale. We use this method to combine data from five studies that examine the diagnostic abilities of different behavioral rating scales and EEG assessments of ADHD, enrolling a total of 56 ADHD and 55 control subjects of different age groups and gender.
journal_name
Methods Enzymoljournal_title
Methods in enzymologyauthors
Robeva R,Penberthy JKdoi
10.1016/S0076-6879(09)67014-2subject
Has Abstractpub_date
2009-01-01 00:00:00pages
357-380eissn
0076-6879issn
1557-7988pii
S0076-6879(09)67014-2journal_volume
467pub_type
杂志文章abstract::The methods of computational chemistry and molecular modeling are becoming more and more accessible to biochemists with the advent of fast, inexpensive graphics workstations and well-tested computer programs. The state of the art in small molecules allows chemists to use these programs as "black boxes" and be confiden...
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journal_title:Methods in enzymology
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journal_title:Methods in enzymology
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abstract::Current methodologies available to quantify changes in mitochondrial turnover are limited to pulse-chase assays or specific assays that quantify mitophagy. Accordingly, new tools that can assess mitochondrial turnover are needed for the study of cellular, subcellular, and spatial parameters of mitochondrial turnover a...
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journal_title:Methods in enzymology
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journal_title:Methods in enzymology
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