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The Role of Technology in Clinical Neuropsychology$
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Robert L. Kane and Thomas D. Parsons

Print publication date: 2017

Print ISBN-13: 9780190234737

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780190234737.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 16 June 2021

Incorporating Neuroimaging into Cognitive Assessment

Incorporating Neuroimaging into Cognitive Assessment

Chapter:
(p.377) 13 Incorporating Neuroimaging into Cognitive Assessment
Source:
The Role of Technology in Clinical Neuropsychology
Author(s):

Erin D. Bigler

Publisher:
Oxford University Press
DOI:10.1093/oso/9780190234737.003.0020

All traditional neuropsychological assessment techniques emerged in an era prior to modern neuroimaging. In fact, question-answer/paper-and-pencil test origins that gained traction with Alfred Binet in 1905 remain the same core techniques today. Indeed, Binet’s efforts began the era of standardized human metrics designed to assess a broad spectrum of cognitive, emotional, and behavioral functions and abilities. During the early part of the 20th century, the concept of an intellectual quotient expressed as a standard score with a mean of 100 and a standard deviation of 15 also initiated the era of quantitative descriptions of mental and emotional functioning (Anastasi, 1968; Stern, 1912). Other descriptive statistical metrics were applied to human measurement, including scaled, percentile, T-score, and z-score statistics. Statistical measures became part of the assessment lexicon and each possessed strength as well as weakness for descriptive purposes, but together proved to be immensely effective for communicating test findings and inferring average and above or below the norm performances. In turn, descriptive statistical methods became the cornerstone for describing neuropsychological findings, typically reported by domain of functioning (memory, excutive, language, etc.; Cipolotti & Warrington, 1995; Lezak, Howieson, Bigler, & Tranel, 2012). As much as psychology and medicine have incorporated descriptive statistics into research and clinical application, a major focus of both disciplines also has been binary classification—normal versus abnormal. This dichotomization recognizes some variability and individual differences within a test score or laboratory procedure, but at some point the clinician makes the binary decision of normal or abnormal. In the beginnings of neuroimaging, which are discussed more thoroughly below, interpretation of computed tomographic (CT) or magnetic resonance imaging (MRI) scans mostly was approached in this manner. Although lots of information was available from CT and MRI images, if nothing obviously abnormal was seen, the radiological conclusion merely stated in the Impression section, “Normal CT (or MRI) of the brain,” with no other qualification (or quantification) of why the findings were deemed normal other than the image appeared that way. Until recently, quantification of information in an image required hand editing and was excruciatingly time consuming.

Keywords:   Block Design, Desikan approach, NeuroQuant software, body temperature, brainstem, computed tomography (CT), deformation mapping, finger oscillation test, frontal impairment, gyrification, hippocampus, magnetoencephalography (MEG), subcortical volume, sulcal depth measurements, thermometers, vertex-wise measurements, voxel-based morphometry, white matter volume

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