Cognitive and neuropsychological examination of the elderly

Handbook of Clinical Neurology, Vol. 167 (3rd series) Geriatric Neurology S.T. DeKosky and S. Asthana, Editors https://doi.org/10.1016/B978-0-12-804766-8.00006-6 Copyright © 2019 Elsevier B.V. All rights reserved

Chapter 6

Cognitive and neuropsychological examination of the elderly ELIANA PASTERNAK AND GLENN SMITH* Department of Clinical and Health Psychology, University of Florida, Gainesville, FL, United States

Abstract Neuropsychological assessment plays a prominent role in the evaluation and care of patients with neurodegenerative diseases throughout the dynamic course of disease. As a biomarker of disease, neuropsychological measurement can distinguish normal from pathologic aging processes. Further, neuropsychological data can help distinguish and classify underlying pathologies in dementing diseases, augmenting imaging and biofluid markers in this area. Neuropsychological data can predict increased or reduced risk for dementia conferred by multiple factors, and describe disease trajectory in affected individuals. Cognitive evaluation can also estimate and address functional outcomes that are most important to patients and their loved ones and that are clinically relevant to diagnostic staging. In informing intervention and patient care needs, areas of cognitive weakness highlight targets for support/intervention, while areas of cognitive strength can be capitalized upon to modify the clinical course of disease. These functions can be accomplished through the complementary use of brief screening tools and comprehensive test batteries. However, for neuropsychological data to serve these functions, it is critical to understand neuropsychological test properties and nondisease factors that can account for variance in test performance. This chapter concludes with directions for future research.

With advances in our understanding of the dynamic course of dementia development, we now understand that brain changes are occurring in individuals with neurodegenerative disease years before the first clinical manifestations of disease become evident (Jack Jr. et al., 2010). The current state of science allows for the detection of neurodegenerative brain changes via neuroimaging techniques such as structural magnetic resonance imaging (MRI) (Schoonenboom et al., 2008) and positron emission tomography (PET) scanning with an amyloid binding tracer (Klunk et al., 2004), and assays of biomarkers such as the amyloid fragment bamyloid (Ab42), tau181,or phosphorylated tau (p-tau) in cerebrospinal fluid (CSF) or serum (Clark et al., 2003; Schoonenboom et al., 2008; Shaw et al., 2009). Indeed, the field has become increasingly focused on the role of biomarkers in detection and diagnosis. This is evident in the 2011 publications of the NIA-Alzheimer’s

Association (NIA-AA) workgroups on revising the diagnostic criteria for dementia due to Alzheimer’s disease (AD; McKhann, 2011), mild cognitive impairment (MCI; Albert et al., 2011), and preclinical AD (Sperling et al., 2011). It is also explicit in the absence of cognitive measurement from the 2018 NIA-AA proposed framework for defining/diagnosing AD in research studies (Jack Jr. et al., 2018). With these proposals to permit the diagnosis of neurodegenerative conditions in advance of measurable cognitive changes (Jack Jr. et al., 2011, 2018; Sperling et al., 2011), have come related questions of the utility of neuropsychological measurement in neurodegenerative disease. As we will describe, neuropsychological biomarkers continue to serve a critical function in the detection, evaluation, diagnosis, and treatment of neurodegenerative disease. In this chapter, we will summarize the roles of neuropsychological measurement in preclinical and clinical

*Correspondence to: Glenn Smith, Ph.D., ABPP-CN, Elizabeth Faulk Professor and Chair, Department of Clinical and Health Psychology, 1225 Center Drive, RM 3154, P.O. Box 100165, University of Florida, Gainesville, FL, United States. Tel: +1-352-2736556, Fax: +1-352-273-6156, E-mail: [email protected]

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dementia populations. We will then discuss current best practice models in screening and comprehensive testing approaches, and understanding and optimizing the function of neuropsychological measures in an older adult population. We will conclude with a discussion of cross-cultural evaluation considerations, and the importance of quality of life assessment in older adults with neurodegenerative disease.

UTILITY OF NEUROPSYCHOLOGICAL MEASUREMENT IN PRE/CLINICAL DEMENTIA As discussed elsewhere (Fields et al., 2011; Smith and Bondi, 2013), neuropsychological measurement plays an important role in preclinical and clinical dementia evaluation and care in (1) serving as a biomarker for disease; (2) predicting near term development of cognitive impairment and dementia; (3) dynamically capturing countervailing influences on disease trajectory; (4) measuring functional abilities; and (5) identifying targets for intervention. We will briefly discuss these points here.

Neuropsychological measures as biomarkers Neuropsychological measures meet all criteria for a biomarker as defined by the NIH as “a characteristic that is objectively measured and evaluated [criteria 1]; as an indicator of normal biologic processes [criteria 2]; pathogenic processes [criteria 3]; or pharmacologic responses to a therapeutic intervention [criteria 4]” (Biomarkers Definitions Working, 2001). The highly standardized procedures of neuropsychological assessment and strong reliability of neuropsychological data (Lezak et al., 2004; Busch et al., 2006) most certainly meet the criteria for “objective measurement” [criteria 1]. Additionally, cognitive measures have long served as objective outcome measures in clinical trials [criteria 4], as illustrated by the FDA’s support for the use of cognition as an outcome measure in clinical trials (FDA, 2018) and as a requirement for a coprimary cognitive outcome measure in studies seeking to demonstrate drug efficacy in dementia (Leber, 1990). Neuropsychological data have also been shown to indicate both normal (Eyler et al., 2011; Edmonds et al., 2015b; Toepper, 2017) and pathologic processes [criteria 2 and 3] (Ferman et al., 2006; Powell et al., 2006; Chapman et al., 2010; Weissberger et al., 2017). This is supported by a large body of literature demonstrating concordance of neuropsychological data with age-related changes in brain structure and function; studies using principle components and discriminate function analyses showed nearly 90% accuracy in classifying individuals as cognitively normal vs in the early stages of AD using

neuropsychological measurement (Chapman et al., 2010). A recent metaanalysis (Weissberger et al., 2017) demonstrated neuropsychological measures of memory to be highly sensitive and specific in differentiating between patients with AD and healthy elderly controls. Further, the sensitivity and specificity ranges of memory measures were comparable if not superior to published fluid and imaging biomarker data—80%–90% sensitivity and 82%–90% specificity of validated CSF and imaging biomarkers in differentiating AD from nondemented controls (Bloudek et al., 2011). Research has also demonstrated concordance of neuropsychological data with classification of pathological processes; an optimized neuropsychological score obtained at enrollment into an Alzheimer’s Disease Research Center (ADRC) cohort had adequate sensitivity and specificity for neuropathological findings obtained on autopsy an average of 5.5 years later according to the National Institute on Aging (NIA)-Reagan Institute Criteria for the Neuropathological Diagnosis of Alzheimer Disease (NIA-Reagan) diagnostic criteria and Braak neurofibrillary pathology score (Powell et al., 2006). In this context, combining neuropsychological measurement with other relatively inexpensive biomarkers (e.g., MRI) may mitigate the need for more expensive assays (e.g., aPET) (Goryawala et al., 2015). Further, as a biomarker for neurodegenerative disease, neuropsychological data is sensitive and specific in detecting diseases with different regional and functional pathophysiology. Since dementia-associated pathologies have affinities for specific brain regions, neuropsychological findings can help to distinguish the underlying pathology in dementing diseases based upon the observed pattern of cognitive deficits (Fig. 6.1). For example, neuropsychological data can help distinguish patients with AD and Dementia with Lewy Bodies (DLB) in that patients with AD show worse memory and naming performance upon cognitive exam, while patients with DLB show worse attention and visuospatial perception (Marui et al., 2004; Cagnin et al., 2015). Neuropsychological findings can also contribute to differentiation of dementia syndromes that share similar pathology but differ in clinical/phenotypic presentation (Ferman et al., 2006). As is the case with AD and DLB, pathologies in dementia often cooccur (Marui et al., 2004; Walker et al., 2015; Irwin and Hurtig, 2018; Robinson et al., 2018). The cooccurrence of AD pathology in DLB may thus explain the worse anterograde memory impairment seen in patients with DLB that is not typically seen in patient with PDD, despite shared Lewy body disease pathology underlying both DLB and PDD (Boeve, 2009; Fields et al., 2011). AD and cerebrovascular pathology frequently cooccur and underlie the majority of cases of dementia in older adults (Reed et al., 2007; Kapasi et al., 2017). The presentation

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Fig. 6.1. Representative neuropsychological profiles for patients with different autopsy-confirmed forms of dementia. Premorbid z-scores represent estimated intellectual functioning before disease onset. Abbreviations: AVLT LEI, Rey Auditory Verbal Learning Test learning efficiency index; AVLT PCT, Rey Auditory Verbal Learning Test 30 min delay divided by Rey Auditory Verbal Learning Test trial 5; Boston, Boston Naming Test; CAT, Category Fluency Test; COWAT, Controlled Oral Word Association Test; DRS, dementia rating scale; LMI, Wechsler memory scale revised logical memory immediate recall; LM PCT, Wechsler memory scale revised logical memory delay divided by LMI; PO, perceptual organization; TMTA, Trail Making Test A; TMTB, Trail Making Test B; VC, verbal comprehension. Reprinted from Fields, J.A., Ferman, T.J., Boeve, B.F., et al., 2011. Neuropsychological assessment of patients with dementing illness. Nat Rev Neurol 7, 677–687, with permission from Springer Nature.

of vascular dementia is particularly heterogeneous due to differences in extent and location of infarctions, further complicating differential diagnosis. Neuropsychological data may help differentiate the two in that patients with subcortical vascular dementia are more likely to exhibit deficits in executive function, phonemic fluency, attention, and working memory, while patients with AD are more likely to exhibit cortical dysfunction such as episodic memory and naming impairments (Desmond, 2004; Reed et al., 2007; Lamar et al., 2008). This distinguishes the utility of neuropsychological biomarkers, as other disease biomarkers [MRI, Fluorodeoxyglucose (FDG) PET, CSF total tau] are nonspecific indicators of damage that may derive from a variety of etiologies; CSF tau levels, for example, cannot distinguish between AD and frontotemporal dementia. Thus it may ultimately prove short-sighted that cognition was not included in a

proposed descriptive system for categorization of multidomain biomarkers of AD pathology (i.e., the “A/T/N” Amyloid, Tau, and Neurodegeneration/neuronal injury system) (Jack Jr. et al., 2016).

Predicting disease development Neuropsychological biomarkers may serve as early predictors for incipient dementia (Jak et al., 2016; Oltra-Cucarella et al., 2018), with multiple studies demonstrating that neuropsychological measures predict progression from MCI to AD with comparable accuracy to neuroimaging and CSF biomarkers (Landau et al., 2010; Ewers et al., 2012). A metaanalysis based on over 9000 controls and 1200 preclinical AD cases showed large effect sizes for neuropsychological measurement of episodic memory in distinguishing individuals who

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Fig. 6.2. Dynamic biomarkers of Lewy body pathological cascade. Reprinted from Smith, G.E., Bondi, M.W., 2013. Mild cognitive impairment and dementia: definitions, diagnosis, and treatment, Oxford University Press, with permission from Oxford University Press.

progress to AD from those who do not (Backman et al., 2005). Contrasting with the notion of the supposed late appearance of cognitive changes in AD (Jack Jr. et al., 2010), this metaanalysis also demonstrated that multiple brain functions are also affected long before the AD diagnosis (Backman et al., 2005). In synucleinopathies in particular, cognitive change may be more readily detected than any current structural or physiologic neuroimaging marker in predicting disease development (Fig. 6.2). In addition to predictive risk based upon negative deviance on cognitive measures, positive deviance on neuropsychological measures can identify reduced risk of dementia development; the roughly twofold risk associated with advanced age and family history of dementia is neutralized when memory retention performance is one standard deviation above the mean on neuropsychological measurement (Fig. 6.3) (Locke et al., 2009). This permits greater refinement in selection for prevention trials, or referral for more expensive diagnostic tests. Thus, neuropsychological data can be used to titrate medical surveillance and resources consistent with precision medicine principles.

Describing disease trajectory Contrary to the assumption that cognition steadily declines after a critical inflection point is reached, studies have demonstrated that some aspects of mental functioning (notably memory) include a period of stabilization following initial decline that could reflect physiological and/or neuropsychological compensatory mechanisms

(Twamley et al., 2006; Smith et al., 2007). Indeed, after initial memory decline, functional MRI (fMRI) studies have demonstrated the compensatory recruitment of brain regions less affected by neuropathology, associated with a period of stabilized memory performance (Bookheimer et al., 2000; Bondi et al., 2005; Han et al., 2007). These findings imply that findings of mild but stable memory impairment over the course of several years should not be assumed to indicate the absence of neurodegenerative disease or pathologic progression. Just as critically, observation of stable memory function over time in clinical trials should not be interpreted de facto as a response to intervention. Notably, this plateau seen in memory performance does not occur in all cognitive domains, and processes of monotonic decline in verbal comprehension, perceptual organization, learning, and attention occur during this period of stabilized memory retention (Smith et al., 2007). Thus, assessment of other cognitive domains in addition to memory function is critical to accurate assessment of disease progression (Smith and Bondi, 2013). In describing disease trajectory, it is critical to recognize the significant effects of cognitive reserve (i.e., the ability of the brain to actively cope with brain damage through the use of existing or compensatory processes to support brain function) on the association/relationship between disease burden and cognitive function biomarkers (Stern, 2002, 2009). At equivalent levels of noncognitive biomarker abnormalities, high reserve individuals demonstrate better cognitive function for a longer period of time before clinical diagnostic threshold is reached (Fig. 6.4; (Vemuri et al., 2011; see Stern (2009) for comprehensive review of reserve theories).

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Fig. 6.3. Interaction of age, family history, and memory performance in predicting 5-year risk of cognitive impairment. Abbreviations: AVLT, Auditory Verbal Learning Test percentage retention scaled score; RR, relative risk scaled score. Reprinted from Locke, D.E., Ivnik, R.J., Cha, R.H., et al., 2009. Age, family history, and memory and future risk for cognitive impairment. J Clin Exp Neuropsychol 31, 111–116 with permission from Taylor and Francis.

Estimating functional status Cognitive measures address the functional outcomes that are most important to patients and their families (Jack Jr. et al., 2010; Barrios et al., 2016; Smith et al., 2018), such as the patient’s ability to take their medications, manage their finances, and shop and cook, whereas physiologic and imaging biomarkers do not. The presence of functional impairment is key to determining the diagnostic threshold between MCI and clinical dementia (Petersen et al., 1999). However, there is no accepted operational definition or threshold for what constitutes functional decline, and information about functional status is generally underutilized (Bangen et al., 2010; Chang et al., 2011). Together, this diminishes the utility of fluid and imaging biomarkers in studying the MCI construct and predicting progression to dementia. Estimating functional status is also critical to ensure an appropriate level of assistance is being provided, and to help monitor for safety. Functional outcomes are particularly relevant to the neuropsychological

assessment as many older adults are living alone at the time of their assessment, making an informant’s estimate of the patient’s functional status less reliable (Smith et al., 2001). In this context, neuropsychologists are increasingly being asked to answer questions about functional capacity and the effects of cognitive deficits on everyday function (Marcotte et al., 2009). Functional decline is predicted by poor baseline cognitive performance and cognitive measures can serve to estimate functional status (Fields et al., 2010; McAlister et al., 2016). The ability to carry out everyday tasks is most dependent on the measurable integrity of memory and executive functions (Tomaszewski Farias et al., 2009; McAlister and Schmitter-Edgecombe, 2016; Overdorp et al., 2016). Moreover, the commonly used Dementia Rating Scale (DRS; Smith et al., 1994a) allows for the estimation of specific types of functional impairments likely present based on an individual’s cognitive performance. This is based on a pragmatic analysis of the patient’s level of dysfunction across a range of activities of daily living (ADLs) including preparing food, washing and grooming, managing finances, driving,

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Fig. 6.4. Effect of cognitive reserve on the relationship between biomarkers and cognitive function/clinical disease stage. Subjects with (A) low, (B) average, and (C) high cognitive reserve. At an equivalent level of biomarker abnormalities people with high cognitive reserve have better cognitive function and thus greater time to clinical diagnostic threshold. Reprinted from Vemuri, P., Weigand, S.D., Przybelski, S.A., et al., 2011. Cognitive reserve and Alzheimer’s disease biomarkers are independent determinants of cognition. Brain 134, 1479–1492 with permission from Oxford University Press.

and others (Fields et al., 2010). Additional estimates of functional status include self- and informant reports, (although these are subject to response bias; Kalbe et al., 2005; Siafarikas et al., 2018), and performancebased measures (see Moore et al. 2007 for review). Of

note, neuropsychiatric symptoms of depression and apathy are common in older adults with MCI and dementia (Okura et al., 2010) and have also been associated with functional decline early in disease (Lyness et al., 2007). Importantly, the time during which an older adult

COGNITIVE AND NEUROPSYCHOLOGICAL EXAMINATION OF THE ELDERLY remains independent in instrumental ADLs provides a critical opportunity for intervention.

Identifying targets for intervention Information about neuropsychological impairments can highlight weak or impaired domains that should be targeted for support and intervention. Perhaps even more critical, though, is that neuropsychological measurement just as readily identifies areas of cognitive strength and preserved ability. These can and should be capitalized upon to modify the clinical course of disease, preserve function, and limit or prevent dementia and associated morbidity. This is illustrated in research on intervention models for individuals with amnestic MCI that capitalize upon relatively spared procedural memory (which includes the ability to form new habits) in the face of deficits in declarative memory and other areas (Greenaway et al., 2013).

SCREENING AND COMPREHENSIVE TESTING APPROACHES Standardized assessment tools can be used to provide an objective baseline of an individual’s cognitive, behavioral, and functional symptoms associated with dementia, and to monitor symptom progression over time. This can be accomplished through the complementary use of brief screening tools and comprehensive test batteries.

Brief screening Brief cognitive screening tools can be useful in a clinical setting for identifying individuals with cognitive symptoms, staging symptom severity, and tracking progression of symptoms and response to treatment over time. Commonly used screening tools include the Mini-Mental State Examination (MMSE) (Folstein et al., 1975), the Montreal Cognitive Assessment (MoCA) (Kokmen et al., 1975; Nasreddine et al., 2005), and the Clinical Dementia Rating (CDR) scale (Morris, 1993). The MMSE is generally considered a gold standard in clinical practice guidelines and clinical trials for dementia (Arevalo-Rodriguez et al., 2013). It is often used to screen for cognitive impairment and identify individuals in need of more comprehensive evaluation. The MMSE provides brief and basic screening of orientation, attention and concentration, language, visuospatial construction, and memory skills on a 30-point scale (Folstein et al., 2010). At the suggested cut-score of 23 the MMSE had a pooled sensitivity of 88% and a pooled specificity of 86% for detecting dementia in primary care settings (Lin et al., 2013). The test was initially designed to identify impairment in inpatient geriatric settings (Folstein

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et al., 1975). Accordingly, it has shown a ceiling effect (Tombaugh and McIntyre, 1992) for individuals who exhibit mild cognitive symptoms; this limits its utility in detecting cognitive symptoms in community and primary care settings (Hsu et al., 2015), very early in the course of disease, and in individuals with high premorbid function and advanced education (Alves et al., 2013). Conversely, the MMSE has been shown to overestimate impairment in racial and ethnic minority groups and those with low education and literacy (Bohnstedt et al., 1994; Jones and Gallo, 2001). Thus, given its susceptibility to ceiling effects and sociodemographic factors, the MMSE should not be used in isolation to definitively diagnose or rule out dementia. The MoCA is more challenging than the MMSE and is frequently used to screen for mild forms of cognitive impairment. The MoCA had better sensitivity and specificity than the MMSE in detecting MCI and mild dementia (Arevalo-Rodriguez et al., 2015; Trzepacz et al., 2015; Tsoi et al., 2015), and has more tests of executive function. The MoCA may also be useful in identifying nonamnestic forms of MCI and dementia, such as behavioral variant frontotemporal dementia (bvFTD) (Coleman et al., 2016) and Parkinson’s disease (Marras et al., 2013). The CDR scale is a 0–3 point numeric scale derived from clinician rating of cognition and daily function in the domains of memory, orientation, judgment and problem solving, community affairs, home and hobbies, and personal care (Morris, 1993). It is used extensively in research studies and clinical practice to stage dementia severity and monitor disease progression over time (Morris, 1993). As the CDR was initially developed with a relatively narrow focus on the symptoms associated with a classic AD presentation, it has become one of the most reliable and well-validated measures of severity of dementia due to AD (Morris, 1993). Since its development, a version of the CDR has been modified to include domains of language, behavior, comportment, and personality to capture a range of symptoms beyond memory loss associated with less common dementia types (Knopman et al., 2011).

Comprehensive testing More comprehensive evaluation generally involves the assembly of a battery of tests, each one measuring a distinct construct or ability with greater sensitivity and specificity than a screening tool. The evaluation typically includes a clinical interview and targeted assessment of major cognitive domains: intellectual function, attention and concentration, processing speed, higher-order executive function, learning and memory, language, visuospatial/ perceptual, motor skills, and mood/psychiatric symptoms.

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Fig. 6.5. Cognitive domains underlying commonly used neuropsychological measures. The figure illustrates the neuropsychological measures used to assess 5–7 broad cognitive domains (top row of boxes) probably measured by standard clinical batteries. Abbreviations: AVLT, Rey Auditory Verbal Learning Test; BNT, Boston Naming Test; BVFD, Benton Visual Form Discrimination; COWAT, Controlled Oral Word Association Test; Rey–O, Rey–Osterrieth complex figure copy; WRAT-3, Wide Range Achievement Test (reading), 3rd edition. Reprinted from Smith, G.E., Bondi, M.W., 2013. Mild cognitive impairment and dementia: definitions, diagnosis, and treatment, Oxford University Press, with permission from Oxford University Press.

The entire exam can thus take up to several hours. Knowledge of an individual’s specific pattern of strengths and weaknesses across domains, referred to as the neurocognitive profile, can then inform syndromic and etiologic differential diagnosis and individualized treatment targets, as discussed above. For full discussion of assessment techniques and test selection, please see Smith et al. (2008).

UNDERSTANDING AND OPTIMIZING NEUROPSYCHOLOGICAL MEASURES A precondition to the utility of neuropsychological measures as biomarkers in pre/clinical dementia evaluation and care is understanding and optimizing their function. This includes psychometric test properties, as well as noncognitive factors that influence test performance. Psychometric optimization of neuropsychological testing in older adults involves a proper understanding of what a cognitive test is measuring (construct validity), the temporal dynamics of test constructs (test stability), and mitigating extraneous sources of variance through the use of demographically adjusted norms.

Construct validity The primary utility of any test depends upon the extent to which it actually measures the construct it purports to

measure. This is studied by statistically analyzing shared and distinct factors that underlie test performances (Smith and Ivnik, 2003). A series of studies (Smith et al., 1994b; Pedraza et al., 2005; Greenaway et al., 2009) looking at a common set of neuropsychological measures collected in cognitively normal older adults demonstrated that the standard clinical battery likely measures 5–7 broad cognitive domains (Fig. 6.5). This includes assessment of verbal and nonverbal IQ, attention, executive function, motor speed, and the separation of memory into two distinct domains of learning (i.e., encoding or immediate recall) and retention (i.e., delayed recall). The separation of these memory domains is critical as, in a study of individuals with amnestic MCI, a great deal of variability was introduced when they were treated as equivalent (Smith, 2002).

Test stability As mentioned, serial neuropsychological assessment can be helpful in refining diagnosis and indicating disease progression. Within this context, it is critical to determine what constitutes a clinically meaningful change in test performance (Attix et al., 2009; Stein et al., 2010). In an older adult population in particular, physical and cognitive changes associated with normal aging, and concomitant medical and mental health conditions, can influence assessment results. Further, prior assessment

COGNITIVE AND NEUROPSYCHOLOGICAL EXAMINATION OF THE ELDERLY “exposure” or “practice effects” can influence test performance when reassessment occurs over a short span of time. In one study, Ivnik et al. (1999) involving roughly annual assessments of cognitively normal older adults, participants exhibited an initial rise in performance scores at the second time point, then tended to stabilize in subsequent serial assessments. This suggests that in assessments conducted at regular clinical intervals (i.e., roughly annually), any practice effect is based on exposure to the procedures more than the content of the tests. In other words, the examinee learns that they will be expected to match a visual pattern with stimuli, or later recall the list of words being read to them but they do not actually encode the visual patterns or the words. Importantly, though, different cognitive constructs show different longitudinal variances. Over 3–4 years, only 2% of participants showed a 1 standard deviation (SD; mean of 100, SD of 15) change on a verbal comprehension task. In contrast, nearly 25% of the sample exhibited a 1 SD (mean of 100, SD of 15) change on an index of memory retention. Thus a 1 SD change on memory test cannot be assumed to be clinically significant but a similar change on a verbal comprehension test can be interpreted as such (Ivnik et al., 1999). This illustration of the different temporal stabilities of different cognitive measures implies the importance of longitudinal and cross-sectional norms, and suggests that applying a single change threshold across tests is problematic (Fields et al., 2011). Proper understanding of these concepts in test stability is critical to the design of clinical trials and valid interpretation of serial clinical assessments.

Demographically corrected norms Particularly when an individual’s cognitive baseline performance is not available for direct comparison (as is commonly the case), demographically corrected norms are used to account for variance in cognitive performance attributable to demographic factors. This most commonly includes adjustment for age, education, and sometimes gender. More recently there has been a proliferation of research demonstrating the importance of adjusting for culture as well (using ethnicity as proxy) (Corriveau et al., 2017; Daugherty et al., 2017; Freedman and Manly, 2018; Babulal et al., 2019). Demographically corrected norms are critical to the diagnostic utility of neuropsychological data (Smith and Ivnik, 2003) since norms increase specificity (i.e., the true negative rate), and specificity contributes to the enhanced positive predictive value (PPV) of cognitive tests. PPV refers to the probability that a person with an abnormal test score will have a neurodegenerative disease. There is some debate that accounting for agerelated variance undermines test sensitivity (i.e., the true

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positive rate). However, since age is a major risk factor for dementia (O’Connell and Tuokko, 2010), optimization of PPVs (through enhanced specificity using norms) is arguably more critical (Smith and Ivnik, 2003; Fields et al., 2011; Smith and Bondi, 2013). It is increasingly recognized that the development of cognitive measures and norms in specific majority cultures has resulted in cultural bias that has limited the diagnostic utility of these tests in cross-cultural applications (Farias et al., 2004; Heaton et al., 2004; Boone et al., 2007; Heaton et al., 2009). Many groups are now trying to improve the performance of neuropsychological measures across cultures by developing ethnicity-specific norms (Lucas et al., 2005; Norman et al., 2011; Casaletto et al., 2015; O’Bryant et al., 2018). Use of ethnicity-specific norms developed for African American populations yielded greatly improved PPV for cognitive diagnosis as compared to the use of Caucasian norms (Lucas et al., 2005). Indeed, the diagnostic utility of neuropsychological assessment is enhanced when variance due to demographic factors is accounted for (Heaton et al., 2009). However, the cross-cultural application of cognitive tests is complex and factors in addition to ethnicity must be considered (Marcopulos et al., 1997).

CROSS-CULTURAL CONSIDERATIONS IN THE NEUROPSYCHOLOGICAL EXAMINATION OF OLDER ADULTS Epidemiological studies of older adults in the United States have shown higher prevalence and incidence of AD and other dementias in African American and Hispanic older adults than in non-Hispanic White older adults (Babulal et al., 2019; Matthews et al., 2019). It has been suggested that these observed differences in rates of dementia may be explained partly by neuropsychological measurement factors (Manly and Espino, 2004; Freedman and Manly, 2018). With regard to the cognitive exam itself, patient factors including culture, language, measurement bias, and racial differences in cardiovascular disease burden and presentation to memory clinics must be considered in the neuropsychological examination of older adults.

Culture Culture is a critical consideration in the assessment of racial and ethnic minorities. Even after correction for age, education, and sex, substantial differences in neuropsychological test performance exist between cognitively normal older adults from minority populations and those from white European ethnic groups (Farias et al., 2004; Boone et al., 2007). Cultural factors including perceptions of discrimination and acculturation level

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have been shown to impact test performance in African American and Hispanic older adults (Manly et al., 2004; Thompson and Gregory, 2011; Barnes et al., 2012). To illustrate the later, performance on the Boston Naming Test was associated with acculturation variables of the age at which English was first learned, years educated in the United States, and years residing in the United States (Boone et al., 2007). Further, aspects conspicuous to particular cultures may have unique effects on the evaluation process, including the interpersonal dynamic and the type of information disclosed (Pedraza, 2018).

Language and bilingualism Language proficiency is very important to consider in the scoring and interpretation of language-based neuropsychological tests. Bilingual adults may demonstrate varying degrees of proficiency and nonbalanced or mixed skills across different areas of language function (i.e., better reading in one language and better speaking in the other language) (Pedraza, 2018). In the context of neuropsychological assessment, a bilingual patient may use one language for some tasks, but revert to their other language for other tasks. Further, while bilingualism may promote cognitive resources that confer protection in aging (Bialystok et al., 2012; Kroll et al., 2014; Kroll et al., 2015), bilingualism has also been associated with reduced lexical knowledge in each language (Bylund et al., 2019). Upon testing, this may affect the number and accuracy of generated responses on semantic fluency and visual naming tasks, even if responses are allowed in either language (Gollan et al., 2007; Rosselli et al., 2014). These factors are critical for consideration across referral types, and particularly on language-based measures and when a languagepredominant neurodegenerative syndrome (i.e., a primary progressive aphasia) is a consideration in the differential diagnosis.

Measurement bias Observed differences in rates of dementia among minority groups may in part reflect assessment bias, resulting from cross-cultural application of a test instrument, rather than true disparity (Manly et al., 2004; Manly and Espino, 2004; Freedman and Manly, 2018). Bias refers to the presence of systematic error among individuals who have the same underlying trait or ability such that between-group performance differences are due to unwanted artifact (American Educational Research Association, American Psychological Association, National Council on Measurement in Education, 1999; He and van de Vijver, 2012). Sources of bias can be present at the test construct, method, and item levels (He and van de Vijver, 2012). Construct bias arises when a

measured construct, for example, intelligence, is not identical across cultural groups. Methodological bias can be introduced when there is bias in the sample, the instrument, and/or the administration. At the item level, bias refers to the presence of discrepant probability of providing a correct item level response among individuals from different groups, despite equivalence on the trait or ability being measured. This can be caused by poor translation or variance in familiarity with an item by cultural group (Pedraza, 2018).

Normative reference data Availability of appropriate norms should be considered in test selection. However, perfectly appropriate norms may not always be available. In such cases, performance scores should be interpreted cautiously and the relevant documentation provided in the clinical report. Even in the case of appropriate reference groups, there should be recognition that performance differences related to factors such as region and educational quality that are not accounted adequately for by norms. For example, research has demonstrated educational attainment to be a poor surrogate for educational experience/quality among African Americans (VanderWeele and Robinson, 2014; Freedman and Manly, 2018).

Racial differences in presentation Numerous medical factors may contribute to the observed differences in rates of dementia. Most notably, increased cardiovascular disease burden and earlier age of cardiovascular disease onset, combined with racial inequalities in treatment, may contribute to increased cerebrovascular disease burden (Brickman et al., 2008; Reitz et al., 2009; Shepardson et al., 2011). Thus, examination of structural brain imaging should be a fundamental aspect of a comprehensive evaluation. In additional to disparities in medical factors, racial and ethnic minorities are less likely to present to memory disorder clinics, less likely to be referred for a dementia assessment, and are more likely to be diagnosed at later stages of dementia (Dilworth-Anderson et al., 2012). Further, ethnic minorities who do present to clinics are more likely to have neuropsychiatric symptoms than Whites (Salazar et al., 2017; Babulal et al., 2019). Unfortunately, African American and Hispanic older adults tend to be underrepresented in dementia research programs that attempt to explain some of these disparities (Taussig and Talmi, 2001). Therefore, culturally appropriate engagement and recruitment is necessary for clinical and research evaluations among African American and Hispanic older adults (McDougall Jr. et al., 2015; Freedman and Manly, 2018).

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ASSESSMENT OF QUALITY OF LIFE It is critical to address QOL in any clinical or research assessment encounter with an older adult population (Barrios et al., 2016; Smith et al., 2018). As cognitive and functional abilities are lost, older adults with neurodegenerative disease become unable to engage in many of the activities that once gave them a sense of purpose and pleasure (Logsdon et al., 1997). Individuals who experience earlier onset of neurodegenerative changes may face additional challenges related to employment and familial, social, and civic engagement. Further, depression and apathy are common in individuals with neurodegenerative disease and are often intertwined with impairments in daily functioning and related inactivity (Okura et al., 2010; Burton et al., 2018). Each of these factors has a negative impact on QOL, and their confluence makes individuals with neurodegenerative disease particularly vulnerable to reduced QOL (Logsdon and Teri, 2018). As reviewed by Logsdon and Teri (2018), QOL assessment is an important means for individuals and their caregivers to identify factors that are important to their QOL, and find ways to maintain QOL over time. QOL assessment can also evaluate effects of intervention and guide decisions about clinical significance. A variety of approaches have been developed to assess QOL in dementia; the most common are self- and informant report measures. Although self-report measures require that the individual be able to reliably comprehend and respond to questions, this approach is most in line with ethical and clinical guidelines that emphasize the individual’s autonomy and preferences (Fitzpatrick et al., 1998). Informant-report is typically obtained from a family member or caregiver who is familiar with the day-today experiences of the person being assessed, and can be useful particularly in later stages of disease when a reliable self-report cannot be obtained (Rabins and Kasper, 1997). Even then, it is important to bear in mind that caregivers consistently rate QOL lower than the selfreported rating from the individual themselves, and informant ratings are influenced by the affected individual’s mood, activity level, and functional ability as well as by their own levels of depression and burden (Sands et al., 2004; Vogel et al., 2006; Dewitte et al., 2018; Romhild et al., 2018). As a less common alternative, observational measures focus on behavioral indications of affective state and comfort levels, and have been used primarily in residential care settings where individuals with more advanced dementia may struggle to express themselves verbally (Brooker, 2005; Curyto et al., 2008). Assessment of QOL can inform appropriate support and treatment. A range of psychosocial treatment options have demonstrated efficacy in directly or indirectly

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improving QOL for individuals with dementia. These options include behavioral and problem-solving therapy to reduce depressive symptoms and increase activity, caregiver education, and occupational therapy interventions to enhance ADL functioning (Gitlin et al., 2014; Woods et al., 2014; Logsdon and Teri, 2018). Other promising approaches that require additional research include support groups and community-based programs for individuals with mild disease (Logsdon and Teri, 2018).

SUMMARY AND CONCLUSION Neuropsychological assessment continues to have a unique and prominent role in the evaluation and care of patients with neurodegenerative diseases throughout the disease course. Negative and positive deviance on neuropsychological biomarkers contributes to estimation of risk, detection of disease, pathologic and syndromic differential diagnosis, monitoring of disease trajectory, and prediction of functional abilities. Neuropsychological measures can also play important roles in the design of interventions and clinical trials, and interpretation of their effects. Thus, neuropsychological test findings continue to contribute importantly to diagnostic accuracy and treatment methods to reduce morbidity and slow cognitive and functional decline in patients with neurodegenerative disease. Looking forward, continued development of ethnicityspecific norms and culturally unbiased measures will enhance the application and utility of neuropsychological tests among minority older adult groups, a growing population in the US. Further, research on specific MCI subtypes and operational definitions of functional decline will enhance the utility of the MCI construct in studying biomarkers and predicting risk of progression (Edmonds et al., 2015a, b; Parnetti et al., 2019). Finally, emphasis on diagnostic test accuracy statistics (vs null hypothesis testing) for individual neuropsychological measures (vs composites of multiple measures) will further enhance the predictive utility of neuropsychological measurement (Weissberger et al., 2017).

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