Introduction
Since the publication of criteria for mild cognitive impairment (MCI) by Petersen et al. (Reference Petersen, Smith, Waring, Ivnik, Tangalos and Kokmen1999), there has been an exponential growth in publications focused on MCI (Geda & Nedelska, Reference Geda and Nedelska2012), and it has become one the most highly studied topics in the fields of behavioral neurology and neuropsychology. Initially conceived as a means to describe the borderland between early and mild forms of memory impairment and dementia due to Alzheimer's disease (AD), it has undergone revisions to describe non-amnestic forms of cognitive impairment (Petersen & Morris, Reference Petersen and Morris2005) and to capture non-AD dementia prodromes (e.g., MCI in Parkinson's disease: Tröster, Reference Tröster2011; vascular cognitive impairment: Gorelick et al., 2011; non-amnestic MCI progressing to dementia with Lewy bodies: Ferman et al., Reference Ferman, Smith, Kantarci, Boeve, Pankratz, Dickson and Petersen2013). It has also been revised to capitalize on the latest wave of research investigating the use of biomarkers to improve confidence in the diagnosis of MCI due to AD (Albert et al., Reference Albert, DeKosky, Dickson, Dubois, Feldman, Fox and Phelps2011).
Despite this high volume of research on MCI over the past decade, the concept has been hampered by rather cursory nosologies (e.g., in “non-amnestic” MCI, is it disordered executive function, language, visuospatial skills?), and its operational definitions have been routinely mired in blunt assessment methods. Reliance on single impaired cognitive test scores, simple cognitive screening measures, and measures rating day-to-day function such as the Clinical Dementia Rating (CDR) scale have all likely contributed to inaccuracy and instability in diagnosis (see Smith & Bondi, Reference Smith and Bondi2013). Adding to the poor diagnostic reliability is poor consensus on a uniform set of criteria and MCI diagnosis based on few measures and excessive clinical judgment.
Commenting on the revised 2011 criteria for dementia due to AD, McKhann (Reference McKhann2011) offered that “[t]here are no exact transition points that define when an individual has progressed from the MCI phase to the dementia phase. It is a question of clinical judgment.” However, research consistently shows actuarial methods to be superior to clinical judgment, given the latter method's susceptibility to a host of errors, biases, and occasionally faulty assumptions (see Dawes, Faust, & Meehl, Reference Dawes, Faust and Meehl1989). For example, Saxton et al. (2009) have shown that a neuropsychologically based algorithm for MCI diagnosis better predicted progression than a clinically based method that staged decline via the CDR and which produced more “false positive” diagnostic errors. Chang et al. (Reference Chang, Bondi, McEvoy, Fennema-Notestine, Salmon and Galasko2011) have also found the CDR to be insensitive to severity of cortical thinning as well as impairments in activities of daily living (ADL) in those diagnosed with MCI. Still other studies have used clinical decision-making strategies that assign an individual's lower cognitive test score as out of proportion to their other cognitive scores or to their “expected” level based on educational or occupational attainments (e.g., Jicha et al., Reference Jicha, Parisi, Dickson, Johnson, Cha, Ivnik and Petersen2006). Unfortunately, this clinical judgment rests on a faulty assumption that an individual's abilities are roughly equivalent across cognitive domains, despite evidence that education or IQ explains negligible to modest variance on a variety of memory tests (Delis, Kramer, Kaplan, & Ober, Reference Delis, Kramer, Kaplan and Ober2000; Fastenau, Denburg, & Hufford, Reference Fastenau, Denburg and Hufford1999; Heaton, Taylor, & Manly, Reference Heaton, Taylor and Manly2003; Murayama et al., Reference Murayama, Iseki, Tagaya, Ota, Kasanuki, Fujishiro and Sato2013).
Neuropsychology is uniquely positioned to improve upon this state of the science in MCI research and practice by providing critically important actuarial information on the specific cognitive domains affected by the predominant neurodegenerative disorders leading to dementia as well as on the diagnostic decision-making strategies used in studies. In many cases, neuropsychology provides some of the most valid and reliable distinctions by comparing the patterns and severities of neurocognitive impairment among the dementias (e.g., frontotemporal dementia: Rascovsky et al., Reference Rascovsky, Hodges, Knopman, Mendez, Kramer, Neuhaus and Miller2011; vascular dementia: Gorelick et al., 2011; dementia with Lewy bodies: McKeith et al., Reference McKeith, Dickson, Lowe, Emre, O'Brien and Feldman2005). Outlining the criteria for diagnosing vascular contributions to cognitive impairment and dementia, Gorelick et al. (2011) for example state the “diagnosis of dementia must be based on cognitive testing, and a minimum of 4 cognitive domains should be assessed: executive/attention, memory, language, and visuospatial functions.” [Italics added for emphasis]. Although recent revisions to the criteria for dementia in the DSM-5 (American Psychiatric Association, 2013) or by the NIA-AA (McKhann et al., Reference McKhann, Knopman, Chertkow, Hyman, Jack, Kawas and Phelps2011) encourage the use of neuropsychological assessments, many of these revisions fall short of requiring neuropsychological assessment in their diagnostic schema (e.g., McKhann et al., state that “…either a “bedside” mental status examination or neuropsychological testing…” is sufficient). Consequently, reliability and stability of MCI diagnosis is likely to be diminished when comprehensive neuropsychological assessment is not undertaken.
In this virtual issue, we sample some of the articles published in recent years by JINS that highlight the perils of relying on conventional criteria for MCI diagnosis and that reveal how the reliability of diagnosis is improved when sound neuropsychological approaches are adopted (Brooks, Iverson, Holdnack, & Feldman, Reference Brooks, Iverson, Holdnack and Feldman2008; Clark et al., Reference Clark, Delano-Wood, McDonald, Bangen, Jak, Libon and Bondi2013; Howieson et al., Reference Howieson, Carlson, Moore, Wasserman, Abendroth, Payne-Murphy and Kaye2008; Libon et al., Reference Libon, Eppig, Xie, Wicas, Lippa, Bettcher and Wambach2010). When these requirements are met, we illustrate with a second series of articles that neuropsychological measures associate strongly with neuroimaging and cerebrospinal (CSF) biomarkers in expected patterns and that often reflect pathology beyond or instead of typical AD distributions (Hantke et al., Reference Hantke, Nielson, Woodard, Guidotti Breting, Butts, Seidenberg and Rao2013; Nordlund et al., Reference Nordlund, Rolstad, Klang, Lind, Pedersen, Blennow and Wallin2008; Stricker et al., Reference Stricker, Salat, Foley, Zink, Kellison, McFarland and Leritz2013). Finally, when prerequisite conditions exist, people with MCI may demonstrate mild but identifiable functional difficulties, and a challenge for neuropsychology is how to incorporate this information to better define MCI and delineate it from early dementia (Aretouli, Okonkwo, Samek, & Brandt, Reference Aretouli, Okonkwo, Samek and Brandt2011; Bangen et al., Reference Bangen, Jak, Schiehser, Delano-Wood, Tuminello, Han and Bondi2010; Okonkwo et al., Reference Okonkwo, Griffith, Belue, Lanza, Zamrini, Harrell and Marson2008; Sherod et al., Reference Sherod, Griffith, Copeland, Belue, Krzywanski, Zamrini and Marson2009).
Sound Neuropsychological Approaches Improve MCI Diagnosis
Many if not most MCI studies diagnose participants on the basis of a single impaired test score, the most prevalent of which is an impaired memory score. The Alzheimer's Disease Cooperative Studies group Vitamin E and donepezil trial (Petersen et al., 2005) and the Alzheimer's Disease Neuroimaging Initiative (www.adni-info.org) are but two large-scale examples that routinely use a single impaired memory test score (e.g., delayed recall of Story A of the Wechsler Memory Scale-Revised Logical Memory subtest) in diagnosis. In our first paper to be included in this virtual issue, Brooks et al. (Reference Brooks, Iverson, Holdnack and Feldman2008) demonstrate that reliance on a single impaired memory test score can lead to over-interpretation of low memory scores and thus increase the likelihood of false-positive misclassification. In brief, Brooks et al. (Reference Brooks, Iverson, Holdnack and Feldman2008) demonstrated with the Wechsler Memory Scale-III (Wechsler, Reference Wechsler1997) that 26% of the standardization sample of older adults obtained one or more age-adjusted standard scores at or below 1.5 SDs on the memory tests. A second paper in the series by Howieson et al. (Reference Howieson, Carlson, Moore, Wasserman, Abendroth, Payne-Murphy and Kaye2008) reveals that the common practice of relying on delayed recall of an episodic memory test may miss the earliest manifestations of an evolving dementia unless other neuropsychological functions are assessed. They revealed that not only does verbal episodic memory decline precede the diagnosis of MCI and dementia by at least several years, so too does change in semantic memory and visuospatial skills—and that the trajectory of declines are characterized by unique linear and nonlinear changes (see also Smith et al., Reference Smith, Pankratz, Negash, Machulda, Petersen, Boeve and Ivnik2007). Examination of within-person cognitive change, whether by the change point analyses used by Howieson et al., reliable change indices (e.g., Pedraza et al., Reference Pedraza, Smith, Ivnik, Willis, Ferman, Petersen and Lucas2007) or other methods, will have powerful possibilities to help determine whether change within the individual will better identify trajectories of decline rather than comparisons to group norms.
A third paper in this series by Libon et al. (Reference Libon, Eppig, Xie, Wicas, Lippa, Bettcher and Wambach2010) follows up on the notion that a comprehensive sampling of neuropsychological functions is necessary to better reveal the heterogeneity of cognitive impairments in MCI. In this article, Libon and colleagues employed the use of cluster analysis to statistically determine neuropsychological characterizations of MCI in a clinic-based sample of older adults. This technique provides an actuarial method of identifying homogenous subgroups with similar patterns of neuropsychological dysfunction, and they found evidence for amnestic, dysexecutive, and mixed (impaired memory and language) clusters of MCI participants. These authors have further shown that empirically derived MCI subtypes demonstrate dissociable profiles of forgetting, temporal gradients, interference, and errors (Eppig et al., Reference Eppig, Wambach, Nieves, Price, Lamar, Delano-Wood and Libon2012; Libon et al., Reference Libon, Bondi, Price, Lamar, Eppig, Wambach and Penney2011) that may also reflect distinct underlying neuropathologic substrates. In a fourth paper in this series by Clark et al. (Reference Clark, Delano-Wood, McDonald, Bangen, Jak, Libon and Bondi2013) that also used cluster analysis in a community sample, they found that neuropsychological criteria for MCI diagnosis (see Jak, Bondi, et al., Reference Jak, Bondi, Delano-Wood, Wierenga, Corey-Bloom, Salmon and Delis2009) produced several distinct cognitive phenotypes (e.g., amnestic, dysexecutive) similar to that of the Libon et al. (Reference Libon, Eppig, Xie, Wicas, Lippa, Bettcher and Wambach2010) study, and with differing degrees of severity of cognitive impairment. Remarkably, when cluster analysis was applied to those who had been diagnosed with MCI based on conventional Petersen/Winblad (Petersen & Morris, Reference Petersen and Morris2005; Winblad et al., Reference Winblad, Palmer, Kivipelto, Jelic, Fratiglioni, Wahlund and Petersen2004) criteria (e.g., using a cutoff of 1.5 or more SDs below normative means on at least one measure), a large number of the MCI group performed within normal limits on more extensive and detailed cognitive testing. That is, when the group was diagnosed using Petersen/Winblad criteria it was composed of amnestic and mixed MCI subtypes as well as a third subtype that performed within normal limits across the neuropsychological measures. This Cluster-Derived Normal group included nearly half of the MCI sample and did not differ from a normal control group in terms of cognition or measures of cortical thickness in areas usually affected in MCI or AD. These results suggest a high susceptibility of the conventional diagnosis of MCI to false positive diagnostic errors, reinforcing the conclusions drawn by Brooks et al. (Reference Brooks, Iverson, Holdnack and Feldman2008; see also Brooks, Iverson, & White, Reference Brooks, Iverson and White2007). Further work profiling these potential false positive misclassifications on biomarkers and longtiduinal outcomes represent important next steps.
Neuropsychological Approaches to MCI Diagnosis Improve Biomarker Associations
As the above studies selected for this series suggest, when sound neuropsychological approaches to MCI diagnosis are met, significant improvements are made in characterizing the specific cognitive phenotypes of MCI. This offers the possibility that biomarkers may associate more strongly with phenotypes in expected patterns and may also reveal associations beyond or instead of typical AD pathophysiology (e.g., regions related to semantic memory activation and white matter integrity). Our fifth paper in this series by Nordlund et al. (Reference Nordlund, Rolstad, Klang, Lind, Pedersen, Blennow and Wallin2008) offers one example of the ways in which more comprehensive neuropsychological methods shed light on cerebrospinal fluid (CSF) biomarker associations. Here, they demonstrate that MCI participants diagnosed according to conventional Winblad et al. (Reference Winblad, Palmer, Kivipelto, Jelic, Fratiglioni, Wahlund and Petersen2004) criteria have different neuropsychological profiles if sub-divided on the basis of normal versus abnormal levels of CSF AD biomarkers (low amyloid-β, high total tau concentrations, or both). Specifically, when MCI participants with normal CSF AD biomarkers are compared to healthy control participants, the neuropsychological differences on a vast array of neuropsychological tests of memory, speed and attention, language, executive and visuospatial functions are strikingly small despite their MCI diagnosis. However, those MCI patients with abnormal levels of CSF AD biomarkers showed significant neuropsychological impairments across the five cognitive domains when compared to control participants. This MCI group with abnormal CSF levels also showed impairments on episodic memory, naming, and speed/attention/executive functions (digit symbol, Trails A and B) relative to the MCI group with normal CSF levels.
A sixth paper in this series by Hantke et al. (Reference Hantke, Nielson, Woodard, Guidotti Breting, Butts, Seidenberg and Rao2013) examined cognitively stable and declining groups of older adults to determine if baseline functional magnetic resonance imaging (fMRI) tasks of semantic (famous name discrimination) and episodic (name recognition) memory predicted cognitive outcomes 18 months later. They defined cognitive decline based on a comprehensive neuropsychological assessment at both time points and computed residualized change scores that adjusted for baseline performance, practice effects, and regression to the mean. Participants with standardized residuals of −1.0 or lower on one or more of the primary neuropsychological measures were assigned to the cognitively declining group; the remaining participants were classified as cognitively stable. With this rigorous approach to defining “decline,” they found that fMRI activation during a semantic memory task was more accurate in predicting future cognitive decline than activation during the episodic memory task, echoing the findings of Howieson et al. (Reference Howieson, Carlson, Moore, Wasserman, Abendroth, Payne-Murphy and Kaye2008) discussed in the prior section.
A seventh paper by Stricker et al. (Reference Stricker, Salat, Foley, Zink, Kellison, McFarland and Leritz2013) examined, via diffusion tensor imaging, whether decreased white matter integrity in MCI would persist when controlling for AD-signature cortical thinning. Instead of using conventional MCI diagnostic criteria, the authors applied neuropsychologically based MCI criteria using the more comprehensive scheme described by Jak, Bondi, et al. (Reference Jak, Bondi, Delano-Wood, Wierenga, Corey-Bloom, Salmon and Delis2009). Controlling for cortical thickness, the authors found their MCI group showed decreased fractional anisotropy (FA) in parietal white matter and in white matter underlying the entorhinal and posterior cingulate cortices relative to the NC group. They further observed significant cognitive associations such that medial temporal FA was related to memory and parietal FA was related to executive functioning. Their results provide support for the role of white matter integrity as an early biomarker for individuals at risk for AD and highlight that changes in white matter may be independent of gray matter changes.
Neuropsychological Approaches to MCI Diagnosis Associate with Functional Challenges
Although MCI diagnosis, outcome, and biomarker associations represent the foci of the vast majority of studies, a relatively neglected but important area of MCI research centers on the assessment and predictive utility of functional impairments. Many people with MCI often also demonstrate mild but identifiable functional difficulties. A challenge for neuropsychology is how to incorporate this functional information to better define MCI and distinguish it from early dementia. An eighth paper in this series by Aretouli et al. (Reference Aretouli, Okonkwo, Samek and Brandt2011) nicely illustrates this notion by revealing that progression from MCI to dementia over a 2-year period was best predicted by a combination of informant ratings of subtle functional impairments as well as lower baseline scores on episodic memory, category fluency, and constructional praxis. The ninth and tenth papers in the series from Daniel Marson's group focus specifically on two complex instrumental ADLs critical to independent functioning for older adults: financial capacity (Sherod et al., Reference Sherod, Griffith, Copeland, Belue, Krzywanski, Zamrini and Marson2009) and medical decision-making capacity (Okonkwo et al., Reference Okonkwo, Griffith, Belue, Lanza, Zamrini, Harrell and Marson2008). Marson (Reference Marson2001; Marson et al., Reference Marson, Sawrie, Snyder, McInturff, Stalvey, Boothe and Harrell2000) constructed the Financial Capacity Instrument to directly assess the financial abilities of older adults and Sherod et al. (Reference Sherod, Griffith, Copeland, Belue, Krzywanski, Zamrini and Marson2009) administered it to normally aging, amnestic MCI, and AD groups. They demonstrated that, across the aging-MCI-AD continuum, the same cognitive functions (i.e., arithmetic skills, memory, and executive functions) were associated with both intact financial capacity in older controls and declining financial capacity in patients with MCI and AD. Okonkwo et al. (Reference Okonkwo, Griffith, Belue, Lanza, Zamrini, Harrell and Marson2008) revealed similar findings that medical decision-making capacity was predicted by short-term verbal memory and executive functions in patients with amnestic MCI. A final paper in this series by Bangen et al. (Reference Bangen, Jak, Schiehser, Delano-Wood, Tuminello, Han and Bondi2010) examined whether the type of functional difficulty varies by MCI subtype and found participants with amnestic MCI demonstrated significant decrements in financial management, whereas those with non-amnestic MCI showed poorer performance in abilities related to health and safety. Logistic regression demonstrated that functional abilities accurately predicted MCI subtype.
Results from each of the studies in this latter section generally support the need for better delineation of specific functional declines in MCI. Given the implications of functional status for MCI diagnosis and treatment, the direct or actuarial assessment of functional abilities is recommended. Results further suggest performance-based ADL assessments may have utility in distinguishing MCI subtypes. Such integration of neuropsychological test performances alongside operationally defined measures of instrumental activities of daily living will ultimately more fully characterize the cognitive and functional difficulties that lead to dementia.
Conclusions and Future Directions
The reliance on very limited cognitive testing and staging-based rating scales in most research studies of MCI, and recently also of preclinical AD studies (e.g., Vos et al., Reference Vos, Xiong, Visser, Jasielec, Hassenstab, Grant and Fagan2013), will potentially miss individuals with subtle cognitive declines or mis-diagnose MCI in those who are otherwise cognitively normal on a broader neuropsychological battery of tests. The articles selected for this virtual issue make it clear that neuropsychological measurement is key to the valid and reliable identification of persons that may be having subtle problems in living that are due to a range of pathologies and for which supportive interventions (e.g., see Greenaway, Duncan & Smith, Reference Greenaway, Duncan and Smith2013; Hampstead, Sathian, Bacon Moore, Nalisnick, & Stringer, Reference Hampstead, Sathian, Bacon Moore, Nalisnick and Stringer2008; Lubinsky, Rich, & Anderson, Reference Lubinsky, Rich and Anderson2009) may be indicated. When such efforts to more comprehensively assess neuropsychological functions are undertaken, better characterizations of spared and impaired cognitive and functional abilities result and lead to more convincing associations with other biomarkers (Jak, Urban, et al., Reference Jak, Urban, McCauley, Bangen, Delano-Wood, Corey-Bloom and Bondi2009; Nordlund et al., Reference Nordlund, Rolstad, Klang, Lind, Pedersen, Blennow and Wallin2008) as well as to clinical outcomes (Hantke et al., Reference Hantke, Nielson, Woodard, Guidotti Breting, Butts, Seidenberg and Rao2013; Howieson et al., Reference Howieson, Carlson, Moore, Wasserman, Abendroth, Payne-Murphy and Kaye2008).
Future directions for research in this area include comparing anatomical and functional neuroimaging biomarkers to the empirically derived MCI subtypes obtained by Libon et al. (Reference Libon, Eppig, Xie, Wicas, Lippa, Bettcher and Wambach2010) and Clark et al. (Reference Clark, Delano-Wood, McDonald, Bangen, Jak, Libon and Bondi2013). For example, if it is determined that there are imaging-based regional distinctions between neuropsychologically derived MCI subtypes (e.g., see Delano-Wood et al., Reference Delano-Wood, Bondi, Sacco, Abeles, Jak, Libon and Bozoki2009), all of whom nevertheless progress to AD, then it could have major implications for MCI and possibly preclinical AD diagnostic and treatment efforts. The necessity of a comprehensive canvassing of cognitive domains with sensitive neuropsychological tests would be needed to identify other profiles of cognitive dysfunction leading to AD (e.g., difficulties in semantic memory, executive functions, visuospatial skills). This comprehensive neuropsychological assessment strategy would be especially important for efforts to characterize the “subtle cognitive declines” inherent in the criteria for detection of preclinical AD stagings (Sperling et al., Reference Sperling, Aisen, Beckett, Bennett, Craft, Fagan and Phelps2011) or to derive quantitative MCI phenotypes for genetic or genome-wide association studies (Shen et al., 2013). Finally, empirical determinations of the composition of neuropsychological measures in diagnostic batteries, effects of the addition or subtraction of tests, gradations of test difficulty, and verbal-visual balances of items within cognitive domains would all be of interest for future study.
Acknowledgments
This work was supported by NIH grants R01 AG012674 (MB) and K24 AG026431 (MB). The authors report there are no conflict of interest disclosures.
