Cognitive Domains

Cognitive domains are constructs (intellectual conceptualizations to explain observed phenomena, such as gravity) invoked to provide a coherent framework for analysis and testing of cognitive functions. The various cognitive processes in each domain are more or less related and are more or less independent of processes in other domains. Although these domains do not have strict, entirely separable neuroanatomical substrates, they do each depend on particular (but potentially overlapping) neural networks.¹ In view of the way in which cognitive domains are delineated, it is not surprising that there is some variation in their stated number and properties, but commonly recognized ones with their potential neural substrates are listed in Table 2-1.

TABLE 2-1 Commonly Assessed Cognitive Domains and Their Potential Neural Substrate

Neuropsychological Assessment of Individual Cognitive Domains

In practice, although many neuropsychological tests assess predominantly one domain, very few in routine clinical use are pure tests of a single domain, and almost all can be performed poorly for several reasons. For example, inability to copy the Rey Complex Figure might result from impairments of volition, comprehension, planning, or praxis, as well as from some form of the most obvious cause of visual impairment. Assignment of particular tests to particular domains is therefore to some extent arbitrary; many tests are capable of being assigned to more than one domain. The interested reader is referred to Lezak and colleagues (2004) and Spreen and Strauss (1998) for details of individual tests. Multidimensional tests, such as the Mini-Mental State Examination (MMSE), may be subjected to factor analysis. This type of analysis identifies groupings of test items that correlate with each other and may well assess aspects of the same domain. In this way, the range of domains assessed by such a test may be identified.

Prerequisites for Meaningful Testing

Adequate testing within some domains requires that some others are sufficiently intact. For example, a patient whose sustained, focused attention (concentration) is severely compromised by a delirium is unable to register a word list adequately. Consequently, delayed recall is impaired, even in the absence of a true amnesia or its usual structural correlates. A patient with sufficiently impaired comprehension may perform poorly on the Wisconsin Card Sorting Test because the instructions were not understood, rather than because hypothesis generation was compromised. These considerations give rise to the concept of a pyramid of cognitive domains, with valid testing at each level dependent on the adequacy of lower level performance² (Fig. 2-1).

Figure 2-1 The pyramid of cognitive domains. An unconscious or inattentive patient is not able to comprehend test instructions, even though the relevant linguistic networks may be intact. A patient with severely impaired comprehension may not understand the test instructions for praxis, for example, and so forth.

In addition to intact attention and comprehension, patient performance may be compromised by poor motivation—for example, as a result of depression or in the setting of potential secondary gain—or by anxiety. Neurological impairments (e.g., poor vision, ataxia), psychiatric comorbid conditions, preexisting cognitive impairments (e.g., mental retardation), specific learning difficulties or lack of education (e.g., resulting in illiteracy), and lack of mastery of the testing language can all interfere with valid testing and must be carefully considered by the neuropsychologist in interpreting test results.³

Test Reliability

For a neuropsychological test (or any other test) to be clinically useful, it must be both reliable and valid. A reliable test is one for which differences in scores reflect true differences in what is being measured, rather than random variation (“noise”) or systematic bias (e.g., consistent differences between test scores at different centers). The reliability coefficient of a test is the proportion of total test result variability that is attributable to true differences in test results. It may also be conceptualized as the variability that would remain after multiple administrations of a test resulted in random variations that canceled each other out, with no systematic bias assumed. (An analogy familiar to neurologists would be electronic averaging in evoked potentials.) Reliability coefficients of standard neuropsychological tests typically vary from about 0.70 (acceptable) to 0.95 (high).

Reliability may be assessed in a number of ways. Test-retest reliability accounts for both random variability resulting from the test itself and systematic bias resulting from practice effects, although it cannot enable the clinician to easily distinguish between the two. It presupposes a stable test population, which may be an unattainable ideal over longer periods of time, inasmuch as acute pathological conditions such as results of strokes and traumatic injuries tend to improve and degenerative conditions tend to worsen. The internal consistency of a multi-item test can be gauged by split-half reliability, whereby scores from half the test items are compared with scores from the other half (but this leaves moot how the division is performed), or by calculating the mean reliability coefficient obtained from all possible split-half comparisons. The latter strategy generates a statistic called Cronbach’s α. Sometimes, alternative (parallel) versions of tests are constructed, often in order to facilitate serial testing in an effort to avoid practice effects. The reliabilities of the different versions can then be compared, in a process very similar to split-half reliability testing. The difficulty, of course, is in knowing whether the two versions really are equivalent, so that variance between the two represents unreliability rather than differences in difficulty or in the variable or variables actually being measured.

Interrater reliability accounts for the variation in test scores resulting from administration by different testers. This is clearly important particularly in multicenter studies and is an essential property for semiquantitative clinical rating scales.

The importance of test reliability underlies the importance of test administration with standardized materials in a standardized manner and a conducive environment, and by appropriately trained personnel (e.g., not by an intern in a noisy ward).

Test Validity

A valid test measures what it is purported to measure. Whereas an unreliable test cannot be valid (as score variations reflecting true differences in the intended measured variable are concealed by noise or systematic bias), reliability itself is no guarantee of validity. Consideration of the following test of semantic knowledge illustrates this point:

All readers presumably score 75% on this test, which is therefore absolutely reliable but quite invalid as a test of semantic knowledge.

Validity can be gauged in a number of ways. Criterion validity reflects the utility of the test in decision making. Perhaps the ideal form of criterion validity is predictive validity, in which test results are used to make a decision or prediction, such as in which patients amnestic mild cognitive impairment will convert to Alzheimer’s disease, and the validity of the decision is subsequently established on follow-up. Such studies tend to be long and expensive, however, and so other methods of assessing validity are often required.

Concurrent validity, another form of criterion validity often used instead, involves comparing test results with a nontest parameter of relevance, such as sustained, directed attention in children with their class disciplinary records. Ecological validity, a related concept, reflects the predictive value of the test for performance in real-world situations. For example, neuropsychological tests of visual attention and executive function, but not of other domains, have been found to have reasonable ecological validity for predicting driving safety, in comparison with the “gold standard” of on-road testing.⁴

Construct validity assesses whether, for example, a test purportedly of a particular cognitive domain is correlated with other established tests of that particular domain and functions as tests of that domain are expected to function.

Content validity concerns checking the test items against the boundaries and content of the domain (or portion of the domain) to be assessed. Face validity exists when, to a layperson (such as the subject undergoing testing), a test seems to measure what it is purported to measure. Thus, a driving simulator has good face validity as a test of on-road safety, whereas an overlapping figures test of figure/ground discrimination may not, even though it may actually be relevant to perceptual tasks during driving. More detailed discussions of reliability and validity were given by Mitrushina and associates (2005), Halligan and colleagues (2003), or Murphy and Davidshofer (2004).

Symptom Validity Testing

Symptom validity testing is rather different and is used as a method to reveal nonorganic deficits (e.g., malingering). It relies on the fact that patients with no residual ability in a domain, who are forced to respond to items randomly or by guessing, can nevertheless sometimes be correct by chance. Performance at statistically significantly worse than chance levels can be explained only by some retention of ability in that domain, with that ability being used (consciously or unconsciously) to produce incorrect answers. This forced-choice/statistical analysis concept should already be familiar to neurologists, as it underlies much of psychophysically correct sensory testing. (A fine example is the University of Pennsylvania Smell Identification Test [UPSIT] for evaluation of olfaction.⁵)

Other methods for detecting nonorganic deficits also exist; they depend on recognition of deviation from the usual patterns of cognitive impairment (e.g., recognition memory’s being worse than spontaneous recall) or discrepancy between scores on explicit tests of a domain and behavior or other tests implicitly dependent on that domain (e.g., dysfluency appearing only when “language” is tested). This subject is covered in more detail elsewhere.⁶

Ceiling and Floor Effects

Two further difficulties may limit the use of neuropsychological measures: lack of discrimination across the range of abilities expected and practice effects on repeated testing. An ideal test would reveal a linear decline in ability in the tested domain, from the supremely gifted to the profoundly impaired. In practice, this is rarely, if ever, achieved. Some tests discriminate well between patients with different severities of obvious impairment but are problematic in attempts to detect subtle disorders and fail to stratify the normal population appropriately. This is known as a ceiling effect. On the other hand, some tests sensitive to subtle declines and capable of stratifying the normal population, are too difficult for patients with more profound deficits. Real differences in their residual abilities may be missed. This is a floor effect. Some tests have both ceiling and floor effects, leading to a sigmoid curve of scores versus ability. If patients with Alzheimer’s disease are assumed to decline at a constant rate, then on average, over time, the MMSE shows both ceiling and floor effects (Fig. 2-2).

Figure 2-2 The Mini-Mental State Examination (MMSE) shows both ceiling effects (flatter segment of graph, upper left) and floor effects (flatter segment of graph, to right) in comparison with its sensitivity to change in the middle stages of Alzheimer’s disease (AD).

(From Gauthier S, ed: Clinical Diagnosis and Management of Alzheimer’s Disease. London, UK: Martin Dunitz, 1996.)