In a multi-year study of 81 adults conducted by the Kentucky Neuroscience Institute, the CANS-MCI has been shown to be sensitive to heightened CSF levels of Aβ and tau.
This study examined cerebrospinal fluid (CSF) amyloid β 1-42 (Aβ) and tau levels and performance on a computerized self-administered test battery, the Computer-Administered Neuropsychological Screen for MCI (CANS-MCI).
Methods: CSF was collected from participants who also completed the CANS-MCI near to the time of collection. CSF levels of Aβ (threshold = 250pg/ml) and tau (93pg/ml) were used to segment participants: Aβ-Tau-, Aβ+Tau-, and Aβ+Tau+, with positivity indicating increasing brain deposition and likely preclinical Alzheimer’s disease (AD). ANOVAs and Chi-square tests were used to compare group demographics. Linear regressions were used to compare CANS-MCI performance between groups while controlling for age, sex, education, prior CANS-MCI, and processing speed.
Results: Participants were 81 adults, ages 65 to 94; 51.8% females. The Aβ+Tau- group (mean age = 78, SD = 6.8) was significantly older than Aβ-Tau- (mean age= 73.6, SD = 6.3), p = 0.032, but not Aβ+Tau+ (mean = 76.3, SD = 7.5); no other group differences in age. All participants were Caucasian except 3, who belonged to unique ethnic minority groups. Gender, education, depression, prior CANS-MCI administrations, and time between CSF collection and CANS-MCI completion were similar between groups. Processing speed significantly slowed with age (r = 0.27, p = 0.02).
Compared to the Aβ-Tau- group, the Aβ+Tau- and Aβ+Tau+ groups evidenced CANS-MCI performance deficits in memory, visuospatial, and executive domains. The Aβ+Tau+ group performed below the Aβ-Tau- group on the test of language fluency.
Conclusions: Although the cohort was relatively homogeneous and other factors known to affect test performance were unaccounted for, this study demonstrated that performance on the CANS-MCI is sensitive to heightened Aβ and tau brain deposition.
Future studies should examine CANS-MCI performance over time in relation to changes in Aβ and tau CSF levels as well as the utility of the CANS-MCI in predicting preclinical AD.
In further analysis from a multi-year study of 81 adults conducted by the Kentucky Neuroscience Institute, the CANS-MCI has been as shown to be predictively sensitive to heightened CSF levels of Aβ and tau.
CSF was collected from participants near to the time of completing the baseline Computer- Administered Neuropsychological Screen for MCI (CANS). CSF levels of Aβ (threshold = 250pg/ml) were used to group participants as elevated (Aβ+) and not elevated (Aβ-) brain amyloid. A linear mixed model (LMM) with object identification response time at each (correct) trial as the dependent variable and age, sex, education, depression, and trial as independent variables, a random slope of trial for participants. Slope for each participant was extracted and used to predict brain amyloid status and mean response time on the same task 6 months later (logistic regressions).
Results: Of 96 participants, 57 were Aβ- and 39 were Aβ+. The groups did not differ statistically in sex, education, depression, or prior CANS administrations, ps > 0.33. The Aβ+ group (76.6y) was slightly older than Aβ- (73.9y), p =0.05. The LMM indicated age (β = 0.02, p < 0.001) and depression (β = 0.03, p = 0.04) significantly slowed response time, and response time was significantly faster with successive trials (β = -0.04, p < 0.001). Age (β = -0.06, p = 0.06) and participant slopes for trial (β = -75.16, p = 0.01) correctly classified 36 Aβ- (63.2%) and 28 Aβ+ (71.8%) participants. For the Aβ+ group, follow-up response times varied little by slope, whereas Aβ- participants with steeper slopes had faster times at follow-up.
Conclusion: Aβ+participants exhibited significantly steeper learning slopes than Aβ-. This may reflect slight retrieval delays early in the task and subsequent hyperactivation. Learning slopes at baseline also predicted improved performance at follow-up, in line with existing research on within-session practice effects. Implications and future directions discussed.
The CANS-MCI was developed to solve a problem: there were no tools available that could accurately and economically detect the cognitive changes most predictive of further abnormal decline in adults and the elderly. The most common type of decline in need of early detection was toward Alzheimer’s disease.
The CANS-MCI was designed to measure all of the cognitive domains known to be most predictive of Alzheimer’s disease (AD): immediate and delayed memory; attentional speed and flexibility; executive mental control; clock hand placement fluency; language fluency.
Scores on the CANS-MCI were compared with the results of full neuropsychological examinations that were blind to the CANS-MCI results. Identical analyses were performed using full independent neuropsychological evaluation classifications on the 74 subjects who returned a year later.
To determine the ability of the Computer-Administered Neuropsychological Screen for Mild Cognitive Impairment (CANS-MCI) as an accurate screen for mild cognitive impairment (MCI), scores on the CANS-MCI were compared with the results of a full neuropsychological examinations that were blind to the CANS-MCI results. Logistic regression models were used to predict the dichotomous outcomes of MCI vs. normal cognitive functioning (as determined by the neuropsychological exam). Because education can affect scores on measures of cognitive impairment[1,2] samples were separated into individuals with a high school degree or less (N=26) and those with schooling beyond high school (N=57). Gender and age were included in the model. Receiver operating characteristic (ROC) analyses were performed to calculate the sensitivity (the proportion of persons who have MCI that are defined as having MCI) and specificity (proportion of persons who have normal cognitive functioning that are defined as having normal functioning) of the CANS-MCI.
The regression model statistics were very strong (Table 1) indicating a good fit of the data to the model. The CANS-MCI has extremely high levels of sensitivity and specificity (100%) in classifying those with an education up to a high school degree. The optimum sensitivity and specificity for those with 13+ years of education is lower (100%; 85%) but still excellent (Table 2). These findings indicate the CANS-MCI can be a useful screening measure to determine if a person needs to be assessed for cognitive impairment.
Table 1: CANS-MCI Logistic Regression 3
Education
X2
Nagelkerke R2
Predicted Classification % Correct
Less Than 13 Years
35.4
1.0
100
13 or More Years
50.3
.79
84.2
Table 2: CANS-MCI ROC Analyses
Education
Area Under Curve
% Sensitivity
% Specificity
Less Than 13 Years
1.0
100
100
13 or More Years
.96
100
84.8
The same analyses were performed using full independent neuropsychological evaluation classifications on the 74 subjects who returned a year later. Despite small numbers of subjects to date, these data indicate that the overall probability that a full neuropsychological evaluation will indicate MCI can be effectively predicted by the CANS-MCI a year earlier. The regression model statistics for the 1-year follow-up evaluations were strong but limited by small sample size. The algorithms correctly classified 85% of participants with a high school degree or less (Chi-square = 11.7; Nagelkerke pseudo-R2 =.63 ) and 80% of those with at least some college (Chi-square = 31.4; Nagelkerke pseudo-R2 =.59) indicating a good fit of the data to the model (Table 3). The CANS-MCI has good levels of sensitivity and specificity in classifying those with an education up to a high school degree. The optimum sensitivity and specificity for those with 13+ years of education is lower but still excellent.
ROC curve analyses on the two educational levels revealed that cut-points lead to sensitivities/specificities of .93/.83 (<=12 yrs) and .84/.74 (13+ yrs). Areas under the curve were high: .917 for <= 12 yrs education and .888 for 13+ yrs (Table 4). Of course, there is the possibility that relative to an external standard the CANS-MCI is more accurate than the independent full neuropsychological evaluations. That appears to be the case in the study by Scanlan et al (2012) which found better agreement between the CANS-MCI measures and well known standard tests. They concluded that “an optimal cognitive screen would be expected to detect all of the patients indicated as demented by the DRS2 and those judged to have MCI by the WMS1/WMS2. By those criteria, the CANS-MCI (typically completed in under 35 minutes) appears as sensitive to dementia and MCI detection as a full neuropsychological examination (typically completed in 2.5 hours).” [5]
Mattis and Wechsler Memory Scale Immediate/Delayed
(Percent of subjects found impaired on Mattis Dementia Rating scale or Wechsler Memory Scale (n>168)
Neuropsychological Exam
CANS-MCI Memory alone
3-factor CANS-MCI
Mattis Impaired (Memory & Initiation)
89%
93%
100%
Wechsler Impaired (WMS1 & WMS2)
79%
80%
95%
Independent longitudinal research in progress[6] has produced preliminary data confirming the ability of the CANS-MCI to detect the earliest signs of serious decline as determined by imaging. In the first 48 cases analyzed, the CANS-MCI memory factor score and executive functioning factor score showed significant declines in subjects who had initially tested as completely normal but subsequently showed declines in cognitive ability, but would still not be officially classified as MCI or demented by UDS criteria. A subset of subjects (N=21), were also assessed with volumetric MRI, revealing that the CANS-MCI memory factor score was positively related to hippocampal brain volume (right hippocampus: r=.43, p<.05; left hippocampus: r=.38, p<.08) [6]
The CANS-MCI is meant to be used longitudinally, comparing each person to his or her own previous performance. This allows for even more precise predictive detection even in those people who are still above average and would otherwise not seem worthy of immediate medical attention. If new treatments evolve that slow, stop or reverse the progression of cognitive decline toward dementia, the earliest possible prediction of decline will become critical.
Footnotes
Gifford DR, Cummings JL. Evaluating dementia screening tests: Methodological standards to rate their performance. Neurology 1999;52:224-227.
Lorentz WI, Scanlan JM, Borson S. Brief screening tests for dementia. Canadian Journal of Psychiatry2002;47(8):723-732.
Jane B. Tornatore, PhD, Emory Hill, PhD, Jo A. Laboff & Brian Fogel Validity of Mild Cognitive Impairment Touch Screen Tests: The CANS-MCI Study. International Psychogeriatric Association 11th Congress, Chicago, August, 2003.
Jane B. Tornatore, PhD, Emory Hill, PhD, Jo A. Laboff, MSW, Brian Fogel One year Follow-Up Analyses of Scoring Algorithms for a Mild Cognitive Impairment Screen: The CANS-MCI Study, Washington, D.C., June, 2005.
James Scanlan, PhD, Jo Laboff, MSW, Emory Hill, PhD Self-reported memory fails to substitute for objective memory measures. Alzheimer’s & Dementia 2012; 8 (4): S780.
Frederick A Schmitt, PhD Department of Neurology, Alzheimer’s Disease Research Center, University of Kentucky Medical Center, unpublished presentation at Screen, Inc. Annual Science Meeting, January, 2013.
The CANS-MCI, a computer administered, scored, and interpreted touch screen battery, was evaluated for its ability to screen for mild cognitive impairment. 310 community-dwelling elders enrolled in an NIA-funded study. One-month test-retest reliability correlations were all significant (p<.05-p<.001). Concurrent validity correlations were all significant (p<.001). A high level of diagnostic validity was attained relative to the WMS-R LMS-II test (p<.001). Confirmatory factor analysis supported a three-factor model indicating the tests measure the intended cognitive dimensions of Memory, Language/Spatial Fluency, and Executive Function/Mental Control. Goodness of fit indicators were strong (Bentler Comparative Fit Index = .99; Root Mean Square Error of Approximation=.055). Initial validation analyses indicate that the CANS-MCI shows promise of being a reliable, valid screening tool to determine whether more intensive testing for early cognitive impairment is warranted.
Published in final edited form as: J Neuropsychiatry Clin Neurosci. 2005; 17(1): 98–105.
Open the article at the US National Library of Medicine website:
Automated Primary Care Screening for Mild Cognitive Impairment and Alzheimer’s Disease
Jane B. Tornatore, PhD[1], Emory Hill, PhD[1]
& Kenric W. Hammond, MD[2]
POSTER Presented at The 8th International Conference on Alzheimer’s Disease and Related Disorders, Stockholm, Sweden 2002.
ABSTRACT
Background: People with Mild Cognitive Impairment (MCI) appear to develop Alzheimer’s disease (AD) at a rate of 10-15% a year. Since most new treatments for dementia focus upon slowing the progression of AD, it is critical to test with a screen in primary care at an early stage fo the markers of future cognitive decline & the need for intensive diagnostic evaluation.
Objective: The Computer-Administered Neuropsychological Screen for Mild Cognitive Impairment (CANS-MCI), a self-administered touch screen battery, was designed to test (in primary care offices) for the earliest predictive signs of AD dementia. The CANS-MCI incorporates screening tests of all cognitive dimensions known to predict AD dementia: spatial relations, executive inhibitory functions, memory, & language fluency. The usability of the CANS-MCI tests in primary care offices (acceptability & relevance to subjects, absence of test anxiety & high rate of complete self-administration) was previously established.
Methods: 265 elderly volunteers are enrolled in a 3-year longitudinal NIA-funded study to test the CANS-MCI for screening test usability in primary care, validity & reliability. Findings from baseline MCI test screen data are presented.
Results: Internal consistency of the scales ranged from .515 to .966. One-month test-retest reliability correlation coefficients were all highly significant (p<.001). Concurrent validity correlation coefficients were also all highly significant (p<.001). A high level of diagnostic validity was attained using the criterion of performance on the WMS-R LMS II. Principal component factor analysis established 3 factors that explained 63% of the variance of scores: Recognition Retrieval/Language, Executive Functions & Episodic Memory Acquisition.
Conclusions: As effective treatments for AD emerge, it becomes important to screen people during primary care visits who have the earliest signs of the cognitive impairments most likely to become AD. The CANS-MCI is an easily administered, valuable screening tool in primary care to determine whether more intensive testing for early mild cognitive impairment and Alzheimer’s disease is warranted.
INTRODUCTION
The concept of MCI both as distinct diagnostic entity & as a precursor to Alzheimer’s (AD) suggests that instruments focused upon MCI measurement would provide useful screening test information in primary care offices for decisions concerning full diagnostic evaluations for AD dementia.
No single cognitive/behavioral domain can be used to differentiate persons who will develop Alzheimer’s from those who will not. Several screening test dimensions, when combined, significantly enhance the predictive validity of a test battery because of variations in the initial cognitive deficits associated with early stages of AD. Furthermore, determination of the rate of change in at least two cognitive markers is a better means of predicting the development of AD dementia than is a single assessment. Repeated screening test measures are also necessary to detect changes in high functioning adults who have the cognitive reserve to compensate for early symptoms.
Current methods of Alzheimer’s detection in primary care are costly & often deferred until later in the disease process when interventions to delay MCI and AD are likely to be less effective. Therefore, an effective screening device for MCI would incorporate measures of multiple cognitive domains, measure changes over time, & be cost efficient.
METHODS
Subjects
A total of 265 elderly people were recruited through senior centers & retirement homes in 4 counties of Washington State. Exclusionary criteria were non-English speaking, significant hand tremor, inability to sustain a seated position for a minimum of 45 minutes, very recent surgery, cognitive side effects of drugs, indications of recent alcohol abuse, or inadequacies in visual acuity, hearing, or dominant hand agility.
Test Development
The CANS-MCI is a self-administered screening instrument that measures multiple cognitive domains & has the ability to measure changes over time. Development of the CANS-MCI tests was based upon findings of previous neuropsychological testing research.
The usability of the CANS-MCI by elderly subjects was previously established using the following criteria: acceptability, ease of administration, & completion of all tests entirely by self-administration. Even study volunteers with mild AD were able to complete the tests with minimal assistance. Moreover, the CANS-MCI appeared to offer a way of enhancing perceptions of control over testing & avoiding the activation of interpersonal defenses in primary care doctors’ offices.
Both the stimulus & response characteristics of the CANS-MCI are markedly different from traditional screening tests. The range of responses in the CANS-MCI is limited by the touch screen modality. However, in other populations these touch screen responses have been found to produce error rates similar to those produced with traditional verbal responses.
Instrument Description
The CANS-MCI presents the following progression of tasks to the subjects:
Introduction
Presents progressively more difficult General Reaction Time tasks that prepare the subject for the first test.
.
Word/Picture Matching
Presents 4 pictures of objects with one word, & the user is instructed to touch the picture that goes with the word.
.
Guided Recognition-Immediate
User consecutively learns 5 sets of names of 4 pictures by touching pictures that fit into categories and then being told the name of the pictured object in that category.
User is tested after each set of 4 pictures and the set is re-learned if mistakes were made.
20 3-button displays, each with an object name learned and 2 incorrect names from other categories are presented, with category-guided recall and re-acquisition of missed items.
.
Design Matching
Presents 8 designs paired with letters in non-alphabetical order, & a set of 8 letters in alphabetical order.
1 of the designs appears in the middle of the screen, & the user is instructed to touch the letter paired with it.
Complexity of attention-switching required is increased by within & between-test interference.
Changes are made to the designs halfway through this test to present several types of interference.
.
Clock
10 clock blank faces are presented.
A digital time is presented, & the user is instructed to first touch the hour hand position on the blank click face, then the minute hand position.
.
Stroop
User quickly touches buttons matching names of colors presented.
User is then instructed to touch buttons matching the ink color, not the word name, of the words “Red”, “Blue” or “Green”, presented one at a time in either red, blue, or green ink.
.
Picture Naming
Pictures in multiple categories are presented, each with 4, 2-letter word beginnings, 1 naming the picture.
.
Guided Recognition-Delayed
One additional recognition test trial, with guided recall for missed items.
Scores were only given for the items completed within the time limit. Participants did not all answer the same number of items so we were unable to perform reliability analyses.
† If any of the 20 correct items in a trial are not touched, the subject receives a guided recall test on that item.
Table 2. Test Re-Test Reliability
Test
Time 1 Mean (SD)
Time 2 Mean (SD)
Coefficient Alpha
General Reaction Time
0.77 (.21)
0.73 (.17)
.702
Design Matching (accuracy)
38.05 (11.41)
41.37 (8.94)
.765
Clock Test (accuracy)
30.65 (9.45)
32.86 (8.89)
.792
Stroop Discordant Item (reaction time)
1.69 (.48)
1.61 (.52)
.794
Guided Recognition (accuracy)
Immediate
17.73 (2.01)
18.13 (1.86)
.681
Delayed
17.58 (2.27)
17.83 (2.25)
.607
Immediate & Delayed (combined)
35.23 (4.21)
35.95 (3.87)
.760
Guidance Efficacy
0.86 (.15)
0.90 (.14)
.385
Picture Naming (accuracy)
31.59 (4.81)
32.00 (4.91)
.788
Picture Naming (reaction time)
6.18 (2.13)
5.90 (2.26)
.854
Word/Picture Matching (reaction time)
2.06 (.56)
1.93 (.49)
.833
Table 3: Correlations of Standardized Tests with CANS-MCI Tests
Conceptual Domain (CANS-MCI)
CANS-MCI Test
Standardized Test
Correlation Coefficient
P-value
Attention
General Reaction Time
Digit Symbol
-.585
<.001
Visuospatial ability
Design Matching (accuracy)
Digit Symbol
.537
<.001
Spatial relations
Clock (accuracy)
Digit Symbol
.469
<.001
Mental control
Stroop Discordant Item (latency)
Digit Symbol
-.565
<.001
Memory acquisition
Guided Recognition-Immediate
Mattis Memory WMS LMS-I
.518 .540
<.001 <.001
Guidance Efficacy
Mattis Memory WMS LMS-I
.327 .352
<.001 <.001
Memory retention
Guided Recognition-Delayed
Mattis Memory WMS LMS-II
.447 .440
<.001 <.001
Composite memory score
Guided Recognition-Immediate & Delayed
Mattis Memory WMS LMS-I WMS LMS-II
.486 .519 .525
<.001 <.001 <.001
Picture naming
Picture Naming (accuracy)
Mattis Initiation
.584
<.001
Picture Naming (latency)
Mattis Initiation
.616
<.001
Other fluency tests
Word/Picture Matching (latency)
Mattis Initiation Digit Symbol
-.496 -.636
<.001 <.001
Table 4: Diagnostic Validation using Delayed Memory Criterion
Validity: Concurrent: Pearson correlations with the scores on previously validated measures to provide a standard against which the component tests could be assessed.
Diagnostic: T-tests used to analyze differences between subjects in the lowest 10th percentile of cognitive functioning & those in the highest 90th percentile based on WMS-R LMS II scores.
Factor Analysis: Exploratory principal components factor analysis with Varimax rotation & Kaiser normalization.
Confirmatory factor analysis: presented at the American Association of Geriatric Psychiatry Convention, March, 2003.
RESULTS
Internal Consistency: Only 2 tests, Guided Recognition-Delayed (accuracy) & Guided Recognition-Guidance Efficacy, did not meet the predetermined standard for internal consistency (alpha >= .70) Other alpha coefficients ranged from .76-.97 (Table 1).
1 Month Test-Retest Reliability: Correlations over a 1-month period ranged from .607-.854 (Table 2). All but 3 had scores over .70. Separately, the Guided Recognition Immediate & Delayed accuracy tests had alphas below .70. When the immediate & delayed recall tests were combined to form a more global memory measure, the alpha was an acceptable .76. Guidance Efficacy had a very low test-retest alpha. Because Guidance Efficacy scored below the cut-off criteria in both inter-item & test-retest reliabilities, it was not included in further analyses.
Concurrent Validity: The correlations between the CANS-MCI & the previously standardized measures were moderate but all highly significant. Correlation coefficients ranged from .440 to .636 (p<.001) (Table 3).
Diagnostic Validation: Groups of impaired & intact memory subjects were established to assess the degree to which the CANS-MCI was able to detect impairments in cognitive abilities that are diagnostic of MCI or AD. Significant differences were observed between the memory intact group & the memory-impaired group on all CANS-MCI subtests (p<.001) (Table 4).
Factor Analysis: Results suggest a 3-factor solution that explained 63% of the total variance. The factors were Recognition Retrieval/Language, Executive Functions & Episodic Memory Acquisition (Table 5).
DISCUSSION
Reliability: The CANS-MCI demonstrates a high degree of internal consistency & test-retest reliability. These measures of test stability are comparable to those of the standardized comparison tests. Thus, the CANS-MCI can be reliably used at one or multiple testing sessions. Slight improvements in the mean scores are evident on all tests over the one-month period, probably due to the reduction of anticipatory anxiety & establishment of positive relationships with the participants.
Validity: Cross validation of the CANS-MCI with the WMS-R LMSI & II, WAIS Digit Symbol & Mattis subscales demonstrates that the CANS-MCI subtests produce meaningful score differentiation of the memory impaired & non-memory impaired elderly. This is confirmed by an analysis based upon a WMS-R LMS II diagnostic criterion.
Factors: The factor analysis indicated that CANS-MCI items loaded onto 3 main factors: Recognition Retrieval/Language, Executive Functions, & Episodic Memory Acquisition. Design Matching & Word/Picture Matching loaded heaviest on Executive Functions but also heavily on Recognition Retrieval/Language, reflecting the overlap of cognitive domains when recognition ability is measured with psychomotor speed tests. Immediate & Delayed Recognition loaded most heavily on the Episodic Memory Acquisition factor but also loaded heavily on the Recognition Retrieval/Language factor.
CONCLUSION
As effective treatments for AD emerge, it will become important to identify people in primary care office visits who have the earliest signs of the cognitive impairments most likely to become AD. The CANS-MCI tests are reliable & differentiate memory impaired from normal elderly, as determined by the WMS-R LMS II. The CANS-MCI is an easily self-administered, valuable primary care screening tool for MCI to determine whether more intensive testing for cognitive impairment and possible dementia is warranted.
Footnotes
Screen, Inc. Seattle WA, USA
Department of Veterans Affairs, Seattle, WA, USA
Weschler Memory Scale-Revised Logical Memory Components I & II (WMS-R LMS I & II); Mattis Dementia Rating Subscales (Mattis)-Attention, Conceptualization, Inititation, Memory; Weschler Adult Intelligence Scale, Digit Symbol Component (WAIS)
There is a critical and increasing research focus upon developing ways to identify high-risk MCI patients for early treatment. Such efforts depend upon knowing as much as possible about who is most likely to progress from MCI to Alzheimer’s disease.
A very recently published study[1] of the most significant predictors of progression from MCI to Alzheimer’s indicates, albeit indirectly, that the CANS-MCI is a very powerful tool for the detection of those predictors. This conclusion is based upon multiple known relationships between the CANS-MCI and all of the measurements used in the study.[Table 1] The Korolev study proposed a precise, extensive model to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment. However, identification of high risk MCI patients may require even earlier and more efficient intervention.
The CANS-MCI was designed to measure all of the cognitive domains known to be most predictive of Alzheimer’s disease (AD): immediate and delayed memory; attentional speed and flexibility; executive mental control; clock hand placement fluency; language fluency. The CANS-MCI is the only test battery that does not require the presence, much less training, of a test administrator. Years of CANS-MCI usability research resulted in its ability to be fully self-administered by elderly people with Mild Cognitive Impairment, regardless of computer experience, even by elderly people with Mild Cognitive Impairment without causing anxiety-based cognitive interference during testing.[2] When the tests are complete, the data may be automatically sent to a central server where they are kept for longitudinal comparisons. Scoring and longitudinal comparison test analysis is performed by an independent neuropsychology technician who does not know the identity of person tested. Hardware, software, installation, support and confidential data storage are low single fee costs.
Recently, the “robust” consistency and validity of the CANS-MCI was confirmed despite changes in the images (all CANS-MCI versions are country-specific) and language used.[3] The sensitivity of the CANS-MCI when discriminating MCI from normal functioning is as high as the hand-administered MoCA and much greater than the sensitivity of the MMSE.[4]
All latency and accuracy measures on the CANS-MCI are scalable scores; longitudinal comparisons may be performed accurately and frequently because alternative correct items are presented automatically each time a person returns for re-testing, eliminating practice effects. The entire set of tests takes about 25 minutes.
The early studies of the CANS-MCI established significant correlations between its Memory Factor Score and the Wechsler Memory Scale Logical Memory II.[8] Korolev et al[1] defined MCI by: (a) a subjective memory complaint; (b) objective memory loss, as measured by age- and education-adjusted scores on Wechsler Memory Scale Logical Memory II, but without significant impairment in other cognitive domains. “MCI subjects recruited as part of ADNI-1 were diagnosed based on the original Petersen (Mayo Clinic) criteria for amnestic Mild Cognitive Impairment. Thus, MCI subjects were limited to those with memory-only impairments (without significant impairments in other cognitive domains), also termed single-domain amnestic MCI. However, even among those MCI patients diagnosed using this single-domain amnestic MCI definition, impairments in multiple cognitive domains in addition to memory were predictive of MCI-to-AD progression. This provides empirical support to the recently revised clinical criteria for MCI, where the concept of “MCI due to AD” is proposed to include “impairment in one or more cognitive domains”.[5] The CANS-MCI tests abilities within these cognitive domains, found in previous studies[6] to be associated with greater risk of progression to AD dementia than are people with isolated memory deficits.
Significant correlations have been found between the CANS-MCI and the predictors of progression from MCI to Alzheimer’s found by Korolev.[1]
Even though the cognitive assessments proved to be the most accurate (76.1%) in predicting MCI-to-dementia progression, the imaging dimensions were also valuable predictors, particularly volume/cortical thickness of the left hippocampus. Our preliminary hippocampal imaging results suggest that CANS-MCI-Memory Factor and CANS-MCI-Executive Functions Factor differences accurately reflect differences in brain volume.[7]
There are several other ways in which Korolev’s finding support the exceptional power of the CANS-MCI (compared to other computer-assisted test batteries). The CANS-MCI avoids the variability (noise) contributed by inter-tester differences, since it is administered entirely, without staff input or training, by the computer itself, using single finger touches only. Although most research, such as Korolev’s, repeatedly train technicians in test administration to maximize inter-tester reliability, such efforts cannot be expected in clinical situations. That is particularly true when a primary care practice is not specialized yet serves as the initial detection situation for most patients who will come to the attention of specialists. The outstanding reliability of the CANS-MCI has been previously documented.[8] The exceptional user friendliness of the CANS-MCI has also been widely acknowledged.[9]
Table 1: Known relationships between the CANS-MCI and Korolev study measurements
Rey Auditory Verbal Learning (Learning Efficiency) and CANS-MCI Memory Factor
N=158
r=.349
Significance >.001
Rey Auditory Verbal Learning (Delayed Memory) and CANS-MCI Memory Factor
N=161
r=.426
Significance >.001
Rey Auditory Verbal Learning (Delayed Memory) and CANS-MCI Delayed Memory
N=161
r=.403
Significance >.001
Boston Naming Test and CANS-MCI Naming Test
N=32
r=.413
Significance >.02
Digit-Symbol Coding Test and CANS-MCI Executive Function Factor
N=158
r=686
Significance >.001
Trails A (Visual Scanning) and CANS-MCI Design Matching
N=160
r=.436
Significance >.001
Trails B (Attention Flexibility) and CANS-MCI Design Matching
N=157
r=548
Significance >.001
Trails B (Attention Flexibility) and CANS-MCI Executive Function Factor
N=159
r=.504
Significance >.001
Weschler Logical Memory II (Delayed) and CANS-MCI Memory Factor
N=162
r=.588
Significance >.001
Footnotes
Igor O. Korolev · Laura L. Symonds · Andrea C. Bozoki redicting Progression from Mild Cognitive Impairment to Alzheimer’s Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification. Feb 2016 · PLoS ONE.
Hill, E. and Hammond, KW Usability of Multimedia Automated Psychological Tests to Screen for Alzheimer’s Disease. Proceedings of the American Medical Informatics Association Symposium 2000; 1030.
Cláudia M. Memória, Mônica S. Yassuda, Eduardo Y. Nakano and Orestes V. Forlenza International Psychogeriatrics, Volume 26 / Issue 09 / September 2014, pp 1483-1491.
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