Master thesis
Finger Gnosis as a Detection Method in Probable
Alzheimer’s Disease;
The Use of a Finger Identification test in Neuropsychological Assessment
Kelly WolsUniversity of Amsterdam
Programmagroep Brein & Cognitie GZ-master Klinische Neuropsychologie Date: 3 september 2014
Studentnumber: 6063314
Internal supervisor: Dr. R. Rouw External supervisor: Dr. C.K. Jurgens
Index Abstract 3 Introduction 4 Methods 6 Design Sample Subjects Instruments Procedure Results 8
Table 1; Demographic characteristics
and dexterity on 49 patients with memory complaints
Group differences 9
Table 2; Differences between groups; Neuropsychological assessment
Table 2.1; Differences between groups on neuropsychological assessment; p-values
Finger Identification Test 11
Table 3.1; Descriptives: Number of total correct FIT; Visual trial & Tactile trial
Table 3.2; Correlations between Finger Identification Test and neuropsychological assessment.
Graph 3a; FIT: profile plot Visual trial. Graph 3b; FIT: profile plot Tactile trial.
Discussion 13
References 16
Abstract
Background: For scientific and clinical purposes there is still need for sensitive cognitive instruments
in the early phase of Alzheimer’s disease (AD). Aside from the common cognitive tests, like memory tasks, lesser-known, easy to apply tests, are also of interest in signalling domains that are affected by AD. One of the areas that are associated with neuropathological changes in AD is the dominant angular gyrus, which is found to be involved in finger identification. Therefore assessment of finger gnosis might be a valuable instrument to detect early cognitive changes in Alzheimer’s disease.
Objective: To study the Finger Identification Test (FIT) as a potentially useful early marker of
Alzheimer’s disease.
Methods: We studied finger identification in 24 patients with probable Alzheimer’s disease (AD), 11
patients with Mild Cognitive Impairment (MCI) and 14 patients with subjective memory complaints that were considered normal (Normal). A Finger Identification Test (FIT) was added to a
neuropsychological assessment battery in a memory clinic to examine the presence of a disturbance in finger identification alongside objectified memory impairments.
Results: When patients were allowed to use vision for the identification of the fingers, more mistakes
were observed in patients with AD compared to normal and MCI. When only tactile information is used, no difference is found between AD, MCI and normal.
Conclusion: The results suggest that the FIT is valuable in distinguishing AD from MCI and
subjective memory complainers. More research is needed to validate this technique, which is thought to be a useful contribution to neuropsychological assessment.
Introduction
Dementia is one of the most common diagnoses made in geriatric settings (Davis, Trotter, Hertza, Bell & Dean, 2012). Approximately one in ten people above 65 will get diagnosed with a type of dementia and half of the people above 85 will be affected by the disease (Hersenstichting, 2014).
Neurodegeneration associated with Alzheimer’s Disease (AD) accounts for 50 to 60% of cases of dementia in elderly patients (Hijdra, Koudstraal & Roos, 2010). The prevalence of AD is expanding rapidly because of the aging population and the increasing life expectancy with significant
implications for society (Rijksoverheid, 2014).
Dementia caused by AD is a chronic, neurodegenerative disease with a progressive deterioration of the cognitive functions (Hijdra et al. 2010). The National Institute for Neurological and Communicative disorders and Stroke- Alzheimer’s Disease and Related Disorders Association ( NINCDS-ADRDA) (McKhann, Drachman, Folstein, Katzman, Price & Stadlan, 1984) distinguishes between possible, probable and definite AD. A definite diagnosis can only be confirmed post portem through the observation of the neuro –pathological changes, for example neurofibrillary tangles and neuritic plaques. A patient will be diagnosed with probable AD when multiple cognitive functions (at least two) are disturbed, including impaired memory and one of the following: aphasia, apraxia, agnosia or a disturbance in executive functioning. Furthermore, cognitive deficits can not be ascribed to another disorder and have to interfere with daily functioning (DSM IV-TR, 2000; Hengeveld & van Balkom, 2009).
By the use of neuro-imaging and biomarker techniques, such as MRI and lumbar puncture, science is making progress towards an earlier diagnosis of probable AD by life. However, in clinical settings, the first symptoms of AD are still mainly detected by neuropsychological assessment (Schmand,
Eikelenboom & van Gool, 2011). When a patient or caregiver mentions cognitive problems, a neuropsychological assessment is usually the first step to detect these changes in cognitive
functioning. Some cognitive tests are even capable to show cognitive decline when neuro-imaging is not yet capable of detecting structural brain changes (Schmand, Rienstra, Tamminga, Richard, van Gool, Caan, & Majoie, 2014).
Detecting the disease in an early stage is important because of the development of new medication that might slow down or ameliorate the symptoms of AD. Furthermore when a person gets diagnosed with AD in an early stage, the patient can prepare for the future and is still competent to make their own choices about care and housing, and to make medical decisions, also concerning euthanasia (Boer, Droës, Jonker, Eefsting & Hertogh, 2011). Closely observing AD from the onset is scientifically and clinically very relevant and possibly contributes to slowing down the neurodegenerative process of AD in the future.
Since there is a discrepancy between the cognitive deficits noticed by the patients or caregivers and what the neuropsychological tests can detect there is still need of sensitive cognitive instruments. There is a broad amount of memory tests that proved sensitive in the early stage of AD (Jager, Hogervorst & Combrinck, 2013), but to diagnose a probable AD more cognitive domains have to be affected. Therefore lesser-known, easy to apply tests, are also of interest in signalling domains that are affected by AD.
A variety of research has demonstrated that deterioration of tactile recognition of the individual fingers is associated with cerebral dysfunction (Denburg & Tranel (2003), in Davis et al., 2012; Shenal, Jackson, Crucian, & Heilman, 2006). Finger agnosia is the inability to recognize, name or differentiate between stimulated fingers (Anema, Kessels, de Haan, Kappelle & Leijten, 2008). Even though finger agnosia can be found isolated, for example due to CVA (Anema et al., 2011), it is mostly an
expression of higher-order cognitive disorders, including dementia (Denburg & Tranel, 2003). Davis et al. (2012) found that the presence of finger agnosia was more pronounced in elderly with cognitive deterioration due to AD compared to healthy elderly. The ability to recognize the fingers seems to be localised in the dominant angular gyrus, mostly the left angular gyrus (LAG) (Rusconi, Walsh & Butterworth, 2005; Anema, Overvliet, Smeets, Brenner & Dijkerman, 2011) this area is affected by neuropathological changes in AD (Davis et al. 2012), and is related to the temporoparietal association cortex, which is thought to be affected even in an early stage of AD (Hijdra, 2011; Garasty, Hallidal, Kril, Code, 1999). This leads to the hypothesis that finger gnosis might contribute to the detection of AD, even in the early phase.
Therefore a Finger Identification Test (FIT) (based on the finger identification subtest of the Dean-Woodcock Sensory Motor Battery) (DWSMB; Dean & Dean-Woodcock, 2003) was added to a standard neuropsychological test battery of a memory clinic to assess the presence of possible finger agnosia. Another reason to choose the FIT is the briefness of the test and the little additional exhaustion it causes for the elderly population. The main objective of this study was to examine the possibility to differentiate between elderly with AD and elderly without cognitive decline through the use of the FIT. Based on earlier findings of Davis et al. (2012) the expectation was to observe more mistakes in finger identification in AD compared to normal elderly. A group of patients with Mild Cognitive Impairment (MCI) was added to examine the transient period between normal memory complaints and AD. We hypothesise that patients with MCI will also make more mistakes than people with normal memory complaints.
Methods
Design
A descriptive study was performed among all consecutively referred patients visiting the Bronovo memory clinic for a first or second assessment in the period of December 2013 until April 2014.
Sample
Patients were referred to the outpatient memory clinic of the Bronovo hospital in The Hague by their general practitioner or a medical specialist from the hospital. Patients were included if they received a multidisciplinary diagnosis of probable AD, MCI or memory complainer without objective deficits. They had to be able to co-operate during the assessment, to understand the tasks to a sufficient level, and to speak the Dutch language. Furthermore only patients above 65 years old were included to exclude presenile dementia which is known to have another course than AD (Davis et al. 2012). Patients with other forms of dementia, mostly the patients with no memory loss as a main symptom or significant vascular damage on the scan, were excluded, as were patients with co-existing disorders, like CVA, that could cause symptoms similar to dementia.
Subjects
Fourty-nine patients who were referred with memory complaints were included. They were diagnosed based on clinical observations, a neuropsychological assessment and neuro-imaging (CT –scan). Diagnosis was made by a medical specialist after multidisciplinary consensus. Twenty-four patients were diagnosed with probable AD, 11 with MCI and 14 patients had subjective memory complaints that were considered normal.
Instruments
The neuropsychological test battery in the Bronovo Hospital includes the Mini Mental State Examination (MMSE) (Folstein, Folstein & McHugh, 1975), Cambridge Cognitive Examination-Revised (Camcog-R) (Roth, Huppert, Mountjoy & Tym, 1999), the 6-Cognitive Impairment Test (6-CIT), Wechsler memory Scale (WMS) (Wechsler, 1987), Visual Association Test (VAT) (Lindeboom, Schmand, Tulner, Walstra & Jonker, 2002), Trail Making Test (TMT A and B), and additionally the Finger Identification Test (FIT) was included (attachment A). A test for the detection of finger agnosia originally comprises three parts that assess a visual, a tactile or a complex verbal component. In the visual part the patient is allowed to use vision to recognize the fingers, in the tactile trial a patient has to rely on tactile information only. In the complex verbal trial a verbal cue is given at which the patient has to respond. For example; ‘Touch the right index finger of the test examiner.’ The higher sensitivity of the visual and tactile part is the main reason for using only these components for this study (Kessels et al., 2012).
Procedure
The participants in this study were administered the neuropsychological assessment battery of the Bronovo memory clinic. The cognitive screening procedure took approximately 90 minutes to complete, but depending on the patients’ fatigue and performance, the screening was shortened or extended by other relevant cognitive instruments. The assessment ended with the FIT. Patients were asked to put their hands facing down, on the table. The examiner touched each finger with the rear end of a pen and asked the patient to name the finger and name the side that is touched (e.g. ‘thumb left’ or
‘index finger right’). In the first trial (visual) the patient was allowed to use vision for the recognition of the fingers. In the second trial (tactile) the patient was asked to keep the eyes closed and only use tactile information for identification of the fingers. Both the name of the finger, as well as the side of the hand had to be named correctly to receive a point. In both trials a total of 10 items correct was the maximum score. The examiners for the neuropsychological assessment and the FIT were graduate students neuropsychology with training in neuropsychological assessment. A certified
neuropsychologist supervised the students.
Statistical analysis
SPSS for windows (release IBM 22) was used for data analysis. The level of statistical significance was set at .05. Group differences were evaluated using chi square tests for categorical data, kruskall- Wallis tests for ordinal data, and analysis of (co) variance (ANOVA & ANCOVA) for numerical data. Post hoc comparisons were made with Bonferroni Tests. Kendall’s tau correlations were used to investigate the relationship between different neuropsychological tests and the FIT.
Results
Patients were divided into three groups: normal, MCI and AD, based on their diagnosis. Demographic characteristics and dexterity are presented in table 1. There was a significant difference between groups on educational level. A Post hoc-test (Bonferonni) showed that the normal group was significantly higher educated than the MCI group (p= .021). Most neuropsychological tests were already corrected for educational level and was therefore thought not to interfere with the results in this study. Furthermore there was a small difference in the distribution of male and female patients among groups and a significant difference in age. A Post hoc comparison (Bonferonni) revealed that the AD group was significantly older than the normal group (p= .030). This can be explained by the fact that older people have a higher probability to develop AD (Hijdra et al. 2010).
Table 1; Demographic characteristics and dexterity in 49 patients with memory complaints.
Descriptives Normal (n=14) MCI (n=11) AD (n=24) P*
Gender (M/F)** 9/5 3/8 9/15 Age Mean (SD)*** 78.1(7.5) 80.2(5.9) 83.7 (5.5) .048 Education; Verhage^ Mean (SD) 5.7 (1.3) 4.0 (1.8) 5.1 (1.4) .029 Dexterity** Right handed 13 (92.9%) 10 (90.1%) 22 (91.7%) .112 Ambidextrous 1 (7.1%) 1 (9.9%) 0 Forced right-handed 0 0 2 (8.3%)
Values table 1: * Point Probability. ** Chi square test (Gender & Dexterity). *** Kruskall-Wallis (Education). ^ Anova (Age).
Group differences
Table 2 represents the results of the neuropsychological assessment. Camcog-R, MMSE, 6 CIT, and VAT short version, all corrected for age, showed significant differences between groups (P<.05). Post hoc Bonferonni showed that MCI performed significantly worse on all these tests compared to Normal (p<.05). AD performed significantly worse on all tests compared to normal (p<.001) and MCI (p<.05). Post hoc Bonferonni showed that on the Wechsler Memory Scale MCI and AD performed
significantly worse than normal (p<.001), MCI and AD did not differ significantly. On TMT A AD performed significantly worse than MCI and normal ( p<.01). There was no significant difference found between MCI and normal on this task. There were also no significant differences found in performance on TMT B, this is probably due to the small sample size in AD; patients who crossed the time limit of five minutes, were labelled ‘unable to perform task within five minutes’ and were excluded. Only seven out of 24 AD patients could be included for analyses.
Table 2; Differences between groups; Test scores neuropsychological assessment
Neuropsychological
Assessment, mean (SD) Normal MCI AD
Camcog Revised* 90.2 (2.9) 78.9 (3) 62.2 (2.2) MMSE* 27.5 (.83) 23.9 (.86) 18.3 (.63) 6-CIT* 2.5 (1.5) 8.0 (1.5) 15.5 (1.1) WMS (MQ)** 123.9 (9.5) 97.0 (9.1) 95.8 (10.5) TMT a (sec.)* 61.4 (16.3) 67.1 (31.1) 127.9 (63.1) TMT b (sec.)* 153.9 (72.4) 192.6 (62.2) 241.0 (90.0)
VAT short version A* 5.24 (.44) 3.62 (.46) 1.49 (.34)
Values table 2: * Multivariate Ancova (Mean (SE)); corrected for age . ** One way Anova. (Mean (SE)).
Table 2.1 shows significance levels of performance on the tests of the neuropsychological assessment between groups.
Table 2.1; Differences between groups on neuropsychological assessment; p-values
CAM-COG R total MMSE total 6-CIT total WMS Memory quotient TMT A(sec.) TMT B(sec.) VAT Normal-MCI .026* .012* .034* .000* 1.00 .714 .044* MCI- AD .000^ .000^ .001^ 1.00 .007^ 1.00 .002^ AD-Normal .000** .000** .000** .000** .003** .593 .00**
Values table 2.1: * Normal performs significantly better than MCI, at Bonferonni corrected p-values <.05; ^ MCI performs significantly better than AD, at Bonferonni corrected p-values p<.01; ** AD performs significantly worse than normal, at Bonferonni corrected p-values <.001.
Finger Identification Test
Table 3.1 shows the total number of correct in the visual and tactile trial of the FIT in every group. A multivariate analysis showed that there was a significant difference between groups in the visual trial (p<.01). Post hoc Bonferonni showed significant lower results in the visual trial in AD compared to MCI (p=.005) and normal (p=.006 ). No significant differences were found between MCI and Normal in the visual trial. There were no significant differences found between groups in the tactile trial. Eleven patients made one or more mistakes in naming the fingers (e.g.‘index finger’) in the visual trial, ninety-one percent of these patients had probable AD. Eight patients in total made one or more mistakes in naming the fingers in both the visual and the tactile trial, all these patients had AD.
Table 3.1: Descriptives: Number of total correct FIT; Visual trial & Tactile trial
Descriptives Visual Trial Tactile Trial
Normal (n=14) 9.86 (0.53) 9.5 (1.09)
MCI (n=11) 10.0 (0.00) 9.3 (1.80)
AD (n=24) 8.71 (1.78)* 8.25 (2.23)
Values table 3.1: * Differs significantly from MCI and normal, at Bonferonni corrected p values <.01
Graphs 3a and 3b show the trend analysis of the FIT in both the visual and tactile trial. In the tactile trial, even though not significant, a clear downward trend is visible.
Graph 3b; profile plot: Visual trial
*Score FIT
Diagnosis
Graph 3a; Profile plot: Tactile trial
*Score FIT
Diagnosis
*General linear model, trend analysis. Corrected for Age.
Table 3.2 shows the correlation between FIT (visual and tactile) and the tests used in the
neuropsychological assessment. The test scores in the visual trial correlated strongly with CAMCOG-R and MMSE (p<.01), the correlations between FIT and 6-CIT and TMT A were smaller, yet
significant (p<.05). There is no significant correlation found between TMT B, WMS and FIT. The tactile part of FIT did show a significant correlation with TMT B ( p<.01). The correlation between the tactile part and CAMCOG, MMSE was smaller than the correlation between the visual part and the two neuropsychological tests, but were also significant ( P<.05).
Table 3.2: Correlations between Finger Identification Test and neuropsychological assessment.
CAM-COG R total MMSE total 6-CIT total WMS Memory quotient TMT A(sec.) TMT B(sec.) VAT FIT (n=45) (n=49) (n=47) (n=27) (n=47) (n=28) (n=49)
Visual trial Correlation .385 -.338 -.271 -.191 -.261 -.135 .294
Significance .001* .004* .023** .247 .027** .394 .016*
Tactile trial Correlation .246 .281 -.110 .130 -.218 -.396 .282
significance .034** .013** .339 .417 .056 .009* .017*
Discussion
The aim of the current study was to examine the use of a finger identification test as a detection method in AD. In line with Davis et al (2012) we demonstrated that patients with AD have more difficulties identifying the fingers than patients with normal memory complaints and patients with MCI. These differences were only found when patients were allowed to use vision to recognize the fingers and not when only tactile information was available. This study was performed on senior patients. Loss of sense in one or both hands, for example due to polyneuropathy, is quite common amongst elderly (Eleobot.eu 2014; Hijdra et al., 2010). When people can only rely on sense, more people, than only patients with AD, will experience difficulties in identifying the fingers. When vision is used, there is a possibility to compensate for the potentially tactile loss and this gives a straighter diagnosis of finger agnosia. This is more in line with the definition that notes that finger agnosia is the inability to recognize, name and differentiate between stimulated fingers. We should however notice that we have an extra component since the patient also had to name the side of the finger that had to be identified. Left-right disorientation might interfere with the results. Inspecting the data however we found three patients in both the visual and the tactile trial that made a right-left mistake, all these patients also made mistakes in naming the fingers. This means that all the patients that made a mistake in this study, experienced difficulties naming the fingers.
We could not detect a difference between MCI and the normal group in finger identification. Finger agnosia therefore might be detectable only in the stage when other cognitive domains aside memory are also affected, as in AD. We indeed found a strong association between global cognitive
functioning and the FIT. Considering the definition of probable AD (DSM IV-TR, 2000), more than one domain (memory) should be affected and finger identification implies more than the memory function. Finger identification therefore seems suitable and a helpful addition in diagnosing probable AD with more certainty. A score underneath ten on the visual trial is indicative of AD in this
explorative study population. When a bigger sample size is used for analyses, a cut off score can be obtained underneath which the score is indicative of AD.
Inspecting the data, we however observed a ceiling effect (Cohen et al., 2010). Maybe this is why we could not find differences between the MCI and normal group. Some adaptations of the test are necessary to study its usefulness in early stages, and even MCI. In the further development of the Finger Identification Test a measurement of time, or a time limit, could contribute to the sensitivity and specificity of the test. In this study differences in reaction time are not reported, although we did observe subjectively that some people needed much more time to identify fingers. Furthermore longitudinal research is needed to detect the phase were finger identification is deteriorating. Considering the neuropathological course of AD where both the hippocampal area , associated with memory, and the association areas, associated with gnosis, are affected in an early stage of the disease, it is unclear how the deterioration of finger identification is developing compared to memory
impairment (Perl, 2010; Hijdra, 2011; Kessels, 2012; Davis, 2012). We do know that functional brain changes can precede structural changes (Schmand et al. 2014). Therefore functional brain imaging techniques are a valuable addition to the detection of early brain and cognitive disfunctioning. To the best of our knowledge, finger gnosis has not yet been studied using Functional Magnetic Resonance Imaging (Fmri). The FIT seems an easy to apply, costless test to add to this kind of research in AD. The FIT is a very practical and useful contribution to neuropsychological assessment. Elderly people are more subject to increasing fatigue, than younger patients. Therefore the briefness of the test is suitable for geriatric settings. Another advantage of the test is that it is suitable for people of all educational levels. This implies that a great part of the population is capable of doing this test, also lower educated or analphabetic people. We should however notice that we studied the test to the largest extent in patients from western cultures. The test and the development of neuropsychological assessment however can be of great use is our western society, where AD is expanding and affects a great part of our society. For clinical practice the FIT is also easy to implement because it is free of costs. In the Bronovo Hospital we qualitatively noticed that the FIT helps out, as an addition to the neuropsychological test battery, in the discussion if a patients should get a probable AD diagnosis rather than a MCI diagnosis
For future explorative studies we also suggest the use of other easy to apply tests in
neuropsychological assessment and the exploration of lesser known signs of AD. In this study we focused on a specific form of agnosia, but there are more types of agnosia that were associated with AD. Warrington and McCarthy found category specific deficits in patients with dementia, for example the inability to name ‘living’ objects such as a carrot (Warrington & McCarthy, 1987; Deelman, Elin, de Haan, van Zomeren, 2010). We could think of developing a memory test with ‘living’ objects instead of pictures or words. Patients with agnosia can also experience difficulties in describing an object from memory, for example the colour of a banana (Martin et al., 1996). Neuropsychological assessment is particularly suitable for detecting the loss of specific abilities associated with agnosia and dementia.
Furthermore we suggest interest in testing the abilities that are associated with specific areas in the temporoparietal association cortex, which is thought to be one of the first areas that is affected by neuritic plaques due to AD (Hijdra, 2011). Harasty et al. (1999) found that only specific parts in the temporoparietal association cortex show atrophy due to AD, e.g. the medial temporal gyri, which is thought to be involved in identifying and naming tools (Gazanniga et al. 2010), and the inferior temporal gyri, which is thought to be involved in naming animals (Gazanigga et al. 2010). Heschl’s gyri however, which is thought to be involved in hearing, does not show the pathological changes related to AD (Harasty et al., 1999). This theoretical background should help in choosing the modality to test.
In this study we conclude that the Finger Identification Test is suitable as an additional instrument in the diagnosis of probable AD. In its current construction it does however not seem useful to detect the earliest changes related to AD, like in MCI. Improving the FIT and focussing on other easy to apply tests in neuropsychological assessment that assess the lesser-known deficits in AD research, is of importance to get more insight into the cognitive changes in the prodromal phase, alongside memory deficits.
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Appendix A: Finger IdentificationTest
Vinger Identificatietest
Instructie Visuele Trial:
Laat patiënt de beide handen op het tafelblad leggen, met de vingers gespreid. Raak nu een vinger aan en vraag welke vinger dat is en van welke hand het een vinger is.
L. duim Goed/Fout R. duim Goed/Fout L. wijsvinger Goed/Fout R. ringvinger Goed/Fout L. pink Goed/Fout R. middelvinger Goed/Fout L. middelvinger Goed/Fout L. Ringvinger Goed/Fout R. Wijsvinger Goed/Fout R. Pink Goed/Fout Totaal fout : ….
Instructie Tactiele Trial:
‘Nu gaan we hetzelfde doen, maar dan met de ogen dicht. Sluit u de ogen maar.’ Let erop dat de vingers niet bewogen worden.
Raak de volgende vingers aan:
L. duim Goed/Fout R. duim Goed/Fout L. wijsvinger Goed/Fout R. ringvinger Goed/Fout L. pink Goed/Fout R. middelvinger Goed/Fout L. middelvinger Goed/Fout L. Ringvinger Goed/Fout R. Wijsvinger Goed/Fout R. Pink Goed/Fout Totaal fout : ….
