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Neurological abnormalities and neurocognitive functions in healthy elder people: A structural equation modeling analysis

Neurological abnormalities and neurocognitive functions in healthy elder people: A structural... Background/Aims: Neurological abnormalities have been reported in normal aging population. However, most of them were limited to extrapyramidal signs and soft signs such as motor coordination and sensory integration have received much less attention. Very little is known about the relationship between neurological soft signs and neurocognitive function in healthy elder people. The current study aimed to examine the underlying relationships between neurological soft signs and neurocognition in a group of healthy elderly. Methods: One hundred and eighty healthy elderly participated in the current study. Neurological soft signs were evaluated with the subscales of Cambridge Neurological Inventory. A set of neurocognitive tests was also administered to all the participants. Structural equation modeling was adopted to examine the underlying relationship between neurological soft signs and neurocognition. Results: No significant differences were found between the male and female elder people in neurocognitive function performances and neurological soft signs. The model fitted well in the elderly and indicated the moderate associations between neurological soft signs and neurocognition, specifically verbal memory, visual memory and working memory. Conclusions: The neurological soft signs are more or less statistically equivalent to capture the similar information done by conventional neurocognitive function tests in the elderly. The implication of these findings may serve as a potential neurological marker for the early detection of pathological aging diseases or related mental status such as mild cognitive impairment and Alzheimer’s disease. Keywords: neurological soft signs, neurocognitive impairments, elderly, Chinese Background with advancing age [8]. Past research on neurological Neurological soft signs are minor neurological abnorm- abnormalities in elderly individuals has primarily alities in sensory and motor function commonly focused on evaluating extrapyramidal disturbances or reported in disorders such as schizophrenia spectrum focal signs, which are collectively known as “hard” signs disorders [1,2], autism spectrum disorders [3], and [9-13]. These are impairments of basic motor and sen- obsessive-compulsive disorders [4]. Recent studies sug- sory function, corresponding to alterations in focal and gest that healthy people at different developmental localized brain areas, such as the basal ganglia [14], and stages in their lives also exhibit differential base-rates of they have been reported as increased in aging popula- neurological signs [5-7]. Thisisparticularlytruefor tions. In contrast, although minor neurological abnorm- elderly individuals, because neurological signs increase alities have been observed in pathological aging populations, such as Alzheimer’s disease and Parkinson’s disease, the prevalence of neurological soft signs (such * Correspondence: rckchan@psych.ac.cn as rapid alternate finger tapping or astereognosis) in the Neuropsychology and Applied Cognitive Neuroscience Laboratory, Institute of Psychology, Chinese Academy of Sciences, Beijing, China healthy aging population has not been examined. Full list of author information is available at the end of the article © 2011 Chan et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Chan et al. Behavioral and Brain Functions 2011, 7:32 Page 2 of 7 http://www.behavioralandbrainfunctions.com/content/7/1/32 In contrast to hard signs, the presence of neurological healthy young adults, it was hypothesized that a similar soft signs has been thought to indicate a diffuse damage structural equation model would be observed in healthy of the brain, not related to damage of a specific brain elderly participants region. However, recent structural imaging studies show that neurological soft signs are significantly associated Method with volume changes in specific brain regions, and Ethics Statement reductions in gray matter in both neuropsychiatric dis- The present study was approved by the ethics commit- orders and healthy volunteers [15,16]. Moreover, most tee of the Institute of Psychology, Chinese Academy of recent functional imaging studies also show that, Sciences. Written informed consent was obtained by although no specific single region is responsible for participants. these neurological soft signs, there is a specific neural network linking the right inferior prefrontal cortex to Participants the middle prefrontal cortex underlying their presence One hundred and eighty healthy elderly participants (86 [17,18]. men and 94 women) were recruited from several pro- This is particularly interesting considering that the vinces of China, through household visits and local frontal hypothesis of cognitive aging suggests that the advertisements. Exclusion criteria were: (1) a history of prefrontal cortex deteriorates earlier and disproportio- head trauma resulting in loss of consciousness for > 1 h; nately in comparison to the rest of the cortex [19], (2) a diseases of the central nervous system; (3) a history showing a decline in volume, white matter density, and of any psychotic disorder; (4) current and past alcohol synaptic density [20]. Consistent with changes in frontal or substance abuse. cortex, is the robust finding of a general decline in All participants were evaluated with the Mini Mental executive function (critically regulated by the frontal State Examination (MMSE) [25], which includes tests of cortex) associated with physiological aging processes (e. learning, memory, and verbal ability, to assess cognitive g., [21-23]). It is of note that in fact neurological soft dysfunction. An MMSE score lower than 24 points was signs and cognitive deficits have been thought to be considered to indicate a probable diagnosis of Alzheimer functional correlates of the same pathophysiological Disease [26]. substrate. Interestingly, arecent studyfromour group[24], Materials and procedure adopting structural equation modeling, showed that The Cambridge Neurological Inventory (CNI) was used motor coordination, sensory integration, and disinhibi- for the assessment of neurological soft signs [27]. The tion contributed to the latent construct of neurological CNI has been applied to different age groups in both soft signs, whereas the subset of neurocognitive function healthy and clinical Chinese populations [6,28,29]. The tests contributed to the latent constructs of executive administration the CNI has been described elsewhere attention, verbal memory, and visual memory. Greater (e.g., [6,27]). In brief, The CNI soft signs assessment evidence of neurological soft signs is associated with consisted of motor coordination (e.g., rapid finger tap- more severe impairment of executive attention and ping, fist-edge-palm), sensory integration (e.g., aster- memory functions in a group of patients with schizo- eognosis, agraphesthesia), and inhibition (e.g., mirror phrenia and healthy controls independently. movement, eye blink while performing eye tracking). Therefore, it is important to explore the prevalence Each item were assessed as either “absent” (which cov- rates of neurological soft signs, and their relationship to ered the normal or equivocal scale scores) or “present” executive and other neurocognitive functions in the cog- (which covered the abnormal or grossly abnormal scale nitively intact elderly people. Unfortunately, existing stu- scores). Items that could be scored on both the left dies have been limited to the evaluation of neurological and the right were each treated as independent scores. hard signs, and almost all of them have evaluated indivi- All items were rated by a trained research assistant. duals of Caucasian origin. In contrast, very little is We administered the CNI in a standardized manner, known about individuals from other ethnic groups such as specified for each item, and according to a fixed as the Chinese. order. Thepurposeof thecurrent study was to examinethe The logical memory and visual reproduction subtests underlying relationship between neurological soft signs selected from the Chinese version of the Wechsler and neurocognitive function in a sample of healthy Chi- Memory Scale-Revised [30] were used to measure verbal nese elderly people by means of a standardized neurolo- memory and visual memory. For logical memory, the gical signs scale. Given the previous findings of more or participants was told a short story and asked to recall it less equivalent constructs between neurological soft immediately and again 30 minutes later. For visual signs and neurocognitive functions in schizophrenia and reproduction, the participant was shown two pictures Chan et al. Behavioral and Brain Functions 2011, 7:32 Page 3 of 7 http://www.behavioralandbrainfunctions.com/content/7/1/32 (one at a time) for 10s each, and asked to draw them and neurological soft signs (Motor Coordination, Sen- immediately and again 30 min later. sory Integration, and Disinhibition) were covariant. Working memory was assessed by the Chinese version The SEM was conducted using LISREL 8.70 for Win- of the Letter-Number Span Test (Chinese LN span) [31] dows. All the variables were standardized before being andthe forwardand backward digitspantests.Inthe entered in the model. Five fit indices were recorded to Letter-Number sequencing task, the experimenters verb- evaluate the fitness of goodness of the model. They are ally presented a specified sequence of random digits (1 the Root Mean Square Error of Approximation to 9) mixed with Chinese characters (in a specific (RMSEA), Normed Fit Index (NFI), Non-Normed Fit Index (NNFI), Comparative Fit Index (CFI), and Good- sequence,suchasA, B,Cin English) at arateofone per second. Participants were asked to rearrange the ness of Fit Index (GFI). These indices represent the order so that the digits came first, from small to big, improvement in fit between the assumed model and the and then the characters, in sequence. The number of baseline model of uncorrelatedness between the digits and letters increased with 1 digit or character observed variables. The first four fit indices values of .90 until the participant incorrectly repeated two trials of or above, and an RMSEA value of .08 or less indicated the same length. the model adequately fits the data [32]. Data analysis Results We focused on evaluating the relationship between neu- The age of the participants ranged between 60 to 96 rological soft signs and neurocognitive function with years (mean age 69.35, SD = 6.53). We divided 180 par- structure equation model (SEM). It is hypothesized that ticipants into three group according to the age. The neurocognitive functions co-vary with neurological soft summary scores of neurocognitive function and soft signs. Neurocognitive functions and neurological soft signs were displayed in Table 1. There was a significant signs were latent variables, as measured by the corre- tendency for groups with elder subjects have worse per- sponding scales. Specifically, Logical Memory Immediate formance on logical memory, visual memory (delay Recall and Logical Memory Delayed Recall were indices score) and soft signs total and sensory integration score. of “Verbal Memory"; and the Visual Reproduction However, the tendency was plus to nonsignificant and Immediate Recall and Visual Reproduction Delayed significant for the neurocognitive tasks and soft signs, Recall were indices of “Visual Memory"; the number of respectively, when education was took in to account as correct passed and the number of longest passed items covariance in ANCOVA. described the “Working Memory”.Onthe otherhand, There were no differences in age between males and the Motor Coordination, Sensory Integration, and Disin- females (t = 0.48, p = 0.63). The mean years of educa- hibition contributed to “Neurological Soft Signs”.The tion was 6.84 (SD = 4.38), and education in males was relationship between neurocognitive functions, i.e., Ver- higher than in females (t = 2.07, p = 0.04). The MMSE bal Memory, Visual Memory, and Working Memory scores did not differ between males and females (t = Table 1 Descriptive statistics for neurocognitive functions and neurological soft signs scores for different age groups Age range 60-69 70-79 >= 80 ANOVA (df = 2,177) (n = 109) (n = 56) (n = 15) Mean SD Mean SD Mean SD F p-value Age (years) 65.07 2.80 73.91 2.79 83.40 4.12 358.28 0.000 Education (number of years) 26.37 3.02 25.20 3.34 24.53 3.64 3.92 0.022 MMSE 7.70 3.90 5.57 4.52 5.27 5.70 5.73 0.004 Logical Memory 8.63 3.89 6.93 3.94 6.27 3.79 4.98 0.008 Logical Memory (Delay) 6.56 3.74 4.70 3.54 4.80 3.38 5.48 0.005 Visual reproduction 17.06 5.21 15.25 6.16 14.47 5.33 2.85 0.061 Visual reproduction (Delayed) 14.94 6.20 12.14 6.78 11.73 5.15 4.53 0.012 LN span items passed 8.28 4.47 6.96 4.14 7.52 6.96 1.55 0.216 LN span longest passed 3.83 1.80 3.36 1.84 3.11 2.51 1.76 0.175 Motor coordination 1.80 1.63 2.25 1.80 2.40 1.45 1.85 0.160 Sensory integration 1.70 1.44 2.57 1.54 2.80 1.61 8.46 0.000 Disinhibition 1.73 1.40 2.04 1.40 2.07 1.39 1.05 0.353 Total NSS score 5.23 3.21 6.86 3.39 7.27 3.31 5.99 0.003 LN span: Letter-Number Span Test; MMSE: Mini Mental Status Examination; NSS: Neurological Soft Signs Chan et al. Behavioral and Brain Functions 2011, 7:32 Page 4 of 7 http://www.behavioralandbrainfunctions.com/content/7/1/32 1.15, 0.25), with a mean of 25.85 (SD = 3.23). Table 2 Very few data have been reported on the lifespan perfor- displays the means, SDs and the independent sample t- mance of neurological soft signs in healthy sample. A test results comparison across gender. In neurocognitive longitudinal study has found that neurological soft signs and neurological soft signs performance there were also diminished with age from childhood to adolescence no significant differences between males and females. [33]. The current study did suggest that the prevalence There was however .05 <p < .10 in Logical Memory per- of neurological soft signs increase with age for elderly formance, where males performed better than females, people. Though there is formal publishable data specifi- and for sensory integration, where females tended to cally for the normal aging sample, the data implicated have more sensory integration signs than males. Table 3 by the healthy volunteers as controls for Alzheimer’s shows prevalence rate, chi-square value, and Fisher’sp disease suggest a base-rate of soft signs in this sample, of neurological soft signs for each item. Only the rate of with motor coordination signs as the most commonly one item (Graphesthesia R) was significantly different endorsed items [29]. The significant and robust associa- between males and females. On average, there were no tions found between neurological soft signs and neuro- significant differences found between males and females cognitive function were also consistent with findings in the prevalence rate of individual items of neurological from studies conducted in individuals with schizophre- soft signs. nia and Alzheimer’s disease [29,34-36] suggesting that The model showed a good fit of the structure in this maybe there is common neural substrates between neu- elderly sample. Figure 1 shows the structure path and rological soft signs and neurocognitve function. Most the loadings of the model. The structural paths from recently, Chan et al. [24] adopted a structural equation neurological soft signs to verbal memory, visual memory modeling approach evaluating neurological soft signs and working memory were -0.49, -0.68, and -0.70, and conventional neurocognitive functioning in schizo- respectively. All loadings were statistically significant phrenia and healthy volunteers, and showed that these except the one from neurological soft signs and disinhi- two constructs (neurological soft signs and neurocogni- bition. The chi-square was 60.98 (p < 0.001) with degree tive functioning) significantly overlap, with regression of freedom = 30. The fitness index indicated relatively coefficients higher than 0.5, and capture a similar infor- well fit of the model, RMSEA = 0.076, NFI = 0.93, mation of these two constructs. Interestingly, the same NNFI = 0.96, CFI = 0.97, GFI = 0.93, and Standardized model was observed in the healthy controls. Taken RMR = 0.064. together, these findings suggest that this model is rela- tively robust. However, it should be noted that the sta- Discussion tistical model cannot confirm that neurological soft signs and neurocognitive function share the same neural The findings suggest that neurological soft signs are very common among elderly people. Like neurological hard substrates. The underlying neural mechanism needs to signs such as basal ganglia signs, soft signs appear also be further studied by rigorous experimental design for a to increase in prevalence with advancing age [5,10]. better understanding of the relationship between neuro- logical soft signs and neurocognitive functions in the near future. Table 2 Comparison of neurocognitive functions and The negative correlation between neurological soft neurological soft signs between male and female signs and cognitive performance in our study is consis- Male Female t-test tent with the previous findings that have shown that (N = 86) (N = 94) (df = 178) there were significant differences in neurological soft Mean SD Mean SD T p- signs between older people with and without cognitive value impairment in the Chinese setting. For example, Lam et Logical memory 8.50 4.06 7.36 3.85 1.93 0.055 al. [29] showed that patients with Alzheimer’sdisease Logical memory (Delayed) 6.31 3.87 5.39 3.58 1.66 0.099 have a significantly higher prevalence of neurological Visual reproduction 16.85 4.96 15.76 6.08 1.33 0.187 soft signs than those without dementia. In their clinical Visual reproduction 14.08 6.11 13.55 6.77 0.55 0.582 sample, Lam et al. [29] found the CNI soft signs sub- (Delayed) scales to be very sensitive in discriminating between LN span items passed 8.16 4.74 7.46 5.24 0.86 0.389 cases with dementia and cases without, and this was LN span longest passed 3.85 1.90 3.36 2.08 1.53 0.129 particularly the case for motor coordination signs and Motor coordination 2.00 1.71 1.98 1.66 0.08 0.933 sensory integration signs. Our findings further support Sensory integration 1.85 1.35 2.26 1.68 -1.79 0.075 this claim; at least it is sensitive enough to detect cogni- Disinhibition 1.69 1.36 2.01 1.43 -1.56 0.120 tively impaired and intact cases in terms of motor coor- Total NSS score 5.53 3.32 6.24 3.38 -1.42 0.158 dination. Genetic studies from schizophrenia also LN span: Letter-Number Span Test; MMSE: Mini Mental Status Examination; suggest that a 7 nicotinic cholinergic receptor [37], NSS: Neurological Soft Signs Chan et al. Behavioral and Brain Functions 2011, 7:32 Page 5 of 7 http://www.behavioralandbrainfunctions.com/content/7/1/32 Table 3 Prevalence rates of individual neurological soft signs items Item Male Female (N = 86) (N = 94) No. % No. % chi-square Fisher’s Exact p Finger-thumb tapping L 1 1.16% 2 2.13% 0.255 1.000 Finger-thumb tapping R 1 1.16% 0 0.00% 1.099 0.478 Finger-thumb opposition L 13 15.12% 21 22.34% 1.530 0.255 Finger-thumb opposition R 19 22.09% 19 20.21% 0.095 0.855 Mirror 1 L 14 16.28% 20 21.28% 0.732 0.448 Mirror 1 R 21 24.42% 19 20.21% 0.460 0.591 Diadochokinesia L 10 11.63% 8 8.51% 0.485 0.620 Diadochokinesia R 9 10.47% 4 4.26% 2.585 0.150 Mirror 2 L 8 9.30% 15 15.96% 1.785 0.264 Mirror 2 R 3 3.49% 10 10.64% 3.426 0.085 Fist-edge palm L 45 52.33% 46 48.94% 0.206 0.658 Fist-edge palm R 34 39.53% 41 43.62% 0.308 0.650 Oszeretsky test 40 46.51% 45 47.87% 0.033 0.882 Extinction 2 2.33% 5 5.32% 1.077 0.447 Finger Agnosia L 45 52.33% 54 57.45% 0.476 0.549 Finger Agnosia R 33 38.37% 43 45.74% 1.001 0.366 Stereognosia L 7 8.14% 4 4.26% 1.181 0.365 Stereognosia R 3 3.49% 6 6.38% 0.792 0.051 Graphesthesia L 18 20.93% 30 31.91% 2.771 0.129 Graphesthesia R 26 30.23% 43 45.74% 4.572 0.046 L-R Orientation 25 29.07% 27 28.72% 0.003 1.000 Saccade BLK 5 5.81% 9 9.57% 0.885 0.412 Saccade Head 36 41.86% 31 32.98% 1.516 0.280 Wink 30 34.88% 41 43.62% 1.434 0.285 Go/No-Go 28 32.56% 44 46.81% 3.800 0.067 catechol-O-methytransferase (COMT) and GRM3 that these age group would therefore show even higher genetic variation [38] may be related to the presence of neurological soft signs rates. Third, the current study neurological soft signs. Taken together, the use of neu- did not assess IQ, which has been considered to be rological soft signs may serve as potential sensitive bed- highly associated with neurological soft signs (e.g., [28]). side screening tool for neuropsychiatric or However, since there were no differences in education neurodegenerative disorders. (an indirect measure of IQ), and since controlling for The current study has several limitations. First, our neurocognitive functioning did not affect the results, we findings should be considered preliminary. Because of suggest that the current findings could not be comple- the small sample size and the non-stratified sample tely explained by potential IQ difference between the selection, they cannot be considered to represent preva- two groups. Notwithstanding these limitations, the current study is lence rates of neurological soft signs in Chinese elderly people. The data should not be generalized to the one of the very few studies specifically examining the elderly population of China, which has a total popula- prevalence rates of neurological soft signs in healthy tion of 1.3 billion. About 100.45 million people are over aging people, and Chinese in particular. The observed 65 years of age, occupying 7.69% of the overall popula- prevalence of neurological soft signs adds further evi- tion in China [39]. Secondly, we could not subdivide the dence to the geriatric literature on neurological impair- sample into subgroups of old-old and oldest-old, ments in cognitively intact people, and may provide a because of the relatively limited number of people over base-rate for identifying a potential clinical cut-off in the age of 85. Empirical findings suggest that this old- thenearfuture. Themoderateassociationsof the pre- est-old age group is particularly at risk of developing sence of neurological soft signs in the parent-offspring neurological disorders or neurodegenerative illnesses family units suggest a robust heritability [41]. This study such as dementia or Alzheimer’sdisease,withpreva- suggests that neurological soft signs may represent a lence rates as high as 47% [40]. It could be expected potential neurological marker for the early detection of Chan et al. Behavioral and Brain Functions 2011, 7:32 Page 6 of 7 http://www.behavioralandbrainfunctions.com/content/7/1/32 Logic Memory 0.90 Immed recall Verbal Memory 0.91 Logic Memory Motor Delayed recall -0.49 Coordination 0.52 Visual Rep 0.98 -0.68 Immed recall Sensory 0.49 Neurological Visual Integration Soft Signs Memory 0.84 Visual Rep 0.11 Delayed recall -0.70 Disinhibition Letter Num Span 0.95 Working correct passed Memory 0.97 Letter Num Span Longest passed Figure 1 The structure model of influence of soft signs on cognitive function in elderly. Chi-square = 60.98, p < 0.001, df = 30, NFI = 0.93, NNFI = 0.96, CFI = 0.97, GFI = 0.93, RMSEA = 0.076, Standardized RMR = 0.064. Authors’ contributions pathological aging diseases such as mild cognitive RCKC conceived the idea, designed the study, and wrote the first draft of impairment and Alzheimer’s disease. Future studies the paper. XT collected and analyzed the data. HJL, QZ, HHL, YW, CY, XYC, using a population-based design could be important to YNW, YFS collected the data and assisted data analysis. PD participated in writing up the paper. All authors read and approved the final manuscript. cross-validate our findings. Competing interests The authors declare that they have no competing interests. List of abbreviations BLK: Blink; CFI: Comparative Fit Index; CNI: Cambridge Neurological Received: 19 March 2011 Accepted: 10 August 2011 Inventory; COMT: catechol-O-methytransferase; GFI: Goodness of Fit Index; Published: 10 August 2011 IQ: Intellectual Quotient; LISREL: A software for computing the structural equation modeling; L: left; LN span: Letter-Number Span; MMSE: Mini Mental References State Examination; NFI: Normed Fit Index; NNFI: Non-Normed Fit Index; R: 1. 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Chan RCK, Wang Y, Wang L, Chen EYH, Manschreck TC, Li ZJ, Yu X, Gong QY: Neurological soft signs and their relationships to neurocognitive functions: a re-visit with the structural equation modeling design. Plos One 2009, 4:e8469. 25. Wang ZY, Zhang MY, Qu GY, Chen JX, Zhao J: The application of the Chinese version of Mini Mental State Examination. Shanghai Psychiatry Medicine 1989, 7:108-111. 26. Zhang MY, Qu GY, Jin H, Cai GJ, Wang ZY: Commparison of measures for dementia. Chinese Journal of Psychiatry 1991, 24:194-196 (in Chinese). 27. Chen EYH, Shapleske J, Luque R, Mckenna PJ, Hodges JR, Calloway SP, Hymas NFS, Dening TR, Berrios GE: The Cambridge Neurological Inventory: a clinical instrument for assessment of soft neurological signs in psychiatric patients. Psychiat Res 1995, 56:183-204. Submit your next manuscript to BioMed Central 28. Chen EYH, Chan RCK: The Cambridge Neurological Inventory: clinical, and take full advantage of: demographic, and ethnic correlates. Psychiat Ann 2003, 33:202-210. 29. Lam LCW, Lui VWC, Chiu HFK: Association beween soft neurological signs • Convenient online submission and clinical progression Alzheimer’s disease. Hong Kong Journal of Psychiatry 2005, 15:43-49. • Thorough peer review 30. Gong YX, Jiang DW, Deng JL, Dai ZS, Zhou QZ, Xie GY, Li Y, Hua XX: • No space constraints or color figure charges Manual of Wechsler Memory Scale: Chinese version Changsha: Hunan Medical • Immediate publication on acceptance College; 1989. 31. Chan RCK, Wang Y, Deng YY, Zhang YN, Yiao XL, Zhang C: The • Inclusion in PubMed, CAS, Scopus and Google Scholar development of a Chinese equivalence version of Letter-Number Span • Research which is freely available for redistribution Test. Clin Neuropsychol 2008, 22:112-121. Submit your manuscript at www.biomedcentral.com/submit http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Behavioral and Brain Functions Springer Journals

Neurological abnormalities and neurocognitive functions in healthy elder people: A structural equation modeling analysis

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Springer Journals
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Copyright © 2011 by Chan et al; licensee BioMed Central Ltd.
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Biomedicine; Neurosciences; Neurology; Behavioral Therapy; Psychiatry
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1744-9081
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10.1186/1744-9081-7-32
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21827719
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Abstract

Background/Aims: Neurological abnormalities have been reported in normal aging population. However, most of them were limited to extrapyramidal signs and soft signs such as motor coordination and sensory integration have received much less attention. Very little is known about the relationship between neurological soft signs and neurocognitive function in healthy elder people. The current study aimed to examine the underlying relationships between neurological soft signs and neurocognition in a group of healthy elderly. Methods: One hundred and eighty healthy elderly participated in the current study. Neurological soft signs were evaluated with the subscales of Cambridge Neurological Inventory. A set of neurocognitive tests was also administered to all the participants. Structural equation modeling was adopted to examine the underlying relationship between neurological soft signs and neurocognition. Results: No significant differences were found between the male and female elder people in neurocognitive function performances and neurological soft signs. The model fitted well in the elderly and indicated the moderate associations between neurological soft signs and neurocognition, specifically verbal memory, visual memory and working memory. Conclusions: The neurological soft signs are more or less statistically equivalent to capture the similar information done by conventional neurocognitive function tests in the elderly. The implication of these findings may serve as a potential neurological marker for the early detection of pathological aging diseases or related mental status such as mild cognitive impairment and Alzheimer’s disease. Keywords: neurological soft signs, neurocognitive impairments, elderly, Chinese Background with advancing age [8]. Past research on neurological Neurological soft signs are minor neurological abnorm- abnormalities in elderly individuals has primarily alities in sensory and motor function commonly focused on evaluating extrapyramidal disturbances or reported in disorders such as schizophrenia spectrum focal signs, which are collectively known as “hard” signs disorders [1,2], autism spectrum disorders [3], and [9-13]. These are impairments of basic motor and sen- obsessive-compulsive disorders [4]. Recent studies sug- sory function, corresponding to alterations in focal and gest that healthy people at different developmental localized brain areas, such as the basal ganglia [14], and stages in their lives also exhibit differential base-rates of they have been reported as increased in aging popula- neurological signs [5-7]. Thisisparticularlytruefor tions. In contrast, although minor neurological abnorm- elderly individuals, because neurological signs increase alities have been observed in pathological aging populations, such as Alzheimer’s disease and Parkinson’s disease, the prevalence of neurological soft signs (such * Correspondence: rckchan@psych.ac.cn as rapid alternate finger tapping or astereognosis) in the Neuropsychology and Applied Cognitive Neuroscience Laboratory, Institute of Psychology, Chinese Academy of Sciences, Beijing, China healthy aging population has not been examined. Full list of author information is available at the end of the article © 2011 Chan et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Chan et al. Behavioral and Brain Functions 2011, 7:32 Page 2 of 7 http://www.behavioralandbrainfunctions.com/content/7/1/32 In contrast to hard signs, the presence of neurological healthy young adults, it was hypothesized that a similar soft signs has been thought to indicate a diffuse damage structural equation model would be observed in healthy of the brain, not related to damage of a specific brain elderly participants region. However, recent structural imaging studies show that neurological soft signs are significantly associated Method with volume changes in specific brain regions, and Ethics Statement reductions in gray matter in both neuropsychiatric dis- The present study was approved by the ethics commit- orders and healthy volunteers [15,16]. Moreover, most tee of the Institute of Psychology, Chinese Academy of recent functional imaging studies also show that, Sciences. Written informed consent was obtained by although no specific single region is responsible for participants. these neurological soft signs, there is a specific neural network linking the right inferior prefrontal cortex to Participants the middle prefrontal cortex underlying their presence One hundred and eighty healthy elderly participants (86 [17,18]. men and 94 women) were recruited from several pro- This is particularly interesting considering that the vinces of China, through household visits and local frontal hypothesis of cognitive aging suggests that the advertisements. Exclusion criteria were: (1) a history of prefrontal cortex deteriorates earlier and disproportio- head trauma resulting in loss of consciousness for > 1 h; nately in comparison to the rest of the cortex [19], (2) a diseases of the central nervous system; (3) a history showing a decline in volume, white matter density, and of any psychotic disorder; (4) current and past alcohol synaptic density [20]. Consistent with changes in frontal or substance abuse. cortex, is the robust finding of a general decline in All participants were evaluated with the Mini Mental executive function (critically regulated by the frontal State Examination (MMSE) [25], which includes tests of cortex) associated with physiological aging processes (e. learning, memory, and verbal ability, to assess cognitive g., [21-23]). It is of note that in fact neurological soft dysfunction. An MMSE score lower than 24 points was signs and cognitive deficits have been thought to be considered to indicate a probable diagnosis of Alzheimer functional correlates of the same pathophysiological Disease [26]. substrate. Interestingly, arecent studyfromour group[24], Materials and procedure adopting structural equation modeling, showed that The Cambridge Neurological Inventory (CNI) was used motor coordination, sensory integration, and disinhibi- for the assessment of neurological soft signs [27]. The tion contributed to the latent construct of neurological CNI has been applied to different age groups in both soft signs, whereas the subset of neurocognitive function healthy and clinical Chinese populations [6,28,29]. The tests contributed to the latent constructs of executive administration the CNI has been described elsewhere attention, verbal memory, and visual memory. Greater (e.g., [6,27]). In brief, The CNI soft signs assessment evidence of neurological soft signs is associated with consisted of motor coordination (e.g., rapid finger tap- more severe impairment of executive attention and ping, fist-edge-palm), sensory integration (e.g., aster- memory functions in a group of patients with schizo- eognosis, agraphesthesia), and inhibition (e.g., mirror phrenia and healthy controls independently. movement, eye blink while performing eye tracking). Therefore, it is important to explore the prevalence Each item were assessed as either “absent” (which cov- rates of neurological soft signs, and their relationship to ered the normal or equivocal scale scores) or “present” executive and other neurocognitive functions in the cog- (which covered the abnormal or grossly abnormal scale nitively intact elderly people. Unfortunately, existing stu- scores). Items that could be scored on both the left dies have been limited to the evaluation of neurological and the right were each treated as independent scores. hard signs, and almost all of them have evaluated indivi- All items were rated by a trained research assistant. duals of Caucasian origin. In contrast, very little is We administered the CNI in a standardized manner, known about individuals from other ethnic groups such as specified for each item, and according to a fixed as the Chinese. order. Thepurposeof thecurrent study was to examinethe The logical memory and visual reproduction subtests underlying relationship between neurological soft signs selected from the Chinese version of the Wechsler and neurocognitive function in a sample of healthy Chi- Memory Scale-Revised [30] were used to measure verbal nese elderly people by means of a standardized neurolo- memory and visual memory. For logical memory, the gical signs scale. Given the previous findings of more or participants was told a short story and asked to recall it less equivalent constructs between neurological soft immediately and again 30 minutes later. For visual signs and neurocognitive functions in schizophrenia and reproduction, the participant was shown two pictures Chan et al. Behavioral and Brain Functions 2011, 7:32 Page 3 of 7 http://www.behavioralandbrainfunctions.com/content/7/1/32 (one at a time) for 10s each, and asked to draw them and neurological soft signs (Motor Coordination, Sen- immediately and again 30 min later. sory Integration, and Disinhibition) were covariant. Working memory was assessed by the Chinese version The SEM was conducted using LISREL 8.70 for Win- of the Letter-Number Span Test (Chinese LN span) [31] dows. All the variables were standardized before being andthe forwardand backward digitspantests.Inthe entered in the model. Five fit indices were recorded to Letter-Number sequencing task, the experimenters verb- evaluate the fitness of goodness of the model. They are ally presented a specified sequence of random digits (1 the Root Mean Square Error of Approximation to 9) mixed with Chinese characters (in a specific (RMSEA), Normed Fit Index (NFI), Non-Normed Fit Index (NNFI), Comparative Fit Index (CFI), and Good- sequence,suchasA, B,Cin English) at arateofone per second. Participants were asked to rearrange the ness of Fit Index (GFI). These indices represent the order so that the digits came first, from small to big, improvement in fit between the assumed model and the and then the characters, in sequence. The number of baseline model of uncorrelatedness between the digits and letters increased with 1 digit or character observed variables. The first four fit indices values of .90 until the participant incorrectly repeated two trials of or above, and an RMSEA value of .08 or less indicated the same length. the model adequately fits the data [32]. Data analysis Results We focused on evaluating the relationship between neu- The age of the participants ranged between 60 to 96 rological soft signs and neurocognitive function with years (mean age 69.35, SD = 6.53). We divided 180 par- structure equation model (SEM). It is hypothesized that ticipants into three group according to the age. The neurocognitive functions co-vary with neurological soft summary scores of neurocognitive function and soft signs. Neurocognitive functions and neurological soft signs were displayed in Table 1. There was a significant signs were latent variables, as measured by the corre- tendency for groups with elder subjects have worse per- sponding scales. Specifically, Logical Memory Immediate formance on logical memory, visual memory (delay Recall and Logical Memory Delayed Recall were indices score) and soft signs total and sensory integration score. of “Verbal Memory"; and the Visual Reproduction However, the tendency was plus to nonsignificant and Immediate Recall and Visual Reproduction Delayed significant for the neurocognitive tasks and soft signs, Recall were indices of “Visual Memory"; the number of respectively, when education was took in to account as correct passed and the number of longest passed items covariance in ANCOVA. described the “Working Memory”.Onthe otherhand, There were no differences in age between males and the Motor Coordination, Sensory Integration, and Disin- females (t = 0.48, p = 0.63). The mean years of educa- hibition contributed to “Neurological Soft Signs”.The tion was 6.84 (SD = 4.38), and education in males was relationship between neurocognitive functions, i.e., Ver- higher than in females (t = 2.07, p = 0.04). The MMSE bal Memory, Visual Memory, and Working Memory scores did not differ between males and females (t = Table 1 Descriptive statistics for neurocognitive functions and neurological soft signs scores for different age groups Age range 60-69 70-79 >= 80 ANOVA (df = 2,177) (n = 109) (n = 56) (n = 15) Mean SD Mean SD Mean SD F p-value Age (years) 65.07 2.80 73.91 2.79 83.40 4.12 358.28 0.000 Education (number of years) 26.37 3.02 25.20 3.34 24.53 3.64 3.92 0.022 MMSE 7.70 3.90 5.57 4.52 5.27 5.70 5.73 0.004 Logical Memory 8.63 3.89 6.93 3.94 6.27 3.79 4.98 0.008 Logical Memory (Delay) 6.56 3.74 4.70 3.54 4.80 3.38 5.48 0.005 Visual reproduction 17.06 5.21 15.25 6.16 14.47 5.33 2.85 0.061 Visual reproduction (Delayed) 14.94 6.20 12.14 6.78 11.73 5.15 4.53 0.012 LN span items passed 8.28 4.47 6.96 4.14 7.52 6.96 1.55 0.216 LN span longest passed 3.83 1.80 3.36 1.84 3.11 2.51 1.76 0.175 Motor coordination 1.80 1.63 2.25 1.80 2.40 1.45 1.85 0.160 Sensory integration 1.70 1.44 2.57 1.54 2.80 1.61 8.46 0.000 Disinhibition 1.73 1.40 2.04 1.40 2.07 1.39 1.05 0.353 Total NSS score 5.23 3.21 6.86 3.39 7.27 3.31 5.99 0.003 LN span: Letter-Number Span Test; MMSE: Mini Mental Status Examination; NSS: Neurological Soft Signs Chan et al. Behavioral and Brain Functions 2011, 7:32 Page 4 of 7 http://www.behavioralandbrainfunctions.com/content/7/1/32 1.15, 0.25), with a mean of 25.85 (SD = 3.23). Table 2 Very few data have been reported on the lifespan perfor- displays the means, SDs and the independent sample t- mance of neurological soft signs in healthy sample. A test results comparison across gender. In neurocognitive longitudinal study has found that neurological soft signs and neurological soft signs performance there were also diminished with age from childhood to adolescence no significant differences between males and females. [33]. The current study did suggest that the prevalence There was however .05 <p < .10 in Logical Memory per- of neurological soft signs increase with age for elderly formance, where males performed better than females, people. Though there is formal publishable data specifi- and for sensory integration, where females tended to cally for the normal aging sample, the data implicated have more sensory integration signs than males. Table 3 by the healthy volunteers as controls for Alzheimer’s shows prevalence rate, chi-square value, and Fisher’sp disease suggest a base-rate of soft signs in this sample, of neurological soft signs for each item. Only the rate of with motor coordination signs as the most commonly one item (Graphesthesia R) was significantly different endorsed items [29]. The significant and robust associa- between males and females. On average, there were no tions found between neurological soft signs and neuro- significant differences found between males and females cognitive function were also consistent with findings in the prevalence rate of individual items of neurological from studies conducted in individuals with schizophre- soft signs. nia and Alzheimer’s disease [29,34-36] suggesting that The model showed a good fit of the structure in this maybe there is common neural substrates between neu- elderly sample. Figure 1 shows the structure path and rological soft signs and neurocognitve function. Most the loadings of the model. The structural paths from recently, Chan et al. [24] adopted a structural equation neurological soft signs to verbal memory, visual memory modeling approach evaluating neurological soft signs and working memory were -0.49, -0.68, and -0.70, and conventional neurocognitive functioning in schizo- respectively. All loadings were statistically significant phrenia and healthy volunteers, and showed that these except the one from neurological soft signs and disinhi- two constructs (neurological soft signs and neurocogni- bition. The chi-square was 60.98 (p < 0.001) with degree tive functioning) significantly overlap, with regression of freedom = 30. The fitness index indicated relatively coefficients higher than 0.5, and capture a similar infor- well fit of the model, RMSEA = 0.076, NFI = 0.93, mation of these two constructs. Interestingly, the same NNFI = 0.96, CFI = 0.97, GFI = 0.93, and Standardized model was observed in the healthy controls. Taken RMR = 0.064. together, these findings suggest that this model is rela- tively robust. However, it should be noted that the sta- Discussion tistical model cannot confirm that neurological soft signs and neurocognitive function share the same neural The findings suggest that neurological soft signs are very common among elderly people. Like neurological hard substrates. The underlying neural mechanism needs to signs such as basal ganglia signs, soft signs appear also be further studied by rigorous experimental design for a to increase in prevalence with advancing age [5,10]. better understanding of the relationship between neuro- logical soft signs and neurocognitive functions in the near future. Table 2 Comparison of neurocognitive functions and The negative correlation between neurological soft neurological soft signs between male and female signs and cognitive performance in our study is consis- Male Female t-test tent with the previous findings that have shown that (N = 86) (N = 94) (df = 178) there were significant differences in neurological soft Mean SD Mean SD T p- signs between older people with and without cognitive value impairment in the Chinese setting. For example, Lam et Logical memory 8.50 4.06 7.36 3.85 1.93 0.055 al. [29] showed that patients with Alzheimer’sdisease Logical memory (Delayed) 6.31 3.87 5.39 3.58 1.66 0.099 have a significantly higher prevalence of neurological Visual reproduction 16.85 4.96 15.76 6.08 1.33 0.187 soft signs than those without dementia. In their clinical Visual reproduction 14.08 6.11 13.55 6.77 0.55 0.582 sample, Lam et al. [29] found the CNI soft signs sub- (Delayed) scales to be very sensitive in discriminating between LN span items passed 8.16 4.74 7.46 5.24 0.86 0.389 cases with dementia and cases without, and this was LN span longest passed 3.85 1.90 3.36 2.08 1.53 0.129 particularly the case for motor coordination signs and Motor coordination 2.00 1.71 1.98 1.66 0.08 0.933 sensory integration signs. Our findings further support Sensory integration 1.85 1.35 2.26 1.68 -1.79 0.075 this claim; at least it is sensitive enough to detect cogni- Disinhibition 1.69 1.36 2.01 1.43 -1.56 0.120 tively impaired and intact cases in terms of motor coor- Total NSS score 5.53 3.32 6.24 3.38 -1.42 0.158 dination. Genetic studies from schizophrenia also LN span: Letter-Number Span Test; MMSE: Mini Mental Status Examination; suggest that a 7 nicotinic cholinergic receptor [37], NSS: Neurological Soft Signs Chan et al. Behavioral and Brain Functions 2011, 7:32 Page 5 of 7 http://www.behavioralandbrainfunctions.com/content/7/1/32 Table 3 Prevalence rates of individual neurological soft signs items Item Male Female (N = 86) (N = 94) No. % No. % chi-square Fisher’s Exact p Finger-thumb tapping L 1 1.16% 2 2.13% 0.255 1.000 Finger-thumb tapping R 1 1.16% 0 0.00% 1.099 0.478 Finger-thumb opposition L 13 15.12% 21 22.34% 1.530 0.255 Finger-thumb opposition R 19 22.09% 19 20.21% 0.095 0.855 Mirror 1 L 14 16.28% 20 21.28% 0.732 0.448 Mirror 1 R 21 24.42% 19 20.21% 0.460 0.591 Diadochokinesia L 10 11.63% 8 8.51% 0.485 0.620 Diadochokinesia R 9 10.47% 4 4.26% 2.585 0.150 Mirror 2 L 8 9.30% 15 15.96% 1.785 0.264 Mirror 2 R 3 3.49% 10 10.64% 3.426 0.085 Fist-edge palm L 45 52.33% 46 48.94% 0.206 0.658 Fist-edge palm R 34 39.53% 41 43.62% 0.308 0.650 Oszeretsky test 40 46.51% 45 47.87% 0.033 0.882 Extinction 2 2.33% 5 5.32% 1.077 0.447 Finger Agnosia L 45 52.33% 54 57.45% 0.476 0.549 Finger Agnosia R 33 38.37% 43 45.74% 1.001 0.366 Stereognosia L 7 8.14% 4 4.26% 1.181 0.365 Stereognosia R 3 3.49% 6 6.38% 0.792 0.051 Graphesthesia L 18 20.93% 30 31.91% 2.771 0.129 Graphesthesia R 26 30.23% 43 45.74% 4.572 0.046 L-R Orientation 25 29.07% 27 28.72% 0.003 1.000 Saccade BLK 5 5.81% 9 9.57% 0.885 0.412 Saccade Head 36 41.86% 31 32.98% 1.516 0.280 Wink 30 34.88% 41 43.62% 1.434 0.285 Go/No-Go 28 32.56% 44 46.81% 3.800 0.067 catechol-O-methytransferase (COMT) and GRM3 that these age group would therefore show even higher genetic variation [38] may be related to the presence of neurological soft signs rates. Third, the current study neurological soft signs. Taken together, the use of neu- did not assess IQ, which has been considered to be rological soft signs may serve as potential sensitive bed- highly associated with neurological soft signs (e.g., [28]). side screening tool for neuropsychiatric or However, since there were no differences in education neurodegenerative disorders. (an indirect measure of IQ), and since controlling for The current study has several limitations. First, our neurocognitive functioning did not affect the results, we findings should be considered preliminary. Because of suggest that the current findings could not be comple- the small sample size and the non-stratified sample tely explained by potential IQ difference between the selection, they cannot be considered to represent preva- two groups. Notwithstanding these limitations, the current study is lence rates of neurological soft signs in Chinese elderly people. The data should not be generalized to the one of the very few studies specifically examining the elderly population of China, which has a total popula- prevalence rates of neurological soft signs in healthy tion of 1.3 billion. About 100.45 million people are over aging people, and Chinese in particular. The observed 65 years of age, occupying 7.69% of the overall popula- prevalence of neurological soft signs adds further evi- tion in China [39]. Secondly, we could not subdivide the dence to the geriatric literature on neurological impair- sample into subgroups of old-old and oldest-old, ments in cognitively intact people, and may provide a because of the relatively limited number of people over base-rate for identifying a potential clinical cut-off in the age of 85. Empirical findings suggest that this old- thenearfuture. Themoderateassociationsof the pre- est-old age group is particularly at risk of developing sence of neurological soft signs in the parent-offspring neurological disorders or neurodegenerative illnesses family units suggest a robust heritability [41]. This study such as dementia or Alzheimer’sdisease,withpreva- suggests that neurological soft signs may represent a lence rates as high as 47% [40]. It could be expected potential neurological marker for the early detection of Chan et al. Behavioral and Brain Functions 2011, 7:32 Page 6 of 7 http://www.behavioralandbrainfunctions.com/content/7/1/32 Logic Memory 0.90 Immed recall Verbal Memory 0.91 Logic Memory Motor Delayed recall -0.49 Coordination 0.52 Visual Rep 0.98 -0.68 Immed recall Sensory 0.49 Neurological Visual Integration Soft Signs Memory 0.84 Visual Rep 0.11 Delayed recall -0.70 Disinhibition Letter Num Span 0.95 Working correct passed Memory 0.97 Letter Num Span Longest passed Figure 1 The structure model of influence of soft signs on cognitive function in elderly. Chi-square = 60.98, p < 0.001, df = 30, NFI = 0.93, NNFI = 0.96, CFI = 0.97, GFI = 0.93, RMSEA = 0.076, Standardized RMR = 0.064. Authors’ contributions pathological aging diseases such as mild cognitive RCKC conceived the idea, designed the study, and wrote the first draft of impairment and Alzheimer’s disease. Future studies the paper. XT collected and analyzed the data. HJL, QZ, HHL, YW, CY, XYC, using a population-based design could be important to YNW, YFS collected the data and assisted data analysis. PD participated in writing up the paper. All authors read and approved the final manuscript. cross-validate our findings. Competing interests The authors declare that they have no competing interests. List of abbreviations BLK: Blink; CFI: Comparative Fit Index; CNI: Cambridge Neurological Received: 19 March 2011 Accepted: 10 August 2011 Inventory; COMT: catechol-O-methytransferase; GFI: Goodness of Fit Index; Published: 10 August 2011 IQ: Intellectual Quotient; LISREL: A software for computing the structural equation modeling; L: left; LN span: Letter-Number Span; MMSE: Mini Mental References State Examination; NFI: Normed Fit Index; NNFI: Non-Normed Fit Index; R: 1. Torrey EF, Bowler AE, Taylor EH, Gottesman II: Schizophrenia and manic- Right; RMSEA: Root Mean Square Error of Approximation; SEM: structure depressive disorder: the biological roots of mental illness as revealed by the equation model landmark study of identical twins New York, NY: Basic Books; 1994. 2. Tsuang MT, Faraone SV: The concept of target features in schizophrenia Acknowledgements research. Acta Psychiat Scand 1999, , supplement 395: 2-11. This study was supported partially by the Project-Oriented Hundred Talents 3. Tani P, Lindberg N, Appelberg B, Nieminen-Von Wendt T, Von Wendt L, Programme (O7CX031003), the Knowledge Innovation Project of the Chinese Porkka-Heiskanen T: Clinical neurological abnormalities in young adults Academy of Sciences (KSCX2-YW-R-131 & KSCX2-EW-J-8), a grant from the with Asperger syndrome. Psychiatry and Clinical Neurosciences 2006, National Basic Research Programme (973 Programme No. 2007CB512302), 60:253-255. and a grant from the National Outstanding Young Investigator Award from 4. Hollander E, Kaplan A, Schmeidler J, Yang H, Li D, Koran LM, Barbato LM: National Science Foundation of China (81088001). The funding sources had Neurological soft signs as predictors of treatment response to selective no role in study design, data collection and analysis, decision to publish, or serotonin reuptake inhibitors in Obsessive-Compulsive Disorder. J preparation of the manuscript. Neuropsychiatry Clin Neurosci 2005, 17:472-477. 5. Kodama T, Nakagawa M, Arimura K, Koriyama C, Akiba S, Osame M: Cross- Author details sectional analysis of neurological findings among healthy elderly: study Neuropsychology and Applied Cognitive Neuroscience Laboratory, Institute in a remote island in Kagoshima, Japan. Neuroepidemiology 2002, of Psychology, Chinese Academy of Sciences, Beijing, China. 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