Article

Variability of trail making test, symbol digit test and line trait test in normal people. A normative study taking into account age-dependent decline and sociobiological variables.

Department of Clinical and Experimental Medicine, University of Padova, Italy.
Aging clinical and experimental research (Impact Factor: 1.14). 04/2002; 14(2):117-31. DOI: 10.1007/BF03324425
Source: PubMed

ABSTRACT The influence of sociobiological variables and aging on the variability of the Trail Making Tests (TMT), the Symbol Digit Substituting Test (SDT), and the Line Trait Test (LTT) in the general healthy populations are not well known. Even less is known about the reliability at re-testing. This study aimed at determining the reference range of these tests, taking into account sociobiological variables and age, and the re-testing effect.
We studied 300 healthy subjects from 20 to 80 years of age. The sample was derived by the pooling of two samples stratified by age and sex: a randomized sample of 161 subjects collected from the city registers of Padova, and a convenience sample of 139 subjects collected in 20 towns (mainly rural) of Northern Italy. After normalization, data were assayed for the influence of age, education, job, and gender.
Age was found to be a significant independent predictor for all the tests, education for all but the LTT, job only for the TMT-B and a geometrical version of the same test (TMT-G) which was proved to be highly correlated with the TMT-B (r=0.80, p<0.01). Job and the interaction age x education level influenced the difference TMT-B minus TMT-A. From the predicting equations, the normative data and the formulas to obtain Z scores for each test were derived. Reliability was lowest for LTT errors (CV=67%), highest for the SDT (13%), whereas the TMT obtained intermediate values (22-33%, depending on the test).
This study provides the most reliable normative data range for the TMT, SDT and LTT to date because it considers important demographic variables such as age, education and job.

2 Followers
 · 
160 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.
    The Scientific World Journal 06/2014; 2014(906038):16. DOI:10.1155/2014/906038 · 1.73 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Cognitive impairment and memory dysfunction following stroke diagnosis are common symptoms that significantly affect the survivors' quality of life. Stroke patients have a high potential to develop dementia within the first year of stroke onset. Currently, efforts are being exerted to assess stroke effects on the brain, particularly in the early stages. Numerous neuropsychological assessments are being used to evaluate and differentiate cognitive impairment and dementia following stroke. This article focuses on the role of available neuropsychological assessments in detection of dementia and memory loss after stroke. This review starts with stroke types and risk factors associated with dementia development, followed by a brief description of stroke diagnosis criteria and the effects of stroke on the brain that lead to cognitive impairment and end with memory loss. This review aims to combine available neuropsychological assessments to develop a post-stroke memory assessment (PSMA) scheme based on the most recognized and available studies. The proposed PSMA is expected to assess different types of memory functionalities that are related to different parts of the brain according to stroke location. An optimal therapeutic program that would help stroke patients enjoy additional years with higher quality of life is presented.
    Neuropsychiatric Disease and Treatment 01/2014; 10:1677-91. DOI:10.2147/NDT.S67184 · 2.15 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The influence of carotid stenosis and its surgical treatment on brain function is still poorly defined. We therefore performed a study to assess psychometric and quantified EEG findings after carotid endarterectomy (CEA). Sixty-nine non-demented patients (aged 72 ± 7 years) with severe carotid stenosis (≥70 %) eligible for CEA were studied. Forty patients (group A) had unilateral stenosis, and 29 patients (group B) had bilateral stenosis. Before and 5 months after CEA all the patients were evaluated by the Trail Making Test A, the Symbol Digit Test, and spectral EEG analysis. At baseline, compared to group A, group B patients performed slowly the Trail Making Test A (Z: 1.45 ± 1.4 vs. 0.76 ± 1.3; p < 0.05), but not the Symbol Digit Test (Z: 0.83 ± 1.38 vs. 0.64 ± 1.26; p = 0.59). Altogether, the patients with at least one abnormal psychometric test were 29 % (group A: 26 %; group B: 33 %, p = 0.56). The EEG did not differ significantly between patients of group A compared to group B. After CEA, psychometric tests improved (mean Z score from 0.73 ± 1.12 to 0.45 ± 1.15, p < 0.05). The improvement was similar in group A and B. The EEG mean dominant frequency improved only in group B patients and it was related to the improvement in psychometric tests (r = 0.43, p = 0.05). Low psychometric performance was detectable in about 1/ 3 of non-demented patients with severe carotid stenosis. CEA improved mental performance and, in patients with severe bilateral stenosis, accelerated the EEG frequency.
    Metabolic Brain Disease 07/2014; 30(1). DOI:10.1007/s11011-014-9589-1 · 2.40 Impact Factor

Full-text

Download
80 Downloads
Available from
May 16, 2014