Diagnostic accuracy of early cognitive indicators in mild cognitive impairment

ACURÁCIA DIAGNÓSTICA DE INDICADORES COGNITIVOS PRECOCES EM COMPROMETIMENTO COGNITIVO LEVE

ABSTRACT

The aging of the population leads to an increase in the prevalence of dementia and mild cognitive impairment (MCI). Alzheimer's disease (AD) is the most common cause of dementia. Recent studies highlight the early non-amnestic deficits in AD and MCI. The European Union report shows the importance of thoroughly assessing cognitive aspects that have been poorly evaluated, such as processing speed (PS), which could represent early indicators of cognitive decline.

Objective:

To analyze the diagnostic accuracy of PS measures in older adults with MCI, AD, and those who are cognitively-healthy.

Methods:

A cross-sectional study was conducted by performing an extensive neuropsychological assessment in three samples: 26 control participants, 22 individuals with MCI, and 21 individuals with AD. Analysis of variance (ANOVA) was employed to test the relationship between dependent variables and the clinical group. Post hoc tests (Bonferroni test) were used when a significant ANOVA result was found. Finally, the Receiver Operating Characteristic (ROC) curve for PS measures was performed in older adults with MCI and AD compared with cognitively-healthy older adults.

Results:

The results showed that deficits in PS measures can be early indicators of cognitive decline in cases of MCI, even when executive functions (EFs) and functionality are preserved. Conversely, AD versus MCI presented differences in PS, EFs, and functionality.

Conclusions:

The ROC analyses showed that PS measures had discriminative capacities to differentiate individuals with MCI, AD, and cognitively-healthy older adults.

Keywords:
cognitive dysfunction; Alzheimer disease; diagnostic; cognition

RESUMO

O envelhecimento da população leva ao aumento da prevalência de demência e comprometimento cognitivo leve (CCL). A doença de Alzheimer (DA) é a causa mais comum de demência. Estudos recentes destacam os déficits precoces não amnésicos em DA e CCL. O relatório da União Europeia mostra a necessidade de avaliar em maior profundidade aspectos cognitivos que atualmente são negligenciados, como a velocidade de processamento (VP), e que podem representar indicadores precoces de declínio cognitivo.

Objetivo:

O objetivo deste estudo foi analisar a acurácia diagnóstica de medidas de VP em idosos com CCL, DA e participantes controles.

Métodos:

um estudo transversal foi desenvolvido, no qual realizou-se uma extensa avaliação neuropsicológica em 3 amostras: 26 participantes controles, 22 casos de CCL e 21 DA. A relação entre as variáveis dependentes e o grupo clínico foi testada com uma análise de variância (ANOVA). Se uma ANOVA significativa fosse encontrada, testes post hoc foram utilizados. Por fim, a curva ROC para medidas de VP foi realizada em CCL e DA em comparação com indivíduos controles.

Resultados:

os resultados mostraram que déficits nas medidas de VP podem ser indicadores precoces do declínio cognitivo nos casos de CCL, mesmo quando as funções executivas (FE) e a funcionalidade estão preservadas. Por outro lado, DA versus CCL mostrou diferenças em VP, EF e funcionalidade.

Conclusões:

As análises ROC mostraram que as medidas de PS tinham capacidades discriminativas para diferenciar CCL, DA e participantes controles.

Palavras-chave:
comprometimento cognitivo leve; doença de Alzheimer; diagnóstico; cognição

INTRODUCTION

Aging is usually referred to as a process of gradual deteriorations in the cognitive function that occur as people age. Profiles of cognitive aging include normal age-related cognitive declines from mild cognitive impairment (MCI) to a full stage of dementia.^1,2 Alzheimer's disease (AD) is the most widespread form of dementia,³ and it is estimated that one in 85 individuals would be living with AD by the year 2050.⁴ Although the average duration of the disease varies between 4 and 8 years, some patients may survive up to 20 years with the disease.⁵ Similarly, the aging of the population leads to the prevalence of clinical conditions such as MCI.^6,7 MCI is a clinical entity according to which individuals demonstrate cognitive deficit with minimal impairment of instrumental activities of daily living (IADL)^8,9 and with substantial heterogeneity in etiology, clinical presentation, prognosis, and outcome.¹⁰ The prevalence of MCI varies according to variables such as clinical setting and inclusion criteria. However, this prevalence generally ranges from 11 to 20%.^11,12 To better understand MCI has become a major public health priority. Its causes must be investigated, underlying pathophysiological processes and the earliest possible identification.¹³ Neuropsychological assessment has been effective in discriminating normal aging from mild cognitive impairment.^14,15 Likewise, McKhann et al. reviewed the criteria for AD and found that clear-cut history of worsening of cognition by report or observation is one of the core clinical criteria for probable AD.¹⁶

Neuropsychological instruments are potentially non-invasive methods to identify individuals with MCI or predict the risk of developing MCI or dementia.¹⁷ Neuropsychological assessment is typically used for both descriptive and diagnostic purposes.¹⁸ When using the tests for diagnostic purposes, they provide information about the probability that an individual has — or will have at some moment in the future — to develop a cognitive disorder or deficit such as AD and MCI.¹⁹

The current literature recognizes neuropsychological heterogeneity in MCI by dividing it into subtypes. Most researchers employ four subtypes depending on the number of affected domains, namely: amnestic single-domain MCI (aMCI), amnestic multidomain MCI (aMCI), non-amnestic single-domain MCI (naMCI), and non-amnestic multidomain MCI (naMCI).^9,20–24 The European Union (EU) report highlights the need to assess non-amnestic aspects in MCI, such as motor/perceptual aspects or processing speed (PS), considering that such features may not be thoroughly investigated and could represent early indicators of cognitive decline.²⁵ Most neuropsychological studies involving MCI have focused on disorders of episodic memory, language, and executive functions.^26–31 Actually, information processing speed is included in the diagnostic criteria for neurocognitive disorders of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5).¹⁶ However, there is substantially less research that evaluates deficits in PS in MCI.^32–34 Likewise, recent studies highlight the cognitive heterogeneity in AD, showing the importance of studying other cognitive aspects in addition to episodic memory.^35–38

Normal aging and some psychiatric disorders (such as MCI and AD) were associated with decline in PS.^39,40 PS involves several components of executive control, which vary according to age. Individual differences in PS indicate variation in neural speed,^41,42 as well as age-related changes in neural processing, including the decline of axonal myelination throughout life.^40,41 PS can be conceptualized as either the amount of time it takes to process a specific quantity of information^39,40 or the quantity of information that can be processed within a finite amount of time.⁴³ Decline in PS leads to cognitive deficits that make the ability to simultaneously control information limited. Taking into consideration the lower capacity to process information, it can also conduct to increased errors in the cognitive processing.^39,40

Accurate diagnosis of MCI and AD is very important for timely therapy and possible delay of the disease.⁴⁴ Consequently, the analysis of PS measures in aging is crucially important, especially in the Brazilian context, and neuropsychological tests are fundamental in this process. Based on a brief review of the literature, there are no Brazilian study on the subject to the best of our knowledge. Therefore, the purpose of this study is to analyze the diagnostic accuracy of early neuropsychological indicators, such PS measures, in older adults affected by AD, MCI, and in those who are cognitively-healthy.

METHOD

Participants

A total of 85 individuals were selected from a social program that was offered by the Government of Rio de Janeiro, Brazil. Of these individuals, 36 were control participants (CP), 26 had MCI, and 21 had a probable diagnosis of AD. The assessments were performed between 2016 and 2018 in Rio de Janeiro (state of Rio de Janeiro, Brazil) by a certified board psychiatrist and all neuropsychological evaluations were conducted by a senior neuropsychologist in Rio de Janeiro. The match of the variables “age” and “years of education” was performed; thus, 12 control participants and four individuals with MCI were excluded from the sample (Figure 1). Although the AD group presented a higher mean age, individuals with such diagnosis were not excluded, in such a way 21 AD cases of baseline were maintained. Therefore, the sample resulted in 26 CP and 22 individuals with MCI. All participants aged over 60 years and were proficient in Brazilian Portuguese. The participants agreed to participate in the study and signed the informed consent form. The study was approved by the Research Ethics Committee under authorization No. 965.264.

Figure 1
Flowchart of criteria for sample selection.

Diagnosis

Control participants (CP) were individuals with no changes in cognitive performance tests and without functional impairment. The assessment of CP and individuals with MCI was based on clinical history, neuroimaging, and an initial neuropsychological protocol that included the following tests and scales: 1) Mini-Mental State Examination (MMSE);⁴⁵ 2) Brief Cognitive Screening Battery, which consisted of the following tests: Memory of Figure Test (MFT); Categorical Verbal Fluency Test (CVFT); Clock Drawing Test (CDT); Geriatric Depression Scale (GDS-15); Pfeffer's Functional Activities Questionnaire (FAQ); and Lawton Instrumental Activities of Daily Living Scale (IADL);^46,47 3) Rey Auditory Verbal Learning Test (RAVLT);⁴⁸ 4) Phonemic Verbal Fluency Test (FAS);⁴⁹ and 5) Rey Complex Figure Task.⁵⁰ All participants with MCI who had subjective cognitive complaints underwent a comprehensive neuropsychological assessment. A clinical diagnosis of individuals with MCI was established at a conference for each patient by an interdisciplinary team. Finally, the diagnosis of AD was based on the consensus criteria from the National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer's Disease and Related Disorders Association (NINCDS-ADRDA).¹⁶ Exclusion criteria included: 1) history of cerebral infection or stroke; 2) brain tumor; 3) head injury; 4) ongoing psychiatric illness; 5) history of alcohol or drug abuse; and 6) brain imaging that indicated any possibility of brain lesions other than MCI.

Neuropsychological tests used in measures of processing speed and executive functions

PS was assessed by the tests of the Wechsler Adult Intelligence Scale, Third Edition (WAIS-III),⁵¹ namely: Digit Symbol-Coding (CD) and Symbol Search (SS). In addition, the Processing Speed Index (PSI) was performed for all participants in the sample. Finally, the attention and executive functions were assessed by the Color Trails Test (CTT),⁵² the Victoria Stroop Test (VST, Dot condition — Card 1; Word condition — Card 2; Interference condition — Card 3),⁵³ and the Digit Span Test (DS).⁵¹

Analyses

All analyses were conducted with the Statistical Package for the Social Sciences (SPSS) software, v. 21,, with significance set at p≤0.05. Normality of distribution was determined by a histogram. According to the data, there was no normal distribution, in such a way that parametric and nonparametric tests were performed. The results of the analyses only differed in CTT — Form A (individuals with MCI) and VST (Card 1; individuals with MCI). Measures of Skewness and Kurtosis were analyzed and indicated the use of parametric tests. Data analysis was performed using raw scores of the neuropsychological tests, and only PSI was converted into standard scores. The relationship between dependent variables and the clinical group was tested with analysis of variance (ANOVA). If a significant ANOVA was found, post hoc tests (Bonferroni test) controlling for multiple comparisons were used to identify pairs of clinical groups that significantly differed. Clinical groups were also compared according to demographic characteristics (i.e., age, sex, and years of education). Finally, the Receiver Operating Characteristic (ROC) curve was performed for the following tests and index: CD, SS, and PSI. ROC curves were also plotted in order to determine the degree to which subtests discriminated between CP, individuals with MCI, and individuals with AD. These analyses show the sensitivity versus one minus the specificity for each possible cutoff point. The area under the curve (AUC), with 95% confidence intervals (95%CI), was used as an indicator of the ability of the PS measures in differentiating patients who were CP, individuals with MCI, and individuals with AD.

RESULTS

Demographic characteristics, Mini-Mental State Examination, and Lawton scores

Table 1 summarizes demographic data, Lawton score (patient version), MMSE score, and pairwise comparisons. The repeated measures ANOVA showed differences between age (F (2.66)=5.7; p=.005); MMSE score (F (2.66)=21.5; p<.001), and Lawton score (F (2.65)=28.1; p<.001), but not in years of education (F (2.66)=2.5; p=.084). When comparing individuals with MCI versus CP, pairwise comparisons presented no differences in age (p=.56), years of education (p=.09), MMSE score (p=.22), and Lawton score (p=.66). As expected, AD versus MCI showed differences in MMSE scores (p<.001) and Lawton scores (p<.001), but not in age (p=.14) and years of education (p=1.00).

Table 1
Demographic characteristics (mean and standard deviation), Mini-Mental State Examination scores (maximum: 35 points), Lawton scores (patient version; maximum: 21 points), and pairwise comparisons for the three study groups.

	CP (n=26)	MCI (n=22)	AD (n=21)	F	*p-value	*p-value (CP versus MCI)	*p-value (MCI versus AD)
Age	73.3 (4.9)	75.6 (6.2)	79.2 (6.7)	5.7	0.004	0.568	0.147
Years of education	13.1 (3.0)	10.4 (5.1)	11.4 (4.5)	2.5	2.5	0.087	1.00
MMSE score (max: 35 points)	31.6 (1.8)	29.6 (2.6)	24.4 (5.9)	21.5	<0.001	0.225	<0.001
Lawton score (max: 21 points)	20.8 (0.4)	20.3 (0.6)	18.0 (2.2)	28.1	<0.001	0.665	<0.001
Men	4 (22)	1(21)	7 (14)	*	*	*	*

• ^*
  MMSE: Mini-Mental State Examination; CP: Control Participants; MCI: mild cognitive impairment; AD: Alzheimer's disease.

Executive functions and processing speed measures

Table 2 shows mean and standard deviation (SD) of neuropsychological measures and pairwise comparisons. The ANOVA demonstrated differences in PSI (F (2.66)=25.1; p<.001), SS score (F (2.66)=17.2; p<.001), CD score (F (2.66)=26.3; p<.001), SS errors (F (2.66)=3.6; p=.030), VST-Card 1 (F (2.57)=6.3; p=.003), VST-Card 2 (F (2.57)=6.6; p=.002), VST-Card 3 (F (2.57)=9.5; p<.001), CTT – Form A (F (2.66)=15.5; p<.001), CTT – Form B (F (2.66)=18.0; p<.001), CTT – Form A errors (F (2.66)=4.1; p=.021), and DS (F (2.66)=5,5; p=.006), but no differences in CTT — Form B errors (F (2.66)=3.0; p=.055). When comparing individuals with MCI and CP, only PS measures showed differences, such as: SS score (p=.001), CD score (p<.001), PSI (p<.001), and SS errors (p=.028). There were no differences in EFs measures, except for DS (p=.04). This ability to identify early MCI in individuals can be explained by differences in the performance of PS measures, which is sufficient to distinguish the groups. However, the comparison between MCI and AD demonstrated differences in PS and EFs measures, such as: CD score (p=.010), PSI (p=.036), CTT — Form A (p=.002), CTT — Form B (p<.001), VST-Card 2 (p=.012), and VST-Card 3 (p=.002).

Table 2
Mean and standard deviation of neuropsychological measures and pairwise comparisons.

Neuropsychological measures	CP (n=26)	MCI (n=22)	AD (n=21)	*p-value (CP versus MCI)	*p-value (MCI versus AD)
PSI (max: 146 points)	123.0 (12.5)	107.1 (11.2)	97.1 (14.2)	<0.001	0.036
CD (max: 133 points)	45.7 (13.7)	30.7 (11.5)	18.8 (12.7)	<0.001	0.010
SS (max: 60 points)	24.8 (9.9)	15.5 (6.1)	11.2 (7.5)	0.001	0.277
SS errors	1.5 (1.5)	3.0 (1.7)	2.4 (2.2)	0.028	0.931
CTT – Form A	67.0 (27.0)	94.9 (27.6)	153.9 (88.3)	0.231	0.002
CTT – Form B	130.9 (45.2)	170.3 (38.3)	266.2 (126.2)	0.257	<0.001
CTT – Form A errors	0.04 (0.1)	0.14 (0.4)	0.6 (1.2)	1.00	0.097
CTT – Form B errors	0.15 (0.3)	0.64 (1.0)	1.9 (4.3)	1.00	0.291
VST-Card 1	14.8 (3.6)	21.7 (8.4)	32.3 (25.0)	0.506	0.095
VST-Card 2	21.0 (6.3)	24.7 (7.3)	53.2 (51.6)	1.00	0.012
VST-Card 3	33.6 (13.4)	40.2 (12.8)	87.3 (70.8)	1.00	0.002
Interference	2.0 (0.9)	2.5 (2.3)	2.7 (1.1)	1.00	0.05
DS (max: 14 points)	12.4 (3.7)	10.1 (2.6)	9.6 (2.7)	0.04	1.00

• ^*
  VST: Victoria Stroop Test; CTT: Color Trails Test; SS: Search Symbols (maximum: 60 points); CD: Digit Symbol-Coding (maximum: 133 points); PSI: Processing Speed Index (maimum: 146); DS: Digit Span Test (maximum: 14 points).

Mild cognitive impairment versus control participants

Sensitivity and specificity of the processing speed measures were investigated in the first analysis, namely: SS, CD, and PSI. These diagnostic parameters were used to test the ability of these PS measures in identifying individuals with MCI compared with cognitively-healthy older adults (Figure 2). The estimated AUC for SS was 0.80 (95%CI 0.68–0.93; p<0.01) and for CD, 0.79 (95%CI 0.66–0.92; p<0.01). In addition, the estimated AUC for PSI was 0.83 (95%CI 0.71–0.94; p<0.01). The most appropriate cutoff point (raw score) for SS was 19.5, with sensitivity and specificity of 86 and 76% respectively; as for CD (raw score), the most appropriate cutoff point was 36.5, with sensitivity and specificity of 77 and 80% respectively. Finally, the most appropriate cutoff point for PSI was 114.5, with sensitivity and specificity of 81 and 76% respectively.

Figure 2
Analyses of Receiver Operating Characteristic curve, sensitivity, and specificity for Alzheimer's disease versus control participants.

Alzheimer's disease versus control participants

The diagnostic parameters were used to test the ability of PS measures in identifying cases of Alzheimer's disease compared with cognitively-healthy older adults. The estimated AUC compared with cognitively-healthy older adults. The estimated AUC (Figure 3) for SS was 0.88 (95%CI 0.78–0.98; p<0.01); for CD, 0.92 (95%CI 0.85–0.99; p<0.01). Finally, the estimated AUC for PSI was 0.90 (95%CI 0.81–0.99; p<0.01). The most appropriate cutoff point for SS (raw score) was 17.0, with sensitivity and specificity of 85 and 80% respectively; as for CD (raw score), the most appropriate cutoff point was 35.5, with sensitivity and specificity of 85 and 80% respectively. Finally, the most appropriate cutoff point for PSI was 112, with sensitivity and specificity of 85 and 80% respectively.

Figure 3
Analyses of Receiver Operating Characteristic curve, sensitivity, and specificity for individuals with mild cognitive impairment versus control participants.

DISCUSSION

The results of this study might indicate that individuals with MCI could be early identified according to the performance in PSI and in tests that assess PS, when compared with the control participants. According to Salthouse,^39,40 the decrease in PS can also lead to increased errors in the cognitive processing. Moreover, according to data of the present study, samples of individuals with MCI and CP can be differentiated by the number of errors made in one of the PS tests. However, differences in traditional measures of executive functions were not enough to distinguish control participants from individuals with MCI, except for DS. This fact can be explained because complex measures of EFs, such as working memory, require process of attention and mental manipulation.⁵⁴ Furthermore, the ROC analyses showed that PS measures had discriminative capacities to differentiate individuals with MCI, AD, and cognitively-healthy older adults.

Cognitive domains decrease with advanced age.^55,56 Decline in cognitive function affects more than 50% of people aged over 60 years.⁵⁷ Particularly, memory and PS seem to be more sensitive to age than other cognitive domains.^39,55 A recent study have compared cognitively-healthy older adults, individuals with MCI, and individuals with AD, and showed that PS measures were significant to differentiate the three groups. White matter (WM) brain pathology is often present in patients with MCI and AD. Thus, this study concluded that WM seemed to have the strongest effects on PS measures for the three samples.³³ Likewise, Park et al. assessed cognitively-healthy older adults, individuals with MCI, and those with AD, and concluded that a PS measure could distinguish the three groups.³⁴ These results support our findings and the notion that neuropsychological measures are sensitive to differentiate individuals with AD, MCI, and cognitively-healthy older adults.

According to the American Psychiatric Association (APA) and the NINCDS‐ADRDA, neuropsychological assessment is necessary and consists in an important component for the diagnosis of MCI and AD, respectively.^16,58 Furthermore, neuropsychological testing is an equally valuable and arguably more affordable and less invasive cognitive biomarker of AD.²⁰ In this context, the strength of the study was to provide diagnostic parameters for early neuropsychological indicators in the Brazilian samples. In addition, it provides raw data on the performance of individuals with MCI compared with cognitively-healthy older adults.

However, limitations of this study should be discussed. First, the Brazilian norms of the WAIS-III have limitations and must be revised. In order to minimize these biases, only the raw data from the CD and SS tests were analyzed. Second, the study sample size. Clinical samples and extensive neuropsychological evaluations present major obstacles to evidence-based neuropsychology practice. Noteworthily, the sample of the present study is mainly composed of women. Nevertheless, previous studies showed that women have better performance than men on verbal memory tasks, but sex differences were not evident for speed of information processing and attention.^59,60

In conclusion, the authors emphasize the importance of early indicators of cognitive decline in MCI and the diagnostic parameters in the neuropsychological instruments in the Brazilian clinical settings. Such aspects might impact the prognosis of the disorder and assist in decision-making concerning treatment options, especially those related to cognitive rehabilitation. Nevertheless, further studies on the subject are still necessary.

• This study was conducted at the Department of Psychology, Pontifícia Universidade Católica do Rio de Janeiro, RJ, Brazil.
• Funding: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES): Finance Code 001.

REFERENCES

• ¹
  Chatterji S, Kowal P, Mathers C, Naidoo N, Verdes E, Smith JP, et al. The health of aging populations in China and India. Health Aff (Millwood). 2008; 27(4):1052-63. https://doi.org/10.1377/hlthaff.27.4.1052
  » https://doi.org/10.1377/hlthaff.27.4.1052
• ²
  Zhang D, Shen D. Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer's disease. Neuroimage. 2012;59(2):895-907. https://doi.org/10.1016/j.neuroimage.2011.09.069
  » https://doi.org/10.1016/j.neuroimage.2011.09.069
• ³
  Townsend M. When will Alzheimer's disease be cured? A pharmaceutical perspective. J Alzheimers Dis. 2011;24 Suppl. 2:43-52. https://doi.org/10.3233/JAD-2011-110020
  » https://doi.org/10.3233/JAD-2011-110020
• ⁴
  Brookmeyer R, Johnson E, Ziegler-Graham K, Arrighi HM. Forecasting the global burden of Alzheimer's disease. Alzheimer's Dement. 2007;3(3):186-91. https://doi.org/10.1016/j.jalz.2007.04.381
  » https://doi.org/10.1016/j.jalz.2007.04.381
• ⁵
  Xie J, Brayne C, Matthews FE. Survival times in people with dementia: analysis from population based cohort study with 14 year follow-up. BMJ. 2008;336(7638):258-62. https://doi.org/10.1136/bmj.39433.616678.25
  » https://doi.org/10.1136/bmj.39433.616678.25
• ⁶
  Sachdev PS, Lipnicki DM, Kochan NA, Crawford JD, Thalamuthu A, Andrews G, et al. The prevalence of mild cognitive impairment in diverse geographical and ethnocultural regions: The COSMIC Collaboration. PLoS One. 2015;10(11):e0142388. https://doi.org/10.1371/journal.pone.0142388
  » https://doi.org/10.1371/journal.pone.0142388
• ⁷
  Petersen RC, Lopez O, Armstrong MJ, Getchius TSD, Ganguli M, Gloss D, et al. Practice guideline update summary: mild cognitive impairment: report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology. Neurology. 2018;90(3):126-35. https://doi.org/10.1212/WNL.0000000000004826
  » https://doi.org/10.1212/WNL.0000000000004826
• ⁸
  Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med. 2004;256(3):183-94. https://doi.org/10.1111/j.1365-2796.2004.01388.x
  » https://doi.org/10.1111/j.1365-2796.2004.01388.x
• ⁹
  Winblad B, Palmer K, Kivipelto M, Jelic V, Fratiglioni L, Wahlund L-O, et al. Mild cognitive impairment: beyond controversies, towards a consensus: report of the International Working Group on Mild Cognitive Impairment. J Intern Med. 2004;256(3):240-6. https://doi.org/10.1111/j.1365-2796.2004.01380.x
  » https://doi.org/10.1111/j.1365-2796.2004.01380.x
• ¹⁰
  Petersen RC. Mild cognitive impairment: where are we? Alzheimer Dis Assoc Disord. 2005;19(3):166-9. https://doi.org/10.1097/01.wad.0000179417.95584.90
  » https://doi.org/10.1097/01.wad.0000179417.95584.90
• ¹¹
  Petersen RC, Roberts RO, Knopman DS, Geda YE, Cha RH, Pankratz VS, et al. Prevalence of mild cognitive impairment is higher in men. Neurology. 2010;75(10):889-97. https://doi.org/10.1212/WNL.0b013e3181f11d85
  » https://doi.org/10.1212/WNL.0b013e3181f11d85
• ¹²
  Nie H, Xu Y, Liu B, Zhang Y, Lei T, Hui X, et al. The prevalence of mild cognitive impairment about elderly population in China: a meta-analysis. Int J Geriatr Psychiatry. 2011;26(6):558-63. https://doi.org/10.1002/gps.2579
  » https://doi.org/10.1002/gps.2579
• ¹³
  Winblad B, Amouyel P, Andrieu S, Ballard C, Brayne C, Brodaty H, et al. Defeating Alzheimer's disease and other dementias: a priority for European science and society. Lancet Neurol. 2016;15(5):455-532. https://doi.org/10.1016/S1474-4422(16)00062-4
  » https://doi.org/10.1016/S1474-4422(16)00062-4
• ¹⁴
  De Jager CA, Hogervorst E, Combrinck M, Budge MM. Sensitivity and specificity of neuropsychological tests for mild cognitive impairment, vascular cognitive impairment and Alzheimer's disease. Psychol Med. 2003;33(6):1039-50. https://doi.org/10.1017/s0033291703008031
  » https://doi.org/10.1017/s0033291703008031
• ¹⁵
  Salmon DP, Bondi MW. Neuropsychological assessment of dementia. Annu Rev Psychol. 2009;60:257-82. https://doi.org/10.1146/annurev.psych.57.102904.190024
  » https://doi.org/10.1146/annurev.psych.57.102904.190024
• ¹⁶
  McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Kawas CH, et al. The diagnosis of dementia due to Alzheimer's disease: Recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011;7(3):263-9. https://doi.org/10.1016/j.jalz.2011.03.005
  » https://doi.org/10.1016/j.jalz.2011.03.005
• ¹⁷
  Blacker D, Lee H, Muzikansky A, Martin EC, Tanzi R, McArdle JJ, et al. Neuropsychological measures in normal individuals that predict subsequent cognitive decline. Arch Neurol. 2007;64(6):862-71. https://doi.org/10.1001/archneur.64.6.862
  » https://doi.org/10.1001/archneur.64.6.862
• ¹⁸
  Busch RM, Chelune GJ, Suchy Y. Using norms in neuropsychological assessment of the elderly. In: Attix DK, Welsh-Bohmer KA (Eds.). Geriatric neuropsychology: assessment and intervention. New York: Guilford Publications; 2006. p. 133-57.
• ¹⁹
  Stern RA, White T. Neuropsychological assessment battery. Lutz, FL: Psychological Assessment Resources; 2003.
• ²⁰
  Jak AJ, Preis SR, Beiser AS, Seshadri S, Wolf PA, Bondi MW, et al. Neuropsychological Criteria for Mild Cognitive Impairment and Dementia Risk in the Framingham Heart Study. J Int Neuropsychol Soc. 2016;22(9):937-43. https://doi.org/10.1017/S1355617716000199
  » https://doi.org/10.1017/S1355617716000199
• ²¹
  Lopez OL, Becker JT, Chang YF, Sweet RA, DeKosky ST, Gach, MH, et al. Incidence of mild cognitive impairment in the Pittsburgh Cardiovascular Health Study-Cognition Study. Neurology. 2012;79(15):1599-606. https://doi.org/10.1212/WNL.0b013e31826e25f0
  » https://doi.org/10.1212/WNL.0b013e31826e25f0
• ²²
  Bangen KJ, Jak AJ, Schiehser DM, Delano-Wood L, Tuminello E, Han SD, et al. Complex activities of daily living vary by mild cognitive impairment subtype. J Int Neuropsychol Soc. 2010;16(4):630-9. https://doi.org/10.1017/S1355617710000330
  » https://doi.org/10.1017/S1355617710000330
• ²³
  Petersen RC, Negash S. Mild cognitive impairment: an overview. CNS Spectr. 2008;13(1):45-53. https://doi.org/10.1017/s1092852900016151
  » https://doi.org/10.1017/s1092852900016151
• ²⁴
  Busse A, Hensel A, Guhne U, Angermeyer MC, Riedel-Heller SG. Mild cognitive impairment: long-term course of four clinical subtypes. Neurology. 2006;67(12):2176-85. https://doi.org/10.1212/01.wnl.0000249117.23318.e1
  » https://doi.org/10.1212/01.wnl.0000249117.23318.e1
• ²⁵
  Apostolo J, Holland C, O’Connell MD, Feeney J, Tabares-Seisdedos R, Tadros G, et al. Mild cognitive decline. A position statement of the Cognitive Decline Group of the European Innovation Partnership for Active and Healthy Ageing (EIPAHA). Maturitas. 2016;83:83-93. https://doi.org/10.1016/j.maturitas.2015.10.008
  » https://doi.org/10.1016/j.maturitas.2015.10.008
• ²⁶
  Eppig J, Wambach, D, Nieves C, Price CC, Lamar M, Delano-Wood L, et al. Dysexecutive functioning in mild cognitive impairment: Derailment in temporal gradients. J Int Neuropsychol Soc. 2012;18(1):20-8. https://doi.org/10.1017/S1355617711001238
  » https://doi.org/10.1017/S1355617711001238
• ²⁷
  Hwang YK, Kim E, Kim YB, Kim YW, Nam CM, Cho SH, et al. Diagnostic value of time-constrained naming test in mild cognitive impairment. Dement Geriatr Cogn Disord. 2017;44:171-81. https://doi.org/10.1159/000479149
  » https://doi.org/10.1159/000479149
• ²⁸
  Kirova AM, Bays RB, Lagalwar S. Working memory and executive function decline across normal aging, mild cognitive impairment, and Alzheimer's disease. BioMed Res Int. 2015:748212. https://doi.org/10.1155/2015/748212
  » https://doi.org/10.1155/2015/748212
• ²⁹
  Libon DJ, Bondi MW, Price, CC, Lamar M, Eppig J, Wambach DM, et al. Verbal serial list learning in mild cognitive impairment: A profile analysis of interference, forgetting, and errors. J Int Neuropsychol Soc. 2011;17(5):905-14. https://doi.org/10.1017/S1355617711000944
  » https://doi.org/10.1017/S1355617711000944
• ³⁰
  McCullough KC, Bayles KA, Bouldin ED. Language performance of individuals at risk for mild cognitive impairment. J Speech Lang Hear R. 2019;62(3):706-22. https://doi.org/10.1044/2018_JSLHR-L-18-0232
  » https://doi.org/10.1044/2018_JSLHR-L-18-0232
• ³¹
  Saunders NLJ, Summers MJ. Longitudinal deficits to attention, executive, and working memory in subtypes of mild cognitive impairment. Neuropsychology. 2011;25(2):237-48. https://doi.org/10.1037/a0021134
  » https://doi.org/10.1037/a0021134
• ³²
  Dion C, Arias F, Amini S, Davis R, Penney D, Libon DJ, et al. Cognitive correlates of digital clock drawing metrics in older adults with and without mild cognitive impairment. J Alzheimers Dis. 2020;75(1):73-83. https://doi.org/10.3233/JAD-191089
  » https://doi.org/10.3233/JAD-191089
• ³³
  Kaskikallio A, Karrasch M, Rinne J, Tuokkola T, Parkkola R, Grönholm-Nyman P. Domain-specific cognitive effects of white matter pathology in old age, mild cognitive impairment and Alzheimer's disease. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn. 2020;27(3):453-70. https://doi.org/10.1080/13825585.2019.1628916
  » https://doi.org/10.1080/13825585.2019.1628916
• ³⁴
  Park S, Pyo S, Shin SA, Lee JY, Kim YK, Park, HJ, et al. A quick test of cognitive speed in older adults with Alzheimer's disease and mild cognitive impairment: a preliminary behavioral and brain imaging study. Psychiatry Res Neuroimag. 2018;280:30-8. https://doi.org/10.1016/j.pscychresns.2018.08.004
  » https://doi.org/10.1016/j.pscychresns.2018.08.004
• ³⁵
  Martorelli M, Sudo FK, Fichman-Charchat H. This is not only about memory: a systematic review on neuropsychological heterogeneity in Alzheimer's disease. Psychol Neurosci. 2019;12(2):271-81. https://doi.org/10.1037/pne0000149
  » https://doi.org/10.1037/pne0000149
• ³⁶
  Scheltens NME, Tijms BM, Koene T, Barkhof F, Teunissen CE, Wolfsgruber S, et al. Cognitive subtypes of probable Alzheimer's disease robustly identified in four cohorts. Alzheimers Dement. 2017;13(11):1226-36. https://doi.org/10.1016/j.jalz.2017.03.002
  » https://doi.org/10.1016/j.jalz.2017.03.002
• ³⁷
  Scheltens NM, Galindo-Garre F, Pijnenburg YA, van der Vlies AE, Smits LL, Koene T, et al. The identification of cognitive subtypes in Alzheimer's disease de-mentia using latent class analysis. J Neurol Neurosurg Psychiatry. 2016;87(3):235-43. https://doi.org/10.1136/jnnp-2014-309582
  » https://doi.org/10.1136/jnnp-2014-309582
• ³⁸
  Vardy E, Ford AH, Gallagher P, Watson R, McKeith IG, Blamire A, et al. Distinct cognitive phenotypes in Alzheimer's disease in older people. Int Psychogeriatr. 2013;25(10):1659-66. https://doi.org/10.1017/S1041610213000914
  » https://doi.org/10.1017/S1041610213000914
• ³⁹
  Salthouse TA. The processing-speed theory of adult age differences in cognition. Psychol Rev. 1996;103(3):403-28. https://doi.org/10.1037/0033-295x.103.3.403
  » https://doi.org/10.1037/0033-295x.103.3.403
• ⁴⁰
  Salthouse TA. What and when of cognitive aging. Curr Dir Psychol Sci. 2004;13(4):140-44. https://doi.org/10.1111/j.0963-7214.2004.00293.x
  » https://doi.org/10.1111/j.0963-7214.2004.00293.x
• ⁴¹
  Birren JE, Fisher LM. Aging and speed of behavior: Possible consequences for psychological functioning. Ann Rev Psychol. 1995;46:329-53. https://doi.org/10.1146/annurev.ps.46.020195.001553
  » https://doi.org/10.1146/annurev.ps.46.020195.001553
• ⁴²
  Mendelson JR, Ricketts C. Age-related temporal processing speed deterioration in auditory cortex. Hear Res. 2001;158(1-2):84-94. https://doi.org/10.1016/S0378-5955(01)00294-5
  » https://doi.org/10.1016/S0378-5955(01)00294-5
• ⁴³
  Kalmar JH, Chiaravalloti ND. Information processing speed in multiple sclerosis: a primary deficit? In: DeLuca J, Kalmar JH (Eds.). Studies on neuropsychology, neurology and cognition. Information processing speed in clinical populations. [N.I.] Taylor & Francis; 2008. p. 153-72.
• ⁴⁴
  Zhang D, Shen D, Alzheimer's Disease Neuroimaging Initiative. Predicting future clinical changes of MCI patients using longitudinal and multimodal biomarkers and brain imaging study. PLoS One. 2012;7(3):e33182. https://doi.org/10.1371/journal.pone.0033182
  » https://doi.org/10.1371/journal.pone.0033182
• ⁴⁵
  Brucki S, Ricardo N, Caramelli P, Bertolucci P, Okamoto IH. Suggestions for the use of the mental state mini-exam in Brazil. Arq Neuro-Psiquiatr 2003;61(3-B):777-81. https://doi.org/10.1590/S0004-282X2003000500014
  » https://doi.org/10.1590/S0004-282X2003000500014
• ⁴⁶
  Nitrini R, Lefèvre B, Mathias S, Caramelli P, Carrilho P, Sauaia, et al. Neuropsychological tests of simple application for diagnosing dementia. Arq Neuro-Psiquiatr. 1994;52(4):457-65. https://doi.org/10.1590/s0004-282x1994000400001
  » https://doi.org/10.1590/s0004-282x1994000400001
• ⁴⁷
  Araujo VC, Lima CMB, Barbosa ENB, Furtado FP, Charchat-Fichman H. Impact of age and schooling on performance on the Brief Cognitive Screening Battery: A study of elderly residents in the City of Rio de Janeiro, Brazil. Psychol Neurosci. 2018;11(3):317-28. https://doi.org/10.1037/pne0000118
  » https://doi.org/10.1037/pne0000118
• ⁴⁸
  De Paula J, Malloy-Diniz. Rey Auditory Verbal Learning Test. São Paulo; 2018. Vetor.
• ⁴⁹
  Machado TH, Fichman HC, Santos EL, Carvalho VA, Fialho PP, Koenig AM, et al. Normative data for healthy elderly on the phonemic verbal fluency task FAS. Dement Neuropsychol. 2009;3(1):55-60. https://doi.org/10.1590/S1980-57642009DN30100011
  » https://doi.org/10.1590/S1980-57642009DN30100011
• ⁵⁰
  Oliveira M, Rigoni M. Rey's figure copy and memory reproduction. São Paulo: Casa do Psicólogo; 2017.
• ⁵¹
  Wechsler D. WAIS-III: Wechsler Adult Intelligence Scale - Third edition administration and scoring manual. São Paulo: Psychological Corporation; 2004.
• ⁵²
  Rabelo I, Pacanaro S, Rosseti M, De Sá, I. Trail color test. São Paulo: Casa do Psicólogo; 2010.
• ⁵³
  Regard M. Cognitive rigidity and flexibility: a neuropsychological study. Unpublished Ph.D. dissertation – University of Victoria, British Columbia; 1981.
• ⁵⁴
  Carlozzi NE, Tulsky DS, Chiaravalloti ND, Beaumont JL, Weintraub S, Conway K, et al. NIH Toolbox Cognitive Battery (NIHTB-CB): the NIHTB pattern comparison processing speed test. J Int Neuropsychol Soc. 2014;20(6):630-41. https://doi.org/10.1017/S1355617714000319
  » https://doi.org/10.1017/S1355617714000319
• ⁵⁵
  Christensen H. What cognitive changes can be expected with normal ageing? Aust N Z J Psychiatry. 2001;35(6):768-75. https://doi.org/10.1046/j.1440-1614.2001.00966.x
  » https://doi.org/10.1046/j.1440-1614.2001.00966.x
• ⁵⁶
  Singh-Manoux A, Kivimaki M, Glymour M, Elbaz A, Berr C, Ebmeier K, et al. Timing of onset of cognitive decline: results from Whitehall II prospective cohort study. BMJ. 2012;344:d7622. https://doi.org/10.1136/bmj.d7622
  » https://doi.org/10.1136/bmj.d7622
• ⁵⁷
  Skaper SD, Facci L, Giusti P. Neuroinflammation, microglia and mast cells in the pathophysiology of neurocognitive disorders: a review. CNS Neurol Disord Drug Targets. 2014;13(10):1654-66. https://doi.org/10.2174/1871527313666141130224206
  » https://doi.org/10.2174/1871527313666141130224206
• ⁵⁸
  American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5. ed. Washington, DC: APA; 2013.
• ⁵⁹
  van Hooren SA, Valentijn AM, Bosma H, Ponds RW, van Boxtel MP, Jolles J. Cognitive functioning in healthy older adults aged 64–81: a cohort study into the effects of age, sex, and education. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn. 2007;14(1):40-54. https://doi.org/10.1080/138255890969483
  » https://doi.org/10.1080/138255890969483
• ⁶⁰
  Mougias A, Christidi F, Synetou M, Kotrotsou I, Valkimadi P, Politis A. Differential effect of demographics, processing speed, and depression on cognitive function in 755 non-demented community-dwelling elderly individuals. Cogn Behav Neurol. 2019;32(4):236-46. https://doi.org/10.1097/WNN.0000000000000211
  » https://doi.org/10.1097/WNN.0000000000000211

Publication Dates

• Publication in this collection
  11 Dec 2020
• Date of issue
  Dec 2020

History

• Received
  21 Apr 2020
• Accepted
  08 Sept 2020