Using historical data to facilitate clinical prevention trials in Alzheimer disease? An analysis of longitudinal MCI (mild cognitive impairment) data sets

Abstract Background The Placebo Group Simulation Approach (PGSA) aims at partially replacing randomized placebo-controlled trials (RPCTs), making use of data from historical control groups in order to decrease the needed number of study participants exposed to lengthy placebo treatment. PGSA algorithms to create virtual control groups were originally derived from mild cognitive impairment (MCI) data of the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. To produce more generalizable algorithms, we aimed to compile five different MCI databases in a heuristic manner to create a “standard control algorithm” for use in future clinical trials. Methods We compared data from two North American cohort studies (n=395 and 4328, respectively), one company-sponsored international clinical drug trial (n=831) and two convenience patient samples, one from Germany (n=726), and one from Switzerland (n=1558). Results Despite differences between the five MCI samples regarding inclusion and exclusion criteria, their baseline demographic and cognitive performance data varied less than expected. However, the five samples differed markedly with regard to their subsequent cognitive performance and clinical development: (1) MCI patients from the drug trial did not deteriorate on verbal fluency over 3 years, whereas patients in the other samples did; (2) relatively few patients from the drug trial progressed from MCI to dementia (about 10% after 4 years), in contrast to the other four samples with progression rates over 30%. Conclusion Conventional MCI criteria were insufficient to allow for the creation of well-defined and internationally comparable samples of MCI patients. More recently published criteria for MCI or “MCI due to AD” are unlikely to remedy this situation. The Alzheimer scientific community needs to agree on a standard set of neuropsychological tests including appropriate selection criteria to make MCI a scientifically more useful concept. Patient data from different sources would then be comparable, and the scientific merits of algorithm-based study designs such as the PGSA could be properly assessed.

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Using Historical Data to Facilitate Clinical Prevention Trials in Alzheimer Disease?

10.21203/rs.3.rs-236517/v1 ◽

2021 ◽

Author(s):

Manfred Berres ◽

Andreas U. Monsch ◽

René Spiegel

Keyword(s):

Cognitive Performance ◽

Placebo Treatment ◽

Drug Trial ◽

The Other ◽

Community Needs ◽

Standard Set ◽

Using Data ◽

Clinical Prevention ◽

Study Participants ◽

Study Designs

Abstract BackgroundThe Placebo Group Simulation Approach (PGSA) aims at partially replacing Randomized Placebo-Controlled Trials (RPCTs) using data from historical control groups in order to decrease the needed number of study participants exposed to lengthy placebo treatment. PGSA algorithms were originally derived from Mild Cognitive Impairment (MCI) data of the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. To produce more generalizable algorithms, we aimed to compile five different MCI databases in an heuristic manner to create a ‘standard control algorithm’ for use in future clinical trials.MethodsWe compared data from two North American cohort studies (n= 395 and 4,328, respectively) one international clinical drug trial (n= 831) and two convenience patient samples, one from Germany (n=726), and one from Switzerland (n=1,558).ResultsDespite differences between the five MCI samples regarding inclusion and exclusion criteria, their baseline demographic and cognitive performance data varied less than expected. However, the five samples differed markedly with regard to their subsequent cognitive performance and clinical development: (1) MCI patients from the drug trial did not deteriorate on verbal fluency over 3 years, whereas patients in the other samples did; (2) relatively few patients from the drug trial progressed from MCI to dementia (about 10% after 4 years), in contrast to the other four samples with progression rates over 30 percent.ConclusionConventional MCI criteria were insufficient to allow for the creation of well-defined and internationally comparable samples of MCI patients. More recently published MCI criteria are unlikely to remedy this situation. The Alzheimer scientific community needs to agree on a standard set of neuropsychological tests including appropriate selection criteria to make MCI a scientifically more useful concept. Patient data from different sources would then be comparable and the scientific merits of algorithm-based study designs such as the PGSA could be properly assessed.

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Effect of education on cognitive performance in patients with mild cognitive impairment

Applied Neuropsychology Adult ◽

10.1080/23279095.2021.1887191 ◽

2021 ◽

pp. 1-10

Author(s):

Filipe Godinho ◽

Carolina Maruta ◽

Cláudia Borbinha ◽

Isabel Pavão Martins

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Cognitive Performance

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Multimodal measurement approach to identify individuals with mild cognitive impairment: study protocol for a cross-sectional trial

BMJ Open ◽

10.1136/bmjopen-2020-046879 ◽

2021 ◽

Vol 11 (5) ◽

pp. e046879

Author(s):

Bernhard Grässler ◽

Fabian Herold ◽

Milos Dordevic ◽

Tariq Ali Gujar ◽

Sabine Darius ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Statistical Analysis ◽

Cognitive Decline ◽

Cognitive Performance ◽

Classification Accuracy ◽

Multimodal Approach ◽

Cross Sectional ◽

Age Related ◽

Age Related Cognitive Decline

IntroductionThe diagnosis of mild cognitive impairment (MCI), that is, the transitory phase between normal age-related cognitive decline and dementia, remains a challenging task. It was observed that a multimodal approach (simultaneous analysis of several complementary modalities) can improve the classification accuracy. We will combine three noninvasive measurement modalities: functional near-infrared spectroscopy (fNIRS), electroencephalography and heart rate variability via ECG. Our aim is to explore neurophysiological correlates of cognitive performance and whether our multimodal approach can aid in early identification of individuals with MCI.Methods and analysisThis study will be a cross-sectional with patients with MCI and healthy controls (HC). The neurophysiological signals will be measured during rest and while performing cognitive tasks: (1) Stroop, (2) N-back and (3) verbal fluency test (VFT). Main aims of statistical analysis are to (1) determine the differences in neurophysiological responses of HC and MCI, (2) investigate relationships between measures of cognitive performance and neurophysiological responses and (3) investigate whether the classification accuracy can be improved by using our multimodal approach. To meet these targets, statistical analysis will include machine learning approaches.This is, to the best of our knowledge, the first study that applies simultaneously these three modalities in MCI and HC. We hypothesise that the multimodal approach improves the classification accuracy between HC and MCI as compared with a unimodal approach. If our hypothesis is verified, this study paves the way for additional research on multimodal approaches for dementia research and fosters the exploration of new biomarkers for an early detection of nonphysiological age-related cognitive decline.Ethics and disseminationEthics approval was obtained from the local Ethics Committee (reference: 83/19). Data will be shared with the scientific community no more than 1 year following completion of study and data assembly.Trial registration numberClinicalTrials.gov, NCT04427436, registered on 10 June 2020, https://clinicaltrials.gov/ct2/show/study/NCT04427436.

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Relationship between Cognitive Performance and Measures of Neurodegeneration among Hispanic and White Non-Hispanic Individuals with Normal Cognition, Mild Cognitive Impairment, and Dementia

Journal of the International Neuropsychological Society ◽

10.1017/s1355617717000820 ◽

2017 ◽

Vol 24 (2) ◽

pp. 176-187 ◽

Cited By ~ 5

Author(s):

Shanna L. Burke ◽

Miriam J. Rodriguez ◽

Warren Barker ◽

Maria T Greig-Custo ◽

Monica Rosselli ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Cognitive Performance ◽

Entorhinal Cortex ◽

Rating Scale ◽

Structural Mri ◽

Diagnostic Group ◽

Disease Stage ◽

Clinical Dementia Rating ◽

Mild Dementia

AbstractObjectives:The aim of this study was to determine the presence and severity of potential cultural and language bias in widely used cognitive and other assessment instruments, using structural MRI measures of neurodegeneration as biomarkers of disease stage and severity.Methods:Hispanic (n=75) and White non-Hispanic (WNH) (n=90) subjects were classified as cognitively normal (CN), amnestic mild cognitive impairment (aMCI) and mild dementia. Performance on the culture-fair and educationally fair Fuld Object Memory Evaluation (FOME) and Clinical Dementia Rating Scale (CDR) between Hispanics and WNHs was equivalent, in each diagnostic group. Volumetric and visually rated measures of the hippocampus entorhinal cortex, and inferior lateral ventricles (ILV) were measured on structural MRI scans for all subjects. A series of analyses of covariance, controlling for age, depression, and education, were conducted to compare the level of neurodegeneration on these MRI measures between Hispanics and WNHs in each diagnostic group.Results:Among both Hispanics and WNH groups there was a progressive decrease in volume of the hippocampus and entorhinal cortex, and an increase in volume of the ILV (indicating increasing atrophy in the regions surrounding the ILV) from CN to aMCI to mild dementia. For equivalent levels of performance on the FOME and CDR, WNHs had greater levels of neurodegeneration than did Hispanic subjects.Conclusions:Atrophy in medial temporal regions was found to be greater among WNH than Hispanic diagnostic groups, despite the lack of statistical differences in cognitive performance between these two ethnic groups. Presumably, unmeasured factors result in better cognitive performance among WNH than Hispanics for a given level of neurodegeneration. (JINS, 2018,24, 176–187)

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P3-067: Brazilian version of the Short Cognitive Performance Test (SKT) - psychometric properties and diagnostic accuracy in Alzheimer's disease (AD) and mild cognitive impairment (MCI)

Alzheimer s & Dementia ◽

10.1016/j.jalz.2006.05.1334 ◽

2006 ◽

Vol 2 ◽

pp. S392-S392

Author(s):

Mariana K. Flaks ◽

Mônica S. Yassuda ◽

Ana Carolina Regina ◽

Cândida Helena P. Camargo ◽

Carla G. Cid ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Diagnostic Accuracy ◽

Psychometric Properties ◽

Cognitive Performance ◽

Performance Test ◽

Brazilian Version

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Altered Weibull Degree Distribution in Resting-State Functional Brain Networks Is Associated With Cognitive Decline in Mild Cognitive Impairment

Frontiers in Aging Neuroscience ◽

10.3389/fnagi.2020.599112 ◽

2021 ◽

Vol 12 ◽

Author(s):

Yifei Zhang ◽

Xiaodan Chen ◽

Xinyuan Liang ◽

Zhijiang Wang ◽

Teng Xie ◽

...

Keyword(s):

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Human Brain ◽

Cognitive Performance ◽

Power Law ◽

Resting State ◽

Brain Networks ◽

Functional Networks ◽

Whole Brain ◽

Connectivity Degree

The topological organization of human brain networks can be mathematically characterized by the connectivity degree distribution of network nodes. However, there is no clear consensus on whether the topological structure of brain networks follows a power law or other probability distributions, and whether it is altered in Alzheimer's disease (AD). Here we employed resting-state functional MRI and graph theory approaches to investigate the fitting of degree distributions of the whole-brain functional networks and seven subnetworks in healthy subjects and individuals with amnestic mild cognitive impairment (aMCI), i.e., the prodromal stage of AD, and whether they are altered and correlated with cognitive performance in patients. Forty-one elderly cognitively healthy controls and 30 aMCI subjects were included. We constructed functional connectivity matrices among brain voxels and examined nodal degree distributions that were fitted by maximum likelihood estimation. In the whole-brain networks and all functional subnetworks, the connectivity degree distributions were fitted better by the Weibull distribution [f(x)~x(β−1)e(−λxβ)] than power law or power law with exponential cutoff. Compared with the healthy control group, the aMCI group showed lower Weibull β parameters (shape factor) in both the whole-brain networks and all seven subnetworks (false-discovery rate-corrected, p < 0.05). These decreases of the Weibull β parameters in the whole-brain networks and all subnetworks except for ventral attention were associated with reduced cognitive performance in individuals with aMCI. Thus, we provided a short-tailed model to capture intrinsic connectivity structure of the human brain functional networks in health and disease.

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