scholarly journals Distinct Subgroups of Amnestic Mild Cognitive Impairment as Identified by Soft Independent Modeling of Class Analogy

2019 ◽  
Author(s):  
Sookjaroen Tangwongchai ◽  
Itthipol Tawankanjanachot ◽  
Chavit Tunvirachaisakul ◽  
Thitiporn Supasitthumrong ◽  
Solaphat Hemrungrojn ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Neuropsychological Tests ◽  
Diagnostic Category ◽  
Principal Component ◽  
Amnestic Mild Cognitive Impairment ◽  
Cognitive Profile ◽  
Support Vector ◽  
Patients At Risk ◽  
Normal Controls

Amnestic mild cognitive impairment (aMCI) is a condition characterized by mild deficits in episodic and semantic memory and learning. The conversion rate of aMCI to Alzheimer disease (AD) is significantly higher in aMCI than in the general population. The aim of this study is to examine whether aMCI is a valid diagnostic category or whether aMCI comprises different subgroups based on cognitive functions. We recruited 60 aMCI patients, 60 with AD and 61 healthy controls who completed neuropsychological tests of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-NP) and biomarkers including serum anion gap (AGAP). Principal component analysis, support vector machine and Soft Independent Modeling of Class Analogy (SIMCA) showed that AD patients and controls were highly significantly discrimanted from each other, while patients with aMCI overlap considerably with normal controls. SIMCA showed that 68.3% of the aMCI patients were assigned to the control class (named: aMCI-HC), 15% to AD (aMCI-AD), while 16.6% did not belong to either class (aMCI-strangers). aMCI-HC subjects showed sings of very mild cognitive decline and impaired recall. aMCI-strangers showed signs of mild cognitive impairment with impaired fluency and naming. aMCI-AD cases showed a cognitive profile reminiscent of AD an increased AGAP levels. In conclusion, our SIMCA model may classify subjects afforded a clinical diagnosis of aMCI according to Petersen’s criteria into three clinically relevant subgroups and help in the early detection of AD by identifying aMCI patients at risk to develop AD and those that have an AD prodrome.

Neurocognitive Plasticity Is Associated with Cardiorespiratory Fitness Following Physical Exercise in Older Adults with Amnestic Mild Cognitive Impairment

2021 ◽  
pp. 1-22
Author(s):  
Galit Yogev-Seligmann ◽  
Tamir Eisenstein ◽  
Elissa Ash ◽  
Nir Giladi ◽  
Haggai Sharon ◽  
...  
Keyword(s):  
Older Adults ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Aerobic Exercise ◽  
Cardiorespiratory Fitness ◽  
Neuropsychological Tests ◽  
Aerobic Training ◽  
Amnestic Mild Cognitive Impairment ◽  
Control Group ◽  
Memory Encoding

Background: Aerobic training has been shown to promote structural and functional neurocognitive plasticity in cognitively intact older adults. However, little is known about the neuroplastic potential of aerobic exercise in individuals at risk of Alzheimer’s disease (AD) and dementia. Objective: We aimed to explore the effect of aerobic exercise intervention and cardiorespiratory fitness improvement on brain and cognitive functions in older adults with amnestic mild cognitive impairment (aMCI). Methods: 27 participants with aMCI were randomized to either aerobic training (n = 13) or balance and toning (BAT) control group (n = 14) for a 16-week intervention. Pre- and post-assessments included functional MRI experiments of brain activation during associative memory encoding and neural synchronization during complex information processing, cognitive evaluation using neuropsychological tests, and cardiorespiratory fitness assessment. Results: The aerobic group demonstrated increased frontal activity during memory encoding and increased neural synchronization in higher-order cognitive regions such as the frontal cortex and temporo-parietal junction (TPJ) following the intervention. In contrast, the BAT control group demonstrated decreased brain activity during memory encoding, primarily in occipital, temporal, and parietal areas. Increases in cardiorespiratory fitness were associated with increases in brain activation in both the left inferior frontal and precentral gyri. Furthermore, changes in cardiorespiratory fitness were also correlated with changes in performance on several neuropsychological tests. Conclusion: Aerobic exercise training may result in functional plasticity of high-order cognitive areas, especially, frontal regions, among older adults at risk of AD and dementia. Furthermore, cardiorespiratory fitness may be an important mediating factor of the observed changes in neurocognitive functions.

scholarly journals Classification of Alzheimer’s Disease and Mild Cognitive Impairment Based on Cortical and Subcortical Features from MRI T1 Brain Images Utilizing Four Different Types of Datasets

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Saidjalol Toshkhujaev ◽  
Kun Ho Lee ◽  
Kyu Yeong Choi ◽  
Jang Jae Lee ◽  
Goo-Rak Kwon ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
State Of The Art ◽  
Principal Component ◽  
Label Propagation ◽  
Support Vector ◽  
Brain Volumes

Alzheimer’s disease (AD) is one of the most common neurodegenerative illnesses (dementia) among the elderly. Recently, researchers have developed a new method for the instinctive analysis of AD based on machine learning and its subfield, deep learning. Recent state-of-the-art techniques consider multimodal diagnosis, which has been shown to achieve high accuracy compared to a unimodal prognosis. Furthermore, many studies have used structural magnetic resonance imaging (MRI) to measure brain volumes and the volume of subregions, as well as to search for diffuse changes in white/gray matter in the brain. In this study, T1-weighted structural MRI was used for the early classification of AD. MRI results in high-intensity visible features, making preprocessing and segmentation easy. To use this image modality, we acquired four types of datasets from each dataset’s server. In this work, we downloaded 326 subjects from the National Research Center for Dementia homepage, 123 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) homepage, 121 subjects from the Alzheimer’s Disease Repository Without Borders homepage, and 131 subjects from the National Alzheimer’s Coordinating Center homepage. In our experiment, we used the multiatlas label propagation with expectation–maximization-based refinement segmentation method. We segmented the images into 138 anatomical morphometry images (in which 40 features belonged to subcortical volumes and the remaining 98 features belonged to cortical thickness). The entire dataset was split into a 70 : 30 (training and testing) ratio before classifying the data. A principal component analysis was used for dimensionality reduction. Then, the support vector machine radial basis function classifier was used for classification between two groups—AD versus health control (HC) and early mild cognitive impairment (MCI) (EMCI) versus late MCI (LMCI). The proposed method performed very well for all four types of dataset. For instance, for the AD versus HC group, the classifier achieved an area under curve (AUC) of more than 89% for each dataset. For the EMCI versus LMCI group, the classifier achieved an AUC of more than 80% for every dataset. Moreover, we also calculated Cohen kappa and Jaccard index statistical values for all datasets to evaluate the classification reliability. Finally, we compared our results with those of recently published state-of-the-art methods.

Characteristics of Mild Cognitive Impairment Using the Thai Version of the Consortium to Establish a Registry for Alzheimer’s Disease Tests: A Multivariate and Machine Learning Study

2018 ◽  
Vol 45 (1-2) ◽  
pp. 38-48 ◽  
Author(s):  
Chavit Tunvirachaisakul ◽  
Thitiporn Supasitthumrong ◽  
Sookjareon Tangwongchai ◽  
Solaphat Hemrunroj ◽  
Phenphichcha Chuchuen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Support Vector ◽  
Test Machine ◽  
Characteristic Analysis ◽  
Adaptive Boosting ◽  
Normal Controls

Background: The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) developed a neuropsychological battery (CERAD-NP) to screen patients with Alzheimer’s dementia. Mild cognitive impairment (MCI) has received attention as a pre-dementia stage. Objectives: To delineate the CERAD-NP features of MCI and their clinical utility to externally validate MCI diagnosis. Methods: The study included 60 patients with MCI, diagnosed using the Clinical Dementia Rating, and 63 normal controls. Data were analysed employing receiver operating characteristic analysis, Linear Support Vector Machine, Random Forest, Adaptive Boosting, Neural Network models, and t-distributed stochastic neighbour embedding (t-SNE). Results: MCI patients were best discriminated from normal controls using a combination of Wordlist Recall, Wordlist Memory, and Verbal Fluency Test. Machine learning showed that the CERAD features learned from MCI patients and controls were not strongly predictive of the diagnosis (maximal cross-validation 77.2%), whilst t-SNE showed that there is a considerable overlap between MCI and controls. Conclusions: The most important features of the CERAD-NP differentiating MCI from normal controls indicate impairments in episodic and semantic memory and recall. While these features significantly discriminate MCI patients from normal controls, the tests are not predictive of MCI.

Addenbrooke's cognitive examination III: diagnostic utility for mild cognitive impairment and dementia and correlation with standardized neuropsychological tests

2016 ◽  
Vol 29 (1) ◽  
pp. 105-113 ◽  
Author(s):  
Jordi A. Matias-Guiu ◽  
Ana Cortés-Martínez ◽  
Maria Valles-Salgado ◽  
Teresa Rognoni ◽  
Marta Fernández-Matarrubia ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Neuropsychological Tests ◽  
Pearson Correlation ◽  
Amnestic Mild Cognitive Impairment ◽  
Visuospatial Function ◽  
Early Stages ◽  
Addenbrooke’S Cognitive Examination

ABSTRACTBackground:Addenbrooke's Cognitive Examination III (ACE-III) is a screening test that was recently validated for diagnosing dementia. Since it assesses attention, language, memory, fluency, and visuospatial function separately, it may also be useful for general neuropsychological assessments. The aim of this study was to analyze the tool's ability to detect early stages of Alzheimer's disease and to examine the correlation between ACE-III scores and scores on standardized neuropsychological tests.Methods:Our study included 200 participants categorized as follows: 25 healthy controls, 48 individuals with subjective memory complaints, 47 patients with amnestic mild cognitive impairment and 47 mild Alzheimer's disease, and 33 patients with other neurodegenerative diseases.Results:The ACE-III memory and language domains were highly correlated with the neuropsychological tests specific to those domains (Pearson correlation coefficient of 0.806 for total delayed recall on the Free and Cued Selective Reminding Test vs. 0.744 on the Boston Naming Test). ACE-III scores discriminated between controls and patients with amnestic mild cognitive impairment (AUC: 0.906), and between controls and patients with mild Alzheimer's disease (AUC: 0.978).Conclusion:Our results suggest that ACE-III is a useful neuropsychological test for assessing the cognitive domains of attention, language, memory, and visuospatial function. It also enables detection of Alzheimer's disease in early stages.

Does tip-of-the-tongue for proper names discriminate amnestic mild cognitive impairment?

2012 ◽  
Vol 25 (4) ◽  
pp. 627-634 ◽  
Author(s):  
Onésimo Juncos-Rabadán ◽  
David Facal ◽  
Cristina Lojo-Seoane ◽  
Arturo X. Pereiro
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Verbal Fluency ◽  
Multivariate Regression ◽  
Proper Names ◽  
Naming Task ◽  
Amnestic Mild Cognitive Impairment ◽  
Regression Analyses ◽  
Tip Of The Tongue ◽  
Normal Controls

ABSTRACTBackground: Difficulty in retrieving people's names is very common in the early stages of Alzheimer's disease and mild cognitive impairment. Such difficulty is often observed as the tip-of-the-tongue (TOT) phenomenon. The main aim of this study was to explore whether a famous people's naming task that elicited the TOT state can be used to discriminate between amnestic mild cognitive impairment (aMCI) patients and normal controls.Methods: Eighty-four patients with aMCI and 106 normal controls aged over 50 years performed a task involving naming 50 famous people shown in pictures. Univariate and multivariate regression analyses were used to study the relationships between aMCI and semantic and phonological measures in the TOT paradigm.Results: Univariate regression analyses revealed that all TOT measures significantly predicted aMCI. Multivariate analysis of all these measures correctly classified 70% of controls (specificity) and 71.6% of aMCI patients (sensitivity), with an AUC (area under curve ROC) value of 0.74, but only the phonological measure remained significant. This classification value was similar to that obtained with the Semantic verbal fluency test.Conclusions: TOTs for proper names may effectively discriminate aMCI patients from normal controls through measures that represent one of the naming processes affected, that is, phonological access.

Comparison of Informant Reports and Neuropsychological Assessment in Mild Cognitive Impairment

2011 ◽  
Vol 26 (7) ◽  
pp. 528-534 ◽  
Author(s):  
Carlo Abbate ◽  
Pietro D. Trimarchi ◽  
Paola Nicolini ◽  
Luigi Bergamaschini ◽  
Carlo Vergani ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Retrospective Study ◽  
Mild Cognitive Impairment ◽  
Neuropsychological Assessment ◽  
Neuropsychological Tests ◽  
Subjective Evaluation ◽  
Structured Interview ◽  
Cognitive Status ◽  
Cognitive Profile ◽  
The One

The aim of this retrospective study was to investigate the accuracy of informant reports on cognitive status in mild cognitive impairment (MCI) by comparing the subjective evaluation made by patients’ relatives with the objective results of neuropsychological assessment. We enrolled 119 MCI outpatients and their relatives. Cognitive impairment was assessed by a battery of standardized neuropsychological tests. Informant reports on cognitive functioning were obtained by means of a structured interview. Subjective and objective evaluations of cognitive status were rated according to the same scoring system in order to enable comparison. All but one relative reported cognitive dysfunctions at the interview, but the kind of cognitive profile emerging from their reports was quite different from the one highlighted by neuropsychological assessment. A subjective evaluation of cognitive status based on informant reports could therefore be useful to identify patients with MCI but is unable to define MCI subtypes.

scholarly journals Topological Properties of Large-Scale Cortical Networks Based on Multiple Morphological Features in Amnestic Mild Cognitive Impairment

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Qiongling Li ◽  
Xinwei Li ◽  
Xuetong Wang ◽  
Yuxia Li ◽  
Kuncheng Li ◽  
...  
Keyword(s):  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Cortical Thickness ◽  
Large Scale ◽  
Amnestic Mild Cognitive Impairment ◽  
Geometric Features ◽  
Topological Properties ◽  
Cortical Networks ◽  
Normal Controls ◽  
Sulcal Depth

Previous studies have demonstrated that amnestic mild cognitive impairment (aMCI) has disrupted properties of large-scale cortical networks based on cortical thickness and gray matter volume. However, it is largely unknown whether the topological properties of cortical networks based on geometric measures (i.e., sulcal depth, curvature, and metric distortion) change in aMCI patients compared with normal controls because these geometric features of cerebral cortex may be related to its intrinsic connectivity. Here, we compare properties in cortical networks constructed by six different morphological features in 36 aMCI participants and 36 normal controls. Six cortical features (3 volumetric and 3 geometric features) were extracted for each participant, and brain abnormities in aMCI were identified by cortical network based on graph theory method. All the cortical networks showed small-world properties. Regions showing significant differences mainly located in the medial temporal lobe and supramarginal and right inferior parietal lobe. In addition, we also found that the cortical networks constructed by cortical thickness and sulcal depth showed significant differences between the two groups. Our results indicated that geometric measure (i.e., sulcal depth) can be used to construct network to discriminate individuals with aMCI from controls besides volumetric measures.

scholarly journals Combined Support Vector Machine Classifier and Brain Structural Network Features for the Individual Classification of Amnestic Mild Cognitive Impairment and Subjective Cognitive Decline Patients

2021 ◽  
Vol 13 ◽  
Author(s):  
Weijie Huang ◽  
Xuanyu Li ◽  
Xin Li ◽  
Guixia Kang ◽  
Ying Han ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Classification Performance ◽  
Amnestic Mild Cognitive Impairment ◽  
Subjective Cognitive Decline ◽  
Support Vector ◽  
Weighted Networks ◽  
Structural Network ◽  
Auc Value

ObjectiveIndividuals with subjective cognitive decline (SCD) or amnestic mild cognitive impairment (aMCI) represent important targets for the early detection and intervention of Alzheimer’s disease (AD). In this study, we employed a multi-kernel support vector machine (SVM) to examine whether white matter (WM) structural networks can be used for screening SCD and aMCI.MethodsA total of 138 right-handed participants [51 normal controls (NC), 36 SCD, 51 aMCI] underwent MRI brain scans. For each participant, three types of WM networks with different edge weights were constructed with diffusion MRI data: fiber number-weighted networks, mean fractional anisotropy-weighted networks, and mean diffusivity (MD)-weighted networks. By employing a multiple-kernel SVM, we seek to integrate information from three weighted networks to improve classification performance. The accuracy of classification between each pair of groups was evaluated via leave-one-out cross-validation.ResultsFor the discrimination between SCD and NC, an area under the curve (AUC) value of 0.89 was obtained, with an accuracy of 83.9%. Further analysis revealed that the methods using three types of WM networks outperformed other methods using single WM network. Moreover, we found that most of discriminative features were from MD-weighted networks, which distributed among frontal lobes. Similar classification performance was also reported in the differentiation between subjects with aMCI and NCs (accuracy = 83.3%). Between SCD and aMCI, an AUC value of 0.72 was obtained, with an accuracy of 72.4%, sensitivity of 74.5% and specificity of 69.4%. The highest accuracy was achieved with features only selected from MD-weighted networks.ConclusionWhite matter structural network features help machine learning algorithms accurately identify individuals with SCD and aMCI from NCs. Our findings have significant implications for the development of potential brain imaging markers for the early detection of AD.

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