Discrimination Of Mild Cognitive Impairment Research Articles

Discrimination of mild cognitive impairment based on involuntary changes caused in voice elements.

In this study, the technique associated with the capturing involuntary changes in voice elements caused by diseases is applied to diagnose them and a voice index is proposed to discriminate mild cognitive impairments. The participants in this study included 399 elderly people aged 65 years or older living in Matsumoto City, Nagano Prefecture, Japan. The participants were categorized into healthy and mild cognitive impairment groups based on clinical evaluation. It was hypothesized that as dementia progressed, task performance would become more challenging, and the effects on vocal cords and prosody would change significantly. In the study, voice samples of the participants were recorded while they were engaged in mental calculational tasks and during the reading of the results of the calculations written on paper. The change in prosody during the calculation from that during reading was expressed based on the difference in the acoustics. Principal component analysis was used to aggregate groups of voice features with similar characteristics of feature differences into several principal components. These principal components were combined with logistic regression analysis to propose a voice index to discriminate different mild cognitive impairment types. Discrimination accuracies of 90% and 65% were obtained for discriminations using the proposed index on the training and verification data (obtained from a population different from the training data), respectively. Therefore, it is suggested that the proposed index may be utilized as a means for discriminating mild cognitive impairments.

Potential Diagnostic Applications of Multi-Delay Arterial Spin Labeling in Early Alzheimer's Disease: The Chinese Imaging, Biomarkers, and Lifestyle Study.

BackgroundCerebral blood flow (CBF) alterations are involved in the onset and progression of Alzheimer’s disease (AD) and can be a potential biomarker. However, CBF measured by single-delay arterial spin labeling (ASL) for discrimination of mild cognitive impairment (MCI, an early stage of AD) was lack of accuracy. Multi-delay ASL can not only provide CBF quantification but also provide arterial transit time (ATT). Unfortunately, the technique was scarcely applied to the diagnosis of AD. Here, we detected the utility of ASL with 1-delay and 7-delay in ten regions of interest (ROIs) to identify MCI and AD.Materials and MethodsPseudocontinuous ASL (pCASL) MRI was acquired on a 3T GE scanner in adults from the Chinese Imaging, Biomarkers, and Lifestyle (CIBL) Study of AD cohort, including 26 normal cognition (NC), 37 MCI, and 39 AD. Receiver operating characteristic (ROC) analyses with 1-delay and 7-delay ASL were performed for the identification of MCI and AD. The DeLong test was used to compare ROC curves.ResultsFor CBF of 1-delay or 7-delay the AUCs showed moderate-high performance for the AD/NC and AD/MCI comparisons (AUC = 0.83∼0.96) (p < 0.001). CBF of 1-delay performed poorly in MCI/NC comparison (AUC = 0.69) (p < 0.001), but CBF of 7-delay fared well with an AUC of 0.79 (p < 0.001). The combination of CBF and ATT of 7-delay showed higher performance for AD/NC, AD/MCI, and MCI/NC comparisons with AUCs of 0.96, 0.89, and 0.89, respectively (p < 0.001). Furthermore, combination of CBF, ATT, sex, age, APOE ε4, and education improved further the accuracy (p < 0.001). In subgroups analyses, there were no significant differences in CBF of 7-delay ASL for identification of AD or MCI between age subgroups (p > 0.05).ConclusionThe combination of CBF and ATT with 7-delay ASL showed higher performance for identification of MCI than CBF of 1-delay, when adding to sex, age, APOE ε4 carrier status, and education years, the diagnostic performance was further increased, presenting a potential imaging biomarker in early AD.

Translation and psychometric evaluation of a Persian version of the functional assessment staging scale (I-FAST) in older patients with mild cognitive impairment and Alzheimer's disease in Iran.

The Functional Assessment Staging procedure is a clinical instrument which has been designed for staging Alzheimer's disease (AD) from the stage of no deficits to the pre-clinical stage of subjective deficits, to Mild Cognitive Impairment (MCI), to the stages of AD. This study examined the psychometric properties and the validity of the Persian version of the FAST (I-FAST) in an elderly outpatient population in Iran. We conducted a validation study of the FAST scale at the two referral centers for dementia and cognitive disorders in Tehran, Iran. The participants consisted of subjects with normal cognition, MCI and AD. The scores of the Persian version of the Mini-Mental State Examination (MMSE) and the Persian version of the I-FAST were examined. Demographic variables were also collected. The diagnosis of MCI was made based on Petersen criteria and AD based on the McKhann et al. criteria by a neurologist with expertise in dementia. Data was collected from 219 participants. A total of 54.7% of the sample was female and their mean age was 72.54 ± 8.88years. The area under the ROC curve was calculated 0.952 and 0.982. The I-FAST had a sensitivity of 92.2% and specificity of 98.0% for the differentiation of normal cognition from MCI. The sensitivity of the I-FAST for discrimination of subjects with AD from MCI was 99.0% and the specificity was 93.7%. The I-FAST showed good psychometric characteristics in the discrimination of MCI from both normal elderly and patients with Alzheimer's. The I-FAST is also a sensitive and accurate instrument for staging persons at risk for MCI and Alzheimer's, relatively free of the confounding effects of education, culture and language in comparison with the MMSE.

Complexity of EEG Dynamics for Early Diagnosis of Alzheimer's Disease Using Permutation Entropy Neuromarker

Background and objectiveElectroencephalogram (EEG) is one of the most demanded screening tools that investigates the effects of Alzheimer's Disease (AD) on human brain. Identification of AD in early stage gives rise to efficient treatment in dementia. Mild Cognitive Impairment (MCI) is considered as a conversion stage. Reducing EEG complexity can be used as a marker to detect AD. The aim of this study is to develop a 3-way diagnostic classification using EEG complexity in the detection of MCI/AD in clinical practice. This study also investigates the effects of different eyes states, i.e. eyes-open, eyes-closed on classification performance. MethodsEEG recordings from 85 AD, 85 MCI subjects, and 85 Healthy Controls with eyes-open and eyes- closed are analyzed. Permutation Entropy (PE) values are computed from frontal, central, parietal, temporal, and occipital regions for each EEG epoch. Distribution of PE values are visualized to observe discrimination of MCI/AD with HC. Visual investigations are combined with statistical analysis using ANOVA to determine whether groups are significant or not. Multinomial Logistic Regression model is applied to feature sets in order to classify participants individually. ResultsDistribution of measured PE shows that EEG complexity is lower in AD and higher in HC group. MCI group is observed as an intermediate form due to heterogeneous values. Results from 3-way classification indicate that F1-scores and rates of sensitivity and specificity achieve the highest overall discrimination rates reaching up to 100% for at TP8 for eyes-closed condition; and C3, C4, T8, O2 electrodes for eyes-open condition. Classification of HC from both patient groups is achieved best. Eyes-open state increases discrimination of MCI and AD. ConclusionsThis nonlinear EEG methodology study contributes to literature with high discrimination rates for identification of AD. PE is recommended as a practical diagnostic neuro-marker for AD studies. Resting state EEG at eyes-open condition can be more advantageous over eyes-closed EEG recordings for diagnosis of AD.

The Entorhinal Cortex Atrophy Score Is Diagnostic and Prognostic in Mild Cognitive Impairment.

Structural magnetic resonance imaging (MRI) is routinely performed in patients with mild cognitive impairment (MCI), but diagnostic accuracy to detect early cerebral atrophy is limited. To validate the visual entorhinal cortex atrophy (ERICA) rating scale regarding diagnosis, biomarker status, neuropsychological profile, and dementia risk in MCI. The ERICA score was retrospectively assessed regarding its discrimination of MCI (n = 80) from subjective cognitive decline and Alzheimer's disease (AD) dementia (n = 60, respectively), its prediction of conversion to dementia (median follow-up 28 months) and amyloid/tau biomarker status, and its association with neuropsychological tests. The ERICA score achieved 97% positive predictive value (PPV) for the presence of MCI. Discrimination between MCI and AD dementia (area under the curve: 0.71) was comparable to volumetry, and superior to the medial temporal lobe atrophy (MTA) score (p = 0.006). The PPV of the ERICA score for conversion to dementia was 83%, equivalent to tau status. It achieved 90% PPV for conversion when combined with tau, and 100% negative predictive value with verbal recall. While no measure predicted the predominantly positive amyloid status, the ERICA score was at least comparable to volumetry, and superior to the MTA score in predicting tau positivity (92% PPV for phospho-tau). The ERICA score was associated with verbal learning and memory, and, unlike the MTA score, also with AD-specific deficits in cued verbal recall. The ERICA score is a simple and valuable tool to exploit structural MRI for diagnosis and prognosis in MCI and is non-inferior to volumetry.

Feature Selection and Combination of Information in the Functional Brain Connectome for Discrimination of Mild Cognitive Impairment and Analyses of Altered Brain Patterns

Mild cognitive impairment (MCI) is often considered a critical time window for predicting early conversion to Alzheimer’s disease (AD). Brain functional connectome data (i.e., functional connections, global and nodal graph metrics) based on resting-state functional magnetic resonance imaging (rs-fMRI) provides numerous information about brain networks and has been used to discriminate normal controls (NCs) from subjects with MCI. In this paper, Student’s t-tests and group-least absolute shrinkage and selection operator (group-LASSO) were used to extract functional connections with significant differences and the most discriminative network nodes, respectively. Based on group-LASSO, the middle temporal, inferior temporal, lingual, posterior cingulate, and middle frontal gyri were the most predominant brain regions for nodal observation in MCI patients. Nodal graph metrics (within-module degree, participation coefficient, and degree centrality) showed the maximum discriminative ability. To effectively combine the multipattern information, we employed the multiple kernel learning support vector machine (MKL-SVM). Combined with functional connectome information, the MKL-SVM achieved a good classification performance (area under the receiving operating characteristic curve = 0.9728). Additionally, the altered brain connectome pattern revealed that functional connectivity was generally decreased in the whole-brain network, whereas graph theory topological attributes of some special nodes in the brain network were increased in MCI patients. Our findings demonstrate that optimal feature selection and combination of all connectome features (i.e., functional connections, global and nodal graph metrics) can achieve good performance in discriminating NCs from MCI subjects. Thus, the combination of functional connections and global and nodal graph metrics of brain networks can predict the occurrence of MCI and contribute to the early clinical diagnosis of AD.

Heterogeneity of Amyloid Binding in Cognitively Impaired Patients Consecutively Recruited from a Memory Clinic: Evaluating the Utility of Quantitative 18F-Flutemetamol PET-CT in Discrimination of Mild Cognitive Impairment from Alzheimer's Disease and Other Dementias.

Background:With the more widespread use of 18F-radioligand-based amyloid-β (Aβ) PET-CT imaging, we evaluated Aβ binding and the utility of neocortical 18F-Flutemetamol standardized uptake value ratio (SUVR) as a biomarker.Objective:18F-Flutemetamol SUVR was used to differentiate 1) mild cognitive impairment (MCI) from Alzheimer’s disease (AD), and 2) MCI from other non-AD dementias (OD).Methods:109 patients consecutively recruited from a University memory clinic underwent clinical evaluation, neuropsychological test, MRI and 18F-Flutemetamol PET-CT. The diagnosis was made by consensus of a panel consisting of 1 neuroradiologist and 2 geriatricians. The final cohort included 13 subjective cognitive decline (SCD), 22 AD, 39 MCI, and 35 OD. Quantitative analysis of 16 region-of-interests made by Cortex ID software (GE Healthcare).Results:The global mean 18F-Flutemetamol SUVR in SCD, MCI, AD, and OD were 0.50 (SD-0.08), 0.53 (SD-0.16), 0.76 (SD-0.10), and 0.56 (SD-0.16), respectively, with SUVR in SCD and MCI and OD being significantly lower than AD. Aβ binding in SCD, MCI, and OD was heterogeneous, being 23%, 38.5%, and 42.9% respectively, as compared to 100% amyloid positivity in AD. Using global SUVR, ROC analysis showed AUC of 0.868 and 0.588 in differentiating MCI from AD and MCI from OD respectively.Conclusion:18F-Flutemetamol SUVR differentiated MCI from AD with high efficacy (high negative predictive value), but much lower efficacy from OD. The major benefit of the test was to differentiate cognitively impaired patients (either SCD, MCI, or OD) without AD-related-amyloid-pathology from AD in the clinical setting, which was under-emphasized in the current guidelines proposed by Amyloid Imaging Task Force.

Discrimination of mild cognitive impairment and estimation of cognition using foot pressure information during walking

Discrimination of mild cognitive impairment and estimation of cognition using foot pressure information during walking

Accuracy of verbal fluency tests in the discrimination of mild cognitive impairment and probable Alzheimer’s disease in older Spanish monolingual individuals

ABSTRACT The main objetive was to analyze the accuracy of different verbal fluency tests (VFTs) in discriminating cognitively healthy subjects from individuals with mild cognitive impairment (MCI) and probable Alzheimer’s disease (AD) in a cohort of older Spanish speaking adults. As a result, we aimed to identify the VFT that best predicts conversion from MCI to probable AD. 287 subjects: 170 controls (HC), 90 stable MCI and 27 patients with MCI that evolved into probable AD (MCI-AD) were assessed with a neuropsychological battery test and five VFTs. The animal fluency test produced the best differentiation of HC from MCI (p < .001), of HC from MCI-AD (p < .001) and of MCI from MCI-AD converters (p < .001), with sensitivities 98.8%, 98.8% and 75.6%, respectively. Logistic regression showed that the animal fluency test (p < 0.001) appears to be the most useful and neuropsychological VFT to predict conversion to probable dementia.

Subjective memory complaint as a useful tool for the early detection of Alzheimer's disease.

PurposeDespite their high prevalence in Alzheimer’s disease (AD), and the increasing level of concern they have generated, subjective memory complaints (SMCs) are poorly understood. This study investigated the accuracy with which SMC can separate mild cognitive impairment (MCI) and early AD from cognitive normal (CN), and explored whether the discrimination ability is similar to or better than that of the Mini-Mental State Exam (MMSE).Patients and methodsThis study recruited 175 CN subjects, 52 with MCI, and 66 with probable AD aged 60 years or older. To test the independent contributions of SMC and MMSE scores to the classification of cognitive status (CN vs MCI or early AD), logistic regression analyses were performed, adjusting for the following potential confounding variables: age, gender, Frontal Assessment Battery score, modified Hachinski Ischemic Scale score, and apolipoprotein E ε4 status. Receiver operating characteristic (ROC) curve analyses were used to determine the discrimination accuracy of SMC and MMSE scores, and area under the ROC curve (AUROC) was also calculated.ResultsIn the highly educated (≥7 years), nondepressed (Geriatric Depression Scale ≤15) subgroup, SMC showed good accuracy in discriminating cognitively impaired subjects from CN after adjusting for potential confounding variables (the AUROC of the adjusted SMC was 0.841 for MCI discrimination, and it was 0.858 for MCI plus early AD discrimination). Both SMC and MMSE scores significantly contributed to differentiating between CN and MCI (OR=2.372, 95% CI=1.086–5.177; OR=0.730, 95% CI=0.566–0.941, respectively) after adjusting for the same covariates. However, in the highly educated and nondepressed subgroups, SMC showed significant predictive power for MCI from CN (OR=3.119, 95% CI=1.190–8.176; OR=3.328, 95% CI=1.320–8.396, respectively), whereas MMSE scores did not.ConclusionOur findings support the usefulness of SMC, which was comparable or even superior to MMSE scores, for detecting MCI or early AD.

A novel approach to brain connectivity reveals early structural changes in Alzheimer’s disease

Objective: Recent studies have shown that complex networks along with diffusion weighted imaging (DWI) can be efficient and promising techniques for early detection of structural pathological changes in Alzheimer’s disease. Besides, connectivity studies, specifically assessing the organization of a graph and its topology, could represent the best chance to discover how brain activity is shaped and driven. Accordingly, we propose a methodology to evaluate how Alzheimer’s disease affects brain networks through a novel way to look at graph connectivity. In fact, we use the combination of network features related to brain organization with network features related to the variations in connectivity between several subjects. Approach: From a DWI brain scan we reconstruct a probabilistic tractography by evaluating the number of white matter fibers connecting two anatomical districts, thus obtaining a weighted undirected network. The nodes of this network are the cerebral regions provided by the reference brain atlas, the weights are the intensity of linkage among them. We investigated brain connectivity graphs retrieved from a set of 222 publicly available DWI scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI): 47 Alzheimer’s disease (AD) patients, 52 normal controls (NC) and 123 mild cognitive impairment (MCI) subjects, this latter cohort includes 85 early and 38 late MCI subjects, EMCI and LMCI respectively. Main results: The proposed brain connectivity approach effectively characterizes Alzheimer’s disease, especially in its early stages. In fact, MCI is a prodromal phase of Alzheimer’s disease. We report a accuracy for the discrimination of NC and AD subjects and accuracies of and for the discrimination of MCI from respectively NC and AD subjects. Significance: Our complex network approach offers an innovative and effective instrument to model brain connectivity and detect in DWI tractographies early changes due to Alzheimer’s.

Discrimination of mild cognitive impairment and Alzheimer's disease using transfer entropy measures of scalp EEG.

Mild cognitive impairment (MCI) is a neurological condition related to early stages of dementia including Alzheimer's disease (AD). This study investigates the potential of measures of transfer entropy in scalp EEG for effectively discriminating between normal aging, MCI, and AD participants. Resting EEG records from 48 age-matched participants (mean age 75.7 years)-15 normal controls, 16 MCI, and 17 early AD-are examined. The mean temporal delays corresponding to peaks in inter-regional transfer entropy are computed and used as features to discriminate between the three groups of participants. Three-way classification schemes based on binary support vector machine models demonstrate overall discrimination accuracies of 91.7- 93.8%, depending on the protocol condition. These results demonstrate the potential for EEG transfer entropy measures as biomarkers in identifying early MCI and AD. Moreover, the analyses based on short data segments (two minutes) render the method practical for a primary care setting.

Serial position effects in mild cognitive impairment

Mild cognitive impairment (MCI) is often associated with the preclinical phase of Alzheimer's disease (AD). Special scoring of word-list recall data for serial position has been suggested to improve discrimination of normal aging from dementia. We examined serial position effects in word-list recall for MCI participants compared to Alzheimer patients and controls. Individuals with MCI, like Alzheimer patients, had a diminished primacy effect in recalling words from a list. No alternative scoring system was better than standard scoring of word-list recall in distinguishing MCI patients from controls. Retention weighted scoring improved the discrimination of MCI and AD groups.

Total Scores of the CERAD Neuropsychological Assessment Battery: Validation for Mild Cognitive Impairment and Dementia Patients With Diverse Etiologies

This study aimed to validate the two total scores (TS-I and TS-II) of the Consortium to Establish a Registry for Alzheimer Disease neuropsychological battery (CERAD-NP) for a large elderly population including mild cognitive impairment (MCI) and dementia patients with various etiologic backgrounds. The authors also investigated whether the addition of frontal-executive function score can improve the discrimination accuracy of the total scores for dementia and MCI. One thousand three hundred thirty-six normal comparison (NC), 583 dementia (420 AD, 111 non-AD dementia, and 52 mixed AD with non-AD dementia), and 250 MCI (223 amnestic and 27 nonamnestic MCI) individuals living in the community were included (all aged 60 years and older). Both TS-I and TS-II were highly correlated with other global cognitive and functional scales. Both total scores showed, though modest, superior NC versus MCI discrimination ability to Mini-Mental State Examination (MMSE). Their discrimination ability for NC versus dementia was excellent and significantly better, especially in discriminating very mild dementia, than MMSE. The addition of frontal-executive test score to TS-I or TS-II did not make a significant improvement in dementia or MCI discrimination ability. Both of them also showed higher test-retest and interrater reliability than MMSE or any individual neuropsychological tests in the CERAD-NP. These results strongly support the validity and usefulness of CERAD total scores for early detection and progression monitoring of MCI and dementia in clinical and research settings.

Diagnostic Discrimination of Mild Cognitive Impairment, Alzheimer's Disease, and Other Dementia Groups using the CogState Clinic Battery

Diagnostic Discrimination of Mild Cognitive Impairment, Alzheimer's Disease, and Other Dementia Groups using the CogState Clinic Battery

Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer's disease

Mild cognitive impairment can represent a transitional state between normal ageing and Alzheimer's disease. Non-invasive diagnostic methods are needed to identify mild cognitive impairment individuals for early therapeutic interventions. Our objective was to determine whether automated magnetic resonance imaging-based measures could identify mild cognitive impairment individuals with a high degree of accuracy. Baseline volumetric T1-weighted magnetic resonance imaging scans of 313 individuals from two independent cohorts were examined using automated software tools to identify the volume and mean thickness of 34 neuroanatomic regions. The first cohort included 49 older controls and 48 individuals with mild cognitive impairment, while the second cohort included 94 older controls and 57 mild cognitive impairment individuals. Sixty-five patients with probable Alzheimer's disease were also included for comparison. For the discrimination of mild cognitive impairment, entorhinal cortex thickness, hippocampal volume and supramarginal gyrus thickness demonstrated an area under the curve of 0.91 (specificity 94%, sensitivity 74%, positive likelihood ratio 12.12, negative likelihood ratio 0.29) for the first cohort and an area under the curve of 0.95 (specificity 91%, sensitivity 90%, positive likelihood ratio 10.0, negative likelihood ratio 0.11) for the second cohort. For the discrimination of Alzheimer's disease, these three measures demonstrated an area under the curve of 1.0. The three magnetic resonance imaging measures demonstrated significant correlations with clinical and neuropsychological assessments as well as with cerebrospinal fluid levels of tau, hyperphosphorylated tau and abeta 42 proteins. These results demonstrate that automated magnetic resonance imaging measures can serve as an in vivo surrogate for disease severity, underlying neuropathology and as a non-invasive diagnostic method for mild cognitive impairment and Alzheimer's disease.

Regional analysis of FDG and PIB-PET images in normal aging, mild cognitive impairment, and Alzheimer’s disease

ObjectiveThe objective of the study is to compare the diagnostic value of regional sampling of the cerebral metabolic rate of glucose metabolism (MRglc) using [18F]-fluoro-2-deoxyglucose ([18F]FDG)-positron emission tomography (PET) and amyloid-beta pathology using Pittsburgh Compound-B ([11C]PIB)-PET in the evaluation of patients with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) compared to normal elderly (NL).Materials and methodsAD patients, 7 NL, 13 MCI, and 17, received clinical, neuropsychological, magnetic resonance imaging (MRI), FDG, and PIB-PET exams. Parametric images of PIB uptake and MRglc were sampled using automated regions-of-interest (ROI).ResultsAD showed global MRglc reductions, and MCI showed reduced hippocampus (HIP) and inferior parietal lobe (IP) MRglc compared to NL. On PIB, AD patients showed significantly increased uptake in the middle frontal gyrus (MFG), posterior cingulate cortex (PCC), and IP (ps < 0.05). PIB uptake in MCI subjects was either AD or NL-like. HIP MRglc and MFG PIB uptake were the best discriminators of NL from MCI and NL from AD. These two best measures showed high diagnostic agreement for AD (94%) and poor agreement for MCI (54%). For the NL vs. MCI discrimination, combining the two best measures increased the accuracy for PIB (75%) and for FDG (85%) to 90%.ConclusionFor AD, the pattern of regional involvement for FDG and PIB differ, but both techniques show high diagnostic accuracy and 94% case by case agreement. In the classification of NL and MCI, FDG is superior to PIB, but there is only 54% agreement at a case level. Combining the two modalities improves the diagnostic accuracy for MCI.

Linear measures of temporal lobe atrophy on brain magnetic resonance imaging (MRI) but not visual rating of white matter changes can help discrimination of mild cognitive impairment (MCI) and Alzheimer's disease (AD)

Clinical discrimination of the early stages of AD and MCI is challenging. MRI indices which are simple enough to be applied by non-radiologists on hard copies would be of practical importance in the discrimination. We studied 45 consecutive patients (17 with MCI, 25 with AD, 3 with normal cognitive findings) with at least one white matter lesion (WML) on axial fluid-attenuated inversion recovery (FLAIR) MRI sequences. WML load was evaluated by Fazekas’ scoring system; temporal lobe atrophy by interuncal distance (IUD) measurement. WML pattern had no significant discriminative value of AD and MCI. No significant correlation between periventricular/subcortical WML scores and neuropsychological test results was observed. The mean IUD was significantly smaller in patients with MCI compared to those with AD. The cut-off value of IUD was 28.3 mm with receiver operating curve (ROC) analysis. Area under the curve was 0.925 (95% CI: 0.800–0.983). A significant negative correlation between IUD and the mini mental state examination (MMSE), verbal fluency, clock drawing, and Rey Auditory verbal learning test (AVLT) was noted. The results indicate that measurement of IUD is a clinically useful test in discrimination of AD and MCI patients with WML(s) on brain MRI. However, severity of these lesions is not useful for distinctions.