Differentiation Between Dementia With Lewy Bodies And Alzheimer's Disease Using Voxel-Based Morphometry Of Structural MRI: A Multicenter Study

The Role of Gastrointestinal Dysbiosis and Fecal Transplantation in Various Neurocognitive Disorders

This review explores the critical role of the human microbiome in neurological and neurodegenerative disorders, focusing on gut-brain axis dysfunction caused by dysbiosis, an imbalance in gut bacteria. Dysbiosis has been linked to diseases such as Alzheimer's disease, Parkinson's disease (PD), multiple sclerosis (MS), and stroke. The gut microbiome influences the central nervous system (CNS) through signaling molecules, including short-chain fatty acids, neurotransmitters, and metabolites, impacting brain health and disease progression. Emerging therapies, such as fecal microbiota transplantation (FMT), have shown promise in restoring microbial balance and alleviating neurological symptoms, especially in Alzheimer's and PD. Additionally, nutritional interventions such as probiotics, prebiotics, and specialized diets are being investigated for their ability to modify gut microbiota and improve patient outcomes. This review highlights the therapeutic potential of gut microbiota modulation but emphasizes the need for further clinical trials to establish the safety and efficacy of these interventions in neurological and mental health disorders.

The usefulness of combined analysis using CIScore and VSRAD parameters for differentiating between dementia with Lewy body and Alzheimer's disease

PURPOSE

The Cingulate Island score (CIScore) is useful index for differentiating between dementia with Lewy body (DLB) and Alzheimer's disease (AD) using regional cerebral blood flow (rCBF) SPECT. The Z score standing for medial temporal lobe (MTL) atrophy and the ratio of Z score between dorsal brain stem (DBS) to MTL are useful indices for differentiating between DLB and AD using MRI with VSRAD. The current study investigated the diagnostic ability by the combined use of rCBF SPECT and MRI in the differentiation between AD and DLB.

MATERIALS AND METHODS

In cases with 42 AD and 28 DLB undertaken Tc-99m-ECD SPECT and MRI, we analyzed differential diagnostic ability between AD and DLB among following conditions by single or combined settings. Namely, they were (1) the CIScore as a parameter of rCBF SPECT (DLB ≦ 0.25), (2) Z score value of MTL atrophy (DLB ≦ 2.05), (3) the ratio of Z score of DBS to medial temporal gray matter as a parameter of brain atrophy using VSRAD (DLB ≧ 0.38). Also, we analyzed them both including and omitting the elderly (over 75 years old).

RESULTS

The accuracy of differential diagnosis in this condition was 74% for (1), 69% for (2), and 67% for (3). The accuracy by combination condition was 84% for (1) and (2), 81% for (1) and (3), and 67% for (2) and (3), respectively. The combination method by CIScore and the Z score of MTL showed the best accuracy. When we confined condition to ages younger than 75 years, the accuracy improved to 94% in the combination method.

CONCLUSION

The combined use of CIScore and Z score of MTL was suggested to be useful in the differential diagnosis between DLB and AD particularly in younger than 75 years old.

Differential diagnosis of MCI with Lewy bodies and MCI due to Alzheimer's disease by visual assessment of occipital hypoperfusion on SPECT images

PURPOSE

Predicting progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD) or dementia with Lewy bodies (DLB) is important. We evaluated morphological and functional differences between MCI with Lewy bodies (MCI-LB) and MCI due to AD (MCI-AD), and a method for differentiating between these conditions using brain MRI and brain perfusion SPECT.

METHODS

A continuous series of 101 subjects, who had visited our memory clinic and met the definition of MCI, were enrolled retrospectively. They were consisted of 60 MCI-LB and 41 MCI-AD subjects. Relative cerebral blood flow (rCBF) on SPECT images and relative brain atrophy on MRI images were evaluated. We performed voxel-based analysis and visually inspected brain perfusion SPECT images for regional brain atrophy, occipital hypoperfusion and the cingulate island sign (CIS), for differential diagnosis of MCI-LB and MCI-AD.

RESULTS

MRI showed no significant differences in regional atrophy between the MCI-LB and MCI-AD groups. In MCI-LB subjects, occipital rCBF was significantly decreased compared with MCI-AD subjects (p < 0.01, family wise error [FWE]-corrected). Visual inspection of occipital hypoperfusion had sensitivity, specificity, and accuracy values of 100%, 73.2% and 89.1%, respectively, for differentiating MCI-LB and MCI-AD. Occipital hypoperfusion was offered higher diagnostic utility than the CIS.

CONCLUSIONS

The occipital lobe was the region with significantly decreased rCBF in MCI-LB compared with MCI-AD subjects. Occipital hypoperfusion on brain perfusion SPECT may be a more useful imaging biomarker than the CIS for visually differentiating MCI-LB and MCI-AD.

Motion correction in MR image for analysis of VSRAD using generative adversarial network

Voxel-based specific region analysis systems for Alzheimer's disease (VSRAD) are clinically used to measure the atrophied hippocampus captured by magnetic resonance imaging (MRI). However, motion artifacts during acquisition of images may distort the results of the analysis. This study aims to evaluate the usefulness of the Pix2Pix network in motion correction for the input image of VSRAD analysis. Seventy-three patients examined with MRI were distinguished into the training group (n = 51) and the test group (n = 22). To create artifact images, the k-space images were manipulated. Supervised deep learning was employed to obtain a Pix2Pix that generates motion-corrected images, with artifact images as the input data and original images as the reference data. The results of the VSRAD analysis (severity of voxel of interest (VOI) atrophy, the extent of gray matter (GM) atrophy, and extent of VOI atrophy) were recorded for artifact images and motion-corrected images, and were then compared with the original images. For comparison, the image quality of Pix2Pix generated motion-corrected image was also compared with that of U-Net. The Bland-Altman analysis showed that the mean of the limits of agreement was smaller for the motion-corrected images compared to the artifact images, suggesting successful motion correction by the Pix2Pix. The Spearman's rank correlation coefficients between original and motion-corrected images were almost perfect for all results (severity of VOI atrophy: 0.87-0.99, extent of GM atrophy: 0.88-00.98, extent of VOI atrophy: 0.90-1.00). Pix2Pix generated motion-corrected images that showed generally improved quantitative and qualitative image qualities compared with the U-net generated motion-corrected images. Our findings suggest that motion correction using Pix2Pix is a useful method for VSRAD analysis.

Voxel-based specific regional analysis system for Alzheimer's disease utility as a screening tool for unrecognized cognitive dysfunction of elderly patients in diabetes outpatient clinics: Multicenter retrospective exploratory study

AIMS/INTRODUCTION

An efficient screening strategy for identification of cognitive dysfunction remains a clinical issue in the management of elderly adults with diabetes. A magnetic resonance imaging voxel-based specific regional analysis system for Alzheimer's disease (VSRAD) has been developed as an automated brain morphometry system that includes the hippocampus. We carried out a multicenter retrospective study to evaluate the utility of VSRAD for screening cognitive dysfunction in diabetes outpatient clinics.

MATERIALS AND METHODS

We enrolled patients with diabetes aged >65 years who underwent brain magnetic resonance imaging scans for the purpose of a medical checkup between November 2018 and May 2019. Patients who were already suspected or diagnosed with mild cognitive impairment and/or dementia as well as those with a history of cerebrovascular disease were excluded.

RESULTS

A total of 67 patients were enrolled. Five patients were diagnosed with mild cognitive impairment or dementia (clinical cognitive dysfunction). Patients with clinical cognitive dysfunction showed a significantly higher z-score in VSRAD analysis (2.57 ± 0.47 vs 1.15 ± 0.55, P < 0.01). The sensitivities and specificities for diagnosis of clinical cognitive dysfunction were 80 and 48% for the Mini-Mental State Examination, 100 and 89% for the z-score, and 100 and 90% for the combination of the Mini-Mental State Examination score and z-score, respectively.

CONCLUSIONS

VSRAD analysis can distinguish patients with clinical cognitive dysfunction in the elderly with diabetes, and also shows reasonable sensitivity and specificity compared with the Mini-Mental State Examination alone. Thus, VSRAD analysis can be useful for early identification of clinical cognitive dysfunction in the elderly with diabetes.

Advances in computerized MRI‐based biomarkers in Alzheimer’s disease

The use of neuroimaging examinations is crucial in Alzheimer’s disease (AD), in both research and clinical settings. Over the years, magnetic resonance imaging (MRI)–based computer‐aided diagnosis has been shown to be helpful for early screening and predicting cognitive decline. Meanwhile, an increasing number of studies have adopted machine learning for the classification of AD, with promising results. In this review article, we focus on computerized MRI‐based biomarkers of AD by reviewing representative studies that used computerized techniques to identify AD patients and predict cognitive progression. We categorized these studies based on the following applications: (1) identifying AD from normal control; (2) identifying AD from other dementia types, including vascular dementia, dementia with Lewy bodies, and frontotemporal dementia; and (3) predicting conversion from NC to mild cognitive impairment (MCI) and from MCI to AD. This systematic review could act as a state‐of‐the‐art overview of this emerging field as well as a basis for designing future studies.

Update on neuroimaging in non-Alzheimer's disease dementia: a focus on the Lewy body disease spectrum

PURPOSE OF REVIEW

An accurate differential diagnosis between Alzheimer's disease (AD) and non-AD dementia is of paramount importance to study disease mechanisms, define prognosis, and select patients for disease-specific treatments. The purpose of the present review is to describe the most recent neuroimaging studies in Lewy body disease spectrum (LBDS), focusing on differences with AD.

RECENT FINDINGS

Different neuroimaging methods are used to investigate patterns of alterations, which can be helpful to distinguish LBDS from AD. Positron emission tomography radiotracers and advanced MRI structural and functional methods discriminate these two conditions with increasing accuracy. Prodromal disease stages can be identified, allowing an increasingly earlier diagnosis.

SUMMARY

Neuroimaging biomarkers can aid in obtaining the best diagnostic accuracy in LBDS. Despite the main role of neuroimaging in clinical setting is to exclude secondary causes of dementia, structural and metabolic imaging techniques give an essential help to study in-vivo pathophysiological mechanisms of diseases. The importance of neuroimaging in LBDS is given by the increasing number of imaging biomarker developed and studied in the last years.

Imaging Familial and Sporadic Neurodegenerative Disorders Associated with Parkinsonism

In this paper, the structural and functional imaging changes associated with sporadic and genetic Parkinson's disease and atypical Parkinsonian variants are reviewed. The role of imaging for supporting diagnosis and detecting subclinical disease is discussed, and the potential use and drawbacks of using imaging biomarkers for monitoring disease progression is debated. Imaging changes associated with nonmotor complications of PD are presented. The similarities and differences in imaging findings in Lewy body dementia, Parkinson's disease dementia, and Alzheimer's disease are discussed.

The combination of MMSE with VSRAD and eZIS has greater accuracy for discriminating mild cognitive impairment from early Alzheimer's disease than MMSE alone

Background

Alzheimer’s disease (AD) is assessed by carefully examining a patient’s cognitive impairment. However, previous studies reported inadequate diagnostic accuracy for dementia in primary care settings. Many hospitals use the automated quantitative evaluation method known as the Voxel-based Specific Regional Analysis System for Alzheimer’s Disease (VSRAD), wherein brain MRI data are used to evaluate brain morphological abnormalities associated with AD. Similarly, an automated quantitative evaluation application called the easy Z-score imaging system (eZIS), which uses brain SPECT data to detect regional cerebral blood flow decreases associated with AD, is widely used. These applications have several indicators, each of which is known to correlate with the degree of AD. However, it is not completely known whether these indicators work better when used in combination in real-world clinical practice.

Methods

We included 112 participants with mild cognitive impairment (MCI) and 128 participants with early AD in this study. All participants underwent MRI, SPECT, and the Mini-Mental State Examination (MMSE). Demographic and clinical characteristics were assessed by univariate analysis, and logistic regression analysis with a combination of MMSE, VSRAD and eZIS indicators was performed to verify whether the diagnostic accuracy in discriminating between MCI and early AD was improved.

Results

The area under the receiver operating characteristic curve (AUC) for the MMSE score alone was 0.835. The AUC was significantly improved to 0.870 by combining the MMSE score with two quantitative indicators from the VSRAD and eZIS that assessed the extent of brain abnormalities.

Conclusion

Compared with the MMSE score alone, the combination of the MMSE score with the VSRAD and eZIS indicators significantly improves the accuracy of discrimination between patients with MCI and early AD. Implementing VSRAD and eZIS does not require professional clinical experience in the treatment of dementia. Therefore, the accuracy of dementia diagnosis by physicians may easily be improved in real-world primary care settings.

Differentiating dementia with Lewy bodies from Alzheimer's disease and Parkinson's disease dementia: an update on imaging modalities

Dementia with Lewy bodies is the second most common cause of neurodegenerative dementia after Alzheimer's disease. Dementia with Lewy bodies can provide a diagnostic challenge due to the frequent overlap of clinical signs with other neurodegenerative conditions, namely Parkinson's disease dementia, and Alzheimer's disease. Part of this clinical overlap is due to the neuropathological overlap. Dementia with Lewy bodies is characterized by the accumulation of aggregated α-synuclein protein in Lewy bodies, similar to Parkinson's disease and Parkinson's disease dementia. However, it is also frequently accompanied by aggregation of amyloid-beta and tau, the pathological hallmarks of Alzheimer's disease. Neuroimaging is central to the diagnostic process. This review is an overview of both established and evolving imaging methods that can improve diagnostic accuracy and improve management of this disorder.

Differentiating Dementia with Lewy Bodies and Alzheimer's Disease by Deep Learning to Structural MRI

BACKGROUND AND PURPOSE

Dementia with Lewy bodies (DLB) is the second most prevalent cause of degenerative dementia next to Alzheimer's disease (AD). Though current DLB diagnostic criteria employ several indicative biomarkers, relative preservation of the medial temporal lobe as revealed by structural MRI suffers from low sensitivity and specificity, making them unreliable as sole supporting biomarkers. In this study, we investigated how a deep learning approach would be able to differentiate DLB from AD with structural MRI data.

METHODS

Two-hundred and eight patients (101 DLB, 69 AD, and 38 controls) participated in this retrospective study. Gray matter images were extracted using voxel-based morphometry (VBM). In order to compare the conventional statistical analysis with deep-learning feature extraction, we built a classification model for DLB and AD with a residual neural network (ResNet) type of convolutional neural network architecture, which is one of the deep learning models. The anatomically standardized gray matter images extracted in the same way as for the VBM process were used as inputs, and the classification performance achieved by our model was evaluated.

RESULTS

Conventional statistical analysis detected no significant atrophy other than fine differences on the middle temporal pole and hippocampal regions. The feature extracted by the deep learning method differentiated DLB from AD with 79.15％ accuracy compared to the 68.41% of the conventional method.

CONCLUSIONS

Our results confirmed that the deep learning method with gray matter images can detect fine differences between DLB and AD that may be underestimated by the conventional method.