A multiparametric MRI study of structural brain damage in dementia with lewy bodies: A comparison with Alzheimer's disease

Parkinson's Disease and Dementia with Lewy Bodies: One and the Same

The question whether Parkinson's disease dementia (PDD) and dementia with Lewy bodies (DLB) are expressions of the same underlying disease has been vigorously debated for decades. The recently proposed biological definitions of Lewy body disease, which do not assign any particular importance to the dopamine system over other degenerating neurotransmitter systems, has once more brought the discussion about different types of Lewy body disease to the forefront. Here, we briefly compare PDD and DLB in terms of their symptoms, imaging findings, and neuropathology, ultimately finding them to be indistinguishable. We then present a conceptual framework to demonstrate how one can view different clinical syndromes as manifestations of a shared underlying Lewy body disease. Early Parkinson's disease, isolated RBD, pure autonomic failure and other autonomic symptoms, and perhaps even psychiatric symptoms, represent diverse manifestations of the initial clinical stages of Lewy body disease. They are characterized by heterogeneous and comparatively limited neuronal dysfunction and damage. In contrast, Lewy body dementia, an encompassing term for both PDD and DLB, represents a more uniform and advanced stage of the disease. Patients in this category display extensive and severe Lewy pathology, frequently accompanied by co-existing pathologies, as well as multi-system neuronal dysfunction and degeneration. Thus, we propose that Lewy body disease should be viewed as a single encompassing disease entity. Phenotypic variance is caused by the presence of individual risk factors, disease mechanisms, and co-pathologies. Distinct subtypes of Lewy body disease can therefore be defined by subtype-specific disease mechanisms or biomarkers.

White matter hyperintensities in cognitive impairment with Lewy body disease: a multicentre study

BACKGROUND AND PURPOSE

White matter hyperintensities (WMHs) are associated with cognitive deficits and worse clinical outcomes in dementia, but rare studies have been carried out of cognitive impairment in Lewy body disease (CI-LB) patients. The objective was to investigate the associations between WMHs and clinical manifestations in patients with CI-LB.

METHODS

In this retrospective multicentre cohort study, 929 patients (486 with dementia with Lewy bodies [DLB], 262 with Parkinson's disease dementia [PDD], 74 with mild cognitive impairment [MCI] with Lewy bodies [MCI-LB] and 107 with Parkinson's disease with MCI [PD-MCI]) were analysed from 22 memory clinics between January 2018 and June 2022. Demographic and clinical data were collected by reviewing medical records. WMHs were semi-quantified according to the Fazekas method. Associations between WMHs and clinical manifestations were investigated by multivariate linear or logistic regression models.

RESULTS

Dementia with Lewy bodies patients had the highest Fazekas scores compared with PDD, MCI-LB and PD-MCI. Multivariable regressions showed the Fazekas score was positively associated with the scores of Unified Parkinson's Disease Rating Scale Part III (p = 0.001), Hoehn-Yahn stage (p = 0.004) and total Neuropsychiatric Inventory (p = 0.001) in MCI-LB and PD-MCI patients. In patients with DLB and PDD, Fazekas scores were associated with the absence of rapid eye movement sleep behaviour disorder (p = 0.041) and scores of Unified Parkinson's Disease Rating Scale Part III (p < 0.001), Hoehn-Yahn stage (p < 0.001) and the Montreal Cognitive Assessment (p = 0.014).

CONCLUSION

White matter hyperintensity burden of DLB was higher than for PDD, MCI-LB and PD-MCI. The greater WMH burden was significantly associated with poorer cognitive performance, worse motor function and more severe neuropsychiatric symptoms in CI-LB.

Accurate Detection of Alzheimer's Disease Using Lightweight Deep Learning Model on MRI Data

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive impairment and aberrant protein deposition in the brain. Therefore, the early detection of AD is crucial for the development of effective treatments and interventions, as the disease is more responsive to treatment in its early stages. It is worth mentioning that deep learning techniques have been successfully applied in recent years to a wide range of medical imaging tasks, including the detection of AD. These techniques have the ability to automatically learn and extract features from large datasets, making them well suited for the analysis of complex medical images. In this paper, we propose an improved lightweight deep learning model for the accurate detection of AD from magnetic resonance imaging (MRI) images. Our proposed model achieves high detection performance without the need for deeper layers and eliminates the use of traditional methods such as feature extraction and classification by combining them all into one stage. Furthermore, our proposed method consists of only seven layers, making the system less complex than other previous deep models and less time-consuming to process. We evaluate our proposed model using a publicly available Kaggle dataset, which contains a large number of records in a small dataset size of only 36 Megabytes. Our model achieved an overall accuracy of 99.22% for binary classification and 95.93% for multi-classification tasks, which outperformed other previous models. Our study is the first to combine all methods used in the publicly available Kaggle dataset for AD detection, enabling researchers to work on a dataset with new challenges. Our findings show the effectiveness of our lightweight deep learning framework to achieve high accuracy in the classification of AD.

Imaging Technologies for Cerebral Pharmacokinetic Studies: Progress and Perspectives

Pharmacokinetic assessment of drug disposition processes in vivo is critical in predicting pharmacodynamics and toxicology to reduce the risk of inappropriate drug development. The blood–brain barrier (BBB), a special physiological structure in brain tissue, hinders the entry of targeted drugs into the central nervous system (CNS), making the drug concentrations in target tissue correlate poorly with the blood drug concentrations. Additionally, once non-CNS drugs act directly on the fragile and important brain tissue, they may produce extra-therapeutic effects that may impair CNS function. Thus, an intracerebral pharmacokinetic study was developed to reflect the disposition and course of action of drugs following intracerebral absorption. Through an increasing understanding of the fine structure in the brain and the rapid development of analytical techniques, cerebral pharmacokinetic techniques have developed into non-invasive imaging techniques. Through non-invasive imaging techniques, molecules can be tracked and visualized in the entire BBB, visualizing how they enter the BBB, allowing quantitative tools to be combined with the imaging system to derive reliable pharmacokinetic profiles. The advent of imaging-based pharmacokinetic techniques in the brain has made the field of intracerebral pharmacokinetics more complete and reliable, paving the way for elucidating the dynamics of drug action in the brain and predicting its course. The paper reviews the development and application of imaging technologies for cerebral pharmacokinetic study, represented by optical imaging, radiographic autoradiography, radionuclide imaging and mass spectrometry imaging, and objectively evaluates the advantages and limitations of these methods for predicting the pharmacodynamic and toxic effects of drugs in brain tissues.