Multiparametric MRI for the improved diagnostic accuracy of Alzheimer's disease and mild cognitive impairment: Research protocol of a case-control study design

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

Detection of Alzheimer's disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning

The scientists are dedicated to studying the detection of Alzheimer's disease onset to find a cure, or at the very least, medication that can slow the progression of the disease. This article explores the effectiveness of longitudinal data analysis, artificial intelligence, and machine learning approaches based on magnetic resonance imaging and positron emission tomography neuroimaging modalities for progression estimation and the detection of Alzheimer's disease onset. The significance of feature extraction in highly complex neuroimaging data, identification of vulnerable brain regions, and the determination of the threshold values for plaques, tangles, and neurodegeneration of these regions will extensively be evaluated. Developing automated methods to improve the aforementioned research areas would enable specialists to determine the progression of the disease and find the link between the biomarkers and more accurate detection of Alzheimer's disease onset.

Myelin in Alzheimer's disease: culprit or bystander?

Alzheimer's disease (AD) is a neurodegenerative disorder with neuronal and synaptic losses due to the accumulation of toxic amyloid β (Αβ) peptide oligomers, plaques, and tangles containing tau (tubulin-associated unit) protein. While familial AD is caused by specific mutations, the sporadic disease is more common and appears to result from a complex chronic brain neuroinflammation with mitochondriopathies, inducing free radicals' accumulation. In aged brain, mutations in DNA and several unfolded proteins participate in a chronic amyloidosis response with a toxic effect on myelin sheath and axons, leading to cognitive deficits and dementia. Αβ peptides are the most frequent form of toxic amyloid oligomers. Accumulations of misfolded proteins during several years alters different metabolic mechanisms, induce chronic inflammatory and immune responses with toxic consequences on neuronal cells. Myelin composition and architecture may appear to be an early target for the toxic activity of Aβ peptides and others hydrophobic misfolded proteins. In this work, we describe the possible role of early myelin alterations in the genesis of neuronal alterations and the onset of symptomatology. We propose that some pathophysiological and clinical forms of the disease may arise from structural and metabolic disorders in the processes of myelination/demyelination of brain regions where the accumulation of non-functional toxic proteins is important. In these forms, the primacy of the deleterious role of amyloid peptides would be a matter of questioning and the initiating role of neuropathology would be primarily the fact of dysmyelination.

[Diagnostic potential of magnetic resonance spectroscopy in cognitive impairment in the elderly]

OBJECTIVE

To explore the potential use of magnetic resonance spectroscopy (MRS) in the diagnosis of pre-dementia cognitive disorders in elderly people.

MATERIAL AND METHODS

A total of 65 elderly individuals (37 individuals with mild cognitive impairment (MCI) according to NIA-AA criteria, mean age 67.2 years; 28 controls, mean age 65.2 years) underwent MRS (3.0 T) with posterior cingulate cortex as the region of interest. Absolute concentrations of metabolites (tCr, NAA, Glx, mI, Cho, NAA) were calculated based on their signal intensities. Statistical analysis was performed to assess intergroup differences and correlations.

RESULTS

Statistically significant differences were observed between the clinical and control groups in the absolute concentrations of metabolites: mI (MCI 4.97±0.13; controls 4.76±0.15; p=0.04) and NAA/mI (MCI 1.61±0.04; controls 1.73±0.04; p=0.04), as well as in the intensities of the choline-containing compounds signal (MCI 0.215±0.015; controls 0.205±0.005; p=0.04) in the posterior cingulate cortex region. No significant correlations between these changes and age were observed, suggesting the predominant role of neurodegeneration in the pathological process under investigation.

CONCLUSION

The findings highlight the promising nature of MRS as a tool to find the neurodegeneration biomarker.

Disrupted metabolic and spontaneous neuronal activity of hippocampus in sepsis associated encephalopathy rats: A study combining magnetic resonance spectroscopy and resting-state functional magnetic resonance imaging

BACKGROUND

The diagnosis of sepsis associated encephalopathy (SAE) remains challenging in clinical settings because of a lack of specific biomarkers. Functional magnetic resonance imaging (fMRI) and proton magnetic resonance spectroscopy (1H-MRS) can be used to aid in the diagnosis of cognition related diseases. This study investigated changes in functional activities and brain metabolites in the hippocampus in SAE rats by fMRI and 1H-MRS.

MATERIALS AND METHODS

Sepsis associated encephalopathy rats underwent cecal ligation and perforation (CLP) surgery. The Morris water maze (MWM) test was then used to evaluate cognitive function. Resting state-fMRI and 1H-MRS scanning were performed 7 and 14 days after CLP surgery to reveal spontaneous neuronal activity and metabolite changes in the hippocampus. The amplitude of low-frequency fluctuation (ALFF) was used to evaluate spontaneous neuronal activity in the hippocampus. Creatine (Cr), Myo-inositol (mI), and glutamine/glutamate (Glx) levels were measured with 1H-MRS scanning. Immunofluorescence and levels of interleukin (IL)-1β, interleukin (IL)-6, and C-reactive protein (CRP) in the hippocampus were additionally detected to evaluate microglial mediated inflammatory responses. Statistical analysis was performed to evaluate correlations between hippocampal metabolism and behavioral findings.

RESULTS

Cecal ligation and perforation treated rats exhibited impaired learning and memory function in the MWM test at days 7 and 14. Elevation of IL-1β in the hippocampus, as well as immunofluorescence results, confirmed severe neuro inflammation in the hippocampus in SAE rats. Compared with the sham group, the ALFF of the right CA-1 area of the hippocampus was higher at day 7after CLP surgery. The Glx/Cr and mI/Cr ratios were enhanced at day 7 after CLP surgery and slightly lower at day 14 after CLP surgery. The ALFF value, and Glx/Cr and mI/Cr ratios were negatively correlated with time spent in the target quadrant in the MWM test.

CONCLUSION

Spontaneous neuronal activity and metabolites showed significant alterations in SAE rats. The elevated ALFF value, Glx/Cr ratio, and mI/Cr ratio in the hippocampus were positively associated with cognitive deficits. Changes in ALFF and metabolites in hippocampus may serve as potential neuroimaging biomarkers of cognitive disorders in patients with SAE.

Research on Voxel-Based Features Detection and Analysis of Alzheimer’s Disease Using Random Survey Support Vector Machine

Alzheimer’s disease (AD) is a degenerative disease of the central nervous system characterized by memory and cognitive dysfunction, as well as abnormal changes in behavior and personality. The research focused on how machine learning classified AD became a recent hotspot. In this study, we proposed a novel voxel-based feature detection framework for AD. Specifically, using 649 voxel-based morphometry (VBM) methods obtained from MRI in Alzheimer’s Disease Neuroimaging Initiative (ADNI), we proposed a feature detection method according to the Random Survey Support Vector Machines (RS-SVM) and combined the research process based on image-, gene-, and pathway-level analysis for AD prediction. Particularly, we constructed 136, 141, and 113 novel voxel-based features for EMCI (early mild cognitive impairment)-HC (healthy control), LMCI (late mild cognitive impairment)-HC, and AD-HC groups, respectively. We applied linear regression model, least absolute shrinkage and selection operator (Lasso), partial least squares (PLS), SVM, and RS-SVM five methods to test and compare the accuracy of these features in these three groups. The prediction accuracy of the AD-HC group using the RS-SVM method was higher than 90%. In addition, we performed functional analysis of the features to explain the biological significance. The experimental results using five machine learning indicate that the identified features are effective for AD and HC classification, the RS-SVM framework has the best classification accuracy, and our strategy can identify important brain regions for AD.