The Combination of Metabolic Posterior Cingulate Cortical Abnormalities and Structural Asymmetries Improves the Differential Diagnosis Between Primary Progressive Aphasia and Alzheimer's Disease

Sulcal Morphometry Predicts Mild Cognitive Impairment Conversion to Alzheimer's Disease

Background

Being able to differentiate mild cognitive impairment (MCI) patients who would eventually convert (MCIc) to Alzheimer's disease (AD) from those who would not (MCInc) is a key challenge for prognosis.

Objective

This study aimed to investigate the ability of sulcal morphometry to predict MCI progression to AD, dedicating special attention to an accurate identification of sulci.

Methods

Twenty-five AD patients, thirty-seven MCI and twenty-five healthy controls (HC) underwent a brain-MR protocol (1.5T scanner) including a high-resolution T1-weighted sequence. MCI patients underwent a neuropsychological assessment at baseline and were clinically re-evaluated after a mean of 2.3 years. At follow-up, 12 MCI were classified as MCInc and 25 as MCIc. Sulcal morphometry was investigated using the BrainVISA framework. Consistency of sulci across subjects was ensured by visual inspection and manual correction of the automatic labelling in each subject. Sulcal surface, depth, length, and width were retrieved from 106 sulci. Features were compared across groups and their classification accuracy in predicting MCI conversion was tested. Potential relationships between sulcal features and cognitive scores were explored using Spearman's correlation.

Results

The width of sulci in the temporo-occipital region strongly differentiated between each pair of groups. Comparing MCIc and MCInc, the width of several sulci in the bilateral temporo-occipital and left frontal areas was significantly altered. Higher width of frontal sulci was associated with worse performances in short-term verbal memory and phonemic fluency.

Conclusions

Sulcal morphometry emerged as a strong tool for differentiating HC, MCI, and AD, demonstrating its potential prognostic value for the MCI population.

Meta-analysis and open-source database for in vivo brain Magnetic Resonance spectroscopy in health and disease

Proton (1H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo. Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Meta-analysis and Open-source Database for In Vivo Brain Magnetic Resonance Spectroscopy in Health and Disease

Proton ( 1 H) Magnetic Resonance Spectroscopy (MRS) is a non-invasive tool capable of quantifying brain metabolite concentrations in vivo . Prioritization of standardization and accessibility in the field has led to the development of universal pulse sequences, methodological consensus recommendations, and the development of open-source analysis software packages. One on-going challenge is methodological validation with ground-truth data. As ground-truths are rarely available for in vivo measurements, data simulations have become an important tool. The diverse literature of metabolite measurements has made it challenging to define ranges to be used within simulations. Especially for the development of deep learning and machine learning algorithms, simulations must be able to produce accurate spectra capturing all the nuances of in vivo data. Therefore, we sought to determine the physiological ranges and relaxation rates of brain metabolites which can be used both in data simulations and as reference estimates. Using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, we've identified relevant MRS research articles and created an open-source database containing methods, results, and other article information as a resource. Using this database, expectation values and ranges for metabolite concentrations and T 2 relaxation times are established based upon a meta-analyses of healthy and diseased brains.

Brain total creatine differs between primary progressive aphasia (PPA) subtypes and correlates with disease severity

Primary progressive aphasia (PPA) is comprised of three subtypes: logopenic (lvPPA), non-fluent (nfvPPA), and semantic (svPPA). We used magnetic resonance spectroscopy (MRS) to measure tissue-corrected metabolite levels in the left inferior frontal gyrus (IFG) and right sensorimotor cortex (SMC) from 61 PPA patients. We aimed to: (1) characterize subtype differences in metabolites; and (2) test for metabolite associations with symptom severity. tCr differed by subtype across the left IFG and right SMC. tCr levels were lowest in lvPPA and highest in svPPA. tCr levels predicted lvPPA versus svPPA diagnosis. Higher IFG tCr and lower Glx correlated with greater disease severity. As tCr is involved in brain energy metabolism, svPPA pathology might involve changes in specific cellular energy processes. Perturbations to cellular energy homeostasis in language areas may contribute to symptoms. Reduced cortical excitatory capacity (i.e. lower Glx) in language regions may also contribute to symptoms. Thus, tCr may be useful for differentiating between PPA subtypes, and both tCr and Glx might have utility in understanding PPA mechanisms and tracking progression.

1H-MRS neurometabolites and associations with neurite microstructures and cognitive functions in amnestic mild cognitive impairment

Alzheimer's disease (AD) pathogenesis is associated with alterations in neurometabolites and cortical microstructure. However, our understanding of alterations in neurochemicals in the prefrontal cortex and their relationship with changes in cortical microstructure in AD remains unclear. Here, we studied the levels of neurometabolites in the left dorsolateral prefrontal cortex (DLPFC) in healthy older adults and patients with amnestic Mild Cognitive Impairments (aMCI) using single-voxel proton-magnetic resonance spectroscopy (¹H-MRS). N-acetyl aspartate (NAA), glutamate+glutamate (Glx), Myo-inositol (mI), and γ-aminobutyric acid (GABA) brain metabolite levels were quantified relative to total creatine (tCr = Cr + PCr). aMCI had significantly decreased NAA/tCr, Glx/tCr, NAA/mI, and increased mI/tCr levels compared with healthy controls. Further, we leveraged advanced diffusion MRI to extract neurite properties in the left DLPFC and found a significant positive correlation between NAA/tCr, related to neuronal intracellular compartment, and neurite density (ICVF, intracellular volume fraction), and a negative correlation between mI/tCr and neurite orientation (ODI) only in healthy older adults. These data suggest a potential decoupling in the relationship between neurite microstructures and NAA and mI concentrations in DLPFC in the early stage of AD. Together, our results confirm altered DLPFC neurometabolites in prodromal phase of AD and provide unique evidence regarding the imbalance in the association between neurometabolites and neurite microstructure in early stage of AD.