(1)H-MRS asymmetry changes in the anterior and posterior cingulate gyrus in patients with mild cognitive impairment and mild Alzheimer's disease

Innovative Multivariable Model Combining MRI Radiomics and Plasma Indexes Predicts Alzheimer's Disease Conversion: Evidence from a 2-Cohort Longitudinal Study

To explore the complementary relationship between magnetic resonance imaging (MRI) radiomic and plasma biomarkers in the early diagnosis and conversion prediction of Alzheimer's disease (AD), our study aims to develop an innovative multivariable prediction model that integrates those two for predicting conversion results in AD. This longitudinal multicentric cohort study included 2 independent cohorts: the Sino Longitudinal Study on Cognitive Decline (SILCODE) project and the Alzheimer Disease Neuroimaging Initiative (ADNI). We collected comprehensive assessments, MRI, plasma samples, and amyloid positron emission tomography data. A multivariable logistic regression analysis was applied to combine plasma and MRI radiomics biomarkers and generate a new composite indicator. The optimal model's performance and generalizability were assessed across populations in 2 cross-racial cohorts. A total of 897 subjects were included, including 635 from the SILCODE cohort (mean [SD] age, 64.93 [6.78] years; 343 [63%] female) and 262 from the ADNI cohort (mean [SD] age, 73.96 [7.06] years; 140 [53%] female). The area under the receiver operating characteristic curve of the optimal model was 0.9414 and 0.8979 in the training and validation dataset, respectively. A calibration analysis displayed excellent consistency between the prognosis and actual observation. The findings of the present study provide a valuable diagnostic tool for identifying at-risk individuals for AD and highlight the pivotal role of the radiomic biomarker. Importantly, built upon data-driven analyses commonly seen in previous radiomics studies, our research delves into AD pathology to further elucidate the underlying reasons behind the robust predictive performance of the MRI radiomic predictor.

Multimodal magnetic resonance scans of patients with mild cognitive impairment

The advancement of neuroimaging technology offers a pivotal reference for the early detection of mild cognitive impairment (MCI), a significant area of focus in contemporary cognitive function research. Structural MRI scans present visual and quantitative manifestations of alterations in brain tissue, whereas functional MRI scans depict the metabolic and functional state of brain tissues from diverse perspectives. As various magnetic resonance techniques possess both strengths and constraints, this review examines the methodologies and outcomes of multimodal magnetic resonance technology in MCI diagnosis, laying the groundwork for subsequent diagnostic and therapeutic interventions for MCI.

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.

Neuroimaging modalities in the detection of Alzheimer's disease-associated biomarkers

Alzheimer's disease (AD) is the most common cause of dementia. Neuropathological changes in AD patients occur up to 10-20 years before the emergence of clinical symptoms. Specific diagnosis and appropriate intervention strategies are crucial during the phase of mild cognitive impairment (MCI) and AD. The detection of biomarkers has emerged as a promising tool for tracking the efficacy of potential therapies, making an early disease diagnosis, and prejudging treatment prognosis. Specifically, multiple neuroimaging modalities, including magnetic resonance imaging (MRI), positron emission tomography, optical imaging, and single photon emission-computed tomography, have provided a few potential biomarkers for clinical application. The MRI modalities described in this review include structural MRI, functional MRI, diffusion tensor imaging, magnetic resonance spectroscopy, and arterial spin labelling. These techniques allow the detection of presymptomatic diagnostic biomarkers in the brains of cognitively normal elderly people and might also be used to monitor AD disease progression after the onset of clinical symptoms. This review highlights potential biomarkers, merits, and demerits of different neuroimaging modalities and their clinical value in MCI and AD patients. Further studies are necessary to explore more biomarkers and overcome the limitations of multiple neuroimaging modalities for inclusion in diagnostic criteria for AD.

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.

Detecting Biomarkers of Alzheimer's Disease Based on Multi-constrained Uncertainty-Aware Adaptive Sparse Multi-view Canonical Correlation Analysis

Image genetics mainly explores the pathogenesis of Alzheimer's disease (AD) by studying the relationship between genetic data (such as SNP, gene expression data, and DNA methylation) and imaging data (such as structural MRI (sMRI), fMRI, and PET). Most of the existing research on brain imaging genomics uses two-way or three-way bi-multivariate methods to explore the correlation analysis between genes and brain imaging. However, many of these methods are still affected by the gradient domination or cannot take into account the effect of feature redundancy on the results, so that the typical correlation coefficient and program running speed are not significantly improved. In order to solve the above problems, this paper proposes a multi-constrained uncertainty-aware adaptive sparse multi-view canonical correlation analysis method (MC-unAdaSMCCA) to explore associations among SNPs, gene expression data, and sMRI; that is, based on traditional unAdaSMCCA, orthogonal constraints are imposed on the weights of the three data features through linear programming, which can reduce the redundancy of feature weights to improve the correlation between the data and reduce the complexity of the algorithm to significantly speed up the running speed of the program. Three adaptive sparse multi-view canonical correlation analysis methods are used as benchmarks to evaluate the difference between real neuroimaging data and synthetic data. Compared with the other three methods, our proposed method has obtained better or comparable typical correlation coefficients and typical weights. Moreover, the following experimental results show that the MC-unAdaSMCCA method cannot only identify biomarkers related to AD and mild cognitive impairment (MCI), but also has a strong ability to resist noise and process high-dimensional data. Therefore, our proposed method provides a reliable approach to multi-modal imaging genetic researches.

Mitochondrial dysfunction, oxidative stress, neuroinflammation, and metabolic alterations in the progression of Alzheimer's disease: A meta-analysis of in vivo magnetic resonance spectroscopy studies

Accumulating evidence demonstrates that metabolic changes in the brain associated with neuroinflammation, oxidative stress, and mitochondrial dysfunction play an important role in the pathophysiology of mild cognitive impairment (MCI) and Alzheimer's disease (AD). However, the neural signatures associated with these metabolic alterations and underlying molecular mechanisms are still elusive. Accordingly, we reviewed the literature on in vivo human brain 1H and 31P-MRS studies and use meta-analyses to identify patterns of brain metabolic alterations in MCI and AD. 40 and 39 studies on MCI and AD, respectively, were classified according to brain regions. Our results indicate decreased N-acetyl aspartate and creatine but increased myo-inositol levels in both MCI and AD, decreased glutathione level in MCI as well as disrupted energy metabolism in AD. In addition, the hippocampus shows the strongest alterations in most of these metabolites. This meta-analysis also illustrates progressive metabolite alterations from MCI to AD. Taken together, it suggests that 1) neuroinflammation and oxidative stress may occur in the early stages of AD, and likely precede neuron loss in its progression; 2) the hippocampus is a sensitive region of interest for early diagnosis and monitoring the response of interventions; 3) targeting bioenergetics associated with neuroinflammation/oxidative stress is a promising approach for treating AD.

Meta-Analysis of Neurochemical Changes Estimated via Magnetic Resonance Spectroscopy in Mild Cognitive Impairment and Alzheimer's Disease

The changes of neurochemicals in mild cognitive impairment (MCI) and Alzheimer's disease (AD) patients has been observed via magnetic resonance spectroscopy in several studies. However, whether it exists the consistent pattern of changes of neurochemicals in the encephalic region during the progression of MCI to AD were still not clear. The study performed meta-analysis to investigate the patterns of neurochemical changes in the encephalic region in the progress of AD. We searched the PubMed, Embase, Cochrane Library, and Web of Science databases, and finally included 63 studies comprising 1,086 MCI patients, 1,256 AD patients, and 1,907 healthy controls. It showed that during the progression from MCI to AD, N-acetyl aspartate (NAA) decreased continuously in the posterior cingulate (PC) (SMD: −0.42 [95% CI: −0.62 to −0.21], z = −3.89, P < 0.05), NAA/Cr (creatine) was consistently reduced in PC (SMD: −0.58 [95% CI: −0.86 to −0.30], z = −4.06, P < 0.05) and hippocampus (SMD: −0.65 [95% CI: −1.11 to −0.12], z = −2.44, P < 0.05), while myo-inositol (mI) (SMD: 0.44 [95% CI: 0.26–0.61], z = 4.97, P < 0.05) and mI/Cr (SMD: 0.43 [95% CI: 0.17–0.68], z = 3.30, P < 0.05) were raised in PC. Furthermore, these results were further verified by a sustained decrease in the NAA/mI of PC (SMD: −0.94 [95% CI: −1.24 to −0.65], z = −6.26, P < 0.05). Therefore, the levels of NAA and mI were associated with the cognitive decline and might be used as potentially biomarkers to predict the possible progression from MCI to AD.

Systematic Review Registration:https://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42020200308.

Changes in Gray Matter Asymmetries of the Fusiform and Parahippocampal Gyruses in Patients With Subcortical Ischemic Vascular Disease

Objective: Changes in the normal asymmetry of the human brain often mean pathology. Current studies on the correlation between asymmetry and cognitive impairment have focused on Alzheimer's disease (AD) and AD-related mild cognitive impairment (MCI). The purpose of this study was to investigate changes in gray matter asymmetry and their relationship with cognitive impairment in patients with subcortical ischemic vascular disease (SIVD) by using voxel-based morphological measurements. Methods: Fifty-nine SIVD patients with (subcortical vascular cognitive impairment, SVCI, N = 30) and without (pre-SVCI, N = 29) cognitive impairment and 30 normal controls (NC, N = 30) underwent high-resolution structural MRI and neuropsychological examinations. The differences in gray matter asymmetry among the three groups were estimated by using one-way ANOVA. Moreover, partial correlation analysis was performed to explore the relationships between the asymmetry index (AI) values and cognitive assessments controlled for age, sex, and education. Results: The gray matter asymmetries in the fusiform and parahippocampal gyruses of the SVCI group were significantly different from those of the NC group and the pre-SVCI group, while no differences were found between the NC group and the pre-SVCI group in the same areas. More specifically, in the fusiform and parahippocampal gyruses, the SVCI group displayed a dramatic rightward asymmetry, whereas the NC group and pre-SVCI group exhibited a marked leftward asymmetry. The results of the correlation analysis showed that the "mean AI" in significant cluster was strongly correlated with the changes in cognitive outcomes. Conclusion: This study demonstrated different lateralization in the fusiform and parahippocampal gyruses of SIVD patients with cognitive impairment compared to healthy subjects and SIVD patients without cognitive decline. Our findings may contribute to better understanding the possible mechanism of cognitive impairment in patients with SIVD, and they suggest the possibility of using gray matter asymmetry as a biomarker for disease progression.

Disrupted White Matter Networks from Subjective Memory Impairment to Amnestic Mild Cognitive Impairment

BACKGROUND AND OBJECTIVE

Subjective memory impairment (SMI) is a preclinical stage prior to amnestic mild cognitive impairment (aMCI) along with the Alzheimer's disease (AD) continuum. We hypothesized that SMI patients had white matter (WM) network disruptions similar to those in aMCI patients.

METHODS

We used diffusion-tensor magnetic resonance imaging and graph theory to construct, analyze, and compare the WM networks among 20 normal controls (NC), 20 SMI patients, and 20 aMCI patients.

RESULTS

Compared with the NC group, the SMI group had significantly decreased global and local efficiency and an increased shortest path length. Moreover, similar to the aMCI group, the SMI group had lower nodal efficiency in regions located in the frontal and parietal lobes, limbic systems, and caudate nucleus compared to that of the NC group.

CONCLUSION

Similar to aMCI patient, SMI patients exhibited WM network disruptions, and detection of these disruptions could facilitate the early detection of SMI.

Proton Magnetic Resonance Spectroscopy in Common Dementias-Current Status and Perspectives

Dementia occurs mainly in the elderly and is associated with cognitive decline and impairment of activities of daily living. The most common forms of dementia are Alzheimer's disease (AD), vascular dementia (VD), dementia with Lewy bodies (DLB), and frontotemporal dementia (FTD). To date, there are no causal options for therapy, but drug and non-drug treatments can positively modulate the course of the disease. Valid biomarkers are needed for the earliest possible and reliable diagnosis, but so far, such biomarkers have only been established for AD and require invasive and expensive procedures. In this context, proton magnetic resonance spectroscopy (¹H-MRS) provides a non-invasive and widely available technique for investigating the biochemical milieu of brain tissue in vivo. Numerous studies have been conducted for AD, but for VD, DLB, and FTD the number of studies is limited. Nevertheless, MRS can detect measurable metabolic alterations in common dementias. However, most of the studies conducted are too heterogeneous to assess the potential use of MRS technology in clinical applications. In the future, technological advances may increase the value of MRS in dementia diagnosis and treatment. This review summarizes the results of MRS studies conducted in common dementias and discusses the reasons for the lack of transfer into clinical routine.

Inhibitory motor dysfunction in parkinson's disease subtypes

BACKGROUND

Parkinson's disease (PD) is divided into postural instability gait difficulty (PIGD) and tremor-dominant (TD) subtypes. Increasing evidence has suggested that the GABAergic neurotransmitter system is involved in the pathogenesis of PD.

PURPOSE

To evaluate the differences of GABA levels between PD motor subtypes using MEscher-GArwood Point Resolved Spectroscopy (MEGA-PRESS).

STUDY TYPE

COHORT.: SUBJECTS: PD patients were classified into PIGD (n = 13) and TD groups (n = 9); 16 age- and sex-matched healthy controls were also recruited. All subjects were right-handed.

SEQUENCE

All subjects underwent an magnetic resonance spectroscopy scan including MEGA-PRESS at 3.0T.

ASSESSMENT

The detected GABA signal also contains signal from macromolecules (MM) and homocarnosine, so it is referred to as GABA+. GABA + levels and Creatine (Cr) levels were quantified in the left basal ganglia (BG) using Gannet 2.0 by Tao Gong.

STATISTICAL TESTS

Differences in GABA + levels between the three groups were analyzed using analysis of covariance. The relationship between GABA levels and a unified PD rating scale (UPDRS) was also analyzed.

RESULTS

GABA + levels were significantly lower in left BG regions of PD patients compared with healthy controls (P < 0.001). In PD patients, the GABA concentration was lower in the TD group than the PIGD group (P = 0.019). Cr levels in PIGD and TD were lower than controls (P = 0.020; P = 0.002). A significant negative correlation was found in PIGD between GABA levels and UPDRS (r = -0.572, P = 0.041), while no correlation was found in TD (r = -0.339, P = 0.372).

DATA CONCLUSION

Low BG GABA levels in PD patients, and differences between PIGD/TD patients, suggest that GABAergic dysfunction may play an important role in the pathogenesis of Parkinson's disease.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:1610-1615.