Amyloid-beta (Aß) and tau tangles are hallmarks of Alzheimer’s disease. Aß distributions in the tau-defined Braak staging regions and their multivariate predictive relationships with mild cognitive impairment (MCI) are not known. In this study, we used PiB PET data from 60 participants (33 with MCI and 27 controls), quantified Aß as distribution volume ratio (DVR) in Braak regions and compared between MCI and controls to test the hypothesis that DVR alters with declining cognition. We found elevated DVR in participants with MCI, especially in the spatial distribution of Braak stages III-IV and V-VII, while an alteration in Braak stage I-II was near the statistical significance. DVR markers correlated with cognitive status, especially in Braak stages III-IV and VI-V. To evaluate whether these markers are predictive of cognitive impairment, we designed support vector machine and artificial neural network models. These methods showed predictive multivariate relationships between Aß makers of Braak regions and cognitive impairment. Overall, these results highlight the importance of computer-aided research efforts for understanding AD pathophysiology.
Abstract Recent studies indicate disrupted functional mechanisms of salience network regions, especially right anterior insula (RAI), left AI (LAI), and anterior cingulate cortex (ACC), in mild cognitive impairment (MCI). However, the underlying neuro-anatomical and neuro-molecular mechanisms in these regions are not clearly understood yet. It is also unknown whether integration of multi-modal neuro-anatomical and neuro-molecular markers could predict cognitive impairment better in MCI. Herein we quantified neuro-anatomical volumetric markers via structural magnetic resonance imaging (sMRI) and neuro-molecular amyloid markers via positron emission tomography with Pittsburgh compound B (PET PiB) in SN regions of MCI (n = 33) and healthy controls (n = 27). We found that neuro-anatomical markers are significantly reduced while neuro-molecular markers are significantly elevated in SN nodes of MCI compared to healthy controls (p < 0.05). These altered markers in MCI patients were associated with their worse cognitive performance (p < 0.05). Our machine learning-based modeling further suggested that the integration of multi-modal markers predicts cognitive impairment in MCI superiorly compared to using single modality-specific markers. Overall, these findings shed light on the underlying neuro-anatomical volumetric and neuro-molecular amyloid alterations in SN regions and show the significance of multi-modal markers integration approach in better predicting cognitive impairment in MCI.
Deuterium metabolic imaging (DMI) has brought about a renewed interest in the application of 2 H-labeled substrates to map metabolism in vivo, yet deuterium spectroscopy remains challenging. We have shown that metabolism of deuterium-labeled glucose can be observed in the proton spectrum through a reduction in signal of downstream metabolites in a technique called quantitative exchange label turnover MRS (qMRS). Here, we show that prior knowledge fitting that includes unlabeled as well as deuterium-labeled forms of glutamate can be used to map neural metabolism reliably with qMRS.
Abstract Neuroimaging studies suggest that the human brain consists of intrinsically organized large-scale neural networks. Among those networks, the interplay among default-mode network (DMN), salience network (SN), and central-executive network (CEN)has been widely employed to understand the functional interaction patterns in health and diseases. This triple network model suggests that SN causally controls DMN and CEN in healthy individuals. This interaction is often referred to as the dynamic controlling mechanism of SN. However, such interactions are not well understood in individuals with schizophrenia. In this study, we leveraged resting state functional magnetic resonance imaging (fMRI) data of schizophrenia (n = 67) and healthy controls (n = 81) to evaluate the functional interactions among DMN, SN, and CEN using dynamical causal modeling. In healthy controls, our analyses replicated previous findings that SN regulates DMN and CEN activities (Mann-Whitney U test; p < 10 −8 ). In schizophrenia, however, our analyses revealed the disrupted SN-based controlling mechanism on DMN and CEN (Mann-Whitney U test; p < 10 −16 ). These results indicate that the disrupted controlling mechanism of SN on two other neural networks may be a candidate neuroimaging phenotype in schizophrenia.
Purpose Two‐dimensional creatine CEST (2D‐CrCEST), with a slice thickness of 10‐20 mm and temporal resolution (τ Res ) of about 30 seconds, has previously been shown to capture the creatine‐recovery kinetics in healthy controls and in patients with abnormal creatine‐kinase kinetics following the mild plantar flexion exercise. Since the distribution of disease burden may vary across the muscle length for many musculoskeletal disorders, there is a need to increase coverage in the slice‐encoding direction. Here, we demonstrate the feasibility of 3D‐CrCEST with τ Res of about 30 seconds, and propose an improved voxel‐wise ‐calibration approach for CrCEST. Methods The current 7T study with enrollment of 5 volunteers involved collecting the baseline CrCEST imaging for the first 2 minutes, followed by 2 minutes of plantar flexion exercise and then 8 minutes of postexercise CrCEST imaging, to detect the temporal evolution of creatine concentration following exercise. Results Very good repeatability of 3D‐CrCEST findings for activated muscle groups on an intraday and interday basis was established, with coefficient of variance of creatine recovery constants (τ Cr ) being 7%‐15.7%, 7.5%, and 5.8% for lateral gastrocnemius, medial gastrocnemius, and peroneus longus, respectively. We also established a good intraday and interday scan repeatability for 3D‐CrCEST and also showed good correspondence between τ Cr measurements using 2D‐CrCEST and 3D‐CrCEST acquisitions. Conclusion In this study, we demonstrated for the first time the feasibility and the repeatability of the 3D‐CrCEST method in calf muscle with improved correction to measure creatine‐recovery kinetics within a large 3D volume of calf muscle.
Recent studies indicate disrupted functional mechanisms of salience network (SN) regions-right anterior insula, left anterior insula, and anterior cingulate cortex-in mild cognitive impairment (MCI). However, the underlying anatomical and molecular mechanisms in these regions are not clearly understood yet. It is also unknown whether integration of multimodal-anatomical and molecular-markers could predict cognitive impairment better in MCI.Herein we quantified anatomical volumetric markers via structural MRI and molecular amyloid markers via PET with Pittsburgh compound B in SN regions of MCI (n = 33) and healthy controls (n = 27). From these markers, we built support vector machine learning models aiming to estimate cognitive dysfunction in MCI.We found that anatomical markers are significantly reduced and molecular markers are significantly elevated in SN nodes of MCI compared to healthy controls (p < .05). These altered markers in MCI patients were associated with their worse cognitive performance (p < .05). Our machine learning-based modeling further suggested that the integration of multimodal markers predicts cognitive impairment in MCI superiorly compared to using single modality-specific markers.These findings shed light on the underlying anatomical volumetric and molecular amyloid alterations in SN regions and show the significance of multimodal markers integration approach in better predicting cognitive impairment in MCI.
ABSTRACT Amyloid-beta ( Aβ ) and tau tangles are hallmarks of Alzheimer’s disease. Aβ distributions in the tau-defined Braak staging regions and their multivariate predictive relationships with mild cognitive impairment (MCI) are not known. In this study, we used PiB PET data from 60 participants (33 with MCI and 27 healthy controls (HC)), quantified Aβ as distribution volume ratio (DVR) in Braak regions, and compared between MCI and controls to test the hypothesis that DVR alters with declining cognition. We found elevated DVR in participants with MCI, especially in the spatial distribution of Braak stages III-IV and V-VII, while an alteration in Braak stage I-II was near the statistical significance. DVR markers correlated with cognitive status, especially in Braak stages III-IV and VI-V. To evaluate whether these markers are predictive of cognitive dysfunction, we designed support vector machine and artificial neural network models. These methods showed predictive multivariate relationships between Aβ makers of Braak regions and cognitive impairment. Overall, these results highlight the importance of computer-aided research efforts for understanding AD pathophysiology.
Mild cognitive impairment (MCI) is a transitional stage between normal aging and early Alzheimer's disease (AD). The presence of extracellular amyloid-beta (Aβ) in Braak regions suggests a connection with cognitive dysfunction in MCI/AD. Investigating the multivariate predictive relationships between regional Aβ biomarkers and cognitive function can aid in the early detection and prevention of AD. We introduced machine learning approaches to estimate cognitive dysfunction from regional Aβ biomarkers and identify the Aβ-related dominant brain regions involved with cognitive impairment. We employed Aβ biomarkers and cognitive measurements from the same individuals to train support vector regression (SVR) and artificial neural network (ANN) models and predict cognitive performance solely based on Aβ biomarkers on the test set. To identify Aβ-related dominant brain regions involved in cognitive prediction, we built the local interpretable model-agnostic explanations (LIME) model. We found elevated Aβ in MCI compared to controls and a stronger correlation between Aβ and cognition, particularly in Braak stages III-IV and V-VII (p < 0.05) biomarkers. Both SVR and ANN, especially ANN, showed strong predictive relationships between regional Aβ biomarkers and cognitive impairment (p < 0.05). LIME integrated with ANN showed that the parahippocampal gyrus, inferior temporal gyrus, and hippocampus were the most decisive Braak regions for predicting cognitive decline. Consistent with previous findings, this new approach suggests relationships between Aβ biomarkers and cognitive impairment. The proposed analytical framework can estimate cognitive impairment from Braak staging Aβ biomarkers and delineate the dominant brain regions collectively involved in AD pathophysiology.
Purpose Nuclear Overhauser effect (NOE) is based on dipolar cross‐relaxation mechanism that enables the indirect detection of aliphatic protons via the water proton signal. This work focuses on determining the reproducibility of NOE magnetization transfer ratio (NOE MTR ) and isolated or relayed NOE (rNOE) contributions to the NOE MRI of the healthy human brain at 7 Tesla (T). Methods We optimized the amplitude and length of the saturation pulse by acquiring NOE images with different values with multiple saturation lengths. Repeated NOE MRI measurements were made on five healthy volunteers by using optimized saturation pulse parameters including correction of B 0 and inhomogeneities. To isolate the individual contributions from z‐spectra, we have fit the NOE z‐spectra using multiple Lorentzians and calculated the total contribution from each pool contributing to the overall NOE MTR contrast. Results We found that a saturation amplitude of 0.72 μT and a length of 3 s provided the highest contrast. We found that the mean NOE MTR value in gray matter (GM) was 26%, and in white matter (WM) was 33.3% across the 3D slab of the brain. The mean rNOE contributions from GM and WM values were 8.9% and 9.6%, which were ∼10% of the corresponding total NOE MTR signal. The intersubject coefficient of variations (CoVs) of NOE MTR from GM and WM were 4.5% and 6.5%, respectively, whereas the CoVs of rNOE were 4.8% and 5.6%, respectively. The intrasubject CoVs of the NOE MTR range was 2.1%–4.2%, and rNOE range was 2.9%–10.5%. Conclusion This work has demonstrated an excellent reproducibility of both inter‐ and intrasubject NOE MTR and rNOE metrics in healthy human brains at 7 T.
