In vivo detection of microstructural correlates of brain pathology in preclinical and early Alzheimer Disease with magnetic resonance imaging

Unveiling metabolic patterns in dementia: Insights from high-resolution quantitative blood-oxygenation-level-dependent MRI

BACKGROUND

Oxygen extraction fraction (OEF) and deoxyhemoglobin (DoHb) levels reflect variations in cerebral oxygen metabolism in demented patients.

PURPOSE

Delineating the metabolic profiles evident throughout different phases of dementia necessitates an integrated analysis of OEF and DoHb levels. This is enabled by leveraging high-resolution quantitative blood oxygenation level dependent (qBOLD) analysis of magnitude images obtained from a multi-echo gradient-echo MRI (mGRE) scan performed on a 3.0 Tesla scanner.

METHODS

Achieving superior spatial resolution in qBOLD necessitates the utilization of an mGRE scan with only four echoes, which in turn limits the number of measurements compared to the parameters within the qBOLD model. Consequently, it becomes imperative to discard non-essential parameters to facilitate further analysis. This process entails transforming the qBOLD model into a format suitable for fitting the log-magnitude difference (L-MDif) profiles of the four echo magnitudes present in each brain voxel. In order to bolster spatial specificity, the log-difference qBOLD model undergoes refinement into a representative form, termed as r-qBOLD, particularly when applied to class-averaged L-MDif signals derived through k-means clustering of L-MDif signals from all brain voxels into a predetermined number of clusters. The agreement between parameters estimated using r-qBOLD for different cluster sizes is validated using Bland-Altman analysis, and the model's goodness-of-fit is evaluated using aχ 2 ${\chi ^2}$ -test. Retrospective MRI data of Alzheimer's disease (AD), mild cognitive impairment (MCI), and non-demented patients without neuropathological disorders, pacemakers, other implants, or psychiatric disorders, who completed a minimum of three visits prior to MRI enrolment, are utilized for the study.

RESULTS

Utilizing a cohort comprising 30 demented patients aged 65-83 years in stages 4-6 representing mild, moderate, and severe stages according to the clinical dementia rating (CDR), matched with an age-matched non-demented control group of 18 individuals, we conducted joint observations of OEF and DoHb levels estimated using r-qBOLD. The observations elucidate metabolic signatures in dementia based on OEF and DoHb levels in each voxel. Our principal findings highlight the significance of spatial patterns of metabolic profiles (metabolic patterns) within two distinct regimes: OEF levels exceeding the normal range (S1-regime), and OEF levels below the normal range (S2-regime). The S1-regime, accompanied by low DoHb levels, predominantly manifests in fronto-parietal and perivascular regions with increase in dementia severity. Conversely, the S2-regime, accompanied by low DoHb levels, is observed in medial temporal (MTL) regions. Other regions with abnormal metabolic patterns included the orbitofrontal cortex (OFC), medial-orbital prefrontal cortex (MOPFC), hypothalamus, ventro-medial prefrontal cortex (VMPFC), and retrosplenial cortex (RSP). Dysfunction in the OFC and MOPFC indicated cognitive and emotional impairment, while hypothalamic involvement potentially indicated preclinical dementia. Reduced metabolic activity in the RSP suggested early-stage AD related functional abnormalities.

CONCLUSIONS

Integrated analysis of OEF and DoHb levels using r-qBOLD reveals distinct metabolic signatures across dementia phases, highlighting regions susceptible to neuronal loss, vascular involvement, and preclinical indicators.

T1 and FLAIR signal intensities are related to tau pathology in dominantly inherited Alzheimer disease

Carriers of mutations responsible for dominantly inherited Alzheimer disease provide a unique opportunity to study potential imaging biomarkers. Biomarkers based on routinely acquired clinical MR images, could supplement the extant invasive or logistically challenging) biomarker studies. We used 1104 longitudinal MR, 324 amyloid beta, and 87 tau positron emission tomography imaging sessions from 525 participants enrolled in the Dominantly Inherited Alzheimer Network Observational Study to extract novel imaging metrics representing the mean (μ) and standard deviation (σ) of standardized image intensities of T1-weighted and Fluid attenuated inversion recovery (FLAIR) MR scans. There was an exponential decrease in FLAIR-μ in mutation carriers and an increase in FLAIR and T1 signal heterogeneity (T1-σ and FLAIR-σ) as participants approached the symptom onset in both supramarginal, the right postcentral and right superior temporal gyri as well as both caudate nuclei, putamina, thalami, and amygdalae. After controlling for the effect of regional atrophy, FLAIR-μ decreased and T1-σ and FLAIR-σ increased with increasing amyloid beta and tau deposition in numerous cortical regions. In symptomatic mutation carriers and independent of the effect of regional atrophy, tau pathology demonstrated a stronger relationship with image intensity metrics, compared with amyloid pathology. We propose novel MR imaging intensity-based metrics using standard clinical T1 and FLAIR images which strongly associates with the progression of pathology in dominantly inherited Alzheimer disease. We suggest that tau pathology may be a key driver of the observed changes in this cohort of patients.

Olfactory Three-Needle Electroacupuncture Improved Synaptic Plasticity and Gut Microbiota of SAMP8 Mice by Stimulating Olfactory Nerve

OBJECTIVE

To investigate the effects and mechanisms of olfactory three-needle (OTN) electroacupuncture (EA) stimulation of the olfactory system on cognitive dysfunction, synaptic plasticity, and the gut microbiota in senescence-accelerated mouse prone 8 (SAMP8) mice.

METHODS

Thirty-six SAMP8 mice were randomly divided into the SAMP8 (P8), SAMP8+OTN (P8-OT), and SAMP8+nerve transection+OTN (P8-N-OT) groups according to a random number table (n=12 per group), and 12 accelerated senescence-resistant (SAMR1) mice were used as the control (R1) group. EA was performed at the Yintang (GV 29) and bilateral Yingxiang (LI 20) acupoints of SAMP8 mice for 4 weeks. The Morris water maze test, transmission electron microscopy, terminal deoxynucleotidyl transferase-mediated dUTP nick-end labeling (TUNEL) staining, Nissl staining, Golgi staining, Western blot, and 16S rRNA sequencing were performed, respectively.

RESULTS

Compared with the P8 group, OTN improved the cognitive behavior of SAMP8 mice, inhibited neuronal apoptosis, increased neuronal activity, and attenuated hippocampal synaptic dysfunction (P<0.05 or P<0.01). Moreover, the expression levels of synaptic plasticity-related proteins N-methyl-D-aspartate receptor 1 (NMDAR1), NMDAR2B, synaptophysin (SYN), and postsynaptic density protein-95 (PSD95) in hippocampus were increased by OTN treatment (P<0.05 or P<0.01). Furthermore, OTN greatly enhanced the brain-derived neurotrophic factor (BDNF)/cAMP-response element binding (CREB) signaling and phosphatidylinositol 3-kinase/protein kinase B/mammalian target of rapamycin (PI3K/AKT/mTOR) signaling compared with the P8 group (P<0.05 or P<0.01). However, the neuroprotective effect of OTN was attenuated by olfactory nerve truncation. Compared with the P8 group, OTN had a very limited effect on the fecal microbial structure and composition of SAMP8 mice, while specifically increased the genera Oscillospira and Sutterella (P<0.05). Interestingly, the P8-N-OT group showed an abnormal fecal microbiota with higher microbial α-diversity, Firmicutes/Bacteroidetes ratio and pathogenic bacteria (P<0.05 or P<0.01).

CONCLUSIONS

OTN improved cognitive deficits and hippocampal synaptic plasticity by stimulating the olfactory nerve and activating the BDNF/CREB and PI3K/AKT/mTOR signaling pathways. Although the gut microbiota was not the main therapeutic target of OTN for Alzheimer's disease, the olfactory nerve was essential to maintain the homeostasis of gut microbiota.

De novo CLPTM1 variants with reduced GABAA R current response in patients with epilepsy

OBJECTIVE

To investigate the clinical features and potential pathogenesis mechanism of de novo CLPTM1 variants associated with epilepsy.

METHODS

Identify de novo genetic variants associated with epilepsy by reanalyzing trio-based whole-exome sequencing data. We analyzed the clinical characteristics of patients with these variants and performed functional in vitro studies in cells expressing mutant complementary DNA for these variants using whole-cell voltage-clamp current recordings and outside-out patch-clamp recordings from transiently transfected human embryonic kidney (HEK) cells.

RESULTS

Two de novo missense variants related to epilepsy were identified in the CLPTM1 gene. Functional studies indicated that CLPTM1-p.R454H and CLPTM1-p.R568Q variants reduced the γ-aminobutyric acid A receptor (GABAA R) current response amplitude recorded under voltage clamp compared to the wild-type receptors. These variants also reduced the charge transfer and altered the time course of desensitization and deactivation following rapid removal of GABA. The surface expression of the GABAA R γ2 subunit from the CLPTM1-p.R568Q group was significantly reduced compared to CLPTM1-WT.

SIGNIFICANCE

This is the first report of functionally relevant variants within the CLPTM1 gene. Patch-clamp recordings showed that these de novo CLPTM1 variants reduce GABAA R currents and charge transfer, which should promote excitation and hypersynchronous activity. This study may provide insights into the molecular mechanisms of the CLPTM1 variants underlying the patients' phenotypes, as well as for exploring potential therapeutic targets for epilepsy.

A Graph Convolutional Network Based on Univariate Neurodegeneration Biomarker for Alzheimer's Disease Diagnosis

OBJECTIVE

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease that is not easily detectable in the early stage. This study proposed an efficient method of applying a graph convolutional network (GCN) on the early prediction of AD.

METHODS

We proposed a univariate neurodegeneration biomarker (UNB) based GCN semi-supervised classification framework. We generated UNB by comparing the similarity of individual morphological atrophy pattern and the atrophy pattern of [Formula: see text] AD group according to the brain morphological abnormalities induced by AD. For the GCN semi-supervised classification model, we took the UNBs of individuals as the features of nodes and constructed the weight of edges according to the similarity of phenotypic information between individuals, which explored the essential features of individuals through spectral graph convolution. The attention module was constructed and embedded into the GCN framework, which may refine the input morphological features to highlight the main impact of AD on the cerebral cortex and weaken the instability caused by individual diversities, thereby identifying the significant ROIs affected by AD and improving the classification accuracy.

RESULTS

We tested the UNB-GCN framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The estimated minimum sample sizes were 156, 349 and 423 for the longitudinal [Formula: see text] AD, [Formula: see text] mild cognitive impairment (MCI) and [Formula: see text] cognitively unimpaired (CU) groups, respectively. And the proposed UNB-GCN framework combined with the attention module can effectively improve the classification performance with 93.90% classification accuracy for AD vs. CU and 82.05% for AD vs. MCI on the validation set.

CONCLUSION

The proposed UNB measures were superior to the conventional volume measures in describing the AD-induced cerebral cortex morphological changes. And the UNB-GCN framework combined with attention module may effectively improve the classification performance between MCI subjects and AD patients. Clinical and Translational Impact Statement: This study aims to predict the early AD patients, so as to help clinicians develop effective interventions to delay the deterioration of AD symptoms.

Deep learning-based Accelerated and Noise-Suppressed Estimation (DANSE) of quantitative Gradient-Recalled Echo (qGRE) magnetic resonance imaging metrics associated with human brain neuronal structure and hemodynamic properties

The purpose of the current study was to introduce a Deep learning-based Accelerated and Noise-Suppressed Estimation (DANSE) method for reconstructing quantitative maps of biological tissue cellular-specific, R2t*, and hemodynamic-specific, R2', metrics of quantitative gradient-recalled echo (qGRE) MRI. The DANSE method adapts a supervised learning paradigm to train a convolutional neural network for robust estimation of R2t* and R2' maps with significantly reduced sensitivity to noise and the adverse effects of macroscopic (B₀ ) magnetic field inhomogeneities directly from the gradient-recalled echo (GRE) magnitude images. The R2t* and R2' maps for training were generated by means of a voxel-by-voxel fitting of a previously developed biophysical quantitative qGRE model accounting for tissue, hemodynamic, and B₀ -inhomogeneities contributions to multigradient-echo GRE signal using a nonlinear least squares (NLLS) algorithm. We show that the DANSE model efficiently estimates the aforementioned qGRE maps and preserves all the features of the NLLS approach with significant improvements including noise suppression and computation speed (from many hours to seconds). The noise-suppression feature of DANSE is especially prominent for data with low signal-to-noise ratio (SNR ~ 50-100), where DANSE-generated R2t* and R2' maps had up to three times smaller errors than that of the NLLS method. The DANSE method enables fast reconstruction of qGRE maps with significantly reduced sensitivity to noise and magnetic field inhomogeneities. The DANSE method does not require any information about field inhomogeneities during application. It exploits spatial and gradient echo time-dependent patterns in the GRE data and previously gained knowledge from the biophysical model, thus producing high quality qGRE maps, even in environments with high noise levels. These features along with fast computational speed can lead to broad qGRE clinical and research applications.

Mitigating the impact of flip angle and orientation dependence in single compartment R2* estimates via 2-pool modeling

PURPOSE

The effective transverse relaxation rate (R 2 * $$ {\mathrm{R}}_2^{\ast } $$ ) is influenced by biological features that make it a useful means of probing brain microstructure. However, confounding factors such as dependence on flip angle (α) and fiber orientation with respect to the main field (θ $$ \uptheta $$ ) complicate interpretation. The α- andθ $$ \uptheta $$ -dependence stem from the existence of multiple sub-voxel micro-environments (e.g., myelin and non-myelin water compartments). Ordinarily, it is challenging to quantify these sub-compartments; therefore, neuroscientific studies commonly make the simplifying assumption of a mono-exponential decay obtaining a singleR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimate per voxel. In this work, we investigated how the multi-compartment nature of tissue microstructure affects single compartmentR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimates.

METHODS

We used 2-pool (myelin and non-myelin water) simulations to characterize the bias in single compartmentR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimates. Based on our numeric observations, we introduced a linear model that partitionsR 2 * $$ {\mathrm{R}}_2^{\ast } $$ into α-dependent and α-independent components and validated this in vivo at 7T. We investigated the dependence of both components on the sub-compartment properties and assessed their robustness, orientation dependence, and reproducibility empirically.

RESULTS

R 2 * $$ {\mathrm{R}}_2^{\ast } $$ increased with myelin water fraction and residency time leading to a linear dependence on α. We observed excellent agreement between our numeric and empirical results. Furthermore, the α-independent component of the proposed linear model was robust to the choice of α and reduced dependence on fiber orientation, although it suffered from marginally higher noise sensitivity.

CONCLUSION

We have demonstrated and validated a simple approach that mitigates flip angle and orientation biases in single-compartmentR 2 * $$ {\mathrm{R}}_2^{\ast } $$ estimates.

Transfer learning-based modified inception model for the diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative ailment, which gradually deteriorates memory and weakens the cognitive functions and capacities of the body, such as recall and logic. To diagnose this disease, CT, MRI, PET, etc. are used. However, these methods are time-consuming and sometimes yield inaccurate results. Thus, deep learning models are utilized, which are less time-consuming and yield results with better accuracy, and could be used with ease. This article proposes a transfer learning-based modified inception model with pre-processing methods of normalization and data addition. The proposed model achieved an accuracy of 94.92 and a sensitivity of 94.94. It is concluded from the results that the proposed model performs better than other state-of-the-art models. For training purposes, a Kaggle dataset was used comprising 6,200 images, with 896 mild demented (M.D) images, 64 moderate demented (Mod.D) images, and 3,200 non-demented (N.D) images, and 1,966 veritably mild demented (V.M.D) images. These models could be employed for developing clinically useful results that are suitable to descry announcements in MRI images.

Using quantitative MRI to study the association of isocitrate dehydrogenase (IDH) status with oxygen metabolism and cellular structure changes in glioma

OBJECTIVE

To investigate the characteristics of oxygen metabolism and the cellular structure of glioma using quantitative MRI to predict the isocitrate dehydrogenase 1 (IDH1) status and to further understand the biological characteristics of gliomas.

METHODS

In this retrospective study, 94 patients with gliomas eventually received quantitative MRI measures to study oxygen metabolism. The oxygen metabolism biomarker maps (oxygen extraction fraction [OEF] and cerebral metabolic rate of oxygen [CMRO₂]) and the tissue-cellular-specific (R₂t*) MRI relaxation parameter were evaluated in different regions of glioma.

RESULTS

MRI results showed differences in oxygen metabolism measures in all patients with gliomas of different IDH1 statuses. Compared to patients with IDH1 mutant gliomas, patients with IDH1 wild type gliomas showed increased (P < 0.01) CMRO₂, OEF, cerebral blood volume [CBF], and R₂t* measures in tumor regions, while only OEF, CBF and R₂t* were found to be increased (P < 0.05) in the peritumoral area. OEF achieved the best performance for distinguishing IDH1 wild type and mutant gliomas in the tumor area (AUC = 0.732, P < 0.001). R₂t* values correlated with Ki-67(R = 0.35, P < 0.001) in the tumor area, while no significant correlations between Ki-67 and R₂t* were found in the peritumoral area (R = 0.19, P = 0.072).

CONCLUSION

Quantitative MRI has potential applications in studying the tumor and peritumoral areas of glioma, and it has the ability to predict and reveal the characteristics of oxygen metabolism and cellular structure in different regions of gliomas.

HTLML: Hybrid AI Based Model for Detection of Alzheimer’s Disease

Alzheimer’s disease (AD) is a degenerative condition of the brain that affects the memory and reasoning abilities of patients. Memory is steadily wiped out by this condition, which gradually affects the brain’s ability to think, recall, and form intentions. In order to properly identify this disease, a variety of manual imaging modalities including CT, MRI, PET, etc. are being used. These methods, however, are time-consuming and troublesome in the context of early diagnostics. This is why deep learning models have been devised that are less time-intensive, require less high-tech hardware or human interaction, continue to improve in performance, and are useful for the prediction of AD, which can also be verified by experimental results obtained by doctors in medical institutions or health care facilities. In this paper, we propose a hybrid-based AI-based model that includes the combination of both transfer learning (TL) and permutation-based machine learning (ML) voting classifier in terms of two basic phases. In the first phase of implementation, it comprises two TL-based models: namely, DenseNet-121 and Densenet-201 for features extraction, whereas in the second phase of implementation, it carries out three different ML classifiers like SVM, Naïve base and XGBoost for classification purposes. The final classifier outcomes are evaluated by means of permutations of the voting mechanism. The proposed model achieved accuracy of 91.75%, specificity of 96.5%, and an F1-score of 90.25. The dataset used for training was obtained from Kaggle and contains 6200 photos, including 896 images classified as mildly demented, 64 images classified as moderately demented, 3200 images classified as non-demented, and 1966 images classified as extremely mildly demented. The results show that the suggested model outperforms current state-of-the-art models. These models could be used to generate therapeutically viable methods for detecting AD in MRI images based on these results for clinical prospective.

Phytochemicals from medicinal plants from African forests with potentials in rheumatoid arthritis management

OBJECTIVES

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by inflammation, pain, and cartilage and bone damage. There is currently no cure for RA. It is however managed using nonsteroidal anti-inflammatory drugs, corticosteroids and disease-modifying anti-rheumatic drugs, often with severe side effects. Hidden within Africa's lush vegetation are plants with diverse medicinal properties including anti-RA potentials. This paper reviews the scientific literature for medicinal plants, growing in Africa, with reported anti-RA activities and identifies the most abundant phytochemicals deserving research attention. A search of relevant published scientific literature, using the major search engines, such as Pubmed/Medline, Scopus, Google Scholar, etc. was conducted to identify medicinal plants, growing in Africa, with anti-RA potentials.

KEY FINDINGS

Twenty plants belonging to 17 families were identified. The plants are rich in phytochemicals, predominantly quercetin, rutin, catechin, kaempferol, etc., known to affect some pathways relevant in RA initiation and progression, and therefore useful in its management.

SUMMARY

Targeted research is needed to unlock the potentials of medicinal plants by developing easy-to-use technologies for preparing medicines from them. Research attention should focus on how best to exploit the major phytochemicals identified in this review for the development of anti-RA 'green pharmaceuticals'.

An Increased Aspartate to Alanine Aminotransferase Ratio Is Associated With a Higher Risk of Cognitive Impairment

Background

Recent Alzheimer's disease (AD) hypotheses implicate that hepatic metabolic disorders might contribute to the disease pathogenesis of AD, but the mechanism remains unclear.

Aims

To investigate whether the elevated aspartate aminotransferase (AST) and Alanine aminotransferase (ALT) ratio is associated with future cognitive decline, and to explore the possible mechanisms of liver enzymes affecting cognitive function.

Methods

Three different clinical cohorts were included in the current study, including one cross-sectional study (Cohort 1) and two longitudinal follow-up studies (Cohort 2 and 3). All participants completed a detailed clinical evaluation, neuropsychological tests, and liver enzyme tests. In addition, some of them also underwent structural magnetic resonance imaging (MRI) scans.

Results

Cohort 1 was derived from the CRC2017ZD02 program, including 135 amnestic mild cognitive impairment (aMCI) patients, 22 AD patients, and 319 normal controls. In this cross-sectional study, we found that the AST/ALT ratio was associated with AD (p = 0.014, OR = 1.848, 95%CI: 1.133∼3.012), but not aMCI; Cohort 2 was derived from the Shanghai Brain Health Program. A total of 260 community elderly people with normal cognitive function were included in the study and followed up for 2 years. In this 2-year longitudinal follow-up study, we found that a higher AST/ALT ratio was a risk factor for future development of aMCI (p = 0.014, HR = 1.848, 95%CI: 1.133∼3.021); Cohort 3 was derived from the China longitudinal aging study (CLAS) Program. A total of 94 community elderly people with normal cognitive function were followed up for 7 years, and all of them completed MRI scans. In this 7-year longitudinal follow-up study, we found that a higher AST/ALT ratio was a risk factor for future development of aMCI (p = 0.006, HR = 2.247, 95%CI: 1.248∼4.049), and the AST/ALT ratio was negatively correlated with right hippocampal volume (r = -0.148, p = 0.043).

Conclusion

An increased ratio of AST to ALT is associated with a higher risk of cognitive impairment and may impair cognitive function by affecting hippocampal volume.

Learning-based motion artifact removal networks for quantitative R 2 ∗ mapping

PURPOSE

To introduce two novel learning-based motion artifact removal networks (LEARN) for the estimation of quantitative motion- and B 0 -inhomogeneity-corrected R 2 ∗ maps from motion-corrupted multi-Gradient-Recalled Echo (mGRE) MRI data.

METHODS

We train two convolutional neural networks (CNNs) to correct motion artifacts for high-quality estimation of quantitative B 0 -inhomogeneity-corrected R 2 ∗ maps from mGRE sequences. The first CNN, LEARN-IMG, performs motion correction on complex mGRE images, to enable the subsequent computation of high-quality motion-free quantitative R 2 ∗ (and any other mGRE-enabled) maps using the standard voxel-wise analysis or machine learning-based analysis. The second CNN, LEARN-BIO, is trained to directly generate motion- and B 0 -inhomogeneity-corrected quantitative R 2 ∗ maps from motion-corrupted magnitude-only mGRE images by taking advantage of the biophysical model describing the mGRE signal decay.

RESULTS

We show that both CNNs trained on synthetic MR images are capable of suppressing motion artifacts while preserving details in the predicted quantitative R 2 ∗ maps. Significant reduction of motion artifacts on experimental in vivo motion-corrupted data has also been achieved by using our trained models.

CONCLUSION

Both LEARN-IMG and LEARN-BIO can enable the computation of high-quality motion- and B 0 -inhomogeneity-corrected R 2 ∗ maps. LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of R 2 ∗ maps, while LEARN-BIO directly performs motion- and B 0 -inhomogeneity-corrected R 2 ∗ estimation. Both LEARN-IMG and LEARN-BIO jointly process all the available gradient echoes, which enables them to exploit spatial patterns available in the data. The high computational speed of LEARN-BIO is an advantage that can lead to a broader clinical application.

Connectivity and variability of related cognitive subregions lead to different stages of progression toward Alzheimer's disease

Single modality MRI data is not enough to depict and discern the cause of the underlying brain pathology of Alzheimer's disease (AD). Most existing studies do not perform well with multi-group classification. To reveal the structural, functional connectivity and functional topological relationships among different stages of mild cognitive impairment (MCI) and AD, a novel method was proposed in this paper for the analysis of regional importance with an improved deep learning model. Obvious drift of related cognitive regions can be observed in the prefrontal lobe and surrounding the cingulate area in the right hemisphere when comparing AD and healthy controls (HC) based on absolute weights in the classification mode. Alterations of these regions being responsible for cognitive impairment have been previously reported. Different parcellation atlases of the human cerebral cortex were compared, and the fine-grained multimodal parcellation HCPMMP performed the best with 180 cortical areas per hemisphere. In multi-group classification, the highest accuracy achieved was 96.86% with the utilization of structural and functional topological modalities as input to the training model. Weights in the trained model with perfect discriminating ability quantify the importance of each cortical region. This is the first time such a phenomenon is discovered and weights in cortical areas are precisely described in AD and its prodromal stages to the best of our knowledge. Our findings can establish other study models to differentiate the patterns in various diseases with cognitive impairments and help to identify the underlying pathology.

Quantitative Gradient Echo MRI Identifies Dark Matter as a New Imaging Biomarker of Neurodegeneration that Precedes Tisssue Atrophy in Early Alzheimer's Disease

BACKGROUND

Currently, brain tissue atrophy serves as an in vivo MRI biomarker of neurodegeneration in Alzheimer's disease (AD). However, postmortem histopathological studies show that neuronal loss in AD exceeds volumetric loss of tissue and that loss of memory in AD begins when neurons and synapses are lost. Therefore, in vivo detection of neuronal loss prior to detectable atrophy in MRI is essential for early AD diagnosis.

OBJECTIVE

To apply a recently developed quantitative Gradient Recalled Echo (qGRE) MRI technique for in vivo evaluation of neuronal loss in human hippocampus.

METHODS

Seventy participants were recruited from the Knight Alzheimer Disease Research Center, representing three groups: Healthy controls [Clinical Dementia Rating® (CDR®) = 0, amyloid β (Aβ)-negative, n = 34]; Preclinical AD (CDR = 0, Aβ-positive, n = 19); and mild AD (CDR = 0.5 or 1, Aβ-positive, n = 17).

RESULTS

In hippocampal tissue, qGRE identified two types of regions: one, practically devoid of neurons, we designate as "Dark Matter", and the other, with relatively preserved neurons, "Viable Tissue". Data showed a greater loss of neurons than defined by atrophy in the mild AD group compared with the healthy control group; neuronal loss ranged between 31% and 43%, while volume loss ranged only between 10% and 19%. The concept of Dark Matter was confirmed with histopathological study of one participant who underwent in vivo qGRE 14 months prior to expiration.

CONCLUSION

In vivo qGRE method identifies neuronal loss that is associated with impaired AD-related cognition but is not recognized by MRI measurements of tissue atrophy, therefore providing new biomarkers for early AD detection.

Communicating 5-Year Risk of Alzheimer's Disease Dementia: Development and Evaluation of Materials that Incorporate Multiple Genetic and Biomarker Research Results

BACKGROUND

Cognitively normal (CN) older adults participating in Alzheimer's disease (AD) research increasingly ask for their research results-including genetic and neuroimaging findings-to understand their risk of developing AD dementia. AD research results are typically not returned for multiple reasons, including possible psychosocial harms of knowing one is at risk of a highly feared and untreatable disease.

OBJECTIVE

We developed materials that convey information about 5-year absolute risk of developing AD dementia based on research results.

METHODS

20 CN older adults who received a research brain MRI result were interviewed regarding their wishes for research results to inform material development (Pilot 1). Following material development, 17 CN older adults evaluated the materials for clarity and acceptability (Pilot 2). All participants were community-dwelling older adults participating in longitudinal studies of aging at a single site.

RESULTS

Participants want information on their risk of developing AD dementia to better understand their own health, satisfy curiosity, inform family, and future planning. Some articulated concerns, but the majority wanted to know their risk despite the limitations of information. Participants found the educational materials and results report clear and acceptable, and the majority would want to know their research results after reviewing them.

CONCLUSION

These materials will be used in a clinical study examining the psychosocial and cognitive effects of offering research results to a cohort of CN older adults. Future AD research may incorporate the return of complex risk information to CN older adults, and materials are needed to communicate this information.

Machine learning based on the multimodal connectome can predict the preclinical stage of Alzheimer's disease: a preliminary study

OBJECTIVES

Subjective cognitive decline (SCD) may be a preclinical stage of Alzheimer's disease (AD). Neuroimaging studies suggest that abnormal brain connectivity plays an important role in the pathophysiology of SCD. However, most previous studies focused on single modalities only. Multimodal combinations can more effectively utilize various information and little is known about their diagnostic value in SCD.

METHODS

One hundred ten SCD individuals and well-matched healthy controls (HCs) were recruited in this study (the primary sample: 35 SCD and 36 HC; the validation sample: 21 SCD and 18 HC). Multimodal imaging data were used to construct functional, anatomical, and morphological networks, respectively. These networks were used in combination with a multiple kernel learning-support vector machine to predict SCD individuals. We validated our model on another independent sample. Multiple linear regression (MLR) analyses were conducted to investigate the relationships among network metrics, cognition, and pathological biomarkers.

RESULTS

We found that the characteristics identified from the multimodal network were primarily located in the default mode network (DMN) and salience network (SN), achieving an accuracy of 88.73% (an accuracy of 79.49% for an independent sample) based on the integration of the three modalities. MLR analyses showed that increased AV45 SUVRs were significantly associated with impaired memory function, the enhanced functional connectivity, and the decreased morphological connectivity.

CONCLUSION

This study suggests that abnormal multimodal connections within DMN and SN can be used as effective biomarkers to identify SCD and provide insight into understanding the pathophysiological mechanisms underlying SCD.

KEY POINTS

• Multimodal brain networks improve the detection accuracy of SCD. • Abnormal connections within DMN and SN can be used as effective biomarkers for the identification of SCD.

Library-driven approach for fast implementation of the voxel spread function to correct magnetic field inhomogeneity artifacts for gradient-echo sequences

PURPOSE

Previously developed Voxel Spread Function (VSF) method (Yablonskiy, et al, MRM, 2013;70:1283) provides solution to correct artifacts induced by macroscopic magnetic field inhomogeneity in the images obtained by multi-Gradient-Recalled-Echo (mGRE) techniques. The goal of this study was to develop a library-driven approach for fast VSF implementation.

METHODS

The VSF approach describes the contribution of the magnetic field inhomogeneity effects on the mGRE signal decay in terms of the F-function calculated from mGRE phase and magnitude images. A pre-calculated library accounting for a variety of background field gradients caused by magnetic field inhomogeneity was used herein to speed up the calculation of F-function. Quantitative R2* maps from the mGRE data collected from two healthy volunteers were generated using the library as validation.

RESULTS

As compared with direct calculation of the F-function based on a voxel-wise approach, the new library-driven method substantially reduces computational time from several hours to few minutes, while, at the same time, providing similar accuracy of R2* mapping.

CONCLUSION

The new procedure proposed in this study provides a fast post-processing algorithm that can be incorporated in the quantitative analysis of mGRE data to account for background field inhomogeneity artifacts, thus can facilitate the applications of mGRE-based quantitative techniques in clinical practices.

Ultra-High Field MRI in Alzheimer's Disease: Effective Transverse Relaxation Rate and Quantitative Susceptibility Mapping of Human Brain In Vivo and Ex Vivo compared to Histology

Alzheimer's disease (AD) is the most common cause of dementia worldwide. So far, diagnosis of AD is only unequivocally defined through postmortem histology. Amyloid plaques are a classical hallmark of AD and amyloid load is currently quantified by Positron Emission tomography (PET) in vivo. Ultra-high field magnetic resonance imaging (UHF-MRI) can potentially provide a non-invasive biomarker for AD by allowing imaging of pathological processes at a very-high spatial resolution. The first aim of this work was to reproduce the characteristic cortical pattern previously observed in vivo in AD patients using weighted-imaging at 7T. We extended these findings using quantitative susceptibility mapping (QSM) and quantification of the effective transverse relaxation rate (R2*) at 9.4T. The second aim was to investigate the origin of the contrast patterns observed in vivo in the cortex of AD patients at 9.4T by comparing quantitative UHF-MRI (9.4T and 14.1T) of postmortem samples with histology. We observed a distinctive cortical pattern in vivo in patients compared to healthy controls (HC), and these findings were confirmed ex vivo. Specifically, we found a close link between the signal changes detected by QSM in the AD sample at 14.1T and the distribution pattern of amyloid plaques in the histological sections of the same specimen. Our findings showed that QSM and R2* maps can distinguish AD from HC at UHF by detecting cortical alterations directly related to amyloid plaques in AD patients. Furthermore, we provided a method to quantify amyloid plaque load in AD patients at UHF non-invasively.

Quantitative signal properties from standardized MRIs correlate with multiple sclerosis disability

OBJECTIVE

To enable use of clinical magnetic resonance images (MRIs) to quantify abnormalities in normal appearing (NA) white matter (WM) and gray matter (GM) in multiple sclerosis (MS) and to determine associations with MS-related disability. Identification of these abnormalities heretofore has required specialized scans not routinely available in clinical practice.

METHODS

We developed an analytic technique which normalizes image intensities based on an intensity atlas for quantification of WM and GM abnormalities in standardized MRIs obtained with clinical sequences. Gaussian mixture modeling is applied to summarize image intensity distributions from T1-weighted and 3D-FLAIR (T2-weighted) images from 5010 participants enrolled in a multinational database of MS patients which collected imaging, neuroperformance and disability measures.

RESULTS

Intensity distribution metrics distinguished MS patients from control participants based on normalized non-lesional signal differences. This analysis revealed non-lesional differences between relapsing MS versus progressive MS subtypes. Further, the correlation between our non-lesional measures and disability was approximately three times greater than that between total lesion volume and disability, measured using the patient derived disease steps. Multivariate modeling revealed that measures of extra-lesional tissue integrity and atrophy contribute uniquely, and approximately equally, to the prediction of MS-related disability.

INTERPRETATION

These results support the notion that non-lesional abnormalities correlate more strongly with MS-related disability than lesion burden and provide new insight into the basis of abnormalities in NA WM. Non-lesional abnormalities distinguish relapsing from progressive MS but do not distinguish between progressive subtypes suggesting a common progressive pathophysiology. Image intensity parameters and existing biomarkers each independently correlate with MS-related disability.

Developing univariate neurodegeneration biomarkers with low-rank and sparse subspace decomposition

Cognitive decline due to Alzheimer's disease (AD) is closely associated with brain structure alterations captured by structural magnetic resonance imaging (sMRI). It supports the validity to develop sMRI-based univariate neurodegeneration biomarkers (UNB). However, existing UNB work either fails to model large group variances or does not capture AD dementia (ADD) induced changes. We propose a novel low-rank and sparse subspace decomposition method capable of stably quantifying the morphological changes induced by ADD. Specifically, we propose a numerically efficient rank minimization mechanism to extract group common structure and impose regularization constraints to encode the original 3D morphometry connectivity. Further, we generate regions-of-interest (ROI) with group difference study between common subspaces of Aβ+AD and Aβ-cognitively unimpaired (CU) groups. A univariate morphometry index (UMI) is constructed from these ROIs by summarizing individual morphological characteristics weighted by normalized difference between Aβ+AD and Aβ-CU groups. We use hippocampal surface radial distance feature to compute the UMIs and validate our work in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. With hippocampal UMIs, the estimated minimum sample sizes needed to detect a 25% reduction in the mean annual change with 80% power and two-tailed P=0.05are 116, 279 and 387 for the longitudinal Aβ+AD, Aβ+mild cognitive impairment (MCI) and Aβ+CU groups, respectively. Additionally, for MCI patients, UMIs well correlate with hazard ratio of conversion to AD (4.3, 95% CI = 2.3-8.2) within 18 months. Our experimental results outperform traditional hippocampal volume measures and suggest the application of UMI as a potential UNB.

The compensatory phenomenon of the functional connectome related to pathological biomarkers in individuals with subjective cognitive decline

Background

Subjective cognitive decline (SCD) is a preclinical stage along the Alzheimer’s disease (AD) continuum. However, little is known about the aberrant patterns of connectivity and topological alterations of the brain functional connectome and their diagnostic value in SCD.

Methods

Resting-state functional magnetic resonance imaging and graph theory analyses were used to investigate the alterations of the functional connectome in 66 SCD individuals and 64 healthy controls (HC). Pearson correlation analysis was computed to assess the relationships among network metrics, neuropsychological performance and pathological biomarkers. Finally, we used the multiple kernel learning-support vector machine (MKL-SVM) to differentiate the SCD and HC individuals.

Results

SCD individuals showed higher nodal topological properties (including nodal strength, nodal global efficiency and nodal local efficiency) associated with amyloid-β levels and memory function than the HC, and these regions were mainly located in the default mode network (DMN). Moreover, increased local and medium-range connectivity mainly between the bilateral parahippocampal gyrus (PHG) and other DMN-related regions was found in SCD individuals compared with HC individuals. These aberrant functional network measures exhibited good classification performance in the differentiation of SCD individuals from HC individuals at an accuracy up to 79.23%.

Conclusion

The findings of this study provide insight into the compensatory mechanism of the functional connectome underlying SCD. The proposed classification method highlights the potential of connectome-based metrics for the identification of the preclinical stage of AD.

In vivo evolution of biopsy-proven inflammatory demyelination quantified by R2t* mapping

A 35‐year‐old man with an enhancing tumefactive brain lesion underwent biopsy, revealing inflammatory demyelination. We used quantitative Gradient‐Recalled‐Echo (qGRE) MRI to visualize and measure tissue damage in the lesion. Two weeks after biopsy, qGRE showed significant R2t* reduction in the left optic radiation and surrounding tissue, consistent with the histopathological and clinical findings. qGRE was repeated 6 and 14 months later, demonstrating partially recovered optic radiation R2t*, in concert with improvement of the hemianopia to ultimately involve only the lower right visual quadrant. These results support qGRE metrics as in vivo biomarkers for tissue damage and longitudinal monitoring of demyelinating disease.

Alonso S, Prieto MJ. Combined Therapy for Alzheimer's Disease: Tacrine and PAMAM Dendrimers Co-Administration Reduces the Side Effects of the Drug without Modifying its Activity

Alzheimer's disease has become a public health priority, so an investigation of new therapies is required. Tacrine (TAC) was licensed for treatments; however, its oral administration caused hepatotoxicity, so it is essential to reduce the side effects. PAMAM dendrimer generation 4.0 and 4.5 (DG4.0 and DG4.5) can be used as drug delivery systems and as nanodrugs per se. Our work aims to propose a combined therapy based on TAC and PAMAM dendrimer co-administration. TAC and dendrimer interactions were studied by in vitro drug release, drug stability, and FTIR. The toxicity profile of co-administration was evaluated in human red blood cells, in Neuro-2a cell culture, and in zebrafish larvae. Also, the anti-acetylcholinesterase activity was studied in cell culture. It was possible to obtain DG4.0-TAC and DG4.5-TAC suspensions, without reducing the drug solubility and stability. FTIR and in vitro release studies confirmed that interaction between TAC and DG4.5 was of the electrostatic type. No toxicity effects on human red blood cells were observed, whereas the co-administration with DG4.5 reduced cytotoxicity of TAC on the Neuro-2a cell line. Moreover, in vivo co-administration of both DG4.0-TAC and DG4.5-TAC reduced the morphological and hepatotoxic effects of TAC in zebrafish larvae. The reduction of TAC toxicity was not accompanied by a reduction in its activity since the anti-acetylcholinesterase activity remains when it is co-administrated with dendrimers. In conclusion, the co-administration of TAC with both DG4.0 and DG4.5 is a novel therapy since it was less-toxic, was more biocompatible, and has the same effectiveness than the free drug. Graphical abstract.

Integrating Convolutional Neural Networks and Multi-Task Dictionary Learning for Cognitive Decline Prediction with Longitudinal Images

BACKGROUND

Disease progression prediction based on neuroimaging biomarkers is vital in Alzheimer's disease (AD) research. Convolutional neural networks (CNN) have been proved to be powerful for various computer vision research by refining reliable and high-level feature maps from image patches.

OBJECTIVE

A key challenge in applying CNN to neuroimaging research is the limited labeled samples with high dimensional features. Another challenge is how to improve the prediction accuracy by joint analysis of multiple data sources (i.e., multiple time points or multiple biomarkers). To address these two challenges, we propose a novel multi-task learning framework based on CNN.

METHODS

First, we pre-trained CNN on the ImageNet dataset and transferred the knowledge from the pre-trained model to neuroimaging representation. We used this deep model as feature extractor to generate high-level feature maps of different tasks. Then a novel unsupervised learning method, termed Multi-task Stochastic Coordinate Coding (MSCC), was proposed for learning sparse features of multi-task feature maps by using shared and individual dictionaries. Finally, Lasso regression was performed on these multi-task sparse features to predict AD progression measured by the Mini-Mental State Examination (MMSE) and the Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-Cog).

RESULTS

We applied this novel CNN-MSCC system on the Alzheimer's Disease Neuroimaging Initiative dataset to predict future MMSE/ADAS-Cog scales. We found our method achieved superior performances compared with seven other methods.

CONCLUSION

Our work may add new insights into data augmentation and multi-task deep model research and facilitate the adoption of deep models in neuroimaging research.

Single-slice Alzheimer's disease classification and disease regional analysis with Supervised Switching Autoencoders

BACKGROUND

Alzheimer's disease (AD) is a difficult to diagnose pathology of the brain that progressively impairs cognitive functions. Computer-assisted diagnosis of AD based on image analysis is an emerging tool to support AD diagnosis. In this article, we explore the application of Supervised Switching Autoencoders (SSAs) to perform AD classification using only one structural Magnetic Resonance Imaging (sMRI) slice. SSAs are revised supervised autoencoder architectures, combining unsupervised representation and supervised classification as one unified model. In this work, we study the capabilities of SSAs to capture complex visual neurodegeneration patterns, and fuse disease semantics simultaneously. We also examine how regions associated to disease state can be discovered by SSAs following a local patch-based approach.

METHODS

Patch-based SSAs models are trained on individual patches extracted from a single 2D slice, independently for Axial, Coronal, and Sagittal anatomical planes of the brain at selected informative locations, exploring different patch sizes and network parameterizations. Then, models perform binary class prediction - healthy (CDR = 0) or AD-demented (CDR > 0) - on test data at patch level. The final subject classification is performed employing a majority rule from the ensemble of patch predictions. In addition, relevant regions are identified, by computing accuracy densities from patch-level predictions, and analyzed, supported by Atlas-based regional definitions.

RESULTS

Our experiments employing a single 2D T1-w sMRI slice per subject show that SSAs perform similarly to previous proposals that rely on full volumetric information and feature-engineered representations. SSAs classification accuracy on slices extracted along the Axial, Coronal, and Sagittal anatomical planes from a balanced cohort of 40 independent test subjects was 87.5%, 90.0%, and 90.0%, respectively. A top sensitivity of 95.0% on both Coronal and Sagittal planes was also obtained.

CONCLUSIONS

SSAs provided well-ranked accuracy performance among previous classification proposals, including feature-engineered and feature learning based methods, using only one scan slice per subject, instead of the whole 3D volume, as it is conventionally done. In addition, regions identified as relevant by SSAs' were, in most part, coherent or partially coherent in regard to relevant regions reported on previous works. These regions were also associated with findings from medical knowledge, which gives value to our methodology as a potential analytical aid for disease understanding.

Automated Detection of Alzheimer's Disease Using Brain MRI Images- A Study with Various Feature Extraction Techniques

The aim of this work is to develop a Computer-Aided-Brain-Diagnosis (CABD) system that can determine if a brain scan shows signs of Alzheimer's disease. The method utilizes Magnetic Resonance Imaging (MRI) for classification with several feature extraction techniques. MRI is a non-invasive procedure, widely adopted in hospitals to examine cognitive abnormalities. Images are acquired using the T2 imaging sequence. The paradigm consists of a series of quantitative techniques: filtering, feature extraction, Student's t-test based feature selection, and k-Nearest Neighbor (KNN) based classification. Additionally, a comparative analysis is done by implementing other feature extraction procedures that are described in the literature. Our findings suggest that the Shearlet Transform (ST) feature extraction technique offers improved results for Alzheimer's diagnosis as compared to alternative methods. The proposed CABD tool with the ST + KNN technique provided accuracy of 94.54%, precision of 88.33%, sensitivity of 96.30% and specificity of 93.64%. Furthermore, this tool also offered an accuracy, precision, sensitivity and specificity of 98.48%, 100%, 96.97% and 100%, respectively, with the benchmark MRI database.

A Novel Gradient Echo Plural Contrast Imaging Method Detects Brain Tissue Abnormalities in Patients With TBI Without Evident Anatomical Changes on Clinical MRI: A Pilot Study

RESEARCH OBJECTIVES

It is widely accepted that mild traumatic brain injury (mTBI) causes injury to the white matter, but the extent of gray matter (GM) damage in mTBI is less clear.

METHODS

We tested 26 civilian healthy controls and 14 civilian adult subacute-chronic mTBI patients using quantitative features of MRI-based Gradient Echo Plural Contrast Imaging (GEPCI) technique. GEPCI data were reconstructed using previously developed algorithms allowing the separation of R2t*, a cellular-specific part of gradient echo MRI relaxation rate constant, from global R2* affected by BOLD effect and background gradients.

RESULTS

Single-subject voxel-wise analysis (comparing each mTBI patient to the sample of 26 control subjects) revealed GM abnormalities that were not visible on standard MRI images (T1w and T2w). Analysis of spatial overlap for voxels with low R2t* revealed tissue abnormalities in multiple GM regions, especially in the frontal and temporal regions, that are frequently damaged after mTBI. The left posterior insula was the region with abnormalities found in the highest proportion (50%) of mTBI patients.

CONCLUSIONS

Our data suggest that GEPCI quantitative R2t* metric has potential to detect abnormalities in GM cellular integrity in individual TBI patients, including abnormalities that are not detectable by a standard clinical MRI.

Fluorinated MRI contrast agents and their versatile applications in the biomedical field

MRI has been recognized as one of the most applied medical imaging techniques in clinical practice. However, the presence of background signal coming from water protons in surrounding tissues makes sometimes the visualization of local contrast agents difficult. To remedy this, fluorine has been introduced as a reliable perspective, thanks to its magnetic properties being relatively close to those of protons. In this review, we aim to give an overall description of fluorine incorporation in contrast agents for MRI. The different kinds of fluorinated probes such as perfluorocarbons, fluorinated dendrimers, polymers and paramagnetic probes will be described, as will their imaging applications such as chemical exchange saturation transfer (CEST) imaging, physico-chemical changes detection, drug delivery, cell tracking and inflammation or tumors detection.

Treating Alzheimer's disease by targeting iron

No disease modifying drugs have been approved for Alzheimer's disease despite recent major investments by industry and governments throughout the world. The burden of Alzheimer's disease is becoming increasingly unsustainable, and given the last decade of clinical trial failures, a renewed understanding of the disease mechanism is called for, and trialling of new therapeutic approaches to slow disease progression is warranted. Here, we review the evidence and rational for targeting brain iron in Alzheimer's disease. Although iron elevation in Alzheimer's disease was reported in the 1950s, renewed interest has been stimulated by the advancement of fluid and imaging biomarkers of brain iron that predict disease progression, and the recent discovery of the iron-dependent cell death pathway termed ferroptosis. We review these emerging clinical and biochemical findings and propose how this pathway may be targeted therapeutically to slow Alzheimer's disease progression. LINKED ARTICLES: This article is part of a themed section on Therapeutics for Dementia and Alzheimer's Disease: New Directions for Precision Medicine. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.18/issuetoc.

Comparison of reconstruction and acquisition choices for quantitative T2* maps and synthetic contrasts

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Phase images have artifacts if reconstructed with a vendor’s sum of squares mode.

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Quantitative T2* values can be obtained from DICOM data instead of k-space data.

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Reconstruction from DICOM data does not reduce white matter/gray matter contrast.

Aim and scope

A Gradient Echo Plural Contrast Imaging technique (GEPCI) is a post-processing method, which can be used to obtain quantitative T2* values and generate multiple synthetic contrasts from a single acquisition. However, scan duration and image reconstruction from k-space data present challenges in a clinical workflow. This study aimed at optimizing image reconstruction and acquisition duration to facilitate a post-processing method for synthetic image contrast creation in clinical settings.

Materials and methods

This study consists of tests using the American College of Radiology (ACR) image quality phantom, two healthy volunteers, four mild traumatic brain injury patients and four small vessel disease patients. The measurements were carried out on a 3.0 T scanner with multiple echo times. Reconstruction from k-space data and DICOM data with two different coil-channel combination modes were investigated. Partial Fourier techniques were tested to optimize the scanning time.

Conclusions

Sum of squares coil-channel combination produced artifacts in phase images, but images created with adaptive combination were artifact-free. The voxel-wise median signed difference of T2* between the vendor’s adaptive channel combination and k-space reconstruction modes was 2.9 ± 0.7 ms for white matter and 4.5 ± 0.6 ms for gray matter. Relative white matter/gray matter contrast of all synthetic images and contrast-to-noise ratio of synthetic T1-weighted images were almost equal between reconstruction modes. Our results indicate that synthetic contrasts can be generated from the vendor’s DICOM data with the adaptive combination mode without affecting the quantitative T2* values or white matter/gray matter contrast.

Advances in Multi-Functional Ligands and the Need for Metal-Related Pharmacology for the Management of Alzheimer Disease

Alzheimer’s disease (AD) is the age linked neurodegenerative disorder with no disease modifying therapy currently available. The available therapy only offers short term symptomatic relief. Several hypotheses have been suggested for the pathogenesis of the disease while the molecules developed as possible therapeutic agent in the last decade, largely failed in the clinical trials. Several factors like tau protein hyperphosphorylation, amyloid-β (Aβ) peptide aggregation, decline in acetyl cholinesterase and oxidative stress might be contributing toward the pathogenesis of AD. Additionally, biometals dyshomeostasis (Iron, Copper, and Zinc) in the brain are also reported to be involved in the pathogenesis of AD. Thus, targeting these metal ions may be an effective strategy for the development of a drug to treat AD. Chelation therapy is currently employed for the metal intoxication but we lack a safe and effective chelating agents with additional biological properties for their possible use as multi target directed ligands for a complex disease like AD. Chelating agents possess the ability to disaggregate Aβ aggregation, dissolve amyloid plaques, and delay the cognitive impairment. Thus there is an urgent need to develop disease modifying therapeutic molecules with multiple beneficial features like targeting more than one factor responsible of the disease. These molecules, as disease modifying therapeutic agents for AD, should possess the potential to inhibit Aβ-metal interactions, the formation of toxic Aβ aggregates; and the capacity to reinstate metal homeostasis.

MR approaches in neurodegenerative disorders

Neurodegenerative disease is the umbrella term which refers to a range of clinical conditions causing degeneration of neurons within the central nervous system leading to loss of brain function and eventual death. The most prevalent of these is Alzheimer's disease (AD), which affects approximately 50 million people worldwide and is predicted to reach 75 million by 2030. Neurodegenerative diseases can only be fully diagnosed at post mortem by neuropathological assessment of the type and distribution of protein deposits which characterise each different condition, but there is a clear role for imaging technologies in aiding patient diagnoses in life. Magnetic resonance imaging (MRI) and spectroscopy (MRS) techniques have been applied to study these conditions for many years. In this review, we consider the range of MR-based measurements and describe the findings in AD, but also contrast these with the second most common dementia, dementia with Lewy bodies (DLB). The most definitive observation is the major structural brain changes seen in AD using conventional T1-weighted (T1w) MRI, where medial temporal lobe structures are notably atrophied in most symptomatic patients with AD, but often preserved in DLB. Indeed these findings are sufficiently robust to have been incorporated into clinical diagnostic criteria. Diffusion tensor imaging (DTI) reveals widespread changes in tissue microstructure, with increased mean diffusivity and decreased fractional anisotropy reflecting the degeneration of the white matter structures. There are suggestions that there are subtle differences between AD and DLB populations. At the metabolic level, atrophy-corrected MRS demonstrates reduced density of healthy neurons in brain areas with altered perfusion and in regions known to show higher deposits of pathogenic proteins. As studies have moved from patients with advanced disease and clear dysfunction to patients with earlier presentation such as with mild cognitive impairment (MCI), which in some represents the first signs of their ensuing dementia, the ability of MRI to detect differences has been weaker and further work is still required, ideally in much larger cohorts than previously studied. The vast majority of imaging research in dementia populations has been univariate with respect to the MR-derived parameters considered. To date, none of these measurements has uniquely replicated the patterns of tissue involvement seen by neuropathology, and the ability of MR techniques to deliver a non-invasive diagnosis eludes us. Future opportunities may lie in combining MR and nuclear medicine approaches (position emission tomography, PET) to provide a more complete view of structural and metabolic changes. Such developments will require multi-variate analyses, possibly combined with artificial intelligence or deep learning algorithms, to enhance our ability to combine the array of image-derived information, genetic, gender and lifestyle factors.

Genetically defined cellular correlates of the baseline brain MRI signal

fMRI revolutionized neuroscience by allowing in vivo real-time detection of human brain activity. While the nature of the fMRI signal is understood as resulting from variations in the MRI signal due to brain-activity-induced changes in the blood oxygenation level (BOLD effect), these variations constitute a very minor part of a baseline MRI signal. Hence, the fundamental (and not addressed) questions are how underlying brain cellular composition defines this baseline MRI signal and how a baseline MRI signal relates to fMRI. Herein we investigate these questions by using a multimodality approach that includes quantitative gradient recalled echo (qGRE), volumetric and functional connectivity MRI, and gene expression data from the Allen Human Brain Atlas. We demonstrate that in vivo measurement of the major baseline component of a GRE signal decay rate parameter (R2t*) provides a unique genetic perspective into the cellular constituents of the human cortex and serves as a previously unidentified link between cortical tissue composition and fMRI signal. Data show that areas of the brain cortex characterized by higher R2t* have high neuronal density and have stronger functional connections to other brain areas. Interestingly, these areas have a relatively smaller concentration of synapses and glial cells, suggesting that myelinated cortical axons are likely key cortical structures that contribute to functional connectivity. Given these associations, R2t* is expected to be a useful signal in assessing microstructural changes in the human brain during development and aging in health and disease.

Iron and Alzheimer's Disease: From Pathogenesis to Therapeutic Implications

As people age, iron deposits in different areas of the brain may impair normal cognitive function and behavior. Abnormal iron metabolism generates hydroxyl radicals through the Fenton reaction, triggers oxidative stress reactions, damages cell lipids, protein and DNA structure and function, and ultimately leads to cell death. There is an imbalance in iron homeostasis in Alzheimer's disease (AD). Excessive iron contributes to the deposition of β-amyloid and the formation of neurofibrillary tangles, which in turn, promotes the development of AD. Therefore, iron-targeted therapeutic strategies have become a new direction. Iron chelators, such as desferoxamine, deferiprone, deferasirox, and clioquinol, have received a great deal of attention and have obtained good results in scientific experiments and some clinical trials. Given the limitations and side effects of the long-term application of traditional iron chelators, alpha-lipoic acid and lactoferrin, as self-synthesized naturally small molecules, have shown very intriguing biological activities in blocking Aβ-aggregation, tauopathy and neuronal damage. Despite a lack of evidence for any clinical benefits, the conjecture that therapeutic chelation, with a special focus on iron ions, is a valuable approach for treating AD remains widespread.

Single scan quantitative gradient recalled echo MRI for evaluation of tissue damage in lesions and normal appearing gray and white matter in multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a chronic disease affecting the human central nervous system (CNS) and leading to neurologic disability. Although conventional MRI techniques can readily detect focal white matter (WM) lesions, it remains challenging to quantify tissue damage in normal-appearing gray matter (GM) and WM.

PURPOSE

To demonstrate that a new MRI biomarker, R2t*, can provide quantitative analysis of tissue damage across the brain in MS patients in a single scan.

STUDY TYPE

Prospective.

SUBJECTS

Forty-four MS patients and 19 healthy controls (HC).

FIELD STRENGTH/SEQUENCE

3T, quantitative gradient-recalled-echo (qGRE), Magnetization-prepared rapid gradient-echo, fluid-attenuated inversion recovery.

ASSESSMENT

Severity of tissue damage was assessed by reduced R2t*. Tissue atrophy was assessed by cortical thickness and cervical spinal cord cross-sectional area (CSA). Multiple Sclerosis Functional Composite was used for clinical assessment.

RESULTS

R2t* in cortical GM was more sensitive to MS damage than cortical atrophy. Using more than two standard deviations (SD) reduction versus age-matched HC as the cutoff, 48% of MS patients showed lower R2t*, versus only 9% with lower cortical thickness. Significant correlations between severities of tissue injury were identified among 1) upper cervical cord and several cortical regions, including motor cortex (P < 0.001), and 2) adjacent regions of GM and subcortical WM (P < 0.001). R2t*-defined tissue cellular damage in cortical GM was greater relative to adjacent WM. Reductions in cortical R2t* correlated with cognitive impairment (P < 0.01). Motor-related clinical signs correlated most with cervical cord CSA (P < 0.001).

DATA CONCLUSION

Reductions in R2t* within cortical GM was more sensitive to tissue damage than atrophy, potentially allowing a reduced sample size in clinical trials. R2t* together with structural morphometry suggested topographic patterns of regions showing correlated tissue damage throughout the brain and the cervical spinal cord of MS patients.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:487-498.

Assessment of pathological features in Alzheimer's disease brain tissue with a large field-of-view visible-light optical coherence microscope

We implemented a wide field-of-view visible-light optical coherence microscope (OCM) for investigating ex-vivo brain tissue of patients diagnosed with Alzheimer's disease (AD) and of a mouse model of AD. A submicrometer axial resolution in tissue was achieved using a broad visible light spectrum. The use of various objective lenses enabled reaching micrometer transversal resolution and the acquisition of images of microscopic brain features, such as cell structures, vessels, and white matter tracts. Amyloid-beta plaques in the range of 10 to 70    μ m were visualized. Large field-of-view images of young and old mouse brain sections were imaged using an automated x - y - z stage. The plaque load was characterized, revealing an age-related increase. Human brain tissue affected by cerebral amyloid angiopathy was investigated and hyperscattering structures resembling amyloid beta accumulations in the vessel walls were identified. All results were in good agreement with histology. A comparison of plaque features in both human and mouse brain tissue was performed, revealing an increase in plaque load and a decrease in reflectivity for mouse as compared with human brain tissue. Based on the promising outcome of our experiments, visible light OCM might be a powerful tool for investigating microscopic features in ex-vivo brain tissue.

Immunomodulatory effects of Sceletium tortuosum (Trimesemine™) elucidated in vitro: Implications for chronic disease

ETHNOPHARMACOLOGICAL RELEVANCE

Sceletium tortuosum, among other Sceletium species, was traditionally used by the Khoisan people of Southern Africa for relief of pain-related ailments. However, the commercial availability of this supplement has greatly expanded due to anecdotal claims of its mood-elevating and anxiolytic properties. Unrelated research has elucidated a significant link between cytokines and the mediation of depression. Therefore, the effect of Sceletium supplementation on immune cell functionality is of interest, since the efficacy of potential depression treatments could, at least in part, rely on downregulation of pro-inflammatory signalling.

AIM OF THE STUDY

The current study evaluated the immunomodulatory effects of a Sceletium extract, both basally and in the context of acute endotoxin stimulation.

MATERIALS AND METHODS

Primary human monocytes were supplemented with either a 0.01mg/ml or 1mg/ml Sceletium extract dose, with or without E. coli LPS stimulation in vitro, for 24h. Mitochondrial viability, as an indirect measure of cytotoxicity, and cytokine release in response to the treatment intervention were assessed.

RESULTS

Sceletium extract treatment was associated with increased mitochondrial viability, as well as up-regulated IL-10 release under basal conditions. LPS exposure significantly decreased mitochondrial viability, but this was prevented completely under Sceletium-treated conditions. The acute inflammatory response to LPS stimulation was not negatively affected. Sceletium treatment conferred most significant effects at a dose of 0.01mg/ml.

CONCLUSIONS

Sceletium exerts significant cytoprotective effects in the setting of endotoxin stimulation. Cytokine assessment indicated that Sceletium possesses mild anti-inflammatory properties, but does not hinder the mounting of an adequate immune response to acute immune challenge. These findings indicate that Sceletium may be beneficial for the attenuation of cytokine-induced depression, as well as in systemic low-grade inflammation.

Immune-pineal axis - acute inflammatory responses coordinate melatonin synthesis by pinealocytes and phagocytes

Melatonin is well known for its circadian production by the pineal gland, and there is a growing body of data showing that it is also produced by many other cells and organs, including immune cells. The chronobiotic role of pineal melatonin, as well as its protective effects in vitro and in vivo, have been extensively explored. However, the interaction between the chronobiotic and defence functions of endogenous melatonin has been little investigated. This review details the current knowledge regarding the coordinated shift in melatonin synthesis from the pineal gland (circadian and monitoring roles) to the regulation of acute immune responses via immune cell production and autocrine effects, producing systemic interactions termed the immune-pineal axis. An acute inflammatory response drives the transcription factor, NFκB, to switch melatonin synthesis from pinealocytes to macrophages/microglia and, upon acute inflammatory resolution, back to pinealocytes. The potential pathophysiological relevance of immune-pineal axis dysregulation is highlighted, with both research and clinical implications, across several medical conditions, including host/parasite interaction, neurodegenerative diseases and cancer. LINKED ARTICLES: This article is part of a themed section on Recent Developments in Research of Melatonin and its Potential Therapeutic Applications. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.16/issuetoc.

Limbic system damage in MS: MRI assessment and correlations with clinical testing

Volume loss in some limbic region structures has been observed in multiple sclerosis (MS) patients. However, in vivo evaluation of existing tissue cellular microstructure integrity has received less attention. The goal of studies reported here was to quantitatively assess loss of limbic system volumes and tissue integrity, and to evaluate associations of these measures with cognitive and physical dysfunction in MS patients. Thirty-one healthy controls (HC) and 80 MS patients, including 32 relapsing remitting (RRMS), 32 secondary progressive (SPMS) and 16 primary progressive (PPMS), participated in this study. Tissue cellular integrity was evaluated by means of recently introduced tissue-specific parameter R2t* that was calculated from multi-gradient-echo MRI signals using a recently developed method that separates R2t* from BOLD (blood oxygen level dependent) contributions to GRE signal decay rate constant (R2*), and accounting for physiological fluctuations and artifacts from background gradients. Volumes in limbic system regions, normalized to skull size (NV), were measured from standard MPRAGE images. MS patients had lower R2t* and smaller normalized volumes in the hippocampus, amygdala, and several other limbic system regions, compared to HC. Alterations in R2t* of several limbic system regions correlated with clinical and neurocognitive test scores in MS patients. In contrast, smaller normalized volumes in MS were only correlated with neurocognitive test scores in the hippocampus and amygdala. This study reports the novel finding that R2t*, a measure that estimates tissue integrity, is more sensitive to tissue damage in limbic system structures than is atrophy. R2t* measurements can serve as a biomarker that is distinct from and complementary to volume measurements.

Tissue magnetic susceptibility mapping as a marker of tau pathology in Alzheimer's disease

Alzheimer's disease is connected to a number of other neurodegenerative conditions, known collectively as 'tauopathies', by the presence of aggregated tau protein in the brain. Neuroinflammation and oxidative stress in AD are associated with tau pathology and both the breakdown of axonal sheaths in white matter tracts and excess iron accumulation grey matter brain regions. Despite the identification of myelin and iron concentration as major sources of contrast in quantitative susceptibility maps of the brain, the sensitivity of this technique to tau pathology has yet to be explored. In this study, we perform Quantitative Susceptibility Mapping (QSM) and T2* mapping in the rTg4510, a mouse model of tauopathy, both in vivo and ex vivo. Significant correlations were observed between histological measures of myelin content and both mean regional magnetic susceptibility and T2* values. These results suggest that magnetic susceptibility is sensitive to tissue myelin concentrations across different regions of the brain. Differences in magnetic susceptibility were detected in the corpus callosum, striatum, hippocampus and thalamus of the rTg4510 mice relative to wild type controls. The concentration of neurofibrillary tangles was found to be low to intermediate in these brain regions indicating that QSM may be a useful biomarker for early stage detection of tau pathology in neurodegenerative diseases.

Cerebral quantitative susceptibility mapping predicts amyloid-β-related cognitive decline

See Derry and Kent (doi:10.1093/awx167) for a scientific commentary on this article.The large variance in cognitive deterioration in subjects who test positive for amyloid-β by positron emission tomography indicates that convergent pathologies, such as iron accumulation, might combine with amyloid-β to accelerate Alzheimer's disease progression. Here, we applied quantitative susceptibility mapping, a relatively new magnetic resonance imaging method sensitive to tissue iron, to assess the relationship between iron, amyloid-β load, and cognitive decline in 117 subjects who underwent baseline magnetic resonance imaging and amyloid-β positron emission tomography from the Australian Imaging, Biomarkers and Lifestyle study (AIBL). Cognitive function data were collected every 18 months for up to 6 years from 100 volunteers who were either cognitively normal (n = 64) or diagnosed with mild cognitive impairment (n = 17) or Alzheimer's disease (n = 19). Among participants with amyloid pathology (n = 45), higher hippocampal quantitative susceptibility mapping levels predicted accelerated deterioration in composite cognition tests for episodic memory [β(standard error) = -0.169 (0.034), P = 9.2 × 10-7], executive function [β(standard error) = -0.139 (0.048), P = 0.004), and attention [β(standard error) = -0.074 (0.029), P = 0.012]. Deteriorating performance in a composite of language tests was predicted by higher quantitative susceptibility mapping levels in temporal lobe [β(standard error) = -0.104 (0.05), P = 0.036] and frontal lobe [β(standard error) = -0.154 (0.055), P = 0.006]. These findings indicate that brain iron might combine with amyloid-β to accelerate clinical progression and that quantitative susceptibility mapping could be used in combination with amyloid-β positron emission tomography to stratify individuals at risk of decline.