Robust Quantitative Susceptibility Mapping via Approximate Message Passing

Purpose: It is challenging to recover magnetic susceptibility in the presence of phase errors, which may be caused by noise or strong local-susceptibility shifts in cases of brain hemorrhage and calcification. We propose a Bayesian formulation for quantitative susceptibility mapping (QSM) where a customized Gaussian-mixture distribution is used to model the long-tailed noise distribution. Theory: Complex exponential functions of the phase are used as nonlinear measurements. Wavelet coefficients of the susceptibility map are modeled by the Laplace distribution. Measurement noise is modeled by a two-component Gaussian-mixture distribution, where the second component is reserved to model the noise outliers. The susceptibility map and distribution parameters are jointly recovered using approximate message passing (AMP). Methods: The proposed AMP with built-in parameter estimation (AMP-PE) is compared with the state-of-the-art nonlinear L1-QSM and MEDI approaches that adopt the L1-norm and L2-norm data-fidelity terms respectively. They are tested on the simulated and in vivo datasets. Results: On the simulated Sim2Snr1 dataset, AMP-PE achieved the lowest NRMSE and SSIM, MEDI achieved the lowest HFEN. On the in vivo datasets, AMP-PE is more robust and better at preserving structural details and removing streaking artifacts in the hemorrhage cases than L1-QSM and MEDI. Conclusion: By leveraging a customized Gaussian-mixture noise prior, AMP-PE achieves better performance in challenging cases of brain hemorrhage and calcification. It is equipped with built-in parameter estimation, which avoids subjective bias from the usual visual-tuning step of in vivo reconstruction.Comment: Keywords: Approximate message passing, Compressive sensing, Parameter estimation, QS

Model-based T1, T2* and Proton Density Mapping Using a Bayesian Approach with Parameter Estimation and Complementary Undersampling Patterns

Purpose: To achieve automatic hyperparameter estimation for the joint recovery of quantitative MR images, we propose a Bayesian formulation of the reconstruction problem that incorporates the signal model. Additionally, we investigate the use of complementary undersampling patterns to determine optimal undersampling schemes for quantitative MRI. Theory: We introduce a novel nonlinear approximate message passing framework, referred to as ``AMP-PE'', that enables the simultaneous recovery of distribution parameters and quantitative maps. Methods: We employed the variable flip angle multi-echo (VFA-ME) method to acquire measurements. Both retrospective and prospective undersampling approaches were utilized to obtain Fourier measurements using variable-density and Poisson-disk patterns. Furthermore, we extensively explored various undersampling schemes, incorporating complementary patterns across different flip angles and/or echo times. Results: AMP-PE adopts a model-based joint recovery strategy, it outperforms the $l_1$-norm minimization approach that follows a decoupled recovery strategy. A comparison with an existing joint-recovery approach further demonstrates the advantageous outcomes of AMP-PE. For quantitative $T_1$ mapping using VFA-ME, employing identical k-space sampling patterns across different echo times produced the best performance. Whereas for $T_2^*$ and proton density mappings, using complementary sampling patterns across different flip angles yielded the best performance. Conclusion: AMP-PE is equipped with built-in parameter estimation, and works naturally in clinical settings with varying acquisition protocols and scanners. It also achieves improved performance by combining information from the MR signal model and the sparse prior on images

Sortilin, SorCS1b, and SorLA Vps10p sorting receptors, are novel γ-secretase substrates

BACKGROUND: The mammalian Vps10p sorting receptor family is a group of 5 type I membrane homologs (Sortilin, SorLA, and SorCS1-3). These receptors bind various cargo proteins via their luminal Vps10p domains and have been shown to mediate a variety of intracellular sorting and trafficking functions. These proteins are highly expressed in the brain. SorLA has been shown to be down regulated in Alzheimer's disease brains, interact with ApoE, and modulate Aβ production. Sortilin has been shown to be part of proNGF mediated death signaling that results from a complex of Sortilin, p75(NTR )and proNGF. We have investigated and provide evidence for γ-secretase cleavage of this family of proteins. RESULTS: We provide evidence that these receptors are substrates for presenilin dependent γ-secretase cleavage. γ-Secretase cleavage of these sorting receptors is inhibited by γ-secretase inhibitors and does not occur in PS1/PS2 knockout cells. Like most γ-secretase substrates, we find that ectodomain shedding precedes γ-secretase cleavage. The ectodomain cleavage is inhibited by a metalloprotease inhibitor and activated by PMA suggesting that it is mediated by an α-secretase like cleavage. CONCLUSION: These data indicate that the α- and γ-secretase cleavages of the mammalian Vps10p sorting receptors occur in a fashion analogous to other known γ-secretase substrates, and could possibly regulate the biological functions of these proteins

Proteomic Analysis of Hippocampal Dentate Granule Cells in Frontotemporal Lobar Degeneration: Application of Laser Capture Technology

Frontotemporal lobar degeneration (FTLD) is the most common cause of dementia with pre-senile onset, accounting for as many as 20% of cases. A common subset of FTLD cases is characterized by the presence of ubiquitinated inclusions in vulnerable neurons (FTLD-U). While the pathophysiological mechanisms underlying neurodegeneration in FTLD-U have not yet been elucidated, the presence of inclusions in this disease indicates enhanced aggregation of one or several proteins. Moreover, these inclusions suggest altered expression, processing, or degradation of proteins during FTLD-U pathogenesis. Thus, one approach to understanding disease mechanisms is to delineate the molecular changes in protein composition in FTLD-U brain. Using a combined approach consisting of laser capture microdissection (LCM) and high-resolution liquid chromatography-tandem mass spectrometry (LC–MS/MS), we identified 1252 proteins in hippocampal dentate granule cells excised from three post-mortem FTLD-U and three unaffected control cases processed in parallel. Additionally, we employed a labeling-free quantification technique to compare the abundance of the identified proteins between FTLD-U and control cases. Quantification revealed 54 proteins with selective enrichment in FTLD-U, including TAR–DNA binding protein 43 (TDP-43), a recently identified component of ubiquitinated inclusions. Moreover, 19 proteins were selectively decreased in FTLD-U. Subsequent immunohistochemical analysis of TDP-43 and three additional protein candidates suggests that our proteomic profiling of FTLD-U dentate granule cells reveals both inclusion-associated proteins and non-aggregated disease-specific proteins. Application of LCM is a valuable tool in the molecular analysis of complex tissues, and its application in the proteomic characterization of neurodegenerative disorders such as FTLD-U may be used to identify proteins altered in disease

Development of a Rapid Screening Instrument for Mild Cognitive Impairment and Undiagnosed Dementia

Mild cognitive impairment (MCI) often presages development of Alzheimer’s disease (AD). We recently completed a cross-sectional study to test the hypothesis that a combination of a brief cognitive screening instrument (Mini-Cog) with a functional scale (Functional Activities Questionnaire; FAQ) would accurately identify individuals with MCI and undiagnosed dementia. The Mini-Cog consists of a clock drawing task and 3-item recall, and takes less than 5 minutes to administer. The FAQ is a 30-item questionnaire completed by an informant. In addition to the Mini-Cog and FAQ, a traditional cognitive test battery was administered, and two neurologists and a neuropsychologist determined a consensus diagnosis of Normal, MCI, or Dementia. A classification tree algorithm was used to pick optimal cutpoints, and, using these cutpoints, the combined Mini-Cog and FAQ (MC-FAQ) predicted the consensus diagnosis with an accuracy of 83% and a weighted kappa of 0.81. When the population was divided into Normal and Abnormal, the sensitivity, specificity and positive predictive value were 89%, 90%, and 95%, respectively. The MC-FAQ discriminates individuals with MCI from cognitively normal individuals and those with dementia, and its ease of administration makes it an attractive screening instrument to aid detection of cognitive impairment in the elderly

Large-scale proteomic analysis of human brain identifies proteins associated with cognitive trajectory in advanced age

In advanced age, some individuals maintain a stable cognitive trajectory while others experience a rapid decline. Such variation in cognitive trajectory is only partially explained by traditional neurodegenerative pathologies. Hence, to identify new processes underlying variation in cognitive trajectory, we perform an unbiased proteome-wide association study of cognitive trajectory in a discovery (n = 104) and replication cohort (n = 39) of initially cognitively unimpaired, longitudinally assessed older-adult brain donors. We find 579 proteins associated with cognitive trajectory after meta-analysis. Notably, we present evidence for increased neuronal mitochondrial activities in cognitive stability regardless of the burden of traditional neuropathologies. Furthermore, we provide additional evidence for increased synaptic abundance and decreased inflammation and apoptosis in cognitive stability. Importantly, we nominate proteins associated with cognitive trajectory, particularly the 38 proteins that act independently of neuropathologies and are also hub proteins of protein co-expression networks, as promising targets for future mechanistic studies of cognitive trajectory.Accelerating Medicine Partnership for AD [U01AG046161, U01 AG061357]; Emory Alzheimer's Disease Research Center [P50 AG025688]; NINDS Emory Neuroscience Core [P30 NS055077]; intramural program of the National Institute on Aging (NIA); Alzheimer's Association; Alzheimer's Research UK; Michael J. Fox Foundation for Parkinson's Research; Weston Brain Institute Biomarkers Across Neurodegenerative Diseases Grant [11060]; National Institute of Neurological Disorders and Stroke [U24 NS072026]; National Institute on Aging [P30 AG19610]; Arizona Department of Health Services [211002]; Arizona Biomedical Research Commission [4001, 0011, 05-901, 1001]; [R01 AG056533]; [R01 AG053960]; [U01 MH115484]; [I01 BX003853]; [IK2 BX001820]; [R01 AG061800]; [R01 AG057911]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

U1 small nuclear ribonucleoproteins (snRNPs) aggregate in Alzheimer’s disease due to autosomal dominant genetic mutations and trisomy 21

BACKGROUND: We recently identified U1 small nuclear ribonucleoprotein (snRNP) tangle-like aggregates and RNA splicing abnormalities in sporadic Alzheimer’s disease (AD). However little is known about snRNP biology in early onset AD due to autosomal dominant genetic mutations or trisomy 21 in Down syndrome. Therefore we investigated snRNP biochemical and pathologic features in these disorders. FINDINGS: We performed quantitative proteomics and immunohistochemistry in postmortem brain from genetic AD cases. Electron microscopy was used to characterize ultrastructural features of pathologic aggregates. U1-70k and other snRNPs were biochemically enriched in the insoluble fraction of human brain from subjects with presenilin 1 (PS1) mutations. Aggregates of U1 snRNP-immunoreactivity formed cytoplasmic tangle-like structures in cortex of AD subjects with PS1 and amyloid precursor protein (APP) mutations as well as trisomy 21. Ultrastructural analysis with electron microscopy in an APP mutation case demonstrated snRNP immunogold labeling of paired helical filaments (PHF). CONCLUSIONS: These studies identify U1 snRNP pathologic changes in brain of early onset genetic forms of AD. Since dominant genetic mutations and trisomy 21 result in dysfunctional amyloid processing, the findings suggest that aberrant β-amyloid processing may influence U1 snRNP aggregate formation

Effects of APOE Genotype on Brain Proteomic Network and Cell Type Changes in Alzheimer's Disease

Polymorphic alleles in the apolipoprotein E (APOE) gene are the main genetic determinants of late-onset Alzheimer's disease (AD) risk. Individuals carrying the APOE E4 allele are at increased risk to develop AD compared to those carrying the more common E3 allele, whereas those carrying the E2 allele are at decreased risk for developing AD. How ApoE isoforms influence risk for AD remains unclear. To help fill this gap in knowledge, we performed a comparative unbiased mass spectrometry-based proteomic analysis of post-mortem brain cortical tissues from pathologically-defined AD or control cases of different APOE genotypes. Control cases (n = 10) were homozygous for the common E3 allele, whereas AD cases (n = 24) were equally distributed among E2/3, E3/3, and E4/4 genotypes. We used differential protein expression and co-expression analytical approaches to assess how changes in the brain proteome are related to APOE genotype. We observed similar levels of amyloid-β, but reduced levels of neurofibrillary tau, in E2/3 brains compared to E3/3 and E4/4 AD brains. Weighted co-expression network analysis revealed 33 modules of co-expressed proteins, 12 of which were significantly different by APOE genotype in AD. The modules that were significantly different by APOE genotype were associated with synaptic transmission and inflammation, among other biological processes. Deconvolution and analysis of brain cell type changes revealed that the E2 allele suppressed homeostatic and disease-associated cell type changes in astrocytes, microglia, oligodendroglia, and endothelia. The E2 allele-specific effect on brain cell type changes was validated in a separate cohort of 130 brains. Our systems-level proteomic analyses of AD brain reveal alterations in the brain proteome and brain cell types associated with allelic variants in APOE, and suggest further areas for investigation into the upstream mechanisms that drive ApoE-associated risk for AD