Partial covariance based functional connectivity computation using Ledoit-Wolf covariance regularization

Highlights •We use the well characterized matrix regularization technique described by Ledoit and Wolf to calculate high dimensional partial correlations in fMRI data. •Using this approach we demonstrate that partial correlations reveal RSN structure suggesting that RSNs are defined by widely and uniquely shared variance. •Partial correlation functional connectivity is sensitive to changes in brain state indicating that they contain functional information. Functional connectivity refers to shared signals among brain regions and is typically assessed in a task free state. Functional connectivity commonly is quantified between signal pairs using Pearson correlation. However, resting-state fMRI is a multivariate process exhibiting a complicated covariance structure. Partial covariance assesses the unique variance shared between two brain regions excluding any widely shared variance, hence is appropriate for the analysis of multivariate fMRI datasets. However, calculation of partial covariance requires inversion of the covariance matrix, which, in most functional connectivity studies, is not invertible owing to rank deficiency. Here we apply Ledoit–Wolf shrinkage (L2 regularization) to invert the high dimensional BOLD covariance matrix. We investigate the network organization and brain-state dependence of partial covariance-based functional connectivity. Although RSNs are conventionally defined in terms of shared variance, removal of widely shared variance, surprisingly, improved the separation of RSNs in a spring embedded graphical model. This result suggests that pair-wise unique shared variance plays a heretofore unrecognized role in RSN covariance organization. In addition, application of partial correlation to fMRI data acquired in the eyes open vs. eyes closed states revealed focal changes in uniquely shared variance between the thalamus and visual cortices. This result suggests that partial correlation of resting state BOLD time series reflect functional processes in addition to structural connectivity

Local and distributed PiB accumulation associated with development of preclinical Alzheimer\u27s disease

Amyloid-beta plaques are a hallmark of Alzheimer\u27s disease (AD) that can be assessed by amyloid imaging (e.g., Pittsburgh B compound [PiB]) and summarized as a scalar value. Summary values may have clinical utility but are an average over many regions of interest, potentially obscuring important topography. This study investigates the longitudinal evolution of amyloid topographies in cognitively normal older adults who had normal (N = 131) or abnormal (N = 26) PiB scans at baseline. At 3 years follow-up, 16 participants with a previously normal PiB scan had conversion to PiB scans consistent with preclinical AD. We investigated the multivariate relationship (canonical correlation) between baseline and follow-up PiB topographies. Furthermore, we used penalized regression to investigate the added information derived from PiB topography compared to summary measures. PiB accumulation can be local, that is, a topography predicting the same topography in the future, and/or distributed, that is, one topography predicting another. Both local and distributed PiB accumulation was associated with conversion of PiB status. Additionally, elements of the multivariate topography, and not the commonly used summary scalar, correlated with future PiB changes. Consideration of the entire multivariate PiB topography provides additional information regarding the development of amyloid-beta pathology in very early preclinical AD

Brain age predicts disability accumulation in multiple sclerosis

OBJECTIVE: Neurodegenerative conditions often manifest radiologically with the appearance of premature aging. Multiple sclerosis (MS) biomarkers related to lesion burden are well developed, but measures of neurodegeneration are less well-developed. The appearance of premature aging quantified by machine learning applied to structural MRI assesses neurodegenerative pathology. We assess the explanatory and predictive power of brain age analysis on disability in MS using a large, real-world dataset. METHODS: Brain age analysis is predicated on the over-estimation of predicted brain age in patients with more advanced pathology. We compared the performance of three brain age algorithms in a large, longitudinal dataset (\u3e13,000 imaging sessions from \u3e6,000 individual MS patients). Effects of MS, MS disease course, disability, lesion burden, and DMT efficacy were assessed using linear mixed effects models. RESULTS: MS was associated with advanced predicted brain age cross-sectionally and accelerated brain aging longitudinally in all techniques. While MS disease course (relapsing vs. progressive) did contribute to advanced brain age, disability was the primary correlate of advanced brain age. We found that advanced brain age at study enrollment predicted more disability accumulation longitudinally. Lastly, a more youthful appearing brain (predicted brain age less than actual age) was associated with decreased disability. INTERPRETATION: Brain age is a technically tractable and clinically relevant biomarker of disease pathology that correlates with and predicts increasing disability in MS. Advanced brain age predicts future disability accumulation

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

Deep-Sea Exploration of the US Gulf of Mexico with NOAA Ship Okeanos Explorer

Oceanography articles are licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution, and reproduction in any medium or format as long as users cite the materials appropriately (e.g., authors, Oceanography, volume number, issue number, page number[s], figure number[s], and DOI for the article), provide a link to the Creative Commons license, and indicate the changes that were made to the original content

Tau and Aβ imaging, CSF measures, and cognition in Alzheimer\u27s disease

Alzheimer’s disease (AD) is characterized by two molecular pathologies: cerebral β-amyloidosis in the form of β-amyloid (Aβ) plaques and tauopathy in the form of neurofibrillary tangles, neuritic plaques, and neuropil threads. Until recently, only Aβ could be studied in humans using positron emission tomography (PET) imaging owing to a lack of tau PET imaging agents. Clinical pathological studies have linked tau pathology closely to the onset and progression of cognitive symptoms in patients with AD. We report PET imaging of tau and Aβ in a cohort of cognitively normal older adults and those with mild AD. Multivariate analyses identified unique disease-related stereotypical spatial patterns (topographies) for deposition of tau and Aβ. These PET imaging tau and Aβ topographies were spatially distinct but correlated with disease progression. Cerebrospinal fluid measures of tau, often used to stage preclinical AD, correlated with tau deposition in the temporal lobe. Tau deposition in the temporal lobe more closely tracked dementia status and was a better predictor of cognitive performance than Aβ deposition in any region of the brain. These data support models of AD where tau pathology closely tracks changes in brain function that are responsible for the onset of early symptoms in AD

Covariance-based vs. correlation-based functional connectivity dissociates healthy aging from Alzheimer disease

Prior studies of aging and Alzheimer disease have evaluated resting state functional connectivity (FC) using either seed-based correlation (SBC) or independent component analysis (ICA), with a focus on particular functional systems. SBC and ICA both are insensitive to differences in signal amplitude. At the same time, accumulating evidence indicates that the amplitude of spontaneous BOLD signal fluctuations is physiologically meaningful. We systematically compared covariance-based FC, which is sensitive to amplitude, vs. correlation-based FC, which is not, in affected individuals and controls drawn from two cohorts of participants including autosomal dominant Alzheimer disease (ADAD), late onset Alzheimer disease (LOAD), and age-matched controls. Functional connectivity was computed over 222 regions of interest and group differences were evaluated in terms of components projected onto a space of lower dimension. Our principal observations are: (1) Aging is associated with global loss of resting state fMRI signal amplitude that is approximately uniform across resting state networks. (2) Thus, covariance FC measures decrease with age whereas correlation FC is relatively preserved in healthy aging. (3) In contrast, symptomatic ADAD and LOAD both lead to loss of spontaneous activity amplitude as well as severely degraded correlation structure. These results demonstrate a double dissociation between age vs. Alzheimer disease and the amplitude vs. correlation structure of resting state BOLD signals. Modeling results suggest that the AD-associated loss of correlation structure is attributable to a relative increase in the fraction of locally restricted as opposed to widely shared variance

Inadequacy of Existing Clinical Prediction Models for Predicting Mortality after Transcatheter Aortic Valve Implantation

Background: The performance of emerging transcatheter aortic valve implantation (TAVI) clinical prediction models (CPMs) in national TAVI cohorts distinct from those where they have been derived is unknown. This study aimed to investigate the performance of the German Aortic Valve, FRANCE-2, OBSERVANT and American College of Cardiology (ACC) TAVI CPMs compared with the performance of historic cardiac CPMs such as the EuroSCORE and STS-PROM, in a large national TAVI registry. Methods: The calibration and discrimination of each CPM were analyzed in 6676 patients from the UK TAVI registry, as a whole cohort and across several subgroups. Strata included gender, diabetes status, access route, and valve type. Furthermore, the amount of agreement in risk classification between each of the considered CPMs was analyzed at an individual patient level. Results: The observed 30-day mortality rate was 5.4%. In the whole cohort, the majority of CPMs over-estimated the risk of 30-day mortality, although the mean ACC score (5.2%) approximately matched the observed mortality rate. The areas under ROC curve were between 0.57 for OBSERVANT and 0.64 for ACC. Risk classification agreement was low across all models, with Fleiss's kappa values between 0.17 and 0.50. Conclusions: Although the FRANCE-2 and ACC models outperformed all other CPMs, the performance of current TAVI-CPMs was low when applied to an independent cohort of TAVI patients. Hence, TAVI specific CPMs need to be derived outside populations previously used for model derivation, either by adapting existing CPMs or developing new risk scores in large national registries