Biology subjects

Craddock, C.

Publications and source records attributed to Craddock, C..

12 recordsLinked to original sources

bioRxiv2022

A single episode of CMV reactivation initiates long-term expansion and differentiation of the NK cell repertoire

NK cells play an important role in suppression of viral replication and are critical for effective control of persistent infections such as herpesviruses. Cytomegalovirus infection is associated with expansion of adaptive-memory NK cells with a characteristic CD16brightCD56dim NKG2C+ phenotype but the mechanisms by which this population is maintained remain uncertain. We studied NK cell reconstitution in patients undergoing haemopoietic stem cell transplantation and related this to CMV reactivation. NK cells expanded in the early post-transplant period but then remained stable in the absence of viral reactivation. However, a single episode of CMV reactivation led to a rapid and sustained 10-fold increase in NK cell number. NKG2C expression was increased on all NK subsets although the kinetics of expansion peaked at 6 months on immature CD56bright cells whilst continuing to rise on the mature CD56dim pool. Phenotypic maturation was observed by acquisition of CD57 and KIR expression. Transplantation from CMV-seropositive donors was associated with more rapid NK expansion and superior control of viral reactivation. Effective control of viral reactivation was seen when the peripheral NK cell count reached 20,000/ml. These data show that a single episode of CMV reactivation acts to reprogramme hemopoiesis to drive a sustained modulation and expansion of the NK cell pool and reveal further insight into long term regulation of the innate immune repertoire by infectious challenge. Author Summaryim to highlight where your work fits within a broader context; present the significance or possible implications of your work simply and objectively; and avoid the use of acronyms and complex terminology wherever possible. The goal is to make your findings accessible to a wide audience that includes both scientists and non-scientists Innate immune cells respond rapidly to infectious challenge and it has previously been thought that they were unable to learn from specific infecitons but provided only short term control. However, in recent years it has become apparent that some subsets of innate cells, termed adaptive natural killer (NK) cells, are permanently increased in people who have become infected with cytomegalovirus. The mechanism by which this is maintained is not known. We studied the kinetics of adaptive NK cell expansion in patients who had recently undergone stem cell (bone marrow) transplantation as CMV very often reactivates in this setting and allows study of how this affects the immune system. We find that one short term episode of viral replication led to a large and prolonged expansion of adaptive NK cells. Indeed, their levls increasd 11-fold over 10 months.

immunology↗

bioRxiv2021

Moving Beyond Processing and Analysis-Related Variation in Neuroscience

When fields lack consensus standard methods and accessible ground truths, reproducibility can be more of an ideal than a reality. Such has been the case for functional neuroimaging, where there exists a sprawling space of tools and processing pipelines. We provide a critical evaluation of the impact of differences across five independently developed minimal preprocessing pipelines for functional MRI. We show that even when handling identical data, inter-pipeline agreement was only moderate, critically shedding light on a factor that limits cross-study reproducibility. We show that low inter-pipeline agreement mainly becomes appreciable when the reliability of the underlying data is high, which is increasingly the case as the field progresses. Crucially, we show that when inter-pipeline agreement is compromised, so too are the consistency of insights from brainwide association studies. We highlight the importance of comparing analytic configurations, as both widely discussed and commonly overlooked decisions can lead to marked variation.

neuroscience↗

bioRxiv2020

Determinants of Real-Time fMRI Neurofeedback Performance and Improvement: a Machine Learning Mega-Analysis

Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinical symptoms in patient populations. However, a considerably large ratio of participants undergoing neurofeedback training do not learn to control their own brain signals and, consequently, do not benefit from neurofeedback interventions, which limits clinical efficacy of neurofeedback interventions. As neurofeedback success varies between studies and participants, it is important to identify factors that might influence neurofeedback success. Here, for the first time, we employed a big data machine learning approach to investigate the influence of 20 different design-specific (e.g. activity vs. connectivity feedback), region of interest-specific (e.g. cortical vs. subcortical) and subject-specific factors (e.g. age) on neurofeedback performance and improvement in 608 participants from 28 independent experiments. With a classification accuracy of 60% (considerably different from chance level), we identified two factors that significantly influenced neurofeedback performance: Both the inclusion of a pre-training no-feedback run before neurofeedback training and neurofeedback training of patients as compared to healthy participants were associated with better neurofeedback performance. The positive effect of pre-training no-feedback runs on neurofeedback performance might be due to the familiarization of participants with the neurofeedback setup and the mental imagery task before neurofeedback training runs. Better performance of patients as compared to healthy participants might be driven by higher motivation of patients, higher ranges for the regulation of dysfunctional brain signals, or a more extensive piloting of clinical experimental paradigms. Due to the large heterogeneity of our dataset, these findings likely generalize across neurofeedback studies, thus providing guidance for designing more efficient neurofeedback studies specifically for improving clinical neurofeedback-based interventions. To facilitate the development of data-driven recommendations for specific design details and subpopulations the field would benefit from stronger engagement in Open Science and data sharing.

neuroscience↗

bioRxiv2020

TipQAD: an automated tool for quantifying apical fluorescence dynamics in tip-growing cells

Cell polarity is a fundamental property essential for the function and development of all cellular organisms. Tip growth is an extreme form of polar growth requiring spatiotemporally dynamic but highly coordinated cellular activities. Quantification of these dynamic activities is important for the systematic study of the mechanisms controlling cell polarity, but an automated and unbiased method for analyzing and quantification of tip-growing cells has been missing. In this paper, we developed a computational model and an associated tool called TipQAD for quantifying the spatiotemporal dynamics of fluorescence protein (FP) labeled molecules or structures in tip-growing cells. This tool robustly and accurately measured the spatial distribution and temporal dynamics of FP-labeled proteins in the cytoplasm or the plasma membrane at the tip region of several types of tip-growing cells. We also demonstrated its ability to analyze data from FRAP experiments. In conclusion, this tool showed great potential for automated measurements of the spatiotemporal dynamics of fluorescent-labeled cellular molecules and structures.

plant biology↗

bioRxiv2020

How does group differences in motion scrubbing affect false positives in functional connectivity studies?

Analyzing resting state fMRI data is difficult due to a weak signal and several noise sources. Head motion is also a major problem and it is common to apply motion scrubbing, i.e. to remove time points where a subject has moved more than some pre-defined motion threshold. A problem arises if one cohort on average moves more than another, since the remaining temporal degrees of freedom are then different for the two groups. The effect of this is that the uncertainty of the functional connectivity estimates (e.g. Pearson correlations) are different for the two groups, but this is seldom modelled in resting state fMRI. We demonstrate that group differences in motion scrubbing can result in inflated false positives, depending on how the temporal auto correlation is modelled when performing the Fisher r-to-z transform.

neuroscience↗

bioRxiv2020

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

Neurofeedback training has been shown to influence behavior in healthy participants as well as to alleviate clinical symptoms in neurological, psychosomatic, and psychiatric patient populations. However, many real-time fMRI neurofeedback studies report large interindividual differences in learning success. The factors that cause this vast variability between participants remain unknown and their identification could enhance treatment success. Thus, here we employed a meta-analytic approach including data from 24 different neurofeedback studies with a total of 401 participants, including 140 patients, to determine whether levels of activity in target brain regions during pre-training functional localizer or no-feedback runs (i.e., self-regulation in the absence of neurofeedback) could predict neurofeedback learning success. We observed a slightly positive correlation between pre-training activity levels during a functional localizer run and neurofeedback learning success, but we were not able to identify common brain-based success predictors across our diverse cohort of studies. Therefore, advances need to be made in finding robust models and measures of general neurofeedback learning, and in increasing the current study database to allow for investigating further factors that might influence neurofeedback learning.

neuroscience↗

bioRxiv2019

Optimal Experimental Design for Big Data: Applications in Brain Imaging

Replicability, the ability to replicate scientific findings, is a prerequisite for scientific discovery and clinical utility. Troublingly, we are in the midst of a replicability crisis. A key to replicability is that multiple measurements of the same item (e.g., experimental sample or clinical participant) under fixed experimental constraints are relatively similar to one another. Thus, statistics that quantify the relative contributions of accidental deviations--such as measurement error--as compared to systematic deviations--such as individual differences--are critical. We demonstrate that existing replicability statistics, such as intra-class correlation coefficient and fingerprinting, fail to adequately differentiate between accidental and systematic deviations in very simple settings. We therefore propose a novel statistic, discriminability, which quantifies the degree to which an individuals samples are relatively similar to one another, without restricting the data to be univariate, Gaussian, or even Euclidean. Using this statistic, we introduce the possibility of optimizing experimental design via increasing discriminability and prove that optimizing discriminability improves performance bounds in subsequent inference tasks. In extensive simulated and real datasets (focusing on brain imaging and demonstrating on genomics), only optimizing data discriminability improves performance on all subsequent inference tasks for each dataset. We therefore suggest that designing experiments and analyses to optimize discriminability may be a crucial step in solving the replicability crisis, and more generally, mitigating accidental measurement error. Author SummaryIn recent decades, the size and complexity of data has grown exponentially. Unfortunately, the increased scale of modern datasets brings many new challenges. At present, we are in the midst of a replicability crisis, in which scientific discoveries fail to replicate to new datasets. Difficulties in the measurement procedure and measurement processing pipelines coupled with the influx of complex high-resolution measurements, we believe, are at the core of the replicability crisis. If measurements themselves are not replicable, what hope can we have that we will be able to use the measurements for replicable scientific findings? We introduce the "discriminability" statistic, which quantifies how discriminable measurements are from one another, without limitations on the structure of the underlying measurements. We prove that discriminable strategies tend to be strategies which provide better accuracy on downstream scientific questions. We demonstrate the utility of discriminability over competing approaches in this context on two disparate datasets from both neuroimaging and genomics. Together, we believe these results suggest the value of designing experimental protocols and analysis procedures which optimize the discriminability.

neuroscience↗

bioRxiv2019

Relative Concentration of Brain Iron (rcFe) Derived from Standard Functional MRI.

Brain iron plays key roles in catecholaminergic neurotransmitter synthesis and early life brain development. It is also central to cellular energetics and neurotransmitter metabolism throughout the lifespan. Disturbances in brain iron have been implicated in a growing number of psychiatric and late-life neurodegenerative disorders. Additionally, brain iron accumulations are thought to play a deleterious role in neuroinflammatory processes in later life. Despite its importance, the role of brain iron in development, aging, and psychiatric disorders remains comparatively understudied. This is partly due to technical challenges inherent in implementation and analysis of formal iron imaging protocols and practical constraints on scan session durations. Here, we introduce a method to estimate relative brain iron concentrations that is 1) computationally simple, 2) shows excellent correspondence with formal iron imaging in-vivo, 3) replicates clinically-relevant findings from formal iron imaging, 4) yields novel insights into brain iron and cognition across the lifespan, and 5) leverages a widely available and frequently shared brain imaging modality: functional MRI. The computationally simple nature of the measure, coupled with the availability of fMRI datasets across the lifespan and disorders, has the potential to transform our understanding of the complex and critical relationship between iron and brain health.

neuroscience↗

bioRxiv2018

Evaluating fMRI-Based Estimation of Eye Movement during Naturalistic Viewing

The collection of eye gaze information during functional magnetic resonance imaging (fMRI) is important for monitoring variations in attention and task compliance, particularly for naturalistic viewing paradigms (e.g., movies). However, the complexity and setup requirements of current in-scanner eye-tracking solutions can preclude many researchers from accessing such information. Predictive eye estimation regression (PEER) is a previously developed support vector regression-based method for retrospectively estimating eye gaze from the fMRI signal in the eyes orbit using a 1.5-minute calibration scan. Here, we provide confirmatory validation of the PEER methods ability to infer eye gaze on a TR-by-TR basis during movie viewing, using simultaneously acquired eye tracking data in five individuals (median angular deviation < 2{degrees}). Then, we examine variations in the predictive validity of PEER models across individuals in a subset of data (n=448) from the Child Mind Institute Healthy Brain Network Biobank, identifying head motion as a primary determinant. Finally, we accurately classify which of two movies is being watched based on the predicted eye gaze patterns (area under the curve = .90 {+/-} .02) and map the neural correlates of eye movements derived from PEER. PEER is a freely available and easy-to-use tool for determining eye fixations during naturalistic viewing.

neuroscience↗

bioRxiv2017

Assessment of the impact of shared data on the scientific literature

Data sharing is increasingly recommended as a means of accelerating science by facilitating collaboration, transparency, and reproducibility. While few oppose data sharing philosophically, a range of barriers deter most researchers from implementing it in practice (e.g., workforce and infrastructural demands, sociocultural and privacy concerns, lack of standardization). To justify the significant effort required for sharing data (e.g., organization, curation, distribution), funding agencies, institutions, and investigators need clear evidence of benefit. Here, using the International Neuroimaging Data-sharing Initiative, we present a brain imaging case study that provides direct evidence of the impact of open sharing on data use and resulting publications over a seven-year period (2010-2017). We dispel the myth that scientific findings using shared data cannot be published in high-impact journals and demonstrate rapid growth in the publication of such journal articles, scholarly theses, and conference proceedings. In contrast to commonly used pay to play models, we demonstrate that openly shared data can increase the scale (i.e., sample size) of scientific studies conducted by data contributors, and can recruit scientists from a broader range of disciplines. These findings suggest the transformative power of data sharing for accelerating science and underscore the need for the scientific ecosystem to embrace the challenge of implementing data sharing universally.

bioinformatics↗

bioRxiv2017

Delineating the macroscale areal organization of the macaque cortex in vivo

Complementing longstanding traditions centered around histology, fMRI approaches are rapidly maturing in delineating brain areal organization at the macroscale. The non-human primate (NHP) provides the opportunity to overcome critical barriers in translational research. Here, we establish the data and scanning conditions for achieving reproducible, stable and internally valid areal parcellations in individuals. We demonstrate that these functional boundaries serve as a functional fingerprint of the individual animals, and can be achieved under anesthesia or awake conditions (rest, naturalistic viewing), though differences between awake and anesthetized states precluded the detection of individual differences across states. Comparison of awake and anesthetized states suggested a more nuanced picture of changes in connectivity for higher order association areas, as well as visual and motor cortex. These results establish feasibility and data requirements for the generation of reproducible individual-specific parcellations in NHP, as well as provide insights into the impact of scan state and motivate efforts toward harmonizing protocols.

neuroscience↗

bioRxiv2017

Clinically Useful Brain Imaging for Neuropsychiatry: How Can We Get There?

Despite decades of research, visions of transforming neuropsychiatry through the development of brain imaging-based growth charts or lab tests have remained out of reach. In recent years, there is renewed enthusiasm about the prospect of achieving clinically useful tools capable of aiding the diagnosis and management of neuropsychiatric disorders. The present work explores the basis for this enthusiasm. We assert that there is no single advance that currently has the potential to drive the field of clinical brain imaging forward. Instead, there has been a constellation of advances that, if combined, could lead to the identification of objective brain imaging-based markers of illness. In particular, we focus on advances that are helping to: 1) elucidate the research agenda for biological psychiatry (e.g., neuroscience focus, precision medicine), 2) shift research models for clinical brain imaging (e.g., big data exploration, standardization), 3) break down research silos (e.g., open science, calls for reproducibility and transparency), and 4) improve imaging technologies and methods. While an arduous road remains ahead, these advances are repositioning the brain imaging community for long-term success.

neuroscience↗