Network mechanisms of intentional learning.

The ability to learn new tasks rapidly is a prominent characteristic of human behaviour. This ability relies on flexible cognitive systems that adapt in order to encode temporary programs for processing non-automated tasks. Previous functional imaging studies have revealed distinct roles for the lateral frontal cortices (LFCs) and the ventral striatum in intentional learning processes. However, the human LFCs are complex; they house multiple distinct sub-regions, each of which co-activates with a different functional network. It remains unclear how these LFC networks differ in their functions and how they coordinate with each other, and the ventral striatum, to support intentional learning. Here, we apply a suite of fMRI connectivity methods to determine how LFC networks activate and interact at different stages of two novel tasks, in which arbitrary stimulus-response rules are learnt either from explicit instruction or by trial-and-error. We report that the networks activate en masse and in synchrony when novel rules are being learnt from instruction. However, these networks are not homogeneous in their functions; instead, the directed connectivities between them vary asymmetrically across the learning timecourse and they disengage from the task sequentially along a rostro-caudal axis. Furthermore, when negative feedback indicates the need to switch to alternative stimulus-response rules, there is additional input to the LFC networks from the ventral striatum. These results support the hypotheses that LFC networks interact as a hierarchical system during intentional learning and that signals from the ventral striatum have a driving influence on this system when the internal program for processing the task is updated.This work was supported by Medical Research Council Grant (U1055.01.002.00001.01) and a European Research GrantPCIG13-GA-2013-618351 to AH. JBR is supported by the Wellcome Trust (103838). The authors report no conflicts of interest.This is the final version of the article. It first appeared from Elsevier via http://dx.doi.org/10.1016/j.neuroimage.2015.11.06

Active acquisition for multimodal neuroimaging

In many clinical and scientific situations the optimal neuroimaging sequence may not be known prior to scanning and may differ for each individual being scanned, depending on the exact nature and location of abnormalities. Despite this, the standard approach to data acquisition, in such situations, is to specify the sequence of neuroimaging scans prior to data acquisition and to apply the same scans to all individuals. In this paper, we propose and illustrate an alternative approach, in which data would be analysed as it is acquired and used to choose the future scanning sequence: Active Acquisition. We propose three Active Acquisition scenarios based around multiple MRI modalities. In Scenario 1, we propose a simple use of near-real time analysis to decide whether to acquire more or higher resolution data, or acquire data with a different field-of-view. In Scenario 2, we simulate how multimodal MR data could be actively acquired and combined with a decision tree to classify a known outcome variable (in the simple example here, age). In Scenario 3, we simulate using Bayesian optimisation to actively search across multiple MRI modalities to find those which are most abnormal. These simulations suggest that by actively acquiring data, the scanning sequence can be adapted to each individual. We also consider the many outstanding practical and technical challenges involving normative data acquisition, MR physics, statistical modelling and clinical relevance. Despite these, we argue that Active Acquisition allows for potentially far more powerful, sensitive or rapid data acquisition, and may open up different perspectives on individual differences, clinical conditions, and biomarker discovery

Associations between dimensions of behaviour, personality traits, and mental-health during the COVID-19 pandemic in the United Kingdom.

The COVID-19 pandemic (including lockdown) is likely to have had profound but diverse implications for mental health and well-being, yet little is known about individual experiences of the pandemic (positive and negative) and how this relates to mental health and well-being, as well as other important contextual variables. Here, we analyse data sampled in a large-scale manner from 379,875 people in the United Kingdom (UK) during 2020 to identify population variables associated with mood and mental health during the COVID-19 pandemic, and to investigate self-perceived pandemic impact in relation to those variables. We report that while there are relatively small population-level differences in mood assessment scores pre- to peak-UK lockdown, the size of the differences is larger for people from specific groups, e.g. older adults and people with lower incomes. Multiple dimensions underlie peoples' perceptions, both positive and negative, of the pandemic's impact on daily life. These dimensions explain variance in mental health and can be statistically predicted from age, demographics, home and work circumstances, pre-existing conditions, maladaptive technology use and personality traits (e.g., compulsivity). We conclude that a holistic view, incorporating the broad range of relevant population factors, can better characterise people whose mental health is most at risk during the COVID-19 pandemic

Generation and characterization of radiation in biomedical applications

This Creative Inquiry, Generation and Characterization of Radiation in Biomedical Applications, fuses two scientific disciplines, physics and bioengineering, seeking a common goal. Students under Dr. Takacs and Dr. Dean, including a doctoral candidate, are designing experiments to irradiate various biomaterials, including proteins and cancer cells, with monochromatic x-rays between 1000 eV to 15000 eV, and then study the results of those interactions. This specific creative inquiry\u27s (PHYS 2990-005 and BIOE 4510-025) goal for this semester is to further understand x-ray interactions with matter, specifically biomaterials. The bioengineering students are devising specific ways to cultivate certain proteins and cell cultures, and the physicists are designing parameters for the experiments, including the production and spectroscopy of x-rays. Several of the experiments will also be utilizing Clemson\u27s EBIT (electron beam ion trap, one of two in the country) as one of the sources for such radiation. With so little data collected using instrumentation of this precision, we feel that even our short-term goals will have far reaching implications

Tuberculosis in Dr Granville's mummy: a molecular re-examination of the earliest known Egyptian mummy to be scientifically examined and given a medical diagnosis

‘Dr Granville's mummy’ was described to the Royal Society of London in 1825 and was the first ancient Egyptian mummy to be subjected to a scientific autopsy. The remains are those of a woman, Irtyersenu, aged about 50, from the necropolis of Thebes and dated to about 600 BC. Augustus Bozzi Granville (1783–1872), an eminent physician and obstetrician, described many organs still in situ and attributed the cause of death to a tumour of the ovary. However, subsequent histological investigations indicate that the tumour is a benign cystadenoma. Histology of the lungs demonstrated a potentially fatal pulmonary exudate and earlier studies attempted to associate this with particular disease conditions. Palaeopathology and ancient DNA analyses show that tuberculosis was widespread in ancient Egypt, so a systematic search for tuberculosis was made, using specific DNA and lipid biomarker analyses. Clear evidence for Mycobacterium tuberculosis complex DNA was obtained in lung tissue and gall bladder samples, based on nested PCR of the IS6110 locus. Lung and femurs were positive for specific M. tuberculosis complex cell-wall mycolic acids, demonstrated by high-performance liquid chromatography of pyrenebutyric acid–pentafluorobenzyl mycolates. Therefore, tuberculosis is likely to have been the major cause of death of Irtyersenu

Low-pathogenicity Mycoplasma spp. alter human monocyte and macrophage function and are highly prevalent among patients with ventilator-acquired pneumonia.

BACKGROUND: Ventilator-acquired pneumonia (VAP) remains a significant problem within intensive care units (ICUs). There is a growing recognition of the impact of critical-illness-induced immunoparesis on the pathogenesis of VAP, but the mechanisms remain incompletely understood. We hypothesised that, because of limitations in their routine detection, Mycoplasmataceae are more prevalent among patients with VAP than previously recognised, and that these organisms potentially impair immune cell function. METHODS AND SETTING: 159 patients were recruited from 12 UK ICUs. All patients had suspected VAP and underwent bronchoscopy and bronchoalveolar lavage (BAL). VAP was defined as growth of organisms at >10(4) colony forming units per ml of BAL fluid on conventional culture. Samples were tested for Mycoplasmataceae (Mycoplasma and Ureaplasma spp.) by PCR, and positive samples underwent sequencing for speciation. 36 healthy donors underwent BAL for comparison. Additionally, healthy donor monocytes and macrophages were exposed to Mycoplasma salivarium and their ability to respond to lipopolysaccharide and undertake phagocytosis was assessed. RESULTS: Mycoplasmataceae were found in 49% (95% CI 33% to 65%) of patients with VAP, compared with 14% (95% CI 9% to 25%) of patients without VAP. Patients with sterile BAL fluid had a similar prevalence to healthy donor BAL fluid (10% (95% CI 4% to 20%) vs 8% (95% CI 2% to 22%)). The most common organism identified was M. salivarium. Blood monocytes from healthy volunteers incubated with M. salivarium displayed an impaired TNF-α response to lipopolysaccharide (p=0.0003), as did monocyte-derived macrophages (MDMs) (p=0.024). MDM exposed to M. salivarium demonstrated impaired phagocytosis (p=0.005). DISCUSSION AND CONCLUSIONS: This study demonstrates a high prevalence of Mycoplasmataceae among patients with VAP, with a markedly lower prevalence among patients with suspected VAP in whom subsequent cultures refuted the diagnosis. The most common organism found, M. salivarium, is able to alter the functions of key immune cells. Mycoplasmataceae may contribute to VAP pathogenesis.This study was funded by the Hospital Infection Society, Wellcome Trust/Department of Health Health Innovation Challenge Fund (HICF)(0510/078) and Sir Jules Thorn Charitable Trust (03/JTA).This is the final version of the article. It first appeared from BMJ Publishing Group via http://dx.doi.org/10.1136/thoraxjnl-2015-20805

An automated machine learning approach to predict brain age from cortical anatomical measures

The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated ML (autoML) has been gaining attention. Here, we apply an autoML library called Tree-based Pipeline Optimisation Tool (TPOT) which uses a tree-based representation of ML pipelines and conducts a genetic programming-based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging data sets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean absolute error [MAE]: 4.612 ± .124 years) and a relevance vector regression (MAE 5.474 ± .140 years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications