Impact of polycyclic aromatic hydrocarbon exposure on cognitive function and neurodegeneration in humans:A systematic review and meta-analysis

INTRODUCTION: This article documents an emerging body of evidence concerning the neurological effect of polycyclic aromatic hydrocarbon (PAH) exposure with regard to cognitive function and increased risk of neurodegeneration. METHODS: Two electronic databases, PubMed and Web of Science, were systematically searched. RESULTS: The 37/428 studies selected included outcomes measuring cognitive function, neurobehavioral symptoms of impaired cognition, and pathologies associated with neurodegeneration from pre-natal (21/37 studies), childhood (14/37 studies), and adult (8/37 studies) PAH exposure. Sufficient evidence was found surrounding pre-natal exposure negatively impacting child intelligence, mental development, average overall development, verbal IQ, and memory; externalizing, internalizing, anxious, and depressed behaviors; and behavioral development and child attentiveness. Evidence concerning exposure during childhood and as an adult was scarce and highly heterogeneous; however, the presence of neurodegenerative biomarkers and increased concentrations of cryptic “self” antigens in serum and cerebrospinal fluid samples suggest a higher risk of neurodegenerative disease. Associations with lowered cognitive ability and impaired attentiveness were found in children and memory disturbances, specifically auditory memory, verbal learning, and general memory in adults. DISCUSSION: Although evidence is not yet conclusive and further research is needed, the studies included supported the hypothesis that PAH exposure negatively impacts cognitive function and increases the risk of neurodegeneration in humans, and recommends considering the introduction of a variable “rural vs. urban” as covariate for adjusting analyses, where the neurological functions affected (as result of our review) are outcome variables

Reconstruction Schemes for MR Data. Discussion Session

The high fidelity reconstruction of compressed and low-resolution magnetic resonance (MR) data is essential for simultaneously improving patient care, accuracy in diagnosis and quality in clinical research. Sponsored by the Royal Society through the Newton Mobility Grant Scheme, a half-day workshop on reconstruction schemes for MR data was held on the 17th of August 2016 to discuss new ideas from related research fields that could be useful to overcome the shortcomings of the conventional reconstruction methods that have been evaluated up to date. Participants were 21 university students, computer scientists, image analysts, engineers and physicists from institutions from 6 different countries. The discussion evolved around exploring new avenues to achieve high resolution, high quality and fast acquisition of MR imaging. This presentation introduces and reflects on these discussion topics

Automatic Spatial Estimation of White Matter Hyperintensities Evolution in Brain MRI using Disease Evolution Predictor Deep Neural Networks

Funds from the Indonesia Endowment Fund for Education (LPDP), Ministry of Finance, Republic of Indonesia (MFR); Row Fogo Charitable Trust (Grant No. BRO-D.FID3668413)(MCVH); Wellcome Trust (patient recruitment, scanning, primary study Ref No. WT088134/Z/09/A); Fondation Leducq (Perivascular Spaces Transatlantic Network of Excellence); EU Horizon 2020 (SVDs@Target); and the MRC UK Dementia Research Institute at the University of Edinburgh (Wardlaw programme) are gratefully acknowledged. The Titan Xp used for this research was donated by the NVIDIA Corporation.Peer reviewedPublisher PD

The possible causes for sulcal hyperintensities on FLAIR images on brain MRI: the dataset derived from a systematic review

This report describes the data related to the article entitiled: “Relationship between inferior frontal sulcal hyperintensities on brain MRI, ageing and cerebral small vessel disease”. This systematic review was conducted to assess possible causes for sulcal hyperintensities on fluid-attenuated inversion recovery (FLAIR) images on brain MRI

A comparison of the peers method and traditional methodologies, and risk behaviors in studies of the prevalence of drug consumption in a population of female, chilean students

Este estudio investiga el consumo de drogas y los comportamientos de riesgo de alumnas entre los 8º y 12º cursos en dos comunidades de la ciudad de Santiago, Chile. Empleando dos métodos para investigar el consumo, y basándonos en los estudios de poblaciones universitarias, hipotetizamos que los autoinformes de las alumnas sobre su consumo de drogas legales tenderán a ser consistentes con el uso real de dichas drogas, mientras que su consumo de drogas ilícitas será susceptible y tenderán a infra-informar de ello cuando se evalúa a través de medidas tradicionales de autoinforme. Para comparar los dos métodos, se diseñó un estudio transversal que compararía el consumo informado, primero empleando el método de pares, y después por medio del autoinforme tradicional, en un grupo de 350 alumnas. Los resultados muestran que los informes de las chicas sobre su consumo de drogas legales convergen empleando los dos métodos, mientras que, como se ha sugerido, infra-informaban sobre su consumo de drogas ilegales cuando se empleaban metodologías tradicionales.The present study investigates drug consumption and risk behaviors among female students between 8th and 12th grade in two communities in the city of Santiago, Chile. Based on studies of university populations, we hypothesize that the students’ reports of their consumption of licit drugs will tend to be consistent with their actual use of said drugs using two methods to investigate consumption, while their consumption of illicit drugs will be susceptible, and will tend to be under reported when evaluated through traditional self-report measures. In order to compare the two methods, a cross-sectional study was designed that would compare reported drug consumption, first using the peers method, and then by means of traditional self-report in a group of 350 students. The results show that women’s reports of licit drug consumption converge using the two methods, while as earlier suggested, women’s illicit drug consumption was found to be under reported when traditional methodologies were used

Evaluation of Enhanced Learning Techniques for Segmenting Ischaemic Stroke Lesions in Brain Magnetic Resonance Perfusion Images Using a Convolutional Neural Network Scheme

Magnetic resonance (MR) perfusion imaging non-invasively measures cerebral perfusion, which describes the blood's passage through the brain's vascular network. Therefore, it is widely used to assess cerebral ischaemia. Convolutional Neural Networks (CNN) constitute the state-of-the-art method in automatic pattern recognition and hence, in segmentation tasks. But none of the CNN architectures developed to date have achieved high accuracy when segmenting ischaemic stroke lesions, being the main reasons their heterogeneity in location, shape, size, image intensity and texture, especially in this imaging modality. We use a freely available CNN framework, developed for MR imaging lesion segmentation, as core algorithm to evaluate the impact of enhanced machine learning techniques, namely data augmentation, transfer learning and post-processing, in the segmentation of stroke lesions using the ISLES 2017 dataset, which contains expert annotated diffusion-weighted perfusion and diffusion brain MRI of 43 stroke patients. Of all the techniques evaluated, data augmentation with binary closing achieved the best results, improving the mean Dice score in 17% over the baseline model. Consistent with previous works, better performance was obtained in the presence of large lesions

Mediterranean-type diet and brain structural change from 73 to 79 years in the Lothian Birth Cohort 1936

Acknowledgements: We thank the LBC1936 members and project staff. Study Funding: The LBC1936 and this research are supported by Age UK (Disconnected Mind project), the UK Medical Research Council [MRC; G0701120, G1001245, MR/M013111/1, MR/R024065/1], and the University of Edinburgh. SRC, MEB, and IJD were also supported by a National Institutes of Health (NIH) research grant R01AG054628. JMW, IJD are also supported by a Wellcome Trust Strategic Award (Ref 104036/Z/14/Z). MCVH is funded by the Row Fogo Charitable Trust (grant No. BROD.FID3668413). SRC was also supported by a Sir Henry Dale Fellowship jointly funded by the Wellcome Trust and the Royal Society (Grant Number 221890/Z/20/Z). Imaging and image analysis was performed at the Brain Research Imaging Centre (http://www.sbirc.ed.ac.uk/), Edinburgh, supported by the Scottish Funding Council SINAPSE Collaboration. LCAC acknowledges 4 Luciano funding from the Scottish Government’s Rural and Environment Science and Analytical Services (RESAS) division. MVH is funded by the Row Fogo Charitable Trust Centre for Research into Ageing and the Brain (Ref No: AD.ROW4.35. BRO-D.FID3668413). JMW received funding from the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer’s Society and Alzheimer’s Research UK.Peer reviewedPublisher PD