Patterns of neural response in scene-selective regions of the human brain are affected by low-level manipulations of spatial frequency

Neuroimaging studies have found distinct patterns of response to different categories of scenes. However, the relative importance of low-level image properties in generating these response patterns is not fully understood. To address this issue, we directly manipulated the low level properties of scenes in a way that preserved the ability to perceive the category. We then measured the effect of these manipulations on category-selective patterns of fMRI response in the PPA, RSC and OPA. In Experiment 1, a horizontal-pass or vertical-pass orientation filter was applied to images of indoor and natural scenes. The image filter did not have a large effect on the patterns of response. For example, vertical- and horizontal-pass filtered indoor images generated similar patterns of response. Similarly, vertical- and horizontal-pass filtered natural scenes generated similar patterns of response. In Experiment 2, low-pass or high-pass spatial frequency filters were applied to the images. We found that image filter had a marked effect on the patterns of response in scene-selective regions. For example, low-pass indoor images generated similar patterns of response to low-pass natural images. The effect of filter varied across different scene-selective regions, suggesting differences in the way that scenes are represented in these regions. These results indicate that patterns of response in scene-selective regions are sensitive to the low-level properties of the image, particularly the spatial frequency content

A data driven approach to understanding the organization of high-level visual cortex

The neural representation in scene-selective regions of human visual cortex, such as the PPA, has been linked to the semantic and categorical properties of the images. However, the extent to which patterns of neural response in these regions reflect more fundamental organizing principles is not yet clear. Existing studies generally employ stimulus conditions chosen by the experimenter, potentially obscuring the contribution of more basic stimulus dimensions. To address this issue, we used a data-driven approach to describe a large database of scenes (>100,000 images) in terms of their visual properties (orientation, spatial frequency, spatial location). K-means clustering was then used to select images from distinct regions of this feature space. Images in each cluster did not correspond to typical scene categories. Nevertheless, they elicited distinct patterns of neural response in the PPA. Moreover, the similarity of the neural response to different clusters in the PPA could be predicted by the similarity in their image properties. Interestingly, the neural response in the PPA was also predicted by perceptual responses to the scenes, but not by their semantic properties. These findings provide an image-based explanation for the emergence of higher-level representations in scene-selective regions of the human brain

Stratigraphic Overview of Palaeogene Tuffs in the Faroe-Shetland Basin, NE Atlantic Margin

Acknowledgements We are very grateful to PGS for generously donating seismic datasets. Seismic interpretation was carried out using IHS Kingdom software, and wells were downloaded from the UK Oil & Gas Common Data Access Welllog interpretation was conducted using Schlumberger Techlog software. D.W. would also like to thank C. Telford for insights regarding the identification of tuffs in ditch cuttings and Total (UK) for material concerning the Vaila Formation. Attendees of VMRC workshops from academia and industry provided important insights into the stratigraphy of the FSB. Finally,D.W.would like to acknowledge the late Robert Knox, without whom our knowledge of North Atlantic explosive volcanism would be considerably poorer. The reviews of P. Reynolds and J. Ólavsdóttir greatly improved the paper. Funding This work is part of D.W.’s PhD research, which is funded by a University of Aberdeen College of Physical Sciences Scholarship.Peer reviewedPostprin

Connectopic mapping techniques do not reflect functional gradients in the brain

Functional gradients, in which response properties change gradually across a brain region, have been proposed as a key organising principle of the brain. Recent studies using both resting-state and natural viewing paradigms have indicated that these gradients may be reconstructed from functional connectivity patterns via “connectopic mapping” analyses. However, local connectivity patterns may be confounded by spatial autocorrelations artificially introduced during data analysis, for instance by spatial smoothing or interpolation between coordinate spaces. Here, we investigate whether such confounds can produce illusory connectopic gradients. We generated datasets comprising random white noise in subjects’ functional volume spaces, then optionally applied spatial smoothing and/or interpolated the data to a different volume or surface space. Both smoothing and interpolation induced spatial autocorrelations sufficient for connectopic mapping to produce both volume- and surface-based local gradients in numerous brain regions. Furthermore, these gradients appeared highly similar to those obtained from real natural viewing data, although gradients generated from real and random data were statistically different in certain scenarios. We also reconstructed global gradients across the whole-brain – while these appeared less susceptible to artificial spatial autocorrelations, the ability to reproduce previously reported gradients was closely linked to specific features of the analysis pipeline. These results indicate that previously reported gradients identified by connectopic mapping techniques may be confounded by artificial spatial autocorrelations introduced during the analysis, and in some cases may reproduce poorly across different analysis pipelines. These findings imply that connectopic gradients need to be interpreted with caution

Identification of transcriptional regulatory networks specific to pilocytic astrocytoma.

BackgroundPilocytic Astrocytomas (PAs) are common low-grade central nervous system malignancies for which few recurrent and specific genetic alterations have been identified. In an effort to better understand the molecular biology underlying the pathogenesis of these pediatric brain tumors, we performed higher-order transcriptional network analysis of a large gene expression dataset to identify gene regulatory pathways that are specific to this tumor type, relative to other, more aggressive glial or histologically distinct brain tumours.MethodsRNA derived from frozen human PA tumours was subjected to microarray-based gene expression profiling, using Affymetrix U133Plus2 GeneChip microarrays. This data set was compared to similar data sets previously generated from non-malignant human brain tissue and other brain tumour types, after appropriate normalization.ResultsIn this study, we examined gene expression in 66 PA tumors compared to 15 non-malignant cortical brain tissues, and identified 792 genes that demonstrated consistent differential expression between independent sets of PA and non-malignant specimens. From this entire 792 gene set, we used the previously described PAP tool to assemble a core transcriptional regulatory network composed of 6 transcription factor genes (TFs) and 24 target genes, for a total of 55 interactions. A similar analysis of oligodendroglioma and glioblastoma multiforme (GBM) gene expression data sets identified distinct, but overlapping, networks. Most importantly, comparison of each of the brain tumor type-specific networks revealed a network unique to PA that included repressed expression of ONECUT2, a gene frequently methylated in other tumor types, and 13 other uniquely predicted TF-gene interactions.ConclusionsThese results suggest specific transcriptional pathways that may operate to create the unique molecular phenotype of PA and thus opportunities for corresponding targeted therapeutic intervention. Moreover, this study also demonstrates how integration of gene expression data with TF-gene and TF-TF interaction data is a powerful approach to generating testable hypotheses to better understand cell-type specific genetic programs relevant to cancer

Exceptional longevity and potential determinants of successful ageing in a cohort of 39 Labrador retrievers: results of a prospective longitudinal study.

BACKGROUND: The aim of this study was to describe the longevity and causes of mortality in 39 (12 males, 27 females) pedigree adult neutered Labrador retrievers with a median age of 6.5 years at the start of the study and kept under similar housing and management conditions. Body condition score was maintained between two and four on a 5-point scale by varying food allowances quarterly. The impact of change in body weight (BW) and body composition on longevity was analysed using linear mixed models with random slopes and intercepts. RESULTS: On 31 July 2014, 10 years after study start, dogs were classified into three lifespan groups: 13 (33 %) Expected (≥9 to ≤12.9 years), 15 (39 %) Long (≥13 to ≤15.5 years) and 11 (28 %) Exceptional (≥15.6 years) with five still alive. Gender and age at neutering were not associated with longevity (P ≥ 0.06). BW increased similarly for all lifespan groups up to age 9, thereafter, from 9 to 13 years, Exceptional dogs gained and Long-lifespan dogs lost weight (P = 0.007). Dual-energy x-ray absorptiometer scans revealed that absolute fat mass increase was slower to age 13 for Long compared with Expected lifespan dogs (P = 0.003) whilst all groups lost a similar amount of absolute lean mass (P > 0.05). Percent fat increase and percent lean loss were slower, whilst the change in fat:lean was smaller, in both the Exceptional and Long lifespan compared with Expected dogs to age 13 (P ≤ 0.02). Total bone mineral density was significantly lower for Expected compared to Exceptional and Long lifespan dogs (P < 0.04). CONCLUSIONS: This study shows that life-long maintenance of lean body mass and attenuated accumulation of body fat were key factors in achieving a longer lifespan. The results suggest that a combination of a high quality plane of nutrition with appropriate husbandry and healthcare are important in obtaining a greater than expected proportion of Labrador retrievers living well beyond that of the expected breed lifespan: 89.7 % (95 % CI 74.8-96.7 %) dogs were alive at 12 years of age and 28.2 % (95 % CI 15.6-45.1 %) reaching an exceptional lifespan of ≥15.6 years.The authors wish to thank all the former P&G Research & Development team involved for their assistance in this study since its inception over 10 years ago. The authors also wish to acknowledge the role of P&G for their financial support of this study and Spectrum Brands for supporting the analysis and preparation of this manuscript.This is the final version of the article. It first appeared from BioMed Central via http://dx.doi.org/10.1186/s13028-016-0206-

Body weight at 10 years of age and change in body composition between 8 and 10 years of age were related to survival in a longitudinal study of 39 Labrador retriever dogs

Abstract: Background: Overweight and obesity have been adversely associated with longevity in dogs but there is scarce knowledge on the relation between body composition and lifespan. We aimed to investigate the effects of body composition, and within-dog changes over time, on survival in adult Labradors using a prospective cohort study design. The dogs had a median age of 6.5 years at study start and were kept in similar housing and management conditions throughout. The effects of the various predictors, including the effect of individual monthly-recorded change in body weight as a time varying covariate, were evaluated using survival analysis. Results: All dogs were followed to end-of-life; median age at end-of-life was 14.0 years. Body composition was measured annually with dual-energy x-ray absorptiometer (DEXA) scans between 6.2 and 17.0 years. All 39 dogs had DEXA recorded at 8, 9 and 10 years of age. During the study the mean (± SD) percent of fat (PF) and lean mass (PL) was 32.8 (± 5.6) and 64.2 (± 5.5) %, respectively, with a mean lean:fat ratio (LFR) of 2.1 (± 0.6); body weight (BW) varied from 17.5 to 44.0 kg with a mean BW change of 9.9 kg (± 3.0). There was increased hazard of dying for every kg increase in BW at 10 years of age; for each additional kg of BW at 10 years, dogs had a 19% higher hazard (HR = 1.19, P = 0.004). For the change in both lean mass (LM) and LFR variables, it was protective to have a higher lean and/or lower fat mass (FM) at 10 years of age compared to 8 years of age, although the HR for change in LM was very close to 1.0. For age at study start, older dogs had an increased hazard. There was no observed effect for the potential confounders sex, coat colour and height at shoulders, or of the time-varying covariate. Conclusions: These results suggest that even rather late-life control efforts on body weight and the relationship between lean and fat mass may influence survival in dogs. Such “windows of opportunity” can be used to develop healthcare strategies that would help promote an increased healthspan in dogs