Brains of verbal memory specialists show anatomical differences in language, memory and visual systems

Abstract We studied a group of verbal memory specialists to determine whether intensive oral text memory is associated with structural features of hippocampal and lateral-temporal regions implicated in language processing. Professional Vedic Sanskrit Pandits in India train from childhood for around 10 years in an ancient, formalized tradition of oral Sanskrit text memorization and recitation, mastering the exact pronunciation and invariant content of multiple 40,000–100,000 word oral texts. We conducted structural analysis of gray matter density, cortical thickness, local gyrification, and white matter structure, relative to matched controls. We found massive gray matter density and cortical thickness increases in Pandit brains in language, memory and visual systems, including i ) bilateral lateral temporal cortices and ii ) the anterior cingulate cortex and the hippocampus, regions associated with long and short-term memory. Differences in hippocampal morphometry matched those previously documented for expert spatial navigators and individuals with good verbal working memory. The findings provide unique insight into the brain organization implementing formalized oral knowledge systems

The Social and Economic Long Term Monitoring Program (SELTMP) 2014: Recreation in the Great Barrier Reef

[Extract] Introduction.\ud People love to spend their recreational time visiting the Great Barrier Reef World Heritage Area (GBRWHA), (GBRMPA, 2009), and many people are doing it! The recent SELTMP surveys revealed that 95% of residents of coastal town adjacent to the GBR had visited the GBRWHA for recreation at least once, and 87% had visited in the previous 12 months. Many of these visits appeared to be to a mainland beach to walk, swim, and relax. However, 68% of people who told us about their recent trips had been beyond the mainland beach to islands, reefs, shoals, etc., to take part in activities such as fishing, snorkelling and diving. Other activities include boating, sailing, jet skiing, camping, kayaking, sight-seeing, photography, and wildlife viewing, to name a few. Recreational visitors are currently very satisfied with their use of the Marine Park.\ud \ud While most trips beyond the beach were made by ferry, about a third of these trips were accessed by residents' own or someone else's boat. While not everyone is using their vessel very frequently, vessel registration by coastal residents has increased substantially in recent years (Old Department of Transport, unpublished data, 2011).\ud \ud Given all of this activity, it is not surprising that recreation in the GBRWHA provides significant social and cultural benefits as well as many health and wellbeing benefits associated with the psychological interaction with nature (Synergies Economic Consulting, 2012). In economic terms, recreation (defined by Deloitte Access Economics as GBR catchment residents visiting an island, sailing, boating and fishing), contributed $126m in direct value or$243.9m value added to the Australian economy in 2011/12 (Deloitte Access Economics, 2013). This estimate did not include beach visits.\ud Importantly, recreation differs from tourism. The Great Barrier Reef Marine Park Authority define recreation as an independent visit for enjoyment that is not part of a commercial operation (GBRMPA, 2012). For the purposes of the SELTMP Surveys (outline following), any resident of the GBR catchment who visits the GBRWHA is included within recreation; while tourists are defined as those residing outside of the GBR catchment

Advances in monitoring the human dimension of natural resource systems: an example from the Great Barrier Reef

The aim of this paper is to demonstrate the feasibility and potential utility of decision-centric social-economic monitoring using data collected from Great Barrier Reef (Reef) region. The social and economic long term monitoring program (SELTMP) for the Reef is a novel attempt to monitor the social and economic dimensions of social-ecological change in a globally and nationally important region. It represents the current status and condition of the major user groups of the Reef with the potential to simultaneously consider trends, interconnections, conflicts, dependencies and vulnerabilities. Our approach was to combine a well-established conceptual framework with a strong governance structure and partnership arrangement that enabled the co-production of knowledge. The framework is a modification of the Millennium Ecosystem Assessment and it was used to guide indicator choice. Indicators were categorised as; (i) resource use and dependency, (ii) ecosystem benefits and well-being, and (iii) drivers of change. Data were collected through secondary datasets where existing and new datasets were created where not, using standard survey techniques. Here we present an overview of baseline results of new survey data from commercial-fishers (n =210), marine-based tourism operators (n =119), tourists (n =2877), local residents (n =3181), and other Australians (n =2002). The indicators chosen describe both social and economic components of the Reef system and represent an unprecedented insight into the ways in which people currently use and depend on the Reef, the benefits that they derive, and how they perceive, value and relate to the Reef and each other. However, the success of a program such as the SELTMP can only occur with well-translated cutting-edge data and knowledge that are collaboratively produced, adaptive, and directly feeds into current management processes. We discuss how data from the SELTMP have already been incorporated into Reef management decision-making through substantial inclusion in three key policy documents

Brain Areas Specific for Attentional Load in a Motion-Tracking Task

Although visual attention is known to modulate brain activity in the posterior parietal, prefrontal, and visual sensory areas, the unique roles of these areas in the control of attentional resources have remained unclear. Here, we report a dissociation in the response profiles of these areas. In a parametric functional magnetic resonance imaging (fMRI) study, subjects performed a covert motion-tracking task, in which we manipulated “attentional load” by varying the number of tracked balls. While strong effects of attention—independent of attentional load—were widespread, robust linear increases of brain activity with number of balls tracked were seen primarily in the posterior parietal areas, including the intraparietal sulcus (IPS) and superior parietal lobule (SPL). Thus, variations in attentional load revealed different response profiles in sensory areas as compared to control areas. Our results suggest a general role for posterior parietal areas in the deployment of visual attentional resources

Pulse Sequence Resilient Fast Brain Segmentation

Accurate automatic segmentation of brain anatomy from $T_1$-weighted~($T_1$-w) magnetic resonance images~(MRI) has been a computationally intensive bottleneck in neuroimaging pipelines, with state-of-the-art results obtained by unsupervised intensity modeling-based methods and multi-atlas registration and label fusion. With the advent of powerful supervised convolutional neural networks~(CNN)-based learning algorithms, it is now possible to produce a high quality brain segmentation within seconds. However, the very supervised nature of these methods makes it difficult to generalize them on data different from what they have been trained on. Modern neuroimaging studies are necessarily multi-center initiatives with a wide variety of acquisition protocols. Despite stringent protocol harmonization practices, it is not possible to standardize the whole gamut of MRI imaging parameters across scanners, field strengths, receive coils etc., that affect image contrast. In this paper we propose a CNN-based segmentation algorithm that, in addition to being highly accurate and fast, is also resilient to variation in the input $T_1$-w acquisition. Our approach relies on building approximate forward models of $T_1$-w pulse sequences that produce a typical test image. We use the forward models to augment the training data with test data specific training examples. These augmented data can be used to update and/or build a more robust segmentation model that is more attuned to the test data imaging properties. Our method generates highly accurate, state-of-the-art segmentation results~(overall Dice overlap=0.94), within seconds and is consistent across a wide-range of protocols.Comment: Accepted at MICCAI 201