All posts filed under “StudynWorks

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Connected Brains – Oct 2015

  • Gu S, Satterthwaite TD, Medaglia JD, Yang M, Gur RE, Gur RC, Bassett DS. Emergence of system roles in normative neurodevelopment. Proc Natl Acad Sci U S A. 2015 Oct 19. pii: 201502829. [Epub ahead of print]
    • Comment: Brain networks consist of different modules or subnetworks that are activated during various cognitive tasks. In this large study of 780 subjects between 8-22 years the authors show that modules display a characteristic pattern of development which is related to the function and topology of the modules. Individual variation of this pattern correlates with variation in cognitive performance.

  • Barttfeld P, Bekinschtein TA, Salles A, Stamatakis EA, Adapa R, Menon DK, Sigman M. Factoring the brain signatures of anesthesia concentration and level of arousal across individuals.Neuroimage Clin. 2015 Sep 3;9:385-91.
    • Comment: Resting-state functional brain networks were studied in subjects sedated with Propofol and their clinical level of consciousness was assessed. Factorial analysis showed that Propofol levels were associated with a frontal parietal loss of functional connectivity, while decreased responsiveness was associated with lower thalamic frontal, and higher thalamic temporal / occipital connectivity.

  • Wu X, Zou Q, Hu J, Tang W, Mao Y, Gao L, Zhu J, Jin Y, Wu X, Lu L, Zhang Y, Zhang Y, Dai Z, Gao JH, Weng X, Zhou L, Northoff G, Giacino JT, He Y, Yang Y. Intrinsic Functional Connectivity Patterns Predict Consciousness Level and Recovery Outcome in Acquired Brain Injury. J Neurosci. 2015 Sep 16;35(37):12932-46.
    • Comment: Resting-state fMRI was used in 99 patients with acquired brain injury (ABI) with various levels of disturbed consciousness and healthy controls. Loss of functional connectivity of a subset of regions was correlated with loss of consciousness and outcome after three months. With support vector machine analysis recovery of consciousness could be predicted with an accuracy of 81.25%. The most predictive regions were the posterior cingulate gyrus and the precuneus. This study illustrates the importance of hub connectivity for consciousness, and the potential of machine learning applied to connectivity networks for clinical application.

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The hubs of the human connectome are generally implicated in the anatomy of brain disorders

Brain networks or ‘connectomes’ include a minority of highly connected hub nodes that are functionally valuable, because their topological centrality supports integrative processing and adaptive behaviours. Recent studies also suggest that hubs have higher metabolic demands and longer-distance connections than other brain regions, and therefore could be considered biologically costly.
Assuming that hubs thus normally combine both high topological value and high biological cost, we predicted that pathological brain lesions would be concentrated in hub regions. To test this general hypothesis, we first identified the hubs of brain anatomical networks estimated from diffusion tensor imaging data on healthy volunteers (n = 56), and showed that computational attacks targeted on hubs disproportionally degraded the efficiency of brain networks compared to random attacks. We then prepared grey matter lesion maps, based on meta-analyses of published magnetic resonance imaging data on more than 20 000 subjects and 26 different brain disorders. Magnetic resonance imaging lesions that were common across all brain disorders were more likely to be located in hubs of the normal brain connectome (P5104, permutation test). Specifically, nine brain disorders had lesions that were significantly more likely to be located in hubs (P50.05, permutation test), including schizophrenia and Alzheimer’s disease. Both these disorders had significantly hub-concentrated lesion distributions, although (almost completely) distinct subsets of cortical hubs were lesioned in each disorder: temporal lobe hubs specifically were associated with higher lesion probability in Alzheimer’s disease, whereas in schizophrenia lesions were concentrated in both frontal and temporal cortical hubs. These results linking pathological lesions to the topological centrality of nodes in the normal diffusion tensor imaging connectome were generally replicated when hubs were defined instead by the meta-analysis of more than 1500 task-related functional neuroimaging studies of healthy volunteers to create a normative functional co-activation network. We conclude that the high cost/high value hubs of human brain networks are more likely to be anatomically abnormal than non-hubs in many (if not all) brain disorders.

Reference : Crossley NA, Mechelli A, Scott J, Carletti F, Fox PT, McGuire P, Bullmore ET. The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain. 2014 Aug;137(Pt 8):2382-95.

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A Deep Learning Tutorial: From Perceptrons to Deep Networks

In recent years, there’s been a resurgence in the field of Artificial Intelligence. It’s spread beyond the academic world with major players like Google, Microsoft, and Facebook creating their own research teams and making some impressive acquisitions.

Some this can be attributed to the abundance of raw data generated by social network users, much of which needs to be analyzed, as well as to the cheap computational power available via GPGPUs.

But beyond these phenomena, this resurgence has been powered in no small part by a new trend in AI, specifically in machine learning, known as “Deep Learning”. In this tutorial, I’ll introduce you to the key concepts and algorithms behind deep learning, beginning with the simplest unit of composition and building to the concepts of machine learning in Java.

(For full disclosure: I’m also the author of a Java deep learning library, available here, and the examples in this article are implemented using the above library. If you like it, you can support it by giving it a star on GitHub, for which I would be grateful. Usage instructions are available on the homepage.)

http://www.toptal.com/machine-learning/an-introduction-to-deep-learning-from-perceptrons-to-deep-networks

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A Potential Role of the Curry Spice Curcumin in Alzheimer’s Disease

There is substantial in-vitro data indicating that curcumin has antioxidant, anti-inflammatory, and anti-amyloid activity. In addition, studies in animal models of Alzheimer’s disease (AD) indicate a direct effect of curcumin in decreasing the amyloid pathology of AD. As the widespread use of curcumin as a food additive and relatively small short-term studies in humans suggest safety, curcumin is a promising agent in the treatment and/or prevention of AD. Nonetheless, important information regarding curcumin bioavailability, safety and tolerability, particularly in an elderly population is lacking. We are therefore performing a study of curcumin in patients with AD to gather this information in addition to data on the effect of curcumin on biomarkers of AD pathology.

Reference :
John M. Ringman et al, “A Porential Role of the Curry Spice Curcumin in Alzheimer’s Disease”, Curr Alzheimer Res. 2005 April; 2(2): 131-136

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