15 research outputs found

    Consensus classification of posterior cortical atrophy

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    INTRODUCTION: A classification framework for posterior cortical atrophy (PCA) is proposed to improve the uniformity of definition of the syndrome in a variety of research settings. METHODS: Consensus statements about PCA were developed through a detailed literature review, the formation of an international multidisciplinary working party which convened on four occasions, and a Web-based quantitative survey regarding symptom frequency and the conceptualization of PCA. RESULTS: A three-level classification framework for PCA is described comprising both syndrome- and disease-level descriptions. Classification level 1 (PCA) defines the core clinical, cognitive, and neuroimaging features and exclusion criteria of the clinico-radiological syndrome. Classification level 2 (PCA-pure, PCA-plus) establishes whether, in addition to the core PCA syndrome, the core features of any other neurodegenerative syndromes are present. Classification level 3 (PCA attributable to AD [PCA-AD], Lewy body disease [PCA-LBD], corticobasal degeneration [PCA-CBD], prion disease [PCA-prion]) provides a more formal determination of the underlying cause of the PCA syndrome, based on available pathophysiological biomarker evidence. The issue of additional syndrome-level descriptors is discussed in relation to the challenges of defining stages of syndrome severity and characterizing phenotypic heterogeneity within the PCA spectrum. DISCUSSION: There was strong agreement regarding the definition of the core clinico-radiological syndrome, meaning that the current consensus statement should be regarded as a refinement, development, and extension of previous single-center PCA criteria rather than any wholesale alteration or redescription of the syndrome. The framework and terminology may facilitate the interpretation of research data across studies, be applicable across a broad range of research scenarios (e.g., behavioral interventions, pharmacological trials), and provide a foundation for future collaborative work

    A New Image Encryption Scheme Based on Hybrid Chaotic Maps

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    Chaos-based encryption algorithms offer many advantages over conventional cryptographic algorithms, such as speed, high security, affordable overheads for computation, and procedure power. In this paper, we propose a novel perturbation algorithm for data encryption based on double chaotic systems. A new image encryption algorithm based on the proposed chaotic maps is introduced. The proposed chaotification method is a hybrid technique that parallels and combines the chaotic maps. It is based on combination between Discrete Wavelet Transform (DWT) to decompose the original image into sub-bands and both permutation and diffusion properties are attained using the chaotic states and parameters of the proposed maps, which are then concerned in shuffling of pixel and operations of substitution, respectively. Security, statistical test analyses, and comparison with other techniques indicate that the proposed algorithm has promising effect and it can resist several common attacks. Namely, the average values for UACI and NPCR metrics were 33.6248% and 99.6472%, respectively. Additionally, unscrambling quality can fulfill security and execution prerequisites as evidenced by PSNR (9.005955) and entropy (7.999275) values. In sum, the proposed method has enough ability to achieve low residual intelligibility with high quality recovered data, high sensitivity, and high security performance compared to some other recent literature approaches
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