The Scottish Brain Health Service Model: Rationale and Scientific Basis for a National Care Pathway of Brain Health Services in Scotland.

In order to address the oft-cited societal, economic, and health and social care impacts of neurodegenerative diseases, such as Alzheimer's disease, we must move decisively from reactive to proactive clinical practice and to embed evidence-based brain health education throughout society. Most disease processes can be at least partially prevented, slowed, or reversed. We have long neglected to intervene in neurodegenerative disease processes, largely due to a misconception that their predominant symptom - cognitive decline - is a normal, age-related process, but also due to a lack of multi-disciplinary collaboration. We now understand that there are modifiable risk factors for neurodegenerative diseases, that successful management of common comorbidities (such as diabetes and hypertension) can reduce the incidence of neurodegenerative disease, and that disease processes begin (and, crucially, can be detected, reduced, and delayed, prevented, or treated) decades earlier in life than had previously been appreciated. Brain Health Scotland, established by Scottish Government and working in partnership with Alzheimer Scotland, propose far-reaching public health and clinical practice approaches to reduce neurodegenerative disease incidence. Focusing here on Brain Health Scotland's clinical offerings, we present the Scottish Model for Brain Health Services. To our knowledge, the Scottish Model for Brain Health, built on foundations of personalised risk profiling, targeted risk reduction and prevention, early disease detection, equity of access, and harnessing comprehensive data to assist in clinical decision-making, marks the first example of a nationwide approach to overhauling clinical, societal, and political approaches to the prevention, assessment, and treatment of neurodegenerative disease.

Measuring perceived differences in surface texture due to changes in higher order statistics.

We investigate the ability of humans to perceive changes in the appearance of images of surface texture caused by the variation of their higher order statistics. We incrementally randomize their phase spectra while holding their first and second order statistics constant in order to ensure that the change in the appearance is due solely to changes in third and other higher order statistics. Stimuli comprise both natural and synthetically generated naturalistic images, with the latter being used to prevent observers from making pixel-wise comparisons. A difference scaling method is used to derive the perceptual scales for each observer, which show a sigmoidal relationship with the degree of randomization. Observers were maximally sensitive to changes within the 20%-60% randomization range. In order to account for this behavior we propose a biologically plausible model that computes the variance of local measurements of phase congruency.

Visual search for a target against a 1/f(beta) continuous textured background.

We present synthetic surface textures as a novel class of stimuli for use in visual search experiments. Surface textures have certain advantages over both the arrays of abstract discrete items commonly used in search studies and photographs of natural scenes. In this study we investigate how changing the properties of the surface and target influence the difficulty of a search task. We present a comparison with Itti and Koch's saliency model and find that it fails to model human behaviour on these surfaces. In particular it does not respond to changes in orientation in the same manner as human observers.

Evaluating Kube and Pentland's fractal imaging model.

The paper assesses the validity of a model, proposed by Kube and Pentland (1988), that relates a rough surface to its image texture. Simulation was used to assess whether a linear approximation is appropriate, and whether the optimal linear filter agrees with the predictions of Kube and Pentland's model. The predictions of the model about the image directionality were also assessed on real images. It was found that a linear model is capable of modeling the imaging process for surfaces of moderate roughness and Lambertian reflectance, and that, subject to a small modification, Kube and Pentland's model accurately predicts the relationship between surface and image spectra.