Our findings show significant correlations between functional connectivity changes in key areas affected in SCA2 and these patients' motor and neuropsychological impairments, adding an important insight to our understanding of the pathophysiology of SCA2.
Spinocerebellar ataxia type 7 (SCA7) is an autosomal-dominant neurodegenerative disorder characterized by progressive ataxia and retinal dystrophy. It is caused by a CAG trinucleotide expansion in the ataxin7 gene. Anatomical studies have shown severe cerebellar degeneration and region-specific neocortical atrophy in SCA7 patients. However, the impact of the neurodegeneration on the functional integration of the remaining tissue is still unknown. The aim of this study was to examine functional connectivity abnormalities in areas with significant gray matter atrophy in SCA7 patients and their relationship with number of CAG repeats. Using a combination of voxel-based morphometry and resting-state fMRI, we studied 26 genetically confirmed SCA7 patients and aged-matched healthy controls. In SCA7 patients we found reduced functional interaction between the cerebellum and the middle and superior frontal gyri, disrupted functional connectivity between the visual and motor cortices, and increased functional coordination between atrophied areas of the cerebellum and a range of visual cortical areas compared with healthy controls. The degree of mutation expansion showed a negative effect on both the functional interaction between the right anterior cerebellum and the left superior frontal gyrus and the connectivity between the right anterior cerebellum and left parahippocampal gyrus. We found abnormal functional connectivity patterns, including both hypo- and hyperconnectivity, compared with controls. These abnormal patterns show reasonable association with the severity of gene mutation. Our findings suggest that aberrant changes are prevalent in both motor and visual systems, adding significantly to our understanding of the pathophysiology of SCA7.
The application of Value-based Healthcare requires not only the identification of key processes in the clinical domain but also an adequate analysis of the value chain delivered to the patient. Data Science and Big Data approaches are technologies that enable the creation of accurate systems that model reality. However, classical Data Mining techniques are presented by professionals as black boxes. This evokes a lack of trust in those techniques in the medical domain. Process Mining technologies are human-understandable Data Science tools that can fill this gap to support the application of Value-Based Healthcare in real domains. The aim of this paper is to perform an analysis of the ways in which Process Mining techniques can support health professionals in the application of Value-Based Technologies. For this purpose, we explored these techniques by analyzing emergency processes and applying the critical timing of Stroke treatment and a Question-Driven methodology. To demonstrate the possibilities of Process Mining in the characterization of the emergency process, we used a real log with 9046 emergency episodes from 2145 stroke patients that occurred from January 2010 to June 2017. Our results demonstrate how Process Mining technology can highlight the differences between the flow of stroke patients compared with that of other patients in an emergency. Further, we show that support for health professionals can be provided by improving their understanding of these techniques and enhancing the quality of care.
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