Computer assisted image acquisition techniques, including confocal microscopy, require efficient tools for an automatic sorting of vast amount of generated image data. The complexity of the classification process, absence of adequate tools, and insufficient amount of reference data has made the automated processing of images challenging. Mastering of this issue would allow implementation of statistical analysis in research areas such as in research on formation of t-tubules in cardiac myocytes. We developed a system aimed at automatic assessment of cardiomyocyte development stages (SAACS). The system classifies confocal images of cardiomyocytes with fluorescent dye stained sarcolemma. We based SAACS on a densely connected convolutional network (DenseNet) topology. We created a set of labelled source images, proposed an appropriate data augmentation technique and designed a class probability graph. We showed that the DenseNet topology, in combination with the augmentation technique is suitable for the given task, and that high-resolution images are instrumental for image categorization. SAACS, in combination with the automatic high-throughput confocal imaging, will allow application of statistical analysis in the research of the tubular system development or remodelling and loss.

• MeSH
• Cell Differentiation MeSH
• Fluorescent Dyes MeSH
• Myocytes, Cardiac cytology   ultrastructure   MeSH
• Microscopy, Confocal methods   MeSH
• Rats MeSH
• Models, Cardiovascular MeSH
• Neural Networks, Computer MeSH
• Image Processing, Computer-Assisted methods   MeSH
• Sarcolemma ultrastructure   MeSH
• Machine Learning MeSH
• Artificial Intelligence MeSH
• Animals MeSH
• Check Tag
• Rats MeSH
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

BACKGROUND: Cognitive impairment in Parkinson's disease (PD) is associated with altered connectivity of the resting state networks (RSNs). Longitudinal studies in well cognitively characterized PD subgroups are missing. OBJECTIVES: To assess changes of the whole-brain connectivity and between-network connectivity (BNC) of large-scale functional networks related to cognition in well characterized PD patients using a longitudinal study design and various analytical methods. METHODS: We explored the whole-brain connectivity and BNC of the frontoparietal control network (FPCN) and the default mode, dorsal attention, and visual networks in PD with normal cognition (PD-NC, n = 17) and mild cognitive impairment (PD-MCI, n = 22) as compared to 51 healthy controls (HC). We applied regions of interest-based, partial least squares, and graph theory based network analyses. The differences among groups were analyzed at baseline and at the one-year follow-up visit (37 HC, 23 PD all). RESULTS: The BNC of the FPCN and other RSNs was reduced, and the whole-brain analysis revealed increased characteristic path length and decreased average node strength, clustering coefficient, and global efficiency in PD-NC compared to HC. Values of all measures in PD-MCI were between that of HC and PD-NC. After one year, the BNC was further increased in the PD-all group; no changes were detected in HC. No cognitive domain z-scores deteriorated in either group. CONCLUSION: As compared to HC, PD-NC patients display a less efficient transfer of information globally and reduced BNC of the visual and frontoparietal control network. The BNC increases with time and MCI status, reflecting compensatory efforts.

• MeSH
• Adult MeSH
• Cognitive Dysfunction etiology   pathology   psychology   MeSH
• Middle Aged MeSH
• Humans MeSH
• Longitudinal Studies MeSH
• Magnetic Resonance Imaging MeSH
• Brain diagnostic imaging   pathology   MeSH
• Nerve Net diagnostic imaging   pathology   MeSH
• Neuroimaging MeSH
• Parkinson Disease complications   pathology   psychology   MeSH
• Prefrontal Cortex pathology   MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Case-Control Studies MeSH
• Parietal Lobe pathology   MeSH
• Mental Status and Dementia Tests MeSH
• Visual Cortex pathology   MeSH
• Check Tag
• Adult MeSH
• Middle Aged MeSH
• Humans MeSH
• Male MeSH
• Aged, 80 and over MeSH
• Aged MeSH
• Female MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH

• MeSH
• Mathematical Computing MeSH
• Mathematics MeSH
• Numerical Analysis, Computer-Assisted * MeSH
• Publication type
• Monograph MeSH

• Conspectus
• Počítačová věda. Výpočetní technika. Informační technologie
• NML Fields
• přírodní vědy
• přírodní vědy