Brain Structural Connectivity Differences in Patients with Normal Cognition and Cognitive Impairment.
Nauris ZdanovskisArdis PlatkājisAndrejs KostiksGuntis KarelisOļesja GrigorjevaPublished in: Brain sciences (2021)
Advances in magnetic resonance imaging, particularly diffusion imaging, have allowed researchers to analyze brain connectivity. Identification of structural connectivity differences between patients with normal cognition, cognitive impairment, and dementia could lead to new biomarker discoveries that could improve dementia diagnostics. In our study, we analyzed 22 patients (11 control group patients, 11 dementia group patients) that underwent 3T MRI diffusion tensor imaging (DTI) scans and the Montreal Cognitive Assessment (MoCA) test. We reconstructed DTI images and used the Desikan-Killiany-Tourville cortical parcellation atlas. The connectivity matrix was calculated, and graph theoretical analysis was conducted using DSI Studio. We found statistically significant differences between groups in the graph density, network characteristic path length, small-worldness, global efficiency, and rich club organization. We did not find statistically significant differences between groups in the average clustering coefficient and the assortativity coefficient. These statistically significant graph theory measures could potentially be used as quantitative biomarkers in cognitive impairment and dementia diagnostics.
Keyphrases
- cognitive impairment
- white matter
- mild cognitive impairment
- magnetic resonance imaging
- resting state
- end stage renal disease
- ejection fraction
- newly diagnosed
- functional connectivity
- prognostic factors
- computed tomography
- high resolution
- convolutional neural network
- deep learning
- magnetic resonance
- machine learning
- photodynamic therapy
- patient reported outcomes
- contrast enhanced
- neural network
- cerebral ischemia
- network analysis