| Autor: |
Khokhar SK; Department of Neuroimaging and Interventional Radiology, and National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India., Kumar M; Department of Neuroimaging and Interventional Radiology, and National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India., Kumar S; Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India., Manae T; Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India., Thanissery N; Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India., Ramakrishnan S; Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India., Arshad F; Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India., Nagaraj C; Department of Neuroimaging and Interventional Radiology, and National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India., Mangalore S; Department of Neuroimaging and Interventional Radiology, and National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India., Alladi S; Department of Neurology, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India., Gandhi TK; Department of Electrical Engineering, Indian Institute of Technology (IIT) Delhi, New Delhi, Delhi, India., Bharath RD; Department of Neuroimaging and Interventional Radiology, and National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, Karnataka, India. |
| Abstrakt: |
Introduction: Unraveling the network pathobiology in neurodegenerative disorders is a popular and promising field in research. We use a relatively newer network measure of assortativity in metabolic connectivity to understand network differences in patients with Alzheimer's Disease (AD), compared with those with mild cognitive impairment (MCI). Methods: Eighty-three demographically matched patients with dementia (56 AD and 27 MCI) who underwent positron emission tomography-magnetic resonance imaging (PET-MRI) study were recruited for this exploratory study. Global and nodal network measures obtained using the BRain Analysis using graPH theory toolbox were used to derive group-level differences (corrected p < 0.05). The methods were validated in age, and gender-matched 23 cognitively normal, 25 MCI, and 53 AD patients from the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Regions that revealed significant differences were correlated with the Addenbrooke's Cognitive Examination-III (ACE-III) scores. Results: Patients with AD revealed significantly increased global assortativity compared with the MCI group. In addition, they also revealed increased modularity and decreased participation coefficient. These findings were validated in the ADNI data. We also found that the regional standard uptake values of the right superior parietal and left superior temporal lobes were proportional to the ACE-III memory subdomain scores. Conclusion: Global errors associated with network assortativity are found in patients with AD, making the networks more regular and less resilient. Since the regional measures of these network errors were proportional to memory deficits, these measures could be useful in understanding the network pathobiology in AD. |
