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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Episodic memory in mild cognitive impairment inversely correlates with the global modularity of the cerebral blood
Carlos A Sánchez-Catasús1, Antoon Willemsen2, Ronald Boellaard2
1Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, GZ 9713, the Netherlands; Center for Neurological Restoration (CIREN), Ave. 25, No. 15 805, Playa, La Habana 11300, Cuba.
Abstract:
Cerebral blood flow (CBF) SPECT is an interesting methodology to study brain connectivity in mild cognitive impairment (MCI) since it is accessible worldwide and can be used as a biomarker of neuronal injury in MCI. In CBF SPECT, connectivity is grounded in group-based correlation networks. Therefore, topological metrics derived from the CBF correlation network cannot be used to support diagnosis and prognosis individually. However, methods to extract the individual patient contribution to topological metrics of group-based correlation networks were developed although not yet applied to MCI patients. Here, we investigate whether the episodic memory of 24 amnestic MCI patients correlates with individual patient contributions to topological metrics of the CBF correlation network. We first compared topological metrics of the MCI group network with the network corresponding to 26 controls. Metrics that showed significant differences were then used for the individual patient contribution analysis. We found that the global network modularity was increased while global efficiency decreased in the MCI network compared to the control. Most importantly, we found that episodic memory inversely correlates with the patient contribution to the global network modularity, which highlights the potential of this approach to develop a CBF connectivity-based biomarker at the individual level.
Insights
Cerebral blood flow SPECT reveals altered brain connectivity in mild cognitive impairment (MCI). Individual patient contributions to network modularity correlate with episodic memory, suggesting a potential biomarker for MCI.
Area of Science:
- Neuroimaging
- Neurology
- Network Science
Background:
- Mild cognitive impairment (MCI) affects brain connectivity.
- Cerebral blood flow (CBF) SPECT is an accessible tool for assessing neuronal injury.
- Current CBF SPECT connectivity analysis relies on group networks, limiting individual diagnostic potential.
Purpose of the Study:
- To investigate the correlation between episodic memory and individual contributions to brain network topology in MCI patients.
- To explore the potential of developing an individual-level CBF connectivity biomarker for MCI.
Main Methods:
- Compared topological metrics of CBF SPECT correlation networks between 24 MCI patients and 26 controls.
- Analyzed individual patient contributions to network metrics that differed significantly between groups.
- Correlated episodic memory scores with these individual contributions.
Main Results:
- The MCI group exhibited increased global network modularity and decreased global efficiency compared to controls.
- Episodic memory showed an inverse correlation with the individual patient's contribution to global network modularity.
Conclusions:
- Individualized analysis of CBF SPECT networks can reveal significant findings in MCI.
- The correlation between episodic memory and network modularity contribution suggests a promising avenue for developing individual biomarkers for MCI.
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