P2‐441: Characterizing Structural Brain Alterations in Alzheimer's Disease Patients With Machine Learning

Hwang, Gyujoon; Nair, Veena A.; Alexander, Andrew L.; Antuono, Piero; Asthana, Sanjay; Birn, Rasmus M.; Carlsson, Cynthia M.; Chen, Guangyu; Edwards, Dorothy Farrar; Franczak, Malgorzata; Goveas, Joseph S.; Johnson, Sterling C.; Kecskemeti, Steven; Kulkarni, Arman P.; Mohanty, Rosaleena; Nencka, Andrew S.; Okonkwo, Ozioma C.; Pasquesi, Mary‐Elizabeth; Rivera-Bonet, Charlene N.; Taylor, Ingrid Kristine; Tellapragada, Neelima; Williams, Leroy; Li, Shi‐Jiang; Bendlin, Barbara B.; Prabhakaran, Vivek

doi:10.1016/j.jalz.2018.06.1133

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“…The patient group consisted of 29 left, 15 right, and 4 bilateral TLE patients, and 12 TLE patients of uncertain seizure laterality. To closely match the TLE and control samples, 12 of the healthy control data sets were taken from the Alzheimer Disease Connectome Project (ADCP; 1UF1AG051216-01A1) (Hwang et al, 2018), which uses the same set of MRI scanners at MCW and UW-Madison and the same imaging protocols for structural and rs-fMRI scans as the ECP. The MCW Institutional Review Board has approved the use of human participants for ADCP and the sharing of deidentified data sets from this study.…”

Section: Detecting Temporal Lobe Epilepsy With Machine Learningmentioning

confidence: 99%

Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning

et al. 2019

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The National Institutes of Health-sponsored Epilepsy Connectome Project aims to characterize connectivity changes in temporal lobe epilepsy (TLE) patients. The magnetic resonance imaging protocol follows that used in the Human Connectome Project, and includes 20 min of resting-state functional magnetic resonance imaging acquired at 3T using 8-band multiband imaging. Glasser parcellation atlas was combined with the Free-Surfer subcortical regions to generate resting-state functional connectivity (RSFC), amplitude of low-frequency fluctuations (ALFFs), and fractional ALFF measures. Seven different frequency ranges such as Slow-5 (0.01-0.027 Hz) and Slow-4 (0.027-0.073 Hz) were selected to compute these measures. The goal was to train machine learning classification models to discriminate TLE patients from healthy controls, and to determine which combination of the resting state measure and frequency range produced the best classification model. The samples included age-and gender-matched groups of 60 TLE patients and 59 healthy controls. Three traditional machine learning models were trained: support vector machine, linear discriminant analysis, and naive Bayes classifier. The highest classification accuracy was obtained using RSFC measures in the Slow-4 + 5 band (0.01-0.073 Hz) as features. Leave-one-out cross-validation accuracies were *83%, with receiver operating characteristic area-under-the-curve reaching close to 90%. Increased connectivity from right area posterior 9-46v in TLE patients contributed to the high accuracies. With increased sample sizes in the near future, better machine learning models will be trained not only to aid the diagnosis of TLE, but also as a tool to understand this brain disorder.

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Section: Detecting Temporal Lobe Epilepsy With Machine Learningmentioning

confidence: 99%