ECP Data Collection
The Problem
- A lot of the data was collected improperly
- Seems that this could impact a fair number of publications
Cook, C. J. et al. Effective Connectivity Within the Default Mode Network in Left Temporal Lobe Epilepsy: Findings from the Epilepsy Connectome Project. Brain Connectivity 9, 174–183 (2019).
Hwang, G. et al. Using Low-Frequency Oscillations to Detect Temporal Lobe Epilepsy with Machine Learning. Brain Connectivity 9, 184–193 (2019).
Hwang, G. et al. Cognitive slowing and its underlying neurobiology in temporal lobe epilepsy. Cortex 117, 41–52 (2019).
Hwang, G. et al. Brain aging in temporal lobe epilepsy: Chronological, structural, and functional. Neuroimage Clin 25, 102183 (2020).
Hermann, B. et al. Network, clinical and sociodemographic features of cognitive phenotypes in temporal lobe epilepsy. Neuroimage Clin 27, 102341 (2020).
Bonet, C. N. R. et al. Neuroticism in temporal lobe epilepsy is associated with altered limbic-frontal lobe resting-state functional connectivity. Epilepsy Behav 110, 107172 (2020).
Struck, A. F. et al. Regional and global resting-state functional MR connectivity in temporal lobe epilepsy: Results from the Epilepsy Connectome Project. Epilepsy Behav 117, 107841 (2021).
Size of the Problem
Every post-upgrade Madison session has functional scans collected in the same direction
Madison TLE Patients
38 pre
29 post
Individual Papers
Connectivity, 2019
- looks at differences in connectivity between TLE and HC
- Used Spectral Dynamic Causal Modeling
- Final sample includes 34 participants
- 17 L-TLE and 17 Controls
- Both MCW and UW
Probably okay
Connectivity, 2019
- Goal was to train ML classification models to discriminate TLE patients from healthy controls
- Wanted to know which resting state measure and frequency range produced the best classification model
- Final sample includes 119 participants
- 60 TLE (29 L, 15 R, 4 Bilateral, 12 uncertain)
- Also took 12 controls from ADCP
- Models included SVM, Linear Discriminant, naive bayes
Cortex, 2019
- 55 TLE patients and 58 healthy controls
- All 71,631 connections were tested for their correlation with the age-corrected PCPS scores (using FDR correction)
NeuroImage, 2020
- Support vector regression was used to predict TLE patient brain ages
- 104 TLE patients (151 HC)
NeuroImage, 2020
- Support vector regression was used to predict TLE patient brain ages
- 111 TLE patients (83 HC)
"Resting state fMRI revealed stepwise anomalies as a function of cluster membership"
Unique pairwise correlations were compared in the three clusters using a standard GLM
NeuroImage, 2020
Seems that if this is the case, that global efficiency should separate Generalized-TLE from HC near 100%?
Epilepsy & Behavior, 2020
- 93 people with TLE and 40 HC
- Did ANOVA and then t-tests
Epilepsy & Behavior, 2021
- 102 people with TLE and 55 HC
- Did ANOVA and then t-tests
EC2130
T1w_restore
EC2130
REST1_AP
EC2130
T1w_restore
EC2130
REST1_PA
EC2130
REST1_PA
deck
By smazurchuk
deck
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