Back

In vivo Characterization of MRI-based T1w/T2w Ratios and Covariance Network in Temporal Lobe Epilepsy

Jiang, Y.; Li, W.; Qin, Y.; Zhang, L.; Tong, X.; Xiao, F.; Gong, Q.; Zhou, D.; Yao, D.; An, D.; Luo, C.

2020-12-04 radiology and imaging
10.1101/2020.12.03.20243238 medRxiv
Show abstract

Temporal lobe epilepsy (TLE) is the most common type of intractable epilepsy in adults. A novel method based on the ratio of T1-weighted (T1w) and T2-weighted (T2w) magnetic resonance images can investigate brain microstructural changes and how these regional changes interact with each other. This study estimated T1w/T2w ratios in 42 left TLE (LTLE) and 42 right TLE (RTLE) patients and 41 healthy controls (HC). A T1w/T2w structural covariance network (SCN) was built by calculating correlations between any two regions across subjects and analysed by graph theory. Voxel-wise comparisons of T1w/T2w laterality were performed among the three groups. Compared to HC, both patient groups showed decreased T1w/T2w in frontotemporal regions, amygdala and thalamus; however, the LTLE showed lower T1w/T2w in left medial temporal regions than RTLE. Moreover, the LTLE exhibited decreased global efficiency compared with HC and more increased connections than RTLE. The laterality in putamen was differently altered between the two patient groups: higher laterality at posterior putamen in LTLE and higher laterality at anterior putamen in RTLE. This study demonstrated T1w/T2w reductions in frontotemporal and subcortical regions and extensive disconnections of SCN, providing evidence that TLE is a system disorder with widespread disruptions at regional and network levels. The putamen may play a transfer station role in damage spreading induced by epileptic seizures from the hippocampus.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.