Structural-functional coupling changes in temporal lobe epilepsy.

TL;DR: Overall, studying the connectome in focal epilepsy is a critical endeavor that may lead to improved strategies for epileptogenic‐zone localization, surgical outcome prediction, and a better understanding of the neuropsychological implications of recurrent seizures.

TL;DR: It is concluded that multi-modal techniques of neuroimaging are required such as an improvement on methodologies for obtaining structural and functional connectivity, and future models should be multifactorial to elucidate multi-scale relationships and to infer disorder mechanisms.

TL;DR: In an assessment of the relationship between exposure to environmental lead and child development, change-in-estimate (CE) and significance testing (ST) criteria were compared in identifying potential confounders and the CE criterion appears to be more stringent than the ST method.

TL;DR: This study quantifies structure-function coupling, specifically between white matter connections and intracranial EEG, across pre-ictal and ictal periods in 45 seizures from nine patients with unilateral drug-resistant focal epilepsy and suggests that seizures harness the underlying structural connectome as they propagate.

TL;DR: This article seeks to make theorists and researchers aware of the importance of not using the terms moderator and mediator interchangeably by carefully elaborating the many ways in which moderators and mediators differ, and delineates the conceptual and strategic implications of making use of such distinctions with regard to a wide range of phenomena.

TL;DR: It is shown that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation, and an algorithm called LARS‐EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lamba.

TL;DR: An anatomical parcellation of the spatially normalized single-subject high-resolution T1 volume provided by the Montreal Neurological Institute was performed and it is believed that this tool is an improvement for the macroscopical labeling of activated area compared to labeling assessed using the Talairach atlas brain.

TL;DR: The results indicate that for real-word datasets similar to the authors', the best method to use for model selection is ten fold stratified cross validation even if computation power allows using more folds.