Repetitive deep TMS in alcohol dependent patients halts progression of white matter changes in early abstinence.

Aim: Alcohol use disorder (AUD) is the most prevalent form of addiction, with a great burden on society and limited treatment options. A recent clinical trial reported significant clinical benefits of deep transcranial magnetic stimulations (Deep TMS) targeting midline frontocortical areas. However, the underlying biological substrate remained elusive.

Repetitive Transcranial Magnetic Stimulation in Alcohol Dependence: A Randomized, Double-Blind, Sham-Controlled Proof-of-Concept Trial Targeting the Medial Prefrontal and Anterior Cingulate Cortices.

Background: Alcohol addiction is associated with a high disease burden, and treatment options are limited. In a proof-of-concept study, we used deep repetitive transcranial magnetic stimulation (dTMS) to target circuitry associated with the pathophysiology of alcohol addiction. We evaluated clinical outcomes and explored associated neural signatures using functional magnetic resonance imaging.

Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests.

We propose novel model transfer-learning methods that refine a decision forest model M learned within a "source" domain using a training set sampled from a "target" domain, assumed to be a variation of the source. We present two random forest transfer algorithms. The first algorithm searches greedily for locally optimal modifications of each tree structure by trying to locally expand or reduce the tree around individual nodes.

From "sense of number" to "sense of magnitude": The role of continuous magnitudes in numerical cognition.

In this review, we are pitting two theories against each other: the more accepted theory, the number sense theory, suggesting that a sense of number is innate and non-symbolic numerosity is being processed independently of continuous magnitudes (e.g., size, area, and density); and the newly emerging theory suggesting that (1) both numerosities and continuous magnitudes are processed holistically when comparing numerosities and (2) a sense of number might not be innate.

The Perturbed Variation.

We introduce a new discrepancy measure between two distributions that gives an indication on their similarity. The new measure, termed the Perturbed Variation (PV), gives an intuitive interpretation of similarity; it optimally perturbs the distributions so that they best fit each other. The PV is defined between continuous and discrete distributions, and can be efficiently estimated from samples.