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. Here, we report the effect of Deep TMS on the microstructure of white matter. METHODS: A total of 37 (14 females) AUD treatment-seeking patients were randomized to sham or active Deep TMS. Twenty (six females) age-matched healthy controls were included. White matter integrity was evaluated by fractional anisotropy (FA). Secondary measures included brain functional connectivity and self-reports of craving and drinking units in the 3 months of follow-up period. RESULTS: White matter integrity was compromised in patients with AUD relative to healthy controls, as reflected by the widespread reduction in FA. This alteration progressed during early abstinence (3 weeks) in the absence of Deep TMS. However, stimulation of midline frontocortical areas arrested the progression of FA changes in association with decreased craving and relapse scores. Reconstruction of axonal tracts from white-matter regions showing preserved FA values identified cortical regions in the posterior cingulate and dorsomedial prefrontal cortices where functional connectivity was persistently modulated. These effects were absent in the sham-stimulated group. CONCLUSIONS: By integrating brain structure and function to characterize the alcohol-dependent brain, this study provides mechanistic insights into the TMS effect, pointing to myelin plasticity as a possible mediator.

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. METHODS: This was a double-blind, randomized, sham-controlled trial. A total of 51 recently abstinent treatment-seeking patients with alcohol use disorder (moderate to severe) were randomized to sham or active dTMS, using an H7 coil targeting midline frontocortical areas, including the medial prefrontal and anterior cingulate cortices. Treatment included 15 sessions over 3 weeks, followed by five sessions over 3 months of follow-up. Each session delivered 100 trains of 30 pulses at 10 Hz. The primary predefined outcome was reduction in percentage of heavy drinking days, obtained using timeline follow-back interviews. Secondary analyses included self-reports of craving, ethyl glucuronide in urine, and brain imaging measures. RESULTS: Both craving after treatment and percentage of heavy drinking days during follow-up were significantly lower in the active versus sham control group (percentage of heavy drinking days = 2.9 ± 0.8% vs. 10.6 ± 1.9%, p = .037). Active dTMS was associated with decreased resting-state functional connectivity of the dorsal anterior cingulate cortex with the caudate nucleus and decreased connectivity of the medial prefrontal cortex to the subgenual anterior cingulate cortex. CONCLUSIONS: We provide initial proof-of-concept for dTMS targeting midline frontocortical structures as a treatment for alcohol addiction. These data strongly support a rationale for a full-scale confirmatory multicenter trial. Therapeutic benefits of dTMS appear to be associated with persistent changes in brain network activity.

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. The second algorithm does not modify structure, but only the parameter (thresholds) associated with decision nodes. We also propose to combine both methods by considering an ensemble that contains the union of the two forests. The proposed methods exhibit impressive experimental results over a range of problems.

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. In the first part of this review, we discuss the number sense theory. Against this background, we demonstrate how the natural correlation between numerosities and continuous magnitudes makes it nearly impossible to study non-symbolic numerosity processing in isolation from continuous magnitudes, and therefore, the results of behavioral and imaging studies with infants, adults, and animals can be explained, at least in part, by relying on continuous magnitudes. In the second part, we explain the sense of magnitude theory and review studies that directly demonstrate that continuous magnitudes are more automatic and basic than numerosities. Finally, we present outstanding questions. Our conclusion is that there is not enough convincing evidence to support the number sense theory anymore. Therefore, we encourage researchers not to assume that number sense is simply innate, but to put this hypothesis to the test and consider whether such an assumption is even testable in the light of the correlation of numerosity and continuous magnitudes.

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. We provide bounds on the convergence of the estimated score to its distributional value, as well as robustness analysis of the PV to outliers. A number of possible applications of the score are presented, and its ability to detect similarity is compared with that of other known measures on real data. We also present a new visual tracking algorithm based on the PV, and compare its performance with known tracking algorithms.