Evidence-based umbrella review of cognitive effects of prefrontal tDCS

Abstract Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique, which has been increasingly used as an investigational tool in neuroscience. In social and affective neuroscience research, the prefrontal cortex has been primarily targeted, since this brain region is critically involved in complex psychobiological processes subserving both 'hot' and 'cold' domains. Although several studies have suggested that prefrontal tDCS can enhance neuropsychological outcomes, meta-analyses have reported conflicting results. Therefore, we aimed to assess the available evidence by performing an umbrella review of meta-analyses. We evaluated the effects of prefrontal active vs sham tDCS on different domains of cognition among healthy and neuropsychiatric individuals. A MeaSurement Tool to Assess Systematic Reviews 2 was employed to evaluate the quality of meta-analyses, and the GRADE system was employed to grade the quality of evidence of every comparison from each meta-analysis. PubMed/MEDLINE, PsycINFO and the Cochrane Database of Systematic Reviews were searched, and 11 meta-analyses were included resulting in 55 comparisons. Only 16 comparisons reported significant effects favoring tDCS, but 13 of them had either very low or low quality of evidence. Of the remaining 39 comparisons which reported non-significant effects, 38 had either very low or low quality of evidence. Meta-analyses were rated as having critically low and low quality. Among several reasons to explain these findings, the lack of consensus and reproducibility in tDCS research is discussed

Transcranial direct current stimulation as an add-on treatment to cognitive-behavior therapy in first episode drug-naive major depression patients: the ESAP study protocol

Background: Major Depressive Disorder (MDD) affects more than 264 million people worldwide. Current treatments include the use of psychotherapy and/or drugs, however similar to 30% of patients either do not respond to these treatments, or do not tolerate the side effects associated to the use of pharmacological interventions. Thus, it is important to study non-pharmacological interventions targeting mechanisms not directly involved with the regulation of neurotransmitters. Several studies demonstrated that transcranial Direct Current Stimulation (tDCS) can be effective for symptoms relief in MDD. However, tDCS seems to have a better effect when used as an add-on treatment to other interventions.Methods/Design: This is a study protocol for a parallel, randomized, triple-blind, sham-controlled clinical trial in which a total of 90 drug-naive, first-episode MDD patients (45 per arm) will be randomized to one of two groups to receive a 6-weeks of CBT combined with either active or sham tDCS to the dorsolateral prefrontal cortex (DLPFC). The primary outcome will depressive symptoms improvement as assessed by the Montgomery-Asberg Depression Rating Scale (MADRS) at 6-weeks. The secondary aim is to test whether CBT combined with tDCS can engage the proposed mechanistic target of restoring the prefrontal imbalance and connectivity through the bilateral modulation of the DLPFC, as assessed by changes over resting-state and emotional task eliciting EEG.Discussion: This study evaluates the synergetic clinical effects of CBT and tDCS in the first episode, drug-naive, patients with MDD. First episode MDD patients provide an interesting opportunity, as their brains were not changed by the pharmacological treatments, by the time course, or by the recurrence of MDD episodes (and other comorbidities).This work was partially supported by FEDER funds through the Programa Operacional Factores de Competitividade-COMPETE and by national funds through FCT-Fundacao para a Ciencia e a Tecnologia through the calls IF/00091/2015 and PTDC/PSI-ESP/29701/2017. The sponsors had no role in the study design, implementation, data analysis or publication

A Hybrid Heuristic for the k-medoids Clustering Problem

Clustering is an important tool for data analysis, since it allows the exploration of datasets with no or very little prior information. Its main goal is to group a set of data based on their similarity (dissimilarity). A well known mathematical formulation for clustering is the k-medoids problem. Current versions of k-medoids rely on heuristics, with good results reported in the literature. However, few methods that analyze the quality of the partitions found by the heuristics have been proposed. in this paper, we propose a hybrid Lagrangian heuristic for the k-medoids. We compare the performance of the proposed Lagrangian heuristic with other heuristics for the k-medoids problem found in literature. Experimental results presented that the proposed Lagrangian heuristic outperformed the other algorithms.UNIFESP, Inst Ciencia & Tecnol, BR-12230280 Sao Jose Dos Campos, SP, BrazilUNIFESP, Inst Ciencia & Tecnol, BR-12230280 Sao Jose Dos Campos, SP, BrazilWeb of Scienc

Coarse graining a non-Markovian collisional model

The dynamics of systems subjected to noise is called Markovian in the absence of memory effects, i.e., when its immediate future depends only on its present. Time correlations in the noise source may generate non-Markovian effects that, sometimes, can be erased by appropriately coarse graining the time evolution of the system. In general, the coarse-graining time tCG is taken to be much larger than the correlation time τ but no direct relation between them is established. Here we analytically obtain a relation between tCG and τ for the dynamics of a qubit subjected to a time-correlated environment. Our results can be applied in principle to any distribution of the environmental correlations and can be tested through a collisional model where the qubit sequentially interacts with correlated qutritsThis work is part of the INCT-IQ from CNPq and also part of the Australian Research Council Centre of Excellence for Quantum Computation and Communication Technology (Project No. CE110001027)

The NoiseFiltersR Package: Label Noise Preprocessing in R

In Data Mining, the value of extracted knowledge is directly related to the quality of the used data. This makes data preprocessing one of the most important steps in the knowledge discovery process. A common problem affecting data quality is the presence of noise. A training set with label noise can reduce the predictive performance of classification learning techniques and increase the overfitting of classification models. In this work we present the NoiseFiltersR package. It contains the first extensive R implementation of classical and state-of-the-art label noise filters, which are the most common techniques for preprocessing label noise. The algorithms used for the implementation of the label noise filters are appropriately documented and referenced. They can be called in a R-user-friendly manner, and their results are unified by means of the "filter" class, which also benefits from adapted print and summary methods.Spanish Research ProjectAndalusian Research PlanBrazilian grant-CeMEAI-FAPESPFAPESPUniv Granada, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, SpainUniv Sao Paulo, Inst Ciencias Matemat & Comp, Trabalhador Sao Carlense Av 400, BR-13560970 Sao Carlos, SP, BrazilUniv Fed Sao Paulo, Inst Ciencia & Tecnol, Talim St 330, BR-12231280 Sao Jose Dos Campos, SP, BrazilUniv Fed Sao Paulo, Inst Ciencia & Tecnol, Talim St 330, BR-12231280 Sao Jose Dos Campos, SP, BrazilSpanish Research Project: TIN2014-57251-PAndalusian Research Plan: P11-TIC-7765CeMEAI-FAPESP: 2013/07375-0FAPESP: 2012/22608-8FAPESP: 2011/14602-7Web of Scienc

Mitochondrial genetic haplogroups and depressive symptoms: A large study among people in North America

Background: A possible relationship between mitochondrial haplogroups and psychiatric diseases (e.g. schizophrenia and bipolar disorder) has been postulated, but data regarding depression is still limited. We investigated whether any mitochondrial haplogroup carried a significant higher risk of depressive symptoms in a large prospective cohort of North American people included in the Osteoarthritis Initiative. Methods: Cross sectional data was derived from the Osteoarthritis Initiative. The haplogroup was assigned through a combination of sequencing and PCR-RFLP techniques. All the mitochondrial haplogroups were named following this nomenclature: H, U, K, J, T, V, SuperHV, I, W, X or Others. Depression was ascertained through the 20-item Center for Epidemiologic Studies- Depression (CES-D), with >16 indicating depressive symptoms. Results: Overall, 3,601 Caucasian participants (55.9% women), mean age of 61.7±9.3 years were included. No difference was observed in mitochondrial haplogroups frequency among those with depressive symptoms (n=285, =7.9% of the baseline population) compared to participants with no depressive symptoms (N=3,316) (chi-square test=0.53). Using a logistic regression analysis, adjusted for eight potential confounders, with those having the haplogroup H as the reference group (the most common haplogroup), no significant mitochondrial haplogroup was associated with prevalent depressive symptoms. The same results were evident in secondary analysis in which we matched depressed and non-depressed participants for age and sex. Limitations: cross-sectional design; only CES-D for evaluating mood; participants not totally representative of general population. Conclusions: We found no evidence of any relationship between specific mitochondrial haplogroups and depressive symptoms. Future longitudinal research is required to confirm/ refute these findings