Behavior, sensitivity, and power of activation likelihood estimation characterized by massive empirical simulation

Given the increasing number of neuroimaging publications, the automated knowledge extraction on brain-behavior associations by quantitative meta-analyses has become a highly important and rapidly growing field of research. Among several methods to perform coordinate-based neuroimaging meta-analyses, Activation Likelihood Estimation (ALE) has been widely adopted. In this paper, we addressed two pressing questions related to ALE meta-analysis: i) Which thresholding method is most appropriate to perform statistical inference? ii) Which sample size, i.e., number of experiments, is needed to perform robust meta-analyses? We provided quantitative answers to these questions by simulating more than 120,000 meta-analysis datasets using empirical parameters (i.e., number of subjects, number of reported foci, distribution of activation foci) derived from the BrainMap database. This allowed to characterize the behavior of ALE analyses, to derive first power estimates for neuroimaging meta-analyses, and to thus formulate recommendations for future ALE studies. We could show as a first consequence that cluster-level family-wise error (FWE) correction represents the most appropriate method for statistical inference, while voxel-level FWE correction is valid but more conservative. In contrast, uncorrected inference and false-discovery rate correction should be avoided. As a second consequence, researchers should aim to include at least 20 experiments into an ALE meta-analysis to achieve sufficient power for moderate effects. We would like to note, though, that these calculations and recommendations are specific to ALE and may not be extrapolated to other approaches for (neuroimaging) meta-analysis

Active network management for electrical distribution systems: problem formulation, benchmark, and approximate solution

With the increasing share of renewable and distributed generation in electrical distribution systems, Active Network Management (ANM) becomes a valuable option for a distribution system operator to operate his system in a secure and cost-effective way without relying solely on network reinforcement. ANM strategies are short-term policies that control the power injected by generators and/or taken off by loads in order to avoid congestion or voltage issues. Advanced ANM strategies imply that the system operator has to solve large-scale optimal sequential decision-making problems under uncertainty. For example, decisions taken at a given moment constrain the future decisions that can be taken and uncertainty must be explicitly accounted for because neither demand nor generation can be accurately forecasted. We first formulate the ANM problem, which in addition to be sequential and uncertain, has a nonlinear nature stemming from the power flow equations and a discrete nature arising from the activation of power modulation signals. This ANM problem is then cast as a stochastic mixed-integer nonlinear program, as well as second-order cone and linear counterparts, for which we provide quantitative results using state of the art solvers and perform a sensitivity analysis over the size of the system, the amount of available flexibility, and the number of scenarios considered in the deterministic equivalent of the stochastic program. To foster further research on this problem, we make available at http://www.montefiore.ulg.ac.be/~anm/ three test beds based on distribution networks of 5, 33, and 77 buses. These test beds contain a simulator of the distribution system, with stochastic models for the generation and consumption devices, and callbacks to implement and test various ANM strategies

Centrality of prefrontal and motor preparation cortices to Tourette Syndrome revealed by meta-analysis of task-based neuroimaging studies

Tourette Syndrome (TS) is a neurodevelopmental condition characterized by chronic multiple tics, which are experienced as compulsive and ‘unwilled’. Patients with TS can differ markedly in the frequency, severity, and bodily distribution of tics. Moreover, there are high comorbidity rates with attention deficit hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), anxiety disorders, and depression. This complex clinical profile may account for apparent variability of findings across neuroimaging studies that connect neural function to cognitive and motor behavior in TS. Here we crystalized information from neuroimaging regarding the functional circuitry of TS, and furthermore, tested specifically for neural determinants of tic severity, by applying activation likelihood estimation (ALE) meta-analyses of neuroimaging (activation) studies of TS. Fourteen task-based studies (13 fMRI and one H2O-PET) met rigorous inclusion criteria. These studies, encompassing 25 experiments and 651 participants, tested for differences between TS participants and healthy controls across cognitive, motor, perceptual and somatosensory domains. Relative to controls, TS participants showed distributed differences in the activation of prefrontal (inferior, middle, and superior frontal gyri), anterior cingulate, and motor preparation cortices (lateral premotor cortex and supplementary motor area; SMA). Differences also extended into sensory (somatosensory cortex and the lingual gyrus; V4); and temporo-parietal association cortices (posterior superior temporal sulcus, supramarginal gyrus, and retrosplenial cortex). Within TS participants, tic severity (reported using the Yale Global Tic Severity Scale; YGTSS) selectively correlated with engagement of SMA, precentral gyrus, and middle frontal gyrus across tasks. The dispersed involvement of multiple cortical regions with differences in functional reactivity may account for heterogeneity in the symptomatic expression of TS and its comorbidities. More specifically for tics and tic severity, the findings reinforce previously proposed contributions of premotor and lateral prefrontal cortices to tic expression

A meta-analysis of fMRI studies of language comprehension in children

The neural representation of language comprehension has been examined in several meta-analyses of fMRI studies with human adults. To complement this work from a developmental perspective, we conducted a meta-analysis of fMRI studies of auditory language comprehension in human children. Our analysis included 27 independent experiments involving n = 625 children (49% girls) with a mean age of 8.9 years. Activation likelihood estimation and seed-based effect size mapping revealed activation peaks in the pars triangularis of the left inferior frontal gyrus and bilateral superior and middle temporal gyri. In contrast to this distribution of activation in children, previous work in adults found activation peaks in the pars opercularis of the left inferior frontal gyrus and more left-lateralized temporal activation peaks. Accordingly, brain responses during language comprehension may shift from bilateral temporal and left pars triangularis peaks in childhood to left temporal and pars opercularis peaks in adulthood. This shift could be related to the gradually increasing sensitivity of the developing brain to syntactic information

Research Review: Neural response to threat in children, adolescents, and adults after child maltreatment – a quantitative meta‐analysis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136280/1/jcpp12651_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136280/2/jcpp12651.pd

Infinite Factorial Finite State Machine for Blind Multiuser Channel Estimation

New communication standards need to deal with machine-to-machine communications, in which users may start or stop transmitting at any time in an asynchronous manner. Thus, the number of users is an unknown and time-varying parameter that needs to be accurately estimated in order to properly recover the symbols transmitted by all users in the system. In this paper, we address the problem of joint channel parameter and data estimation in a multiuser communication channel in which the number of transmitters is not known. For that purpose, we develop the infinite factorial finite state machine model, a Bayesian nonparametric model based on the Markov Indian buffet that allows for an unbounded number of transmitters with arbitrary channel length. We propose an inference algorithm that makes use of slice sampling and particle Gibbs with ancestor sampling. Our approach is fully blind as it does not require a prior channel estimation step, prior knowledge of the number of transmitters, or any signaling information. Our experimental results, loosely based on the LTE random access channel, show that the proposed approach can effectively recover the data-generating process for a wide range of scenarios, with varying number of transmitters, number of receivers, constellation order, channel length, and signal-to-noise ratio.Comment: 15 pages, 15 figure