An exact permutation method for testing any effect in balanced and unbalanced fixed effect ANOVA

A regression framework for brain network distance metrics

Analyzing brain networks has long been a prominent research topic in neuroimaging. However, statistical methods to detect differences between these networks and relate them to phenotypic traits are still sorely needed. Our previous work developed a novel permutation testing framework to detect differences between two groups. Here we advance that work to allow both assessing differences by continuous phenotypes and controlling for confounding variables. To achieve this, we propose an innovative regression framework to relate distances (or similarities) between brain network features to functions of absolute differences in continuous covariates and indicators of difference for categorical variables. We explore several similarity metrics for comparing distances (or similarities) between connection matrices, and adapt several standard methods for estimation and inference within our framework: standard F test, F test with individual level effects (ILE), feasible generalized least squares (FGLS), and permutation. Via simulation studies, we assess all approaches for estimation and inference while comparing them with existing multivariate distance matrix regression (MDMR) methods. We then illustrate the utility of our framework by analyzing the relationship between fluid intelligence and brain network distances in Human Connectome Project (HCP) data.

The cluster depth tests: Toward point-wise strong control of the family-wise error rate in massively univariate tests with application to M/EEG

The cluster mass test has been widely used for massively univariate tests in M/EEG, fMRI and, recently, pupillometry analysis. It is a powerful method for detecting effects while controlling weakly the family-wise error rate (FWER), although its correct interpretation can only be performed at the cluster level without any point-wise conclusion. It implies that the discoveries of a cluster mass test cannot be precisely localized in time or in space. We propose a new multiple comparisons procedure, the cluster depth tests, that both controls the FWER while allowing an interpretation at the time point level. We show the conditions for a strong control of the FWER, and a simulation study shows that the cluster depth tests achieve large power and guarantee the FWER even in the presence of physiologically plausible effects. By having an interpretation at the time point/voxel level, the cluster depth tests make it possible to take full advantage of the high temporal resolution of EEG recording and give a precise timing of the start and end of the significant effects.

Efficacy of psychotherapy for anxiety reduction in hospital management of women successfully treated for preterm labor: a randomized controlled trial

Preterm labor (PTL) is associated with high anxiety and stress in pregnant women. The study investigated the effect of adding psychotherapy to hospital management of pregnant women with successfully treated for PTLon improving anxiety, pregnancy stress, and perceived control. In a randomized controlled trial study, sixty pregnant women with gestational ages of 24 to 37 weeks and successfully treated forPTL were randomly divided into the experimental (n = 30) and control group (n = 30). The experimental group received psychotherapy along with medical care in individual face-to-facesessions, 1 h per day for 6 consecutive days.The control group received medical care for PTL. Objective outcomes were assessed via the State-Anxiety Inventory, Prenatal Distress Questionnaire (NuPDQ), and Perceived Pregnancy Control at pre-trial and post-trial. The results revealed that psychotherapy significantly improved mean pregnancy distress (45.80 ± 2.29 vs. 43.80 ± 1.93), state anxiety (47.46 ± 5.92 vs. 41.60 ± 5.46), and perceived control (2.86 ± 1.59 vs. 5.26 ± 1.38) in women with PTL from beginning to end of the intervention. The findings suggest that adding psychotherapy to hospital management of women successfully treated for PTLcan be considered a potential method to reduce anxiety and pregnancy-specific stress.

Whole-Language and Item-Specific Inhibition in Bilingual Language Switching: The Role of Domain-General Inhibitory Control

A prominent theory of bilingual speech production holds that appropriate language selection is achieved via inhibitory control. Such inhibition may operate on the whole-language and/or item-specific level. In this study, we examined these two levels of control in parallel, by introducing a novel element into the traditional cued language switching paradigm: half of the stimuli were univalent (each required naming in the same language every time it appeared), and the other half were bivalent (each required naming in different languages on different trials). Contrasting switch and stay trials provided an index for whole-language inhibition, while contrasting bivalent and univalent stimuli provided an index for item-specific inhibition. We then investigated the involvement of domain-general brain mechanisms in these two levels of language control. Neuroimaging studies report activation of the pre-supplementary motor area (pre-SMA), a key region in the executive control brain network, during language switching tasks. However, it is unclear whether or not the pre-SMA plays a causal role in language control, and at which level it exerts control. Using repetitive transcranial magnetic stimulation (TMS) to transiently disrupt the pre-SMA, we observed an essential role of this brain region in general speech execution, while evidence for its specific involvement in each level of inhibition remains inconclusive.

Permutation inference for canonical correlation analysis

Canonical correlation analysis (CCA) has become a key tool for population neuroimaging, allowing investigation of associations between many imaging and non-imaging measurements. As age, sex and other variables are often a source of variability not of direct interest, previous work has used CCA on residuals from a model that removes these effects, then proceeded directly to permutation inference. We show that a simple permutation test, as typically used to identify significant modes of shared variation on such data adjusted for nuisance variables, produces inflated error rates. The reason is that residualisation introduces dependencies among the observations that violate the exchangeability assumption. Even in the absence of nuisance variables, however, a simple permutation test for CCA also leads to excess error rates for all canonical correlations other than the first. The reason is that a simple permutation scheme does not ignore the variability already explained by previous canonical variables. Here we propose solutions for both problems: in the case of nuisance variables, we show that transforming the residuals to a lower dimensional basis where exchangeability holds results in a valid permutation test; for more general cases, with or without nuisance variables, we propose estimating the canonical correlations in a stepwise manner, removing at each iteration the variance already explained, while dealing with different number of variables in both sides. We also discuss how to address the multiplicity of tests, proposing an admissible test that is not conservative, and provide a complete algorithm for permutation inference for CCA.

Mnemonic vulnerability induced by post-activation time-dependent new-learning

Reactivation renders consolidated memory labile again, and the ensuing temporary reconsolidation process is highly susceptible to mnemonic modification. Here, we show that memories in such an unstable state could be influenced by sheer behavioral means, bypassing the need for pharmacological intervention. Across several experiments using a "face-location association" paradigm in which participants experienced a "Learning - New-learning - Final-test" procedure, we demonstrate that reactivated memory traces were hampered when the new learning was strategically administered at between 0-min and 20-min delay. Using fMRI, we further advance our theoretical understanding that this lability can be mechanistically explained by the differential activation in the hippocampal-amygdala memory system implicated by the post-activation new-learning whereas the mnemonic intrusion caused by newly learned memories is efficaciously reconciled by the left inferior frontal gyrus.

The Beneficial Effects of Cognitive Training With Simple Calculation and Reading Aloud (SCRA) in the Elderly Postoperative Population: A Pilot Randomized Controlled Trial

Background: There has been little research conducted regarding cognitive treatments for the elderly postsurgical population. Patients aged ≥60 years have an increased risk of postoperative cognitive decline, a condition in which cognitive functions are negatively affected. This cognitive decline can lead to a decline in quality of life. In order to maintain a high quality of life, the elderly postsurgical population may benefit from treatment to maintain and/or improve their cognitive functions. This pilot study investigates the effect of simple calculation and reading aloud (SCRA) cognitive training in elderly Japanese postsurgical patients.

Methods: Elderly patients undergoing non-cardiovascular thoracic surgery under general anesthesia were recruited (n = 12). Subjects were randomly divided into two groups—one that receives 12 weeks of SCRA intervention, and a waitlisted control group. Before and after the intervention, we measured cognitive function [Mini-Mental Status Exam-Japanese (MMSE-J), Frontal Assessment Battery (FAB), computerized Cogstate Brief Battery (CBB)] and emotional state [General Health Questionnaire-12 (GHQ-12), Geriatric Depression Scale (GDS), Quality of Life Scale-5 (QOL-5)].

Results: Group difference analyses using ANCOVA with permutation test showed that the intervention SCRA group had a significant improvement in FAB motor programming sub-score, GDS, and QOL-5 compared to the control group. Within-group analyses using Wilcoxon signed-rank test to compare baseline and follow-up showed that the SCRA intervention group total FAB scores, FAB motor programming sub-scores, and QOL-5 scores were significantly improved.

Discussion: This pilot study showed that there are important implications for the beneficial effects of SCRA intervention on cognitive function and emotional state in the postoperative elderly population; however, further investigations are necessary to reach any conclusions.

Trial registration: This study was registered with the University Hospital Medical Information Network (UMIN) Clinical Trial Registry (UMIN000019832).

iMap4: An open source toolbox for the statistical fixation mapping of eye movement data with linear mixed modeling

A major challenge in modern eye movement research is to statistically map where observers are looking, by isolating the significant differences between groups and conditions. As compared to the signals from contemporary neuroscience measures, such as magneto/electroencephalography and functional magnetic resonance imaging, eye movement data are sparser, with much larger variations in space across trials and participants. As a result, the implementation of a conventional linear modeling approach on two-dimensional fixation distributions often returns unstable estimations and underpowered results, leaving this statistical problem unresolved (Liversedge, Gilchrist, & Everling, 2011). Here, we present a new version of the iMap toolbox (Caldara & Miellet, 2011) that tackles this issue by implementing a statistical framework comparable to those developed in state-of-the-art neuroimaging data-processing toolboxes. iMap4 uses univariate, pixel-wise linear mixed models on smoothed fixation data, with the flexibility of coding for multiple between- and within-subjects comparisons and performing all possible linear contrasts for the fixed effects (main effects, interactions, etc.). Importantly, we also introduced novel nonparametric tests based on resampling, to assess statistical significance. Finally, we validated this approach by using both experimental and Monte Carlo simulation data. iMap4 is a freely available MATLAB open source toolbox for the statistical fixation mapping of eye movement data, with a user-friendly interface providing straightforward, easy-to-interpret statistical graphical outputs. iMap4 matches the standards of robust statistical neuroimaging methods and represents an important step in the data-driven processing of eye movement fixation data, an important field of vision sciences.

Phase-Amplitude Coupling of the Electroencephalogram in the Auditory Cortex in Schizophrenia

BACKGROUND

Cross-frequency interactions may coordinate neural circuits operating at different frequencies. While neural oscillations associated with particular circuits in schizophrenia (SZ) are impaired, few studies have examined cross-frequency interactions. Here we examined phase-amplitude coupling (PAC) in the electroencephalograms of individuals with SZ and healthy control subjects (HCs). We computed PAC during the baseline period of 40-Hz auditory steady-state stimulation and rest. We hypothesized that subjects with SZ would show abnormal theta/gamma coupling during stimulation, especially in the left auditory cortex, and coupling with high frequencies would be higher during stimulation than during rest.

METHODS

We reanalyzed data from 18 subjects with SZ and 18 HCs. Auditory cortex electroencephalogram activity was estimated using dipole source localization. PAC was computed using the debiased PAC measure, calculated with the generalized Morse wavelet transform. PAC clusters were identified using cluster-corrected permutation testing and interrogated in analyses of variance with correction for multiple tests.

RESULTS

Overall, coupling of high beta and gamma amplitude was higher during the auditory steady-state response, while alpha/beta PAC was higher during rest. Theta/alpha PAC was higher in subjects with SZ than in HCs. Theta/gamma PAC was lateralized to the left hemisphere in HCs but was not lateralized in subjects with SZ.

CONCLUSIONS

PAC involving high frequencies was state dependent and not abnormal in SZ. Increased theta/alpha PAC in subjects with SZ was consistent with other evidence of increased low-frequency activity. Hemispheric lateralization of theta/gamma PAC was reduced in subjects with SZ, consistent with evidence for left hemisphere auditory cortex abnormalities in subjects with SZ. PAC may reveal new insights into neural circuitry abnormalities in SZ and other neuropsychiatric disorders.

Sensitivity Analysis of the MGMT-STP27 Model and Impact of Genetic and Epigenetic Context to Predict the MGMT Methylation Status in Gliomas and Other Tumors

The methylation status of the O(6)-methylguanine-DNA methyltransferase (MGMT) gene is an important predictive biomarker for benefit from alkylating agent therapy in glioblastoma. Our model MGMT-STP27 allows prediction of the methylation status of the MGMT promoter using data from the Illumina's Human Methylation BeadChips (HM-27K and HM-450K) that is publically available for many cancer data sets. Here, we investigate the impact of the context of genetic and epigenetic alterations and tumor type on the classification and report on technical aspects, such as robustness of cutoff definition and preprocessing of the data. The association between gene copy number variation, predicted MGMT methylation, and MGMT expression revealed a gene dosage effect on MGMT expression in lower grade glioma (World Health Organization grade II/III) that in contrast to glioblastoma usually carry two copies of chromosome 10 on which MGMT resides (10q26.3). This implies some MGMT expression, potentially conferring residual repair function blunting the therapeutic effect of alkylating agents. A sensitivity analyses corroborated the performance of the original cutoff for various optimization criteria and for most data preprocessing methods. Finally, we propose an R package mgmtstp27 that allows prediction of the methylation status of the MGMT promoter and calculation of appropriate confidence and/or prediction intervals. Overall, MGMT-STP27 is a robust model for MGMT classification that is independent of tumor type and is adapted for single sample prediction.

Permutation inference for the general linear model

Permutation methods can provide exact control of false positives and allow the use of non-standard statistics, making only weak assumptions about the data. With the availability of fast and inexpensive computing, their main limitation would be some lack of flexibility to work with arbitrary experimental designs. In this paper we report on results on approximate permutation methods that are more flexible with respect to the experimental design and nuisance variables, and conduct detailed simulations to identify the best method for settings that are typical for imaging research scenarios. We present a generic framework for permutation inference for complex general linear models (GLMS) when the errors are exchangeable and/or have a symmetric distribution, and show that, even in the presence of nuisance effects, these permutation inferences are powerful while providing excellent control of false positives in a wide range of common and relevant imaging research scenarios. We also demonstrate how the inference on GLM parameters, originally intended for independent data, can be used in certain special but useful cases in which independence is violated. Detailed examples of common neuroimaging applications are provided, as well as a complete algorithm - the "randomise" algorithm - for permutation inference with the GLM.

A cautionary note on ignoring polygenic background when mapping quantitative trait loci via recombinant congenic strains

In gene mapping, it is common to test for association between the phenotype and the genotype at a large number of loci, i.e., the same response variable is used repeatedly to test a large number of non-independent and non-nested hypotheses. In many of these genetic problems, the underlying model is a mixed model consistent of one or very few major genes concurrently with a genetic background effect, usually thought as of polygenic nature and, consequently, modeled through a random effects term with a well-defined covariance structure dependent upon the kinship between individuals. Either because the interest lies only on the major genes or to simplify the analysis, it is habitual to drop the random effects term and use a simple linear regression model, sometimes complemented with testing via resampling as an attempt to minimize the consequences of this practice. Here, it is shown that dropping the random effects term has not only extreme negative effects on the control of the type I error rate, but it is also unlikely to be fixed by resampling because, whenever the mixed model is correct, this practice does not allow to meet some basic requirements of resampling in a gene mapping context. Furthermore, simulations show that the type I error rates when the random term is ignored can be unacceptably high. As an alternative, this paper introduces a new bootstrap procedure to handle the specific case of mapping by using recombinant congenic strains under a linear mixed model. A simulation study showed that the type I error rates of the proposed procedure are very close to the nominal ones, although they tend to be slightly inflated for larger values of the random effects variance. Overall, this paper illustrates the extent of the adverse consequences of ignoring random effects term due to polygenic factors while testing for genetic linkage and warns us of potential modeling issues whenever simple linear regression for a major gene yields multiple significant linkage peaks.

Brain training game boosts executive functions, working memory and processing speed in the young adults: a randomized controlled trial

BACKGROUND

Do brain training games work? The beneficial effects of brain training games are expected to transfer to other cognitive functions. Yet in all honesty, beneficial transfer effects of the commercial brain training games in young adults have little scientific basis. Here we investigated the impact of the brain training game (Brain Age) on a wide range of cognitive functions in young adults.

METHODS

We conducted a double-blind (de facto masking) randomized controlled trial using a popular brain training game (Brain Age) and a popular puzzle game (Tetris). Thirty-two volunteers were recruited through an advertisement in the local newspaper and randomly assigned to either of two game groups (Brain Age, Tetris). Participants in both the Brain Age and the Tetris groups played their game for about 15 minutes per day, at least 5 days per week, for 4 weeks. Measures of the cognitive functions were conducted before and after training. Measures of the cognitive functions fell into eight categories (fluid intelligence, executive function, working memory, short-term memory, attention, processing speed, visual ability, and reading ability).

RESULTS AND DISCUSSION

Our results showed that commercial brain training game improves executive functions, working memory, and processing speed in young adults. Moreover, the popular puzzle game can engender improvement attention and visuo-spatial ability compared to playing the brain training game. The present study showed the scientific evidence which the brain training game had the beneficial effects on cognitive functions (executive functions, working memory and processing speed) in the healthy young adults.

CONCLUSIONS

Our results do not indicate that everyone should play brain training games. However, the commercial brain training game might be a simple and convenient means to improve some cognitive functions. We believe that our findings are highly relevant to applications in educational and clinical fields.

TRIAL REGISTRATION

UMIN Clinical Trial Registry 000005618.

Forest restoration in a fog oasis: evidence indicates need for cultural awareness in constructing the reference

Background

In the Peruvian Coastal Desert, an archipelago of fog oases, locally called lomas, are centers of biodiversity and of past human activity. Fog interception by a tree canopy, dominated by the legume tree tara (Caesalpinia spinosa), enables the occurrence in the Atiquipa lomas (southern Peru) of an environmental island with a diverse flora and high productivity. Although this forest provides essential services to the local population, it has suffered 90% anthropogenic reduction in area. Restoration efforts are now getting under way, including discussion as to the most appropriate reference ecosystem to use.

Methodology/Principal Findings

Genetic diversity of tara was studied in the Atiquipa population and over a wide geographical and ecological range. Neither exclusive plastid haplotypes to loma formations nor clear geographical structuring of the genetic diversity was found. Photosynthetic performance and growth of seedlings naturally recruited in remnant patches of loma forest were compared with those of seedlings recruited or planted in the adjacent deforested area. Despite the greater water and nitrogen availability under tree canopy, growth of forest seedlings did not differ from that of those recruited into the deforested area, and was lower than that of planted seedlings. Tara seedlings exhibited tight stomatal control of photosynthesis, and a structural photoprotection by leaflet closure. These drought-avoiding mechanisms did not optimize seedling performance under the conditions produced by forest interception of fog moisture.

Conclusions/Significance

Both weak geographic partitioning of genetic variation and lack of physiological specialization of seedlings to the forest water regime strongly suggest that tara was introduced to lomas by humans. Therefore, the most diverse fragment of lomas is the result of landscape management and resource use by pre-Columbian cultures. We argue that an appropriate reference ecosystem for ecological restoration of lomas should include sustainable agroforestry practices that emulate the outcomes of ancient uses.