Statistical nonparametric mapping: Multivariate permutation tests for location, correlation, and regression problems in neuroimaging

Transcallosal white matter and cortical gray matter variations in autistic adults ages 30-73 years: A bi-tensor free water imaging approach

Background: Autism spectrum disorder (ASD) has long been recognized as a lifelong condition, but brain aging studies in autistic adults aged >30 years are limited. Free water, a novel brain imaging marker derived from diffusion MRI (dMRI), has shown promise in differentiating typical and pathological aging and monitoring brain degeneration. We aimed to examine free water and free water corrected dMRI measures to assess white and gray matter microstructure and their associations with age in autistic adults. Methods: Forty-three autistic adults ages 30-73 years and 43 age, sex, and IQ matched neurotypical controls participated in this cross-sectional study. We quantified fractional anisotropy (FA), free water, and free water-corrected FA (fwcFA) across 32 transcallosal white matter tracts and 94 gray matter areas in autistic adults and neurotypical controls. Follow-up analyses assessed age effect on dMRI metrics of the whole brain for both groups and the relationship between dMRI metrics and clinical measures of ASD in regions that significantly differentiated autistic adults from controls. Results: We found globally elevated free water in 24 transcallosal tracts in autistic adults. We identified negligible differences in dMRI metrics in gray matter between the two groups. Age-associated FA reductions and free water increases were featured in neurotypical controls; however, this brain aging profile was largely absent in autistic adults. Additionally, greater autism quotient (AQ) total raw score was associated with increased free water in the inferior frontal gyrus pars orbitalis and lateral orbital gyrus in autistic adults. Limitations: All autistic adults were cognitively capable individuals, minimizing the generalizability of the research findings across the spectrum. This study also involved a cross-sectional design, which limited inferences about the longitudinal microstructural changes of white and gray matter in ASD. Conclusions: We identified differential microstructural configurations between white and gray matter in autistic adults and that autistic individuals present more heterogeneous brain aging profiles compared to controls. Our clinical correlation analysis offered new evidence that elevated free water in some localized white matter tracts may critically contribute to autistic traits in ASD. Our findings underscored the importance of quantifying free water in dMRI studies of ASD.

Turgor loss point explains climate-driven growth reductions in trees in Central Europe

As climate change thrives, and the frequency of intense droughts is affecting many forested regions, a mechanistic understanding of the factors conferring drought tolerance in trees is increasingly important. However, studies linking the observed growth reduction to mechanistic traits are still rare. We compared the median growth anomalies of 16 native tree species, gathered across a network of study plots in Bavaria, with the mean species-specific turgor loss point (πtlp) measured at five locations in Central Europe πtlp explained 37% of the growth anomalies observed in response to the intense droughts between 2018 and 2020 compared to the pre-drought period between 2006 and 2017 across sites. πtlp constitutes an important leaf drought tolerance trait and influences the growth response of native tree species during extraordinary dry periods. As climate change-induced droughts intensify, tree species with drought-tolerant leaves will be less vulnerable to growth reductions. πtlp provides a useful indicator for selecting tree species to adapt forest management systems to climate change.

Assessing the cardiac autonomic response to bicycle exercise in Olympic athletes with different loads of endurance training: new insights from statistical indicators based on multilevel exploratory factor analysis

Aim: The mechanisms governing the organism's response to exercise are complex and difficult to study. Spectral analysis of heart rate variability (HRV) could represent a convenient methodology for studying humans' autonomic nervous system (ANS). However, difficulties in interpreting the multitude of correlated HRV-derived indices, mainly when computed over different time segments, may represent a barrier to its usage. This preliminary investigation addressed to elite athletes proposes a novel method describing the cardiac autonomic response to exercise based on multilevel exploratory factor analysis (MEFA), which reduces the multitude of HRV-derived indices to fewer uncorrelated ANS indicators capable of accounting for their interrelationships and overcoming the above difficulties. Methods: The study involved 30 Italian Olympic athletes, divided into 15 cyclists (prevalent high-intensity endurance training) and 15 shooters (prevalent technical training with low-intensity endurance component). All athletes underwent a complete test of a dynamic protocol, constituted by a rest-stand test followed by a stepwise bicycle stress test subdivided into a single bout of progressive endurance (from aerobic to anaerobic) exercise and recovery. Then, by spectral analysis, values of 12 ANS proxies were computed at each time segment (9 epochs in all) of the complete test. Results: We obtained two global ANS indicators (amplitude and frequency), expressing the athletes' overall autonomic response to the complete test, and three dynamic ANS indicators (amplitude, signal self-similarity, and oscillatory), describing the principal dynamics over time of the variability of RR interval (RRV). Globally, cyclists have significantly higher amplitude levels (median ± MAD: cyclists 69.9 ± 20.5; shooters 37.2 ± 19.4) and lower frequency levels (median ± MAD: cyclists 37.4 ± 14.8; shooters 78.2 ± 10.2) than shooters, i.e., a parasympathetic predominance compared to shooters. Regarding the RRV dynamics, the signal self-similarity and oscillatory indicators have the strongest sensitivity in detecting the rest-stand change; the amplitude indicator is highly effective in detecting the athletes' autonomic changes in the exercise fraction; the amplitude and oscillatory indicators present significant differences between cyclists and shooters in specific test epochs. Conclusion: This MEFA application permits a more straightforward representation of the complexity characterizing ANS modulation during exercise, simplifying the interpretation of the HRV-derived indices and facilitating the possible real-life use of this non-invasive methodology.

Prediction of moment-by-moment heart rate and skin conductance changes in the context of varying emotional arousal

Autonomic nervous system (ANS) responses such as heart rate (HR) and galvanic skin responses (GSR) have been linked with cerebral activity in the context of emotion. Although much work has focused on the summative effect of emotions on ANS responses, their interaction in a continuously changing context is less clear. Here, we used a multimodal data set of human affective states, which includes electroencephalogram (EEG) and peripheral physiological signals of participants' moment-by-moment reactions to emotional provoking video clips and modeled HR and GSR changes using machine learning techniques, specifically, long short-term memory (LSTM), decision tree (DT), and linear regression (LR). We found that LSTM achieved a significantly lower error rate compared with DT and LR due to its inherent ability to handle sequential data. Importantly, the prediction error was significantly reduced for DT and LR when used together with particle swarm optimization to select relevant/important features for these algorithms. Unlike summative analysis, and contrary to expectations, we found a significantly lower error rate when the prediction was made across different participants than within a participant. Moreover, the predictive selected features suggest that the patterns predictive of HR and GSR were substantially different across electrode sites and frequency bands. Overall, these results indicate that specific patterns of cerebral activity track autonomic body responses. Although individual cerebral differences are important, they might not be the only factors influencing the moment-by-moment changes in ANS responses.

Permutation-based true discovery proportions for functional magnetic resonance imaging cluster analysis

We propose a permutation-based method for testing a large collection of hypotheses simultaneously. Our method provides lower bounds for the number of true discoveries in any selected subset of hypotheses. These bounds are simultaneously valid with high confidence. The methodology is particularly useful in functional Magnetic Resonance Imaging cluster analysis, where it provides a confidence statement on the percentage of truly activated voxels within clusters of voxels, avoiding the well-known spatial specificity paradox. We offer a user-friendly tool to estimate the percentage of true discoveries for each cluster while controlling the family-wise error rate for multiple testing and taking into account that the cluster was chosen in a data-driven way. The method adapts to the spatial correlation structure that characterizes functional Magnetic Resonance Imaging data, gaining power over parametric approaches.

Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognition

Despite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based functional magnetic resonance imaging (fMRI). Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n = 3,989). We compared 9 predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and ordinary least squares (OLS) multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than nonlinear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects, and Friedman's H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.

Robust Permutation Tests for Penalized Splines

Penalized splines are frequently used in applied research for understanding functional relationships between variables. In most applications, statistical inference for penalized splines is conducted using the random effects or Bayesian interpretation of a smoothing spline. These interpretations can be used to assess the uncertainty of the fitted values and the estimated component functions. However, statistical tests about the nature of the function are more difficult, because such tests often involve testing a null hypothesis that a variance component is equal to zero. Furthermore, valid statistical inference using the random effects or Bayesian interpretation depends on the validity of the utilized parametric assumptions. To overcome these limitations, I propose a flexible and robust permutation testing framework for inference with penalized splines. The proposed approach can be used to test omnibus hypotheses about functional relationships, as well as more flexible hypotheses about conditional relationships. I establish the conditions under which the methods will produce exact results, as well as the asymptotic behavior of the various permutation tests. Additionally, I present extensive simulation results to demonstrate the robustness and superiority of the proposed approach compared to commonly used methods.

Navigating the Statistical Minefield of Model Selection and Clustering in Neuroscience

Model selection is often implicit: when performing an ANOVA, one assumes that the normal distribution is a good model of the data; fitting a tuning curve implies that an additive and a multiplicative scaler describes the behavior of the neuron; even calculating an average implicitly assumes that the data were sampled from a distribution that has a finite first statistical moment: the mean. Model selection may be explicit, when the aim is to test whether one model provides a better description of the data than a competing one. As a special case, clustering algorithms identify groups with similar properties within the data. They are widely used from spike sorting to cell type identification to gene expression analysis. We discuss model selection and clustering techniques from a statistician's point of view, revealing the assumptions behind, and the logic that governs the various approaches. We also showcase important neuroscience applications and provide suggestions how neuroscientists could put model selection algorithms to best use as well as what mistakes should be avoided.

Exploring the Representations of Individual Entities in the Brain Combining EEG and Distributional Semantics

Semantic knowledge about individual entities (i.e., the referents of proper names such as Jacinta Ardern) is fine-grained, episodic, and strongly social in nature, when compared with knowledge about generic entities (the referents of common nouns such as politician). We investigate the semantic representations of individual entities in the brain; and for the first time we approach this question using both neural data, in the form of newly-acquired EEG data, and distributional models of word meaning, employing them to isolate semantic information regarding individual entities in the brain. We ran two sets of analyses. The first set of analyses is only concerned with the evoked responses to individual entities and their categories. We find that it is possible to classify them according to both their coarse and their fine-grained category at appropriate timepoints, but that it is hard to map representational information learned from individuals to their categories. In the second set of analyses, we learn to decode from evoked responses to distributional word vectors. These results indicate that such a mapping can be learnt successfully: this counts not only as a demonstration that representations of individuals can be discriminated in EEG responses, but also as a first brain-based validation of distributional semantic models as representations of individual entities. Finally, in-depth analyses of the decoder performance provide additional evidence that the referents of proper names and categories have little in common when it comes to their representation in the brain.

Innate Muscle Patterns Reproduction During Afferent Somatosensory Input With Vojta Therapy in Healthy Adults. A Randomized Controlled Trial

OBJECTIVE

Vojta therapy describes stereotypic widespread motor responses as a pattern of tonic muscle contractions during a peripherical pressure stimulation. The present work proposes to characterize the responses at muscles level to a specific tactile input based on Vojta therapy, assessed by sEMG, compared to a sham stimulation in healthy subjects.

METHODS

Surface electromyography (sEMG) signal was acquired with dipolar electrodes placed at wrist extensors of both forearms, right tibialis anterior, and top part of rectus abdominus, ground channel placed over the right olecranon. It was amplified and digitized by a 4-channel hub Biosignalsplux device (Plux Wireless Biosignals S.A., Lisboa, Portugal), sampled at 1000 Hz with 16-bit per channel. A continuous 10-minute record of the sEMG signal from the four electrodes were registered. Resting EEG during the first minute before the stimulation period was recorded by 64 active electrodes.

RESULTS

Statistically significant differences were showed between sham and experimental group. Experimental group participants were subjected to cluster analysis based on their muscle activation patterns, generating three different models of activation. Differences in the previous resting cortical activity in left superior frontal area were found between clusters that activated limb muscles and the cluster that did not.

CONCLUSIONS

Vojta specific stimulation area activates innate muscle responses assessed by sEMG in healthy subjects, compared to a sham stimulation.

SIGNIFICANCE

This characterization might be helpful to the prescription and application of Vojta therapy in an individual-basis for non-neurophysiologically damaged adult subjects.

Calorie deprivation impairs the self-control of eating, but not of other behaviors

OBJECTIVE

Sustained weight loss is difficult to achieve, and weight regain is common due to biological and psychological changes caused by calorie deprivation. These changes are thought to undermine weight loss efforts by making self-control more difficult. However, there is a lack of evidence showing a causal relationship between calorie deprivation and behavioral self-control.

DESIGN

In this longitudinal field experiment, we tested whether a ten-day period of calorie deprivation leads to the impairment of behavioral self-control. Participants were randomly assigned to either restrict their calorie intake or to continue eating normally for the study period.

MAIN OUTCOME MEASURES

Participants were given a box of food and non-food 'treats' (i.e., chocolates and lottery tickets) that they were asked to resist until the end of the study. On the last day, researchers recorded the number of treats that remained for each participant.

RESULTS

Nonparametric permutation tests revealed that calorie-deprived participants ate significantly more chocolates than control participants did (p = 0.036), but that participants did not differ in the number of lottery tickets 'scratched' by condition (p = 0.332).

CONCLUSION

This pattern of findings suggests that calorie deprivation impairs food-related self-control, but that this self-control deficit may not generalize beyond food-related tasks.

Cortical activity during sensorial tactile stimulation in healthy adults through Vojta therapy. A randomized pilot controlled trial

Background

Brain’s is stimulated by Vojta Therapy through selected body areas activating stored innate motor programs which are exported as coordinate movement and muscle contractions to trunk and limbs. The aim of this pilot study is to know the responses at cortical level to a specific tactile input, assessed by electroencephalography (EEG), compared to a sham stimulation, in healthy subjects.

Methods

A randomized-controlled trial was conducted. Participants were randomly distributed into two groups: a non-specific tactile input-group (non-STI-group) (n = 20) and a Vojta specific tactile input-group (V-STI-group) (n = 20). The non-STI-group was stimulated in a non specific area (quadriceps distal area) and V-STI-group was stimulated in a specific area (intercostal space, at the mammillary line between the 7th and 8th ribs) according to the Vojta therapy. Recording was performed with EEG for 10 min considering a first minute of rest, 8 min during the stimulus and 1 min after the stimulus. EEG activity was recorded from 32 positions with active Ag/AgCl scalp electrodes following the 10–20 system. The continuous EEG signal was split into consecutive segments of one minute.

Results

The V-STI-group showed statistically significant differences in the theta, low alpha and high alpha bands, bilaterally in the supplementary motor (SMA) and premotor (PMA) areas (BA6 and BA8), superior parietal cortex (BA5, BA7) and the posterior cingulate cortex (BA23, BA31). For the V-STI-group, all frequency bands presented an initial bilateral activation of the superior and medial SMA (BA6) during the first minute. This activation was maintained until the fourth minute. During the fourth minute, the activation decreased in the three frequency bands. From the fifth minute, the activation in the superior and medial SMA rose again in the three frequency bands

Conclusions

Our findings highlight that the specific stimulation area at intercostal space, on the mammillary line between 7 and 8th ribs according to Vojta therapy differentially increased bilateral activation in SMA (BA6) and Pre-SMA (BA8), BA5, BA7, BA23 and BA31 in the theta, low and high alpha bands in healthy subjects. These results could indicate the activation of innate locomotor circuits during stimulation of the pectoral area according to the Vojta therapy.

Trial registration Retrospectively registered. This randomized controlled trial has been registered at ClinicalTrials.gov Identifier: NCT04317950 (March 23, 2020).

The effects of internal jugular vein compression for modulating and preserving white matter following a season of American tackle football: A prospective longitudinal evaluation of differential head impact exposure

The purpose of this clinical trial was to examine whether internal jugular vein compression (JVC)-using an externally worn neck collar-modulated the relationships between differential head impact exposure levels and pre- to postseason changes in diffusion tensor imaging (DTI)-derived diffusivity and anisotropy metrics of white matter following a season of American tackle football. Male high-school athletes (n = 284) were prospectively assigned to a non-collar group or a collar group. Magnetic resonance imaging data were collected from participants pre- and postseason and head impact exposure was monitored by accelerometers during every practice and game throughout the competitive season. Athletes' accumulated head impact exposure was systematically thresholded based on the frequency of impacts of progressively higher magnitudes (10 g intervals between 20 to 150 g) and modeled with pre- to postseason changes in DTI measures of white matter as a function of JVC neck collar wear. The findings revealed that the JVC neck collar modulated the relationships between greater high-magnitude head impact exposure (110 to 140 g) and longitudinal changes to white matter, with each group showing associations that varied in directionality. Results also revealed that the JVC neck collar group partially preserved longitudinal changes in DTI metrics. Collectively, these data indicate that a JVC neck collar can provide a mechanistic response to the diffusion and anisotropic properties of brain white matter following the highly diverse exposure to repetitive head impacts in American tackle football. Clinicaltrials.gov: NCT# 04068883.

Prior cortical activity differences during an action observation plus motor imagery task related to motor adaptation performance of a coordinated multi-limb complex task

Motor adaptation is the ability to develop new motor skills that makes performing a consolidated motor task under different psychophysical conditions possible. There exists a proven relationship between prior brain activity at rest and motor adaptation. However, the brain activity at rest is highly variable both between and within subjects. Here we hypothesize that the cortical activity during the original task to be later adapted is a more reliable and stronger determinant of motor adaptation. Consequently, we present a study to find cortical areas whose activity, both at rest and during first-person virtual reality simulation of bicycle riding, characterizes the subjects who did and did not adapt to ride a reverse steering bicycle, a complex motor adaptation task involving all limbs and balance. The results showed that cortical activity differences during the simulated task were higher, more significant, spatially larger, and spectrally wider than at rest for good performers. In this sense, the activity of the left anterior insula, left dorsolateral and ventrolateral inferior prefrontal areas, and left inferior premotor cortex (action understanding hub of the mirror neuron circuit) during simulated bicycle riding are the areas with the most descriptive power for the ability of adapting the motor task. Trials registration Trial was registered with the NIH Clinical Trials Registry (clinicaltrials.gov), with the registration number NCT02999516 (21/12/2016).

Multiple testing correction over contrasts for brain imaging

The multiple testing problem arises not only when there are many voxels or vertices in an image representation of the brain, but also when multiple contrasts of parameter estimates (that represent hypotheses) are tested in the same general linear model. We argue that a correction for this multiplicity must be performed to avoid excess of false positives. Various methods for correction have been proposed in the literature, but few have been applied to brain imaging. Here we discuss and compare different methods to make such correction in different scenarios, showing that one classical and well known method is invalid, and argue that permutation is the best option to perform such correction due to its exactness and flexibility to handle a variety of common imaging situations.

Robust nonparametric tests of general linear model coefficients: A comparison of permutation methods and test statistics

Statistical inference in neuroimaging research often involves testing the significance of regression coefficients in a general linear model. In many applications, the researcher assumes a model of the form Y=α+Xβ+Zγ+ε, where Y is the observed brain signal, and X and Z contain explanatory variables that are thought to be related to the brain signal. The goal is to test the null hypothesis H₀:β=0 with the nuisance parameters γ included in the model. Several nonparametric (permutation) methods have been proposed for this problem, and each method uses some variant of the F ratio as the test statistic. However, recent research suggests that the F ratio can produce invalid permutation tests of H₀:β=0 when the ε terms are heteroscedastic (i.e., have non-constant variance), which can occur for a variety of reasons. This study compares the classic F test statistic to the robust W (Wald) test statistic using eight different permutation methods. The results reveal that permutation tests using the F ratio can produce accurate results when the errors are homoscedastic, but high false positive rates when the errors are heteroscedastic. In contrast, permutation tests using the W test statistic produced valid results when the errors were homoscedastic, and asymptotically valid results when the errors were heteroscedastic. In the situation with homoscedastic errors, permutation tests using the W statistic showed slightly reduced power compared to the F statistic, but the difference disappeared as the sample size n increased. Consequently, the W test statistic is recommended for robust nonparametric hypothesis tests of regression coefficients in neuroimaging research.