Mass-univariate Analysis Research Articles

Sex differences in the functional topography of association networks in youth

Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here, we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8 to 23 y) who underwent functional MRI as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 82.9% accuracy (P < 0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention, default mode, and frontoparietal networks. Mass univariate analyses using generalized additive models with penalized splines provided convergent results. Furthermore, transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography were spatially correlated with the expression of genes on the X chromosome. These results highlight the role of sex as a biological variable in shaping functional topography.

Parsimonious model for mass-univariate vertexwise analysis.

Purpose: Covariance between gray-matter measurements can reflect structural or functional brain networks though it has also been shown to be influenced by confounding factors (e.g., age, head size, and scanner), which could lead to lower mapping precision (increased size of associated clusters) and create distal false positives associations in mass-univariate vertexwise analyses. Approach: We evaluated this concern by performing state-of-the-art mass-univariate analyses (general linear model, GLM) on traits simulated from real vertex-wise gray matter data (including cortical and subcortical thickness and surface area). We contrasted the results with those from linear mixed models (LMMs), which have been shown to overcome similar issues in omics association studies. Results: We showed that when performed on a large sample ( , UK Biobank), GLMs yielded greatly inflated false positive rate (cluster false discovery rate ). We showed that LMMs resulted in more parsimonious results: smaller clusters and reduced false positive rate but at a cost of increased computation. Next, we performed mass-univariate association analyses on five real UKB traits (age, sex, BMI, fluid intelligence, and smoking status) and LMM yielded fewer and more localized associations. We identified 19 significant clusters displaying small associations with age, sex, and BMI, which suggest a complex architecture of at least dozens of associated areas with those phenotypes. Conclusions: The published literature could contain a large proportion of redundant (possibly confounded) associations that are largely prevented using LMMs. The parsimony of LMMs results from controlling for the joint effect of all vertices, which prevents local and distal redundant associations from reaching significance.

Spatial frequency impacts perceptual and attentional ERP components across cultures

Culture impacts visual perception in several ways.To identify stages of perceptual processing that differ between cultures, we usedelectroencephalography measures of perceptual and attentional responses to simple visual stimuli.Gabor patches of higher or lower spatialfrequencywere presented at high contrast to 25 American and 31 East Asian participants while they were watching for the onset of aninfrequent, oddball stimulus. Region of interest and mass univariate analyses assessed how cultural background and stimuli spatial frequency affected the visual evoked response potentials. Across both groups, the Gabor of lower spatial frequency produced stronger evoked response potentials in the anterior N1 and P3 than did the higher frequency Gabor. The mass univariate analyses also revealed effects of spatial frequency, including a frontal negativity around 150 ms and a widespread posterior positivity around 300 ms. The effects of spatial frequency generally differed little across cultures; although there was some evidence for cultural differences in the P3 response to different frequencies at the Pz electrode, this effect did not emerge in the mass univariate analyses. We discuss these results in relation to those from previous studies, and explore the potential advantages of mass univariate analyses for cultural neuroscience.

NBS-Predict: A prediction-based extension of the network-based statistic

Graph models of the brain hold great promise as a framework to study functional and structural brain connectivity across scales and species. The network-based statistic (NBS) is a well-known tool for performing statistical inference on brain graphs, which controls the family-wise error rate in a mass univariate analysis by combining the cluster-based permutation technique and the graph-theoretical concept of connected components. As the NBS is based on group-level inference statistics, it does not inherently enable informed decisions at the level of individuals, which is, however, necessary for the realm of precision medicine. Here we introduce NBS-Predict, a new approach that combines the powerful features of machine learning (ML) and the NBS in a user-friendly graphical user interface (GUI). By combining ML models with connected components in a cross-validation (CV) structure, the new methodology provides a fast and convenient tool to identify generalizable neuroimaging-based biomarkers. The purpose of this paper is to (i) introduce NBS-Predict and evaluate its performance using two sets of simulated data with known ground truths, (ii) demonstrate the application of NBS-Predict in a real case-control study, including resting-state functional magnetic resonance imaging (rs-fMRI) data acquired from patients with schizophrenia, (iii) evaluate NBS-Predict using rs-fMRI data from the Human Connectome Project 1200 subjects release. We found that: (i) NBS-Predict achieved good statistical power on two sets of simulated data; (ii) NBS-Predict classified schizophrenia with an accuracy of 90% using subjects’ functional connectivity matrices and identified a subnetwork with reduced connections in the group with schizophrenia, mainly comprising brain regions localized in frontotemporal, visual, and motor areas, as well as in the subcortex; (iii) NBS-Predict also predicted general intelligence scores from resting-state fMRI connectivity matrices with a prediction score of r = 0.2 and identified a large-scale subnetwork associated with general intelligence. Overall results showed that NBS-Predict performed comparable to or better than pre-existing feature selection algorithms (lasso, elastic net, top 5%, p-value thresholding) and connectome-based predictive modeling (CPM) in terms of identifying relevant features and prediction accuracy.

The missing N1 or jittered P2: Electrophysiological correlates of pattern glare in the time and frequency domain

Excessive sensitivity to certain visual stimuli (cortical hyperexcitability) is associated with a number of neurological disorders including migraine, epilepsy, multiple sclerosis, autism and possibly dyslexia. Others show disruptive sensitivity to visual stimuli with no other obvious pathology or symptom profile (visual stress) which can extend to discomfort and nausea. We used event‐related potentials (ERPs) to explore the neural correlates of visual stress and headache proneness. We analysed ERPs in response to thick (0.37 cycles per degree [c/deg]), medium (3 c/deg) and thin (12 c/deg) gratings, using mass univariate analysis, considering three factors in the general population: headache proneness, visual stress and discomfort. We found relationships between ERP features and the headache and discomfort factors. Stimulus main effects were driven by the medium stimulus regardless of participant characteristics. Participants with high discomfort ratings had larger P1 components for the initial presentation of medium stimuli, suggesting initial cortical hyperexcitability that is later suppressed. The participants with high headache ratings showed atypical N1‐P2 components for medium stripes relative to the other stimuli. This effect was present only after repeated stimulus presentation. These effects were also explored in the frequency domain, suggesting variations in intertrial theta band phase coherence. Our results suggest that discomfort and headache in response to striped stimuli are related to different neural processes; however, more exploration is needed to determine whether the results translate to a clinical migraine population.

Examining the transition of novel information toward familiarity

Throughout their lives, humans encounter multiple instances of new information that can be inconsistent with prior knowledge (novel). Over time, the once-novel information becomes integrated into their established knowledge base, shifting from novelty to familiarity. In this study, we investigated the processes by which the first steps of this transition take place. We hypothesized that the neural representations of initially novel items gradually change over the course of repeated presentations, expressing a shift toward familiarity. We further assumed that this shift could be traced by examining neural patterns using fMRI. In two experiments, while being scanned, participants read noun-adjective word pairs that were either consistent or inconsistent with their prior knowledge. Stimuli were repeated 3–6 times within the scans. Employing mass univariate and multivariate similarity analyses, we showed that the neural representations associated with the initial presentation of familiar versus novel objects differed in lateral frontal and temporal regions, the medial prefrontal cortex, and the medial temporal lobe. Importantly, the neural representations of novel stimuli gradually changed throughout repetitions until they became indistinguishable from their respective familiar items. We interpret these findings as indicating that an early phase of familiarization can be completed within a few repetitions. This initial familiarization can then serve as the prerequisite to the integration of novel items into existing knowledge. Future empirical and theoretical works can build on the current findings to develop a comprehensive model of the transition from novelty to familiarity.

I can see it in your eyes: Perceived gaze direction impacts ERP and behavioural measures of affective theory of mind

Looking at someone's eyes is thought to be important for affective theory of mind (aTOM), our ability to infer their emotional state. However, it is unknown whether an individual's gaze direction influences our aTOM judgements and what the time course of this influence might be. We presented participants with sentences describing individuals in positive, negative or neutral scenarios, followed by direct or averted gaze neutral face pictures of those individuals. Participants made aTOM judgements about each person's mental state, including their affective valence and arousal, and we investigated whether the face gaze direction impacted those judgements. Participants rated that gazers were feeling more positive when they displayed direct gaze as opposed to averted gaze, and that they were feeling more aroused during negative contexts when gaze was averted as opposed to direct. Event-related potentials associated with face perception and affective processing were examined using mass-univariate analyses to track the time-course of this eye-gaze and affective processing interaction at a neural level. Both positive and negative trials were differentiated from neutral trials at many stages of processing. This included the early N200 and EPN components, believed to reflect automatic emotion areas activation and attentional selection respectively. This also included the later P300 and LPP components, thought to reflect elaborative cognitive appraisal of emotional content. Critically, sentence valence and gaze direction interacted over these later components, which may reflect the incorporation of eye-gaze in the cognitive evaluation of another's emotional state. The results suggest that gaze perception directly impacts aTOM processes, and that altered eye-gaze processing in clinical populations may contribute to associated aTOM impairments.

Neurobiological Basis of Reinforcement-Based Decision-Making in Adults With ADHD Treated With Lisdexamfetamine Dimesylate

Background: The objective of this study was to examine changes in the activation of the brain reward system following treatment with lisdexamfetamine (LDX) vs. placebo (PL) as a function of clinical improvement in attention deficit/hyperactivity disorder (ADHD) symptoms. Methods: Twenty adults with ADHD were included in a randomized cross-over study. Participants underwent two functional magnetic resonance imaging (fMRI) scans, after receiving 3 to 5 weeks of treatment with both LDX and PL. During scanning, participants performed the passive-avoidance learning task to assess reward-related learning using computational variables (e.g., estimated value and prediction error). Pre-treatment to post-treatment symptom change was assessed via the ADHD Rating Scale (ADHD-RS). The imaging contrasts were Object Choose or Object Refuse during the object choice component of the task, modulated by expected value (reward vs. nonreward cue), and Reward vs. Punishment during feedback, modulated by prediction error (expected vs. actual outcome). To address the primary objective, we performed group-level mass univariate analyses between pre-treatment to post-treatment percent change of the ADHD-RS total scores and the four contrast images under the choice and feedback conditions, with significance set at a whole-brain voxel-wise threshold of p < .05 with family-wise error (FWE) correction and an extent (cluster) threshold of 50 contiguous voxels. Results: Improvement in ADHD symptoms was accompanied by significant increases of brain activation during the Object Refuse, Reward and Punishment contrasts in a widespread network including left caudate and putamen, and right orbitofrontal cortex (i.e., reward-related signaling) and left middle frontal, superior frontal, and precentral gyri (i.e., executive control). Conclusions: These findings are the first to show that the increase in responsiveness of systems engaged in reward processing with LDX treatment is positively related to symptom improvement. Results support the hypothesis that LDX treatment may restore balance to dysfunction (e.g., hypoactivation) within the brain reward circuitry in adults with ADHD.

Spatiotemporal Dynamics of Covert Versus Overt Processing of Happy, Fearful and Sad Facial Expressions.

Behavioral and electrophysiological correlates of the influence of task demands on the processing of happy, sad, and fearful expressions were investigated in a within-subjects study that compared a perceptual distraction condition with task-irrelevant faces (e.g., covert emotion task) to an emotion task-relevant categorization condition (e.g., overt emotion task). A state-of-the-art non-parametric mass univariate analysis method was used to address the limitations of previous studies. Behaviorally, participants responded faster to overtly categorized happy faces and were slower and less accurate to categorize sad and fearful faces; there were no behavioral differences in the covert task. Event-related potential (ERP) responses to the emotional expressions included the N170 (140–180 ms), which was enhanced by emotion irrespective of task, with happy and sad expressions eliciting greater amplitudes than neutral expressions. EPN (200–400 ms) amplitude was modulated by task, with greater voltages in the overt condition, and by emotion, however, there was no interaction of emotion and task. ERP activity was modulated by emotion as a function of task only at a late processing stage, which included the LPP (500–800 ms), with fearful and sad faces showing greater amplitude enhancements than happy faces. This study reveals that affective content does not necessarily require attention in the early stages of face processing, supporting recent evidence that the core and extended parts of the face processing system act in parallel, rather than serially. The role of voluntary attention starts at an intermediate stage, and fully modulates the response to emotional content in the final stage of processing.

Sex Differences in Functional Topography of Association Networks

Prior work has shown that there is substantial interindividual variation in the spatial distribution of functional networks across the cerebral cortex, or functional topography. However, it remains unknown whether there are sex differences in the topography of individualized networks in youth. Here we leveraged an advanced machine learning method (sparsity-regularized non-negative matrix factorization) to define individualized functional networks in 693 youth (ages 8-23 years) who underwent functional magnetic resonance imaging as part of the Philadelphia Neurodevelopmental Cohort. Multivariate pattern analysis using support vector machines classified participant sex based on functional topography with 83% accuracy (p<0.0001). Brain regions most effective in classifying participant sex belonged to association networks, including the ventral attention and default mode networks. Mass-univariate analyses using generalized additive models with penalized splines provided convergent results. Comparative analysis using transcriptomic data from the Allen Human Brain Atlas revealed that sex differences in multivariate patterns of functional topography correlated with the expression of genes on the X-chromosome. These results identify normative developmental sex differences in the functional topography of association networks and highlight the role of sex as a biological variable in shaping brain development in youth.

The early processing of fearful and happy facial expressions is independent of task demands – Support from mass univariate analyses

Most ERP studies on facial expressions of emotion have yielded inconsistent results regarding the time course of emotion effects and their possible modulation by task demands. Most studies have used classical statistical methods with a high likelihood of type I and type II errors, which can be limited with Mass Univariate statistics. FMUT and LIMO are currently the only two available toolboxes for Mass Univariate analysis of ERP data and use different fundamental statistics. Yet, no direct comparison of their output has been performed on the same dataset. Given the current push to transition to robust statistics to increase results replicability, here we compared the output of these toolboxes on data previously analyzed using classic approaches (Itier & Neath-Tavares, 2017). The early (0–352 ms) processing of fearful, happy, and neutral faces was investigated under three tasks in a within-subject design that also controlled gaze fixation location. Both toolboxes revealed main effects of emotion and task but neither yielded an interaction between the two, confirming the early processing of fear and happy expressions is largely independent of task demands. Both toolboxes found virtually no difference between neutral and happy expressions, while fearful (compared to neutral and happy) expressions modulated the N170 and EPN but elicited maximum effects after the N170 peak, around 190 ms. Similarities and differences in the spatial and temporal extent of these effects are discussed in comparison to the published classical analysis and the rest of the ERP literature.

Flogging a Dead Salmon? Reduced Dose Posterior to Prostate Correlates With Increased PSA Progression in Voxel-Based Analysis of 3 Randomized Phase 3 Trials

Flogging a Dead Salmon? Reduced Dose Posterior to Prostate Correlates With Increased PSA Progression in Voxel-Based Analysis of 3 Randomized Phase 3 Trials

Probability waves: Adaptive cluster-based correction by convolution of p-value series from mass univariate analysis

BackgroundMethods for p-value correction are criticized for either increasing Type II error or improperly reducing Type I error in large exploratory data analysis. This text considers patterns in probability vectors resulting from mass univariate analysis to correct p-values, where clusters of significant p-values may indicate true H0 rejection. New methodWe used ERP experimental data from control and ADHD boys to test the method. The Log10 of p-vector was convolved with a Gaussian window whose length was set as the shortest lag above which autocorrelation of each ERP wave may be assumed to have vanished. We realized Monte-Carlo simulations (MC) to (1) evaluate confidence intervals of rejected and non-rejected areas of our data, (2) to evaluate differences between corrected and uncorrected p-vectors or simulated ones in terms of distribution of significant p-values, and (3) to empirically verify the type-I error rate (comparing 10,000 pairs of mixed samples whit control and ADHD subjects). ResultsThe differences between simulation or raw p-vector and corrected p-vectors were, respectively, minimal and maximal for window length set by autocorrelation in p-vector convolution. Comparison with existing methodsOur method was less conservative while FDR methods rejected basically all significant p-values.The MC simulations presented 2.78 ± 4.83% of difference (20 channels) from corrected p-vector, while difference from raw p-vector was 596 ± 5.00% (p = 0.0003). ConclusionAs a cluster-based correction, the present new method seems to be biological and statistically suitable to correct p-values in mass univariate analysis of ERP waves, which adopts adaptive parameters to correction.

Focused Ultrasound Thalamotomy Sensory Side Effects Follow the Thalamic Structural Homunculus.

Focused ultrasound thalamotomy is an effective treatment for tremor; however, side effects may occur. The purpose of the present study was to investigate the spatial relationship between thalamotomies and specific sensory side effects and their functional connectivity with somatosensory cortex and relationship to the medial lemniscus (ML). Sensory adverse effects were categorized into 4 groups based on the location of the disturbance: face/mouth/tongue numbness/paresthesia, hand-only paresthesia, hemibody/limb paresthesia, and dysgeusia. Then, areas of significant risk (ASRs) for each category were defined using voxel-wise mass univariate analysis and overlaid on corresponding odds ratio maps. The ASR associated with the maximum risk was used as a region of interest in a normative functional connectome to determine side effect-specific functional connectivity. Finally, each ASR was overlaid on the ML derived from normative template. Of 103 patients, 17 developed sensory side effects after thalamotomy persisting 3 months after the procedures. Lesions producing sensory side effects extended posteriorly into the principle sensory nucleus of the thalamus or below the thalamus in the ML. The topography of sensory adverse effects followed the known somatotopy of the ML and the sensory nucleus. Functional connectivity patterns between each sensory-specific thalamic seed and the primary somatosensory areas supported the role of the middle insula in processing of gustatory information and in multisensory integration. Distinct regions in the sensory thalamus and its afferent connections rise to specific sensory disturbances. These findings demonstrate the relationship between the sensory thalamus, ML, and bilateral sensory cortical areas.

Remodeling of brain morphology in temporal lobe epilepsy.

BackgroundMesial temporal lobe epilepsy (TLE) is one of the most widespread neurological network disorders. Computational anatomy MRI studies demonstrate a robust pattern of cortical volume loss. Most statistical analyses provide information about localization of significant focal differences in a segregationist way. Multivariate Bayesian modeling provides a framework allowing inferences about inter‐regional dependencies. We adopt this approach to answer following questions: Which structures within a pattern of dynamic epilepsy‐associated brain anatomy reorganization best predict TLE pathology. Do these structures differ between TLE subtypes?MethodsWe acquire clinical and MRI data from TLE patients with and without hippocampus sclerosis (n = 128) additional to healthy volunteers (n = 120). MRI data were analyzed in the computational anatomy framework of SPM12 using classical mass‐univariate analysis followed by multivariate Bayesian modeling.ResultsAfter obtaining TLE‐associated brain anatomy pattern, we estimate predictive power for disease and TLE subtypes using Bayesian model selection and comparison. We show that ipsilateral para‐/hippocampal regions contribute most to disease‐related differences between TLE and healthy controls independent of TLE laterality and subtype. Prefrontal cortical changes are more discriminative for left‐sided TLE, whereas thalamus and temporal pole for right‐sided TLE. The presence of hippocampus sclerosis was linked to stronger involvement of thalamus and temporal lobe regions; frontoparietal involvement was predominant in absence of sclerosis.ConclusionsOur topology inferences on brain anatomy demonstrate a differential contribution of structures within limbic and extralimbic circuits linked to main effects of TLE and hippocampal sclerosis. We interpret our results as evidence for TLE‐related spatial modulation of anatomical networks.

Fast Estimation of L1-Regularized Linear Models in the Mass-Univariate Setting

In certain modeling approaches, activation analyses of task-based fMRI data can involve a relatively large number of predictors. For example, in the encoding model approach, complex stimuli are represented in a high-dimensional feature space, resulting in design matrices with many predictors. Similarly, single-trial models and finite impulse response models may also encompass a large number of predictors. In settings where only few of those predictors are expected to be informative, a sparse model fit can be obtained via L1-regularization. However, estimating L1-regularized models requires an iterative fitting procedure, which considerably increases computation time compared to estimating unregularized or L2-regularized models, and complicates the application of L1-regularization on whole-brain data and large sample sizes. Here we provide several functions for estimating L1-regularized models that are optimized for the mass-univariate analysis approach. The package includes a parallel implementation of the coordinate descent algorithm for CPU-only systems and two implementations of the alternating direction method of multipliers algorithm requiring a GPU device. While the core algorithms are implemented in C++/CUDA, data input/output and parameter settings can be conveniently handled via Matlab. The CPU-based implementation is highly memory-efficient and provides considerable speed-up compared to the standard implementation not optimized for the mass-univariate approach. Further acceleration can be achieved on systems equipped with a CUDA-enabled GPU. Using the fastest GPU-based implementation, computation time for whole-brain estimates can be reduced from 9 h to 5 min in an exemplary data setting. Overall, the provided package facilitates the use of L1-regularization for fMRI activation analyses and enables an efficient employment of L1-regularization on whole-brain data and large sample sizes.

Tools of the Trade Multivoxel pattern analysis in fMRI: a practical introduction for social and affective neuroscientists.

The family of neuroimaging analytical techniques known as multivoxel pattern analysis (MVPA) has dramatically increased in popularity over the past decade, particularly in social and affective neuroscience research using functional magnetic resonance imaging (fMRI). MVPA examines patterns of neural responses, rather than analyzing single voxel- or region-based values, as is customary in conventional univariate analyses. Here, we provide a practical introduction to MVPA and its most popular variants (namely, representational similarity analysis (RSA) and decoding analyses, such as classification using machine learning) for social and affective neuroscientists of all levels, particularly those new to such methods. We discuss how MVPA differs from traditional mass-univariate analyses, the benefits MVPA offers to social neuroscientists, experimental design and analysis considerations, step-by-step instructions for how to implement specific analyses in one’s own dataset and issues that are currently facing research using MVPA methods.

Multi-time-point analysis: A time course analysis with functional near-infrared spectroscopy.

In the data analysis of functional near-infrared spectroscopy (fNIRS), linear model frameworks, in particular mass univariate analysis, are often used when researchers consider examining the difference between conditions at each sampled time point. However, some statistical issues, such as assumptions of linearity, autocorrelation and multiple comparison problems, influence statistical inferences when mass univariate analysis is used on fNIRS time course data. In order to address these issues, the present study proposes a novel perspective, multi-time-point analysis (MTPA), to discriminate signal differences between conditions by combining temporal information from multiple time points in fNIRS. In addition, MTPA adopts the random forest algorithm from the statistical learning domain, followed by a series of cross-validation procedures, providing reasonable power for detecting significant time points and ensuring generalizability. Using a real fNIRS data set, the proposed MTPA outperformed mass univariate analysis in detecting more time points, showing significant differences between experimental conditions. Finally, MTPA was also able to make comparisons between different areas, leading to a novel viewpoint of fNIRS time course analysis and providing additional theoretical implications for future fNIRS studies. The data set and all source code are available for researchers to replicate the analyses and to adapt the program for their own needs in future fNIRS studies.

Selective peak inference: Unbiased estimation of raw and standardized effect size at local maxima

The spatial signals in neuroimaging mass univariate analyses can be characterized in a number of ways, but one widely used approach is peak inference: the identification of peaks in the image. Peak locations and magnitudes provide a useful summary of activation and are routinely reported, however, the magnitudes reflect selection bias as these points have both survived a threshold and are local maxima. In this paper we propose the use of resampling methods to estimate and correct this bias in order to estimate both the raw units change as well as standardized effect size measured with Cohen's d and partial R2. We evaluate our method with a massive open dataset, and discuss how the corrected estimates can be used to perform power analyses. Keywords: fMRI, selective inference, winner's curse, regression to the mean, bias, bootstrap, local maxima, UK biobank, power analyses, massive linear modeling.

A Guide to Representational Similarity Analysis for Social Neuroscience.

Representational similarity analysis (RSA) is a computational technique that uses pairwise comparisons of stimuli to reveal their representation in higher-order space. In the context of neuroimaging, mass-univariate analyses and other multivariate analyses can provide information on what and where information is represented but have limitations in their ability to address how information is represented. Social neuroscience is a field that can particularly benefit from incorporating RSA techniques to explore hypotheses regarding the representation of multidimensional data, how representations can predict behavior, how representations differ between groups and how multimodal data can be compared to inform theories. The goal of this paper is to provide a practical as well as theoretical guide to implementing RSA in social neuroscience studies.