The confound of hemodynamic response function variability in human resting-state functional MRI studies

Functional magnetic resonance imaging (fMRI) is an indirect measure of neural activity with the hemodynamic response function (HRF) coupling it with unmeasured neural activity. The HRF, modulated by several non-neural factors, is variable across brain regions, individuals and populations. Yet, a majority of human resting-state fMRI connectivity studies continue to assume a non-variable HRF. In this article, with supportive prior evidence, we argue that HRF variability cannot be ignored as it substantially confounds within-subject connectivity estimates and between-subjects connectivity group differences. We also discuss its clinical relevance with connectivity impairments confounded by HRF aberrations in several disorders. We present limited data on HRF differences between women and men, which resulted in a 15.4% median error in functional connectivity estimates in a group-level comparison. We also discuss the implications of HRF variability for fMRI studies in the spinal cord. There is a need for more dialogue within the community on the HRF confound, and we hope that our article is a catalyst in the process.

Accounting for Bias in the Estimation of r 2 between Two Sets of Noisy Neural Responses

The Pearson correlation coefficient squared, r 2, is an important tool used in the analysis of neural data to quantify the similarity between neural tuning curves. Yet this metric is biased by trial-to-trial variability; as trial-to-trial variability increases, measured correlation decreases. Major lines of research are confounded by this bias, including those involving the study of invariance of neural tuning across conditions and the analysis of the similarity of tuning across neurons. To address this, we extend an estimator, [Formula: see text], that was recently developed for estimating model-to-neuron correlation, in which a noisy signal is compared with a noise-free prediction, to the case of neuron-to-neuron correlation, in which two noisy signals are compared with each other. We compare the performance of our novel estimator to a prior method developed by Spearman, commonly used in other fields but widely overlooked in neuroscience, and find that our method has less bias. We then apply our estimator to demonstrate how it avoids drastic confounds introduced by trial-to-trial variability using data collected in two prior studies (macaque, both sexes) that examined two different forms of invariance in the neural encoding of visual inputs-translation invariance and fill-outline invariance. Our results quantify for the first time the gradual falloff with spatial offset of translation-invariant shape selectivity within visual cortical neuronal receptive fields and offer a principled method to compare invariance in noisy biological systems to that in noise-free models.SIGNIFICANCE STATEMENT Quantifying the similarity between two sets of averaged neural responses is fundamental to the analysis of neural data. A ubiquitous metric of similarity, the correlation coefficient, is attenuated by trial-to-trial variability that arises from many irrelevant factors. Spearman recognized this problem and proposed corrected methods that have been extended over a century. We show this method has large asymptotic biases that can be overcome using a novel estimator. Despite the frequent use of the correlation coefficient in neuroscience, consensus on how to address this fundamental statistical issue has not been reached. We provide an accurate estimator of the correlation coefficient and apply it to gain insight into visual invariance.

How to remove or control confounds in predictive models, with applications to brain biomarkers

BACKGROUND

With increasing data sizes and more easily available computational methods, neurosciences rely more and more on predictive modeling with machine learning, e.g., to extract disease biomarkers. Yet, a successful prediction may capture a confounding effect correlated with the outcome instead of brain features specific to the outcome of interest. For instance, because patients tend to move more in the scanner than controls, imaging biomarkers of a disease condition may mostly reflect head motion, leading to inefficient use of resources and wrong interpretation of the biomarkers.

RESULTS

Here we study how to adapt statistical methods that control for confounds to predictive modeling settings. We review how to train predictors that are not driven by such spurious effects. We also show how to measure the unbiased predictive accuracy of these biomarkers, based on a confounded dataset. For this purpose, cross-validation must be modified to account for the nuisance effect. To guide understanding and practical recommendations, we apply various strategies to assess predictive models in the presence of confounds on simulated data and population brain imaging settings. Theoretical and empirical studies show that deconfounding should not be applied to the train and test data jointly: modeling the effect of confounds, on the training data only, should instead be decoupled from removing confounds.

CONCLUSIONS

Cross-validation that isolates nuisance effects gives an additional piece of information: confound-free prediction accuracy.

Accounting for Biases in the Estimation of Neuronal Signal Correlation

Signal correlation (rs) is commonly defined as the correlation between the tuning curves of two neurons and is widely used as a metric of tuning similarity. It is fundamental to how populations of neurons represent stimuli and has been central to many studies of neural coding. Yet the classic estimate, Pearson's correlation coefficient, [Formula: see text], between the average responses of two neurons to a set of stimuli suffers from confounding biases. The estimate [Formula: see text] can be downwardly biased by trial-to-trial variability and also upwardly biased by trial-to-trial correlation between neurons, and these biases can hide important aspects of neural coding. Here we provide analytic results on the source of these biases and explore them for ranges of parameters that are relevant for electrophysiological experiments. We then provide corrections for these biases that we validate in simulation. Furthermore, we apply these corrected estimators to make the following novel experimental observation in cortical area MT: pairs of nearby neurons that are strongly tuned for motion direction tend to have high signal correlation, and pairs that are weakly tuned tend to have low signal correlation. We dismiss a trivial explanation for this and find that an analogous trend holds for orientation tuning in the primary visual cortex. We also consider the potential consequences for encoding whereby the association of signal correlation and tuning strength naturally regularizes the dimensionality of downstream computations.SIGNIFICANCE STATEMENT Fundamental to how cortical neurons encode information about the environment is their functional similarity, that is, the redundancy in what they encode and their shared noise. These properties have been extensively studied theoretically and experimentally throughout the nervous system, but here we show that a common estimator of functional similarity has confounding biases. We characterize these biases and provide estimators that do not suffer from them. Using our improved estimators, we demonstrate a novel result, that is, there is a positive relationship between tuning curve similarity and amplitude for nearby neurons in the visual cortical motion area MT. We provide a simple stochastic model explaining this relationship and discuss how it would naturally regularize the dimensionality of neural encoding.

The influence of low-level stimulus features on the representation of contexts, items, and their mnemonic associations

Since the earliest attempts to characterize the "receptive fields" of neurons, a central aim of many neuroscience experiments is to elucidate the information that is represented in various regions of the brain. Recent studies suggest that, in the service of memory, information is represented in the medial temporal lobe in a conjunctive or associative form with the contextual aspects of the experience being the primary factor or highest level of the conjunctive hierarchy. A critical question is whether the information that has been observed in these studies reflects notions such as a cognitive representation of context or whether the information reflects the low-level sensory differences between stimuli. We performed two functional magnetic resonance imaging experiments to address this question and we found that associative representations observed between context and item (and order) in the human brain can be highly influenced by low-level sensory differences between stimuli. Our results place clear constraints on the experimental design of studies that aim to investigate the representation of contexts and items during performance of associative memory tasks. Moreover, our results raise interesting theoretical questions regarding the disambiguation of memory-related representations from processing-related representations.

Differentiating challenge reactivity from psychomotor activity in studies of children's psychophysiology: considerations for theory and measurement

Current methods of assessing children's physiological "stress reactivity" may be confounded by psychomotor activity, biasing estimates of the relation between reactivity and health. We examined the joint and independent contributions of psychomotor activity and challenge reactivity during a protocol for 5- and 6-year-old children (N = 338). Measures of parasympathetic reactivity (respiratory sinus arrhythmia [RSA]) and sympathetic reactivity (preejection period [PEP]) were calculated for social, cognitive, sensory, and emotional challenge tasks. Reactivity was calculated relative to both resting and a paired comparison task that accounted for psychomotor activity effects during each challenge. Results indicated that comparison tasks themselves elicited RSA and PEP responses, and reactivity adjusted for psychomotor activity was incongruent with reactivity calculated using rest. Findings demonstrate the importance of accounting for confounding psychomotor activity effects on physiological reactivity.