Toward a reliable detection of arachnophobia: subjective, behavioral, and neurophysiological measures of fear response

Introduction

The administration of questionnaires presents an easy way of obtaining important knowledge about phobic patients. However, it is not well known how these subjective measurements correspond to the patient's objective condition. Our study aimed to compare scores on questionnaires and image evaluation to the objective measurements of the behavioral approach test (BAT) and the neurophysiological effect of spiders extracted from fMRI measurements. The objective was to explore how reliably subjective statements about spiders and physiological and behavioral parameters discriminate between phobics and non-phobics, and what are the best predictors of overall brain activation.

Methods

Based on a clinical interview, 165 subjects were assigned to either a "phobic" or low-fear "control" group. Finally, 30 arachnophobic and 32 healthy control subjects (with low fear of spiders) participated in this study. They completed several questionnaires (SPQ, SNAQ, DS-R) and underwent a behavioral approach test (BAT) with a live tarantula. Then, they were measured in fMRI while watching blocks of pictures including spiders and snakes. Finally, the respondents rated all the visual stimuli according to perceived fear. We proposed the Spider Fear Index (SFI) as a value characterizing the level of spider fear, computed based on the fMRI measurements. We then treated this variable as the "neurophysiological effect of spiders" and examined its contribution to the respondents' fear ratings of the stimuli seen during the fMRI using the redundancy analysis (RDA).

Results

The results for fear ranks revealed that the SFI, SNAQ, DS-R, and SPQ scores had a significant effect, while BAT and SPQ scores loaded in the same direction of the first multivariate axis. The SFI was strongly correlated with both SPQ and BAT scores in the pooled sample of arachnophobic and healthy control subjects.

Discussion

Both SPQ and BAT scores have a high informative value about the subject's fear of spiders and together with subjective emotional evaluation of picture stimuli can be reliable predictors of spider phobia. These parameters provide easy and non-expensive but reliable measurement wherever more expensive devices such as magnetic resonance are not available. However, SFI still reflects individual variability within the phobic group, identifying individuals with higher brain activation, which may relate to more severe phobic reactions or other sources of fMRI signal variability.

Tackling the challenges of group network inference from intracranial EEG data

INTRODUCTION

Intracranial EEG (iEEG) data is a powerful way to map brain function, characterized by high temporal and spatial resolution, allowing the study of interactions among neuronal populations that orchestrate cognitive processing. However, the statistical inference and analysis of brain networks using iEEG data faces many challenges related to its sparse brain coverage, and its inhomogeneity across patients.

METHODS

We review these challenges and develop a methodological pipeline for estimation of network structure not obtainable from any single patient, illustrated on the inference of the interaction among visual streams using a dataset of 27 human iEEG recordings from a visual experiment employing visual scene stimuli. 100 ms sliding window and multiple band-pass filtered signals are used to provide temporal and spectral resolution. For the connectivity analysis we showcase two connectivity measures reflecting different types of interaction between regions of interest (ROI): Phase Locking Value as a symmetric measure of synchrony, and Directed Transfer Function-asymmetric measure describing causal interaction. For each two channels, initial uncorrected significance testing at p < 0.05 for every time-frequency point is carried out by comparison of the data-derived connectivity to a baseline surrogate-based null distribution, providing a binary time-frequency connectivity map. For each ROI pair, a connectivity density map is obtained by averaging across all pairs of channels spanning them, effectively agglomerating data across relevant channels and subjects. Finally, the difference of the mean map value after and before the stimulation is compared to the same statistic in surrogate data to assess link significance.

RESULTS

The analysis confirmed the function of the parieto-medial temporal pathway, mediating visuospatial information between dorsal and ventral visual streams during visual scene analysis. Moreover, we observed the anterior hippocampal connectivity with more posterior areas in the medial temporal lobe, and found the reciprocal information flow between early processing areas and medial place area.

DISCUSSION

To summarize, we developed an approach for estimating network connectivity, dealing with the challenge of sparse individual coverage of intracranial EEG electrodes. Its application provided new insights into the interaction between the dorsal and ventral visual streams, one of the iconic dualities in human cognition.

Promises and pitfalls of topological data analysis for brain connectivity analysis

Developing sensitive and reliable methods to distinguish normal and abnormal brain states is a key neuroscientific challenge. Topological Data Analysis, despite its relative novelty, already generated many promising applications, including in neuroscience. We conjecture its prominent tool of persistent homology may benefit from going beyond analysing structural and functional connectivity to effective connectivity graphs capturing the direct causal interactions or information flows. Therefore, we assess the potential of persistent homology to directed brain network analysis by testing its discriminatory power in two distinctive examples of disease-related brain connectivity alterations: epilepsy and schizophrenia. We estimate connectivity from functional magnetic resonance imaging and electrophysiology data, employ Persistent Homology and quantify its ability to distinguish healthy from diseased brain states by applying a support vector machine to features quantifying persistent homology structure. We show how this novel approach compares to classification using standard undirected approaches and original connectivity matrices. In the schizophrenia classification, topological data analysis generally performs close to random, while classifications from raw connectivity perform substantially better; potentially due to topographical, rather than topological, specificity of the differences. In the easier task of seizure discrimination from scalp electroencephalography data, classification based on persistent homology features generally reached comparable performance to using raw connectivity, albeit with typically smaller accuracies obtained for the directed (effective) connectivity compared to the undirected (functional) connectivity. Specific applications for topological data analysis may open when direct comparison of connectivity matrices is unsuitable - such as for intracranial electrophysiology with individual number and location of measurements. While standard homology performed overall better than directed homology, this could be due to notorious technical problems of accurate effective connectivity estimation.

Typicality of functional connectivity robustly captures motion artifacts in rs-fMRI across datasets, atlases, and preprocessing pipelines

Functional connectivity analysis of resting-state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix is a useful approximate representation of the brain's connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, Typicality of Functional Connectivity, to capture deviations from standard brain functional connectivity patterns. In a resting-state fMRI dataset of 245 healthy subjects, this measure was significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity, preprocessing options, and other datasets, including 1,081 subjects from the Human Connectome Project. In principle, Typicality of Functional Connectivity should be sensitive also to other types of artifacts, processing errors, and possibly also brain pathology, allowing extensive use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.

BRAD: Software for BRain Activity Detection from hemodynamic response

BACKGROUND AND OBJECTIVE

Precise estimation of neuronal activity from neuroimaging data is one of the central challenges of the application of noninvasive neuroimaging methods. One of the widely used methods for studying brain activity is functional magnetic resonance imaging, which is a neuroimaging procedure that measures brain activity based on the blood oxygenation level dependent effect. The blood oxygenation level dependent signal can be modeled as a linear convolution of a hemodynamic response function with an input signal corresponding to the neuronal activity. Estimating such input signals is a complicated problem.

METHODS

We present a software tool for estimation of brain neuronal activity, which uses a combination of Wiener filtering with deconvolution methods, including the least absolute shrinkage and selection operator, the ordinary least squares method, and the Dantzig selector. The latter two are equipped with both established selection criteria (Akaike and Bayesian information criterion) as well as newly developed mixture criteria for selection of activations.

RESULTS

The software tool was tested on two types of data: measurements during basic visual experiments and during complex naturalistic audiovisual stimulation (watching a movie segment). During testing the software showed reasonable results, with the mixture criteria performing well for temporally extended activations.

CONCLUSIONS

The presented software tool can be used for estimation, visualization, and analysis of brain neuronal activity from functional magnetic resonance imaging blood oxygenation level dependent measurements. The implemented methods provide valid results not only in the sparse activity scenario studied previously but also for temporally extended activations.