The neural correlates of visuospatial perceptual and oculomotor extrapolation

Underlying neurological mechanisms associated with symptomatic convergence insufficiency

Convergence insufficiency (CI) is the most common binocular vision problem, associated with blurred/double vision, headaches, and sore eyes that are exacerbated when doing prolonged near work, such as reading. The Convergence Insufficiency Neuro-mechanism Adult Population Study (NCT03593031) investigates the mechanistic neural differences between 50 binocularly normal controls (BNC) and 50 symptomatic CI participants by examining the fast and slow fusional disparity vergence systems. The fast fusional system is preprogrammed and is assessed with convergence peak velocity. The slow fusional system optimizes vergence effort and is assessed by measuring the phoria adaptation magnitude and rate. For the fast fusional system, significant differences are observed between the BNC and CI groups for convergence peak velocity, final position amplitude, and functional imaging activity within the secondary visual cortex, right cuneus, and oculomotor vermis. For the slow fusional system, the phoria adaptation magnitude and rate, and the medial cuneus functional activity, are significantly different between the groups. Significant correlations are observed between vergence peak velocity and right cuneus functional activity (p = 0.002) and the rate of phoria adaptation and medial cuneus functional activity (p = 0.02). These results map the brain-behavior of vergence. Future therapeutic interventions may consider implementing procedures that increase cuneus activity for this debilitating disorder.

A likelihood ratio approach for functional localization in fMRI

BACKGROUND

To increase power when analyzing fMRI data, researchers often define functional regions of interest (fROIs). It is crucial that this fROI is defined with an optimal balance between both false positives and false negatives to ensure maximal spatial accuracy and to avoid potentially biased results in the main fMRI experiment. Additionally, since the fROI is defined in each subject separately, the used method should attune to the general level of activation of the individual.

NEW METHOD

We investigate the benefits of the maximized likelihood ratio (mLR) method. This method is based on the likelihood paradigm where likelihood ratios are used to reflect relative statistical evidence in favor of an a priori defined practically relevant alternative hypothesis as compared to the null hypothesis of no activation.

RESULTS

Through both simulations and real data, we show that the mLR method provides cumulative evidence for voxels that are active with an effect size that is larger than the one a priori defined in the alternative. Furthermore, an optimal balance between Type I and Type II errors is achieved when the alternative is an underestimation of the true effect size.

COMPARISON WITH EXISTING METHODS

The mLR method is compared with false discovery rate corrected null hypothesis significance testing and regular likelihood ratio testing. It performs as good as or outperformed both methods in both detection of practically relevant voxels and the trade- off between false positives and false negatives.

CONCLUSIONS

The mLR method provides fROIs that are both spatially accurate and practically relevant.

Introducing Alternative-Based Thresholding for Defining Functional Regions of Interest in fMRI

In fMRI research, one often aims to examine activation in specific functional regions of interest (fROIs). Current statistical methods tend to localize fROIs inconsistently, focusing on avoiding detection of false activation. Not missing true activation is however equally important in this context. In this study, we explored the potential of an alternative-based thresholding (ABT) procedure, where evidence against the null hypothesis of no effect and evidence against a prespecified alternative hypothesis is measured to control both false positives and false negatives directly. The procedure was validated in the context of localizer tasks on simulated brain images and using a real data set of 100 runs per subject. Voxels categorized as active with ABT can be confidently included in the definition of the fROI, while inactive voxels can be confidently excluded. Additionally, the ABT method complements classic null hypothesis significance testing with valuable information by making a distinction between voxels that show evidence against both the null and alternative and voxels for which the alternative hypothesis cannot be rejected despite lack of evidence against the null.

Improving the reliability of functional localizers

A critical assumption underlying the practice of functional localization is that the voxels identified by functional localization are essentially the same as those activated in the main experiment for a particular anatomical area. Violations of this assumption bias the resulting analyses and can dramatically increase the likelihood of both Type I and Type II errors. Here we investigated how the amount of data affects the reliability of a set of common functionally-defined regions-of-interest (fROIs). Four participants were scanned ten times each to functionally localize extrastriate regions sensitive to visually presented words, objects and faces. A within-subject random-effects analysis was used as the "gold standard" for identifying the fROIs and the results were compared to within-subject, fixed-effect analyses typically used for functional localization. By varying the quantity of data included in the analyses, we empirically assessed the amount needed to ensure reliable identification of the fROIs. The results demonstrated that the most consistent fROIs were based on either stringent statistical thresholding (Z>5.0) of large quantities of data or on lenient thresholding (Z>2.3) of a modest amount of data, with both methods yielding 70-80% overlap between the functional localization results and the "gold standard." Stringent statistical thresholds on typical quantities of localizer data led to the poorest reliability (<20% overlap). These findings suggest that the most reliable and cost-efficient method for functional localization involves collecting a relatively small amount of data (~10 min) and using a lenient statistical threshold to identify all voxels in a given region that are sensitive to the process-of-interest.