Functional connectivity in vergence and saccade eye movement tasks assessed using Granger Causality analysis

Effects of visual distractors on vergence eye movements

Visual attention is an important aspect of everyday life, which can be incorporated in the assessment of many diagnoses. Another important characteristic of visual attention is that it can be improved via therapeutic interventions. Fifteen subjects with normal binocular vision were presented with visual distractor stimuli at various spatial locations while initiating disparity vergence eye movements (inward or outward rotation of eyes) within a haploscope system. First, a stationary distractor stimulus was presented in either the far, middle, or near visual spaces while the subjects were instructed to follow a target stimulus that was either stationary, converging (moving toward subject), or diverging (moving away from subject). For the second experiment, a dynamic distractor stimulus within the far, middle, or near visual space that was converging or diverging was presented while the target stimulus was also converging or diverging. The subjects were instructed to visually follow the target stimulus and ignore the distractor stimulus. The vergence responses had a final vergence angle between the target and distractor stimuli which has been termed a center of gravity (CoG) effect. Statistically significant differences were observed between the convergence peak velocities (p < 0.001) and response amplitudes (p < 0.001) comparing responses without distractors to responses with the presence of a vergence distractor. The results support that vergence eye movements are influenced by visual distractors, which is similar to how distractors influence saccadic eye movements. The influence of visual distractors within vergence eye movements may be useful to assess binocular dysfunction and visual distraction which are common post brain injury.

Adaptation to Progressive Additive Lenses: Potential Factors to Consider

People develop presbyopia as part of the normal aging process. Most presbyopes adapt to progressive additive lens (PALs), while others do not. This investigation sought to determine whether the ability to modify disparity vergence or phoria was correlated to PALs adaptation. In experiment 1, a double-step paradigm quantified the ability to modify convergence responses in sixteen presbyopes. In experiment 2, thirty-one incipient presbyopes participated in a 5-minute sustained fixation task to evoke phoria adaptation where the magnitude and rate of phoria adaptation were measured. Then, the experiment was repeated after wearing PALs for one month. Linear regression analyses were conducted between the following parameters: near point of convergence, positive fusional vergence at near, vergence facility, net change in the magnitude of phoria adaptation, and the rate of phoria adaptation. The ability to change convergence average peak velocity was significantly greater (p < 0.03) in presbyopic PALs adapters compared to presbyopic PALs non-adapters. The rate of phoria adaptation and vergence facility were significantly greater (p < 0.03) in incipient presbyopic PALs adapters compared to incipient presbyopic PALs non-adapters. Vergence facility and the rate of phoria adaptation may have potential clinical utility in differentiating which patients may adapt to PALs and which ones will have more difficulty.

Comparison of causality analysis on simultaneously measured fMRI and NIRS signals during motor tasks

Brain activity can be measured using different modalities. Since most of the modalities tend to complement each other, it seems promising to measure them simultaneously. In to be presented research, the data recorded from Functional Magnetic Resonance Imaging (fMRI) and Near Infrared Spectroscopy (NIRS), simultaneously, are subjected to causality analysis using time-resolved partial directed coherence (tPDC). Time-resolved partial directed coherence uses the principle of state space modelling to estimate Multivariate Autoregressive (MVAR) coefficients. This method is useful to visualize both frequency and time dynamics of causality between the time series. Afterwards, causality results from different modalities are compared by estimating the Spearman correlation. In to be presented study, we used directionality vectors to analyze correlation, rather than actual signal vectors. Results show that causality analysis of the fMRI correlates more closely to causality results of oxy-NIRS as compared to deoxy-NIRS in case of a finger sequencing task. However, in case of simple finger tapping, no clear difference between oxy-fMRI and deoxy-fMRI correlation is identified.

Directionality analysis on functional magnetic resonance imaging during motor task using Granger causality

Directionality analysis of signals originating from different parts of brain during motor tasks has gained a lot of interest. Since brain activity can be recorded over time, methods of time series analysis can be applied to medical time series as well. Granger Causality is a method to find a causal relationship between time series. Such causality can be referred to as a directional connection and is not necessarily bidirectional. The aim of this study is to differentiate between different motor tasks on the basis of activation maps and also to understand the nature of connections present between different parts of the brain. In this paper, three different motor tasks (finger tapping, simple finger sequencing, and complex finger sequencing) are analyzed. Time series for each task were extracted from functional magnetic resonance imaging (fMRI) data, which have a very good spatial resolution and can look into the sub-cortical regions of the brain. Activation maps based on fMRI images show that, in case of complex finger sequencing, most parts of the brain are active, unlike finger tapping during which only limited regions show activity. Directionality analysis on time series extracted from contralateral motor cortex (CMC), supplementary motor area (SMA), and cerebellum (CER) show bidirectional connections between these parts of the brain. In case of simple finger sequencing and complex finger sequencing, the strongest connections originate from SMA and CMC, while connections originating from CER in either direction are the weakest ones in magnitude during all paradigms.