Kunjan D. Rana

Magnetoencephalography (MEG) captures the magnetic fields generated by neuronal current sources with sensors outside the head. In MEG analysis these current sources are estimated from the measured data to identify the locations and time courses of neural activity. Since there is no unique solution to this so-called inverse problem, multiple source estimation techniques have been developed. The nulling beamformer (NB), a modified form of the linearly constrained minimum variance (LCMV) beamformer, is specifically used in the process of inferring interregional interactions and is designed to eliminate shared signal contributions, or cross-talk, between regions of interest (ROIs) that would otherwise interfere with the connectivity analyses. The nulling beamformer applies the truncated singular value decomposition (TSVD) to remove small signal contributions from a ROI to the sensor signals. However, ROIs with strong crosstalk will have high separating power in the weaker components, which may be removed by the TSVD operation. To address this issue we propose a new method, the nulling beamformer with subspace suppression (NBSS). This method, controlled by a tuning parameter, reweights the singular values of the gain matrix mapping from source to sensor space such that components with high overlap are reduced. By doing so, we are able to measure signals between nearby source locations with limited cross-talk interference, allowing for reliable cortical connectivity analysis between them. In two simulations, we demonstrated that NBSS reduces cross-talk while retaining ROIs' signal power, and has higher separating power than both the minimum norm estimate (MNE) and the nulling beamformer without subspace suppression. We also showed that NBSS successfully localized the auditory M100 event-related field in primary auditory cortex, measured from a subject undergoing an auditory localizer task, and suppressed cross-talk in a nearby region in the superior temporal sulcus.

We investigated, in young healthy subjects, how the affective content of subliminally presented priming images and their specific visual attributes impacted conscious perception of facial expressions. The priming images were broadly categorised as aggressive, pleasant, or neutral and further subcategorised by the presence of a face and by the centricity (egocentric or allocentric vantage-point) of the image content. Subjects responded to the emotion portrayed in a pixelated target-face by indicating via key-press if the expression was angry or neutral. Priming images containing a face compared to those not containing a face significantly impaired performance on neutral or angry target-face evaluation. Recognition of angry target-face expressions was selectively impaired by pleasant prime images which contained a face. For egocentric primes, recognition of neutral target-face expressions was significantly better than of angry expressions. Our results suggest that, first, the affective primacy hypothesis which predicts that affective information can be accessed automatically, preceding conscious cognition, holds true in subliminal priming only when the priming image contains a face. Second, egocentric primes interfere with the perception of angry target-face expressions suggesting that this vantage-point, directly relevant to the viewer, perhaps engages processes involved in action preparation which may weaken the priority of affect processing.

Human perception, cognition, and action are supported by a complex network of interconnected brain regions. There is an increasing interest in measuring and characterizing these networks as a function of time and frequency, and inter-areal phase locking is often used to reveal these networks. This measure assesses the consistency of phase angles between the electrophysiological activity in two areas at a specific time and frequency. Noninvasively, the signals from which phase locking is computed can be measured with magnetoencephalography (MEG) and electroencephalography (EEG). However, due to the lack of spatial specificity of reconstructed source signals in MEG and EEG, inter-areal phase locking may be confounded by false positives resulting from crosstalk. Traditional phase locking estimates assume that no phase locking exists when the distribution of phase angles is uniform. However, this conjecture is not true when crosstalk is present. We propose a novel method to improve the reliability of the phase-locking measure by sampling phase angles from a baseline, such as from a prestimulus period or from resting-state data, and by contrasting this distribution against one observed during the time period of interest.

An important and unresolved problem in the assessment of perceptual and cognitive deficits in neurological patients is how to choose from the many existing behavioral tests, a subset that is sufficient for an appropriate diagnosis. This problem has to be dealt with in clinical trials, as well as in rehabilitation settings and often even at bedside in acute care hospitals. The need for efficient, cost effective and accurate diagnostic-evaluations, in the context of clinician time constraints and concerns for patients' fatigue in long testing sessions, make it imperative to select a set of tests that will provide the best classification of the patient's deficits. However, the small sample size of the patient population complicates the selection methodology and the potential accuracy of the classifier. We propose a method that allows for ordering tests based on having progressive increases in classification using cross-validation to assess the classification power of the chosen test set. This method applies forward linear regression to find an ordering of the tests with leave-one-out cross-validation to quantify, without biasing to the training set, the classification power of the chosen tests.

The detection of looming, the motion of objects in depth, underlies many behavioral tasks, including the perception of self-motion and time-to-collision. A number of studies have demonstrated that one of the most important cues for looming detection is optic flow, the pattern of motion across the retina. Schrater et al. have suggested that changes in spatial frequency over time, or scale changes, may also support looming detection in the absence of optic flow (P. R. Schrater, D. C. Knill, & E. P. Simoncelli, 2001). Here we used an adaptation paradigm to determine whether the perception of looming from optic flow and scale changes is mediated by single or separate mechanisms. We show first that when the adaptation and test stimuli were the same (both optic flow or both scale change), observer performance was significantly impaired compared to a dynamic (non-motion, non-scale change) null adaptation control. Second, we found no evidence of cross-cue adaptation, either from optic flow to scale change, or vice versa. Taken together, our data suggest that optic flow and scale changes are processed by separate mechanisms, providing multiple pathways for the detection of looming.