Measuring Granger Causality between Cortical Regions from Voxelwise fMRI BOLD Signals with LASSO

Functional brain network studies using the Blood Oxygen-Level Dependent (BOLD) signal from functional Magnetic Resonance Imaging (fMRI) are becoming increasingly prevalent in research on the neural basis of human cognition. An important problem in functional brain network analysis is to understand directed functional interactions between brain regions during cognitive performance. This problem has important implications for understanding top-down influences from frontal and parietal control regions to visual occipital cortex in visuospatial attention, the goal motivating the present study. A common approach to measuring directed functional interactions between two brain regions is to first create nodal signals by averaging the BOLD signals of all the voxels in each region, and to then measure directed functional interactions between the nodal signals. Another approach, that avoids averaging, is to measure directed functional interactions between all pairwise combinations of voxels in the two regions. Here we employ an alternative approach that avoids the drawbacks of both averaging and pairwise voxel measures. In this approach, we first use the Least Absolute Shrinkage Selection Operator (LASSO) to pre-select voxels for analysis, then compute a Multivariate Vector AutoRegressive (MVAR) model from the time series of the selected voxels, and finally compute summary Granger Causality (GC) statistics from the model to represent directed interregional interactions. We demonstrate the effectiveness of this approach on both simulated and empirical fMRI data. We also show that averaging regional BOLD activity to create a nodal signal may lead to biased GC estimation of directed interregional interactions. The approach presented here makes it feasible to compute GC between brain regions without the need for averaging. Our results suggest that in the analysis of functional brain networks, careful consideration must be given to the way that network nodes and edges are defined because those definitions may have important implications for the validity of the analysis

Large Vector Auto Regressions

One popular approach for nonstructural economic and financial forecasting is to include a large number of economic and financial variables, which has been shown to lead to significant improvements for forecasting, for example, by the dynamic factor models. A challenging issue is to determine which variables and (their) lags are relevant, especially when there is a mixture of serial correlation (temporal dynamics), high dimensional (spatial) dependence structure and moderate sample size (relative to dimensionality and lags). To this end, an \textit{integrated} solution that addresses these three challenges simultaneously is appealing. We study the large vector auto regressions here with three types of estimates. We treat each variable's own lags different from other variables' lags, distinguish various lags over time, and is able to select the variables and lags simultaneously. We first show the consequences of using Lasso type estimate directly for time series without considering the temporal dependence. In contrast, our proposed method can still produce an estimate as efficient as an \textit{oracle} under such scenarios. The tuning parameters are chosen via a data driven "rolling scheme" method to optimize the forecasting performance. A macroeconomic and financial forecasting problem is considered to illustrate its superiority over existing estimators

Large Vector Auto Regressions

One popular approach for nonstructural economic and financial forecasting is to include a large number of economic and financial variables, which has been shown to lead to significant improvements for forecasting, for example, by the dynamic factor models. A challenging issue is to determine which variables and (their) lags are relevant, especially when there is a mixture of serial correlation (temporal dynamics), high dimensional (spatial) dependence structure and moderate sample size (relative to dimensionality and lags). To this end, an integrated solution that addresses these three challenges simultaneously is appealing. We study the large vector auto regressions here with three types of estimates. We treat each variable's own lags different from other variables' lags, distinguish various lags over time, and is able to select the variables and lags simultaneously. We first show the consequences of using Lasso type estimate directly for time series without considering the temporal dependence. In contrast, our proposed method can still produce an estimate as efficient as an oracle under such scenarios. The tuning parameters are chosen via a data driven "rolling scheme" method to optimize the forecasting performance. A macroeconomic and financial forecasting problem is considered to illustrate its superiority over existing estimators.Time Series, Vector Auto Regression, Regularization, Lasso, Group Lasso, Oracle estimator

Semantic radical consistency and character transparency effects in Chinese: an ERP study

BACKGROUND: This event-related potential (ERP) study aims to investigate the representation and temporal dynamics of Chinese orthography-to-semantics mappings by simultaneously manipulating character transparency and semantic radical consistency. Character components, referred to as radicals, make up the building blocks used dur...postprin

Auditory enrichment for arousal reduction in non-vocal learning species

Passively listening to music and other auditory enrichments has been repeatedly demonstrated to be effective at reducing physiological arousal in a wide range of non-human species. Although statistically significant arousal reduction has been demonstrated, the size of this effect in most studies is generally small. To strengthen the previously demonstrated arousal-reducing effects of auditory enrichment, the aims of this thesis are: To understand what specific aspects of auditory enrichment have the greatest influence on arousal in dogs and horses. To determine if dogs have the auditory perceptive abilities that justify any assumptions of musical appreciation. To establish if a positive association with specific music can influence how that music can manipulate arousal in dogs. Classical music with the pitch and tempo altered; music based on the owner’s voice; and a range of metronome beats where trialled. Heartrate variability was the primary measure of effect and methods of measurement were validated in both dogs and horses prior to these studies. To test perception, a two-choice go/go selection paradigm was used. Changing the pitch or tempo of music made no difference to the arousal of dogs or horses. Bespoke music based on the owners’ voices had an equivalent effect on arousal in dogs as classical music and white noise. Limited testing of perception in dogs failed to demonstrate any ability to discriminate between different tempos, but the results of forming a positive association were suggestive of an increase in effect. An incidental finding was that auditory enrichment was more effective at reducing arousal when used in a noisy environment than when used in a quiet environment. Auditory enrichment has been demonstrated to have an arousal-reducing effect, however, the arousal-reducing effects of auditory enrichment in non-vocal learners may be stemming from simple mechanisms such as acoustic masking and/or the formation of positive associations

A study of event-related electrocortical oscillatory dynamics associated with cued motor-response inhibition during performance of the Go/NoGo task within a sample of prenatally alcohol-exposed children and age-matched controls

Fetal alcohol spectrum disorders (FASDs) are a spectrum of disorders that occur due to prenatal alcohol exposure (PAE). Response inhibition refers to the ability to inhibit/suppress a prepotent behavioural tendency set in motion during an experimental task. Our research explored neocortical processing in heavy-exposed children from Cape Town, South Africa, performing the Go/NoGo response inhibition task. We utilised event-related electroencephalographic methodologies to examine event-related potentials (ERP) and eventrelated changes in induced oscillatory power - event-related desynchronisation (ERD)/eventrelated synchronisation (ERS). Across visual and auditory Go/NoGo tasks, we observed equivalent levels of inhibitory control between heavy-exposed (HE) participants and normally-developing controls; however, HEs demonstrated significantly slower reaction times relative to the control group. In an auditory ERP study, we observed a number of alcohol-related changes in ERP waveform morphology, such as decreased P2 amplitude, reduced P3 amplitude, and longer P3 peak latency. In addition, within the HE group, late in the trials, a slow-wave component was observed in both experimental conditions. A significant difference in N2 amplitude across conditions that has consistently been observed in normally-developing samples was not observed in the HE group. We extended previous research findings in the visual domain by analysing induced oscillatory responses. We observed within the normally-developing sample: (1) in both experimental conditions, a frontal induced beta-band ERS related to decision-making; and (2) in the NoGo-condition, a frontal gamma-band ERS related to cognitive-control. Within the HE group, the beta-ERS was not observed in either of the experimental conditions, neither was the gamma-ERS observed in the NoGo-condition. Frontal induced beta-power was predictive of performance accuracy in the HE group, but not in the control group. The observed alcohol-related effects were not explained and/or mediated by IQ (WISC-IQ), socio-economic circumstances, comorbid ADHD, or teratogenic effects related to postnatal lead exposure and prenatal cigarette-smoke exposure. Our results point to alterations in scalp-measured event-related neocortical oscillatory dynamics and slower processing of task demands due to heavy PAE. These alcohol-related effects are observable on ERP component measures, primarily related to conflict-monitoring and attention-based processing. PAE also affects induced classes of neocortical oscillatory dynamics related to decision-making and cognitive-control processes required to inhibit a prepotent motor-response