Lesion and functional brain imaging studies have suggested that there are two anatomically nonoverlapping attention networks. The dorsal frontoparietal network controls goal-oriented top-down deployment of attention; the ventral frontoparietal network mediates stimulus-driven bottom-up attentional reorienting. The interaction between the two networks and its functional significance has been considered in the past but no direct test has been carried out. We addressed this problem by recording fMRI data from human subjects performing a trial-by-trial cued visual spatial attention task in which the subject had to respond to target stimuli in the attended hemifield and ignore all stimuli in the unattended hemifield. Correlating Granger causal influences between regions of interest with behavioral performance, we report two main results. First, stronger Granger causal influences from the dorsal attention network (DAN) to the ventral attention network (VAN), i.e., DAN3 VAN, are generally associated with enhanced performance, with right intraparietal sulcus (IPS), left IPS, and right frontal eye field being the main sources of behavior-enhancing influences. Second, stronger Granger causal influences from VAN to DAN, i.e., VAN3 DAN, are generally associated with degraded performance, with right temporal-parietal junction being the main sources of behavior-degrading influences. These results support the hypothesis that signals from DAN to VAN suppress and filter out unimportant distracter information, whereas signals from VAN to DAN break the attentional set maintained by the dorsal attention network to enable attentional reorienting.
Dorsal anterior cingulate and bilateral anterior insula form a task control network (TCN) whose primary function includes initiating and maintaining task-level cognitive set and exerting top-down regulation of sensorimotor processing. The default mode network (DMN), comprising an anatomically distinct set of cortical areas, mediates introspection and self-referential processes. Resting-state data show that TCN and DMN interact. The functional ramifications of their interaction remain elusive. Recording fMRI data from human subjects performing a visual spatial attention task and correlating Granger causal influences with behavioral performance and blood-oxygen-level-dependent (BOLD) activity we report three main findings. First, causal influences from TCN to DMN, i.e., TCN→DMN, are positively correlated with behavioral performance. Second, causal influences from DMN to TCN, i.e., DMN→TCN, are negatively correlated with behavioral performance. Third, stronger DMN→TCN are associated with less elevated BOLD activity in TCN, whereas the relationship between TCN→DMN and DMN BOLD activity is unsystematic. These results suggest that during visual spatial attention, top-down signals from TCN to DMN regulate the activity in DMN to enhance behavioral performance, whereas signals from DMN to TCN, acting possibly as internal noise, interfere with task control, leading to degraded behavioral performance.
Multivariate neural data provide the basis for assessing interactions in brain networks. Among myriad connectivity measures, Granger causality (GC) has proven to be statistically intuitive, easy to implement, and generate meaningful results. Although its application to functional MRI (fMRI) data is increasing, several factors have been identified that appear to hinder its neural interpretability: (a) latency differences in hemodynamic response function (HRF) across different brain regions, (b) low-sampling rates, and (c) noise. Recognizing that in basic and clinical neuroscience, it is often the change of a dependent variable (e.g., GC) between experimental conditions and between normal and pathology that is of interest, we address the question of whether there exist systematic relationships between GC at the fMRI level and that at the neural level. Simulated neural signals were convolved with a canonical HRF, down-sampled, and noise-added to generate simulated fMRI data. As the coupling parameters in the model were varied, fMRI GC and neural GC were calculated, and their relationship examined. Three main results were found: (1) GC following HRF convolution is a monotonically increasing function of neural GC; (2) this monotonicity can be reliably detected as a positive correlation when realistic fMRI temporal resolution and noise level were used; and (3) although the detectability of monotonicity declined due to the presence of HRF latency differences, substantial recovery of detectability occurred after correcting for latency differences. These results suggest that Granger causality is a viable technique for analyzing fMRI data when the questions are appropriately formulated.
Granger causality is increasingly being applied to multi-electrode neurophysiological and functional imaging data to characterize directional interactions between neurons and brain regions. For a multivariate dataset, one might be interested in different subsets of the recorded neurons or brain regions. According to the current estimation framework, for each subset, one conducts a separate autoregressive model fitting process, introducing the potential for unwanted variability and uncertainty. In this paper, we propose a multivariate framework for estimating Granger causality. It is based on spectral density matrix factorization and offers the advantage that the estimation of such a matrix needs to be done only once for the entire multivariate dataset. For any subset of recorded data, Granger causality can be calculated through factorizing the appropriate submatrix of the overall spectral density matrix.
An increase in the incidence of water-borne human diseases, such as diarrhea and emesis, has occurred due to drinking polluted water. These water-borne diseases can lead to death, if correct treatment is not provided. Assuring that drinking water quality is safe has been a crucial challenge for public health. Water contamination with pathogenic microorganisms represents a seriously increased threat to human health. Currently, different microorganisms are being used as the primary indicator to assess water quality total coliform and Escherichia coli (E. coli) being the most common. However, increasing the occurrence of water-borne illness from sources deemed safe by the microbial standard criteria has raised the question—are these microbial indicators reliable and sensitive enough to ensure water quality? Currently, other microorganisms including bacteria, enteric virus, and protozoa are being tested and used in different countries as alternative indicators to monitor water quality. It is necessary to study the diverse water quality indicator systems used throughout the world and their efficacy with the present water quality. Although water quality standards suggest adding pathogenic microorganisms such as enteric virus as an indicator, China only uses pathogenic E. coli, protozoa. Pin-pointing the shortage of the current water quality indicator system in China is crucial in order to propose changes in future water quality indicator systems.
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