In this article we consider the application of parametric spectral analysis to multichannel event-related potentials (ERPs) during cognitive experiments. We show that with proper data preprocessing, Adaptive MultiVariate AutoRegressive (AMVAR) modeling is an effective technique for dealing with nonstationary ERP time series. We propose a bootstrap procedure to assess the variability in the estimated spectral quantities. Finally, we apply AMVAR spectral analysis to a visuomotor integration task, revealing rapidly changing cortical dynamics during different stages of task processing.
The visual processing of behaviorally relevant stimuli is enhanced through top-down attentional feedback. One possibility is that feedback targets early visual areas first and the attentional enhancement builds up at progressively later stages of the visual hierarchy. An alternative possibility is that the feedback targets the higher-order areas first and the attentional effects are communicated "backward" to early visual areas. Here, we compared the magnitude and latency of attentional enhancement of firing rates in V1, V2, and V4 in the same animals performing the same task. We found a reverse order of attentional effects, such that attentional enhancement was larger and earlier in V4 and smaller and later in V1, with intermediate results in V2. These results suggest that attentional mechanisms operate via feedback from higher-order areas to lower-order ones.attention | Macaque | vision | feedback N europhysiologic and brain imaging studies in monkeys and humans have shown that attended stimuli evoke larger responses in visual cortex than unattended distracters (1-6), giving attended stimuli a competitive advantage for representation in the cortex (7). These top-down attentional effects are thought to be mediated in part by feedback from prefrontal and posterior parietal cortex (8-12) acting directly or indirectly on all visual areas in the dorsal and ventral stream, including V1. However, the mechanism of this feedback is unclear. In particular, a first-order question is whether the top-down feedback targets V1 [or even the lateral geniculate nucleus (LGN)] first and then is passed on successively to later areas, or whether it targets higher-order areas first and then is fed back to successively lower areas. Without an understanding of the basic functional anatomy of the attentional feedback, it will be difficult to make progress in unraveling the circuitry for attention.The magnitude and timing of attentional effects on visual responses in all of the different visual structures should, in principle, give insight into the direction of attentional effects along the visual pathways. However, a comparison of the magnitude of attentional effects across visual areas in different studies leads to a confusing picture. On the one hand, imaging studies in humans typically find that attentional effects on evoked responses become larger as one moves from V1 into higher-order areas (13,14). Several neurophysiologic studies in monkeys also often report small (4) or even nonexistent (6, 15, 16) attentional enhancement of firing rates to stimuli in the receptive fields (RFs) of V1 cells (17-19), compared with reliable findings of attentional effects in higher-order areas such as V4 in conventional target detection or discrimination paradigms. On the other hand, other primate studies report substantial attentional effects in V1 in complex tasks such as covertly tracking along curved lines with spatially directed attention (18). It has recently been demonstrated that even with a relatively simple selection task, very large at...
Brain age prediction using machine-learning techniques has recently attracted growing attention, as it has the potential to serve as a biomarker for characterizing the typical brain development and neuropsychiatric disorders. Yet one long-standing problem is that the predicted brain age is overestimated in younger subjects and underestimated in older. There is a plethora of claims as to the bias origins, both methodologically and in data itself. With a large neuroanatomical dataset (N = 2,026; 6-89 years of age) from multiple shared datasets, we show this bias is neither data-dependent nor specific to particular method including deep neural network. We present an alternative account that offers a statistical explanation for the bias and describe a simple, yet efficient, method using general linear model to adjust the bias. We demonstrate the effectiveness of bias adjustment with a large multi-modal neuroimaging data (N = 804; 8-21 years of age) for both healthy controls and post-traumatic stress disorders patients obtained from the Philadelphia Neurodevelopmental Cohort.
The traditional view on visual processing emphasizes a hierarchy: local line segments are first linked into global contours, which in turn are assembled into more complex forms. Distinct from this bottom-up viewpoint, here we provide evidence for a theoretical framework whereby objects and their parts are processed almost concurrently in a bidirectional cortico-cortical loop. By simultaneous recordings from V1 and V4 in awake monkeys, we found that information about global contours in a cluttered background emerged initially in V4, started ∼40 ms later in V1, and continued to develop in parallel in both areas. Detailed analysis of neuronal response properties implicated contour integration to emerge from both bottom-up and reentrant processes. Our results point to an incremental integration mechanism: feedforward assembling accompanied by feedback disambiguating to define and enhance the global contours and to suppress background noise. The consequence is a parallel accumulation of contour information over multiple cortical areas.
We have developed a Matlab/C toolbox, Brain-SMART (System for Multivariate AutoRegressive Time series, or BSMART), for spectral analysis of continuous neural time series data recorded simultaneously from multiple sensors. Available functions include time series data importing/exporting, preprocessing (normalization and trend removal), AutoRegressive (AR) modeling (multivariate/bivariate model estimation and validation), spectral quantity estimation (auto power, coherence and Granger causality spectra), network analysis (including coherence and causality networks) and visualization (including data, power, coherence and causality views). The tools for investigating causal network structures in respect of frequency bands are unique functions provided by this toolbox. All functionality has been integrated into a simple and user-friendly graphical user interface (GUI) environment designed for easy accessibility. Although we have tested the toolbox only on Windows and Linux operating systems, BSMART itself is system independent. This toolbox is freely available (http://www.brain-smart.org) under the GNU public license for open source development.
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