Spatio-temporal information analysis of event-related BOLD responses

Abstract

A new approach for analysis of event related fMRI (BOLD) signals is proposed. The technique is based on measures from information theory and is used both for spatial localization of task related activity, as well as for extracting temporal information regarding the task dependent propagation of activation across different brain regions. This approach enables whole brain visualization of voxels (areas) most involved in coding of a specific task condition, the time at which they are most informative about the condition, as well as their average amplitude at that preferred time. The approach does not require prior assumptions about the shape of the hemodynamic response function (HRF), nor about linear relations between BOLD response and presented stimuli (or task conditions). We show that relative delays between different brain regions can also be computed without prior knowledge of the experimental design, suggesting a general method that could be applied for analysis of differential time delays that occur during natural, uncontrolled conditions. Here we analyze BOLD signals recorded during performance of a motor learning task. We show that during motor learning, the BOLD response of unimodal motor cortical areas precedes the response in higher-order multimodal association areas, including posterior parietal cortex. Brain areas found to be associated with reduced activity during motor learning, predominantly in prefrontal brain regions, are informative about the task typically at significantly later times.

The Amplitude and Timing of the BOLD Signal Reflects the Relationship between Local Field Potential Power at Different Frequencies

“…As a result, information can capture the contributions of correlations at all orders. Moreover, and unlike for example the general linear model (Friston et al, 1994;Worsley and Friston, 1995), it can also properly capture the effect of any nonlinearity between the two signals (Fuhrmann Alpert et al, 2007). Having said this, it is trivial to model nonlinear dependencies within the general linear model by including nonlinear terms (e.g., polynomial expansions or classical interactions).…”

Information-theoretic methods for studying population codes

“…The mutual information has a number of important qualities that make it well suited to characterizing how well a neural response is modulated by the stimulus (recently reviewed for example by Borst & Theunissen (1999);Fuhrmann Alpert et al (2007); Panzeri et al (2008);Quian Quiroga & Panzeri (2009)). First, as outlined above, information theoretic techniques quantify information gains in single trials (rather than on average across trials) and this makes them biologically relevant, because brains recognize sensory stimuli and take decisions on single trials.…”

Propagation of Brain Activity during Audiovisual Integration

“…Compared with conventional GLM, the advantages of mutual information are that it measures not only the linear but also the nonlinear relationship between two random variables, and that no prior assumption about the shape of the relationship [e.g., hemodynamic response function (HRF)] is required (Fuhrmann Alpert et al, 2007). By estimating the mutual information between the preceding stimulus condition and the BOLD responses for each voxel and the latency after the onset of the stimuli, this approach can detect both brain activation and the preferred latency that maximizes the information content of the BOLD signal about the preceding stimuli.…”

“…The authors have argued previously that this novel method could be generalized for the analysis of sustained stimuli or short intertrial intervals (ITIs) because it does not require any assumption of linearity between the stimulus and BOLD response (Fuhrmann Alpert et al, 2007). However, for short ITI designs, more than one stimulus may contribute to the BOLD signal at short latencies because the BOLD response is sluggish and sustained, such that the response to the first stimulus has not returned to the baseline when the following stimulus evokes a subsequent response.…”

“…It is known that different brain areas may have different HRFs (Lee et al, 1995). In their previous study (Fuhrmann Alpert et al, 2007), the authors argued that one advantage of this novel information-theoretic approach is that it does not require any assumption of the shape of HRF. For activation detection, this is indeed true when compared with conventional GLM.…”