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Measuring information processing in neural data: The application of transfer entropy in neuroscience
(2017)
It is a common notion in neuroscience research that the brain and neural systems in general "perform computations" to generate their complex, everyday behavior (Schnitzer, 2002). Understanding these computations is thus an important step in understanding neural systems as a whole (Carandini, 2012;Clark, 2013; Schnitzer, 2002; de-Wit, 2016). It has been proposed that one way to analyze these computations is by quantifying basic information processing operations necessary for computation, namely the transfer, storage, and modification of information (Langton, 1990; Mitchell, 2011; Mitchell, 1993;Wibral, 2015). A framework for the analysis of these operations has been emerging (Lizier2010thesis), using measures from information theory (Shannon, 1948) to analyze computation in arbitrary information processing systems (e.g., Lizier, 2012b). Of these measures transfer entropy (TE) (Schreiber2000), a measure of information transfer, is the most widely used in neuroscience today (e.g., Vicente, 2011; Wibral, 2011; Gourevitch, 2007; Vakorin, 2010; Besserve, 2010; Lizier, 2011; Richter, 2016; Huang, 2015; Rivolta, 2015; Roux, 2013). Yet, despite this popularity, open theoretical and practical problems in the application of TE remain (e.g., Vicente, 2011; Wibral, 2014a). The present work addresses some of the most prominent of these methodological problems in three studies.
The first study presents an efficient implementation for the estimation of TE from non-stationary data. The statistical properties of non-stationary data are not invariant over time such that TE can not be easily estimated from these observations. Instead, necessary observations can be collected over an ensemble of data, i.e., observations of physical or temporal replications of the same process (Gomez-Herrero, 2010). The latter approach is computationally more demanding than the estimation from observations over time. The present study demonstrates how to handles this increased computational demand by presenting a highly-parallel implementation of the estimator using graphics processing units.
The second study addresses the problem of estimating bivariate TE from multivariate data. Neuroscience research often investigates interactions between more than two (sub-)systems. It is common to analyze these interactions by iteratively estimating TE between pairs of variables, because a fully multivariate approach to TE-estimation is computationally intractable (Lizier, 2012a; Das, 2008; Welch, 1982). Yet, the estimation of bivariate TE from multivariate data may yield spurious, false-positive results (Lizier, 2012a;Kaminski, 2001; Blinowska, 2004). The present study proposes that such spurious links can be identified by characteristic coupling-motifs and the timings of their information transfer delays in networks of bivariate TE-estimates. The study presents a graph-algorithm that detects these coupling motifs and marks potentially spurious links. The algorithm thus partially corrects for spurious results due to multivariate effects and yields a more conservative approximation of the true network of multivariate information transfer.
The third study investigates the TE between pre-frontal and primary visual cortical areas of two ferrets under different levels of anesthesia. Additionally, the study investigates local information processing in source and target of the TE by estimating information storage (Lizier, 2012) and signal entropy. Results of this study indicate an alternative explanation for the commonly observed reduction in TE under anesthesia (Imas, 2005; Ku, 2011; Lee, 2013; Jordan, 2013; Untergehrer, 2014), which is often explained by changes in the underlying coupling between areas. Instead, the present study proposes that reduced TE may be due to a reduction in information generation measured by signal entropy in the source of TE. The study thus demonstrates how interpreting changes in TE as evidence for changes in causal coupling may lead to erroneous conclusions. The study further discusses current bast-practice in the estimation of TE, namely the use of state-of-the-art estimators over approximative methods and the use of optimization procedures for estimation parameters over the use of ad-hoc choices. It is demonstrated how not following this best-practice may lead to over- or under-estimation of TE or failure to detect TE altogether.
In summary, the present work proposes an implementation for the efficient estimation of TE from non-stationary data, it presents a correction for spurious effects in bivariate TE-estimation from multivariate data, and it presents current best-practice in the estimation and interpretation of TE. Taken together, the work presents solutions to some of the most pressing problems of the estimation of TE in neuroscience, improving the robust estimation of TE as a measure of information transfer in neural systems.
Reading is an essential ability to master everyday life in our society. The ability to read is based on specific connections between brain regions involved in the reading process – so-called cortical networks for reading. These cortical networks for reading allow us to learn the correct identification of visual words. The use of visual words is based on knowledge about the orthography (lexical) and the meaning of words (semantic). This knowledge must be acquired by beginning readers (first grader), i.e. beginning readers learn in a first step to link letters to a whole word and in a second step associate this whole word with meaning. To retrieve this knowledge during visual word recognition (VWR) a cortical network for lexical-semantic process must be activated. However, it is currently unclear whether beginning readers and reading experts activate the same neuronal network during VWR. Therefore, the aim of this thesis was to investigate the question whether beginning readers (first grader, children) and reading experts (adults) use different cortical networks for the lexical-semantic processing in VWR.
To address this question we recorded electroencephalographic (EEG) activity during VWR in children and adults. Children and adults were instructed to read a visualizable word to compare this word with a following picture stimulus. The first part of this thesis is concerned with the analysis of ERPs for visual word recognition in children and adults at sensor level. For both groups we observed the typical ERP components P100 and N170 for visual word recognition. These components differed in amplitude and time course between both groups. The second part of this thesis investigated the neuronal generators (brain areas) of ERPs during VWR and possible differences between children and adults at source level. We observed a high overlap in brain areas involved during VWR in children and adults. However, the brain areas differed in activation and time course between children and adults. Finally, the third and most important part of the thesis investigated the question whether children and adults use different cortical networks for the lexical-semantic processing in VWR over time. To address this question Dynamic Causal Modeling (DCM) and Bayesian model comparison were used. We compared nine biologically plausible cortical network models underlying the ventral lexical-semantic path in VWR. In addition, increasing time intervals were used to consider possible changes of network structure during VWR. The network models included eight brain regions (four bilateral pairs) involved in the lexical-semantic processing in VWR: occipital cortex (OC), temporo-occipital part of inferior temporal gyrus (ITG), temporal pole (TP), and inferior frontal gyrus (IFG). In almost all time intervals we found evidence that children and adults use the same cortical networks for the lexical-semantic processing in VWR. However, we found differences between adults and children in the connection strengths of the favoured model. Interestingly, we found a stronger direct connection from OC to IFG in adults compared to children.
In conclusion, our results suggest that children and adults activate largely the same lexical-semantic networks during VWR over time. This supports the notion that children and adults use the same biological fiber connections for VWR. However in contrast to children, adults showed increased use of the shortcut pathway from OC to IFG. The increased use of the shortcut pathway from OC to IFG in adults can be interpreted as consequence of learning. Learning causes in accordance with the Hebbian learning rule (“neurons that fire together, wire together” (Hebb, 1949)) synaptic change. Consequently the frequent coactivation of the input and output stage of OC and IFG during the lexical-semantic process facilitates the stronger direct connection between both brain areas. The stronger direct connection from OC to IFG most likely allows adult reading experts to speed up the lexical-semantic process during VWR. Accordingly, we conclude that the stronger direct connections from OC to IFG in adults compared to children underlay the different reading capabilities in both groups.
Magnetoencephalography (MEG) measures neural activity non-invasively and at an excellent temporal resolution. Since its invention (Cohen, 1968, 1972), MEG has proven a most valuable tool in neurocognitive (Salmelin et al., 1994) and clinical research (Stufflebeam et al., 2009; Van ’t Ent et al., 2003). MEG is able to measure rapid changes in electrophysiological neural signals related to sensory and cognitive processes. The magnetic fields measured outside the head by MEG directly reflect the cortical currents generated by the synchronised activity of thousands of neuronal sources. This distinguishes MEG from functional magnetic resonance imaging (fMRI), where measurements are only indirectly related to electrophysiological activity through neurovascular coupling...