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Scaling errors in measures of brain activity cause erroneous estimates of effective connectivity
KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
2010 (English)In: NeuroImage, ISSN 1053-8119, E-ISSN 1095-9572, Vol. 49, no 1, 621-630 p.Article in journal (Refereed) Published
Abstract [en]

Effective connectivity (EC) is the collective term for various measures of the interaction between the nodes in a network of neurons or neural populations during a certain experimental condition. Here, I investigated three types of EC that differ with respect to signal normalization, and therefore measure different aspects of neural interactions. Unnormalized EC measures pure connection strength. Amplitude-scaled EC measures the combined influence of signal amplitude and connection strength on neural activity. Finally, normalized EC measures the influence of one node on the activity of another relative to all influences on that node. With a theoretical analysis, I investigated the sensitivity of EC to signal scaling (the ratio of the amplitude of the measured signal and the underlying neural activity) and found that scaling affects the conclusions of the analysis of unnormalized EC severely, whereas normalized EC is not affected by the scaling problem. In an analysis of previously published hemodynamic response functions (Handwerker, D. A., Ollinger, J. M., D'Esposito, M., 2004. Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. Neuroimage 21, 1639-1651), I tested the predictions of the theoretical analysis. The empirical analysis indicated that signal scaling contributes to a large extent to measurement errors of unnormalized EC, although hemodynamic response function shape variability also contributed. Normalized EC, on the other hand, was only affected by shape differences and not by scaling. In addition to being more accurate, normalized EC is also an appropriate type of measure of neural interactivity if one is interested in the relative influence of one node on another, rather than absolute connection strengths per se.

Place, publisher, year, edition, pages
2010. Vol. 49, no 1, 621-630 p.
Keyword [en]
amplitude modulation; article; brain function; brain region; controlled study; nerve cell network; nuclear magnetic resonance imaging; priority journal
National Category
Biological Sciences
URN: urn:nbn:se:kth:diva-7810DOI: 10.1016/j.neuroimage.2009.07.007ISI: 000272031700062ScopusID: 2-s2.0-70349971123OAI: diva2:12943
QC 20100705. Uppdaterad från Accepted till Published i DiVA 20100705.Available from: 2007-12-12 Created: 2007-12-12 Last updated: 2010-09-21Bibliographically approved
In thesis
1. Neural Mechanisms Determining Visuospatial Working Memory Tasks: Biophysical Modeling, Functional MRI and EEG
Open this publication in new window or tab >>Neural Mechanisms Determining Visuospatial Working Memory Tasks: Biophysical Modeling, Functional MRI and EEG
2007 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

Visuospatial working memory (vsWM) is the ability to temporarily retain goal-relevant visuospatial information in memory. It is a key cognitive function related to general intelligence, and it improves throughout childhood and through WM training. Information is maintained in vsWM through persistent neuronal activity in a fronto-parietal network that consists of the intraparietal sulcus (IPS) and the frontal eye field (FEF). This network is regulated by the dorsolateral prefrontal cortex (dlPFC).

The features of brain structure and activity that regulate the access to and storage capacity of visuospatial WM (vsWM) are still unknown. The aim of my doctoral work has been to find such features by combining a biophysically based model of vsWM activity with functional MRI (fMRI) and EEG experiments.

In study I, we combined modeling and fMRI and showed that stronger fronto-parietal synaptic connections result in developmental increases in brain activity and in improved vsWM during development. This causal relationship was established by ruling out other previously suggested mechanisms, such as myelination or synaptic pruning,

In study II, we combined modeling and EEG to further explore the connectivity of the network. We showed that FEF→IPS connections are stronger than IPS→FEF connections, and that stimuli enter IPS. This arrangement of connections prevents distracting stimuli from being stored.

Study III was a theoretical study showing that errors in measurements of the amplitude of brain activity affect the estimation of effective connection strength.

In study IV, we analyzed EEG data from WM training in children with epilepsy. Improvements on the trained task were accompanied by increased frontal and parietal signal power, but not fronto-parietal coherence. This indicates that local changes in FEF and IPS could underlie improvements on the trained task.

dlPFC is important for the performance on a large variety of cognitive tasks.

In study V, we combined modeling with fMRI to test the hypothesis that dlPFC improves vsWM capacity by providing stabilizing excitatory inputs to IPS, and that dlPFC filters distracters by specifically lowering the capacity of neurons storing distracters. fMRI data confirmed the model hypothesis. We further showed that a dysfunctional dlPFC could explain the link between vsWM capacity and distractibility, as is found in ADHD. The model suggests that dlPFC carries out its multifaceted behavior not by performing advanced calculations itself, but by providing bias signals that control operations performed in the regions it connects to.

A specific aim of this thesis has been to describe the mechanistic model in a way that is accessible to people without a modeling background.

Place, publisher, year, edition, pages
Stockhollm: KTH, 2007. vi, 57 p.
Trita-CSC-A, ISSN 1653-5723 ; 2007:23
National Category
Computer Science
urn:nbn:se:kth:diva-4577 (URN)978-91-7178-832-0 (ISBN)
Public defence
2008-01-11, Sal D2, KTH, Lindstedtsvägen 5, Stockholm, 13:00
QC 20100705Available from: 2007-12-12 Created: 2007-12-12 Last updated: 2010-07-05Bibliographically approved

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