Scott Makeig
Swartz Center for Computational Neuroscience, Institute for Neural Computation, UCSD

Viewing event-related brain dynamics from the top down

Tuesday 22nd of November 2005 at 04:00pm
5101 Tolman

Increasing evidence as well as simple logic point to the predominance of 'top-down' over 'bottom-up' roles for macroscopic field activities and fMRI BOLD signals recorded non-invasively from the human brain. Unfortunately, standard analysis techniques based on response-locked averaging are ideal only for modeling stereotyped, stimulus time- and phase-locked (bottom-up) brain 'impulse' responses. Furthermore, response averaging methods both proceed from and force the assumption that the set of events used to select the data epochs to be averaged must evoke or induce equivalent brain respones.
A quite different point of view leads to a different analytic approach, It begins with the assumption that the ultimate and actual purpose of brain activity is to prepare the subject (or organism) to respond immediatesly and most appropriately to the anticipated consequences of events, while actively guiding perception to best anticipate those consequences. The role of macroscopic brain activity in active 'top-down' perception involves active moment-to-moment regulation of the distribution of attention, both among sensory modalities and between sensation and association, within a moving 'present context' sustained by a (poorly understood) contextual 'working memory' system. From this point of view, each experimental event in any event-related task paradigm poses a fresh and potentially unique challenge. Superficially identical events (e.g., 'targets') in a behavioral task paradigm occur in different perceptual and behavioral contexts - posing a challenge that the brain must respond to directly with renewed or adjusted distributed attentional focus and motor readiness.
The challenge for computational cognitive neuroscience is, therefore, to develop analysis methods capable of discovering relationships between high-dimensional brain activity recordings and the significance-in-context of sensory events and subject actions. The growing field of information- based signal processing suggests a promising approach to this problem which I will illustrate with simple examples. The consequences of this approach may lead to better understanding of integrative ('top-down') brain function -- understanding unlikely to develop from analysis of microscale recordings alone. These methods could also be more readily applied to buildng soon-feasible clinical and workplace brain monitoring systems.

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