Dynamic Causal Modelling - Karl Friston

so in the past 30 years possibly 20 years there's been a revolution in cognitive neuroscience and systems neuroscience a revolution that's been [Music] accelerated by the capability to look at the brain in action to image the brain using either metabolic or hemodynamic tools like functional magnetic resonance imaging or indeed using electromagnetic responses as measured non-invasively by EEG and M eg the picture that is emerging of how the brain works has two aspects on the one side it is clear that different parts of the brain are specialized for processing particular aspects of our sensorial for doing particular cognitive operations like a memory or attentional processing of emotions nearly every job that your brain does that you can conceive of probably has a dedicated brain system or set of areas or regions that talk to each other so that's the principle of functional specialization and when that specialization becomes segregated in a little cortical area say the size of my thumbnail for example visual motion processing roughly in this part of the brain here that segregation is known as functional segregation so a segregation of a functional specialization for doing a particular thing of the many things that the brain does we have for the first 10 years spent a lot of time with careful experimental design rigorous data analysis trying to assign functional specialization to different brain areas to build a map of how what different parts of brain are responsible for this is known as cartography it's also being criticized as neo phrenology so phonology was a procedure many centuries ago whereby was thought visually by palpating the skull and feeling for little lumps and knobs and bumped one can diagnose and infer the sort of person and the competences and the functional processes that the brain was engaged in simply by palpating and many people think that functional neuroimaging functional imaging suffers from the same philosophical shortcoming just by looking at bumps in metabolic activity or regional hotspots often refer to as blogs you're just recapitulating and the same sort of conceptual error that the phonologists of the of the 19th century were committing that Christmas my think is greatly mitigated by the second principle and the second principle is functional integration from the one hand we have these segregated brain regions sometimes referred to as nodes in in a graph or a distributed network and then we have to think about how those nodes or those regions are coupled or connected how they talk to each other how they are integrated how that processing is distributed over nodes or regions in a coordinated and organized and functional way and that integration of the distributed responses the description presence is punctual integration so now you're in the game of having established or your favorite function specialized areas now you want to know how they are integrated how they talk to each other how they are coupled and over the past decade or so is to come together there are two ways in which one can characterize this coupling some you can either to look at correlations in activity of two brain regions so say we imagine we have two parts of the brain this one dealing with visual information from the one side of the visual field and this one dealing with vision information the other side of the visual field and if they talk to each other and share information we might expect that during our brain imaging experiments or during our eg experiments as the activity in one of these areas goes up so will the activity in the other area so there's now a correlation or a statistical dependency between the measured responses in each of these functions segregated or specialized brain areas as well as functional connectivity it's easy to measure it's operationally defined and what it tells you is that somehow the processing over time of these two different brain areas are coupled in the sense that they're likely to be doing similar things so they are both engaged in the same distributed pattern of activity what it doesn't tell you is how the activity here influences the activity here and vice versa so just knowing two things are correlated or functionally connected it doesn't tell you about the directed influence that one brain region exerts over another and that's called affective connectivity so functional connectivity correlations dependences and operational definition affective connectivity directed causal connections mediated by long slender external neuronal processes so that you're driving activity here in a way that depends upon the activity here so that's where dynamic causal modeling comes in so dynamic calls and modeling speaks to the fact that in order to make sense of brain imaging data for example or EEG data or M eg data you have to have a model of how this part of the system influences this part of the system and vice-versa you have to do that in order to interpret the data and put very simply once you've established a model the model of coupling then you can ask what coupling parameters what model parameters of that causal model a model of the causal inferences of this part of the system or node on this part of the system best account the observed data so this is in a sense a model fitting exercise where you've got this distributed pattern of activity throughout the brain and you want to fit this particular model to explain the data and this particular model is all about the dynamics of fluctuations of brain regions that are causing activity in other brain regions hence dynamic causal modeling technically it's just a state space model it's a sort of models which people engage in any time series analysis would normally call upon to understand how saying the weather unfolds so technically speaking these are exactly the same sort of model vbu de beautiful weather forecasting or in economics the fluctuations in the markets how one event over here causes changes in an event over here and how that unfolds over time as many distributed events all calls each other in reciprocal and recurrent way so that is in essence dynamical the modeling it's the the technology that has been brought to bear on deep questions about functional integration about functional architectures so we've moved beyond the functional anatomy of functional specialization and segregation and now we're talking about networks is to be depressing an architecture that equipped not just with where stuff is happening but how stuff here is distributed and influences and is step over here and then there all sorts of interesting questions about the brain network about what has recently be called the connectome how does that architecture inform our understanding of how the brain works so one simple example here would be the notion of a brain hierarchy the idea that there are certain nodes or regions in the brain that are very close to sensory information say the back of the brain in receipt of visual information per primary auditory cortex and on the side the drain directly in receivable hearing or auditory information and these parts of the brain would be at a higher octave lower level and yet if we move into the hierarchy deeper in the brain say towards the front of the brain for example then we have this notion that there are part of the brain engaged in higher-level more abstract representations modeling of the causes of the sensory inputs because if you have a model of a brain as a hierarchy of interconnected regions with some levels of the hierarchy being subordinate or lower to higher levels of the hierarchy then that presupposes there's a difference between bottom-up connections and top-down connections so that distinction is absolutely fundamental to understand functional brain architectures and implicit in that distinction between bottom up from the sensorium from the century' cortical areas through to higher cortical areas in the prefrontal cortex at the front of the brain then you are talking about the difference between directed connections which of course requires you to measure this directed affective connectivity so many of the applications of genomic all the modeling are to understand data from an imaging experiment either with fMRI or the electromagnetic sort in terms of the distinction between what an app processing and top-down processing and one important aspect of that top-down processing is to contextualize and to select the channels that can provide the bottom of input so as I'm talking them I am selecting specifically certain cues in terms of your when I yo should say certain words and where I am in terms of the narrative that I am pursuing and in that selection I am giving weight to and modulating and contextualize and sourcing information that I need to sample to work out what I'm going to do next so that practically simply means switching on some connections or switching off other connections so what am i saying here that well to understand the context sensitive nature of functional architectures in the brain we need to understand how the connection strengths the effective connectivity between different brain areas is itself contextualized and controlled on a moment-to-moment basis so that's probably the most interesting aspect of dynamic causal modeling it is not the architecture in and of itself although that is very important it's how those connection strengths that coupling changes as a function of what I am doing what am i tending to what I'm intending to do so all that higher cognitive function becomes then characterized in terms of tuning the coupling and selecting which connections are in play any one time dyma calls and modeling in conclusion is a modeling procedure that allows one to pose questions about functional brain architectures or indeed the architectures of any coupled a dynamical system to date them to ask questions not only about the which connections are present and how they're deployed is this essential people or is a hierarchical structure fully connected is if they sparse diffic have small world characteristics all of these characteristics and where understanding networks depend upon knowing which connections are present and which are not present and which are in play and which are not in plane furthermore beyond that I can equip these models with a context sensitivity by saying in this condition these sets of connections will be active and in this situation they won't be and I can have a connection context sensitivity built into my model and I can estimate that and start to tell you which connections you're using at the moment while whilst listening to me

Channel: Serious Science

Views: 16,794

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Keywords: science, lecture, Serious Science, brain imaging, neuroimaging, fMRI, MRI, neuroscience, brain, functional connectivity, brain structure

Id: RXTizOtvsE8

Length: 13min 59sec (839 seconds)

Published: Mon May 01 2017