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Session IV
Electro-Vascular Coupling
and Brain Energy Budget
Barry Horwitz, NIH (Bethesda) – Chair
David Attwell, Univ College London
The Brain’s Energy Budget and the Control of Cerebral Blood Flow
Dae-Shik Kim, Boston U.
Spatial Coupling Between fMRI and Electrophysiology
Jorge Riera, Tohoku University (Sendai)
The Multiple Roles of Nitric Oxide (NO) in Neurovascular Coupling:
A Model of NO Concentration in Brain Tissue
Why is this issue at a connectivity meeting?
1. Important for its own sake
2. Important because of its relation to
interpreting fMRI and PET/rCBF data
3. Important because of its relation to
neural modeling
Recent efforts at large-scale modeling require
integrating two models:
1. model of the neural activity
2. model that transforms the relevant
neural activity to fMRI or PET signals (or EEG/MEG signals)
1993: Meeting on Neural Modeling and
Functional Brain Imaging
• Brought together modelers and functional brain
imagers for the first time.
• Tried to determine what research questions modelers
could address
• The four questions:
– Relation between neural activity and imaging
signals
– Effective connectivity evaluation from imaging data
– Integration of multimodality imaging data
– Connecting multi-regional neural data to imaging
data
Horwitz, B. and Sporns, O.: Neural modeling and functional
neuroimaging. Human Brain Mapping 1: 269-283, 1994.
Example
Visual objects - Tagamets & Horwitz, Cerebral Cortex, 1998
Auditory objects - Husain et al., Neuroimage, 2004
Excitatory EE
Horizontal
selective
units
Inhibitory EI
FS
Horizontal
selective
units
Corner
selective
units
LGN
Vertical
selective
units
IT
Vertical
selective
units
FR
D2
Attention
V1/V2
D1
PFC
V4
Model can do the following:
1. Simulate neural activity in each region
3. Simulate fMRI/PET activity in each region
5. Simulate MEG activity
2. Simulate human behavioral performance
4. Predicted existence of a type of neuron
6. Simulate functional & effective connectivity
Assumptions Used
(1) BOLD and rCBF reflect the input to neurons;
(2) excitatory and inhibitory inputs lead to increased BOLD and rCBF
•
•
fMRI
– Integrate the absolute value of the synaptic activity over
50msec
– Convolve with a hemodynamic response function (e.g.,
Boynton model)
– Downsample every TR to get fMRI data
MEG
– Local MEG signal is proportional to the difference between the
excitatory and inhibitory synaptic activity on the excitatory
units (e.g., pyramidal neurons)
Some Issues Relevant to Modeling
1. What is the relation between neural activity and neuroimaging data?
2. Is it the same everywhere in the brain?
3. How do we take account of the activity in the different cortical
layers?
4. There is a spread of neural activity due to local processing. Is it the
same or different than the spread of hemodynamic change?