Claudia AM Wheeler-Kingshott, Ph.D.
University Reader in Magnetic Resonance Physics, Department of Neuroinflammation, UCL Institute of Neurology, London, UK
Abstract
Detecting neuronal activity in vivo non-invasively is possible with a number of techniques. Amongst these, in 1990 functional magnetic resonance imaging (fMRI) was proposed as a technique that has a great ability to spatially map brain activity by exploiting the blood oxygenation level dependent (BOLD) contrast mechanism [1, 2]. In fact, neuronal activation triggers a demand for oxygen and induces a localised increase in blood flow and blood volume, which actually exceeds the metabolic needs. This in turns causes an increase of oxyhaemoglobin in the venous compartment, which is a transient phenomenon and is accompanied by a transient change (decrease) in the concentration of deoxyhaemoglobin. Due to its paramagnetic properties, the amount of deoxyhaemoglobin present in the venous blood affects the local magnetic field seen by the spins (protons) and determines the local properties of the MR signal. A decrease in deoxyhaemoglobin during neuronal activity, therefore, induces local variations of this magnetic field that increases the average transverse relaxation time of tissue, measured via the T2* parameter [3]. This means that there is an increase of the MR signal (of the order of a few %, typically <5%) linked to metabolic changes happening during brain function. Activation can be inferred at different brain locations by performing tasks while acquiring the MR signal and comparing periods of rest to periods of activity.
The macroscopic changes of the BOLD signal are well characterised, while the reason for the increased blood supply, exceeding demands, needs further thoughts. Here we will discuss two approaches for explaining the BOLD phenomenon, one that links it to adenosine triphosphate production [4] and enzyme saturation, the other that relates it to the very slow diffusion of oxygen through the blood-brain-barrier with a consequent compensatory high demand of oxygen [5]. Some evidence of restricted oxygen diffusion has been shown by means of hypercapnia [6], although it is not excluded that both mechanisms may be present.
Overall, the BOLD signal changes theory and its physiological basis will be presented and discussed.
References
- Ogawa, S., et al., Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A, 1990. 87(24): p. 9868-72.
- Kwong, K.K., et al., Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci U S A, 1992. 89(12): p. 5675-9.
- Bandettini PA, et al. Spin-echo and gradient-echo EPI of human brain activation using BOLD contrast: a comparative study at 1.5 T. NMR Biomed. 1994 Mar;7(1-2):12-20
- Fox, P.T., et al., Nonoxidative glucose consumption during focal physiologic neural activity. Science, 1988. 241(4864): p. 462-4.
- Gjedde, A., et al. Reduction of functional capillary density in human brain after stroke. J Cereb Blood Flow Metab, 1990. 10(3): p. 317-26.
- Hoge, R.D., et al., Linear coupling between cerebral blood flow and oxygen consumption in activated human cortex. Proc Natl Acad Sci U S A, 1999. 96(16): p. 9403-8.
Egidio D’Angelo, MD, Ph.D.
Full Professor of Physiology & Director, Brain Connectivity Center, University of Pavia, Italy
Realistic modeling: new insight into the functions of the cerebellar network
Realistic modeling is an approach based on the careful reconstruction of neurons synapses starting from biological details at the molecular and cellular level. This technique, combined with the connection topologies derived from histological measurements, allows the reconstruction of precise neuronal networks. Finally, the advent of specific software platforms (PYTHON-NEURON) and of super-computers allows large-scale network simulation to be performed in reasonable time. This approach inverts the logics of older theoretical models, which anticipated an intuition on how the network might work. In realistic modeling, network properties “emerge” from the numerous biological properties embedded into the model.
This approach is illustrated here through an outstanding application of realistic modeling to the cerebellar cortex network. The neurons (over 105) are reproduced at a high level of detail generating non-linear network effects like population oscillations and resonance, phase-reset, bursting, rebounds, short-term and long-term plasticity, spatiotemporal redistrbution of input patterns. The model is currently being used in the context of he HUMAN BRAIN PROJECT to investigate the cerebellar network function.
Correspondence should be addressed to
Dr. EgidioD’Angelo,
Laboratory of Neurophysiology
Via Forlanini 6, 27100 Pavia, Italy
Phone: 0039 (0) 382 987606
Fax: 0039 (0) 382 987527
dangelo@unipv.it
Acknowledgments
This work was supported by grants from European Union to ED (CEREBNET FP7-ITN238686, REALNET FP7-ICT270434) and by grants from the Italian Ministry of Health to ED (RF-2009-1475845).
