1. Electrophysiology & fMRI

2. Neurons Neural computation Neural selectivity Hierarchy of neural processing

3. Integration of information Retinal ganglion cell receptive fields V1 neuron receptive field (Hubel & Wiesel) Integrate

4. Grandma cell vs. distributed population Sparse coding Narrow selectivity

5. Grandma cell vs. distributed population Distributed coding Broad selectivity

6. Cortical columns Neighboring neurons often share the same selectivity and are strongly connected. “units of computation/selectivity” Why such redundancy? True for early sensory cortices.

7. Neurovascular coupling Iadecola et. al. 2007

8. Synaptic activity “Input” versus “output” of a neuron Ions moving across the membrane

9. Energy consumption Brain activity consumes 20% of the body’s energy at rest. Glucose + oxygen => ATP 80% of the brain’s ATP is used to keep sodium potassium gradient.

10. Energy consumption The power required for neural “Signaling” is a sum of both neural output (spikes) and input: Lennie P. Curr Bio 2003 Input Output

11. Neural activity costs Input is more expensive than output. Neurons spend more energy on “listening” than “talking” Dogma: Neural output (firing rate) is the interesting part.

12. Hemodynamic changes Neural input or output? Combination of both?

13. Electrophysiology Different techniques: 1.Intra-cellular recordings 2.Extra-cellular recordings 3.Fluorescence imaging Different spatial resolutions: 1.Single neuron activity 2.Multi unit activity 3.Local field potential

14. Extra-cellular recordings Separate the recorded signal into different components. High frequencies (>500 Hz): Low frequencies (<100 Hz):

15. Simultaneous measurements Measure simultaneous electrophysiology and fMRI and compare. Logothetis et. al. Nature 2001

16. Simultaneous measurements Before separating electrophysiology into different components

17. Simultaneous measurements LFP and BOLD responses are sustained while MUA seems to adapt very quickly. Anything strange?

18. BOLD – spiking dissociations Several other studies have reported such dissociations… Viswanathan et. al. Nat. Neurosci. 2007

19. Spatial Sampling MUA is a local measure, summing neural spikes only of neurons surrounding the immediate electrode tip. LFP and BOLD are wider measures, summing dendritic/synaptic activity several mm surrounding the electrode.

20. Sampling bias Multi unit activity is mainly generated by large layer 5 pyramidal cells. These are the main “output” neurons of the cortex. LFP and BOLD sum across all cell sizes in all layers.

21. Cortical structure In cortex, 80% of a neurons output synapses are located within 1 mm of its soma. Strong recurrent innervation. Only 6% of V1 synapses (mostly layer 4) are from thalamic neurons. Input without output? Increased LFP without spiking?

22. BOLD, LFP, and spikes In general, in cortex, there is a strong relationship. Epilepsy patients implanted with electrodes in auditory cortex: Mukamel et. al. Science 2005

23. BOLD, LFP, and spikes Neural activity correlated with fMRI:

24. BOLD, LFP, and spikes Different LFP frequencies showed different relationship to BOLD:

25. Optogenetics Hyung Lee et. al. Nature 2010

26. Optogenetics Inject virus into motor cortex. Axons of infected cells reach thalamus. Stimulate in motor cortex and measure activity in both locations.

27. During rest What about spontaneous activity? Shmuel et. al. HBM 2008

28. During rest Significant correlations between neural activity and BOLD during rest…

29. Negative BOLD? Shmuel et. al. Nat. Neurosci. 2006

30. Negative BOLD?

32. Cerebellum Subcortical brain areas might demand more caution. Architecture is different: no recurrent innervation. There is a difference between input and output. BOLD coupled to input. Caeser et. al. PNAS 2003 GABA agonist halts PC spikes BOLD continues

33. One more thought The effects of neuro-modulators (caffeine, hormones, noradrenalin, dopamine, serotonin, etc…) on particulars of neural activity and neurovascular coupling are unknown.

34. To the lab