Jody Culham Brain and Mind Institute Department of Psychology Western University http://www.fmri4newbies.com/

How Neurons Become BOLD

Last Update: September 23, 2014 Last Course: Psychology 9223, F2014, Western University

Section 1 The BOLD Signal

Hemoglobin (Hb)

Deoxygenated Blood  Signal Loss rat breathing pure oxygen • • • Oxygenated blood?

Diamagnetic Doesn ’t distort surrounding magnetic field No signal loss… rat breathing normal air (less than pure oxygen) • • • Deoxygenated blood?

Paramagnetic Distorts surrounding magnetic field Signal loss !!!

Images from Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging based on two papers from Ogawa et al., 1990, both in Magnetic Resonance in Medicine

BOLD signal B lood O xygen L evel D ependent signal  neural activity   blood flow   oxyhemoglobin   T2*   MR signal At Rest: Active:

M xy Signal M o sin



S task S control T 2 * task T 2 * control



S TE optimum time

Source: Jorge Jovicich

Figure Source: Huettel, Song & McCarthy,

2004, Functional Magnetic Resonance Imaging

Perhaps it should be BDLD?

Blood DE -oxygenation level-dependent signal?

• Technically, “BOLD” is a misnomer • The fMRI signal is dependent on deoxygenation rather than oxygenation per se • The more deoxy-Hb there is the lower the signal fMRI Signal Amount of deoxy-Hb

“BDLD” idea from Bruce Pike, MNI

Susceptibility A single bobby pin  susceptibility artifacts (drops in signal and distortions nearby)

Brain at rest Initial Dip Vaso dilation Hb deoxy ~= bobby pins T2*-weighted signal

Evolution of BOLD Response

Hu et al., 1997, MRM

BOLD Time Course Blood Oxygenation Level-Dependent Signal Positive BOLD response 3 2 1 0 Initial Dip Overshoot Post-stimulus Undershoot Time Stimulus

Trial-to-Trial Variability

Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

Variability of HRF Between Subjects Aguirre, Zarahn & D ’Esposito, 1998 • HRF shows considerable variability between subjects

different subjects

• Within subjects, responses are more consistent, although there is still some variability between sessions

same subject, same session same subject, different session

Variability of HRF Between Areas Possible caveat: HRF may also vary between

areas

, not just subjects • Buckner et al., 1996: • noted a delay of .5-1 sec between visual and prefrontal regions • vasculature difference?

• processing latency?

• Bug or feature? • Menon & Kim – mental chronometry

Buckner et al., 1996

Variability Between Subjects/Areas • greater variability between subjects than between regions • deviations from canonical HRF cause false negatives (Type II errors) • Consider including a run to establish subject specific HRFs from robust area like M1

Handwerker et al., 2004, Neuroimage

Factors That Affect HRF • drugs: alcohol, caffeine • digestion: fat consumption • aging • disease: dementia

Reviewed in Handwerker et al. 2012, NeuroImage

Effect of caffeine

Liu et al, 2004, NeuroImage

Sampling Rate

Huettel, Song & McCarthy, 2004, Functional Magnetic Resonance Imaging

Linearity of BOLD response Linearity: “Do things add up?” red = 2 - 1 green = 3 - 2 Sync each trial response to start of trial Not quite linear but good enough!

Source: Dale & Buckner, 1997

Section 2 From Neurons to BOLD

From Neurons to BOLD Positive BOLD Response 1 40 0 -55 -70 0 Refractory period Undershoot Time (ms) Time (s) • Any similarity in the shapes of the curves for action potentials and the BOLD response is purely coincidental (but still kinda cool)

Stimulus to BOLD Source: Arthurs & Boniface, 2002,

Trends in Neurosciences

Neural Networks

Post-Synaptic Potentials • • The inputs to a neuron (post-synaptic potentials) increase (excitatory PSPs) or decrease (inhibitory PSPs) the membrane voltage If the summed PSPs at the axon hillock push the voltage above the threshold, the neuron will fire an action potential

What does electrophysiology measure?

Raw microelectrode signal Filter out low frequencies  Action Potentials (APs) Filter out high frequencies  Local Field Potentials (LFPs) Source: http://www.cin.uni-tuebingen.de/research/methods-in-neuroscience/networks.php

BOLD Correlations 24 s stimulus 12 s stimulus 4 s stimulus

Source: Logothetis et al., 2001, Nature

• • Local Field Potentials (LFP) reflect post-synaptic potentials similar to what EEG (ERPs) and MEG measure • • Multi-Unit Activity (MUA) reflects action potentials similar to what most electrophysiology measures • • Logothetis et al. (2001) combined BOLD fMRI and electrophysiological recordings found that BOLD activity is more closely related to LFPs than MUA

Correlations between BOLD and LFP frequencies α (8-12 Hz) β (18-30 Hz) γ (40-100 Hz) gamma shares most info with BOLD

Even Simple Circuits Aren ’t Simple Lower tier area (e.g., thalamus) Middle tier area (e.g., V1, primary visual cortex) Higher tier area (e.g., V2, secondary visual cortex) gray matter (dendrites, cell bodies & synapses) white matter (axons) Will BOLD activation from the blue voxel reflect: • output of the black neuron (action potentials)?

• excitatory input (green synapses)?

• inhibitory input (red synapses)?

• inputs from the same layer (which constitute ~80% of synapses)?

• feedforward projections (from lower-tier areas)?

• feedback projections (from higher-tier areas)?

Comparing Electrophysiolgy and BOLD Data Source: Disbrow et al., 2000

, PNAS

Figure Source, Huettel, Song & McCarthy,

Functional Magnetic Resonance Imaging

fMRI Measures the Population Activity

Ideas from: Scannell & Young, 1999, Proc Biol Sci

fMRI for Dummies

Effects of Practice Verb generation Verb generation after 15 min practice

Raichle & Posner, Images of Mind cover image

Bug or feature?

• fMRI adaptation enables us to study the tuning of neurons fMRI for Dummies

Stimulus to BOLD Source: Arthurs & Boniface, 2002,

Trends in Neurosciences

Brain and Blood The brain is ~2% of the body by weight …but it uses about 20% of the body ’s oxygen supply and 20-25% of its glucose supply

Vascular system

Vascular system

Contents of a Voxel

Source: Logothetis, 2008, Nature

Capillary beds within the cortex

Source: Duvernoy, Delon & Vannson, 1981, Brain Research Bulletin

Vasculature: Brain vs. Vein

Source: Menon & Kim, TICS

“Brain vs. Vein” • large vessels produce BOLD activation further from the true site of activation than small vessels (especially problematic for high-resolution fMRI) • large vessels line the sulci and make it hard to tell which bank of a sulcus the activity arises from • the % signal change in large vessels can be considerably higher than in small vessels (e.g., 10% vs. 2%) • activation in large vessels occurs up to 3 s later than in small ones Source: Ono et al., 1990,

Atlas of the Cerebral Sulci

Vessel Valves Source: Harrison et al. (2002).

Cerebral Cortex

.

stim Vasodilation vasodilation could be induced by either electrical stimulation or release of Ca 2+ max dilation ~3-6 s after stim • biggest changes in arteriole dilation occurred near stimulation; however, effects could also be observed several mm upstream Source: Adapted from Takano et al., 2006,

Nat Neurosci

, by Huettel, et al., 2nd ed.

Upstream Effects arteriole veins • biggest changes in arteriole dilation occurred near stimulation; however, effects could also be observed several mm upstream Source: Adapted from Iadecola et al., 1997,

J Neurophysiol

, by Huettel, et al., 2nd ed.

Don ’t Trust Sinus Activity • You will sometimes see bogus “activity” in the sagittal sinus

The Forgotten Brain Cells

Common (i.e., Wrong) Wisdom

“Glial cells are probably not essential for processing information” (Kandel, Schwartz & Jessell, Principles of Neural Science 3 rd Ed.)

• Tripartite Synapse Astrocytes are adjacent to both synapses and blood vessels – well poised to adjust vascular response to neural activity • Astrocytes outnumber neurons by at least 10:1 and comprise ~50% of the total CNS volume  Astrocytes perform a number of critically important functions: 1.

2.

3.

4.

Neurotransmitter uptake and recycling Neurometabolic regulation Cerebrovascular regulation Release of signaling molecules ( “gliotransmitters”)

Source: Figley & Stroman, 2011, EJN

Glycolysis

Source: Raichle, 2001, Nature

Energy Budget

Data Source: Howarth et al., 2012 Figure Source, Huettel, Song & McCarthy, Functional Magnetic Resonance Imaging, 3 rd ed.

Vasoactive Substances • substances that cause the vessels to dilate • potassium ions (K + ) – move from intra- to extra-cellular space during synaptic activity • adenosine – increases with high metabolic activity • nitric oxide – released by local and distant activation • gap junctions • calcium (Ca 2+ ) – triggered by neuronal activation • dopamine

Information Source: Huettel, Song & McCarthy, 2nd ed.

What about inhibitory synapses?

• GABA = inhibitory neurotransmitter  hyperpolarization (IPSP) • less metabolically demanding than excitatory (glutamatergic) activity • GABA can be taken up presynaptically rather than recycled through astrocytes • Therefore, neurotransmission at inhibitory synapses likely influences the BOLD signal less than at excitatory synapses

Non-Neuronal Effects

Leopold, 2009, based on data of Sirotin & Das, 2009, Nature

Sirotin & Das, 2009 • two components to blood flow in visual cortex (V1) 1. related to neuronal responses to visual stimuli 2. related anticipation of neural events

Stimulus to BOLD Source: Arthurs & Boniface, 2002,

Trends in Neurosciences

Gradient Echo vs. Spin Echo Gradient Echo • high SNR • strong contribution of vessels Spin Echo • lower SNR • weaker contribution of vessels

Source: Logothetis, 2008, Nature

We sort of understand this (e.g., psychophysics, neurophysiology) The Concise Summary We ’re *&^%$#@ clueless here!

We sort of understand this (MR Physics)

Is the fMRI Sky Falling?

Don ’t Panic • BOLD imaging is well correlated with results from other methods • BOLD imaging can resolve activation at a fairly small scale (e.g., retinotopic mapping) • PSPs and action potentials are correlated so either way, it ’s getting at something meaningful • even if BOLD activation doesn’t correlate completely with electrophysiology, that doesn ’t mean it’s wrong – may be picking up other processing info (e.g., PSPs, synchrony) – maybe anticipatory changes in blood flow are interesting too

Section 3 Spatial Limits of fMRI

MID-SAGITTAL SCOUT IMAGE

for slice selection Number of Slices e.g., 12 slices 6 mm thick Terminology

ONE VOLUME

one set of all slices

VOXEL (Volumetric Pixel)

In-plane resolution e.g., 192 mm / 64 = 3 mm 3 mm 6 mm

IN-PLANE SLICE

3 mm Matrix Size e.g., 64 x 64 Field of View (FOV) e.g., 19.2 cm

Typically ~36 slice planes

1 2 3 4 5 6 7 8 9 10 11 12 For simplicity… 12 slices

12 slices

12 11 10 4 3 2 1 9 8 7 6 5 Ascending, Non-Interleaved

1 2 3 4 5 6 7 8 9 10 11 12 Descending, Non-Interleaved

1 2 3 4 5 6 7 8 9 10 11 12 How we think slices profiles work

1 2 3 4 5 6 7 8 9 10 11 12 How slice profiles actually work slice sensitivity

Slices without Gap 1 2 3 4 5 6 7 8 9 10 11 12 • ensures brain tissue isn’t missed • but some brain tissue may be sampled on two adjacent slices • exciting one slice may affect adjacent slices • interleaved sequences recommended slice sensitivity

Descending, Interleaved 10 11 12 5 6 7 8 9 1 2 3 4 • interleaving ensures information doesn’t bleed between slices

Slices with Gap e.g., 5-mm slice thickness with 1-mm gap 1 2 3 4 5 6 7 8 9 10 11 12 • allows whole brain coverage with fewer slices • prevents bleeding between sequential slices slice sensitivity

Multiband Imaging 9 10 11 12 1 2 3 4 5 6 7 8 • collect multiple slices (e.g., 3 slices) in the same image and then separate the slices using known coil sensitivities • reduces the time required to sample the whole brain (e.g., to 1/3 for 3 slice multiband) • may reduce vulnerability to artifacts like head motion

4-slice multiband

non-isotropic Voxel Size isotropic

same size in 3 directions

non-isotropic 3 x 3 x 6 = 54 mm 3 e.g., SNR = 100 3 x 3 x 3 = 27 mm 3 e.g., SNR = 71 2.1 x 2.1 x 6 = 27 mm 3 e.g., SNR = 71 In general, larger voxels buy you more SNR.

EXCEPT when the activated region does not fill the voxel (partial voluming)

Partial Voluming Partial volume effects: The combination, within a single voxel, of signal contributions from two or more distinct tissue types or functional regions (Huettel, Song & McCarthy, 2004) This voxel contains mostly gray matter This voxel contains mostly white matter This voxel contains both gray and white matter. Even if neurons within the voxel are strongly activated, the signal may be washed out by the absence of activation in white matter.

Partial voluming becomes more of a problem with larger voxel sizes Worst case scenario: A 22 cm x 22 cm x 22 cm voxel would contain the whole brain

fMRI in the Big Picture

What Limits Spatial Resolution • noise – smaller voxels have lower SNR • head motion – the smaller your voxels, the more contamination head motion induces • temporal resolution – the smaller your voxels, the longer it takes to acquire the same volume • 4 mm x 4 mm at 16 slices/sec • OR 1 mm x 1 mm at 1 slice/sec • vasculature – depends on pulse sequences • e.g., spin echo sequences reduce contributions from large vessels – some preprocessing techniques may reduce contribution of large vessels (Menon, 2002, MRM)

Ocular Dominance Columns • Columns on the order of ~0.5 mm have been observed with fMRI

Submillimeter Resolution Spin Echo Functional (activation localized to Layer IV) Gradient Echo Functional (superficial activation includes vessels) Stria of Gennari (Layer IV) Spin Echo Anatomical vein Gradient Echo Anatomical • •

Goenze, Zappe & Logothetis, 2007, Magnetic Resonance Imaging

anaesthetized monkey; 4.7 T; contrast agent (MION) ~0.3 x 0.3 x 2 mm

Can you follow some of this Methods section now?

stuff you should understand stuff your MR physicist or MR tech can explain

Gallivan et al., 2011, J. Neurosci.