Neurovascular

Coupling

Jorge Riera Diaz

jrieradi@fiu.edu

Neu

ron

al A

cti

vit

y

“Ele

ctr

ical E

ven

ts”

Fu

ncti

on

al

Imag

ing

“BO

LD

Effect”

Neurovascular

Coupling

Vascular

Dynamics

Linear System

Linear Convolution Model r

BOLD k HRF ky t h t d • Relation with physiology? • Dysfunctions? Diseases

Physiology of the neurovascular coupling Biophysical models for vascular dynamics

Particulars for Dementia (AD) & Epilepsy

Topics for this talks

Physiology of the neurovascular coupling Biophysical models for vascular dynamics

Particulars for Dementia (AD) & Epilepsy

Topics for this talks

Hamel, J Appl Physiol, 2006

EXTRINSIC INNERVATION

superior cervical (SCG) sphenopalatine (SPG) otic (OG) trigeminal (TG)

Ganglion

SUBCORTICAL VASOACTIVE PATHWAYS

NA- norepinephrine NKA- neurokinin A NPY- neuropeptide Y VIP- vasointentinal polypeptide SP- substance P CGRP- calcitonin gene-related peptide PACAP- pituitary adenylate-cyclase activating polypeptide NOS- nitric oxide synthase ACh- Acetylcholine 5-HT- serotonin SOM- somatostatin

Cauli et al., J Neurosci, 2004

Single interneuron stimulation induces

dilation or constriction of cortical blood

vessels

Hamel, J Appl Physiol, 2006

SUBCORTICAL VASOACTIVE PATHWAYS

Drew

el al. P

NA

S, 20

11

Phasic Pathway (NO, PGE2)

Tonic Pathway (Astrocytes)

Riera, Brain Product Press Release, 2011

Gas Diffusion

NO

NO NO

1

nNOS

Phasic Pathway NADPHd-positive

neuron

Cortical neurons synthetizing NO

Estrada and DeFelipe, Cerebral Cortex, 1998

Nitrergic Neurons

Stefanovic et al., JCBFM 2007

7NI – Inhibitor nNOS (specific)

killed

PGE2 Lipid Diffusion

2

PGE2 Lipid Diffusion

Phasic Pathway

Glutamatergic Neurons

Wang et al., Cerebral Cortex, 2005

Large COX-2 labeled dendrites (COX-d1 and d2) are apposed (black block arrows) by nNOS-

containing terminals (NOS-t1 and t2)

W, Whisker; F, forepaw; H, hindpaw; T, trunk; VC, visual cortex.

Boorman et al., J Neurosci, 2010

Goense et al., (2012)

PBR

NBR

CBF and CBV increased in the center of the cortex

“Laminar Differences in Neurovascular Coupling

For positive BOLD responses:

For negative BOLD responses: CBF decreased superficially while CBV increased in the center

Patrick Drew (Personal Communication) Penn State

Neuronal Astrocytes

Vascular Astrocytes

Cx43 (blue) and Cx30 (red)

Gia

um

e e

t al

., N

RN

, 20

10

Domains do not overlap

Differentially expressed in barrels (dark) and septa (light)

G βPLC

R

IP3R [Ca2+]ER

Cytosol [Ca2+]

Agonist

SERCA

[Ca2+] Extrusion

SCO

PLC

influx

micro-domain

[Ca2+] CCE

[Ca

2+]

Multiple-Channel Patches

IP3R “hot regions” (clusters) ~ 10 M

Channel

Cluster

Riera et al., Biophysical J (2010)

A biophysical model (astrocytes)

Population Astrocytic Ca2+ activity (WT mice)

Nimmerjahn et al., Nat Methods (2004)

Cerebral Cortex (in vivo)

Ca2+ activity

Propagation

Hippocampus (in vitro)

Riera et al., unpublished

1

2

Fluo-4 AM

10 m

Takano et al., Nature Neuroscience 2006

FITC-Dextran (green) Rhod-2 (red)

Wang et al., Nature Neuroscience 2006

Neuronal Activity

Neuronal ATP Consumption

Neuronal O2

Consumption

Tissue

pO2

Level

Ca2+

Waves

Glu Glu

Glu Glu

NO NO

NO

NO

Gas Diffusion

AA AA

AA

AA

AA

Acid Diffusion

AA

CYP2C

COX-1

PGT

PGE2 PGE2

LACTATE LACTATE

LACTATE

glycolysis LDH

MCT-1&4

NO +

EET

+

PGE2

+

AA

20-HETE

-

+

NO -

NO

cGMP Modulation

Astrocyte

Riera & Sumiyoshi, CON 2010

Tonic Pathway

soluble Guanylate Cyclase

lactate dehydrogenase (LDH) monocarboxylate transporter (MCT)

Gordon et al., Nature 2008

• NO-mediated 20-HETE/EET unbalance hypothesis for vasoconstriction and vasodilations

• Lactate–PGE2 coupling hypothesis

Tonic Pathway

Physiology of the neurovascular coupling Biophysical models for vascular dynamics

Particulars for Dementia (AD) & Epilepsy

Topics for this talks

, , , , , ,r t r t t r t r fx x i

,k kt k tr t gy x ε

a) Performing statistical inference about the states,

exogenous inputs and network topology

b) Combining different modalities of functional neuroimaging

data

Why Are Model Needed?

State-Space Model

Discrete nonparametric models

Continuous parametric models , ,l l l l l

l ld t t t dt d t fx x u G w

, 0, , , ,k k k k k

l l l l

t l t l t l tk N

fx x u

,, 2l l

l lE t t

Dw w

The Fokker-Planck equation describes the time evolution of the probability density function

2, 1, , , ,

2

l

l l l l l l

l l l ij l

ij i j

dp tt t p t dt H p t

dt x x

f

xx u x x

2l l lH DGG

The high-dimensionality of the Fokker-Planck equation can be reduced by using the mean-field approximation

, ,l k l

l l kkp t p tx x

, , , ,k l l l

l k k k k lt t t f x u

l l

k kt E t x

Riera, Encyclopedia Computation Neuroscience, 2014

Hemodynamic Model

Riera et al., Hum Brain Map, 2006

Windkessel Theory

Capillary OEF Mass Balance Equation

Wn

ag e

t al

., N

eu

roIm

age

, 20

06

spike-density function

Transient Neural Response

Logothetis et al., Nature ,2001

BOLD signal & EEG recordings

X = -40 mm Y = 8 mm Z = -24 mm

Sumiyoshi et al., Neuroimage, 2011

Wistar Rats (Adult)

Sumiyoshi et al., Neuroimage 2012, In Press

Coefficient P-value

Intercept (b0) -5.27000 0.100

Regression slope for "PaO2" (bO2) +0.00037 0.700

Regression slope for "PaCO2" (bCO2) -0.00067 0.800

Regression slope for "pH" (bpH) +0.78000 0.060

Regression slope for "MABP" (bMABP) -0.00140 0.070

Regression slope for "HR" (bHR) +0.00094 0.007

Multiple Compartmental Model

Lay

er V

Py

ram

idal

Cel

l

Riera et al., Hum Brain Map, 2006

Dendrite + Soma

Dendrite

(Tuft)

Dendrite

(Basal)

Soma

Electrotonic Propagation

Intra- & Extra- Cellular Components

GA

BA

ergic

In

tern

euro

ns

Neurona Mass Model

Phasic (NO) Hemodynamic Model

NO

W

u t t C d

,

lIN PC

NO IN IN PC PC

l T F

C t g S g S

Lin

ear F

ilte

r

(Diffu

sio

n a

nd S

cavenges)

21 exp /k C k C kg I I

Estrada and DeFelipe, Cerebral Cortex, 1998

(NADPHd)

histochemistry

Riera et al., Hum Brain Map, 2006

Visual Integration

Contrast SPM2 t-test

PCD Orientations

BOLD Effect (Time Course)

Riera et al., Hum Brain Map, 2007

PCD Amplitude (Time Course) Where are these components

coming from?

C1

C2

C3

C4

C2

Riera et al., Hum Brain Map, 2007

Mes

o-s

co

pic

Vari

ab

les

NO

dHb

CBF

CBV

Synaptic Inputs

Visual Integration

INTEGRATOR

(apical trunk)

LENGTH

MEAN±SD(μm)

DIAMETER

MEAN±SD(μm)

Branch 1 205.89±25.00 1.26±0.05

Last Compartment 2.48±0.61 2.48±0.61

PROPAGATOR (dendritic trunk)

LENGTH

MEAN±SD(μm)

DIAMETER

MEAN±SD(μm)

Compartment 10 61.64±4.99 2.76±0.25

Compartment 9 60.47±7.68 2.69±0.55

Compartment 8 62.05±5.71 2.80±0.48

Compartment 7 61.66±8.69 2.71±0.38

Compartment 6 63.29±7.01 2.91±0.47

Compartment 5 62.51±7.44 3.01±0.79

Compartment 4 61.43±8.69 2.99±0.45

Compartment 3 61.93±6.68 3.05±0.45

Compartment 2 61.47±5.12 3.33±0.20

Compartment 1 60.66±9.06 4.34±0.46

DIAMETER

MEAN±SD(μm)

COLLECTOR

(soma) 13.40±1.40

Electrotonic Characteristics of layer V PCs

Wang et al., Neural Comp, 2013

10

0

m

Crivaro et al., J Microscopy, 2011

Extended Split-Gradient Deconvolution Method

0 1 2 3

1 1 1k

k

t k k

k

q tb V k q t k k v t

v t

van der Zwaag et al., (2009) & Uludag et al., (2009)

BOLD Signal Model 1 0

12k E

20k

31k

7T MRI

Scanner

1

0

0

0

, 1 1 11

f t

EE f E g

g

00.49g g

Vazquez et al., JCBM (2009)

10

0

,dq t E f Ef t v t q t

dt E

11dv t

f t v tdt

Windkessels Theory

4

f t r t Generalized Poiseuille's Equation

2sNO NO PGE PGE EET EET HETE HETE d

dr tn n n n r t

dt

Phasic Response

Stephenson et al., (1994); Golanov et al., (1994);

Alkayed et al., (1995); Ma et al., (1996); Lindauer et al., (1996);

Alonso-Galicia et al., (1997); Sun et al., (2000);

Niwa et al., (2001); Bakalova et al., (2002); Peng et al., (2002);

Xu et al., (2003); Takano et al., (2006); Stefanovic et al., (2007);

Hoffmeyer et al., (2007); Leithner et al., (2009)

34%sPGE

34%

EET

19%HETE

13%NO

2

2 2 2

2

1 0 0 2

PGE

PGE PGE COX PGT in PGE COX

dn tn t Ca t C L t L n t E t

dt

2

0

EET

EET EET

dn tn t Ca t C

dt P2C

2

0

NOHETE n t

HETE HETE

dn tn t Ca t C e

dt

P4A

2

2

NO O f

NO NO NO NO O NO E I

dn tn t n t n t n f t E t I t

dt

2 2 2 2

2 2 2 0,

O O O O

O O pp E E O

dn tn t l t E t I t E f E

dt

2

2

0 0in in in

in G NO f

L L L NO L PGT in PGE

E I pp

TCA TCA TCA

dL tn t Ca t C n t f t L t L n t

dt

E t I t l t

Dynamic Model Metabolites Astrocytes

Blinder et al. Nature, 2013

Communities Resistances

Vascular-steal/leakage effect

CBF CBV

CBF CBV CBF CBV

NA

O2

Pa

PIC = PCV

CW1 RW1

Ra1(t)

CW2 RW2

Ra2

Rv Rv R12

Windkessel Model Parallel Compartment

Rak (t0)+ RWk0

+ Rv = 1

0.60 % 0.30 % 0.10 %

bA

-β1

kk

V V WkC

0

k

kk

V

VV RR W0Wk

bwo0RF

0V A

- Laminar Flow b - Diminished Volume Reserve P

0 2 4 6 8 10 12 14 16 18 200.985

0.99

0.995

1

1.005

1.01

1.015

Time (Sec)

[Hb

r]

2 4 6 8 10 12 14 16 18 20

1

1.05

1.1

2 4 6 8 10 12 14 16 18

0.994

0.996

0.998

1

Time (Sec)

CB

F

0 2 4 6 8 10 12 14 16 18 200.9

1

1.1

0 2 4 6 8 10 12 14 16 18 200.995

1

1.005

1.01

1.015

Time (Sec)

CB

V

0 2 4 6 8 10 12 14 16 18 20-0.03

-0.02

-0.01

0

0.01

0.02

0.03

Time (Sec)

BO

LD

PBR

NBR

R12 =[.1, .45, .8, 1.15, 1.5]

Physiology of the neurovascular coupling Biophysical models for vascular dynamics

Particulars for Epilepsy and Dementia (AD)

Topics for this talks

L R

A

P D V

A

B

C

D

R L

2.08 mm

Song et al. OHBM Poster 2264

32 Channel EEG

Mini-Cap

MRI based template

Sharp-Wave

Spikes

EEG Waveforms Topography Tomography

EEG Source Analysis

Bae et al. OHBM Poster 2198

Magnetic Resonance Research

Center

Yale University

sL

OR

ETA

(Co

rtica

l)

EE

G-T

rigg

ere

d B

OL

D

BOLD Effect

vs.

Current Source Imaging

Yinchen Song, PhD Candidate

1 2

Electrophysiological Recording

Laser Doppler Flowmetry

System

for R

eco

rdin

g In

trinsic O

ptical Sign

als

A B

Electrocorticography (ECoG)

Pediatric Focal Epilepsy

500 nm 700 nm

Intrinsic Optical Imaging

Low-Frequency Oscillations

Out-of-phase

Rat FCD

WT APP-Tg

Mouse strain C57BL/6J C57BL/6J

Genotype WT BE-APP65Sw/Lo

Phenotype normal Promotion of Ab deposition Increase in senile plaque Being prone to dementia

Early: 6-10 months

Middle: 11-20 months

Late: 21-28 months

GROUPS

Animal Model

Riera et al., J Biophysics, 2011

Pike et al., Exp. Neurol., (1995)

Gliosis in Alzheimer’s Disease

Astrocytes surround the area of

diffuse amyloid accumulation with

long processes

Primitive “Classic” Plaques

- Increases in relation to neurite

dystrophy (Mrak et al., J Neuropathol

Exp Neurol., 1996)

- Contributes to the immunological

cascade, e.g., by initiating an

inflammation with increased IL-6 release

(Huell et al., Acta Neuropathol, 1995;

Wood et al., Brain Res., 1993)

Advanced Stage Plaques

Takano et al., Ann. N.Y. Acad. Sci., (2007)

Age: 2-4 months

fluo-4 AM

Sulforhodamine 101

Fluorescent Indicators

Triple transgenic (3XTg-AD)

Dutch/Iowa mutation (DI)

APP Sweden Tg-2576 (APP)

Mouse Stream

Kuchibhotla et al., Science, (2009)

• Mutant human Ab precursor

protein (APP)

• Mutant presenilin 1 (PS1) in

neurons

(APPswe:PS1ΔE9)

Mouse Stream

Age: 6-8 months OGB-1 AM + (FLIM)

methoxy-XO4 (Ab)

Sulforhodamine 101

Fluorescent Indicators

Dysfunctions in Alzheimer disease

Riera et al. Biophysical Journal, 2011

De

ns

ity o

f C

on

ne

cti

on

s

WT APP-Tg

Str

en

gth

o

f c

on

ne

cti

on

s

0.025

0.020

0.015

0.010

0.005

0 APP-Tg WT

mean±SD

Astrocytic connections: density (WT<APP) and strength (WT=APP)

Percentage of active cells with age: increase (WT) and reduce (APP)

High frequency: > 6 transients / 10 min

WT APP-Tg

Pe

rce

nta

ge

of

ce

lls

Wit

h h

igh

fre

qu

en

cy (

%)

Perc

enta

ge o

f active c

ells

(%

) mean±SD

APP-Tg

WT

Early Middle Late

Hippocampus L

ate

Ag

e

1.0

0.8

0.6

0.4

0.2

0.0

Probability Map

(White Noise)

WT APP

Long-term neuronal activity deprivation decreased Aβ plaque formation/growth

Bero et al., Nat Neurosci (2011)

Sustained Metabolic Activation in the

brain’s default-mode networks

Early Middle Late

age

Ca

2+

activity

APP

WT Reactivity Dysfunction

Functionality of astrocytes is very

compromised in AD Development

Plasticity

A severely impairment of the cerebral autoregulation in AD

Claassen & Zhang, Cereb Blood Flow Metab (2011)

Model

“Neurons”

Vasculature

LFP/Unit-Activity

EEG/MEG

Large-Scale

Small-Scale

fMRI-BOLD

Large-Scale

Astrocytes

Vasomotions

Neuronal

Modulation

Thanks