Laboratory 13 : NeurophysiologyDr. Craelius

• Autonomic (Involuntary) and Somatic (Voluntary) Nervous Systems)

• Methodologies for measuring and controlling both.

• Relevant to Neuroprostheses. I.e. how can we “decode” what the brain is telling our organs and muscles, so that when the information channels are blocked, we can replace them?

Action Potentialof a Neuron

The output is a pulse train: Its frequency contains the information.In general the higher the frequency, the greater the informationcontent. Neurons in the MI cortex are specialized for such operationsAs kinematics or dynamics.

Neurophysiology Basics

• Muscles and neurons are excitable, and carry information through their pulse rate.

• I = log2(fm*t +1)• Action Potential - a reversal in relative

polarity or change in electrical potential of a cell Neurotransmitters- chemical messengers

• Central neurons are specialized for function.

ANS Background

• Spectral analysis of HRV reveals 2 limbs of the ANS.

• ULF (diurnal) HRV is predictive of cardiac performance.

• HRV signatures using Cepstral Analysis (US patent 6,390,986).

• HRV manipulation can improve asthma (Vaschillo, Lehrer et al, 2004).

• Portable digital recorders of ECG facilitate analysis.

Influences: Autonomic + Mechanical+ Metabolic + Environmental

(HP)

Complexity

Frequency

Time Domain

Heart Rate (HR)

Heart Period (HP)

(HP)

Beat-to-beat (RR)

Pacemaker: Sino-atrial node

HRV Analysis Pyramid

Inverse Problem

Autonomic Nervous System & Heart Rate Variability

• Exerts effects on every organ, but the heart is the most “visible” organ we can examine.

• Para slows it, Sympa speeds it.

Pacemaker

Para

Sympa

Beatmaker

Autonomic (Para and Symp) modulations of HR: A model

What modulates HR?

• SNS has a periodicity of ~ 20 seconds, and possibly others.

• ANS has a periodicity = breathing rate, and possibly others.

• Thoracic motions alone can modulate, and vagal nerve drive of respiration can contribute.

• Thermoregulation, daily activities can modulate long periodicities.

Low and High Frequency variations in blood pressure

Respiration controlsHF

What limb of the ANS controls the LF?

High RSA during sleep @ 11:30 PM

RR

RESP

RR

RESP

Tachogram

Low RSA, High LF During sleep12:50 AM

11:30 PM 1:00 AM

RSA disappeared during sleep

13 b

13 breaths/min

Can HRV identify disease or specific individuals?

• Age-related normal ranges of overall HRV = SDNN, are known and are predictive of survival after MI.

• A brief record of HR can be a signature of an individual using HR vector cepstral methods *.

*Curcie, D, Craelius W: Recognition of Individual Heart Rate Patterns with Cepstral Vectors, Biological Cybernetics, 77/2:103-109, 1997

Analyzing HRV

• Collect sufficiently long , ‘clean’, epoch, I.e. need at least a few cycles of the rhythm- so for LF , get > 3 X 20 seconds.

• First examine tachogram, edit artifacts.

• Do time domain stats, ie, S.D.

• Do spectral analysis if you have sufficient data, ie. Need several cycles to detect.

Cardiovascular Resonance

• Vaschillo & Lehrer et. Al.

• Get ANS into resonance by biofeedback.

• Deep breathing at resonant rate is key.

• Resonance can influence performance.

Time Domain Indices

Task Force: Circulation 93:1043-1065, 1996.

Geometric Indices

Task Force: Circulation 93:1043-1065, 1996

TINN analysis

RR Interval histogram

Frequency Bands

0.0001-0.003

0.003-0.04

0.04-0.15

0.15-0.4

ULF VLF LF HFPower(ms2)

Frequency (Hz)

Ratios

• LF/HF : estimates sympathetic to parasympathetic activities

• LF-tilt/HF-supine: a more specific estimate

Normal Range Variable Units Normal Values

(mean +- S.D.)SDNN ms 141 39SDANN ms 127 35RMSSD ms 27 12Triangular index ms 37 15Total Power ms2 3466 1018LF ms2 1170 416HF ms2 975 203LF nu 54 4HF nu 29 3LF/HF 1.5- 2.0

HRV Oscillations

Frequency Component

Range

(Hz)

Likely Origin

HF 0.15 - 0.40 Parasympathetic outflow

LF 0.05 - 0.15 Mostly Sympathetic in standing position

VLF 0.003 - 0.05

Possibly thermoregulatory or plasma rennin activity

ULF <0.003 Wide range of determinants like posture, behavioral variables

CollectECG- Lead

II

Detect fiducial R

points

Instantaneous Heart Rate

Stationary?

Free of Artifact?

Time Domain

Measures

Frequency Domain

Power in Bands

Modelling HR Vector

Estimate autonomic activities

Classify individuals

Overall HRV

(Pulse record in our lab)

Processing Pulse Record

Unfiltered Pulses High Pass Filtered @ 0.2 HzBaseline correction: If you filter too much, you differentiate.

Somatic Nervous System:Signals

• Originate in a Motor Neuron– Activated by conscious thought or afferent

input (i.e. reflex)

• Travel through the nervous system to the target muscle(s) via, depolarization (action potential) and neurotransmitters : Signal Degradation

Motor Homunculus: Map of functions

Controller

Proprioception

Vision

+

+_

External Load

Volition

Motion Control Volition + Load -(sensation) = error

Motor Regions for ULIndex Finger Forearm

Biceps

Areas for placingelectrodes

Bionic Approaches to Restoring Mobility

• Mobility can be restored by several neuroprosthetic approaches *.

Muscles

Computer

Computer

Muscles

Robot

Brain

BCIAction

Action

Action

Action

BMI

HybridBMI

PMI

Figure 3

1.* Craelius,W.: "The Bionic Man: Restoring Mobility", Science, Vol 295, 1018-1021, 2002.

Training primates to move arm by decoding neuron signals

Brain-Machine/Computer Interfaces

• Monkeys in Brooklyn moving arms in North Carolina, fast learning (Wessberg et al.)

• Completely paralyzed persons moving cursors and robotic arms (Kennedy, PR, et el.)

• Paraplegic with implanted SC chip using switches on walker (Rabischong)

Record Inside the brain ?

• Need > 1000 Implanted electrodes

• Hence need wireless control from external controller

• Electrode biocompatibility and migration

• But decoding volition from motoneurons is surprisingly easy: simple cumulative summation of firing rates (linear)

Volitional Degradation/Restoration

• G = H · V ( G and V are column vectors)

• G is the measured response

• H is the degradation through the system

• V is the volition

• To Retrieve volition:

GHV

1

VolitionBrainTask

Muscular

Output

Motors

Register

ContextEnvironment

Controller

Filter

1/H

State

Degradation

H

State Vector

G

V

V

Linear filter is simplestAnd best decoder

How to measure performance of decoding?

• How accurate is positioning of arm?

• Euclidean distance:

• Speed versus accuracy

Speed/Accuracy Tradeoff

TimeAs your need for attention Accuracy

Attention Accuracy

How to quantify?

Measuring performance with Speed/Accuracy tradeoff : Specific

targeting task90°

Fitts

SAT

SAT test

MT = a + b log2(2A/W) where• MT = average movement time = Time/# of hits • A = amplitude (distance) of movement between

targets• W = width of the target • a = intercept• b = slope• log2(2A/W) == difficulty level

SAT graph

Difficulty index

Protocol

1. Pulse recording 5 min --- file

2. SAT test 5 min ---- file

3. Deep Breathing w/pulse recording 5 min--- file.

4. SAT test 5 min ---- file

5. Pulse recording 5 min --- file

6. SAT test 5 min ---- file

Analysis

1. Prepare pulse files, with HP filtering if necessary.

2. Use RR interval program to get intervals- optimize for minimal artifacts.

3. Put RR & SAT data in Excel- analyze & graph.

4. If time, further analyze RR data with Log-a-Rhythm.

Bionic Interface TypesBMI For “partial” paralysis Robotic arms

BCI For complete paralysis Screen Cursor

PMI For amputees or with weak muscles

Robotic Limbs

HBMI SC injuries FreeHand

CBI Parkinson’s Activa Tremor Control