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Biological Intelligence. Artificial Intelligence Biological Sensors Cognitive Neuroscience Cognitive Science Neuronal Pattern Analysis. Physiological Saline. MALDI Matrix Solution. Cell. Matrix. Sample Plate. Single Cell and Subcellular MALDI Mass Spectrometry

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Biological Intelligence

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Biological intelligence

Biological Intelligence

Artificial Intelligence

Biological Sensors

Cognitive Neuroscience

Cognitive Science

Neuronal Pattern Analysis


Biological intelligence

Physiological

Saline

MALDI Matrix

Solution

Cell

Matrix

Sample Plate

Single Cell and Subcellular MALDI Mass Spectrometry

for the Direct Assay of the Neuropeptides

Neuropeptides and hormones can be directly detected from biological samples ranging in size from femtoliter peptidergic vesicles to large invertebrate neurons. When combined with genetic information, the complete processing of prohormones into biologically active peptides can be measured in a single cell. Current work involves developing mass spectrometric imaging (to determine the precise locations of the peptides) and the ability to measure peptide release from single cells and brain slices.


Biological intelligence

Placenta vs. Brain – 3800 Placenta Array

cy3cy5


Center for biomedical computing

Narendra Ahuja

Bill Greenough

William O'Brien

Mark Band

Steve Boppart

Sariel Har-Peled

Art Kramer

Harris Lewin

Zhi-Pei Liang

Lei Liu

Greg Miller

Jean Ponce

Jim Zachary

Center for Biomedical Computing


Micro patterned neuronal networks in culture recent progress

Micro Patterned Neuronal Networks in CultureRecent Progress

Robustness:

Neurons Stay in Patterns for One Month

Designability:

Neurons Can Be Guided Over Electrodes on a Microelectrode Array

Single fibers

superimposed on electrodes

Patterned fiber track superimposed on electrodes

Bruce C. Wheeler, Member of the Neuronal Pattern Analysis and Biosensor Research Groups, Faculty in the Electrical and Computer Engineering Department


Micro patterned neuronal networks in culture recent progress1

40

30

% Active Electrodes

20

10

0

100

200

300

400

500

Local Cell Density (per mm2)

Micro Patterned Neuronal Networks in CultureRecent Progress

Input/Output:

Multiple Channel Electrical Recordings Can be Obtained Routinely

Function: Are Neurons in Patterns More Active?

A. Patterned Networks Have Greater Activity

Without Patterns: 1% ± 3% active electrodes

With Patterns: 16%  12% active electrodes

B. Activity Increases with Cell Density

PatternedNeuron Cultures

Bruce C. Wheeler, Member of the Neuronal Pattern Analysis and Biosensor Research Groups, Faculty in the Electrical and Computer Engineering Department


Biological intelligence

Detection of weak signals in noisy spike trains

B) Signal superimposed on noisy spike train

A) Signal due to small prey

C) Signal superimposed

on regular afferent spike train

Model of electric fish with electroreceptors distributed over its body. (A) The

change in afferent firing activity due to a small prey. (B) the signal due to the

prey superimposed on fluctuations due to spontaneous activity, in the case of a

standard (binomial) model for afferent firing activity with the same firing rate as

the afferent and (C) the signal superimposed on the actual afferent baseline

activity. In contrast to (B), the afferent spike train exhibits long-term regularity

(memory). This limits the fluctuations in baseline firing rate, making weak

signals easier to detect.

number of spikes in a 10 ms window

with mean subtracted


Production of transgenic mice using cre loxp technology

cre mice

loxP mice

Production of transgenic mice using cre-loxP technology

  • Create cell-type specific knockout mice

  • Two lines of mice required:

    • cre mice which express cre in desired cells

    • loxP mice with loxP sites flanking the gene of interest


Biological intelligence

Creation of NR1 loxP mice


Biological intelligence

Characterization of NR1 loxP mice


Biological intelligence

KO

CTL


Dendritic development in barrel cortex

Dendritic Development in Barrel Cortex


Dendritic development in barrel cortex1

Dendritic Development in Barrel Cortex

Chang and Greenough, 1988


Dendritic development in barrel cortex2

Dendritic Development in Barrel Cortex

CTL KO


Biological intelligence

rt

ey

h

g

v

i

Optical Coherence Tomography

Fiber-Optic OCT Instrument

High-Resolution OCT of Cell Mitosis & Migration

Real-Time Endoscopic Imaging

Non-Invasive Imaging of Developing Biology


Non invasive optical imaging

Non-invasive optical imaging

  • New group of procedures for measuring the optical parameters of the cortex

    • Scattering and absorption of near-infrared (NIR) photons traveling through tissue

  • These parameters can be inferred by measuring:

    • The degree of light attenuation (intensity)

    • The degree of photon (phase)delay


Biological intelligence

Assessment of exposed tissue Assessment of deep tissue

(UV and visible light) (Near infrared light)

Intrinsic

Contrast

Contrast

Agents

Contrast

Agents

Intrinsic

Contrast

Light

Scatter

Brain cell

swelling

during

functional

activity?

FAST

NIRS-Signals

EROS

Absorp-

tion

[Cytochrome-

C-Oxidase]

[Oxy-Hb]

[Deoxy-Hb]

‘Intrinsic

Brain signals’?

Fluores-

cence

[NADH]

[oxy-

Flaveo-

proteins]

Light

Scatter

Brain Cell

Swelling

during

functional

activity?

‘Intrinsic

Brain signals’?

Doppler

Shift

Blood

Flow

Blood

Volume

Blood Cell

Velocity

LDF

Absorp-

tion

Blood Flow

(e.g.Indicator

Dilution

with

Cardiogreen)

Fluores-

cence

Ion-Conc

(Ca, K, Mg)

Voltage

Sensitive

Dyes

Micro-

circulation

Fluores-

cence

Principally

feasible,

depending

on tracer

development?

Doppler

Shift

Blood

Flow

Fluores-

cence

?

Absorp-

tion

[Cytochrome-

C-Oxidase]

[Oxy-Hb]

[Deoxy-Hb]

NIRS

Absorp-

tion

Blood

Flow

(Indicator dilution with Cardio-

green

oxygen)

NIRS

Optical Methods

Modified from A. Villringer


Optical effects

Optical effects

  • “Slow” effects

    • develop over several seconds after stimulation

    • correspond to effects observed with fMRI and PET

    • are presumably due to hemodynamic changes

  • “Fast” effects (EROS)

    • develop within the first 500 ms after stimulation

    • are most visible on the photon delay parameter

    • are presumably due to neuronal changes


Biological intelligence

0

.

0

.

2

0

.

0

-

0

.

2

0

.

2

0

.

0

-

0

.

2

-

0

.

4

-

0

.

6

0

5

1

0

1

5

2

0

2

5

3

0

3

5

4

0

4

5

5

0

5

5

6

0

Hb oxygenation in visual cortex

0

.

6

[oxy-Hb]

4

Concentration changes / microM

[deoxy-Hb]

Time / s

Modified from A. Villringer


Biological intelligence

 [oxy-Hb]

vs.

 CBF

 [deoxy-Hb]

vs.

 CBF

 [total-Hb]

vs.

 CBF

12

12

12

 CBF (PET)

 CBF (PET)

 CBF (PET)

-14

-14

-14

-30

-15

-20

15

30

15

 oxy-Hb (NIRS)

 deoxy-Hb (NIRS)

 total-Hb (NIRS)

Comparison of PET and NIRS

Modified from A. Villringer


Nir absorption spectra

0.5

Water

Hb

0.4

AbsorptionCoefficient (cm-1)

0.3

0.2

0.1

HbO2

0.0

600

700

800

900

1000

Wavelength (nm)

NIR Absorption Spectra


In vitro scattering effects

In-vitro scattering effects

Scattering changes during

an action potential

Scattering changes during

tetanic activation of a

hippocampal slice

voltage

scattering


Eros methods

Phase

delay

measured

at 5 kHz

Synthesizer

Delays (ps)

Delays (ps)

1 2 3 Time (s)

Stimulus

Signal

Averaging

112 MHz

PMT

Optic

fiber

LED

Head

surface

200 400 Time (ms)

Cerebral cortex

Volume described

by photons reaching fiber

Average Evoked Response

EROS: Methods


Recording helmet

Recording helmet


Neuro vascular relationship

Neuro-Vascular Relationship

  • The hemodynamic (NIRS) effect is proportional to the size of the neuronal (EROS) effect integrated over time

  • This supports the use of hemodynamic brain imaging methods to quantify neuronal activity

Gratton, Goodman-Wood, & Fabiani,

HBM, in press


Upper left visual stimulation

fMRI

EROS

pre-stimulus

baseline

100 ms latency

200 ms

latency

RHLH

Upper-left visual stimulation

Gratton et al., NeuroImage, 1997


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

+

Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

+

Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


Biological intelligence

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Right Visual Field Stimulation

Left Hemisphere Response

Screen


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