Removing Cross-Talk from Deep Brain Printing: Hippocampal ......organization and brain plasticity...
1
` Jessica Voight ‘17 and Catherine Poirier ’17 Advisors: Professor Taikang Ning, Ph.D, and Professor Harry Blaise, Ph.D Senior Capstone Design Project, Trinity College Department of Engineering Background • While the mechanisms that underlie REM sleep are widely unknown, it is understood to be linked to dreaming, memory acquisition, and learning 1 • The hippocampus’ main function is to transfer memories from short term to long term storage • Theta wave oscillations during REM sleep have been a “reliable” link between memory consolidation and brain plasticity 2 • The hippocampal granule cells in the DG undergo neurogenesis, whereas the pyramidal cells in the CA1 remain unchanged from birth 4,5 • It is expected that comparison of EEG recorded simultaneously from each subfield will yield age- dependent developmental differences in neuronal organization and brain plasticity Problem Statement Rapid eye movement (REM) sleep data from adolescent rats will be examined using linearly independent data collected simultaneously from the CA1 and dentate gyrus (DG) regions of the hippocampus. An experimental and analytical methodology will be designed with the goal of removing cross-talk induced noise between the two regions. Methods Electrophysiology • Perform stereotaxic surgery on anesthetized animals under a heat lamp to maintain a body temperature of 32 C • Restricted animal with ear bars • Make small incision on the sagittal plane atop the skull • Use histology to determine proper CA1 and DG locations • Insert monopolar recording electrodes in the ipsilateral CA1 and DG subfields and two recording electrodes in the contralateral brain to act as grounds • Secure all electrodes using a crown of dental cement • Allow animals to recover for at least 24 hours before placing in recording chamber Results Conclusions • Using the methodology created, ICA can successfully remove linear dependencies with statistical significance • PSE and MSC can be utilized to visualize and confirm that data is linearly dependent in the DG and CA1 regions • For future data collection, the data acquisition system must be updated to remove additional noise, as newly collected data was not able to be used using this methodology Removing Cross-Talk from Deep Brain Hippocampal EEG in Adolescent Rats Diagram of a rat brain, with the hippocampal region highlighted in red. Diagram of a rat hippocampus, with the regions of interest, the CA1 and dentate gyrus, highlighted. Data Acquisition • A polygraph machine was used for signal acquisition • 60 Hz notch filter • Differential amplifier • Recording continued for 4 hours to capture REM sleep Digital Signal Processing MATLAB programming was used to design a code that would process the REM sleep data through the following analysis methods: • Power Spectrum Analysis (PSE) - • Data was identified as REM or non-REM based on frequency peaks • 6-10 Hz = Theta wave band during REM • Magnitude Squared Coherence (MSC) – • Identifies correlated data based on likelihood it will be similar • Independent Component Analysis (ICA) – • Identifies linear dependencies in the data and removes them • Low MSC proves ICA has successfully removed crosstalk and that discrete signals from the CA1 and DG have been extracted from the data Power Spectrum Magnitude (dB) Frequency (Hz) MSC (pre ICA) Coherence Frequency (Hz) MSC (post ICA) Coherence Frequency (Hz) CA1 PSE DG PSE MSC CA1 with ICA DG with ICA MSC with ICA Magnitude (dB) Frequency (Hz) Coherence • REM data was selected from PSE analysis based on peaks in the 6-10 Hz range, shown in red • MSC shows that there was high correlation between the DG and CA1 prior to ICA, shown in red • DG and CA1 had low coherence after ICA processing in the 6 – 10 Hz region shown in the MSC and PSE plots above " =$ & ()*+"& , ,(- &./ ℎ 45-(67 () = 45-(67 () 45- () 67 () 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Adolescent 1 Adolescent 2 Adolescent 3 Adolescent 4 Adolescent 5 Adolescent 6 Adolescent 7 Coherence Magnitude Squared Coherence Before and After ICA Processing Before ICA After ICA Magnitude (dB) Magnitude (dB) Frequency (Hz) Coherence Magnitude (dB) • It is necessary to identify discrete brain activity from each region during REM sleep • Noise distortion or “cross- talk,” can occur during signal acquisition because of the close proximity of the subfields, and must be removed before analysis • Independent Component Analysis (ICA) is an algorithm capable of extracting linear dependencies between the two recording sites 3 [1]"What Is REM Sleep?" [2]Cantero, Jose, et al. [3]Mendhurwar, Kaustubha, et al. [4]Ryu, Jae Ryun et al. [5]Blaise, Harry and Ning, Taikang We would like to thank our faculty advisors Professor Blaise and Professor Ning, Professor Mertens, Andrew Muslin, Jenny Nord, and all of the other student research assistants in the Electrophysiology Laboratory References and Acknowledgements • ICA successfully removed crosstalk. There was a statistically significant difference in MSC before ICA compared to after ICA processing (p < 0.0001 for all n) Diagram of the sleep cycle in adult humans
Transcript of Removing Cross-Talk from Deep Brain Printing: Hippocampal ......organization and brain plasticity...
`
Printing:
Jessica Voight ‘17 and Catherine Poirier ’17Advisors: Professor Taikang Ning, Ph.D, and Professor Harry Blaise, Ph.D
Senior Capstone Design Project, Trinity College Department of Engineering
Background• While the mechanisms that
underlie REM sleep are widely unknown, it is understood to be linked to dreaming, memory acquisition, and learning1
• The hippocampus’ main function is to transfer memories from short term to long term storage
• Theta wave oscillations during REM sleep have been a “reliable” link between memory consolidation and brain plasticity2
• The hippocampal granule cells in the DG undergo neurogenesis, whereas the pyramidal cells in the CA1 remain unchanged from birth4,5
• It is expected that comparison of EEG recorded simultaneously from each subfield will yield age-dependent developmental differences in neuronal organization and brain plasticity
Problem Statement Rapid eye movement (REM) sleep data from adolescent rats will be examined using linearly independent data collected simultaneously from the CA1 and dentate gyrus (DG) regions of the hippocampus. An experimental and analytical methodology will be designed with the goal of removing cross-talk induced noise between the two regions.
MethodsElectrophysiology• Perform stereotaxic surgery on anesthetized
animals under a heat lamp to maintain a body temperature of 32 C
• Restricted animal with ear bars• Make small incision on the sagittal plane atop
the skull• Use histology to determine proper CA1 and
DG locations• Insert monopolar recording electrodes in the
ipsilateral CA1 and DG subfields and two recording electrodes in the contralateral brain to act as grounds
• Secure all electrodes using a crown of dental cement
• Allow animals to recover for at least 24 hours before placing in recording chamber
Results
Conclusions• Using the methodology created, ICA can successfully remove linear
dependencies with statistical significance• PSE and MSC can be utilized to visualize and confirm that data is linearly
dependent in the DG and CA1 regions• For future data collection, the data acquisition system must be updated to
remove additional noise, as newly collected data was not able to be used using this methodology
Removing Cross-Talk from Deep Brain Hippocampal EEG in Adolescent Rats
Diagram of a rat brain, with the hippocampal region highlighted in
red.
Diagram of a rat hippocampus, with the regions of interest, the CA1 and
dentate gyrus, highlighted.
Data Acquisition• A polygraph machine was used for signal
acquisition • 60 Hz notch filter• Differential amplifier
• Recording continued for 4 hours to capture REM sleep
Digital Signal ProcessingMATLAB programming was used to design a code that would process the REM sleep data through the following analysis methods:• Power Spectrum Analysis (PSE) -
• Data was identified as REM or non-REM based on frequency peaks
• 6-10 Hz = Theta wave band during REM• Magnitude Squared Coherence (MSC) –
• Identifies correlated data based on likelihood it will be similar
• Independent Component Analysis (ICA) –• Identifies linear dependencies in the data
and removes them• Low MSC proves ICA has successfully
removed crosstalk and that discrete signals from the CA1 and DG have been extracted from the data
Power Spectrum
Mag
nitu
de (d
B)
Frequency (Hz)
MSC (pre ICA)
Coh
eren
ce
Frequency (Hz)
MSC (post ICA)
Coh
eren
ce
Frequency (Hz)
CA1 PSE DG PSE MSC
CA1 with ICA DG with ICA MSC with ICA
Mag
nitu
de (d
B)
Frequency (Hz)
Coh
eren
ce
• REM data was selected from PSE analysis based on peaks in the 6-10 Hz range, shown in red
• MSC shows that there was high correlation between the DG and CA1 prior to ICA, shown in red
• DG and CA1 had low coherence after ICA processing in the 6 –10 Hz region shown in the MSC and PSE plots above
𝑋" = $ 𝑥&𝑒()*+"&
,
,(-
&./
𝑐𝑜ℎ45-(67(𝜔) =𝑋45-(67(𝜔)𝑋45-(𝜔)𝑋67(𝜔)
�
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Adolescent 1
Adolescent 2
Adolescent 3
Adolescent 4
Adolescent 5
Adolescent 6
Adolescent 7
Coh
eren
ce
Magnitude Squared Coherence Before and After ICA Processing
Before ICA After ICA
Mag
nitu
de (d
B)
Mag
nitu
de (d
B)
Frequency (Hz)
Coh
eren
ce
Mag
nitu
de (d
B)
• It is necessary to identify discrete brain activity from each region during REM sleep
• Noise distortion or “cross-talk,” can occur during signal acquisition because of the close proximity of the subfields, and must be removed before analysis
• Independent Component Analysis (ICA) is an algorithm capable of extracting linear dependencies between the two recording sites3
[1]"What Is REM Sleep?" [2]Cantero, Jose, et al. [3]Mendhurwar, Kaustubha, et al. [4]Ryu, Jae Ryun et al. [5]Blaise, Harry and Ning, TaikangWe would like to thank our faculty advisors Professor Blaise and Professor Ning, Professor Mertens, Andrew Muslin, Jenny Nord, and all of the other student research assistants in the Electrophysiology Laboratory
References and Acknowledgements
• ICA successfully removed crosstalk. There was a statistically significant difference in MSC before ICA compared to after ICA processing (p < 0.0001 for all n)
Diagram of the sleep cycle in adult humans
