Three-dimensional reengineering of neuronal microcircuits [Elektronische Ressource] : the cortical column in silico / put forward by Marcel Oberländer

Sujets

• Physik

Informations

• Publié par: ruprecht-karls-universitat_heidelberg
• Publié le: 01 janvier 2009
• Nombre de lectures: 38
• Langue: Deutsch
• Poids de l'ouvrage: 3 Mo

Extrait

Dissertation
submitted to the
Combined Faculties for the Natural Sciences and for Mathematics
of the Ruperto-Carola University of Heidelberg, Germany
for the degree of
Doctor of Natural Sciences
Put forward by
Diplom-Physicist: Marcel Oberl˜ander
Born in: Blankenburg (Harz), Germany
Oral examination: December the 16th, 2009Three-dimensional reengineering of neuronal
microcircuits
The cortical column in silico
Referees:
Prof. Dr. Bert Sakmann
Prof. Dr. Karlheinz Meier
iiiAbstract German
Die hier pr˜asentierte Dissertation beschreibt eine Pipeline zum Nachbau drei-dimensionaler,
anatomisch realistischer, funktioneller neuronaler Netzwerke mit subzellul˜arer Au ˜osung. Die
Pipeline besteht aus funf˜ Methoden:
1. "NeuroCount"ergibtdieAnzahlunddrei-dimensionaleVerteilungallerneuronalenZellk˜orper
in gro…en Hirnregionen.
2. "NeuroMorph" ergibt verl˜assliche neuronale Rekonstruktionen, inklusive dendritischer and
axonaler Morphologien.
3. "daVinci" registriert neuronale Morphologien in ein standartisiertes Bezugssystem.
4. "NeuroCluster" gruppiert die standartisierten Rekonstruktionen objektiv in anatomische
Neurontypen.
5. "NeuroNet" kombiniert die Anzahl und Verteilung der Neuronen mit den standartisierten
Rekonstruktionen und bestimmt die Anzahl synaptischer Kontakte, in Abh˜angigkeit von
Neurontyp und Neuronposition, fur˜ jedwede zwei Neurontypen.
DieentwickeltenMethodenwerdenanhanddesNachbauseinesNetzwerkesimsomatosensorischen
System der Ratte demonstriert. Dort existiert eine eins-zu-eins Repr˜asentation zwischen der
sensorischen Information, aufgenommen durch ein einzelnes Barthaar, und abgetrennten Bere-
ichen im Thalamus und Kortex. Der Nachbau dieses Kreislaufes resultiert in einem zylin-
derf˜ormigen Netzwerk Modell bestehend aus …15200 anregenden kortikalen, durch Komparte-
mente repr˜asentierten, Neuronen. Dieses Netzwerk ist mit …285 pr˜asynaptischen thalamischen
Neuronen verbunden. Anregung dieser "kortikalen Kolumne in silico" mit gemessenen physiol-
ogischen Signalen, wird einen Beitrag zum Verst˜andni… neuronalen Informationsverarbeitung in
S˜augetiergehirnen leisten.
ivAbstract English
The presented thesis will describe a pipeline to reengineer three-dimensional, anatomically re-
alistic, functional neuronal networks with subcellular resolution. The pipeline consists of flve
methods:
1. "NeuroCount"providesthenumberandthree-dimensionaldistributionofallneuronsomata
in large brain regions.
2. "NeuroMorph"providesauthenticneurontracings,comprisingdendriteandaxonmorphol-
ogy.
3. "daVinci" registers the neuron morphologies to a standardized reference framework.
4. "NeuroCluster"objectivelygroupsthestandardizedtracingsintoanatomicalneurontypes.
5. "NeuroNet" combines the number and distribution of neurons and neuron-types with the
standardized tracings and determines the neuron-type- and position-speciflc number of
synaptic connections for any two types of neuron.
The developed methods are demonstrated by reengineering the thalamocortical lemniscal micro-
circuit in the somatosensory system of rats. There exists an one-to-one correspondence between
the sensory information obtained by a single facial whisker and segregated areas in the thala-
mus and cortex. The reengineering of this pathway results in a column-shaped network model of
…15200excitatoryfull-compartmentalcorticalneurons. Thisnetworkissynapticallyconnectedto
…285pre-synapticthalamicneurons. Animationofthis"corticalcolumnin silico"withmeasured
physiologicalinputwillhelptogainamechanisticunderstandingofneuronalsensoryinformation
processing in the mammalian brain.
vAcknowledgements
A very special Thanks to:
† my supervisor Prof. Dr. Bert Sakmann for his unlimited support, trustfulness and
encouragement during the past years and the opportunity to continue the presented
work at the Max Planck Florida Institute.
† my family and friends, especially to my parents, who supported me during my entire
life and the past years of study.
† my girlfriend Aline, who supported me during the past flve years and took care that
I also spent some time outside the institute.
A special Thanks to:
† my collaborators: Albert Berman, Dr. Philip J. Broser, Dr. Randy M. Bruno, Dr.
Christiaan P. J. de Kock, Vincent J. Dercksen, Dr. Moritz Helmst˜adter, Dr. Stefan
Hippler, Dr. Stefan Lang and Hanno-Sebastian Meyer. Without teamwork and your
help, none of the presented results would have been possible.
† theundergraduatestudentsundermysupervision: Caroline, Firas, Jasmin, Kristina,
Melanie, Rita, Robert, Stefan, Stefanie and Tatjana. Without you doing all the te-
dious, time-consuming tracings, counts, programming and sample preparation, none
of the presented results would have been possible within three years.
† my thesis committee, Prof. Dr. Bert Sakmann, Prof. Dr. Alexander Borst and Prof.
Dr. Tobias Bonh˜ofier, for fruitful discussions and continuous encouragement.
† Chris Roome, Wulf Kaiser, Klaus Bauer and Marlies Kaiser, who helped me with
every technical problem, even after I moved to Munich.
viContents
1 Introduction 2
1.1 Neuronal circuits: from in vitro/in vivo towards in silico . . . . . . . . . . 2
1.2 The "whisker-barrel-system" in rats . . . . . . . . . . . . . . . . . . . . . . 10
1.2.1 Anatomy of the whisker system . . . . . . . . . . . . . . . . . . . . 11
1.2.2 Functional organization of the barrel cortex . . . . . . . . . . . . . 15
1.2.3 Whisker-related behavior and plasticity . . . . . . . . . . . . . . . . 20
2 Methods 23
2.1 3D counting of neuron somata . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.1.1 Sample preparation and imaging. . . . . . . . . . . . . . . . . . . . 28
2.1.2 Manual detection of soma positions . . . . . . . . . . . . . . . . . . 29
2.1.3 Computing hard- and software . . . . . . . . . . . . . . . . . . . . . 29
2.1.4 Threshold-based flltering (pre-processing). . . . . . . . . . . . . . . 31
2.1.5 Morphological flltering . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.1.6 Model-based cluster splitting. . . . . . . . . . . . . . . . . . . . . . 40
2.1.7 Colocalisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.2 3D reconstruction of neuron morphologies . . . . . . . . . . . . . . . . . . 48
2.2.1 Sample preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
vii2.2.2 Image acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
2.2.3 Shack-Hartmann-analysis-based deconvolution . . . . . . . . . . . . 55
2.2.4 Computing hard- and software . . . . . . . . . . . . . . . . . . . . . 68
2.2.5 Automated image processing . . . . . . . . . . . . . . . . . . . . . . 69
2.2.6 Semi-automated post-processing . . . . . . . . . . . . . . . . . . . . 79
2.3 3D registration of neuron morphologies . . . . . . . . . . . . . . . . . . . . 83
2.3.1 3D reconstruction of reference contours . . . . . . . . . . . . . . . . 84
2.3.2 Calculation of most likely vertical column axis . . . . . . . . . . . . 86
2.3.3 Translation and rotation to standard barrel system . . . . . . . . . 86
2.3.4 Inhomogeneous z-scaling to barrel system . . . . . . . . . 88
2.4 3D classiflcation of neuronal cell-types . . . . . . . . . . . . . . . . . . . . 88
2.4.1 Cluster algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
2.4.2 Cluster features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
2.5 3D reengineering of average neuronal networks . . . . . . . . . . . . . . . . 97
2.5.1 NeuroNet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
3 Methodical results 106
3.1 NeuroCount: 3D counting of neuron somata . . . . . . . . . . . . . . . . . 106
3.1.1 Automated vs manual counting . . . . . . . . . . . . . . . . . . . . 107
3.2 NeuroMorph: 3D reconstruction of single neuron morphologies . . . . . . . 111
3.2.1 Optical aberrations of cortical tissue . . . . . . . . . . . . . . . . . 111
3.2.2 Automated vs manual reconstruction . . . . . . . . . . . . . . . . . 118
4 Anatomical results 122
4.1 Quantitative 3D structure of S1 . . . . . . . . . . . . . . . . . . . . . . . . 124
4.1.1 3D distribution of neuron somata in S1 . . . . . . . . . . . . . . . . 125
viii4.1.2 3D average cortical column of neuron somata . . . . . . . . . . . . 131
4.1.3 Dendritic excitatory neuronal cell types in S1 . . . . . . . . . . . . 134
4.1.4 Axonal projections of L5A neurons within S1 . . . . . . . . . . . . 141
4.2 Quantitative 3D structure of VPM . . . . . . . . . . . . . . . . . . . . . . 145
4.2.1 3D distribution of neuron somata in VPM . . . . . . . . . . . . . . 146
4.2.2 Axonal excitatory neuronal cell types in VPM . . . . . . . . . . . . 150
4.3 3D reconstruction of lemniscal thalamocortical pathway . . . . . . . . . . . 154
4.3.1 The standardized 3D cortical column in silico . . . . . . . . . . . . 155
4.3.2 Number and 3D distribution of VPM synapses in S1 . . . . . . . . 160
5 Discussion 170
ixList of Tables
3.1 Manual vs automated neuron counting . . . . . . . . . . . . . . . . . . . . 108
3.2 False positive/negative analysis of neuron counting . . . . . . . . . . . . . 110
4.1 Comparison of NeuroCount and sparse sampling results . . . . . . . . . . . 130
4.2 Dendritic and axonal leng