337 research outputs found
Binary Matrix Shuffling Filter for Feature Selection in Neuronal Morphology Classification
A prerequisite to understand neuronal function and characteristic is to classify neuron correctly. The existing classification techniques are usually based on structural characteristic and employ principal component analysis to reduce feature dimension. In this work, we dedicate to classify neurons based on neuronal morphology. A new feature selection method named binary matrix shuffling filter was used in neuronal morphology classification. This method, coupled with support vector machine for implementation, usually selects a small amount of features for easy interpretation. The reserved features are used to build classification models with support vector classification and another two commonly used classifiers. Compared with referred feature selection methods, the binary matrix shuffling filter showed optimal performance and exhibited broad generalization ability in five random replications of neuron datasets. Besides, the binary matrix shuffling filter was able to distinguish each neuron type from other types correctly; for each neuron type, private features were also obtained
Binary Matrix Shuffling Filter for Feature Selection in Neuronal Morphology Classification
A prerequisite to understand neuronal function and characteristic is to classify neuron correctly. The existing classification techniques are usually based on structural characteristic and employ principal component analysis to reduce feature dimension. In this work, we dedicate to classify neurons based on neuronal morphology. A new feature selection method named binary matrix shuffling filter was used in neuronal morphology classification. This method, coupled with support vector machine for implementation, usually selects a small amount of features for easy interpretation. The reserved features are used to build classification models with support vector classification and another two commonly used classifiers. Compared with referred feature selection methods, the binary matrix shuffling filter showed optimal performance and exhibited broad generalization ability in five random replications of neuron datasets. Besides, the binary matrix shuffling filter was able to distinguish each neuron type from other types correctly; for each neuron type, private features were also obtained
Cellular Classes in the Human Brain Revealed In Vivo by Heartbeat-Related Modulation of the Extracellular Action Potential Waveform
Determining cell types is critical for understanding neural circuits but remains elusive in the living human brain. Current approaches discriminate units into putative cell classes using features of the extracellular action potential (EAP); in absence of ground truth data, this remains a problematic procedure. We find that EAPs in deep structures of the brain exhibit robust and systematic variability during the cardiac cycle. These cardiac-related features refine neural classification. We use these features to link bio-realistic models generated from in vitro human whole-cell recordings of morphologically classified neurons to in vivo recordings. We differentiate aspiny inhibitory and spiny excitatory human hippocampal neurons and, in a second stage, demonstrate that cardiac-motion features reveal two types of spiny neurons with distinct intrinsic electrophysiological properties and phase-locking characteristics to endogenous oscillations. This multi-modal approach markedly improves cell classification in humans, offers interpretable cell classes, and is applicable to other brain areas and species
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Neuronal dynamics and connectivity analysis of neuronal cultures on multi electrode arrays
Despite a number of attempts over the past two decades, research into reliable, controlled induction of long term evoked responses, mimicking low level learning and memory in dissociated cell cultures remains challenging. In addition, a full understanding of the stimulus-response relationships that underlie synaptic plasticity has not yet been achieved, and many of the underlying principles remain largely unknown. Plasticity studies have been predominantly limited to low density Multi/Micro Electrode Arrays (MEAs). With the advent of complementary metal-oxide-semiconductor (CMOS) based High-Density (HD) MEAs, unprecedented spatial and temporal resolution is now possible. In this thesis, an attempt to bridge the gap between studies of neural plasticity and the use of CMOS based HD-MEAs with thousands of electrodes, is reported. Additionally, since such HD-MEAs generate a large volume of data and require advanced analytics to efficiently process and analyse recordings, computational tools and novel algorithms to infer connectivity during plasticity have been developed.
The study showed that the responsiveness, stability and initial firing rate of neuronal cultures are the deciding factors to reliably induce evoked responses. With multi-site stimulation, sustained long term potentiation was achieved, which was validated both by evoked response plots and overall firing rates measured at five different time points - before and after repeated stimulation, and at a three day time points. In contrast, while depression responses were observed, it was found that the effects were not sustained over many days. The findings of the study suggest that appropriate selection of neuronal cultures is crucial for inducing desired evoked responses and criteria for this have been developed. Furthermore, it is concluded that the initial responses to test stimuli can be used to determine whether potentiated or depressed responses are to be expected.
To analyse the recordings, pipeline of computational tools was developed. Firstly, neuronal synchrony metrics were adapted for the first time for large HD-MEA recordings and shown to correspond effectively to the firing dynamics. To analyse functional connectivity, an information theoretic approach, Transfer Entropy(TE), was utilised. The method showed accurate estimation of functional connectivity with mid 80th percentile accuracy on simulated data. A superimposition method was proposed to enhance confidence in the connectivity estimation. To statistically evaluate connectivity estimation, a new surrogate method, based on ISI distribution approach, was proposed and validated with a simulated Izhikevich network. The method achieved improved accuracy, compared to the existing ISI shuffling method. This newly developed method was later utilised to infer connectivity and refine connections during the learning process of real neuronal cultures over many days of stimulation. The connectivity inference corresponded accurately to both the spontaneous and stimulated networks during evoked responses and the proposed method permitted observation of the evolution of connections for the potentiated network
Towards Accurate Forecasting of Epileptic Seizures: Artificial Intelligence and Effective Connectivity Findings
L’épilepsie est une des maladies neurologiques les plus fréquentes, touchant près d’un
pourcent de la population mondiale. De nos jours, bien qu’environ deux tiers des patients
épileptiques répondent adéquatement aux traitements pharmacologiques, il reste qu’un tiers des
patients doivent vivre avec des crises invalidantes et imprévisibles. Quoique la chirurgie
d’épilepsie puisse être une autre option thérapeutique envisageable, le recours à la chirurgie de
résection demeure très faible en partie pour des raisons diverses (taux de réussite modeste, peur
des complications, perceptions négatives). D’autres avenues de traitement sont donc souhaitables.
Une piste actuellement explorée par des groupes de chercheurs est de tenter de prédire les crises à
partir d’enregistrements de l’activité cérébrale des patients. La capacité de prédire la survenue de
crises permettrait notamment aux patients, aidants naturels ou personnels médical de prendre des
mesures de précaution pour éviter les désagréments reliés aux crises voire même instaurer un
traitement pour les faire avorter. Au cours des dernières années, d’importants efforts ont été
déployés pour développer des algorithmes de prédiction de crises et d’en améliorer les
performances.
Toutefois, le manque d’enregistrements électroencéphalographiques intracrâniens (iEEG) de
longue durée de qualité, la quantité limitée de crises, ainsi que la courte durée des périodes
interictales constituaient des obstacles majeurs à une évaluation adéquate de la performance des
algorithmes de prédiction de crises. Récemment, la disponibilité en ligne d’enregistrements iEEG
continus avec échantillonnage bilatéral (des deux hémisphères) acquis chez des chiens atteints
d’épilepsie focale à l’aide du dispositif de surveillance ambulatoire implantable NeuroVista a
partiellement facilité cette tâche. Cependant, une des limitations associées à l’utilisation de ces
données durant la conception d’un algorithme de prédiction de crises était l’absence
d’information concernant la zone exacte de début des crises (information non fournie par les
gestionnaires de cette base de données en ligne). Le premier objectif de cette thèse était la mise
en oeuvre d’un algorithme précis de prédiction de crises basé sur des enregistrements iEEG canins
de longue durée. Les principales contributions à cet égard incluent une localisation quantitative
de la zone d’apparition des crises (basée sur la fonction de transfert dirigé –DTF), l’utilisation
d’une nouvelle fonction de coût via l’algorithme génétique proposé, ainsi qu’une évaluation
quasi-prospective des performances de prédiction (données de test d’un total de 893 jours). Les résultats ont montré une amélioration des performances de prédiction par rapport aux études
antérieures, atteignant une sensibilité moyenne de 84.82 % et un temps en avertissement de 10 %.
La DTF, utilisée précédemment comme mesure de connectivité pour déterminer le réseau
épileptique (objectif 1), a été préalablement validée pour quantifier les relations causales entre les
canaux lorsque les exigences de quasi-stationnarité sont satisfaites. Ceci est possible dans le cas
des enregistrements canins en raison du nombre relativement faible de canaux. Pour faire face
aux exigences de non-stationnarité, la fonction de transfert adaptatif pondérée par le spectre
(Spectrum weighted adaptive directed transfer function - swADTF) a été introduit en tant qu’une
version variant dans le temps de la DTF. Le second objectif de cette thèse était de valider la
possibilité d’identifier les endroits émetteurs (ou sources) et récepteurs d’activité épileptiques en
appliquant la swADTF sur des enregistrements iEEG de haute densité provenant de patients
admis pour évaluation pré-chirurgicale au CHUM. Les générateurs d’activité épileptique étaient
dans le volume réséqué pour les patients ayant des bons résultats post-chirurgicaux alors que
différents foyers ont été identifiés chez les patients ayant eu de mauvais résultats postchirurgicaux.
Ces résultats démontrent la possibilité d’une identification précise des sources et
récepteurs d’activités épileptiques au moyen de la swADTF ouvrant la porte à la possibilité d’une
meilleure sélection d’électrodes de manière quantitative dans un contexte de développement
d’algorithme de prédiction de crises chez l’humain.
Dans le but d’explorer de nouvelles avenues pour la prédiction de crises épileptiques, un
nouveau précurseur a aussi été étudié combinant l’analyse des spectres d’ordre supérieur et les
réseaux de neurones artificiels (objectif 3). Les résultats ont montré des différences
statistiquement significatives (p<0.05) entre l’état préictal et l’état interictal en utilisant chacune
des caractéristiques extraites du bi-spectre. Utilisées comme entrées à un perceptron multicouche,
l’entropie bispectrale normalisée, l’entropie carré normalisée, et la moyenne ont atteint des
précisions respectives de 78.11 %, 72.64% et 73.26%.
Les résultats de cette thèse confirment la faisabilité de prédiction de crises à partir
d’enregistrements d’électroencéphalographie intracrâniens. Cependant, des efforts
supplémentaires en termes de sélection d’électrodes, d’extraction de caractéristiques, d’utilisation
des techniques d’apprentissage profond et d’implémentation Hardware, sont nécessaires avant
l’intégration de ces approches dans les dispositifs implantables commerciaux.----------ABSTRACT
Epilepsy is a chronic condition characterized by recurrent “unpredictable” seizures. While
the first line of treatment consists of long-term drug therapy about one-third of patients are said to
be pharmacoresistant. In addition, recourse to epilepsy surgery remains low in part due to
persisting negative attitudes towards resective surgery, fear of complications and only moderate
success rates. An important direction of research is to investigate the possibility of predicting
seizures which, if achieved, can lead to novel interventional avenues.
The paucity of intracranial electroencephalography (iEEG) recordings, the limited number of
ictal events, and the short duration of interictal periods have been important obstacles for an
adequate assessment of seizure forecasting. More recently, long-term continuous bilateral iEEG
recordings acquired from dogs with naturally occurring focal epilepsy, using the implantable
NeuroVista ambulatory monitoring device have been made available on line for the benefit of
researchers. Still, an important limitation of these recordings for seizure-prediction studies was
that the seizure onset zone was not disclosed/available. The first objective of this thesis was to
develop an accurate seizure forecasting algorithm based on these canine ambulatory iEEG
recordings. Main contributions include a quantitative, directed transfer function (DTF)-based,
localization of the seizure onset zone (electrode selection), a new fitness function for the
proposed genetic algorithm (feature selection), and a quasi-prospective assessment of seizure
forecasting on long-term continuous iEEG recordings (total of 893 testing days). Results showed
performance improvement compared to previous studies, achieving an average sensitivity of
84.82% and a time in warning of 10 %.
The DTF has been previously validated for quantifying causal relations when quasistationarity
requirements are met. Although such requirements can be fulfilled in the case of
canine recordings due to the relatively low number of channels (objective 1), the identification of
stationary segments would be more challenging in the case of high density iEEG recordings. To
cope with non-stationarity issues, the spectrum weighted adaptive directed transfer function
(swADTF) was recently introduced as a time-varying version of the DTF. The second objective
of this thesis was to validate the feasibility of identifying sources and sinks of seizure activity
based on the swADTF using high-density iEEG recordings of patients admitted for pre-surgical monitoring at the CHUM. Generators of seizure activity were within the resected volume for
patients with good post-surgical outcomes, whereas different or additional seizure foci were
identified in patients with poor post-surgical outcomes. Results confirmed the possibility of
accurate identification of seizure origin and propagation by means of swADTF paving the way
for its use in seizure prediction algorithms by allowing a more tailored electrode selection.
Finally, in an attempt to explore new avenues for seizure forecasting, we proposed a new
precursor of seizure activity by combining higher order spectral analysis and artificial neural
networks (objective 3). Results showed statistically significant differences (p<0.05) between
preictal and interictal states using all the bispectrum-extracted features. Normalized bispectral
entropy, normalized squared entropy and mean of magnitude, when employed as inputs to a
multi-layer perceptron classifier, achieved held-out test accuracies of 78.11%, 72.64%, and
73.26%, respectively.
Results of this thesis confirm the feasibility of seizure forecasting based on iEEG recordings;
the transition into the ictal state is not random and consists of a “build-up”, leading to seizures.
However, additional efforts in terms of electrode selection, feature extraction, hardware and deep
learning implementation, are required before the translation of current approaches into
commercial devices
Cellular Classes in the Human Brain Revealed In Vivo by Heartbeat-Related Modulation of the Extracellular Action Potential Waveform
Determining cell types is critical for understanding neural circuits but remains elusive in the living human brain. Current approaches discriminate units into putative cell classes using features of the extracellular action potential (EAP); in absence of ground truth data, this remains a problematic procedure. We find that EAPs in deep structures of the brain exhibit robust and systematic variability during the cardiac cycle. These cardiac-related features refine neural classification. We use these features to link bio-realistic models generated from in vitro human whole-cell recordings of morphologically classified neurons to in vivo recordings. We differentiate aspiny inhibitory and spiny excitatory human hippocampal neurons and, in a second stage, demonstrate that cardiac-motion features reveal two types of spiny neurons with distinct intrinsic electrophysiological properties and phase-locking characteristics to endogenous oscillations. This multi-modal approach markedly improves cell classification in humans, offers interpretable cell classes, and is applicable to other brain areas and species
Reconstruction of neuronal activity and connectivity patterns in the zebrafish olfactory bulb
In the olfactory bulb (OB), odors evoke distributed patterns of activity across glomeruli that are reorganized by networks of interneurons (INs). This reorganization results in multiple computations including a decorrelation of activity patterns across the output neurons, the mitral cells (MCs). To understand the mechanistic basis of these computations it is essential to analyze the relationship between function and structure of the underlying circuit.
I combined in vivo twophoton calcium imaging with dense circuit reconstruction from complete serial block-face electron microscopy (SBEM) stacks of the larval zebrafish OB (4.5 dpf) with a voxel size of 9x9x25nm. To address bottlenecks in the workflow of SBEM, I developed a novel embedding and staining procedure that effectively reduces surface charging in SBEM and enables to acquire SBEM stacks with at least a ten-fold increase in both, signal-to-noise as well as acquisition speed.
I set up a high throughput neuron reconstruction pipeline with >30 professional tracers that is available for the scientific community (ariadne-service.com). To assure efficient and accurate circuit reconstruction, I developed PyKNOSSOS, a Python software for skeleton tracing and synapse annotation, and CORE, a skeleton consolidation procedure that combines redundant reconstruction with targeted expert input.
Using these procedures I reconstructed all neurons (>1000) in the larval OB. Unlike in the adult OB, INs were rare and appeared to represent specific subtypes, indicating that different sub-circuits develop sequentially. MCs were uniglomerular whereas inter-glomerular projections of INs were complex and biased towards groups of glomeruli that receive input from common types of sensory neurons. Hence, the IN network in the OB exhibits a topological organization that is governed by glomerular identity.
Calcium imaging revealed that the larval OB circuitry already decorrelates activity patterns evoked by similar odors. The comparison of inter-glomerular connectivity to the functional interactions between glomeruli indicates that pattern decorrelation depends on specific, non-random inter-glomerular IN projections. Hence, the topology of IN networks in the OB appears to be an important determinant of circuit function
Profiling of target molecules of human astrocytes for selective transduction by the Adeno-associated virus variant AAV9P1
Astrocytes are among the most abundant cell types in the human central nervous system (CNS). They have critical functions in the brain, including the maintenance of neuronal homeostasis and active contribution to the formation, regulation, and maintenance of synapses and synaptic transmission. Further, astrocytes react to CNS damage with proliferation and have been shown to adapt neuronal functions in mice after the artificial expression of neurogenic factors. Astrocytic dysregulation is associated with a variety of neuropathologies, including psychological and neurodegenerative diseases. Treatment strategies for these diseases that rely on modifying the astrocytic gene or protein expression are subject of current research. The main obstacle in this pursuit is the lack of efficient and specific vectors for targeted astrocyte transduction. Adeno-associated virus (AAV) vectors are considered the gold standard for gene therapy due to their favorable biological characteristic. The variant rAAV9P1 has been described to efficiently transduce astrocytes in vivo and discriminate between astrocytes and neurons. However, the molecular base of this transduction behavior is still elusive.
In this work, we have investigated the underlying molecular profile that enables efficient and selective transduction of astrocytes by rAAV9P1. We could show that rAAV9P1 transduces astrocytic cell lines more efficiently than vectors derived from its parental serotype AAV9 and with higher selectivity than vectors carrying a capsid from the well-investigated serotype AAV2. It was found that rAAV9P1 follows a transduction mechanism that is distinctly different from the HSPG-dependent, ubiquitous transduction of rAAV2. On the molecular level, rAAV9P1 engages with αv-containing integrins, likely via the RGD-sequence in the inserted P1 peptide. These integrins include αvβ8 as a central receptor and αvβ3/αvβ5 as potential redundant auxiliary receptors. Besides, rAAV9P1 transduction is dependent on classical AAV9 receptors such as terminal galactose on N-linked cell surface glycans, the 37/67 kDa laminin receptor (LamR), and the essential AAV receptor KIA00319L (AAVR). Furthermore, a genome-wide CRISPR/Cas9 screening in a human glioblastoma cell line revealed that intra-cellular pathways with astrocyte-relevance might be involved in efficient and selective transduction of astrocytes by rAAV9P1.
Taken together, this work presents the detailed receptor profile of rAAV9P1, which achieves high efficiency and cell-type selectivity by combining the binding to new receptors through capsid modifications with pre-existing receptors of the parental serotype. This multi-factorial binding might pave the road for the future development of more cell-type-selective rAAV vectors, but also refining of rAAV9P1 for future in vivo and gene therapy approaches
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