Machine learning reveals interhemispheric somatosensory coherence as indicator of anesthetic depth

The goal of this study was to identify features in mouse electrocorticogram recordings that indicate the depth of anesthesia as approximated by the administered anesthetic dosage. Anesthetic depth in laboratory animals must be precisely monitored and controlled. However, for the most common lab species (mice) few indicators useful for monitoring anesthetic depth have been established. We used electrocorticogram recordings in mice, coupled with peripheral stimulation, in order to identify features of brain activity modulated by isoflurane anesthesia and explored their usefulness in monitoring anesthetic depth through machine learning techniques. Using a gradient boosting regressor framework we identified interhemispheric somatosensory coherence as the most informative and reliable electrocorticogram feature for determining anesthetic depth, yielding good generalization and performance over many subjects. Knowing that interhemispheric somatosensory coherence indicates the effectively administered isoflurane concentration is an important step for establishing better anesthetic monitoring protocols and closed-loop systems for animal surgeries

Improving the Specificity of EEG for Diagnosing Alzheimer's Disease

Objective. EEG has great potential as a cost-effective screening tool for Alzheimer's disease (AD). However, the specificity of EEG is not yet sufficient to be used in clinical practice. In an earlier study, we presented preliminary results suggesting improved specificity of EEG to early stages of Alzheimer's disease. The key to this improvement is a new method for extracting sparse oscillatory events from EEG signals in the time-frequency domain. Here we provide a more detailed analysis, demonstrating improved EEG specificity for clinical screening of MCI (mild cognitive impairment) patients. Methods. EEG data was recorded of MCI patients and age-matched control subjects, in rest condition with eyes closed. EEG frequency bands of interest were θ (3.5–7.5 Hz), α1 (7.5–9.5 Hz), α2 (9.5–12.5 Hz), and β (12.5–25 Hz). The EEG signals were transformed in the time-frequency domain using complex Morlet wavelets; the resulting time-frequency maps are represented by sparse bump models. Results. Enhanced EEG power in the θ range is more easily detected through sparse bump modeling; this phenomenon explains the improved EEG specificity obtained in our previous studies. Conclusions. Sparse bump modeling yields informative features in EEG signal. These features increase the specificity of EEG for diagnosing AD

A roadmap to integrate astrocytes into Systems Neuroscience.

Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease

Complexity of Brain Dynamics as a Correlate of Consciousness in Anaesthetized Monkeys

The use of anaesthesia is a fundamental tool in the investigation of consciousness. Anesthesia procedures allow to investigate different states of consciousness from sedation to deep anesthesia within controlled scenarios. In this study we use information quantifiers to measure the complexity of electrocorticogram recordings in monkeys. We apply these metrics to compare different stages of general anesthesia for evaluating consciousness in several anesthesia protocols. We find that the complexity of brain activity can be used as a correlate of consciousness. For two of the anaesthetics used, propofol and medetomidine, we find that the anaesthetised state is accompanied by a reduction in the complexity of brain activity. On the other hand we observe that use of ketamine produces an increase in complexity measurements. We relate this observation with increase activity within certain brain regions associated with the ketamine used doses. Our measurements indicate that complexity of brain activity is a good indicator for a general evaluation of different levels of consciousness awareness, both in anesthetized and non anesthetizes states.Fil: Fuentes, Nicolás. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; ArgentinaFil: Garcia, Alexis. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; ArgentinaFil: Guevara, Ramón. Università di Padova; ItaliaFil: Orofino, Roberto. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños Pedro Elizalde (ex Casa Cuna); Argentina. Hospital Español de Buenos Aires;Fil: Mateos, Diego Martín. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Matemática Aplicada del Litoral. Universidad Nacional del Litoral. Instituto de Matemática Aplicada del Litoral; Argentina. Universidad Nacional de Entre Rios. Facultad de Ingeniería. Departamento de Bioingeniería; Argentin

A Quest for Meaning in Spontaneous Brain Activity - From fMRI to Electrophysiology to Complexity Science

The brain is not a silent, complex input/output system waiting to be driven by external stimuli; instead, it is a closed, self-referential system operating on its own with sensory information modulating rather than determining its activity. Ongoing spontaneous brain activity costs the majority of the brain\u27s energy budget, maintains the brain\u27s functional architecture, and makes predictions about the environment and the future. I have completed three separate studies on the functional significance and the organization of spontaneous brain activity. The first study showed that strokes disrupt large-scale network coherence in the spontaneous functional magnetic resonance imaging: fMRI) signals, and that the degree of such disruption predicts the behavioral impairment of the patient. This study established the functional significance of coherent patterns in the spontaneous fMRI signals. In the second study, by combining fMRI and electrophysiology in neurosurgical patients, I identified the neurophysiological signal underlying the coherent patterns in the spontaneous fMRI signal, the slow cortical potential: SCP). The SCP is a novel neural correlate of the fMRI signal, most likely underlying both spontaneous fMRI signal fluctuations and task-evoked fMRI responses. Some theoretical considerations have led me to propose a hypothesis on the involvement of the neural activity indexed by the SCP in the emergence of consciousness. In the last study I investigated the temporal organization across a wide range of frequencies in the spontaneous electrical field potentials recorded from the human brain. This study demonstrated that the arrhythmic, scale-free brain activity often discarded in human and animal electrophysiology studies in fact contains rich, complex structures, and further provided evidence supporting the functional significance of such activity

Interrogating spatiotemporal patterns of resting state neuronal and hemodynamic activity in the awake mouse model

Since the advent of functional magnetic resonance imaging (fMRI) and the rise in popularity of its use for resting state functional connectivity mapping (rs-FCM) to non-invasively detect correlated networks of brain activity in human and animal models, many resting state FCM studies have reported differences in these networks under pathologies such as Alzheimer’s or schizophrenia, highlighting the potential for the method’s diagnostic relevance. A common underlying assumption of this analysis, however, is that the blood oxygen level dependent (BOLD) signal of fMRI is a direct measurement of local neural activity. The BOLD signal is in fact a measurement of the local changes in concentration of deoxy-hemoglobin (HbR). Thus, it is imperative that neurovascular coupling—the relationship between neuronal activity and subsequent hemodynamic activity—be better characterized to enable accurate interpretation of resting state fMRI in the context of clinical usage. This dissertation first describes the development and utility of WFOM paradigm for the robust and easily adaptable imaging of simultaneous neuronal and hemodynamic activity in awake mouse models of health or disease in strains with genetically encoded fluorescent calcium reporters. Subsequent exploration of resting state WFOM data collected in Thy1-GCaMP3 and Thy1-GCaMP6f mouse strains is then presented, namely the characterization of spatiotemporal patterns of neuronal and hemodynamic activity and different modulatory depths of neuronal activity via a toolbox of unsupervised blind source separation (e.g. k-means clustering) and supervised (e.g. non-negative least squares, Pearson correlation) analysis tools. The presence of these different modulatory depths of neuronal activity were then confirmed in another Thy1-jRGECO1a mouse strain using the same imaging scheme. Finally, the dissertation documents the application of the WFOM paradigm and select analysis tools to a novel mouse model of diffusely infiltrating glioma, through which neuronal and hemodynamic activity changes during diffusely infiltrating glioma development which impact temporal coherence of the tumor region activity relative to non-tumor regions activity were recorded and analyzed. The paradigm also allowed for recording of numerous spontaneous occurrences of interictal neuronal activity during which neurovascular coupling is modified in the tumor, as well as occurrences of non-convulsive generalized seizure activity (during which neurovascular is non-linear and cortex eventually suffers hypoxia). The detection of spatiotemporal patterns and different modulatory depths of activity in the awake mouse cortex, as well as observation of changes in functional activity in the context of diffusely infiltrating glioma, provide us with new insights into the possible mechanisms underlying variations in resting state connectivity networks found in resting state fMRI studies comparing health and disease states

Evaluating the impact of intracortical microstimulation on distant cortical brain regions for neuroprosthetic applications

Enhancing functional motor recovery after localized brain injury is a widely recognized priority in healthcare as disorders of the nervous system that cause motor impairment, such as stroke, are among the most common causes of adult-onset disability. Restoring physiological function in a dysfunctional brain to improve quality of life is a primary challenge in scientific and clinical research and could be driven by innovative therapeutic approaches. Recently, techniques using brain stimulation methodologies have been employed to promote post-injury neuroplasticity for the restitution of motor function. One type of closed-loop stimulation, i.e., activity-dependent stimulation (ADS), has been shown to modify existing functional connectivity within either healthy or injured cerebral cortices and used to increase behavioral recovery following cortical injury. The aim of this PhD thesis is to characterize the electrophysiological correlates of such behavioral recovery in both healthy and injured cortical networks using in vivo animal models. We tested the ability of two different intracortical micro-stimulation protocols, i.e., ADS and its randomized open-loop version (RS), to potentiate cortico-cortical connections between two distant cortical locations in both anaesthetized and awake behaving rats. Thus, this dissertation has the following three main goals: 1) to investigate the ability of ADS to induce changes in intra-cortical activity in healthy anesthetized rats, 2) to characterize the electrophysiological signs of brain injury and evaluate the capability of ADS to promote electrophysiological changes in the damaged network, and 3) to investigate the long-term effects of stimulation by repeating the treatment for 21 consecutive days in healthy awake behaving animals. The results of this study indicate that closed-loop activity-dependent stimulation induced greater changes than open-loop random stimulation, further strengthening the idea that Hebbian-inspired protocols might potentiate cortico-cortical connections between distant brain areas. The implications of these results have the potential to lead to novel treatments for various neurological diseases and disorders and inspire new neurorehabilitation therapies

A roadmap to integrate astrocytes into Systems Neuroscience

Systems Neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca$^{2+}$ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in time scales of sub-seconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, are, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration, such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca$^{2+}$ and brain coding may represent a leap forward towards novel approaches in the study of astrocytes in health and disease.Junior Leader Fellowhip Program by 'la Caixa' Banking Foundation, LCF/BQ/LI18/11630006 BFU2017-85936-P BFU2016-75107-P BFU2016-79735-P FLAGERA-PCIN-2015-162-C02-02 HHMI 55008742 FPU13/05377 NIH R01NS099254 NSF 1604544 Agència de Gestio d’Ajuts Universitaris i de Recerca, 2017 SGR54

Spreading depression als endogener antiepileptischer Mechanismus: Das Zusammenspiel in der Grenzzone zwischen Migräne und Epilepsie

Background: Migraine and epilepsy are comorbid neurological diseases that have an intriguing relationship as similar genetic mutations and external stimuli trigger both conditions. While the resemblance of their clinical presentation can thus make a diagnosis difficult, the same prophylactic drugs, in turn, decrease attack frequency in both migraine and epilepsy. Spreading depression/depolarization (SD) and epileptic seizures represent their pathophysiological correlates and are triggered by hyperexcitability. Cortical SD describes an abrupt mass depolarization that creeps along the hemisphere and silences electrical activity for several minutes. In contrast, epileptic seizures rapidly recruit adjacent tissue into a hyperexcitable state. Objectives: To investigate whether epileptic seizures trigger SD, what determines the occurrence of SDs, and to what extent SD contributes to suppression and limitation of seizures. Methods: Four chemoconvulsants were used to induce focal neocortical and generalized seizures in an in vivo mouse model. Wildtype CD1 mice (n = 165), familial hemiplegic migraine mutant (n = 14) and transgenic mice (n = 18) were used. Seizure intensity and spread were quantified by an electrocorticogram, while generalized seizures were examined via intrahippocampal recordings. SDs were simultaneously detected via a slow direct current shift and Intrinsic Optical Imaging (IOS) in the cortex and hippocampus. Cerebral blood volume was used as a surrogate of seizure activity and measured via IOS. The effects of spontaneous, chemically, and optogenetically induced SDs were examined. Results: Focal neocortical and generalized seizures triggered multiple SDs. Severe seizure intensity and generalization were related to SD occurrence. Tetrodotoxin confirmed seizures as the only trigger of SD. SDs exerted an antiseizure effect, thereby inhibiting seizure intensity and limiting its spread. Chemically and optogenetically induced SDs showed a similar antiseizure effect. A single SD was capable of limiting the recurrence of seizures. The results could be reproduced in all mice strains. Pretreatment of the cortex with an SD 5 and 30 minutes before applying the chemoconvulsant prevented seizure development. Vice versa, the blockage of SDs with MK-801 resulted in greater expansion of the seizures. Conclusions: SD might function as an endogenous defense mechanism to diminish the occurrence of epileptic activity. Following the seizure suppression, SD manifests as a migraine aura. The data supports the understanding of migraine and epilepsy as related disorders linked by cortical hyperexcitability that either result in SD or seizures. It allows for a nuanced view of SD’s role in neurological disease and facilitates the identification of new therapy concepts for migraine and epilepsy.Hintergrund: Migräne und Epilepsie sind komorbide neurologische Erkrankungen, die in einer engen Beziehung stehen, da sie durch die gleichen externen Einflüsse sowie genetischen Mutationen getriggert werden. Während ihre ähnliche klinische Präsentation eine Diagnose erschweren kann, reduziert die gleiche Medikation wiederum die Episodenfrequenz in beiden Erkrankungen. Die Netzwerk-Phänomene Spreading Depression/Depolarization (SD) und epileptische Anfälle stellen hier die pathophysiologischen Korrelate dar und stehen in einer engen wechselseitigen Beziehung: Beide Phänomene werden durch eine Hypererregbarkeit des Kortex getriggert. SD liegt einer abrupten und intensiven Massendepolarisation zugrunde, breitet sich langsam im Kortex aus und supprimiert dort für einige Minuten hirnelektrische Aktivität. Im Gegensatz dazu rekrutieren epileptische Anfälle mit ihrer schnellen Ausbreitung benachbartes Hirngewebe in einen übererregten Zustand. Zielsetzung: Es soll untersucht werden, inwieweit epileptische Anfälle SDs triggern, welche Charakteristika SD-induzierende Anfälle aufweisen und inwieweit SDs zur Abschwächung und Limitierung von Anfällen beitragen. Methoden: Vier pharmakologische Anfällsmodelle wurden genutzt, um fokale neokortikale Anfälle und generalisierte Anfälle zu erzeugen. Es wurden Wildtyp- (n=165), transgene (n=18) und FHM1-Mäuse (n=14) genutzt. Ein Elektrokortikogramm wurde abgeleitet, um die Intensität und Expansion der epileptischen Anfälle zu erfassen. Systemisch induzierte Anfälle wurden über elektrophysiologische Messungen im Hippocampus beurteilt. Das Auftreten von SDs wurde simultan über die langsame Gleichstrompotenzialänderung im Kortex und Hippocampus, sowie über Intrinsic Optical Imaging (IOS) registriert. Das zerebrale Blutvolumen als metabolisches Korrelat einer gesteigerten neuronalen Aktivität wurde ebenfalls über IOS beurteilt. Es wurden Effekte spontaner, chemisch und optogenetisch induzierter SDs quantifiziert. Ergebnisse: Fokale neokortikale und generalisierte Anfälle triggerten multiple SDs. Das Auftreten spontaner SDs korrelierte mit einer schwerwiegenderen Anfallsintensität und -generalisation. SDs übten einen antiepileptischen Effekt auf den Anfall aus und schwächten sowohl Intensität als auch eine Generalisation. Chemisch und optogenetisch induzierte SDs zeigten einen ebenso potenten antiepileptischen Effekt. Eine einzelne SD konnte ein Wiederauftreten des Anfalls verhindern. Die Ergebnisse konnten in allen Maus-Stämmen reproduziert werden. Eine Vorbehandlung des Kortex mit einer einzelnen SD fünf bzw. 30 Minuten vor Applikation der epileptogenen Substanz verhinderte die Bildung eines Anfalls. Eine Inhibition von SDs mit MK-801 führte umgekehrt zu einer ausgedehnteren Generalisation. Schlussfolgerung: SD als endogener antiepileptischer Mechanismus kann epileptische Anfälle ausbremsen, um dann klinisch als Aura in Erscheinung zu treten. Die Daten festigen die These, dass Migräne und Epilepsie zwei sich überschneidende Erkrankungen darstellen, bei denen die Übererregbarkeit des Kortex entweder eine SD oder Anfälle triggert. Dies erlaubt einen nuancierten Blick auf das Phänomen SD in neurologischen Erkrankungen und erleichtert die Identifikation neuer Therapie- Strategien bei Migräne und Epilepsie