Characterization of spatio-temporal epidural event-related potentials for mouse models of psychiatric disorders.

Distinctive features in sensory event-related potentials (ERPs) are endophenotypic biomarkers of psychiatric disorders, widely studied using electroencephalographic (EEG) methods in humans and model animals. Despite the popularity and unique significance of the mouse as a model species in basic research, existing EEG methods applicable to mice are far less powerful than those available for humans and large animals. We developed a new method for multi-channel epidural ERP characterization in behaving mice with high precision, reliability and convenience and report an application to time-domain ERP feature characterization of the Sp4 hypomorphic mouse model for schizophrenia. Compared to previous methods, our spatio-temporal ERP measurement robustly improved the resolving power of key signatures characteristic of the disease model. The high performance and low cost of this technique makes it suitable for high-throughput behavioral and pharmacological studies

A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is however critical both for basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brain-wide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brain-wide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open access data repository; compatibility with existing resources, and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.Comment: 41 page

Development and Localization of Spike-Wave Seizures in Animal Models

Animal models allow for detailed investigation of neuronal function, particularly invasive localization and developmental studies not possible in humans. This thesis will review the technical challenges of simultaneous EEG-fMRI, and epileptogenesis studies in animal models, including issues related to anesthesia, movement, signal artifact, physiology, electrode compatibility, data acquisition, and data analysis, and review recent findings from simultaneous EEG-fMRI studies in epilepsy and other fields. Original research will be presented on the localization of neuronal networks involved during spike-and-wave seizures in the WAG/Rij rat, a model of human absence epilepsy. Simultaneous EEG-fMRI at 9 Tesla, complimented by parallel electrophysiology, including Multiple Unit Activity (MUA), Local Field Potential (LFP), and Cerebral Blood Flow (CBF) measurements were employed to investigate the functioning of neuronal networks. This work indicates that while BOLD signal increases in the Somaotsensory Cortex and Thalamus during SWD are associated with MUA, LFP, and CBF increases, BOLD signal decreases in the Caudate are associated with CBF decreases and relatively larger increase in LFP and smaller increase in MUA. Complimenting the localization studies, original research will also be presented on the development of spike-and-wave epilepsy in the C3H/Hej mouse, a model which will allow for more advanced genetic and molecular investigation. This work shows seizure development progressing though immature, transitional, and mature stages

Analysis of resting-state neurovascular coupling and locomotion-associated neural dynamics using wide-field optical mapping

Understanding the relationship between neural activity and cortical hemodynamics, or neurovascular coupling is the foundation to interpret neuroimaging signals such as functional magnetic resonance imaging (fMRI) which measure local changes in hemodynamics as a proxy for underlying neural activity. Even though the stereotypical stimulus-evoked hemodynamic response pattern with increased concentration of oxy- and total-hemoglobin and decrease in concentration of deoxy-hemoglobin has been well-recognized, the linearity of neurovascular coupling and its variances depending on brain state and tasks haven’t been thoroughly evaluated. To directly assess the cortical neurovascular coupling, simultaneous recordings of neural and hemodynamic activity were imaged by wide-field optical mapping (WFOM) over the bilateral dorsal surface of the mouse brain through a bilateral thinned-skull cranial window. Neural imaging is achieved through wide-field fluorescence imaging in animals expressing genetically encoded calcium sensor (Thy1-GCaMP). Hemodynamics are recorded via simultaneous imaging of multi-spectral reflectance. Significant hemodynamic crosstalk was found in the detected fluorescence signal and the physical model of the contamination, methods of correction as well as electrophysiological verification are presented. A linear model between neural and hemodynamic signals was used to fit spatiotemporal hemodynamics can be predicted by convolving local fluorescence changes with hemodynamic response functions derived through both deconvolution and gamma-variate fitting. Beyond confirming that the resting-state hemodynamics in the awake and anesthetized brain are coupled to underlying neural activity, the patterns of bilaterally symmetric spontaneous neural activity observed by WFOM emulate the functionally connected networks detected by fMRI. This result provides reassurance that resting-state functional connectivity has neural origins. With the access to cortical neural activity at mesoscopic level, we further explore the cortical neural representations preceding and during spontaneous locomotion

Early brain activity : Translations between bedside and laboratory

Neural activity is both a driver of brain development and a readout of developmental processes. Changes in neuronal activity are therefore both the cause and consequence of neurodevelopmental compromises. Here, we review the assessment of neuronal activities in both preclinical models and clinical situations. We focus on issues that require urgent translational research, the challenges and bottlenecks preventing translation of biomedical research into new clinical diagnostics or treatments, and possibilities to overcome these barriers. The key questions are (i) what can be measured in clinical settings versus animal experiments, (ii) how do measurements relate to particular stages of development, and (iii) how can we balance practical and ethical realities with methodological compromises in measurements and treatments.Peer reviewe

Multi-Scale Mathematical Modelling of Brain Networks in Alzheimer's Disease

Perturbations to brain network dynamics on a range of spatial and temporal scales are believed to underpin neurological disorders such as Alzheimer’s disease (AD). This thesis combines quantitative data analysis with tools such as dynamical systems and graph theory to understand how the network dynamics of the brain are altered in AD and experimental models of related pathologies. Firstly, we use a biophysical neuron model to elucidate ionic mechanisms underpinning alterations to the dynamics of principal neurons in the brain’s spatial navigation systems in an animal model of tauopathy. To uncover how synaptic deficits result in alterations to brain dynamics, we subsequently study an animal model featuring local and long-range synaptic degeneration. Synchronous activity (functional connectivity; FC) between neurons within a region of the cortex is analysed using two-photon calcium imaging data. Long-range FC between regions of the brain is analysed using EEG data. Furthermore, a computational model is used to study relationships between networks on these different spatial scales. The latter half of this thesis studies EEG to characterize alterations to macro-scale brain dynamics in clinical AD. Spectral and FC measures are correlated with cognitive test scores to study the hypothesis that impaired integration of the brain’s processing systems underpin cognitive impairment in AD. Whole brain computational modelling is used to gain insight into the role of spectral slowing on FC, and elucidate potential synaptic mechanisms of FC differences in AD. On a finer temporal scale, microstate analyses are used to identify changes to the rapid transitioning behaviour of the brain’s resting state in AD. Finally, the electrophysiological signatures of AD identified throughout the thesis are combined into a predictive model which can accurately separate people with AD and healthy controls based on their EEG, results which are validated on an independent patient cohort. Furthermore, we demonstrate in a small preliminary cohort that this model is a promising tool for predicting future conversion to AD in patients with mild cognitive impairment

小動物におけるEEGとfMRIの同時計測法の確立

Tohoku University川島隆太課

Neural population coding: combining insights from microscopic and mass signals

Behavior relies on the distributed and coordinated activity of neural populations. Population activity can be measured using multi-neuron recordings and neuroimaging. Neural recordings reveal how the heterogeneity, sparseness, timing, and correlation of population activity shape information processing in local networks, whereas neuroimaging shows how long-range coupling and brain states impact on local activity and perception. To obtain an integrated perspective on neural information processing we need to combine knowledge from both levels of investigation. We review recent progress of how neural recordings, neuroimaging, and computational approaches begin to elucidate how interactions between local neural population activity and large-scale dynamics shape the structure and coding capacity of local information representations, make them state-dependent, and control distributed populations that collectively shape behavior