Spatio-temporal Principles of Infra-slow Brain Activity

In the study of systems where basic laws have eluded us, as is largely the case in neuroscience, the simplest approach to progress might be to ask: what are the biggest, most noticeable things the system does when left alone? Without any perturbations or fine dissections, can regularities be found in the basic operations of the system as a whole? In the case of the brain, it turns out that there is an amazing amount of activity even in the absence of explicit environmental inputs or outputs. We call this spontaneous, or resting state, brain activity. Prior work has shown that spontaneous brain activity is dominated by very low frequencies: the biggest changes in brain activity happen relatively slowly, over 10’s-100’s of seconds. Moreover, this very slow activity of the brain is quite metabolically expensive. The brain accounts for 2% of body mass in an adult, but requires 20% of basal metabolic expenditure. Remarkably, the energy required to sustain brain function is nearly constant whether one is engaged in a demanding mental task or simply out to lunch. Furthermore, work over the past three decades has established that the spontaneous activities of the brain are not random, but instead organized into specific patterns, most often characterized by correlations within large brain systems. Yet, how do these correlations arise, and does spontaneous activity support slow signaling within and between neural systems? In this thesis, we approach these questions by providing a comprehensive analysis of the temporal structure of very low frequency spontaneous activity. Specifically, we focus on the direction of travel in low frequency activity, measured using resting state fMRI in humans, but also using electrophysiological techniques in humans and mice, and optical calcium imaging in mice. Our temporal analyses reveal heretofore unknown regularities in the way slow signals move through the brain. We further find that very low frequency activity behaves differently than faster frequencies, that it travels through distinct layers of the cortex, and that its travel patterns give rise to correlations within networks. We also demonstrate that the travel patterns of very low frequency activity are highly dependent on the state of the brain, especially the difference between wake and sleep states. Taken together, the findings in this thesis offer a glimpse into the principles that govern brain activity

Resting-state networks representation of the global phenomena

Resting-state functional magnetic resonance imaging (rsfMRI) has been widely applied to investigate spontaneous neural activity, often based on its macroscopic organization that is termed resting-state networks (RSNs). Although the neurophysiological mechanisms underlying the RSN organization remain largely unknown, accumulating evidence points to a substantial contribution from the global signals to their structured synchronization. This study further explored the phenomenon by taking advantage of the inter- and intra-subject variations of the time delay and correlation coefficient of the signal timeseries in each region using the global mean signal as the reference signal. Consistent with the hypothesis based on the empirical and theoretical findings, the time lag and correlation, which have consistently been proven to represent local hemodynamic status, were shown to organize networks equivalent to RSNs. The results not only provide further evidence that the local hemodynamic status could be the direct source of the RSNs’ spatial patterns but also explain how the regional variations in the hemodynamics, combined with the changes in the global events’ power spectrum, lead to the observations. While the findings pose challenges to interpretations of rsfMRI studies, they further support the view that rsfMRI can offer detailed information related to global neurophysiological phenomena as well as local hemodynamics that would have great potential as biomarkers

Low frequency hippocampal-cortical activity drives brain-wide resting-state functional MRI connectivity

The hippocampus, including the dorsal dentate gyrus (dDG), and cortex engage in bidirectional communication. We propose that low-frequency activity in hippocampal–cortical pathways contributes to brain-wide resting-state connectivity to integrate sensory information. Using optogenetic stimulation and brain-wide fMRI and resting-state fMRI (rsfMRI), we determined the large-scale effects of spatiotemporal-specific downstream propagation of hippocampal activity. Low-frequency (1 Hz), but not high-frequency (40 Hz), stimulation of dDG excitatory neurons evoked robust cortical and subcortical brain-wide fMRI responses. More importantly, it enhanced interhemispheric rsfMRI connectivity in various cortices and hippocampus. Subsequent local field potential recordings revealed an increase in slow oscillations in dorsal hippocampus and visual cortex, interhemispheric visual cortical connectivity, and hippocampal–cortical connectivity. Meanwhile, pharmacological inactivation of dDG neurons decreased interhemispheric rsfMRI connectivity. Functionally, visually evoked fMRI responses in visual regions also increased during and after low-frequency dDG stimulation. Together, our results indicate that low-frequency activity robustly propagates in the dorsal hippocampal–cortical pathway, drives interhemispheric cortical rsfMRI connectivity, and mediates visual processing

The Role Of The Nmda Receptor In Shaping Cortical Activity During Development

Currently, it is estimated that neuropsychiatric disorders will affect 20-25% of humans in their lifetime. These disorders are a major cause of mortality, suffering, and economic cost to society. Within this broad class, neurodevelopmental disorders (NDDs), including intellectual disability, autism spectrum disorder, and schizophrenia, are estimated to affect 2-5% percent of the world population. Devastatingly, we lack fundamental treatments for NDDs, which have proved some of the most imposing disorders to understand scientifically. The challenge is twofold: first, NDDs affect the most complex aspects of human cognition; second, pathogenesis begins early in neural circuit development, but we lack predictive biomarkers before overt behavioral deficits are apparent. Although we have identified many genes associated with these disorders, how underlying genetic disruptions lead to pathological neural network development and function remains unclear. The overarching framework of this dissertation is that all NPDs are disorders of distributed neural networks, and pathophysiology must be understood at this level to effectively intervene clinically. The cerebral cortex is necessary for complex human capacities, and cortical dysfunction is hypothesized to be central to the pathophysiology of NDDs. NMDA glutamate receptors (NMDARs) are important for the development of local circuit features in the cortex, for normal neurocognitive function, and are strongly implicated in NDDs. However, the role of NMDARs in the development of the large-scale cortical network dynamics that underly higher cognition has not been well examined. Understanding the role of NMDARs at this network level is critical because large-scale “functional connectivity” patterns are thought to be hallmarks of normal cortical function, are hypothesized to be disrupted in NDDs, and may be detectable in humans using non-invasive neuroimaging or electrophysiology. In the studies presented in this dissertation, I (in collaboration and with the support of my colleagues) tested the role of the NMDAR in shaping large-scale cortical network organization using in vivo widefield imaging of whole cortex spontaneous activity in developing mice. I found that NMDAR function in the lineage that includes cortical excitatory neurons and glia, specifically, was critical for the elaboration of normal cortical activity patterns and dynamic network organization. In the first set of experiments, NMDARs were deleted in glutamatergic excitatory neurons (Emx1-cre+/WT/Grin1f/f ; referred to as EX-NMDAR KO mice) or GABAergic inhibitory neurons (Nkx2.1+/WT/Grin1f/f; referred to as IN-NMDAR KO mice). The developing cortex normally exhibits a diverse range of spatio-temporal patterns, reflecting the emergence of functionally associated sub-networks. In EX-NMDAR KO mice, normal patterns of spontaneous activity were severely disrupted and reduced to a nearly one-dimensional dynamic space dominated by large, cortex-wide events. Interestingly, in IN-NMDAR KO mice, the structure and complexity of spontaneous activity was largely normal. In the next set of experiments, I tested the role of extrinsic thalamic neurotransmission on cortical activity during development. Deleting the vesicular glutamate transporter from thalamic neurons while leaving cortical NMDARs intact (Sert-Cre+/−,vglut1−/−,vglut2fl/fl; referred to as TH-VG KO mice) led to a shift in cortical activity patterns towards large domains of activity, reminiscent of patterns observed in EX-NMDAR KO mice. This manipulation also reduced the dimensionality of cortical activity, though not as severally as in EX-NMDAR KO mice. In a final set of experiments, I tested cortical activity in three established mouse models of mono-genetic causes of NDDs in humans: the FMR1-KO mouse based on Fragile X Syndrome, the CNTNAP2-KO mouse, and the TS2-neo mouse based on Timothy Syndrome. In all three of these mouse models, I found that large-scale cortical activity patterns were largely normal, but there was a statistically significant shift towards reduced cortex-wide synchrony and increased dimensionality of spontaneous activity, which may be consistent with the disconnectivity hypothesis of autism. In a final set of experiments, we tested our hypothesis, based on past literature and our results in EX-NMDAR KO and TH-VG KO mice, that the disruptions in cortical activity was predominantly due to the developmental loss of activity-dependent wiring of circuits. To test the developmental versus acute role of NMDAR function in shaping cortical activity, I blocked NMDAR pharmacologically in wild-type mice. I found that acute NMDAR blockade shifted cortical activity to a restricted dynamic space similar to that observed in EX-NMDAR KO mice and more extreme than that observed in TH-VG KO mice. These results strongly reinforce the critical role of NMDAR in shaping cortical activity during development, and suggest that a substantial component of that may be through NMDAR’s role in synaptic transmission and moment to moment cortex-wide circuit function. Overall, these results provide critical insight into the role of NMDARs and the glutamatergic system in cortical network functional organization during development. Specifically, they highlight the essential role of NMDARs in excitatory neurons on the functional connectivity and dynamic repertoire of the cortical network during development. These results make novel contribution to our understanding of how NMDARs may contribute to the pathophysiology of NDDs. Specifically, they contribute powerful new insight into to a critical mechanistic question about the cell-specific role of NMDARs in the pathophysiology of schizophrenia and the mechanisms of NMDAR antagonists, which have transformed psychiatry recently due to their rapid-acting anti-depressant and anti-suicidal properties. Furthermore, they identify a patterns of large-scale network dysfunction that might be detectable in humans using noninvasive functional imaging or electrophysiology

Deciphering the brainstem, hippocampal and whole-brain dynamics by neuronal-ensemble event signatures

Intracortically-recorded brain signals display a rich variety of such transient activities: brief, recurring episodes of deflection or oscillatory activities that reflect cooperative neural circuit mechanisms. These network patterns of activity, also called neural events, span multiple spatio-temporal scales, and are believed to be basic computing elements during cognitive processes such as learning and off-line memory consolidation. However, both the large-scale and microscopic-scale cooperative mechanisms associated with these episodes remain poorly understood. This knowledge gap arises partly due to methodological limitations of existing experimental approaches, specifically in measuring simultaneous micro- and macroscopic aspects of neuronal activity in the brain. Therefore, this dissertation sought to study the relationship between ongoing spontaneous neural events in the hippocampus, brainstem and thalamic structures at micro-, meso- and macroscopic scales by combining data from intracortical recordings, multi-compartmental network models, and functional magnetic resonance imaging (fMRI)