328 research outputs found
Wide-band spectral power fluctuations characterize the response of simulated cortical networks to increasing stimulus intensity
No full textThe hippocampus is an anatomically distinct region of the medial temporal lobe that plays a critical role in the formation of declarative memories. Here we show that a computer simulation of simple compartmental cells organized with basic hippocampal connectivity is capable of producing stimulus intensity sensitive wide-band fluctuations of spectral power similar to that seen in real EEG. While previous computational models have been designed to assess the viability of the putative mechanisms of memory storage and retrieval, they have generally been too abstract to allow comparison with empirical data. Furthermore, while the anatomical connectivity and organization of the hippocampus is well defined, many questions regarding the mechanisms that mediate large-scale synaptic integration remain unanswered. For this reason we focus less on the specifics of changing synaptic weights and more on the population dynamics.\ud
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Spectral power in four distinct frequency bands were derived from simulated field potentials of the computational model and found to depend on the intensity of a random input. The majority of power occurred in the lowest frequency band (3-6 Hz) and was greatest to the lowest intensity stimulus condition (1% maximal stimulus). In contrast, higher frequency bands ranging from 7-45 Hz show an increase in power directly related with an increase in stimulus intensity. This trend continues up to a stimulus level of 15% to 20% of the maximal input, above which power falls dramatically. These results suggest that the relative power of intrinsic network oscillations are dependent upon the level of activation and that above threshold levels all frequencies are damped, perhaps due to over activation of inhibitory interneurons
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Corticothalamic Mechanisms of Non-REM Sleep
No full textK-complexes (KCs), spindles, and slow oscillations characterize non-REM (NREM) sleep. Understanding how these large-scale cortical modulations coordinate cortical processing requires knowing where and how they are generated and spread. However, this is largely unknown, especially in humans. In this thesis, the interaction of the thalamus and cortex in giving rise to these sleep events, in particular the KC, was examined using computational modeling and a variety of recordings in humans: scalp electroencephalography (EEG), electrocorticography (ECOG), and stereoelectroencephalography (SEEG). The overall aim to characterize how the KCs arise in the human cortex and how the thalamus may be implicated in this process is addressed in three parts. Chapter 1 presents a novel thalamocortical computational model of NREM stage 2 sleep that produces spindles as well as spontaneous and evoked KCs. Properties of the model are based on evidence outlined in the chapter from EEG, ECOG, and SEEG recordings that KCs may arise synchronously across the cortex. The model suggests the disruption of thalamic spindling via inactivation of the low-threshold Ca2+ current (IT) as a possible mechanism for the production of synchronous KCs. Chapter 2 examines the spatial and temporal dynamics of individual KCs measured locally in the cortex using SEEG recordings. This study addresses where KCs occur, how often they occur, how large they are in amplitude, whether they co-occur across the cortex, and whether they propagate in sequential order across the cortex. Unlike a previously dominant model, this work finds that the KC can start anywhere, and then spread over a large or small cortical area, in any direction. Chapter 3 describes how the downstate differentially groups the cortical and thalamic spindle using simultaneous bipolar SEEG recordings. This study finds that the thalamus leads the cortex in the initiation of spindles, as well as driving individual spindle waves, while the downstate occurs in the cortex before the thalamus. In sum, the body of work presented here furthers our understanding of how corticothalamic mechanisms in humans give rise to stereotyped patterns of brain activity during sleep and suggests how these mechanisms may underlie the functional role of sleep in the brain
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Human cortical ripples as a mechanism for neural binding
No full textThis dissertation investigates the mechanistic role of high-frequency ripple oscillations in facilitating neural integration across distributed brain regions during various cognitive states. Through extensive analysis of rare intracranial recordings from human participants, this research reveals how ripple oscillations (70-100 Hz) orchestrate communication between spatially separated neuronal populations, addressing a fundamental question in systems neuroscience: how does the brain bind information encoded by spatially distributed neurons into coherent cognitive representations?In the first chapter, I demonstrate that co-occurring ripple oscillations significantly enhance neuronal interactions between cortical locations separated by up to 16 mm in both sleep and waking states. Using microelectrode recordings from arrays implanted in human temporal and motor cortices, I show that neurons in co-rippling locations exhibit increased short-latency co-firing, stronger predictability of each other's activity, and enhanced participation in neural assemblies. Through analysis of local field potentials and single-unit activity from 96-channel microelectrode arrays, I establish that these interactions are phase-dependent, with neurons firing at ripple peaks showing the strongest coupling. Critically, I demonstrate that this enhanced neural coordination during co-ripples is not merely a byproduct of increased firing rates but reflects a genuine reorganization of spike timing relationships that persists when controlling for overall activation levels. The effects persist at longer distances without significant decay, suggesting a mechanism specifically adapted for long-range neural communication. The second chapter extends these findings to examine how ripple-mediated neuronal coordination persists across brain-wide spatial scales and whether it is dynamically modulated during active cognitive processing in a structured working memory paradigm. Using recordings from patients implanted with microwire electrodes in the hippocampus, amygdala, ventromedial prefrontal cortex, anterior cingulate cortex, and pre-supplementary motor area, I demonstrate that ripple oscillations significantly increase in all recorded regions during encoding, maintenance, and retrieval phases of a Sternberg working memory task. The co-occurrence of ripples between distant brain regions shows selective enhancement during these cognitive phases, particularly under high memory load conditions. These co-rippling events are associated with substantial increases in cross-region co-firing that scale with memory load during maintenance and retrieval. Furthermore, co-ripples enhance the replay during recognition testing of specific cross-region co-firing previously evoked by the same stimuli at encoding. I establish that ripple-mediated coordination exhibits stronger modulation by cognitive demands compared to other high-frequency oscillations, suggesting a specific role for ripples in orchestrating neural activity during cognitively demanding operations. Critically, these long-distance interactions span several centimeters without significant degradation, between lobes and hemispheres, neocortex and associated subcortical structures. This study appears to provide the first evidence for task-related co-firing across widespread brain regions in humans, and further suggests that ripple synchronization serves as a fundamental mechanism for integrating information across the human brain.Together, these studies advance our understanding of the binding problem in neuroscience, revealing ripple oscillations as a previously unappreciated orchestrator of long-range neural communication that supports complex cognitive processes. By providing direct electrophysiological evidence from human recordings, this research bridges many prior studies in animals with human neuroscience and opens new avenues for investigating the functional role of high-frequency oscillations in both healthy cognition and neuropsychiatric conditions characterized by aberrant neuronal synchrony
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A Motor Theory of Reading: The interaction of visual and auditory language
No full textReading is learned in the presence of an already formed auditory language network. However, unlike auditory language reading is a recent cultural invention made possible by an extensive period of learning. Understanding the relationship of visual language with auditory language is key to understanding the novel human construct of reading. Articulatory motor movements are a potential bridge between the existing auditory language network and the developing visual reading network. Children who vocalize while learning to read and who understand the relationships between letters and sounds learn at a faster and more successful rate. However, in neuroanatomical models of silent reading the precentral gyrus, associated with articulatory motor movements, is largely omitted. The first section of the dissertation presents evidence that the precentral gyrus is involved in the dorsal reading route, putatively in grapheme-to-phoneme conversion. Chapter 1 presents evidence from a speeded semantic decision task. Word-level linguistic effects in the Precentral Gyrus and significant early phase-locking activity between the Fusiform and Precetral Gyrus were identified. Chapter 2 presents evidence from a Match/Mismatch task between sequentially presented graphemes and phonemes. Again, the precentral gyrus is implicated as a central hub by the combination of letter-specific effects, Mismatch effects, and significant connectivity with the Fusiform Gyrus. Chapter 3 examines the overlap and separation of Visual and Auditory language using a semantic decision task performed in each sensory modality. We find that while the Visual language processing that was present significantly overlaps with Auditory language processing, only a fraction of the Auditory language network is recruited during Visual language processing. The second section details methodological advances to aid in the study of language using intracranial iEEG. Chapter 4 details the use of carbon-based electrodes to increase the possible spatial resolution that iEEG can measure while retaining high signal-to-noise ratio. Chapter 5 details a multimedia tablet which was created to facilitate increased data collection on patients without increasing the effort necessary from either patients or staff. By increasing the possible spatial resolution and the possible amount of data collected, these two chapters demonstrate how to build upon the work in the first three chapters
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Cortical replay, hippocampal ripples, and cortico-hippocampal communication in human sleep
No full textNon-rapid eye movement (NREM) sleep is crucial for memory formation in mammals. Cortical replay of awake neural activity in NREM sleep, coordinated by hippocampal ripples (high-frequency oscillations that mark hippocampal replay of waking neuronal firing sequences), is thought to facilitate the consolidation of novel experience. However, direct human electrophysiological evidence for replay is lacking. Moreover, hippocampal ripples and their connection to cortical activity are not well characterized in humans, partly owing to the difficulty in accessing the human hippocampus; while some reports show human sharpwave-ripples as found in animals, others report hippocampal sleep spindles that modulate ripples and co-occur with scalp EEG spindles. Thus, I performed analyses on human intracranial electrophysiological data, separating high-gamma power-based neocortical population firing peaks and hippocampal ripples from interictal activity. In Chapter 1, consistent sequences of high-gamma peaks (“Motifs”) across neocortex were captured during waking. These Motifs were then compared to activity patterns in sleeps preceding (Sleep-Pre) and following (Sleep-Post) waking. Motifs predominantly resembled patterns in Sleep-Post than in Sleep-Pre, thereby constituting human cortical replay. In Chapter 2, hippocampal sharpwave-ripples were characterized in terms of their morphology, spectral characteristics, occurrence rate, and spread. While some traits were shared with animals, such as LFP morphology, others were potentially unique to humans, e.g. preferential occurrence in anterior hippocampus. In Chapter 3, the relationship between hippocampal sharpwave-ripples and neocortical sleep graphoelements in NREM was explored, whereby sharpwave-ripples were found to associate with neocortical theta bursts, spindles, downstates and upstates across the whole cortex, often with characteristic temporal latencies. Neocortical events near sharpwave-ripples also tended to follow specific sequences, i.e. theta burst-downstate-spindle “triplets”. In Chapter 4, the previously published claim of human ripples organized by hippocampal spindles was investigated. While new evidence did support hippocampal spindles modulating ripples, both sharpwave-ripples and spindle-ripples were found in the same hippocampal locations, as well as transitional forms. Hippocampal spindles and spindle-ripples preferentially occurred in posterior hippocampus, were coordinated with cortical NREM graphoelements, and tended to phase-lock with co-occurring neocortical (especially parietal) spindles. In sum, human sharpwave-ripples and spindle-ripples may constitute separate, if complementary, routes toward organization of cortical reactivation and memory consolidation
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Neurophysiology of Human Brain Integration
No full textRipples are brief high-frequency oscillations that have mainly been studied rodent hippocampus where they are important for consolidation of recent memories during non-rapid eye movement sleep (NREM). I investigate in my thesis if human cortical ripples also possess other properties crucial to a possible general role in binding. I analyze in Chapter 1 whether cortical rippling and co-rippling occur in cognitive processing beyond Recent Memory recall during waking, and consolidation during NREM. Do they occur when letters are bound in words, when lexical items are bound to their meanings, or when those meanings in conjunction with internalized task instructions determine the choice and execution of actions? I found a striking pattern of cortical interactions, divided into four stages. The first appeared to conform to well-established sequential-hierarchical labelled-line convergent processing, and was not associated with a typical ripple or co-ripple activation, but rather appeared to trigger ripples in the left fusiform wordform area. In the second stage, fusiform co-ripples with language-related areas in all lobes, but those areas do not co-ripple with each other until the third stage, led by prefrontal-parietal ripples, culminating in an exponential rise in co-rippling, eventually involving all areas, with the last peak in the Rolandic cortex just prior to correct responses. I hypothesize that this pattern of co-ripples may directly represent the cortico-cortical interactions underlying semantic judgements of visually-presented words. In Chapter 2, I analyze the effects of intravenous opioid pain medication in intracranial EEG. We have characterized the overall effects of opioids on the LFP spectrum, revealing a suppression of beta-frequency (15-25 Hz) power in prefrontal and limbic sites. Indeed, intrinsic variability in pain relief by opioids was correlated with the degree of beta suppression in a subset of prefrontal sites. Further, we found a profound decrease in the cross-region coupling of beta phase to high gamma (70-190 Hz) amplitude following opioid administration, which we interpret as possibly due to decreased recurrent pyramidal activation of basket cells. As pain is an inherently integrative phenomenon, involving the binding of sensory perception, valence, attention, the self-model, stress, and threat, we hypothesized that the overt neural signatures of pain relieving drugs, as revealed by LFP analysis, will form systematic relationships with ripples. Finally, we found that intravenous opioids modulated ripple occurrence in a similar manner to beta-frequency amplitude, that ripple co-occurrence was also decreased, particularly between pain-relevant regions, and that ripples were locked to the phase of beta-oscillations. We hypothesize that beta-ripple activation underlies a state of concern-monitoring-focus on the pain sensations, which is the major component of the psychological component, the most recalcitrant to treatment. We propose that beta-ripple activation is worthy of further exploration as a biomarker for the psychological component of pain, and target for its treatment. Tying together the two chapters of my dissertation is the focus on the role of co-ripples in cortical integration in human
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Coordination of hippocampo-cortical waves and single unit spiking in human memory
No full textIn mammals, the consolidation of memories from previous waking is facilitated through the coordination of hippocampo-cortical waves during non-rapid eye movement sleep (NREM). Specifically, it is thought that consolidation in the cortex is guided by the coordination of cortical sleep waves (downstates, spindles, upstates) with high-frequency oscillations in the hippocampus called ‘ripples,’ which in rodents mark the replay of neuronal firing sequences established during waking. However, it is not well-understood how these waves facilitate plasticity. While it has been shown that cortical ripples are involved in memory recall in humans, it is not known whether they are generated during human sleep. Furthermore, it remains unknown how the different elements of a memory are bound across the cortex into a cohesive representation. In this dissertation, I collected and analyzed human intracranial macro- and micro-electrode recordings to investigate how interactions between cortical waves, hippocampal waves, and single unit spiking may underlie memory during sleep and waking. Chapter 1 shows how sleep spindles modulate neuron spike-timing to create conditions necessary for spike-timing-dependent plasticity (STDP). This study reveals that spindles facilitate short latency co-firing between neurons that may lead to STDP. Furthermore, it reports the spatial organization and propagation of spindles and spiking within a few millimeters of cortex. Chapter 2 provides the first report and comprehensive characterization of 80 Hz cortical ripples during human NREM, and provides evidence for their role in memory consolidation. This study shows that ripples are ubiquitous throughout the cortex during NREM as well as waking. During sleep, cortical ripples occur during spindles on down-to-upstates, with unit-firing patterns suggesting generation by pyramidal-interneuron feedback. Furthermore, cortical ripples mark the recurrence of spatiotemporal activity patterns from preceding waking and group co-firing, which could further enhance spindle-mediated STDP. Chapter 3 reveals that ripples occur simultaneously in multiple lobes in both hemispheres, and in the hippocampus, generally during sleep and waking, and with enhancements preceding memory recall. Ripples phase-lock local cell-firing, and phase-synchronize with little decay between locations separated by up to 25 cm, enabling long distance integration. Indeed, co-rippling sites have increased correlation of very-high-frequency activity which reflects cell-firing. Thus, ripples may help bind information across the cortex in memory
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On corticocortical connectivity and its contribution to extracranial potentials
No full textAlthough rarely quantitatively considered together, extracranially recorded electric potentials, or their complementary magnetic gradients, and the underlying neuronal transmembrane ion flows form a unitary phenomenon. Because the conduction of these fields through the tissues of the head are for all biologically relevant intents and purposes instantaneous and additive, the variance and covariance of signals recorded by extracranial sensors are a weighted linear combination of the variance and covariance of current density fluctuations in the gray matter of the cortex. However, for a given sensor variance-covariance matrix, or complex cross-spectral matrix in the frequency domain, there are infinite number of valid cortical source configurations. In order to accurately distinguish between these possibilities assumptions must be made about the true patterns of correlativity, or functional connectivity, among sources which is in turn constrained by the anatomical connectivity of the cortex. In this dissertation, I investigated the structural and functional connectivity of the human cortex and developed computational models informed by these measures that quantitatively link intra- and extra-cranial activity.Chapter 1 examines relative human corticocortical structural connectivity using publicly available diffusion MRI data from the human connectome project. Chapter 2 refines these results by scaling relative connectivity in arbitrary units to absolute connectivity in physical units by taking advantage of the unique properties of the corpus callosum, and in which we find finds that the resulting connectivity is sparse. In chapter 3 the anatomical connectivity is used to inform a whole brain model of non-REM sleep capable of producing synthetic intra- and extra-cranial sensor activity, and in Chapter 4 the composited human invasive stereo-EEG data from epilepsy patients to estimate the spatial dependence of spontaneous cortical functional connectivity in a frequency-resolved manner
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Effects of sub-perceptual vagus nerve stimulation on brain activity measured intra-cranially in humans
No full textThe general state of the human organism is modulated by the Autonomic Nervous System (ANS) comprised of two balancing influences: sympathetic and parasympathetic. The basal forebrain (BF) cholinergic and the locus coeruleus (LC) noradrenergic system within the brain innervates the cerebral cortex and regulates cognitive functions such as arousal, attention, learning, and sleep. The peripheral autonomic nervous system is linked with the cortical projection systems and modulates cortical activity levels in the human brain, which has been shown in various studies of vagus nerve stimulation (VNS) effect on cognitive functions, but its mechanism still remains unclear. The purpose of this study is to determine if sub-perception stimulation on the auricular branch of the vagus nerve will produce mild event-related cortical modulation. Our study provided a detailed view of local neuron population activity through depth electrodes implanted in 5 epileptic subjects, and analyzed the acute effect of VNS on intracranial EEG in post-stimulation period versus baseline in the time and frequency domains. We have found rare but significant (p < .001) small-amplitude modulations: 1) 2 out of 487 channels contain event-related changes in local field potentials activity (0.1-40Hz) located in insula and postcentral gyrus; 2) 4 out of 487 channels contain high gamma (70-190Hz) analytic amplitude decrease located in lateral temporal and frontal cortex, amplitude increase located in cingulate gyrus; and 3) 7 out of 71 channels contain α & β band power reduction located in insula, prefrontal cortex and cingulate gyrus. These results suggest that sub-perception non-invasive VNS may be able to modulate cortical activity in widely distributed regions, which provides a potential method for a variety of illness treatment such as Parkinson disease and Alzheimer’s disease
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Spectral Characterization and Delay Differential Analysis of Human Brain Dynamics
No full textThe brain endogenously generates electrical activity that arises from the complex, nonlinear interactions of its components. During sleep, large amplitude, slow oscillations as well as 10-16 Hz rhythms known as sleep spindles are generated in the cortex and thalamus respectively, and their coupling has been shown to bolster our memory capacities by facilitating cortical plasticity. Identifying where particular sleep rhythms are generated, how they co-occur with other regions, and whether rhythms differ in frequency or other characteristics can inform mechanisms for how they coordinate information exchange during sleep. In this dissertation, I characterized the cortical and thalamic activity of sleep spindles, theta bursts (~6 Hz), a novel sleep rhythm identified here, and the coupling of spindles and theta bursts with slow waves using intracranial recordings from epileptic patients. I also report regional differences in spindle properties, largely inaccessible to non-invasive recordings, that propose a modified view of spindle dynamics across the cortex. The most common characterizations of brain dynamics, including the sleep rhythms reported here, are largely based on linear time-frequency analyses. However, because the brain is a high-dimensional, nonlinear system, applying linear techniques alone may not sufficiently capture the relevant dynamical features. To address this, I helped develop nonlinear tools, based on Delay Differential Analysis (DDA), for analyzing neural time series. I evaluated these tools in simulated, chaotic systems, which are suitable models for recurrent, continuous, and nonlinear dynamics. Specifically, I investigated whether given a set of recorded time series, can we (1) assess whether two signals are causally interacting and (2) rank signals by their amount of dynamical information about the original system. These applications of DDA, in tandem with traditional linear techniques, can improve our understanding of underlying brain activity during seizures and sleep.
In Chapter 1, I characterize a novel sleep rhythm, the theta burst, that is distinct from sleep spindles, recorded in both the cortex and thalamus, and which in both structures, precedes downstates. In Chapter 2, I report distinct sources of spindle variability, including variability across channels within a region, across spindles within a single recording site, and across cycles within a spindle, and how these sources are of a size comparable to the frontal-parietal difference typically used to summarize spindle dynamics. Chapter 3 introduces a novel nonlinear signal processing technique, Cross-Dynamical Delay Differential Analysis (CD-DDA), for inferring causal interactions between time series and applies this approach to track seizure spread in a patient with epilepsy. Chapter 4 applies DDA in simulated chaotic dynamical systems to assess the observability of a time series, i.e. how much dynamical information a variable has about the original system it is a part of
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