Biology subjects

Lucas, T. H.

Publications and source records attributed to Lucas, T. H..

10 recordsLinked to original sources

bioRxiv2022

"Primed to Perform:" Dynamic white matter graph communicability may drive metastable network representations of enhanced preparatory cognitive control

Spontaneous neural activity has become increasingly linked to behavioral and cognitive output. A specific cognitive control mode, proactive control, uses prior information to plan and prepare the brain to be particularly sensitive to incoming goal-directed stimuli. Little is known about specific proactive mechanisms implemented via preparatory patterns of spontaneous neural activity, that may enable dynamically enhanced cognitive performance. In this study, humans implanted with intracranial electrodes performed a simple cognitive task. For each subject, pre-trial spectral power and communicability-based features from both grey and white matter nodes were extracted to identify preparatory control states that were "primed to perform". The anatomical structure and topology of these states across subjects demonstrated a critical role for white matter communicability in decoding and intrinsically controlling preparatory network activity. Our results provide novel insights for putative cognitive network control and may be studied to develop prosthetic approaches for individuals with cognitive deficits.

neuroscience↗

bioRxiv2021

Neurophysiological evidence for cognitive map formation during sequence learning

Humans deftly parse statistics from sequences. Some theories posit that humans learn these statistics by forming cognitive maps, or underlying representations of the latent space which links items in the sequence. Here, an item in the sequence is a node, and the probability of transitioning between two items is an edge. Sequences can then be generated from walks through the latent space, with different spaces giving rise to different sequence statistics. Individual or group differences in sequence learning can be modeled by changing the time scale over which estimates of transition probabilities are built, or in other words, by changing the amount of temporal discounting. Latent space models with temporal discounting bear a resemblance to models of navigation through Euclidean spaces. However, few explicit links have been made between predictions from Euclidean spatial navigation and neural activity during human sequence learning. Here, we use a combination of behavioral modeling and intracranial encephalography (iEEG) recordings to investigate how neural activity might support the formation of space-like cognitive maps through temporal discounting during sequence learning. Specifically, we acquire human reaction times from a sequential reaction time task, to which we fit a model that formulates the amount of temporal discounting as a single free parameter. From the parameter, we calculate each individuals estimate of the latent space. We find that neural activity reflects these estimates mostly in the temporal lobe, including areas involved in spatial navigation. Similar to spatial navigation, we find that low dimensional representations of neural activity allow for easy separation of important features, such as modules, in the latent space. Lastly, we take advantage of the high temporal resolution of iEEG data to determine the time scale on which latent spaces are learned. We find that learning typically happens within the first 500 trials, and is modulated by the underlying latent space and the amount of temporal discounting characteristic of each participant. Ultimately, this work provides important links between behavioral models of sequence learning and neural activity during the same behavior, and contextualizes these results within a broader framework of domain general cognitive maps.

neuroscience↗

bioRxiv2021

Design and validation of a multi-point injection technology for MR-guided convection enhanced delivery in the brain

Convection enhanced delivery (CED) allows direct intracranial administration of neuro-therapeutics. Success of CED relies on specific targeting and broad volume distributions (VD). However, to prevent off-target delivery and tissue damage, CED is typically conducted with small cannulas and at low flow rates, which critically limit the maximum achievable VD. Furthermore, in applications such as gene therapy requiring injections of large fluid volumes into broad subcortical regions, low flow rates translate into long infusion times and multiple surgical trajectories. The cannula design is a major limiting factor in achieving broad VD, while minimizing infusion time and backflow. Here we present and validate a novel multi-point cannula specifically designed to optimize distribution and delivery time in MR-guided intracranial CED of gene-based therapeutics. First, we evaluated the compatibility of our cannula with MRI and common viral vectors for gene therapy. Then, we conducted CED tests in agarose brain phantoms and benchmarked the results against single-needle delivery. 3T MRI in brain phantoms revealed minimal susceptibility-induced artifacts, comparable to the device dimensions. Benchtop CED of adeno-associated virus demonstrated no viral loss or inactivation. CED in agarose brain phantoms at 3, 6, and 9 L/min showed >3x increase in volume distribution and 60% time reduction compared to single-needle delivery. This study confirms the validity of a multi-point delivery approach for improving infusate distribution at clinically-compatible timescales and supports the feasibility of our novel cannula design for advancing safety and efficacy of MR-guided CED to the central nervous system.

bioengineering↗

bioRxiv2021

Fluctuations in functional connectivity associated with interictal epileptiform discharges (IEDs) in intracranial EEG

All epilepsies are defined by a propensity for recurrent seizures, characterized by hypersynchronous electrographic activity. Understanding this overarching property would be advanced by a thorough quantification of how the global synchrony of the epileptic brain responds to small perturbations that do not trigger seizures. Here, we leverage analysis of transient focal bursts of epileptiform activity, termed interictal epileptiform discharges (IEDs), to characterize this response. Specifically, we use a group of 145 participants implanted with intracranial EEG (iEEG) electrodes to quantify changes in five functional connectivity measures associated with three properties of IEDs: their presence, spread, and number. We perform this analysis in five frequency bands in order to contextualize our findings in relation to ongoing neural processes at different spatial and temporal scales. We find that, across frequency bands, both the presence and spread of IEDs tend to lead to independent increases of functional connectivity, but only in functional connectivity measures influenced by the amplitude, rather than the phase, of a signal. We find that these increases are not explained by simple subgroups of connections, such as the weakest connections in the brain, or only connections within the seizure onset zone. Evaluating patterns of similarity across different bands and measure combinations, we find that the presence of IEDs impacts high frequencies (gamma and high gamma) and low frequencies (theta, alpha, and beta) differently, although responses within each group are similar. Using grouped LASSO regression, we identify which individual-level features explain differences in functional connectivity changes associated with IEDs. While no single feature robustly explains observed differences, the most consistently included predictors across bands and measures are the rate of IEDs and the anatomical locus of IEDs. Overall, this work provides compelling evidence for increases in global synchrony associated with IEDs, and delivers a thorough exploration of different functional connectivity measures, frequency bands, and IED properties. These observations show a disruption of several types of ongoing neural dynamics associated with IEDs. Additionally, we provide a starting point for future models of how small perturbations affect neural systems and how those systems support the hypersynchrony seen in epilepsy.

neuroscience↗

bioRxiv2021

MXtrodes: MXene-infused bioelectronic interfaces for multiscale electrophysiology and stimulation

Soft bioelectronic interfaces for mapping and modulating excitable networks at high resolution and at large scale can enable paradigm-shifting diagnostics, monitoring, and treatment strategies. Yet, current technologies largely rely on materials and fabrication schemes that are expensive, do not scale, and critically limit the maximum attainable resolution and coverage. Solution processing is a cost-effective manufacturing alternative, but biocompatible conductive inks matching the performance of conventional metals are lacking. Here, we introduce MXtrodes, a novel class of soft, high-resolution, large-scale bioelectronic interfaces enabled by Ti3C2 MXene and scalable solution processing. We show that the electrochemical properties of MXtrodes exceed those of conventional materials, and do not require conductive gels when used in epidermal electronics. Furthermore, we validate MXtrodes in a number of applications ranging from mapping large scale neuromuscular networks in humans to delivering cortical microstimulation in small animal models. Finally, we demonstrate that MXtrodes are compatible with standard clinical neuroimaging modalities.

bioengineering↗

bioRxiv2018

High interictal connectivity within the resection zone is associated with favorable post-surgical outcomes in focal epilepsy patients

ObjectivePatients with drug-resistant focal epilepsy are often candidates for invasive surgical therapies. In these patients, it is necessary to accurately localize seizure-generators to ensure seizure freedom following intervention. While intracranial electroencephalography (iEEG) is the gold standard for mapping networks for surgery, this approach requires inducing and recording seizures, which may cause patient morbidity. The goal of this study is to evaluate the utility of mapping interictal (non-seizure) iEEG networks to identify targets for surgical treatment.\n\nMethodsWe analyze interictal iEEG recordings and neuroimaging from 27 focal epilepsy patients treated via surgical resection. We generate interictal functional networks by calculating pairwise correlation of iEEG signals across different frequency bands. We identify electrodes falling within surgically resected tissue (i.e. the resection zone), and compute node-level and edge-level synchrony in relation to the resection zone. We associate these metrics with post-surgical outcomes.\n\nResultsGreater overlap between resected electrodes and highly synchronous electrodes is associated with favorable post-surgical outcomes. Additionally, good outcome patients have significantly higher connectivity localized within the resection zone compared to those with poorer postoperative seizure control. This finding persists following normalization by a spatially-constrained null model.\n\nConclusionsThis study suggests that spatially-informed interictal network synchrony measures can distinguish between good and poor post-surgical outcomes. By capturing clinically relevant information during interictal periods, our method may ultimately reduce the need for prolonged invasive implants and provide insights into the pathophysiology of an epileptic brain. We discuss next steps for translating these findings into a prospectively useful clinical tool.

neuroscience↗

bioRxiv2018

Genetic and Neuroanatomical Support for Functional Brain Network Dynamics in Epilepsy

Focal epilepsy is a devastating neurological disorder that affects an overwhelming number of patients world-wide, many of whom prove resistant to medication. The efficacy of current innovative technologies for the treatment of these patients has been stalled by the lack of accurate and effective methods to fuse multimodal neuroimaging data to map anatomical targets driving seizure dynamics. Here we propose a parsimonious model that explains how large-scale anatomical networks and shared genetic constraints shape inter-regional communication in focal epilepsy. In extensive ECoG recordings acquired from a group of patients with medically refractory focal-onset epilepsy, we find that ictal and preictal functional brain network dynamics can be accurately predicted from features of brain anatomy and geometry, patterns of white matter connectivity, and constraints complicit in patterns of gene coexpression, all of which are conserved across healthy adult populations. Moreover, we uncover evidence that markers of non-conserved architecture, potentially driven by idiosyncratic pathology of single subjects, are most prevalent in high frequency ictal dynamics and low frequency preictal dynamics. Finally, we find that ictal dynamics are better predicted by white matter features and more poorly predicted by geometry and genetic constraints than preictal dynamics, suggesting that the functional brain network dynamics manifest in seizures rely on - and may directly propagate along - underlying white matter structure that is largely conserved across humans. Broadly, our work offers insights into the generic architectural principles of the human brain that impact seizure dynamics, and could be extended to further our understanding, models, and predictions of subject-level pathology and response to intervention.

neuroscience↗

bioRxiv2018

White Matter Network Architecture Guides Direct Electrical Stimulation Through Optimal State Transitions

Electrical brain stimulation is currently being investigated as a potential therapy for neurological disease. However, opportunities to optimize and personalize such therapies are challenged by the fact that the beneficial impact (and potential side effects) of focal stimulation on both neighboring and distant regions is not well understood. Here, we use network control theory to build a formal model of brain network function that makes explicit predictions about how stimulation spreads through the brains white matter network and influences large-scale dynamics. We test these predictions using combined electrocorticography (ECoG) and diffusion weighted imaging (DWI) data from patients with medically refractory epilepsy undergoing evaluation for resective surgery, and who volunteered to participate in an extensive stimulation regimen. We posit a specific model-based manner in which white matter tracts constrain stimulation, defining its capacity to drive the brain to new states, including states associated with successful memory encoding. In a first validation of our model, we find that the true pattern of white matter tracts can be used to more accurately predict the state transitions induced by direct electrical stimulation than the artificial patterns of a topological or spatial network null model. We then use a targeted optimal control framework to solve for the optimal energy required to drive the brain to a given state. We show that, intuitively, our model predicts larger energy requirements when starting from states that are farther away from a target memory state. We then suggest testable hypotheses about which structural properties will lead to efficient stimulation for improving memory based on energy requirements. We show that the strength and homogeneity of edges between controlled and uncontrolled nodes, as well as the persistent modal controllability of the stimulated region, predict energy requirements. Our work demonstrates that individual white matter architecture plays a vital role in guiding the dynamics of direct electrical stimulation, more generally offering empirical support for the utility of network control theoretic models of brain response to stimulation.

neuroscience↗

bioRxiv2018

Predictive control of electrophysiological network architecture using direct, single-node neurostimulation in humans

Chronically implantable neurostimulation devices are becoming a clinically viable option for treating patients with neurological disease and psychiatric disorders. Neurostimulation offers the ability to probe and manipulate distributed networks of interacting brain areas in dysfunctional circuits. Here, we use tools from network control theory to examine the dynamic reconfiguration of functionally interacting neuronal ensembles during targeted neurostimulation of cortical and subcortical brain structures. By integrating multi-modal intracranial recordings and diffusion tensor imaging from patients with drug-resistant epilepsy, we test hypothesized structural and functional rules that predict altered patterns of synchronized local field potentials. We demonstrate the ability to predictably reconfigure functional interactions depending on stimulation strength and location. Stimulation of areas with structurally weak connections largely modulates the functional hubness of downstream areas and concurrently propels the brain towards more difficult-to-reach dynamical states. By using focal perturbations to bridge large-scale structure, function, and markers of behavior, our findings suggest that stimulation may be tuned to influence different scales of network interactions driving cognition.

neuroscience↗

bioRxiv2016

Recurring functional interactions predict network architecture of interictal and ictal states in neocortical epilepsy

Human epilepsy patients suffer from spontaneous seizures, which originate in brain regions that also subserve normal function. Prior studies demonstrate focal, neocortical epilepsy is associated with dysfunction, several hours before seizures. How does the epileptic network perpetuate dysfunction during baseline periods? To address this question, we developed an unsupervised machine learning technique to disentangle patterns of functional interactions between brain regions, or subgraphs, from dynamic functional networks constructed from approximately 100 hours of intracranial recordings in each of 22 neocortical epilepsy patients. Using this approach, we found: (i) subgraphs from ictal (seizure) and interictal (baseline) epochs are topologically similar, (ii) interictal subgraph topology and dynamics can predict brain regions that generate seizures, and (iii) subgraphs undergo slower and more coordinated fluctuations during ictal epochs compared to interictal epochs. Our observations suggest that the epileptic network drives dysfunction by controlling dynamics of functional interactions between brain regions that generate seizures and those that underlie normal function.

neuroscience↗