Biology subjects

Bick, S. K.

Publications and source records attributed to Bick, S. K..

9 recordsLinked to original sources

bioRxiv2024

Collapse of Interictal Suppressive Networks Permits Seizure Spread

How do networks in the brain limit seizure activity? In the Interictal Suppression Hypothesis (ISH), we recently postulated that high inward connectivity to seizure onset zones (SOZs) from non-involved zones (NIZs) is a sign of broader network suppression at rest. If broad networks appear to be responsible for interictal SOZ suppression, what changes during seizure initiation, spread, and termination? For patients with drug resistant epilepsy, intracranial monitoring offers a view into the electrographic networks which organize around and in response to the SOZ. In this manuscript, we investigate network dynamics in the peri-ictal periods to assess possible mechanisms of seizure suppression and the consequences of this suppression being overwhelmed. Peri-ictal network dynamics were derived from stereo electroencephalography (SEEG) recordings from 75 patients with drug-resistant epilepsy undergoing pre-surgical evaluation at Vanderbilt University Medical Center. We computed directed connectivity from 5-second windows in the periods between, immediately before, during, and after seizures. After aligning all network connectivity matrices between seizures and patients, we calculated net connectivity changes from the SOZ, propagative zone (PZ), and NIZ. Across all seizure types, we observed two distinct phases as seizures initiated and evolved: a large rapid increase in directed communication towards the SOZ from NIZ followed by a collapse in network connectivity. During this first phase, SOZs could be distinguished from all other regions (One-Way ANOVA, p=8.32x10-19 - 2.22x10-7, lower range to upper range of p-values). In the second phase and post-ictal period, SOZ inward connectivity decreased yet remained distinct (One-Way ANOVA, p= 2.58x10-10-1.66x10-2). Furthermore, NIZs appeared to drive the increase in inward SOZ connectivity while global connectivity between NIZs concordantly decreased. Stratifying by seizure subtype, we found that consciousness-impairing seizures show loss of inward connectivity from the NIZ earlier than conscious sparing seizures (one-way ANOVA, p<0.01 after false discovery correction). Tracking network reorganization against a surrogate for seizure involvement highlighted a possible antagonism between seizure propagation to the NIZ and the NIZs ability to maintain high connectivity to the SOZ. Finally, we found that inclusion of peri-ictal connectivity improved SOZ classification accuracy from previous models to a combined area under the curve of 93%. Overall, NIZs appear to actively respond to seizure onset and increase inhibitory signaling towards the SOZ, possibly in an attempt to thwart seizure activity. This inhibition appears to be insufficient to prevent seizure onset, and furthermore, loss of normal communication in the rest of the brain between NIZs may contribute to loss of consciousness during larger seizures. Dynamic connectivity patterns uncovered in this work may: i) allow more accurate delineation of surgical targets in focal epilepsy, ii) reveal why inward suppression of SOZs interictally may nonetheless be insufficient to prevent all seizures, and iii) provide insight into mechanisms of loss of consciousness during certain seizures.

neuroscience↗

bioRxiv2024

Mesolimbic local field potentials are modulated by motor control

Background and ObjectivesWhile historically cortico-basal ganglia-thalamocortical loops were believed to process limbic and sensorimotor data in parallel, there is now evidence to suggest that the two information streams can be processed in a single open loop. However, the limbic-motor interface remains insufficiently characterized. We sought to further investigate how extrastriatal regions may regulate motor output by examining electrophysiological activity in these areas during a response inhibition paradigm. MethodsWe recorded local field potentials (LFPs) from epilepsy patients implanted with intracranial depth electrodes for seizure localization purposes. Participants performed the stop-signal task, during which they made speeded choice reactions to "go" stimuli and occasionally inhibited their reactions in the incident of a "stop" signal. To compare power during movement and the absence of movement, we applied a Wilcoxon signed-rank test. Additionally, we performed a linear mixed-effects model to relate power in limbic regions to power in the motor cortex. Finally, we implemented exploratory analyses to identify power differences for correct go versus correct stop trials and for correct stop versus incorrect stop trials using cluster-based permutation testing. Results14 patients participated. A comparison between movement and baseline fixation revealed that motor response is associated with reduced beta (15-35 Hz) power in the amygdala, hippocampus, and motor cortex and reduced gamma (35-100 Hz) power in the amygdala and hippocampus. Moreover, average beta and gamma power in the amygdala and hippocampus during motor execution were positively associated with average beta and gamma power in the motor cortex. Additionally, we identified significant differences between correct go and stop trials in delta (1-4 Hz) power for all three regions and in theta (4-8 Hz) power for the amygdala and motor cortex. Likewise, we identified significant differences between correct and incorrect stop trials in delta power for the hippocampus and motor cortex, in theta power for the motor cortex, in alpha (8-15 Hz) and beta power for the amygdala and motor cortex, and in gamma power for all three areas. DiscussionThese correlations between neural oscillations in the hippocampus and amygdala and movement strengthen the notion of mesolimbic modulation of motor activity.

neuroscience↗

bioRxiv2024

Brain-state modeling using electroencephalography: Application to adaptive closed-loop neuromodulation for epilepsy

The progress of developing an effective closed-loop neuromodulation system for many neurological pathologies is hindered by the difficulties in accurately capturing a useful representation of a brains instantaneous functional state. Existing approaches rely on expert labeling of electroencephalography data to develop biomarkers of neurophysiological pathology. These techniques do not capture the highly complex functional states of the brain that are presumed to exist between labeled states or allow for the likely possibility of variation among identically labeled states. Thus, we propose BrainState, a self-supervised technique to model an arbitrarily complex instantaneous functional state of a brain using neural multivariate timeseries data. Application of BrainState to intracranial electroencephalography data from patients with epilepsy was able to capture diverse pre-seizure states and quantify nuanced effects of neuromodulation. We anticipate that BrainState will enable the development of sophisticated closed-loop neuromodulation systems for a diverse array of neurological pathologies.

neuroscience↗

bioRxiv2024

Reward Circuit Local Field Potential Modulations Precede Risk Taking

Risk taking behavior is a symptom of multiple neuropsychiatric disorders and often lacks effective treatments. Reward circuitry regions including the amygdala, orbitofrontal cortex, insula, and anterior cingulate have been implicated in risk-taking by neuroimaging studies. Electrophysiological activity associated with risk taking in these regions is not well understood in humans. Further characterizing the neural signalling that underlies risk-taking may provide therapeutic insight into disorders associated with risk-taking. Eleven patients with pharmacoresistant epilepsy who underwent stereotactic electroencephalography with electrodes in the amygdala, orbitofrontal cortex, insula, and/or anterior cingulate participated. Patients participated in a gambling task where they wagered on a visible playing card being higher than a hidden card, betting $5 or $20 on this outcome, while local field potentials were recorded from implanted electrodes. We used cluster-based permutation testing to identify reward prediction error signals by comparing oscillatory power following unexpected and expected rewards. We also used cluster-based permutation testing to compare power preceding high and low bets in high-risk (<50% chance of winning) trials and two-way ANOVA with bet and risk level to identify signals associated with risky, risk averse, and optimized decisions. We used linear mixed effects models to evaluate the relationship between reward prediction error and risky decision signals across trials, and a linear regression model for associations between risky decision signal power and Barratt Impulsiveness Scale scores for each patient. Reward prediction error signals were identified in the amygdala (p=0.0066), anterior cingulate (p=0.0092), and orbitofrontal cortex (p=6.0E-4, p=4.0E-4). Risky decisions were predicted by increased oscillatory power in high-gamma frequency range during card presentation in the orbitofrontal cortex (p=0.0022), and by increased power following bet cue presentation across the theta-to-beta range in the orbitofrontal cortex (p=0.0022), high-gamma in the anterior cingulate (p=0.0004), and high-gamma in the insula (p=0.0014). Risk averse decisions were predicted by decreased orbitofrontal cortex gamma power (p=2.0E-4). Optimized decisions that maximized earnings were preceded by decreases within the theta to beta range in orbitofrontal cortex (p=2.0E-4), broad frequencies in amygdala (p=2.0E-4), and theta to low-gamma in insula (p=4.0E-4). Insula risky decision power was associated with orbitofrontal cortex high-gamma reward prediction error signal (p=0.0048) and with patient impulsivity (p=0.00478). Our findings identify and help characterize reward circuitry activity predictive of risk-taking in humans. These findings may serve as potential biomarkers to inform the development of novel treatment strategies such as closed loop neuromodulation for disorders of risk taking.

neuroscience↗

bioRxiv2024

Intermittent stimulation of the basal forebrain improves working memory in aged nonhuman primates.

Brain aging contributes to cognitive decline and risk of dementia. Degeneration of the basal forebrain cholinergic system parallels these changes in aging, Alzheimers dementia, Parkinsons dementia, and Lewy body dementia, and thus is a common element linked to executive function across the lifespan and in disease states. Here, we tested the potential of one-hour daily intermittent basal forebrain stimulation to improve cognition in senescent monkeys, and its mechanisms of action. Stimulation in five animals improved working memory duration in 8-12 weeks across all animals, with peak improvements observed in the first four weeks. In an ensuing three month period without stimulation, improvements were retained. With additional stimulation, performance remained above baseline throughout the 15 months of the study. Studies with a cholinesterase inhibitor produced inconsistent improvements in behavior. One of five animals improved significantly. Manipulating the stimulation pattern demonstrated selectivity for both stimulation and recovery period duration. Brain stimulation led to acute increases in cerebrospinal levels of tissue plasminogen activator, which is an activating element for two brain neurotrophins, Nerve Growth Factor (NGF) and Brain-Derived Growth Factor (BDNF). Stimulation also led to improved glucose utilization in stimulated hemispheres relative to contralateral. Glucose utilization also consistently declines with aging and some dementias. Together, these findings suggest that intermittent stimulation of the nucleus basalis of Meynert improves executive function and reverses some aspects of brain aging. HighlightsO_LIThe basal forebrain and its cholinergic projections are the sole source of acetylcholine for the cortical mantle in primates and humans. C_LIO_LIForebrain function tracks cognitive loss throughout the adult lifespan. C_LIO_LIOne hour per day intermittent stimulation of this region improves executive function behaviors and plausibly reverses some aspects of brain aging, a large risk factor in dementias. C_LIO_LIThis stimulation exceeds impacts of standard pharmacotherapies, is enduring, recruits brain neurotrophic pathways and improves cortical glucose utilization. C_LI

neuroscience↗

bioRxiv2024

Depression Attenuates Caudate and Dorsolateral Prefrontal Cortex Alpha and Beta Power Response to Reward

Depression is a prevalent psychiatric condition and a common comorbidity across neurological disorders. Common symptoms include anhedonia, negative emotional biases, and cognitive dysfunction. Beta (15-30 Hz) neural oscillations have been shown to increase during reward-based learning within fronto-striatal reward networks. Corticostriatal beta oscillations have also been implicated in cognitive functions including working memory. However, the relationship between beta oscillations and depression remains unknown. Using intracranial recordings, we aimed to investigate how depression modulates the spectral power of neural oscillations in corticostriatal structures during reward feedback in a working memory task. Thirty movement disorder patients undergoing awake deep brain stimulation surgery with electrode trajectories traversing the caudate or dorsolateral prefrontal cortex (DLPFC) participated in this study. We recorded local field potential data intraoperatively as subjects completed a 2-back verbal working memory task where they identified whether a word matched the word presented two trials prior. Subjects received reward in the form of visual feedback for correct answers. Word stimuli had either a positive, negative, or neutral emotional valence. Subjects completed the Beck Depression Inventory-II preoperatively, and we used a cut-off score of 14 to identify patients with depression. We found that caudate and DLPFC power increased in the alpha (8-15 Hz) and beta range during reward feedback and that this increase was significantly greater for subjects without depression compared to depressed subjects. In non-depressed patients, positive feedback stimuli evoked significantly higher beta power in the caudate during reward compared to neutral and negative stimuli. In depressed patients, emotional valence did not affect reward-related caudate spectral power, while DLPFC alpha power was significantly higher following positive emotional stimuli in comparison to neutral but not negative stimuli. We additionally found that anti-depressant medications (ADMs) generally blunted alpha and beta reward signaling processes in the DLPFC. This blunting effect on reward-related alpha power in the DLPFC, however, was reversed in depressed patients, indicating that the effects of ADMs on reward signaling processes may depend on whether a patient is exhibiting depression symptoms. Our findings suggest that depression suppresses the alpha and beta power response to both reward and emotional stimuli during working memory, indicating power attenuation in these frequency bands may contribute to emotional and cognitive depression symptoms.

neuroscience↗

bioRxiv2022

Corticostriatal Beta Power Changes Associated with Cognitive Function in Parkinsons Disease

Cognitive impairment (CI) is the most frequent nonmotor symptom in Parkinsons Disease (PD) and is associated with deficits in executive functions such as working memory. Previous studies have demonstrated that caudate beta power is involved in learning and working memory. Decreased dopamine in motor cortico-striato-thalamo-cortical (CSTC) circuits results in increased beta power and PD motor symptoms. Analogous changes in cognitive CSTC circuits, including the caudate and dorsolateral prefrontal cortex (DLPFC), may contribute to PD CI. The objective of our study is to evaluate whether beta power changes in caudate and DLPFC contribute to cognitive impairment in PD patients. To investigate this, we used local field potential (LFP) recordings during deep brain stimulation surgery in 15 PD patients. LFP signals from DLPFC and caudate were performed at rest and during a verbal working memory task. We examined beta power changes during the working memory task and relationship of beta power to pre-operative neuropsychological testing results. Beta power decreased in both DLPFC and caudate during encoding of correct trials, whereas beta power increased in DLPFC and caudate during feedback for correct responses. Subjects with cognitive impairment showed smaller decreases in caudate and DLPFC beta power during encoding, greater increases in beta power during feedback, and lower average resting-state beta power. Additionally, reduced caudate beta power during encoding correlated with better memory scores on pre-operative neuropsychological testing, while greater DLPFC beta power during feedback correlated with worse scores in the attention domain. Our findings suggest that similar to the relationship between beta power in motor CSTC circuits and PD motor symptoms, beta power changes in parallel cognitive CSTC circuits may be correlated with cognitive symptoms in PD patients.

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bioRxiv2022

The Interictal Suppression Hypothesis in Focal Epilepsy: Electrographic and Structural Evaluation

Why are people with focal epilepsy not constantly seizing? Previous molecular work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is the high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure onset zones have an increased inward connectivity. Accordingly, we hypothesize that seizure onset zones are actively suppressed by the rest of the brain network during interictal states. We tested this hypothesis on 81 subjects with drug resistant focal epilepsy undergoing presurgical evaluation. We utilized intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, propagative, and non-involved regions. We then utilized diffusion imaging to acquire estimates of white matter connectivity to evaluate structure-function coupling effects on connectivity findings. Finally, using our observations, we generated a resting-state classification model to assist clinicians in detecting seizure onset and propagative zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and propagative zones demonstrate markedly increased inward connectivity and decreased outward connectivity on both resting-state and neurostimulation analyses. When controlling for distance between regions, the difference between inward vs. outward connectivity remained stable up to 80 mm between brain connections. Structure-function coupling analyses revealed that seizure onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue. Using these observations, our classification models achieved a maximum held-out testing set accuracy of 92.0{+/-}2.2%. These results indicate that seizure onset zones are actively segregated and suppressed by a widespread brain network. Furthermore, this electrographically observed functional suppression is disproportionate to any observed structural connectivity alterations of the seizure onset zones. These findings have implications for the identification of seizure onset zones using only brief resting-sate recordings to reduce patient morbidity and augment the presurgical evaluation of drug resistant epilepsy. Furthermore, testing of the interictal suppression hypothesis can provide insight into potential new resective, ablative, and neuromodulation approaches to improve surgical success rates in those suffering from drug resistant focal epilepsy.

neuroscience↗

bioRxiv2022

Localizing temporal lobe seizure onset zones using deep learning on SEEG cortico-cortical evoked potentials

In drug resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localization using brief interictal recordings would supplement presurgical evaluations and improve care. Thus, we sought to localize SOZs by training a multi-channel convolutional neural network on stereo-EEG (SEEG) cortico-cortical evoked potentials. We performed single pulse electrical stimulation with 10 drug resistant temporal lobe epilepsy patients implanted with SEEG. Using the 500,000 unique post-stimulation SEEG epochs, we trained a multi-channel one-dimensional convolutional neural network to determine whether an SOZ was stimulated. SOZs were classified with a mean leave-one-patient-out testing sensitivity of 78.1% and specificity of 74.6%. To achieve maximum accuracy, the model requires a 0-350 ms post stimulation time period. Post-hoc analysis revealed that the model accurately classified unilateral vs bilateral mesial temporal lobe seizure onset, and neocortical SOZs. This is the first demonstration, to our knowledge, that a deep learning framework can be used to accurately classify SOZs using cortico-cortical evoked potentials. Our findings suggest accurate classification of SOZs relies on a complex temporal evolution of evoked potentials within 350 ms of stimulation. Validation in a larger dataset could provide a practical clinical tool for the presurgical evaluation of drug resistant epilepsy.

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