Biology subjects

Dawant, B. M.

Publications and source records attributed to Dawant, B. M..

6 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↗

bioRxiv2023

Brain-wide human oscillatory LFP activity during visual working memory

Oscillatory activity is thought to be a marker of cognitive processes, although its role and distribution across the brain during working memory has been a matter of debate. To understand how oscillatory activity differentiates tasks and brain areas in humans, we recorded local field potentials (LFPs) in 12 adults as they performed visual-spatial and shape-matching memory tasks. Tasks were designed to engage working memory processes at a range of delay intervals between stimulus delivery and response initiation. LFPs were recorded using intracranial depth electrodes implanted to localize seizures for management of intractable epilepsy. Task-related LFP power analyses revealed an extensive network of cortical regions that were activated during the presentation of visual stimuli and during their maintenance in working memory, including occipital, parietal, temporal, insular, and prefrontal cortical areas, and subcortical structures including the amygdala and hippocampus. Across most brain areas, the appearance of a stimulus produced broadband power increase, while gamma power was evident during the delay interval of the working memory task. Notable differences between areas included that occipital cortex was characterized by elevated power in the high gamma (100-150 Hz) range during the 500 ms of visual stimulus presentation, which was less pronounced or absent in other areas. A decrease in power centered in beta frequency (16-40 Hz) was also observed after the stimulus presentation, whose magnitude differed across areas. These results reveal the interplay of oscillatory activity across a broad network, and region-specific signatures of oscillatory processes associated with visual working memory.

neuroscience↗

bioRxiv2022

Deep learning segmentation of the nucleus basalis of Meynert on 3T MRI

The nucleus basalis of Meynert (NBM) is a key subcortical structure that is important in arousal, cognition, brain network modulation, and has been explored as a deep brain stimulation target. It has also been implicated in several disease states, including Alzheimers disease, Parkinsons disease, and temporal lobe epilepsy (TLE). Given the small size of NBM and variability between patients, NBM is difficult to study; thus, accurate, patient-specific segmentation is needed. We investigated whether a deep learning network could produce accurate, patient-specific segmentations of NBM on commonly utilized 3T MRI. It is difficult to accurately segment NBM on 3T MRI, with 7T being preferred. Paired 3T and 7T MRI datasets of 21 healthy subjects were obtained, with 6 completely withheld for testing. NBM was expertly segmented on 7T MRI, providing accurate labels for the paired 3T MRI. An external dataset of 14 patients with TLE was used to test the model on brains with neurological disorders. A 3D-Unet convolutional neural network was constructed, and a 5-fold cross-validation was performed. The model was evaluated on healthy subjects using the held-out test dataset and the external dataset of TLE patients. The model demonstrated significantly improved dice coefficient over the standard probabilistic atlas for both healthy subjects (0.68MEAN{+/-}0.08SD vs. 0.47{+/-}0.06, p=0.0089, t-test) and TLE patients (0.63{+/-}0.08 vs. 0.38{+/-}0.19, p=0.0001). Additionally, the centroid distance was significantly decreased when using the model in patients with TLE (1.22{+/-}0.33mm, 3.25{+/-}2.57mm, p=0.0110). We developed the first model, to our knowledge, for automatic and accurate patient-specific segmentation of the NBM.

neuroscience↗