Biology subjects

Doss, D. J.

Publications and source records attributed to Doss, D. J..

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

Multimodal state-dependent connectivity analysis of arousal and autonomic centers in the brainstem and basal forebrain

Vigilance is a continuously altering state of cortical activation that influences cognition and behavior and is disrupted in multiple brain pathologies. Neuromodulatory nuclei in the brainstem and basal forebrain are implicated in arousal regulation and are key drivers of widespread neuronal activity and communication. However, it is unclear how their large-scale brain network architecture changes across dynamic variations in vigilance state (i.e., alertness and drowsiness). In this study, we leverage simultaneous EEG and 3T multi-echo functional magnetic resonance imaging (fMRI) to elucidate the vigilance-dependent connectivity of arousal regulation centers in the brainstem and basal forebrain. During states of low vigilance, most of the neuromodulatory nuclei investigated here exhibit a stronger global correlation pattern and greater connectivity to the thalamus, precuneus, and sensory and motor cortices. In a more alert state, the nuclei exhibit the strongest connectivity to the salience, default mode, and auditory networks. These vigilance-dependent correlation patterns persist even after applying multiple preprocessing strategies to reduce systemic vascular effects. To validate our findings, we analyze two large 3T and 7T fMRI datasets from the Human Connectome Project and demonstrate that the static and vigilance-dependent connectivity profiles of the arousal nuclei are reproducible across 3T multi-echo, 3T single-echo, and 7T single-echo fMRI modalities. Overall, this work provides novel insights into the role of neuromodulatory systems in vigilance-related brain 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↗

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↗

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.

neuroscience↗