Penn students, many of whom joined the Davis Lab this summer, developed new tools and furthered our understanding of the relationship between neural structure and function in intractable epilepsy patients. Their research strives to improve presurgical localization of seizure onset, a result which could lead to a greater chance of seizure freedom for patients with epilepsy not fully managed by medications.

Penn Today highlighted this work in a new report, emphasizing the impressive contribution of the students to many projects in the Davis Lab. “They take an interdisciplinary approach, drawing from their expertise in imaging analysis, machine learning, network analysis, and signal analysis to solve a very relevant clinical problem,” Penn Today said of the students.

Read the full article by Brandon Baker in Penn Today HERE.

M.D., Ph.D. students John Bernabei and Andy Revell presented grand rounds for the Penn MSTP on Monday. Their lecture focused on the rapid emergence of AI in medicine and how tools currently being used by epilepsy researchers in the CNT and Davis Lab could one day change the fundamentals of clinical practice.

When applied effectively AI tools “can perform staggering computational tasks”, said Bernabei and Revell. In a clinical case study they presented, AI tools were used to sort through 5 million possible genetic variations in an infant presenting with seizures to identify the gene of interest– a time-sensitive feat which would have been impossible to perform manually. Cases such as this highlight an important role for these tools in medicine, they said.

However, Bernabei and Revell also noted that there are key limitations of AI. Machine learning algorithms, for instance, are largely a product of the datasets used to train them. The patterns in data AI is taught to recognize can sometimes be the algorithm’s own undoing. “When AI hears hoofbeats, it’s really only going to ever think of horses, and really never zebras. Humans are better at picking up zebras”, they explained.

Bernabei and Revell concluded that in a future almost certainly involving AI at the bedside, physicians should aim to integrate AI into their practice in a way that effectively leverages both computational tools and human medical expertise.

At this year’s annual meeting of the American Epilepsy Society, former CNT student and current Penn Epilepsy Fellow Erin Conrad, M.D., presented her soon-to-be published findings in a talk titled, “Does the Spatial Distribution or Frequency of IED Predict Seizure Onset Zone?”

The AES annual conference is the largest gathering on epilepsy in the world, and Conrad’s talk was part of an expert-led symposium on how interictal epileptiform discharges, or pathological patterns of brain activity between seizures, can be used to localize seizure onset.

Her findings have been recently accepted to Brain: A Journal of Neurology under the title, “The spatial topology of interictal spikes evolves over time and predicts the location of seizure onset”. To find out more about AES19 and Conrad’s talk, please visit the #AES19 website.

John Bernabei, current M.D., Ph.D. student, and Lohith Kini, M.D., Ph.D., were the primary co-authors on a new publication in Brain on Thursday. In their paper, the authors introduced and shared a set of network-based techniques aimed at predicting the most effective surgical resection zone in epilepsy patients.

Virtual resection, as the technique is called, has been shown to identify patients who are most likely to benefit from the removal of epileptic tissue. Within this patient group the authors hope their tools can specify the most important regions for removal, ensuring the highest probability for post-surgical seizure freedom.

Additionally, the authors show that in some cases tissues close to the seizure onset zone can work to dissipate, or “desynchronize” seizure activity from the start. Identifying these desynchronizing networks enables physicians to plan around them, avoiding their removal in a resection or ablation procedure.

Bernabei and Kini both contributed to this work as members of the CNT, a multidisciplinary environment that seeks to bridge gaps between fields in its research.

“We brought together senior faculty from Neurology, Neurosurgery, Radiology, and Bioengineering to guide a translational project using network neuroscience to guide patient care,” Bernabei says. “I’m fortunate to have mentors here who are dedicated to helping me achieve my goals as a physician-scientist in training.”

To read the full article in Brain click HERE.

Jennifer Stiso, a graduate student in the lab of CNT member Danielle Bassett, Ph.D., recently published findings in Cell Reports detailing how models of neural networks can be used to personalize brain stimulation therapies with the goal of improving memory. The modeling of neural networks using electrode data gathered during the treatment of epilepsy is an ongoing source of collaboration between members of the Bassett Lab and Davis Lab.

Penn Today  highlighted this work on Thursday in an article explaining how brain stimulation therapies, such as those used in the treatment of neurological disorders like epilepsy, can be expanded upon to improve memory or restore motor function.

“This type of study is an important, early step towards developing a fast, generalizable model of an individual’s response to a specific stimulation therapy,” said Stiso.

Models of neural networks can give researchers unprecedented insight into neural function, and collaborations between the Bassett Lab and Davis Lab seek to use these models to develop novel therapies for a wide range of disorders.

Read the full Penn Today article HERE.

Approximately 30% of patients with temporal lobe epilepsy (TLE) do not respond to medication and may turn to surgical removal of the seizure-onset zone. However, 30-40% of patients who undergo surgery still may not achieve seizure freedom, in part due to imprecise localization of the seizure onset zone (SOZ).

Andy Revell, an M.D., Ph.D. candidate in the Davis Lab, aims to develop techniques which more accurately predict the SOZ through his work on graph theory, which he presented this week at the 2019 ICTALS meeting.

By using high resolution imaging of fiber networks in epilepsy patients, Revell developed neural network models that predict how a seizure may spread throughout the brain. He plans to further develop these models with the goal of accurately predicting SOZ and surgical outcomes in individual patients, as developing a greater understanding of epileptic networks and how the spread of seizures relates to the SOZ may provide critical information to clinicians during the surgical planning process.

For patients whose epilepsy is not controlled by medication, surgical intervention may offer the possibility of achieving seizure freedom— but after epileptic tissue is removed, how does the brain respond?

This is the question Ph.D. student Campbell Arnold has been researching at the CNT. At the ICTALS 2019 meeting, he presented his work on developing methods for assessing cortical thickness in patients after epilepsy surgery.

Cortical thickness is a known biomarker for epilepsy progression and is related to connectivity between brain regions. Arnold’s study found that the invasiveness of a patient’s surgical procedure was related to the scope of postoperative cortical thinning.

As new surgical techniques, such as laser ablation, offer minimally invasive surgical options to patients, Campbell plans to further develop and test his methods of assessing the brain’s postoperative development.

Brittany Scheid, a Ph.D. student at the CNT, presented her work this week on controllability dynamics in the context of responsive neurostimulation (RNS) in epilepsy patients. Average controllability, she explained, is a measure of specific brain regions’ ability to affect their surrounding networks.

By applying clinical electrode recordings to models of epileptic network connectivity, Scheid demonstrated that the average controllability of underlying networks is constantly changing.

Additionally, Scheid’s analysis of clinical data revealed that RNS electrode implants were most effective when placed peripheral to the actual seizure onset zone.

These findings may contribute to our understanding of not only how seizures spread in epileptic networks, but also how they might be stopped even before they begin.

Andy Revell, an M.D., Ph.D. student mentored by Dr. Davis at the Center for Neuroengineering and Therapeutics, successfully defended his thesis proposal before a faculty panel Tuesday. Revell’s project focuses on applying graph theory to the modeling of neural networks in the context of epilepsy. Approximately 30% of patients with temporal lobe epilepsy (TLE) do not respond to medication and may turn to surgical removal of the seizure-onset zone. By modeling epileptic networks in individual TLE patients, Revell hopes to better predict surgical outcomes and even localize seizure onset in otherwise ambiguous cases.