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Crone, N.

Publications and source records attributed to Crone, N..

2 recordsLinked to original sources

Sensorimotor cortical-subthalamic network dynamics during force generation

The subthalamic nucleus (STN) is proposed to participate in pausing, or alternately, in dynamic scaling of behavioral responses, roles that have conflicting implications for understanding STN function in the context of deep brain stimulation (DBS) therapy. To examine the nature of event-related STN activity and subthalamic-cortical dynamics, we performed primary motor and somatosensory electrocorticography while subjects (n=10) performed a grip force task during DBS implantation surgery. The results provide the first evidence from humans that STN gamma activity can predict activity in the cortex both prior to and during movement, consistent with the idea that the STN participates in both motor planning and execution. We observed that STN activity appeared to facilitate movement: while both movement onset and termination both coincided with STN-cortical phase-locking, narrow-band gamma power was positively correlated with grip force, and event-related causality measures demonstrated that STN gamma activity predicted cortical gamma activity during movement. STN participation in somatosensory integration also was demonstrated by casual analysis. Information flow from the STN to somatosensory cortex was observed for both beta and gamma range frequencies, specific to particular movement periods and kinematics. Interactions in beta activity between the STN and somatosensory cortex, rather than motor cortex, predicted PD symptom severity. Thus, the STN contributes to multiple aspects of sensorimotor behavior dynamically across time.

neuroscience

Using Network Analysis to Localize the Epileptogenic Zone from Invasive EEG Recordings in Intractable Focal Epilepsy

Treatment of medically intractable focal epilepsy (MIFE) by surgical resection of the epileptogenic zone (EZ) is often effective provided the EZ can be reliably identified. Even with the use of invasive recordings, the clinical differentiation between the EZ and normal brain areas can be quite challenging, mainly in patients without MRI detectable lesions. Consequently, despite relatively large brain regions being removed, surgical success rates barely reach 60-65%. Such variable and unfavorable outcomes associated with high morbidity rates are often caused by imprecise and/or inaccurate EZ localization. We developed a localization algorithm that uses network-based data analytics to process invasive EEG recordings. This network algorithm analyzes the centrality signatures of every contact electrode within the recording network and characterizes contacts into susceptible EZ based on the centrality trends over time. The algorithm was tested in a retrospective study that included 42 patients from four epilepsy centers. Our algorithm had higher agreement with EZ regions identified by clinicians for patients with successful surgical outcomes and less agreement for patients with failed outcomes. These findings suggest that network analytics and a network systems perspective of epilepsy may be useful in assisting clinicians in more accurately localizing the EZ.\n\nAUTHOR SUMMARYEpilepsy is a disease that results in abnormal firing patterns in parts of the brain that comprise the epileptogenic network, known as the epileptogenic zone (EZ). Current methods to localize the EZ for surgical treatment often requires observations of hundreds of thousands of EEG data points measured from many electrodes implanted in a patients brain. In this paper, we used network science to show that EZ regions may exhibit specific network signatures before, during and after seizure events. Our algorithm computes the likelihood of each electrode being in the EZ and tends to agree more with clinicians during successful resections and less during failed surgeries. These results suggest that a networked analysis approach to EZ localization may be valuable in a clinical setting.

neuroscience