Biology subjects

Dawes, H. E.

Publications and source records attributed to Dawes, H. E..

3 recordsLinked to original sources

bioRxiv2025

Towards individualized deep brain stimulation: A stereoencephalography-based workflow for unbiased neurostimulation target identification

ObjectivesDeep brain stimulation (DBS) is increasingly being used to treat a variety of neuropsychiatric conditions, many of which exhibit idiosyncratic symptom presentations and neural correlates across individuals. Thus, we have utilized inpatient stereoelectroencephalography (sEEG) to identify personalized therapeutic stimulation sites for chronic implantation of DBS. Informed by our experience, we have developed a statistics-driven framework for stimulation testing to identify therapeutic targets. Materials and MethodsFourteen participants (major depressive disorder = 6, chronic pain = 6, obsessive-compulsive disorder = 2) underwent inpatient testing using sEEG and symptom monitoring to identify personalized stimulation targets for subsequent DBS implantation. We present a structured approach to this sEEG testing, integrating a Stimulation Testing Decision Tree with power analysis and effect size considerations to inform adequately powered results to detect therapeutic stimulation sites with statistical rigor. ResultsEffect sizes (Hedges g) of stimulation-induced symptom score changes ranged from -1.5 to +2.39. The standard deviation of sham trial responses was a strong predictor of stimulation response variability, as confirmed by a leave-one-out cross-validated linear regression (R2 = 0.67, permutation p<0.001). Thus, early sham trial data could be used to estimate the variability of stimulation responses for power analysis calculations. We show that approximately 10 sham trials were needed to robustly estimate sham variability. Power analysis (using a paired-t test) showed that for effect sizes [≥] 1.1, roughly 10 trials should be used per stimulation site for sufficiently powered results. ConclusionsThe presented workflow is adaptable to multiple indications and is specifically designed to overcome key challenges experienced during stimulation site testing. Through incorporating sham trials, effect size calculations, and tolerability testing, the described approach can be used to identify personalized and clinically efficacious stimulation sites.

neuroscience↗

bioRxiv2020

Naturalistic affective behaviors decoded from spectro-spatial features of multi-day human intracranial recordings

Task-based studies have uncovered distributed neural networks that support emotions, but little is known about how these networks produce affective behaviors in non-laboratory, ecological settings. We obtained continuous intracranial electroencephalography (iEEG) recordings from the emotion network in 11 patients with epilepsy during multi-day hospitalizations. We coded naturalistic affective behaviors (spontaneous expressions of positive or negative affect) from 116 hours of time-locked video recordings obtained over multiple days from subjects hospital rooms and utilized data driven classifiers to determine whether we could decode naturalistic affective behaviors from the neural data. Results indicated that binary within-subject random forest models could decode positive and negative affective behaviors from affectless behaviors (behaviors lacking valence) with up to 93% accuracy. Across the emotion network, positive and negative affective behaviors were associated with increased high frequency activity and decreased lower frequency activity. The anterior insula, amygdala, hippocampus, and anterior cingulate cortex (ACC) made strong contributions to affective behaviors in general. In a subset of subjects, three-state decoders distinguished among the positive, negative, and affectless behaviors using the spectro-spatial features from the emotion network. This study demonstrates that multi-day, highly resolved iEEG recordings in cortical and deep brain structures can reveal the circuit-level physiology of affective behaviors. By measuring behavior in an ecologically valid setting, our findings provide novel insights into the spatially distributed dynamics of local neural populations underlying naturalistic affective behaviors. SignificancePrevious neuroimaging and neurophysiological studies have identified a distributed network that supports emotions, but much remains unknown about how this network produces affective behaviors in ecological settings. We used intracranial electroencephalography recordings from the emotion network to decode naturalistic affective behaviors--spontaneous expressions of positive and negative affect that occurred during multi-day hospital stays--from neural data in patients with epilepsy. Our results complement prior neuroscientific studies of emotion and offer novel insights into the spectral and spatial dynamics of the emotion network that characterize naturalistic affective behaviors. The present study suggests intracranial electroencephalography can uncover new details about emotion network physiology and help to expand current neuroanatomical frameworks of human emotions.

neuroscience↗

bioRxiv2020

Chronic wireless streaming of invasive neural recordings at home for circuit discovery and adaptive stimulation

Invasive neural recording in humans shows promise for understanding the circuit basis of brain disorders. Most recordings have been done for short durations from externalized brain leads in hospital settings, or from first-generation implantable sensing devices that offer only intermittent brief streaming of time series data. Here we report the first human use of an implantable neural interface for wireless multichannel streaming of field potentials over long periods, with and without simultaneous therapeutic neurostimulation, untethered to receiving devices. Four Parkinsons disease patients streamed bilateral 4-channel motor cortical and basal ganglia field potentials at home for over 500 hours, paired with wearable monitors that behaviorally categorize states of inadequate or excessive movement. Motor state during normal home activities was efficiently decoded using either supervised learning or unsupervised clustering algorithms. This platform supports adaptive deep brain stimulation, and may be widely applicable to brain disorders treatable by invasive neuromodulation.

neuroscience↗