Danika Paulo - Academia.edu (original) (raw)

Papers by Danika Paulo

Global Spine Journal, May 1, 2014

S ymptomatic lumbar spinal stenosis (LSS) unresponsive to conservative therapy is commonly treate... more S ymptomatic lumbar spinal stenosis (LSS) unresponsive to conservative therapy is commonly treated using direct surgical decompression. 33 Current guidelines recommend additional arthrodesis in patients with LSS and preexisting spondylolisthesis. 21,24,31,49,50,52 Various techniques are currently used for direct decompression of LSS. Standard open laminectomy has been shown to be an effective procedure for LSS decompression. 36,46,54,60-64,66 However, wide laminectomies violating stabilizing bony and ligamentous structures may exacerbate preexisting spondylolisthesis. 19,37 Minimally invasive laminectomy through tubular or similar retractors is a recently introduced alternative procedure for decompression of LSS. 47 This technique avoids detachment of the paraspinal muscles and may promote preservation of stabilizing ligamentous and bony spinal structures. 29,30,39,41-45 Biomechanical studies indicate that compared with open laminectomy, minimally invasive laminectomy may result in less postoperative instability. 1,6,12,23,34 The current study had two main goals: first, to evaluate the efficacy of minimally invasive laminectomy as a decompressive procedure for the treatment of patients with abbreviatioNS BMI = body mass index; LSS = lumbar spinal stenosis; MCID = minimum clinically important difference; ODI = Oswestry Disability Index; VAS = visual analog scale. accompaNyiNg editorial See pp 337-338.

Neuromodulation: Technology at the Neural Interface

Neurologic Clinics, Nov 1, 2022

bioRxiv (Cold Spring Harbor Laboratory), Jul 10, 2022

bioRxiv (Cold Spring Harbor Laboratory), Jul 5, 2022

The nucleus basalis of Meynert (NBM) is a key subcortical structure that is important in arousal,... more 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 Alzheimer's disease, Parkinson's 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.

bioRxiv (Cold Spring Harbor Laboratory), Jun 30, 2022

Why are people with focal epilepsy not constantly seizing? Previous molecular work has implicated... more 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.

bioRxiv (Cold Spring Harbor Laboratory), Mar 2, 2022

In drug resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localizati... more 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 multichannel 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.

Journal of Neurosurgery, Sep 1, 2022

OBJECTIVE In drug-resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) ... more OBJECTIVE In drug-resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localization that use brief interictal recordings could supplement presurgical evaluations and improve care. Thus, the authors sought to localize SOZs by training a multichannel convolutional neural network on stereoelectroencephalography (SEEG) cortico-cortical evoked potentials. METHODS The authors performed single-pulse electrical stimulation in 10 drug-resistant temporal lobe epilepsy patients implanted with SEEG. Using 500,000 unique poststimulation SEEG epochs, the authors trained a multichannel 1-dimensional convolutional neural network to determine whether an SOZ had been stimulated. RESULTS SOZs were classified with mean sensitivity of 78.1% and specificity of 74.6% according to leave-one-patient-out testing. To achieve maximum accuracy, the model required a 0- to 350-msec poststimulation time period. Post hoc analysis revealed that the model accurately classified unilateral versus bilateral mesial temporal lobe seizure onset, as well as neocortical SOZs. CONCLUSIONS This was the first demonstration, to the authors’ knowledge, that a deep learning framework can be used to accurately classify SOZs with single-pulse electrical stimulation–evoked responses. These findings suggest that accurate classification of SOZs relies on a complex temporal evolution of evoked responses within 350 msec of stimulation. Validation in a larger data set could provide a practical clinical tool for the presurgical evaluation of drug-resistant epilepsy.

Brain, Feb 1, 2023

Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling... more Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure–function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10−13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10−3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10−12). Structure–function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10−21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest 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 electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further 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.

World Neurosurgery, Apr 1, 2020

BACKGROUND:Microvascular decompression (MVD) is highly effective in managing the neuropathic faci... more BACKGROUND:Microvascular decompression (MVD) is highly effective in managing the neuropathic facial pain of trigeminal neuralgia (TN). Its utility in patients with TN and concurrent multiple sclerosis (MS) has been a subject of debate. The goal of this study was to identify demographic and perioperative variables associated with favorable outcome after MVD over the past 20 years in patients from our institution.METHODS:A retrospective analysis of our cohort of 33 patients diagnosed with MS and TN who underwent MVD between 1997 and 2017 to treat neuropathic facial pain was performed. Perioperative variables included MS disease burden, findings on preoperative magnetic resonance imaging (MRI), TN pain severity, and the presence of intraoperative neurovascular compression. MS disease burden was quantified using the Expanded Disability Status Scale. Preoperative and postoperative pain severity was quantified using the Barrow Neurological Institute (BNI) pain severity scale.RESULTS:A total of 33 patients with TN and MS were treated with MVD at our institution (out of the 632 total MVDs performed) between 1997 and 2017. Twenty-two patients (67%) maintained a reduction in pain at a mean follow-up of 53.5 months. Higher preoperative BNI pain intensity score was associated with unfavorable outcome after MVD (P = 0.006). No associations were identified between MS disease burden, presence of neurovascular compression or pontine demyelinating plaques on MRI, or intraoperative findings of neurovascular compression and treatment outcomes.CONCLUSIONS:MVD is a reasonable treatment option for patients with TN and MS, although the rate of freedom from pain is lower than that for the general TN population. Preoperative pain severity may be a predictor of treatment success.

American Journal of Neuroradiology

BACKGROUND AND PURPOSE: The nucleus basalis of Meynert is a key subcortical structure that is imp... more BACKGROUND AND PURPOSE: The nucleus basalis of Meynert is a key subcortical structure that is important in arousal and cognition and has been explored as a deep brain stimulation target but is difficult to study due to its small size, variability among patients, and lack of contrast on 3T MR imaging. Thus, our goal was to establish and evaluate a deep learning network for automatic, accurate, and patient-specific segmentations with 3T MR imaging. MATERIALS AND METHODS: Patient-specific segmentations can be produced manually; however, the nucleus basalis of Meynert is difficult to accurately segment on 3T MR imaging, with 7T being preferred. Thus, paired 3T and 7T MR imaging data sets of 21 healthy subjects were obtained. A test data set of 6 subjects was completely withheld. The nucleus was expertly segmented on 7T, providing accurate labels for the paired 3T MR imaging. An external data set of 14 patients with temporal lobe epilepsy was used to test the model on brains with neurologic disorders. A 3D-Unet convolutional neural network was constructed, and a 5-fold cross-validation was performed. RESULTS: The novel segmentation model demonstrated significantly improved Dice coefficients over the standard probabilistic atlas for both healthy subjects (mean, 0.68 [SD, 0.10] versus 0.45 [SD, 0.11], P ¼ .002, t test) and patients (0.64 [SD, 0.10] versus 0.37 [SD, 0.22], P , .001). Additionally, the model demonstrated significantly decreased centroid distance in patients (1.18 [SD, 0.43] mm, 3.09 [SD, 2.56] mm, P ¼ .007). CONCLUSIONS: We developed the first model, to our knowledge, for automatic and accurate patient-specific segmentation of the nucleus basalis of Meynert. This model may enable further study into the nucleus, impacting new treatments such as deep brain stimulation.

Brain

Cognitive impairment is the most frequent non-motor symptom in Parkinson’s disease and is associa... more Cognitive impairment is the most frequent non-motor symptom in Parkinson’s disease and is associated with deficits in a number of cognitive functions including working memory. However, the pathophysiology of Parkinson’s disease cognitive impairment is poorly understood. Beta oscillations have previously been shown to play an important role in cognitive functions including working memory encoding. Decreased dopamine in motor cortico-striato-thalamo-cortical (CSTC) circuits increases the spectral power of beta oscillations and results in Parkinson’s disease motor symptoms. Analogous changes in parallel cognitive CSTC circuits involving the caudate and dorsolateral prefrontal cortex (DLPFC) may contribute to Parkinson’s disease cognitive impairment. The objective of our study is to evaluate whether changes in beta oscillations in the caudate and DLPFC contribute to cognitive impairment in Parkinson’s disease patients. To investigate this, we used local field potential recordings during...

Neuromodulation: Technology at the Neural Interface

Neuromodulation: Technology at the Neural Interface

Neuromodulation: Technology at the Neural Interface

Neuromodulation: Technology at the Neural Interface

Journal of Pain and Symptom Management

Neurosurgery

INTRODUCTION: Increasing evidence has suggested that identification of regions involved in early ... more INTRODUCTION: Increasing evidence has suggested that identification of regions involved in early seizure propagation (“Propagation Zones”, PZ) is important to predict seizure freedom after epilepsy surgery (Andrews 2019). Resting-state connectivity analyses using stereo-electroencephalography (SEEG) have shown promise in efficiently characterizing seizure onset zones (SOZ) but have found difficulty in distinguishing both SOZs and PZs from non-involved brain regions. Recently, evidence has suggested that ictal structure-function coupling can be used to delineate brain regions important in seizure dynamics (Shah 2019). METHODS: We calculated the resting-state SEEG directed connectivity of 26 consented patients with focal epilepsy undergoing presurgical evaluation. Using preoperative diffusion MRI, we then implemented a custom technique to obtain structural connectivity metrics between SEEG contacts. We calculated the structural connectivity and structure-function coupling of SOZs, PZs...

Neurosurgery

INTRODUCTION: Cognitive dysfunction is a common nonmotor symptom of Parkinson’s disease (PD), wit... more INTRODUCTION: Cognitive dysfunction is a common nonmotor symptom of Parkinson’s disease (PD), with memory impairment being a primary cause of disability. Decreased dopamine in PD elevates beta power in cortico-striato-thalamo-cortico (CSTC) motor circuits and is the basis of closed-loop deep brain stimulation (DBS) strategies. Analogous dysfunction of parallel CSTC cognitive circuitry including caudate and dorsolateral prefrontal cortex (DLPFC) may mediate PD cognitive impairment and be a neuromodulation target for cognitive symptoms. We have previously shown caudate and DLPFC beta power contributes to working memory updating, and suppression of prefrontal cortex beta bursts during working memory encoding has been linked with improved performance. However, how PD affects beta bursting in relation to memory function remains unknown. METHODS: Fourteen PD patients undergoing awake DBS surgery with electrode trajectories traversing the caudate, DLPFC, or both participated in this study....

Global Spine Journal, May 1, 2014

S ymptomatic lumbar spinal stenosis (LSS) unresponsive to conservative therapy is commonly treate... more S ymptomatic lumbar spinal stenosis (LSS) unresponsive to conservative therapy is commonly treated using direct surgical decompression. 33 Current guidelines recommend additional arthrodesis in patients with LSS and preexisting spondylolisthesis. 21,24,31,49,50,52 Various techniques are currently used for direct decompression of LSS. Standard open laminectomy has been shown to be an effective procedure for LSS decompression. 36,46,54,60-64,66 However, wide laminectomies violating stabilizing bony and ligamentous structures may exacerbate preexisting spondylolisthesis. 19,37 Minimally invasive laminectomy through tubular or similar retractors is a recently introduced alternative procedure for decompression of LSS. 47 This technique avoids detachment of the paraspinal muscles and may promote preservation of stabilizing ligamentous and bony spinal structures. 29,30,39,41-45 Biomechanical studies indicate that compared with open laminectomy, minimally invasive laminectomy may result in less postoperative instability. 1,6,12,23,34 The current study had two main goals: first, to evaluate the efficacy of minimally invasive laminectomy as a decompressive procedure for the treatment of patients with abbreviatioNS BMI = body mass index; LSS = lumbar spinal stenosis; MCID = minimum clinically important difference; ODI = Oswestry Disability Index; VAS = visual analog scale. accompaNyiNg editorial See pp 337-338.

Neuromodulation: Technology at the Neural Interface

Neurologic Clinics, Nov 1, 2022

bioRxiv (Cold Spring Harbor Laboratory), Jul 10, 2022

bioRxiv (Cold Spring Harbor Laboratory), Jul 5, 2022

The nucleus basalis of Meynert (NBM) is a key subcortical structure that is important in arousal,... more 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 Alzheimer's disease, Parkinson's 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.

bioRxiv (Cold Spring Harbor Laboratory), Jun 30, 2022

Why are people with focal epilepsy not constantly seizing? Previous molecular work has implicated... more 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.

bioRxiv (Cold Spring Harbor Laboratory), Mar 2, 2022

In drug resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localizati... more 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 multichannel 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.

Journal of Neurosurgery, Sep 1, 2022

OBJECTIVE In drug-resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) ... more OBJECTIVE In drug-resistant temporal lobe epilepsy, automated tools for seizure onset zone (SOZ) localization that use brief interictal recordings could supplement presurgical evaluations and improve care. Thus, the authors sought to localize SOZs by training a multichannel convolutional neural network on stereoelectroencephalography (SEEG) cortico-cortical evoked potentials. METHODS The authors performed single-pulse electrical stimulation in 10 drug-resistant temporal lobe epilepsy patients implanted with SEEG. Using 500,000 unique poststimulation SEEG epochs, the authors trained a multichannel 1-dimensional convolutional neural network to determine whether an SOZ had been stimulated. RESULTS SOZs were classified with mean sensitivity of 78.1% and specificity of 74.6% according to leave-one-patient-out testing. To achieve maximum accuracy, the model required a 0- to 350-msec poststimulation time period. Post hoc analysis revealed that the model accurately classified unilateral versus bilateral mesial temporal lobe seizure onset, as well as neocortical SOZs. CONCLUSIONS This was the first demonstration, to the authors’ knowledge, that a deep learning framework can be used to accurately classify SOZs with single-pulse electrical stimulation–evoked responses. These findings suggest that accurate classification of SOZs relies on a complex temporal evolution of evoked responses within 350 msec of stimulation. Validation in a larger data set could provide a practical clinical tool for the presurgical evaluation of drug-resistant epilepsy.

Brain, Feb 1, 2023

Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling... more Why are people with focal epilepsy not continuously having seizures? Previous neuronal signalling work has implicated gamma-aminobutyric acid balance as integral to seizure generation and termination, but is a high-level distributed brain network involved in suppressing seizures? Recent intracranial electrographic evidence has suggested that seizure-onset zones have increased inward connectivity that could be associated with interictal suppression of seizure activity. Accordingly, we hypothesize that seizure-onset zones are actively suppressed by the rest of the brain network during interictal states. Full testing of this hypothesis would require collaboration across multiple domains of neuroscience. We focused on partially testing this hypothesis at the electrographic network level within 81 individuals with drug-resistant focal epilepsy undergoing presurgical evaluation. We used intracranial electrographic resting-state and neurostimulation recordings to evaluate the network connectivity of seizure onset, early propagation and non-involved zones. We then used diffusion imaging to acquire estimates of white-matter connectivity to evaluate structure–function coupling effects on connectivity findings. Finally, we generated a resting-state classification model to assist clinicians in detecting seizure-onset and propagation zones without the need for multiple ictal recordings. Our findings indicate that seizure onset and early propagation zones demonstrate markedly increased inwards connectivity and decreased outwards connectivity using both resting-state (one-way ANOVA, P-value = 3.13 × 10−13) and neurostimulation analyses to evaluate evoked responses (one-way ANOVA, P-value = 2.5 × 10−3). When controlling for the distance between regions, the difference between inwards and outwards connectivity remained stable up to 80 mm between brain connections (two-way repeated measures ANOVA, group effect P-value of 2.6 × 10−12). Structure–function coupling analyses revealed that seizure-onset zones exhibit abnormally enhanced coupling (hypercoupling) of surrounding regions compared to presumably healthy tissue (two-way repeated measures ANOVA, interaction effect P-value of 9.76 × 10−21). Using these observations, our support vector classification models achieved a maximum held-out testing set accuracy of 92.0 ± 2.2% to classify early propagation and seizure-onset zones. These results suggest 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 electrographic recordings to reduce patient morbidity and augment the presurgical evaluation of drug-resistant epilepsy. Further 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.

World Neurosurgery, Apr 1, 2020

BACKGROUND:Microvascular decompression (MVD) is highly effective in managing the neuropathic faci... more BACKGROUND:Microvascular decompression (MVD) is highly effective in managing the neuropathic facial pain of trigeminal neuralgia (TN). Its utility in patients with TN and concurrent multiple sclerosis (MS) has been a subject of debate. The goal of this study was to identify demographic and perioperative variables associated with favorable outcome after MVD over the past 20 years in patients from our institution.METHODS:A retrospective analysis of our cohort of 33 patients diagnosed with MS and TN who underwent MVD between 1997 and 2017 to treat neuropathic facial pain was performed. Perioperative variables included MS disease burden, findings on preoperative magnetic resonance imaging (MRI), TN pain severity, and the presence of intraoperative neurovascular compression. MS disease burden was quantified using the Expanded Disability Status Scale. Preoperative and postoperative pain severity was quantified using the Barrow Neurological Institute (BNI) pain severity scale.RESULTS:A total of 33 patients with TN and MS were treated with MVD at our institution (out of the 632 total MVDs performed) between 1997 and 2017. Twenty-two patients (67%) maintained a reduction in pain at a mean follow-up of 53.5 months. Higher preoperative BNI pain intensity score was associated with unfavorable outcome after MVD (P = 0.006). No associations were identified between MS disease burden, presence of neurovascular compression or pontine demyelinating plaques on MRI, or intraoperative findings of neurovascular compression and treatment outcomes.CONCLUSIONS:MVD is a reasonable treatment option for patients with TN and MS, although the rate of freedom from pain is lower than that for the general TN population. Preoperative pain severity may be a predictor of treatment success.

American Journal of Neuroradiology

BACKGROUND AND PURPOSE: The nucleus basalis of Meynert is a key subcortical structure that is imp... more BACKGROUND AND PURPOSE: The nucleus basalis of Meynert is a key subcortical structure that is important in arousal and cognition and has been explored as a deep brain stimulation target but is difficult to study due to its small size, variability among patients, and lack of contrast on 3T MR imaging. Thus, our goal was to establish and evaluate a deep learning network for automatic, accurate, and patient-specific segmentations with 3T MR imaging. MATERIALS AND METHODS: Patient-specific segmentations can be produced manually; however, the nucleus basalis of Meynert is difficult to accurately segment on 3T MR imaging, with 7T being preferred. Thus, paired 3T and 7T MR imaging data sets of 21 healthy subjects were obtained. A test data set of 6 subjects was completely withheld. The nucleus was expertly segmented on 7T, providing accurate labels for the paired 3T MR imaging. An external data set of 14 patients with temporal lobe epilepsy was used to test the model on brains with neurologic disorders. A 3D-Unet convolutional neural network was constructed, and a 5-fold cross-validation was performed. RESULTS: The novel segmentation model demonstrated significantly improved Dice coefficients over the standard probabilistic atlas for both healthy subjects (mean, 0.68 [SD, 0.10] versus 0.45 [SD, 0.11], P ¼ .002, t test) and patients (0.64 [SD, 0.10] versus 0.37 [SD, 0.22], P , .001). Additionally, the model demonstrated significantly decreased centroid distance in patients (1.18 [SD, 0.43] mm, 3.09 [SD, 2.56] mm, P ¼ .007). CONCLUSIONS: We developed the first model, to our knowledge, for automatic and accurate patient-specific segmentation of the nucleus basalis of Meynert. This model may enable further study into the nucleus, impacting new treatments such as deep brain stimulation.

Brain

Cognitive impairment is the most frequent non-motor symptom in Parkinson’s disease and is associa... more Cognitive impairment is the most frequent non-motor symptom in Parkinson’s disease and is associated with deficits in a number of cognitive functions including working memory. However, the pathophysiology of Parkinson’s disease cognitive impairment is poorly understood. Beta oscillations have previously been shown to play an important role in cognitive functions including working memory encoding. Decreased dopamine in motor cortico-striato-thalamo-cortical (CSTC) circuits increases the spectral power of beta oscillations and results in Parkinson’s disease motor symptoms. Analogous changes in parallel cognitive CSTC circuits involving the caudate and dorsolateral prefrontal cortex (DLPFC) may contribute to Parkinson’s disease cognitive impairment. The objective of our study is to evaluate whether changes in beta oscillations in the caudate and DLPFC contribute to cognitive impairment in Parkinson’s disease patients. To investigate this, we used local field potential recordings during...

Neuromodulation: Technology at the Neural Interface

Neuromodulation: Technology at the Neural Interface

Neuromodulation: Technology at the Neural Interface

Neuromodulation: Technology at the Neural Interface

Journal of Pain and Symptom Management

Neurosurgery

INTRODUCTION: Increasing evidence has suggested that identification of regions involved in early ... more INTRODUCTION: Increasing evidence has suggested that identification of regions involved in early seizure propagation (“Propagation Zones”, PZ) is important to predict seizure freedom after epilepsy surgery (Andrews 2019). Resting-state connectivity analyses using stereo-electroencephalography (SEEG) have shown promise in efficiently characterizing seizure onset zones (SOZ) but have found difficulty in distinguishing both SOZs and PZs from non-involved brain regions. Recently, evidence has suggested that ictal structure-function coupling can be used to delineate brain regions important in seizure dynamics (Shah 2019). METHODS: We calculated the resting-state SEEG directed connectivity of 26 consented patients with focal epilepsy undergoing presurgical evaluation. Using preoperative diffusion MRI, we then implemented a custom technique to obtain structural connectivity metrics between SEEG contacts. We calculated the structural connectivity and structure-function coupling of SOZs, PZs...

Neurosurgery

INTRODUCTION: Cognitive dysfunction is a common nonmotor symptom of Parkinson’s disease (PD), wit... more INTRODUCTION: Cognitive dysfunction is a common nonmotor symptom of Parkinson’s disease (PD), with memory impairment being a primary cause of disability. Decreased dopamine in PD elevates beta power in cortico-striato-thalamo-cortico (CSTC) motor circuits and is the basis of closed-loop deep brain stimulation (DBS) strategies. Analogous dysfunction of parallel CSTC cognitive circuitry including caudate and dorsolateral prefrontal cortex (DLPFC) may mediate PD cognitive impairment and be a neuromodulation target for cognitive symptoms. We have previously shown caudate and DLPFC beta power contributes to working memory updating, and suppression of prefrontal cortex beta bursts during working memory encoding has been linked with improved performance. However, how PD affects beta bursting in relation to memory function remains unknown. METHODS: Fourteen PD patients undergoing awake DBS surgery with electrode trajectories traversing the caudate, DLPFC, or both participated in this study....