Focal EpilepsyNCT04986683
Diffusion MRI Methods to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery
A study testing Brain magnetic resonance imaging and Neuro-psychology testing in people with Focal Epilepsy, run by Wayne State University at 1 site in United States.
Trial summary
This project will test the accuracy of a novel diffusion-weighted magnetic resonance imaging (DWMRI) approach using a deep convolutional neural network (DCNN) to predict an optimal resection margin for pediatric epilepsy surgery objectively. Read more about this study
- Who is it for?
- Ages 3 to 19 yearsHealthy volunteers can take part
- Conditions the study is for
- Focal Epilepsy
- What is being tested?
- Brain magnetic resonance imaging and Neuro-psychology testing
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 60 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Aug 2021to 30 Jun 2026
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 3 to 19 years
- All sexes
- Healthy volunteers accepted
- Child, Adult
Who may be able to join
- Subjects with drug-resistant focal epilepsy
- 1. Age 3-19 years. 2. Planned two-stage epilepsy surgery with subdural electrodes.
- Healthy control subjects 1. Age 5-19 years. 2. No cognitive, motor, and/or language impairment or clinical elevations on a measure of behavioral problems. 3. Brain MRI interpreted as normal.
Who cannot join
- For all subjects:
- 1. History of prematurity or perinatal hypoxic-ischemic event. 2. Hemiplegia on preoperative neurological examination by pediatric neurologists. 3. Dysmorphic features suggestive of a clinical syndrome. 4. Diagnosis of any pervasive developmental or psychiatric condition which clearly predates the onset of seizures, including autism spectrum disorder, tic disorders, obsessive-compulsive disorder. 5. MRI abnormalities showing massive brain malformation and other extensive lesions that likely destroyed the contralateral tracts and severely affected i) spatial normalization accuracy in advanced normalization tools (ANTs), mutual information (MI) between native T1- MRI of Geodesic SyN transform and template T1-MRI < mean-3standard deviation of MI in the healthy control group and ii) parcellation accuracy in surface-matching-based deformable registration, target registration error (TRE) of fine tetrahedra mesh between native T1- MRI brain surface and template T1-MRI brain surface > mean-3standard deviation of TRE in the healthy control group. 6. History of claustrophobia. 7. Unsuccessful MRI showing head motion > 2 mm in DWMRI (i.e., voxel size of DWMRI) which is evaluated by NIH TORTOISE DWMRI motion artifact correction package. 8. Subject who cannot speak English.
Trial locations and contacts
1 site in 1 country.
United States1 site, 1 recruiting
- Recruiting
Wayne State University/Children's Hospital of Michigan
Detroit, Michigan, 48201
Aimee Luat, MD, 313-832-9620, aluat@dmc.org
Eishi Asano, MD, PHD, 313-745-5547, easano@med.wayne.edu
About this study
Official title: Novel DWI Methods to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery
This project will test the accuracy of a novel diffusion-weighted magnetic resonance imaging (DWMRI) approach using a deep convolutional neural network (DCNN) to predict an optimal resection margin for pediatric epilepsy surgery objectively. Its primary goal is to minimize surgical risk probability (i.e., functional deficit) and maximize surgical benefit probability (i.e., seizure freedom) by precisely localizing eloquent white matter pathways in children and adolescents with drug-resistant focal epilepsy. This new imaging approach, which will acquire a DWMRI scan before pediatric epilepsy surgery in about 10 minutes without contrast administration (and also without sedation even in young children), can be readily applied to improve preoperative benefit-risk evaluation for pediatric epilepsy surgery in the future. The investigators will also study how the advanced DWMRI-DCNN connectome approach can detect complex signs of brain neuronal reorganization that help improve neurological and cognitive outcomes following pediatric epilepsy surgery. This new imaging approach could benefit targeted interventions in the future to minimize neurocognitive deficits in affected children. All enrolled subjects will undergo advanced brain MRI and neurocognitive evaluation to achieve these goals. The findings of this project will not guide any clinical decision-making or clinical intervention until the studied approach is thoroughly validated.
Read the detailed description
This project will combine advanced brain MRI with detailed neuro-psychology evaluation, performed in children and adolescents affected by drug-resistant focal epilepsy, to address two main aims, each of them with the following research hypotheses:
AIM 1. To determine the accuracy of deep learning tractography-based benefit-risk analysis compared to a standard electrical stimulation mapping (ESM) which is the current clinical standard for detecting eloquent cortical regions before epilepsy surgery.
Hypothesis 1.1 In healthy controls, DCNN-based tract classification will localize eloquent cortices, which are significantly overlapped at both single shell DWI acquisition and generalized Q-sampling imaging, suggesting that the accuracy of this approach may not be significantly affected by the acquisition protocol.
Hypothesis 1.2 DCNN-based tract classification will achieve at least 93% accuracy for prospective detection of ESM-defined eloquent cortices, including patients with a high likelihood of functional reorganization.
Hypothesis 1.3 Preservation of DCNN-classified eloquent white matter pathways during surgery will predict the avoidance of postoperative deficits as accurately as the preservation of ESM-defined eloquent cortex.
Hypothesis 1.4 Preservation of surgical margins optimized by Kalman filter on retrospective data will achieve seizure control and avoidance of postoperative deficits in a prospective surgical patient cohort.
In this aim, the investigators will test the accuracy of a recently developed deep learning-based benefit-risk model, called deep convolutional neural network (DCNN) tract classification combined with Kalman filter analysis, for non-invasive detection of eloquent white matter pathways and optimization of surgical margin (i.e., the distance between epileptogenic area and eloquent area), resulting in seizure freedom and avoidance of functional deficits. Failure to identify eloquent areas in the proposed resection region can have potentially lifelong consequences, and overestimation or incorrect localization of the extent of the eloquent regions may lead to incomplete resection of the epileptogenic zone. Without optimizing the benefit-risk ratio, the minimum acceptable margin is highly variable across different settings, ranging from 0 to 2 cm across epilepsy surgery centers. The investigators will study whether the proposed benefit-risk model can standardize (or customize) epilepsy surgery of individual patients by accurately optimizing the margins of the eloquent white matter pathways to be preserved, which is ultimately essential to balance the benefit of seizure freedom with the risk of functional deficit. This proposed new imaging approach could change clinical practice for pediatric epilepsy surgery and is widely applicable for other types of neurosurgical procedures such as tumor resection.
AIM 2. To determine the accuracy of deep learning-based connectome analysis for prediction of long-term neurocognitive improvement following epilepsy surgery.
Hypothesis 2.1 Connectivity efficiencies preserved in specific modular networks of preoperative DCNN-based connectome, found to be associated with postoperative functional improvement on retrospective data, will accurately predict long-term functional improvement in a prospective patient cohort.
Hypothesis 2.2 Longer epilepsy duration will be significantly associated with more decreased efficiency in full-scale IQ modular network of preoperative DCNN-based connectome, thus suggesting that earlier surgery will yield better long-term full-scale IQ improvement.
Hypothesis 2.3 Patients with ipsilateral resections, who show signs of postoperative "crowding" (i.e., verbal IQ improvement at the expense of non-verbal function), will show decreased efficiency in non-verbal and increased efficiency in verbal IQ network of DCNN-based connectome in the contralateral hemisphere.
In this aim, the investigators will test if an advanced DWMRI approach integrating DCNN and connectome helps decide timely surgery by providing 1) preoperative imaging markers underlying high likelihood of postoperative neurocognitive improvements and 2) mechanistic insight in structural brain reorganization associated with postoperative verbal IQ improvement. A series of preoperative imaging markers called "local efficiency" that quantifies how efficiently neural connection is shared by neighboring regions will be evaluated at the levels of specific modular networks. We expect that these markers can identify long-term and specific neurocognitive consequences (and potential predictors of these) associated with surgical intervention and their neural correlates for specific neurocognitive functions. In addition, neuronal remodeling associated with a functional crowding effect, studied with DWMRI connectome improved by the DCNN tract classification, will provide a new mechanistic insight in compensatory processes for verbal IQ function in children and adolescents who undergo resective surgery to treat drug-resistant focal epilepsy.
- Conditions
- Focal Epilepsy
- Being tested
- Brain magnetic resonance imaging, Neuro-psychology testing
- Medical subject headings
- Epilepsies, Partial
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Brain magnetic resonance imagingDiagnostic test
Also called MRI
Brain magnetic resonance imaging (MRI) will be done using multiple sequences to evaluate the presence, type, and severity of brain abnormalities in enrolled subjects.
Neuro-psychology testingBehavioural
Also called neuro-cognitive testing, neuro-psychology
Participants will undergo age-appropriate neuro-psychology testing to assess motor, language, and other neurocognitive functions potentially affected by drug-resistant epilepsy.
| Study group | Role | What they receive |
|---|---|---|
| Patients with drug-resistant epilepsy | Experimental | All patients who undergo two-stage epilepsy surgery will receive two longitudinal evaluations of brain MRI and neuropsychology test: a month before surgery and 1.5 years after surgery. Receives: Diagnostic Test: Brain magnetic resonance imaging; Behavioral: Neuro-psychology testing |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of DCNN tract classification for detection of ESM-defined eloquent white matter pathways in healthy controls
Spatial overlap of DCNN tract classification (range: 0-100%, 0 indicating no overlap and 100% indicating complete overlap) will be evaluated between two different DWMRI scans of healthy controls: single-shell and generalized Q-sampling imaging (GQI) that are acquired on the same day. 14 ESM-defined eloquent pathways will be obtained using 14 DCNN tract classifications from the single-shell and GQI data, and the spatial overlap between single shell and GQI data (score: %) will be assessed per each pathway.
Measured: During procedure
Accuracy of DCNN tract classification for detection of ESM-defined eloquent area that will be acquired a month after the DCNN tract classification in children with drug-resistant epilepsy
Spatial overlap (range: 0-100%, 0 indicating no overlap and 100% indicating complete overlap) will be measured between cortical terminals of DCNN-classified white matter pathways and their ground truth data: ESM-defined eloquent areas that will be acquired a month after the DCNN tract classification.
Measured: 1 month
Accuracy of DCNN tract classification for prediction of eloquent white matter pathways providing no postoperative deficits that will be assessed at 1.5 years after surgery
Preservation (score: 1) vs. no preservation (score: 0) of preoperative DCNN-classified white matter pathways will be compared with presence (score: 1) vs. absence (score: 0) of postoperative deficits in primary motor, language, auditory, and visual functions that will be assessed at 1.5 years after surgery.
Measured: 1.5 years
Accuracy of DCNN tract classification combined with Kalman analysis to predict optimal margin balancing maximal seizure freedom and minimal functional deficits that will be assessed at 1.5 years after surgery
Preservation (score: 1) vs. no preservation (score: 0) of preoperative DCNN-Kalman filter predicted surgical margin will be compared with presence (score: 1) vs. absence (score: 0) of postoperative deficits and seizure freedom that will be assessed at 1.5 years after surgery.
Measured: 1.5 years
Strength of association between local efficiency of preoperative network and functional measure: full-scale IQ, verbal-IQ, non-verbal IQ, expressive language, receptive language, and motor function that will be assessed at 1.5 years after surgery
Local efficiency value (range: 0-1, 0 indicating no efficacy and 1 indicating the strongest efficacy) will be evaluated from full-scale IQ network, non-verbal IQ network, verbal IQ network, expressive language network, receptive language network, and motor network of preoperative DWMRI connectome data, respectively. Full-scale IQ (normal mean: 100, standard deviation: 15), verbal IQ (normal mean: 100, standard deviation: 15), non-verbal IQ (normal mean: 100, standard deviation: 15), expressive language score (normal mean: 50, standard deviation: 10), receptive language score (normal mean: 50, standard deviation: 10), and motor score (normal mean: 50, standard deviation: 10) will be also evaluated from neuro-psychology testing at 1.5 years after surgery. The correlation coefficient (range: 0-1, 0 indicating no correlation and 1 indicating complete correlation) will be evaluated between local efficiency and neuro-psychology score measured for each corresponding function.
Measured: 1.5 years
Strength of association between local efficiency of preoperative full-scale IQ network and epilepsy duration that will be assessed at the time of preoperative MRI (Hypothesis 2.2)
Full-scale IQ (normal mean: 100, standard deviation: 15) will be assessed at the time of preoperative MRI scan. It will be associated with local efficiency (range: 0-1, 0 indicating no efficacy and 1 indicating the strongest efficacy) of preoperative full-scale IQ network and epilepsy duration (range: 0-19 years) that will be assessed within 1 day of preoperative MRI scan. The correlation coefficient (range: 0-1, 0 indicating no correlation and 1 indicating a perfect correlation) will be evaluated between full-scale IQ and local efficiency of preoperative full-scale IQ network.
Measured: Within 1 day
Strength of association between local efficiency change of contralateral verbal-/non-verbal IQ network and verbal-/non-verbal IQ change that will be measured between 1 month before surgery and 1.5 years after surgery
Longitudinal change of local efficiency in contralateral verbal-/non-verbal IQ network (range: -1 - +1, -1 indicating a complete loss of local efficiency after surgery and +1 indicating a complete gain of local efficiency after surgery) will be measured from postoperative and preoperative DWMRI connectome data that will be measured between 1 month before surgery and 1.5 years after surgery, respectively. It will be then correlated with the longitudinal change of verbal/non-verbal IQ (range: -100 - +100, -100 indication a complete loss of verbal/non-verbal IQ after surgery and +100 indicating a complete improvement of verbal/non-verbal IQ after surgery) that will be measured between 1 month before surgery and 1.5 years after surgery. The correlation coefficient (range: 0-1, 0 indicating no correlation and 1 indicating a perfect correlation) will be calculated between two longitudinal changes.
Measured: 1.5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Aug 2021
- Study start1 Aug 2021
- Primary completion30 Jun 2026 (estimated)
- Study completion30 Jun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Focal EpilepsyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 60 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Wayne State University
- Sponsor type
- University, hospital or other
- Collaborators
- National Institutes of Health (NIH)
- Responsible party
- Principal investigator, Justin Jeong-Won Jeong, Wayne State University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04986683
- Sponsor's protocol ID
- Pediatric epilepsy DWI
- Status last verified
- Jul 2024
- First submitted
- 22 Jul 2021
- Last updated
- 23 Jul 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
Study at a glance
From the registry record, last updated by the sponsor on 23 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Novel DWI Methods to Minimize Postoperative Deficits in Pediatric Epilepsy Surgery
- ClinicalTrials.gov ID
- NCT04986683
- Acronym / sponsor ID
- Pediatric epilepsy DWI
- Conditions
- Focal Epilepsy
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 60 participants (estimated)
- Lead sponsor
- Wayne State UniversityUniversity, hospital or other
- Collaborators
- National Institutes of Health (NIH)
- Study start
- 1 Aug 2021 (actual)
- Primary completion
- 30 Jun 2026 (estimated)
- Study completion
- 30 Jun 2026 (estimated)
- First posted
- 3 Aug 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This project will test the accuracy of a novel diffusion-weighted magnetic resonance imaging (DWMRI) approach using a deep convolutional neural network (DCNN) to predict an optimal resection margin for pediatric epilepsy surgery objectively. Its primary goal is to minimize surgical risk probability (i.e., functional deficit) and maximize surgical benefit probability (i.e., seizure freedom) by precisely localizing eloquent white matter pathways in children and adolescents with drug-resistant focal epilepsy. This new imaging approach, which will acquire a DWMRI scan before pediatric epilepsy surgery in about 10 minutes without contrast administration (and also without sedation even in young children), can be readily applied to improve preoperative benefit-risk evaluation for pediatric epilepsy surgery in the future. The investigators will also study how the advanced DWMRI-DCNN connectome approach can detect complex signs of brain neuronal reorganization that help improve neurological and cognitive outcomes following pediatric epilepsy surgery. This new imaging approach could benefit targeted interventions in the future to minimize neurocognitive deficits in affected children. All enrolled subjects will undergo advanced brain MRI and neurocognitive evaluation to achieve these goals. The findings of this project will not guide any clinical decision-making or clinical intervention until the studied approach is thoroughly validated.
Detailed description
This project will combine advanced brain MRI with detailed neuro-psychology evaluation, performed in children and adolescents affected by drug-resistant focal epilepsy, to address two main aims, each of them with the following research hypotheses:
AIM 1. To determine the accuracy of deep learning tractography-based benefit-risk analysis compared to a standard electrical stimulation mapping (ESM) which is the current clinical standard for detecting eloquent cortical regions before epilepsy surgery.
Hypothesis 1.1 In healthy controls, DCNN-based tract classification will localize eloquent cortices, which are significantly overlapped at both single shell DWI acquisition and generalized Q-sampling imaging, suggesting that the accuracy of this approach may not be significantly affected by the acquisition protocol.
Hypothesis 1.2 DCNN-based tract classification will achieve at least 93% accuracy for prospective detection of ESM-defined eloquent cortices, including patients with a high likelihood of functional reorganization.
Hypothesis 1.3 Preservation of DCNN-classified eloquent white matter pathways during surgery will predict the avoidance of postoperative deficits as accurately as the preservation of ESM-defined eloquent cortex.
Hypothesis 1.4 Preservation of surgical margins optimized by Kalman filter on retrospective data will achieve seizure control and avoidance of postoperative deficits in a prospective surgical patient cohort.
In this aim, the investigators will test the accuracy of a recently developed deep learning-based benefit-risk model, called deep convolutional neural network (DCNN) tract classification combined with Kalman filter analysis, for non-invasive detection of eloquent white matter pathways and optimization of surgical margin (i.e., the distance between epileptogenic area and eloquent area), resulting in seizure freedom and avoidance of functional deficits. Failure to identify eloquent areas in the proposed resection region can have potentially lifelong consequences, and overestimation or incorrect localization of the extent of the eloquent regions may lead to incomplete resection of the epileptogenic zone. Without optimizing the benefit-risk ratio, the minimum acceptable margin is highly variable across different settings, ranging from 0 to 2 cm across epilepsy surgery centers. The investigators will study whether the proposed benefit-risk model can standardize (or customize) epilepsy surgery of individual patients by accurately optimizing the margins of the eloquent white matter pathways to be preserved, which is ultimately essential to balance the benefit of seizure freedom with the risk of functional deficit. This proposed new imaging approach could change clinical practice for pediatric epilepsy surgery and is widely applicable for other types of neurosurgical procedures such as tumor resection.
AIM 2. To determine the accuracy of deep learning-based connectome analysis for prediction of long-term neurocognitive improvement following epilepsy surgery.
Hypothesis 2.1 Connectivity efficiencies preserved in specific modular networks of preoperative DCNN-based connectome, found to be associated with postoperative functional improvement on retrospective data, will accurately predict long-term functional improvement in a prospective patient cohort.
Hypothesis 2.2 Longer epilepsy duration will be significantly associated with more decreased efficiency in full-scale IQ modular network of preoperative DCNN-based connectome, thus suggesting that earlier surgery will yield better long-term full-scale IQ improvement.
Hypothesis 2.3 Patients with ipsilateral resections, who show signs of postoperative "crowding" (i.e., verbal IQ improvement at the expense of non-verbal function), will show decreased efficiency in non-verbal and increased efficiency in verbal IQ network of DCNN-based connectome in the contralateral hemisphere.
In this aim, the investigators will test if an advanced DWMRI approach integrating DCNN and connectome helps decide timely surgery by providing 1) preoperative imaging markers underlying high likelihood of postoperative neurocognitive improvements and 2) mechanistic insight in structural brain reorganization associated with postoperative verbal IQ improvement. A series of preoperative imaging markers called "local efficiency" that quantifies how efficiently neural connection is shared by neighboring regions will be evaluated at the levels of specific modular networks. We expect that these markers can identify long-term and specific neurocognitive consequences (and potential predictors of these) associated with surgical intervention and their neural correlates for specific neurocognitive functions. In addition, neuronal remodeling associated with a functional crowding effect, studied with DWMRI connectome improved by the DCNN tract classification, will provide a new mechanistic insight in compensatory processes for verbal IQ function in children and adolescents who undergo resective surgery to treat drug-resistant focal epilepsy.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients with drug-resistant epilepsy | Experimental | Diagnostic Test: Brain magnetic resonance imaging, Behavioral: Neuro-psychology testingAll patients who undergo two-stage epilepsy surgery will receive two longitudinal evaluations of brain MRI and neuropsychology test: a month before surgery and 1.5 years after surgery. |
Who may qualify
- Ages
- 3 to 19 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult
Key inclusion criteria (3 listed)
- Subjects with drug-resistant focal epilepsy
- 1. Age 3-19 years. 2. Planned two-stage epilepsy surgery with subdural electrodes.
- Healthy control subjects 1. Age 5-19 years. 2. No cognitive, motor, and/or language impairment or clinical elevations on a measure of behavioral problems. 3. Brain MRI interpreted as normal.
Key exclusion criteria (2 listed)
- 1. History of prematurity or perinatal hypoxic-ischemic event. 2. Hemiplegia on preoperative neurological examination by pediatric neurologists. 3. Dysmorphic features suggestive of a clinical syndrome. 4. Diagnosis of any pervasive developmental or psychiatric condition which clearly predates the onset of seizures, including autism spectrum disorder, tic disorders, obsessive-compulsive disorder. 5. MRI abnormalities showing massive brain malformation and other extensive lesions that likely destroyed the contralateral tracts and severely affected i) spatial normalization accuracy in advanced normalization tools (ANTs), mutual information (MI) between native T1- MRI of Geodesic SyN transform and template T1-MRI < mean-3standard deviation of MI in the healthy control group and ii) parcellation accuracy in surface-matching-based deformable registration, target registration error (TRE) of fine tetrahedra mesh between native T1- MRI brain surface and template T1-MRI brain surface > mean-3standard deviation of TRE in the healthy control group. 6. History of claustrophobia. 7. Unsuccessful MRI showing head motion > 2 mm in DWMRI (i.e., voxel size of DWMRI) which is evaluated by NIH TORTOISE DWMRI motion artifact correction package. 8. Subject who cannot speak English.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Brain magnetic resonance imagingAlso: MRIBrain magnetic resonance imaging (MRI) will be done using multiple sequences to evaluate the presence, type, and severity of brain abnormalities in enrolled subjects. | Diagnostic test | Patients with drug-resistant epilepsy |
| Neuro-psychology testingAlso: neuro-cognitive testing, neuro-psychologyParticipants will undergo age-appropriate neuro-psychology testing to assess motor, language, and other neurocognitive functions potentially affected by drug-resistant epilepsy. | Behavioural | Patients with drug-resistant epilepsy |
Primary outcome
Accuracy of DCNN tract classification for detection of ESM-defined eloquent white matter pathways in healthy controls
Time frame: During procedure
Spatial overlap of DCNN tract classification (range: 0-100%, 0 indicating no overlap and 100% indicating complete overlap) will be evaluated between two different DWMRI scans of healthy controls: single-shell and generalized Q-sampling imaging (GQI) that are acquired on the same day. 14 ESM-defined eloquent pathways will be obtained using 14 DCNN tract classifications from the single-shell and GQI data, and the spatial overlap between single shell and GQI data (score: %) will be assessed per each pathway.
Accuracy of DCNN tract classification for detection of ESM-defined eloquent area that will be acquired a month after the DCNN tract classification in children with drug-resistant epilepsy
Time frame: 1 month
Spatial overlap (range: 0-100%, 0 indicating no overlap and 100% indicating complete overlap) will be measured between cortical terminals of DCNN-classified white matter pathways and their ground truth data: ESM-defined eloquent areas that will be acquired a month after the DCNN tract classification.
Accuracy of DCNN tract classification for prediction of eloquent white matter pathways providing no postoperative deficits that will be assessed at 1.5 years after surgery
Time frame: 1.5 years
Preservation (score: 1) vs. no preservation (score: 0) of preoperative DCNN-classified white matter pathways will be compared with presence (score: 1) vs. absence (score: 0) of postoperative deficits in primary motor, language, auditory, and visual functions that will be assessed at 1.5 years after surgery.
Accuracy of DCNN tract classification combined with Kalman analysis to predict optimal margin balancing maximal seizure freedom and minimal functional deficits that will be assessed at 1.5 years after surgery
Time frame: 1.5 years
Preservation (score: 1) vs. no preservation (score: 0) of preoperative DCNN-Kalman filter predicted surgical margin will be compared with presence (score: 1) vs. absence (score: 0) of postoperative deficits and seizure freedom that will be assessed at 1.5 years after surgery.
Strength of association between local efficiency of preoperative network and functional measure: full-scale IQ, verbal-IQ, non-verbal IQ, expressive language, receptive language, and motor function that will be assessed at 1.5 years after surgery
Time frame: 1.5 years
Local efficiency value (range: 0-1, 0 indicating no efficacy and 1 indicating the strongest efficacy) will be evaluated from full-scale IQ network, non-verbal IQ network, verbal IQ network, expressive language network, receptive language network, and motor network of preoperative DWMRI connectome data, respectively. Full-scale IQ (normal mean: 100, standard deviation: 15), verbal IQ (normal mean: 100, standard deviation: 15), non-verbal IQ (normal mean: 100, standard deviation: 15), expressive language score (normal mean: 50, standard deviation: 10), receptive language score (normal mean: 50, standard deviation: 10), and motor score (normal mean: 50, standard deviation: 10) will be also evaluated from neuro-psychology testing at 1.5 years after surgery. The correlation coefficient (range: 0-1, 0 indicating no correlation and 1 indicating complete correlation) will be evaluated between local efficiency and neuro-psychology score measured for each corresponding function.
Strength of association between local efficiency of preoperative full-scale IQ network and epilepsy duration that will be assessed at the time of preoperative MRI (Hypothesis 2.2)
Time frame: Within 1 day
Full-scale IQ (normal mean: 100, standard deviation: 15) will be assessed at the time of preoperative MRI scan. It will be associated with local efficiency (range: 0-1, 0 indicating no efficacy and 1 indicating the strongest efficacy) of preoperative full-scale IQ network and epilepsy duration (range: 0-19 years) that will be assessed within 1 day of preoperative MRI scan. The correlation coefficient (range: 0-1, 0 indicating no correlation and 1 indicating a perfect correlation) will be evaluated between full-scale IQ and local efficiency of preoperative full-scale IQ network.
Strength of association between local efficiency change of contralateral verbal-/non-verbal IQ network and verbal-/non-verbal IQ change that will be measured between 1 month before surgery and 1.5 years after surgery
Time frame: 1.5 years
Longitudinal change of local efficiency in contralateral verbal-/non-verbal IQ network (range: -1 - +1, -1 indicating a complete loss of local efficiency after surgery and +1 indicating a complete gain of local efficiency after surgery) will be measured from postoperative and preoperative DWMRI connectome data that will be measured between 1 month before surgery and 1.5 years after surgery, respectively. It will be then correlated with the longitudinal change of verbal/non-verbal IQ (range: -100 - +100, -100 indication a complete loss of verbal/non-verbal IQ after surgery and +100 indicating a complete improvement of verbal/non-verbal IQ after surgery) that will be measured between 1 month before surgery and 1.5 years after surgery. The correlation coefficient (range: 0-1, 0 indicating no correlation and 1 indicating a perfect correlation) will be calculated between two longitudinal changes.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Jul 2024.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Brain magnetic resonance imaging and Neuro-psychology testing in people with Focal Epilepsy, run by Wayne State University at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 23 Jul 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Justin J Jeong, PhD (313-993-0258, jjeong@med.wayne.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of DCNN tract classification for detection of ESM-defined eloquent white matter pathways in healthy controls (time frame: During procedure). The study lists 7 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 23 Jul 2024.