Patent Application
20240099602
The present disclosure generally relates to diagnosis and treatment of epilepsy including imaging techniques and computer-implemented evaluation; and more particularly, to systems and methods for an automated seizure onset zone locator from resting state functional MRI for drug resistant epilepsy.
Epilepsy is devastating, affecting 50 million worldwide (WHO). One in 150 children, 30% have drug resistant epilepsy (DRE) and causes significant morbidity and mortality. A consensus proposal by the ad hoc Task Force of the International League Against Epilepsy (ILAE) proposed the following definition for DRE: “a failure of adequate trials of two tolerated, appropriately chosen and used antiepileptic drug schedules (whether as monotherapies or in combination) to achieve sustained seizure freedom (considered as freedom from all seizures, including auras) for at least 12 months.”
Early diagnosis and treatment of DRE can potentially avoid complications such as evolution into status epilepticus, and Sudden Unexplained Death in Epilepsy (SUDEP), wherein the individual dies due to cardio-respiratory failure from presumed nocturnal seizure activity. Moreover, in children, timely diagnosis, intensive management and treatment are pivotal in minimizing neurological damage. Further, the earliest onset of severe epilepsy in the neonatal population can lead to nearly constant life-threatening seizures requiring urgent need for surgical evaluation early in life.
It is with these observations in mind, among others, that various aspects of the present disclosure were conceived and developed.
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Corresponding reference characters indicate corresponding elements among the view of the drawings. The headings used in the figures do not limit the scope of the claims.
The present disclosure relates to systems and methods for accurate localization of seizure onset zone (SOZ) from independent components (IC) of resting state functional magnetic resonance imaging (rs-fMRI) to improve surgical outcomes in children with Drug Resistant Epilepsy (DRE). Automated IC sorting has limited success in identifying SOZ localizing ICs in adult normal rs-fMRI or uncategorized epilepsy. Children provide unique challenges due to the developing brain and associated surgical risks. The present concept proposes a novel SOZ localization algorithm (EPIK) for children with DRE.
Methods: EPIK is developed in a phased approach, where fMRI noise-related biomarkers are used through high fidelity image processing techniques to eliminate noise ICs. Then SOZ markers are used through a maximum likelihood-based classifier to determine SOZ localizing ICs. The performance of EPIK was evaluated on a unique pediatric DRE dataset (n=52). 24 children underwent surgical resection or ablation of rs-fMRI identified SOZ, concurrently evaluated with EEG and anatomical MRI. Two state-of-art techniques are used for comparison: a) least squares support vector machine, and b) Convolutional Neural Networks. The performance was benchmarked against expert IC sorting and Engel outcomes for surgical SOZ resection or ablation. Analysis is stratified across age and sex.
Results: EPIK outperforms state-of-art techniques for SOZ localizing IC identification with mean accuracy of 84.7% (4% higher), precision 74.1% (22% higher), specificity 81.9% (3.2% higher) and sensitivity 88.6% (16.5% higher). EPIK provides consistent performance across age and sex with best performance in <5-year age. It achieves a -5-fold reduction in the number of ICs to be potentially analyzed during pre-surgical screening.
Significance: Automated SOZ localization from rs-fMRI, validated against surgical outcomes, indicates potential for clinical feasibility. It eliminates the need for expert sorting, outperforms prior automated methods and is consistent across age, and sex.
As indicated above, epilepsy, and DRE in particular, is life-altering for most affected. Various conventional approaches for dealing with these afflictions shall now be described.
Surgery for DRE: The most effective treatment for DRE is surgery. Early surgery is key: “minimally invasive surgical treatment can be a life-changing option for DRE patients: hence management of SOZ requiring disconnecting techniques, or deep sited lesions requiring excision should be considered earlier rather than later”. Notably, recent findings show that ultra-early (before 3 months old) surgical intervention in children evaluated to be DRE after trials of an average of 4 anti-seizure drugs, although seldom performed, has excellent epilepsy outcomes and leads to decrease in usage of anti-seizure drugs, without any increased risk of surgery related permanent morbidity.
Brain Imaging for Pre-surgical screening: Surgical intervention in DRE requires accurate localization of the seizure onset zone (SOZ) for success. Here we make a distinction between epileptic network (EN) and SOZ. EN denotes regions where seizure propagates and may be more extensive than the SOZ. As such it may be difficult as well as unnecessary to surgically eliminate EN since they can incorporate sensitive areas of the brain. Several brain imaging techniques has been explored to identify ictal seizure onset zone (SOZ), propagation zone (i.e., EN), and interictal activity (Table 1, below). This can be done with nuclear medicine-based imaging techniques such as positron emission tomography (PET) or ictal single-photon emission computerized tomography (SPECT). Recent works suggest some SOZ identification capability for PET and SPECT in both adults and children, however, accuracy heavily depends on the timing of the scan. Delay in drug infusion can result in detection of EN instead of SOZ. Invasive modalities such as intracranial EEG (iEEG) is considered as gold standard for SOZ identification and has shown excellent accuracy for both adults and pediatric DRE. Stereo-electroencephalography (SEEG) is minimally invasive which uses a three-dimensional configuration of depth electrodes to localize epileptiform activity and has shown some SOZ identification capability recently.
However, traditional analysis of PET, SPECT, or SEEG is relatively temporally and spatially restricted, whereas functional interpolation of brain activity might allow for noninvasive three-dimensional representation of epileptiform activity and avoid pitfalls inherent of other modalities (Table 1). Recently, magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) based noninvasive techniques have been analyzed for DRE in both adults and children and show descent SOZ identification capability. A combination of MEG and fMRI imaging is also proposed for accurate SOZ identification. However, a major drawback of such brain imaging based SOZ identification techniques is the heavy reliance on manual sorting of images and their components, which not only increases cost but also reduces accessibility and repeatability.
Unfortunately, less than 1% of DRE patients are evaluated for surgery and only 25% of those undergo surgery, part due to the diagnostic and surgical treatment high expensive (>$200,000/patient) and risk of debilitating impairment. Of the 1% evaluated, surgical failure rates are 30-70% despite the use of noninvasive SOZ-localization biomarkers such as anatomical MRI, scalp EEG, simultaneous EEG-fMRI, and magnetoencephalography which are then often confirmed by invasive iEEG. Hence, for surgery to be safe and efficient for wide acceptance, accessible, minimally invasive and accurate SOZ localization is essential.
One of the newer methods showing promise, to this end, is resting state functional MRI (rs-fMRI). Rs-fMRI has shown accurate SOZ-localization capacity by various analysis approaches, but only independent component analysis (ICA) has provided Level 1 evidence and has led to improvement in surgical outcomes and candidacy in DRE. However, expert interpretation of independent components (IC) into sources of noise, normal resting state networks (RSN), and SOZs, limits reproducibility and availability. An automated whole-brain data-driven SOZ-localizing IC identification technique that is rigorously validated against surgical destruction outcomes, reproducible, equally effective across age and sex and epilepsy subtype may greatly improve epilepsy care feasibility, morbidity, and mortality.
fMRI based screening: Referring to flow diagram 100 of
Rs-fMRI ICA results in approximately 100 ICs. Each IC is a spatial-temporal distribution of regions of synchronous activity. In ICA of those with DRE, there are three IC categories: 1) RSNs which are well described and validated in the literature; 2) SOZ, currently, highly dependent on expert sorting; and 3) noise, which is also well understood, resulting from cardiovascular, cerebral-spinal-fluid-pulsation, or scanner artifacts (as described in Boerwinkle et al. 2017 for details and examples). In standard rs-fMRI based pre-surgical screening for DRE children, the entire set of ICs is analyzed by a neurosurgeon or neurologist to determine which ICs capture blood oxygenation changes due to seizure onset. Such an IC is referred to as a “SOZ localizing IC”. The neurosurgeon then determines the location of seizure onset in the brain using the SOZ localizing IC and a recommendation for surgical procedure such as resection or ablation or neurostimulation is made.
Given that ICA results in >100 ICs and only <10% are SOZ localizing ICs, manual sorting of rs-fMRI ICs to search for SOZ localizing IC is a significant time commitment by the neurosurgeon resulting in increased cost, reduces availability, and higher chance of false positives (
Automation of fMRI-based screening: Recent works have considered two automation objectives with rs-fMRI (Table 2): a) classification of subjects with or without epilepsy by identifying epilepsy networks using rs-fMRI BOLD signal z score latency maps, and b) localization of seizure onset zone using rs-fMRI ICs. Epilepsy networks indicate the areas of the brain that are affected by the propagation of seizure. As such they may not indicate the origin of the seizure, which is encapsulated by SOZ. Our work in this paper, tackles the second automation objective of SOZ localization.
Automated classification of rs-fMRI ICs as SOZ or RSN has been explored using supervised shallow machine learning (ML), and using deep learning (DL) in healthy adults to identify the typical RSNs, and is yet to be tested in epilepsy (Table 2 below). Supervised ML indicates that the DRE population has to be divided into two parts: a) training set, which is used to configure the ML, and b) testing set, which is used to test the performance of the ML. Some supervised ML can also choose to utilize a validation set. The performance of the ML technique on the validation set is used to update the training process and improve the performance in the validation set. Hence, the performance on the validation set is excluded from the analysis in Table 2 and only the test set performance is reported. Recent automated methods to classify adult rs-fMRI into RSN, SOZ and noise ICs are of three types: 1) Voxel-based network measures quantifying the number of connections to each voxel in an IC, called voxel degree connectivity (VDC), as indicators for SOZ. Such approaches have small sample size (n≈20) and show maximum reported sensitivity of 77% and specificity of 57% (Table 2); 2) ML based classification, with a sensitivity of 40% and specificity of 77%; and 3) DL approaches for only identifying RSN and noise, but not SOZs, for normal and non-DRE epileptic patients (accuracy 92% in Table 2).
To date, automated approaches have not been successful in classification of RSN, noise, and SOZ, in rs-fMRI for pediatric DRE patients due to challenges including: 1) Lack of normalized pathological rs-fMRI RSN data for children; 2) Databases with balanced instances of RSN, SOZ and noise, large enough for DL techniques to effectively recognize the three IC categories are not available; 3) Potentially inadequate performance of SOZ identification in children with DRE can indicate high risk of developmental disorders post-surgery. Given that each patient only has 5% ICs as SOZ, a 40% sensitivity means only two out of five SOZ ICs are correctly identified, while 14 are wrongly identified as SOZ; and 4) fMRI-based pre-surgical mapping is more complicated for children with DRE due to developmental changes during cognitive maturation, the impairment experienced due to DRE and the normal representation of memory function during development, which may differ from adults. Hence, the efficacy of fMRI classification techniques on adults needs to be reexamined for children with DRE.
tEpileptic Networks were those found to be altered compared to healthy controls, however, were not identified as being causative of epilepsy or a seizure onset zone.
Most current studies (Table 2) focus on adult epilepsy with unknown effect of the degree of hypothesized network disruption effect on localization. Currently available automated IC sorting techniques either only identify SOZ or RSN localizing ICs. Hunyadi et al., the first major work to attempt SOZ localizing ICs identification, used supervised ML but could only achieve a specificity of 77% and sensitivity of 40% on a subset of adult patient population. A more recent technique by Nozais et al. used DL to identify only RSN in healthy adults and reports an accuracy of 92%. The major drawback of DL techniques is the requirement for labelled data on all three IC categories. Table 2 shows that such labelled data is rarely available, even if we combine datasets from different authors, IC data labelled as RSN and SOZ is only available from 212 DRE children. For DL to successfully recognize SOZ it will need at least a balanced distribution of RSN and SOZ. The DL works in this domain utilize RSN data from 2000 healthy subjects for appropriate training (Table 2 Nozais et al and Luckett et al). Hence to achieve balanced data, we would need SOZ from at least 2000 subjects which is not available currently.
There has been one prior unsupervised approach by Zhang et al., however, it was applied to DRE adults and achieved a sensitivity of 78% and specificity of 57%. We cannot replicate that study for this paper, because specific information about parameter settings were not discussed in Zhang et al.
Difference between EPIK and supervised ML: Referring to
In other words, EPIK (ExPert Knowledge based IC categorization) (
To illustrate differences compared to prior ML approaches, we replicate shallow (Borbala Hunyadi, 2014) and implement a Convolution Neural Network (CNN) based DL technique for identification of SOZ localizing ICs from rs-fMRI thereby providing a preliminary comparative study of all three approaches on the same dataset of children with DRE using the standard metrics of accuracy, precision, specificity, and sensitivity. It is hypothesized that EPIK will perform at least equally well as prior methods and consistently across age and sex, due to being informed by developmental and sex informed expert sorting in the pediatric DRE population.
Patients who were determined to be DRE by a treating epileptologist and received surgery evaluation. Most of the patients had focal epilepsy, however, rapid generalization of epileptiform activity from an epileptogenic focus may appear to be generalized epilepsy when evaluated using surface EEG. Hence generalized epilepsy was not an exclusion criterion.
Data Collection method: Referring to the flow diagram 300 of
As part of standard of care the children also received inpatient video EEG and anatomical MRI. This data also aided the manual identification of SOZ localizing ICs in rs-fMRI (
rs-fMRI pre-processing: Oxford Centre FMRIB (Functional MRI of the Brain) Software Library tool MELODIC, was used to analyze the rs-fMRI and extract ICs as detailed in. Pre-processing included deletion of the first 5 volumes to remove T1 saturation effects, passing through a high-pass filter at 100 seconds, slice time correction, spatial smoothing of 1-mm full-width at half maximum, and motion corrected by MCFLIRT, with nonbrain structures removed.
Linear registration was performed between the individual functional scans and patient's high-resolution anatomical scan which was further optimized using boundary-based registration. Individual rs-fMRI data sets then underwent independent component analysis (ICA) as previously reported.
Expert RS-fMRI evaluation methodology: The SOZ were evaluated by the expert epilepsy surgery conference team and deemed to be consistent with the other acquired data (video EEG and anatomical MRI) with high enough evidence to surgically target the SOZ. Further, the confirmation that the SOZ was deemed true by the treatment team as evidenced by the Engel I and II scores 1 year post-operatively.
The ICA results were viewed by two blinded reviewers (1 neurologist and 1 neurosurgeon) and sorted the ICs into 3 categories—noise, resting-state network, and rs-fMRI SOZ—by the criteria below. In case of disagreement between the first two reviewers, the opinion of a third reviewer (a neurologist) was used to make the final determination.
Rs-fMRI was categorized into noise, resting state network (RSN) and SOZ using the following criteria:
Spatial features: The activation should, and in some examples must be located within the gray matter, while not overlapping with the RSN spatial patterns. It must have a bullseye pattern, where two or more overlapping abnormal neuronal IC can be identified. May have alternating activation and deactivation pattern that does not overlap noise zones shown in Table 4. May extend to ventricles through white matter. May have irregular borders.
Temporal features: The SOZ BOLD signal power spectra must contain dominant frequencies greater than 0.073 Hz, the rs-fMRI SOZ must have power spectra at higher frequencies than RSN, the BOLD time series may have irregular patterns.
The rs-fMRI IC were expert sorted and reported to the clinical epilepsy surgery evaluation team. The data includes ICs extracted using the MELODIC module in FSL. Table 3 shows the age and sex distribution and surgical outcome statistics.
Referring to the sample method 400 of
Each IC has multiple slices (around 55 for PCH dataset). The second stage classifier sorts the slices in decreasing order of cluster size (lower panel of
The main aim of the likelihood-based classification step is a standard technique in ML. We want to obtain a 1×4 weight vector ω, such that exi19 ω=ρi corresponds to the likelihood that the largest cluster in IC i is the SOZ. To obtain such a weight vector we solve the following optimization problem:
It should be understood that the above weight configurations are merely exemplary, and the inventive concept can include other weight amounts; i.e., other classifier configurations are contemplated. In addition, the classifier as described can be one of one or more machine learning approaches implemented as part of an artificial intelligence mode (AI model). As such, other AI approaches including different machine learning models may be implemented for identification of SOZ ICs.
Nozais et al. recently proposed a DL based technique where a multi-layer perceptron (MLP) is trained on 12690 RSNs from 282 participants. As such, it does not incorporate any expert knowledge but instead attempts to build its own hypothesis from examples. The technique has not been used to classify SOZ and can currently only identify RSNs. We have implemented CNN based DL for SOZ localization.
For the CNN technique, hyperparameter tuning is one of the most important steps. A Keras-Tuner was implemented to get the optimal values of the hyperparameters. We used hyperband algorithm with the objective of least validation loss to select the best model of CNN by optimizing the following hyperparameters:
4212 ICs were used for training and 1404 ICs were used for validation in the hyperparameter tuning process. The input shape of the IC image was down sampled from 1006×709×3 to 270×400×3 during preprocessing. Binary cross-entropy was used as a loss function and Adam was used as an optimizer. To avoid the overfitting problem, “dropout” and “early_stopping” strategies were implemented. “ReLU” being more computationally efficient was used as an activation function for the input and hidden layers, and “Sigmoid” activation function was used for the output layer. For CNN, weights were initialized using “He uniform” initializer.
The technique proposed in Hunyadi et al. was replicated. rs-fMRI image and BOLD signal features were extracted from the IC images. 60% of the data from the entire pool of ICs was randomly sampled to be used as training data. The remaining 40% was used for testing. The features extracted from the rs-fMRI image and BOLD signal was used to train a Least Squares Support Vector Machine (LS-SVM) as described in Hunyadi et al.
We evaluated performance of each approach for two objectives: a) noise IC removal, and b) SOZ localizing IC identification. For the first objective, we define true positives (TP) as ICs that are classified as RSN or SOZ by both expert and the automated approach, true negatives (TN) as ICs that are classified as noise by both expert and the automated approach, false positives (FP) as ICs classified as noise by expert but RSN or SOZ by automated approach, and false negatives (FN) as ICs classified as RSN or SOZ by expert but noise by automated approach. For the second objective we define TP as ICs classified by both expert and automated approach as SOZ, TN as ICs classified by both expert and automated approach as not SOZ, FP as ICs classified as non-SOZ by expert but SOZ by automated approach, and FN as ICs classified as SOZ by expert but non SOZ by automated approach. From these we derived accuracy, precision, sensitivity, and specificity.
We evaluated the statistical significance of a difference in performance metrics between two approaches by utilizing a one-sided paired t test. The alternate hypothesis was that there is a positive non-zero difference between EPIK and any other approach (LS-SVM or CNN). The alternate hypothesis was rejected if the p-value for the paired t test was less than 0.05.
We also evaluated the effect of age and gender on each approach using a mixed effects model with each parameter as the observation variable and age or gender as the predictor variable. A random effect on the patient ID was also introduced. For each algorithm, a separate mixed effects model was generated for each metric and for each predictor variable age/gender.
We compared performance of EPIK with two competing ML based approaches: shallow learning (LS-SVM) and deep learning (CNN). For the ML based approach, training data was used from every subject. This is also known as user-dependent supervised classification approach and gave us the best performance metrics. For EPIK no such training set is needed. The results in Table 5 show that EPIK outperforms both LS-SVM and CNN approaches for SOZ localizing IC identification task. The CNN approach is more accurate in noise removal, however, performs poorly in the SOZ identification task.
EPIK has high sensitivity in the SOZ identification task with low number of FNs. This implies that EPIK rarely misses any SOZ localizing IC. The LS-SVM approach is poor in noise removal, but its performance improves for the SOZ identification task. The confidence interval is specified as [a,b] for metrics with p<0.5.
Table 6 shows the variation of the performance metrics for EPIK, LS-SVM and CNN with respect to age and gender. The accuracy, precision, and sensitivity of EPIK for noise removal does not have a statistically stable dependence on age or gender. The specificity of EPIK for noise removal decreases with age resulting in more FPs, where noise is categorized as RSN or SOZ. For the SOZ identification task, there is a statistically significant trend for sensitivity to increase and specificity to decrease with age. This implies that as age progresses EPIK tends to classify more RSN or noise as SOZ, however, less SOZs are ignored as noise. Consequently, EPIK is observed to have accuracy greater than 85% at ages below 5, which is higher than those previously reported.
The LS-SVM approach had consistently better performance for SOZ identification task than noise removal. It also had the same pattern of increasing sensitivity and decreasing specificity with age. The LS-SVM approach had higher variance in performance across subjects. This indicates that the hand-crafted features chosen in Hunyadi et al. may be less applicable to specific scenarios of DRE in children.
†Indicates that the result has a p value of less than 0.05 and is statistically significant.
indicates data missing or illegible when filed
CNN approach outperformed EPIK and LS-SVM for all age groups for noise removal. However, it had lower performance for SOZ identification. In the training data there were only 318 SOZ localizing ICs as opposed to 2427 RSN IC. This may have led to underfitting of CNN technique for SOZ identification. For CNN technique in noise removal, both sensitivity and specificity increased with age. This potentially indicates that CNN technique is finding novel hidden features from the ICs that are characteristic of RSN but not SOZ.
Overall, EPIK provided consistent performance across the three age categories considered in this study compared to prior reported methods. Whereas the ML techniques of Hunyadi and CNN have significantly higher variance, possibly indicating inconsistent performance.
Out of the 52 subjects considered in this study 24 underwent surgery. The surgical outcomes were varied with 16 becoming seizure free (Engel I) after surgically destroying expert identified SOZ using rs-fMRI and 7 having reduced post operative seizure frequency (Engel II) (Table 7). We focused on EPIK and LS-SVM for the SOZ identification task on the 24 subjects that underwent surgery, because CNN had significantly poorer performance than the other two.
Table 7 shows that, for subjects whose post operative outcomes are either seizure free or significantly reduced frequency, the agreement between EPIK and expert hand classification is significantly high (88.9% sensitivity and 79% specificity). Although LS-SVM approach has nearly similar accuracy as EPIK, Sensitivity is far lower in LS-SVM, with significant individual variance. To better understand the difference between EPIK and LS-SVM approach, the graph 600 of
For patients undergoing ablation surgery the specificity for EPIK was 82.9% while the sensitivity was 88%. This is preliminarily encouraging result given that ablation is minimally invasive and thus largely accepted as less risky than resection. The specificity and sensitivity in EPIK for patients undergoing resection reduces to 79.5% and 86.6% respectively. Ten out of 15 subjects with ablation procedure were seizure free (Engel 1 outcome), which is slightly better than recently reported statistics (66% in this study, versus. 60.4% reported in other studies).
48%
52%
82%
80%
76%
60%
77%
86%
88%
83%
40%
78%
75%
94%
50%
75%
25%
76%
84%
25%
72%
87%
50%
75%
70%
22%
50%
60%
50%
61%
45%
60%
25%
20%
87%
60%
98%
50%
98%
79% (14.4%)
The ICs marked as SOZ by the EPIK method can be supplied to the Neurosurgeon or neurologist for localization of SOZs in the brain. The number of SOZ classifications in EPIK per subject is 22 (±4). Out of which 16 are true positive SOZ ICs, 2 are noise ICs, and 4 are RSN ICs. These ICs are then evaluated by the neurosurgeon or neurologist for determining SOZ in the brain. This implies that there is ˜5 times reduction in the number of ICs to be analyzed by the neurosurgeon or neurologist. This can significantly aide in pre surgical screening by reducing the cognitive burden of the neurosurgeon or neurologist and improve accuracy of the SOZ identification.
A strength of EPIK, that may increase its utility, is that it does not require any prior training data and hence it is a plug-n-play IC sorting method. EPIK combines spatial and temporal markers specific for RSN and SOZ which results in possibly equivalent or better performance than prior methods. The waterfall technique removes the number of noise ICs using well established expert rules, hence may reduce false positives and increase true positives of SOZ localizing ICs.
For subjects with good post operative outcomes, there was excellent agreement between expert hand sorting and EPIK based SOZ localizing IC identification. Also, EPIK appeared to performance well in those less than 5 years of age, in whom surgery yields improved developmental outcomes.
The LS-SVM approach did not performs as well for the noise identification task, but drastically improves for SOZ identification task. This was expected because the hand selected features proposed by Hunyadi et al. are specifically geared towards the SOZ identification task. However, LS-SVM exhibits significant valiance in performance across subjects resulting in inconsistent accuracy in this study. EPIK had higher and consistent balance in identification of all three categories of IC compared to LS-SVM herein.
The CNN approach had the lower performance for SOZ identification. However, there was a significant improvement in the performance for the noise identification task. This can be explained by the difference in data availability for the two tasks. This gives confidence that CNN can perform better if given adequate number of training-SOZ-localizing ICs, an avenue for future research.
Subtypes of epilepsy-acquired, congenital/genetic, and surgical approaches success metrics should be statistically evaluated with acceptable power. Lastly, repeat studies in same individuals over time would increase knowledge of validity and reproducibility of the tool.
The majority of the subjects in this study received propofol infusion for sedation as part of standard of clinical care for epilepsy surgery evaluation. Head motion maximum was less than 1 mm framewise displacement in any direction. Although propofol use has minimal effect on the overall rs-fMRI BOLD signal, it puts small but additional risks on the child. Several research works have proposed alternate methods of reducing head motion, through engaging the child with videos and post processing by measuring and accounting for head movements through computational methods. An area of future study is to evaluate the effect of sedation on the EPIK SOZ identification accuracy and integration of live motion monitoring and reduction-based approaches towards elimination of head movement artifacts.
Referring to
The computing device 1200 may include various hardware components, such as a processor 1202, a main memory 1204 (e.g., a system memory), and a system bus 1201 that couples various components of the computing device 1200 to the processor 1202. The system bus 1201 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. For example, such architectures may include Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus also known as Mezzanine bus.
The computing device 1200 may further include a variety of memory devices and computer-readable media 1207 that includes removable/non-removable media and volatile/nonvolatile media and/or tangible media, but excludes transitory propagated signals. Computer-readable media 1207 may also include computer storage media and communication media. Computer storage media includes removable/non-removable media and volatile/nonvolatile media implemented in any method or technology for storage of information, such as computer-readable instructions, data structures, program modules or other data, such as RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to store the desired information/data and which may be accessed by the computing device 1200. Communication media includes computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. For example, communication media may include wired media such as a wired network or direct-wired connection and wireless media such as acoustic, RF, infrared, and/or other wireless media, or some combination thereof. Computer-readable media may be embodied as a computer program product, such as software stored on computer storage media.
The main memory 1204 includes computer storage media in the form of volatile/nonvolatile memory such as read only memory (ROM) and random access memory (RAM). A basic input/output system (BIOS), containing the basic routines that help to transfer information between elements within the computing device 1200 (e.g., during start-up) is typically stored in ROM. RAM typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processor 1202. Further, data storage 1206 in the form of Read-Only Memory (ROM) or otherwise may store an operating system, application programs, and other program modules and program data.
The data storage 1206 may also include other removable/non-removable, volatile/nonvolatile computer storage media. For example, the data storage 1206 may be: a hard disk drive that reads from or writes to non-removable, nonvolatile magnetic media; a magnetic disk drive that reads from or writes to a removable, nonvolatile magnetic disk; a solid state drive; and/or an optical disk drive that reads from or writes to a removable, nonvolatile optical disk such as a CD-ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media may include magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The drives and their associated computer storage media provide storage of computer-readable instructions, data structures, program modules, and other data for the computing device 1200.
A user may enter commands and information through a user interface 1240 (displayed via a monitor 1260) by engaging input devices 1245 such as a tablet, electronic digitizer, a microphone, keyboard, and/or pointing device, commonly referred to as mouse, trackball or touch pad. Other input devices 1245 may include a joystick, game pad, satellite dish, scanner, or the like. Additionally, voice inputs, gesture inputs (e.g., via hands or fingers), or other natural user input methods may also be used with the appropriate input devices, such as a microphone, camera, tablet, touch pad, glove, or other sensor. These and other input devices 1245 are in operative connection to the processor 1202 and may be coupled to the system bus 1201, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). The monitor 1260 or other type of display device may also be connected to the system bus 1201. The monitor 1260 may also be integrated with a touch-screen panel or the like.
The computing device 1200 may be implemented in a networked or cloud-computing environment using logical connections of a network interface 1203 to one or more remote devices, such as a remote computer. The remote computer may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computing device 1200. The logical connection may include one or more local area networks (LAN) and one or more wide area networks (WAN) but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet.
When used in a networked or cloud-computing environment, the computing device 1200 may be connected to a public and/or private network through the network interface 1203. In such embodiments, a modem or other means for establishing communications over the network is connected to the system bus 1201 via the network interface 1203 or other appropriate mechanism. A wireless networking component including an interface and antenna may be coupled through a suitable device such as an access point or peer computer to a network. In a networked environment, program modules depicted relative to the computing device 1200, or portions thereof, may be stored in the remote memory storage device.
Certain embodiments are described herein as including one or more modules. Such modules are hardware-implemented, and thus include at least one tangible unit capable of performing certain operations and may be configured or arranged in a certain manner. For example, a hardware-implemented module may comprise dedicated circuitry that is permanently configured (e.g., as a special-purpose processor, such as a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)) to perform certain operations. A hardware-implemented module may also comprise programmable circuitry (e.g., as encompassed within a general-purpose processor or other programmable processor) that is temporarily configured by software or firmware to perform certain operations. In some example embodiments, one or more computer systems (e.g., a standalone system, a client and/or server computer system, or a peer-to-peer computer system) or one or more processors may be configured by software (e.g., an application or application portion) as a hardware-implemented module that operates to perform certain operations as described herein.
Accordingly, the term “hardware-implemented module” encompasses a tangible entity, be that an entity that is physically constructed, permanently configured (e.g., hardwired), or temporarily configured (e.g., programmed) to operate in a certain manner and/or to perform certain operations described herein. Considering embodiments in which hardware-implemented modules are temporarily configured (e.g., programmed), each of the hardware-implemented modules need not be configured or instantiated at any one instance in time. For example, where the hardware-implemented modules comprise a general-purpose processor configured using software, the general-purpose processor may be configured as respective different hardware-implemented modules at different times. Software may accordingly configure the processor 1202, for example, to constitute a particular hardware-implemented module at one instance of time and to constitute a different hardware-implemented module at a different instance of time.
Hardware-implemented modules may provide information to, and/or receive information from, other hardware-implemented modules. Accordingly, the described hardware-implemented modules may be regarded as being communicatively coupled. Where multiple of such hardware-implemented modules exist contemporaneously, communications may be achieved through signal transmission (e.g., over appropriate circuits and buses) that connect the hardware-implemented modules. In embodiments in which multiple hardware-implemented modules are configured or instantiated at different times, communications between such hardware-implemented modules may be achieved, for example, through the storage and retrieval of information in memory structures to which the multiple hardware-implemented modules have access. For example, one hardware-implemented module may perform an operation, and may store the output of that operation in a memory device to which it is communicatively coupled. A further hardware-implemented module may then, at a later time, access the memory device to retrieve and process the stored output. Hardware-implemented modules may also initiate communications with input or output devices.
Computing systems or devices referenced herein may include desktop computers, laptops, tablets e-readers, personal digital assistants, smartphones, gaming devices, servers, and the like. The computing devices may access computer-readable media that include computer-readable storage media and data transmission media. In some embodiments, the computer-readable storage media are tangible storage devices that do not include a transitory propagating signal. Examples include memory such as primary memory, cache memory, and secondary memory (e.g., DVD) and other storage devices. The computer-readable storage media may have instructions recorded on them or may be encoded with computer-executable instructions or logic that implements aspects of the functionality described herein. The data transmission media may be used for transmitting data via transitory, propagating signals or carrier waves (e.g., electromagnetism) via a wired or wireless connection.
It should be understood from the foregoing that, while particular embodiments have been illustrated and described, various modifications can be made thereto without departing from the spirit and scope of the invention as will be apparent to those skilled in the art. Such changes and modifications are within the scope and teachings of this invention as defined in the claims appended hereto.
This is a non-provisional application that claims benefit to U.S. Provisional Application Ser. No. 63/409,677, filed on Sep. 23, 2022, which is herein incorporated by reference in its entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63409677 | Sep 2022 | US |
