Network-targeted approach and postoperative resting-state functional magnetic resonance imaging are associated with seizure outcome

Association Between Postsurgical Functional Connectivity and Seizure Outcome in Patients With Temporal Lobe Epilepsy

BACKGROUND AND OBJECTIVES

Despite the success of presurgical network connectivity studies in predicting short-term (1-year) seizure outcomes, later seizure recurrence occurs in some patients with temporal lobe epilepsy (TLE). To uncover contributors to this recurrence, we investigated the relationship between functional connectivity and seizure outcomes at different time points after surgery in these patients.

METHODS

Patients included were clinically diagnosed with unilateral mesial TLE after a standard clinical evaluation and underwent selective amygdalohippocampectomy. Healthy controls had no history of seizures or head injury. Using resting-state fMRI, we assessed the postsurgical functional connectivity node strength, computed as the node's total strength to all other nodes, between seizure-free (Engel Ia-Ib) and nonseizure-free (Engel Ic-IV) acquisitions. The change over time after surgery in different outcome groups in these nodes was also characterized.

RESULTS

Patients with TLE (n = 32, mean age: 43.1 ± 11.9 years; 46.8% female) and 85 healthy controls (mean age: 37.7 ± 13.5 years; 48.2% female) were included. Resting fMRI was acquired before surgery and at least once after surgery in each patient (range 1-4 scans, 5-60 months). Differences between patients with (n = 30) and without (n = 18) seizure freedom were detected in the posterior insula ipsilateral to the resection (I-PIns: 95% CI -154.8 to -50.1, p = 2.8 × 10-4) and the bilateral central operculum (I-CO: 95% CI -163.2 to -65.1, p = 2.6 × 10-5, C-CO: 95% CI -172.7 to -55.8, p = 2.8 × 10-4). In these nodes, only those who were seizure-free had increased node strength after surgery that increased linearly over time (I-CO: 95% CI 1.0-5.2, p = 4.2 × 10-3, C-CO: 95% CI 1.0-5.2, p = 5.5 × 10-3, I-PIns: 95% CI 1.6-5.5, p = 0.9 × 10-3). Different outcome groups were not distinguished by node strength before surgery.

DISCUSSION

The findings suggest that network evolution in the first 5 years after selective amygdalohippocampectomy surgery is related to seizure outcomes in TLE. This highlights the need to identify presurgical and surgical conditions that lead to disparate postsurgical trajectories between seizure-free and nonseizure-free patients to identify potential contributors to long-term seizure outcomes. However, the lack of including other surgical approaches may affect the generalizability of the results.

Mapping Functional Connectivity Signatures of Pharmacoresistant Focal Cortical Dysplasia-Related Epilepsy

OBJECTIVE

To determine common network alterations in focal cortical dysplasia pharmacoresistant epilepsy (FCD-PRE) using functional connectivity analysis of resting-state functional magnetic resonance imaging (rsfMRI).

METHODS

This is a retrospective imaging cohort from Children's National Hospital (Washington, DC, USA) from January, 2011 to January, 2022. Patients with 3-T MRI-confirmed FCD-PRE underwent rsfMRI as part of routine clinical care. Patients were included if they were age 5-22 years at the time of the scan, and had a minimum of 18 months of follow-up. Healthy, typically-developing controls were included from Children's National Hospital (n = 16) and matched from Human Connectome Project-Development public dataset (n = 100).

RESULTS

A total of 42 FCD-PRE patients (20 M:22 F, aged 14.2 ± 4.1 years) and 116 healthy controls (56 M:60 F, aged 13.7 ± 3.3 years) with rsfMRI were included. Seed-based functional connectivity maps were generated for each FCD, and each seed was used to generate a patient-specific z-scored connectivity map on 116 controls. FCD-PRE patients had mutual altered connectivity in regions of dorsal attention, default mode, and control networks. Functional connectivity was diminished within the FCD dominant functional network, as well as in homotopic regions. Cluster specific connectivity patterns varied by pathological subtype. Higher FCD connectivity to the limbic network was associated with increased odds of Engel I outcome.

INTERPRETATION

This study demonstrates diminished functional connectivity patterns in FCD-PRE, which may represent a neuromarker for the disease, independent of FCD location, involving the dorsal attention, default mode, and control functional networks. Higher connectivity to the limbic network is associated with a seizure-free outcome. Future multicenter, prospective studies are needed to allow for much earlier detection of signatures of treatment-resistant epilepsy. ANN NEUROL 2024.

Motor network dynamic resting state fMRI connectivity of neurotypical children in regions affected by cerebral palsy

Background

Normative childhood motor network resting-state fMRI effective connectivity is undefined, yet necessary for translatable dynamic resting-state-network-informed evaluation in pediatric cerebral palsy.

Methods

Cross-spectral dynamic causal modeling of resting-state-fMRI was investigated in 50 neurotypically developing 5- to 13-year-old children. Fully connected six-node network models per hemisphere included primary motor cortex, striatum, subthalamic nucleus, globus pallidus internus, thalamus, and contralateral cerebellum. Parametric Empirical Bayes with exhaustive Bayesian model reduction and Bayesian modeling averaging informed the model; Purdue Pegboard Test scores of hand motor behavior were the covariate at the group level to determine the effective-connectivity-functional behavior relationship.

Results

Although both hemispheres exhibited similar effective connectivity of motor cortico-basal ganglia-cerebellar networks, magnitudes were slightly greater on the right, except for left-sided connections of the striatum which were more numerous and of opposite polarity. Inter-nodal motor network effective connectivity remained consistent and robust across subjects. Age had a greater impact on connections to the contralateral cerebellum, bilaterally. Motor behavior, however, affected different connections in each hemisphere, exerting a more prominent effect on the left modulatory connections to the subthalamic nucleus, contralateral cerebellum, primary motor cortex, and thalamus.

Discussion

This study revealed a consistent pattern of directed resting-state effective connectivity in healthy children aged 5-13 years within the motor network, encompassing cortical, subcortical, and cerebellar regions, correlated with motor skill proficiency. Both hemispheres exhibited similar effective connectivity within motor cortico-basal ganglia-cerebellar networks reflecting inter-nodal signal direction predicted by other modalities, mainly differing from task-dependent studies due to network differences at rest. Notably, age-related changes were more pronounced in connections to the contralateral cerebellum. Conversely, motor behavior distinctly impacted connections in each hemisphere, emphasizing its role in modulating left sided connections to the subthalamic nucleus, contralateral cerebellum, primary motor cortex, and thalamus. Motor network effective connectivity was correlated with motor behavior, validating its physiological significance. This study is the first to evaluate a normative effective connectivity model for the pediatric motor network using resting-state functional MRI correlating with behavior and serves as a foundation for identifying abnormal findings and optimizing targeted interventions like deep brain stimulation, potentially influencing future therapeutic approaches for children with movement disorders.

Intraoperative changes in large-scale thalamic circuitry following laser ablation of hypothalamic hamartomas

BACKGROUND AND OBJECTIVES

Gelastic seizures due to hypothalamic hamartomas (HH) are challenging to treat, in part due to an incomplete understanding of seizure propagation pathways. Although magnetic resonance imaging-guided laser interstitial thermal therapy (MRgLITT) is a promising intervention to disconnect HH from ictal propagation networks, the optimal site of ablation to achieve seizure freedom is not known. In this study, we investigated intraoperative post-ablation changes in resting-state functional connectivity to identify large-scale networks associated with successful disconnection of HH.

METHODS

Children who underwent MRgLITT for HH at two institutions were consecutively recruited and followed for a minimum of one year. Seizure freedom was defined as Engel score of 1A at the last available follow-up. Immediate pre- and post- ablation resting-state functional MRI scans were acquired while maintaining a constant depth of general anesthetic. Multivariable generalized linear models were used to identify intraoperative changes in large-scale connectivity associated with seizure outcomes.

RESULTS

Twelve patients underwent MRgLITT for HH, five of whom were seizure-free at their last follow-up. Intraprocedural changes in thalamocortical circuitry involving the anterior cingulate cortex were associated with seizure-freedom. Children who were seizure-free demonstrated an increase and decrease in connectivity to the pregenual and dorsal anterior cingulate cortices, respectively. In addition, children who became seizure-free demonstrated increased thalamic connectivity to the periaqueductal gray immediately following MRgLITT.

DISCUSSION

Successful disconnection of HH is associated with intraoperative, large-scale changes in thalamocortical connectivity. These changes provide novel insights into the large-scale basis of gelastic seizures and may represent intraoperative biomarkers of treatment success.

Resting-State Functional Magnetic Resonance Imaging Network Association With Mortality, Epilepsy, Cognition, and Motor Two-Year Outcomes in Suspected Severe Neonatal Acute Brain Injury

BACKGROUND AND OBJECTIVES

In acute brain injury of neonates, resting-state functional magnetic resonance imaging (MRI) (RS) showed incremental association with consciousness, mortality, cognitive and motor development, and epilepsy, with correction for multiple comparisons, at six months postgestation in neonates with suspected acute brain injury (ABI). However, there are relatively few developmental milestones at six months to benchmark against, thus, we extended this cohort study to evaluate two-year outcomes.

METHODS

In 40 consecutive neonates with ABI and RS, ordinal scores of resting-state networks; MRI, magnetic resonance spectroscopy, and electroencephalography; and up to 42-month outcomes of mortality, general and motor development, Pediatric Cerebral Performance Category Scale (PCPC), and epilepsy informed associations between tests and outcomes.

RESULTS

Mean gestational age was 37.8 weeks, 68% were male, and 60% had hypoxic-ischemic encephalopathy. Three died in-hospital, four at six to 42 months, and five were lost to follow-up. Associations included basal ganglia network with PCPC (P = 0.0003), all-mortality (P = 0.005), and motor (P = 0.0004); language/frontoparietal network with developmental delay (P = 0.009), PCPC (P = 0.006), and all-mortality (P = 0.01); default mode network with developmental delay (P = 0.003), PCPC (P = 0.004), neonatal intensive care unit mortality (P = 0.01), and motor (P = 0.009); RS seizure onset zone with epilepsy (P = 0.01); and anatomic MRI with epilepsy (P = 0.01).

CONCLUSION

For the first time, at any age, resting state functional MRI in ABI is associated with long-term epilepsy and RSNs predicted mortality in neonates. Severity of RSN abnormality was associated with incrementally worsened neurodevelopment including cognition, language, and motor function over two years.

iEEG-recon: A fast and scalable pipeline for accurate reconstruction of intracranial electrodes and implantable devices

OBJECTIVE

Clinicians use intracranial electroencephalography (iEEG) in conjunction with noninvasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms.

METHODS

We created iEEG-recon, a scalable electrode reconstruction pipeline for semiautomatic iEEG annotation, rapid image registration, and electrode assignment on brain magnetic resonance imaging (MRI). Its modular architecture includes a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts.

RESULTS

We used iEEG-recon to accurately reconstruct electrodes in both electrocorticography and stereoelectroencephalography cases with a 30-min running time per case (including semiautomatic electrode labeling and reconstruction). iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and postimplant T1-MRI visual inspections. We also found that our use of ANTsPyNet deep learning-based brain segmentation for electrode classification was consistent with the widely used FreeSurfer segmentations.

SIGNIFICANCE

iEEG-recon is a robust pipeline for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting fast data analysis and integration into clinical workflows. iEEG-recon's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide.

Functional profile of perilesional gray matter in focal cortical dysplasia: an fMRI study

Objectives

We aim to investigate the functional profiles of perilesional gray matter (GM) in epileptic patients with focal cortical dysplasia (FCD) and to correlate these profiles with FCD II subtypes, surgical outcomes, and different antiseizure medications (ASMs) treatment response patterns.

Methods

Nine patients with drug-responsive epilepsy and 30 patients with drug-resistant epilepsy (11 were histologically confirmed FCD type IIa, 19 were FCD type IIb) were included. Individual-specific perilesional GM and contralateral homotopic GM layer masks were generated. These masks underwent a two-voxel (2 mm) dilation from the FCD lesion and contralateral homotopic region, resulting in 10 GM layers (20 mm). Layer 1, the innermost, progressed to Layer 10, the outermost. Amplitude of low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) analyses were conducted to assess the functional characteristics of ipsilateral perilesional GM and contralateral homotopic GM.

Results

Compared to the contralateral homotopic GM, a significant reduction of ALFF was detected at ipsilateral perilesional GM layer 1 to 6 in FCD type IIa (after Bonferroni correction p < 0.005, paired t-test), whereas a significant decrease was observed at ipsilateral perilesional GM layer 1 to 2 in FCD type IIb (after Bonferroni correction p < 0.005, paired t-test). Additionally, a significant decrease of the ReHo was detected at ipsilateral perilesional GM layer 1 compared to the CHRs in FCD type IIb. Notably, complete resection of functional perilesional GM alterations did not correlate with surgical outcomes. Compared to the contralateral homotopic GM, a decreased ALFF in the ipsilateral perilesional GM layer was detected in drug-responsive patients, whereas decreased ALFF in the ipsilateral perilesional GM layer 1-6 and decreased ReHo at ipsilateral perilesional GM layer 1 were observed in drug-resistant patients (after Bonferroni correction p < 0.005, paired t-test).

Conclusion

Our findings indicate distinct functional profiles of perilesional GM based on FCD histological subtypes and ASMs' response patterns. Importantly, our study illustrates that the identified functional alterations in perilesional GM may not provide sufficient evidence to determine the epileptogenic boundary required for surgical resection.

The expert's knowledge combined with AI outperforms AI alone in seizure onset zone localization using resting state fMRI

We evaluated whether integration of expert guidance on seizure onset zone (SOZ) identification from resting state functional MRI (rs-fMRI) connectomics combined with deep learning (DL) techniques enhances the SOZ delineation in patients with refractory epilepsy (RE), compared to utilizing DL alone. Rs-fMRI was collected from 52 children with RE who had subsequently undergone ic-EEG and then, if indicated, surgery for seizure control (n = 25). The resting state functional connectomics data were previously independently classified by two expert epileptologists, as indicative of measurement noise, typical resting state network connectivity, or SOZ. An expert knowledge integrated deep network was trained on functional connectomics data to identify SOZ. Expert knowledge integrated with DL showed a SOZ localization accuracy of 84.8 ± 4.5% and F1 score, harmonic mean of positive predictive value and sensitivity, of 91.7 ± 2.6%. Conversely, a DL only model yielded an accuracy of <50% (F1 score 63%). Activations that initiate in gray matter, extend through white matter, and end in vascular regions are seen as the most discriminative expert-identified SOZ characteristics. Integration of expert knowledge of functional connectomics can not only enhance the performance of DL in localizing SOZ in RE but also lead toward potentially useful explanations of prevalent co-activation patterns in SOZ. RE with surgical outcomes and preoperative rs-fMRI studies can yield expert knowledge most salient for SOZ identification.

A scoping review of the functional magnetic resonance imaging-based functional connectivity of focal cortical dysplasia-related epilepsy

Focal cortical dysplasia (FCD) is the most frequent etiology of operable pharmacoresistant epilepsy in children. There is burgeoning evidence that FCD-related epilepsy is a disorder that involves distributed brain networks. Functional magnetic resonance imaging (fMRI) is a tool that allows one to infer neuronal activity and to noninvasively map whole-brain functional networks. Despite its relatively widespread availability at most epilepsy centers, the clinical application of fMRI remains mostly task-based in epilepsy. Another approach is to map and characterize cortical functional networks of individuals using resting state fMRI (rsfMRI). The focus of this scoping review is to summarize the evidence to date of investigations of the network basis of FCD-related epilepsy, and to highlight numerous potential future applications of rsfMRI in the exploration of diagnostic and therapeutic strategies for FCD-related epilepsy. There are numerous studies demonstrating a global disruption of cortical functional networks in FCD-related epilepsy. The underlying pathological subtypes of FCD influence overall functional network patterns. There is evidence that cortical functional network mapping may help to predict postsurgical seizure outcomes, highlighting the translational potential of these findings. Additionally, several studies emphasize the important effect of FCD interaction with cortical networks and the expression of epilepsy and its comorbidities.

Functional MRI for Acute Covert Consciousness: Emerging Data and Implementation Case Series

Although research studies have begun to demonstrate relationships between disorders of consciousness and brain network biomarkers, there are limited data on the practical aspects of obtaining such network biomarkers to potentially guide care. As the state of knowledge continues to evolve, guidelines from professional societies such as the American and European Academies of Neurology and many experts have advocated that the risk-benefit ratio for the assessment of network biomarkers has begun to favor their application toward potentially detecting covert consciousness. Given the lack of detailed operationalization guidance and the context of the ethical implications, herein we offer a roadmap based on local institutional experience with the implementation of functional MRI in the neonatal, pediatric, and adult intensive care units of our local government-supported health system. We provide a case-based demonstrative approach intended to review the current literature and to assist with the initiation of such services at other facilities.

Independent component analysis: a reliable alternative to general linear model for task-based fMRI

Background

Functional magnetic resonance imaging (fMRI) is a valuable tool for the presurgical evaluation of patients undergoing neurosurgeries. Although many pre-processing steps have been modified according to advances in recent years, statistical analysis has remained largely the same since the first days of fMRI. In this study, we examined the ability of Independent Component Analysis (ICA) to separate the activation of a language task in fMRI, and we compared it with the results of the General Lineal Model (GLM).

Methods

Sixty patients undergoing evaluation for brain surgery due to various brain lesions and/or epilepsy and 20 control subjects completed an fMRI language mapping protocol that included three tasks, resulting in 259 fMRI scans. Depending on brain lesion characteristics, patients were allocated to (1) static/chronic not-expanding lesions (Group 1) and (2) progressive/expanding lesions (Group 2). GLM and ICA statistical maps were evaluated by fMRI experts to assess the performance of each technique.

Results

In the control group, ICA and GLM maps were similar without any superiority of either technique. In Group 1 and Group 2, ICA performed statistically better than GLM, with a p-value of < 0.01801 and < 0.0237, respectively. This indicated that ICA performs as well as GLM when the subjects are able to cooperate well (less movement, good task performance), but ICA could outperform GLM in the patient groups. When both techniques were combined, 240 out of 259 scans produced reliable results, showing that the sensitivity of task-based fMRI can be increased when both techniques are integrated with the clinical setup.

Conclusion

ICA may be slightly more advantageous, compared to GLM, in patients with brain lesions, across the range of pathologies included in our population and independent of symptoms chronicity. Our findings suggest that GLM analysis may be more susceptible to brain activity perturbations induced by a variety of lesions or scanner-induced artifacts due to motion or other factors. In our research, we demonstrated that ICA is able to provide fMRI results that can be used in surgery, taking into account patient and task-wise aspects that differ from those when fMRI is used in research.

Treatable brain network biomarkers in children in coma using task and resting-state functional MRI: a case series

The withdrawal of life-sustaining therapies is frequently considered for pediatric patients with severe acute brain injuries who are admitted to the intensive care unit. However, it is worth noting that some children with a resultant poor neurological status may ultimately survive and achieve a positive neurological outcome. Evidence suggests that adults with hidden consciousness may have a more favorable prognosis compared to those without it. Currently, no treatable network disorders have been identified in cases of severe acute brain injury, aside from seizures detectable through an electroencephalogram (EEG) and neurostimulation via amantadine. In this report, we present three cases in which multimodal brain network evaluation played a helpful role in patient care. This evaluation encompassed various assessments such as continuous video EEG, visual-evoked potentials, somatosensory-evoked potentials, auditory brainstem-evoked responses, resting-state functional MRI (rs-fMRI), and passive-based and command-based task-based fMRI. It is worth noting that the latter three evaluations are unique as they have not yet been established as part of the standard care protocol for assessing acute brain injuries in children with suppressed consciousness. The first patient underwent serial fMRIs after experiencing a coma induced by trauma. Subsequently, the patient displayed improvement following the administration of antiseizure medication to address abnormal signals. In the second case, a multimodal brain network evaluation uncovered covert consciousness, a previously undetected condition in a pediatric patient with acute brain injury. In both patients, this discovery potentially influenced decisions concerning the withdrawal of life support. Finally, the third patient serves as a comparative control case, demonstrating the absence of detectable networks. Notably, this patient underwent the first fMRI prior to experiencing brain death as a pediatric patient. Consequently, this case series illustrates the clinical feasibility of employing multimodal brain network evaluation in pediatric patients. This approach holds potential for clinical interventions and may significantly enhance prognostic capabilities beyond what can be achieved through standard testing methods alone.

iEEG-recon: A Fast and Scalable Pipeline for Accurate Reconstruction of Intracranial Electrodes and Implantable Devices

Background

Collaboration between epilepsy centers is essential to integrate multimodal data for epilepsy research. Scalable tools for rapid and reproducible data analysis facilitate multicenter data integration and harmonization. Clinicians use intracranial EEG (iEEG) in conjunction with non-invasive brain imaging to identify epileptic networks and target therapy for drug-resistant epilepsy cases. Our goal was to promote ongoing and future collaboration by automating the process of "electrode reconstruction," which involves the labeling, registration, and assignment of iEEG electrode coordinates on neuroimaging. These tasks are still performed manually in many epilepsy centers. We developed a standalone, modular pipeline that performs electrode reconstruction. We demonstrate our tool's compatibility with clinical and research workflows and its scalability on cloud platforms.

Methods

We created iEEG-recon, a scalable electrode reconstruction pipeline for semi-automatic iEEG annotation, rapid image registration, and electrode assignment on brain MRIs. Its modular architecture includes three modules: a clinical module for electrode labeling and localization, and a research module for automated data processing and electrode contact assignment. To ensure accessibility for users with limited programming and imaging expertise, we packaged iEEG-recon in a containerized format that allows integration into clinical workflows. We propose a cloud-based implementation of iEEG-recon, and test our pipeline on data from 132 patients at two epilepsy centers using retrospective and prospective cohorts.

Results

We used iEEG-recon to accurately reconstruct electrodes in both electrocorticography (ECoG) and stereoelectroencephalography (SEEG) cases with a 10 minute running time per case, and ~20 min for semi-automatic electrode labeling. iEEG-recon generates quality assurance reports and visualizations to support epilepsy surgery discussions. Reconstruction outputs from the clinical module were radiologically validated through pre- and post-implant T1-MRI visual inspections. Our use of ANTsPyNet deep learning approach for brain segmentation and electrode classification was consistent with the widely used Freesurfer segmentation.

Discussion

iEEG-recon is a valuable tool for automating reconstruction of iEEG electrodes and implantable devices on brain MRI, promoting efficient data analysis, and integration into clinical workflows. The tool's accuracy, speed, and compatibility with cloud platforms make it a useful resource for epilepsy centers worldwide. Comprehensive documentation is available at https://ieeg-recon.readthedocs.io/en/latest/.

The presence, nature and network characteristics of behavioural phenotypes in temporal lobe epilepsy

The relationship between temporal lobe epilepsy and psychopathology has had a long and contentious history with diverse views regarding the presence, nature and severity of emotional-behavioural problems in this patient population. To address these controversies, we take a new person-centred approach through the application of unsupervised machine learning techniques to identify underlying latent groups or behavioural phenotypes. Addressed are the distinct psychopathological profiles, their linked frequency, patterns and severity and the disruptions in morphological and network properties that underlie the identified latent groups. A total of 114 patients and 83 controls from the Epilepsy Connectome Project were administered the Achenbach System of Empirically Based Assessment inventory from which six Diagnostic and Statistical Manual of Mental Disorders-oriented scales were analysed by unsupervised machine learning analytics to identify latent patient groups. Identified clusters were contrasted to controls as well as to each other in order to characterize their association with sociodemographic, clinical epilepsy and morphological and functional imaging network features. The concurrent validity of the behavioural phenotypes was examined through other measures of behaviour and quality of life. Patients overall exhibited significantly higher (abnormal) scores compared with controls. However, cluster analysis identified three latent groups: (i) unaffected, with no scale elevations compared with controls (Cluster 1, 37%); (ii) mild symptomatology characterized by significant elevations across several Diagnostic and Statistical Manual of Mental Disorders-oriented scales compared with controls (Cluster 2, 42%); and (iii) severe symptomatology with significant elevations across all scales compared with controls and the other temporal lobe epilepsy behaviour phenotype groups (Cluster 3, 21%). Concurrent validity of the behavioural phenotype grouping was demonstrated through identical stepwise links to abnormalities on independent measures including the National Institutes of Health Toolbox Emotion Battery and quality of life metrics. There were significant associations between cluster membership and sociodemographic (handedness and education), cognition (processing speed), clinical epilepsy (presence and lifetime number of tonic-clonic seizures) and neuroimaging characteristics (cortical volume and thickness and global graph theory metrics of morphology and resting-state functional MRI). Increasingly dispersed volumetric abnormalities and widespread disruptions in underlying network properties were associated with the most abnormal behavioural phenotype. Psychopathology in these patients is characterized by a series of discrete latent groups that harbour accompanying sociodemographic, clinical and neuroimaging correlates. The underlying neurobiological patterns suggest that the degree of psychopathology is linked to increasingly dispersed abnormal brain networks. Similar to cognition, machine learning approaches support a novel developing taxonomy of the comorbidities of epilepsy.

Resting-State Functional MRI/PET Profile as a Potential Alternative to Tri-Modality EEG-MR/PET Imaging: An Exploratory Study in Drug-Refractory Epilepsy

Objective The study explores whether the epileptic networks associate with predetermined seizure onset zone (SOZ) identified from other modalities such as electroencephalogram/video electroencephalogram/structural MRI (EEG/VEEG/sMRI) and with the degree of resting-state functional MRI/positron emission tomography (RS-fMRI/PET) coupling. Here, we have analyzed the subgroup of patients who reported having a seizure on the day of scan as postictal cases and compared the findings with interictal cases (seizure-free interval).

Methods We performed independent component analysis (ICA) on RS-fMRI and 20 ICA were hand-labeled as large scale, noise, downstream, and epilepsy networks (Epinets) based on their profile in spatial, time series, and power spectrum domains. We had a total of 43 cases, with 4 cases in the postictal group (100%). Of 39 cases, 14 cases did not yield any Epinet and 25 cases (61%) were analyzed for the final study. The analysis was done patient-wise and correlated with predetermined SOZ.

Results The yield of finding Epinets on RS-fMRI is more during the postictal period than in the interictal period, although PET and RS-fMRI spatial, time series, and power spectral patterns were similar in both these subgroups. Overlaps between large-scale and downstream networks were noted, indicating that epilepsy propagation can involve large-scale cognition networks. Lateralization to SOZ was noted as blood oxygen level–dependent activation and correlated with sMRI/PET findings. Postoperative surgical failure cases showed residual Epinet profile.

Conclusion RS-fMRI may be a viable option for trimodality imaging to obtain simultaneous physiological information at the functional network and metabolic level.

Proceedings of the First Pediatric Coma and Disorders of Consciousness Symposium by the Curing Coma Campaign, Pediatric Neurocritical Care Research Group, and NINDS: Gearing for Success in Coma Advancements for Children and Neonates

This proceedings article presents the scope of pediatric coma and disorders of consciousness based on presentations and discussions at the First Pediatric Disorders of Consciousness Care and Research symposium held on September 14th, 2021. Herein we review the current state of pediatric coma care and research opportunities as well as shared experiences from seasoned researchers and clinicians. Salient current challenges and opportunities in pediatric and neonatal coma care and research were identified through the contributions of the presenters, who were Jose I. Suarez, MD, Nina F. Schor, MD, PhD, Beth S. Slomine, PhD Erika Molteni, PhD, and Jan-Marino Ramirez, PhD, and moderated by Varina L. Boerwinkle, MD, with overview by Mark Wainwright, MD, and subsequent audience discussion. The program, executively planned by Varina L. Boerwinkle, MD, Mark Wainwright, MD, and Michelle Elena Schober, MD, drove the identification and development of priorities for the pediatric neurocritical care community.

Preoperative functional connectivity by magnetic resonance imaging for refractory neocortical epilepsy

Objective

Patients with refractory epilepsy experience extensive and invasive clinical testing for seizure onset zones treatable by surgical resection. However, surgical resection can fail to provide therapeutic benefit, and patients with neocortical epilepsy have the poorest therapeutic outcomes. This case series studied patients with neocortical epilepsy who were referred for surgical treatment. Prior to surgery, patients volunteered for resting-state functional magnetic resonance imaging (rs-fMRI) in addition to imaging for the clinical standard of care. This work examined the variability of functional connectivity in patients, estimated from rs-fMRI, for associations with surgical outcomes.

Methods

This work examined pre-operative structural imaging, pre-operative rs-fMRI, and post-operative structural imaging from seven epilepsy patients. Review of the clinical record provided Engel classifications for surgical outcomes. A novel method assessed pre-operative rs-fMRI from patients using comparative rs-fMRI from a large cohort of healthy control subjects and estimated Gibbs distributions for functional connectivity in patients compared to healthy controls.

Results

Three patients had Engel classification Ia, one patient had Engel classification IIa, and three patients had Engel classification IV. Metrics for variability of functional connectivity, including absolute differences of the functional connectivity of each patient from healthy control averages and probabilistic scores for absolute differences, were higher for patients classified as Engel IV, for whom epilepsy surgery provided no meaningful improvement.

Significance

This work continues on-going efforts to use rs-fMRI to characterize abnormal functional connectivity in the brain. Preliminary evidence indicates that the topography of variant functional connectivity in epilepsy patients may be clinically relevant for identifying patients unlikely to have favorable outcomes after epilepsy surgery. Widespread topographic variations of functional connectivity also support the hypothesis that epilepsy is a disease of brain resting-state networks.

Automated seizure onset zone locator from resting-state functional MRI in drug-resistant epilepsy

OBJECTIVE

Accurate localization of a seizure onset zone (SOZ) from independent components (IC) of resting-state functional magnetic resonance imaging (rs-fMRI) improves 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 face unique challenges due to the developing brain and its associated surgical risks. This study 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, the 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). A total of 24 children underwent surgical resection or ablation of an rs-fMRI identified SOZ, concurrently evaluated with an EEG and anatomical MRI. Two state-of-art techniques were 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. The analysis was stratified across age and sex.

RESULTS

EPIK outperformed state-of-art techniques for SOZ localizing IC identification with a mean accuracy of 84.7% (4% higher), a precision of 74.1% (22% higher), a specificity of 81.9% (3.2% higher), and a sensitivity of 88.6% (16.5% higher). EPIK showed consistent performance across age and sex with the best performance in those < 5 years of age. It helped achieve 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 the potential for clinical feasibility. It eliminates the need for expert sorting, outperforms prior automated methods, and is consistent across age and sex.

Abnormal Spontaneous Blood Oxygenation Level Dependent Fluctuations in Children with Focal Cortical Dysplasias: Initial Findings in Surgically Confirmed Cases

BACKGROUND

 Focal cortical dysplasias (FCD) are a frequent cause of drug-resistant epilepsy in children but are often undetected on structural magnetic resonance imaging (MRI). We aimed to measure and validate the variation of resting state functional MRI (rs-fMRI) blood oxygenation level dependent (BOLD) metrics in surgically proven FCDs in children, to assess the potential yield for detecting and understanding these lesions.

METHODS

 We prospectively included pediatric patients with surgically proven FCD with inconclusive structural MRI and healthy controls, who underwent a ten-minute rs-fMRI acquired at 3T. Rs-fMRI data was pre-processed and maps of values of regional homogeneity (ReHo), degree centrality (DC), amplitude of low frequency fluctuations (ALFF) and fractional ALFF (fALFF) were calculated. The variations of BOLD metrics within the to-be-resected areas were analyzed visually, and quantitatively using lateralization indices. BOLD metrics variations were also analyzed in fluorodeoxyglucose-positron emission tomography (FDG-PET) hypometabolic areas.

RESULTS

 We included 7 patients (range: 3-15 years) and 6 aged-matched controls (range: 6-17 years). ReHo lateralization indices were positive in the to-be-resected areas in 4/7 patients, and in 6/7 patients in the additional PET hypometabolic areas. These indices were significantly higher compared to controls in 3/7 and 4/7 patients, respectively. Visual analysis revealed a good spatial correlation between high ReHo areas and MRI structural abnormalities (when present) or PET hypometabolic areas. No consistent variation was seen using DC, ALFF, or fALFF.

CONCLUSION

 Resting-state fMRI metrics, noticeably increase in ReHo, may have potential to help detect MRI-negative FCDs in combination with other morphological and functional techniques, used in clinical practice and epilepsy-surgery screening.

Towards network-guided neuromodulation for epilepsy

Epilepsy is well-recognized as a disorder of brain networks. There is a growing body of research to identify critical nodes within dynamic epileptic networks with the aim to target therapies that halt the onset and propagation of seizures. In parallel, intracranial neuromodulation, including deep brain stimulation and responsive neurostimulation, are well-established and expanding as therapies to reduce seizures in adults with focal-onset epilepsy; and there is emerging evidence for their efficacy in children and generalized-onset seizure disorders. The convergence of these advancing fields is driving an era of 'network-guided neuromodulation' for epilepsy. In this review, we distil the current literature on network mechanisms underlying neurostimulation for epilepsy. We discuss the modulation of key 'propagation points' in the epileptogenic network, focusing primarily on thalamic nuclei targeted in current clinical practice. These include (i) the anterior nucleus of thalamus, now a clinically approved and targeted site for open loop stimulation, and increasingly targeted for responsive neurostimulation; and (ii) the centromedian nucleus of the thalamus, a target for both deep brain stimulation and responsive neurostimulation in generalized-onset epilepsies. We discuss briefly the networks associated with other emerging neuromodulation targets, such as the pulvinar of the thalamus, piriform cortex, septal area, subthalamic nucleus, cerebellum and others. We report synergistic findings garnered from multiple modalities of investigation that have revealed structural and functional networks associated with these propagation points - including scalp and invasive EEG, and diffusion and functional MRI. We also report on intracranial recordings from implanted devices which provide us data on the dynamic networks we are aiming to modulate. Finally, we review the continuing evolution of network-guided neuromodulation for epilepsy to accelerate progress towards two translational goals: (i) to use pre-surgical network analyses to determine patient candidacy for neurostimulation for epilepsy by providing network biomarkers that predict efficacy; and (ii) to deliver precise, personalized and effective antiepileptic stimulation to prevent and arrest seizure propagation through mapping and modulation of each patients' individual epileptogenic networks.

Responsive neurostimulation in pediatric patients with drug-resistant epilepsy

OBJECTIVE

Medically refractory epilepsy remains a therapeutic challenge when resective surgery is not a practical option and indirect neurostimulation efficacy may be limited. In these instances responsive neurostimulation (RNS) has been used in adults, with good outcomes in most patients. However, the utility of RNS in children and young adults has not been systematically explored. In this study, the authors present a single institution’s experience with RNS in pediatric patients.

METHODS

A single-center retrospective chart review of patients who underwent RNS implantation at Phoenix Children’s Hospital during the 4-year period between January 2018 and December 2021 was performed.

RESULTS

Following evaluation for epilepsy surgery, 22 patients underwent RNS implantation using different anatomical targets depending on the predetermined epileptic focus/network. In the cohort, 59% of patients were male, the mean age at implantation was 16.4 years (range 6–22 years), and the mean follow-up time was 2.7 years (range 1.0–4.3 years). All patients had a preoperative noninvasive evaluation that included MRI, video-electroencephalography, and resting-state functional MRI. Additionally, 13 patients underwent invasive monitoring with stereo-electroencephalography to help determine RNS targets. All patients had variable positive responses with reduction of seizure frequency and/or intensity. Overall, seizure frequency reduction of > 50% was seen in the majority (86%) of patients. There were two complications: one patient experienced transitory weakness and one generator failed, requiring replacement. A patient died of sudden unexpected death in epilepsy 3 years after implantation despite being seizure free during the previous year.

CONCLUSIONS

RNS used in children with medically refractory epilepsy improved seizure control after implantation, with decreases in seizure frequency > 50% from preoperative baseline in the majority of patients. Preliminary findings indicate that functional MRI and stereo-electroencephalography were helpful for RNS targeting and that RNS can be used safely even in young children.

Functional individual variability development of the neonatal brain

Individual variability in cognition and behavior results from the differences in brain structure and function that have already emerged before birth. However, little is known about individual variability in brain functional architecture at local level in neonates which is of great significance to explore owing to largely undeveloped long-range functional connectivity and segregated functions in early brain development. To address this, resting-state fMRI data of 163 neonates ranged from 32 to 45 postconceptional weeks (PCW) were used in this study, and various functional features including functional parcellation similarity, local brain activity and local functional connectivity were used to characterize individual functional variability. We observed significantly higher local functional individual variability in superior parietal, sensorimotor, and visual cortex, and lower variability in the frontal, insula and cingulate cortex relative to other regions within each hemisphere. The mean local functional individual variability significantly increased with age, and the age effect was found larger in brain regions such as the occipital, temporal, prefrontal and parietal cortex. Our findings promote the understanding of brain plasticity and regional differential maturation in the early stage.

Discerning Seizure-Onset v. Propagation Zone: Pre-and-Post-Operative Resting-State fMRI Directionality and Boerwinkle Neuroplasticity Index

The goal of this study was to determine resting state fMRI (rs-fMRI) effective connectivity (RSEC) capacity, agnostic of epileptogenic events, in distinguishing seizure onset zones (SOZ) from propagation zones (pZ). Consecutive patients (2.1-18.2 years old), with epilepsy and hypothalamic hamartoma, pre-operative rs-fMRI-directed surgery, post-operative imaging, and Engel class I outcomes were collected. Cross-spectral dynamic causal modelling (DCM) was used to estimate RSEC between the ablated rs-fMRI-SOZ to its region of highest connectivity outside the HH, defined as the propagation zone (pZ). Pre-operatively, RSEC from the SOZ and PZ was expected to be positive (excitatory), and pZ to SOZ negative (inhibitory), and post-operatively to be either diminished or non-existent. Sensitivity, accuracy, positive predictive value were determined for node-to-node connections. A Parametric Empirical Bayes (PEB) group analysis on pre-operative data was performed to identify group effects and effects of Engel class outcome and age. Pre-operative RSEC strength was also evaluated for correlation with percent seizure frequency improvement, sex, and region of interest size. Of the SOZ's RSEC, only 3.6% had no connection of significance to the pZ when patient models were individually reduced. Among remaining, 96% were in expected (excitatory signal found from SOZ → pZ and inhibitory signal found from pZ → SOZ) versus 3.6% reversed polarities. Both pre-operative polarity signals were equivalently as expected, with one false signal direction out of 26 each (3.7% total). Sensitivity of 95%, specificity 73%, accuracy of 88%, negative predictive value 88%, and positive predictive value of 88% in identifying and differentiating the SOZ and pZ. Groupwise PEB analysis confirmed SOZ → pZ EC was excitatory, and pZ → SOZ EC was inhibitory. Patients with better outcomes (Engel Ia vs. Ib) showed stronger inhibitory signal (pZ → SOZ). Age was negatively associated with absolute RSEC bidirectionally but had no relationship with Directionality SOZ identification performance. In an additional hierarchical PEB analysis identifying changes from pre-to-post surgery, SOZ → pZ modulation became less excitatory and pZ → SOZ modulation became less inhibitory. This study demonstrates the accuracy of Directionality to identify the origin of excitatory and inhibitory signal between the surgically confirmed SOZ and the region of hypothesized propagation zone in children with DRE due to a HH. Thus, this method validation study in a homogenous DRE population may have potential in narrowing the SOZ-candidates for epileptogenicity in other DRE populations and utility in other neurological disorders.

Integrating Network Neuroscience Into Epilepsy Care: Progress, Barriers, and Next Steps

Drug resistant epilepsy is a disorder involving widespread brain network alterations. Recently, many groups have reported neuroimaging and electrophysiology network analysis techniques to aid medical management, support presurgical planning, and understand postsurgical seizure persistence. While these approaches may supplement standard tests to improve care, they are not yet used clinically or influencing medical or surgical decisions. When will this change? Which approaches have shown the most promise? What are the barriers to translating them into clinical use? How do we facilitate this transition? In this review, we will discuss progress, barriers, and next steps regarding the integration of brain network analysis into the medical and presurgical pipeline.

Predictive value of metabolic and perfusion changes outside the seizure onset zone for postoperative outcome in patients with refractory focal epilepsy

The value of functional molecular changes outside the seizure onset zone as independent predictive factors of surgical outcome has been scarcely evaluated. The aim of this retrospective study was to evaluate relative metabolic and perfusion changes outside the seizure onset zone as predictors of postoperative outcome in patients with unifocal refractory focal epilepsy. Eighty-six unifocal epilepsy patients who underwent ¹⁸F-FDG PET prior to surgery were included. Ictal and interictal perfusion SPECT was available in 65 patients. Good postoperative outcome was defined as the International League against Epilepsy class 1. Using univariate statistical analysis, the predictive ability of volume-of-interest based relative metabolism/perfusion for outcome classification was quantified by AUC ROC-curve, using composite, unilateral cortical (frontal, orbitofrontal, temporal, parietal, occipital) and central volumes-of-interest. The results were cross-validated, and a false discovery rate (FDR) correction was applied. As a secondary objective, a subgroup analysis was performed on temporal lobe epilepsy patients (N = 64). Increased relative ictal perfusion in the contralateral central volume-of-interest was significantly associated with the good surgical outcome both in the total population (AUC 0.79, p_FDR = 0.009) and the temporal lobe epilepsy subgroup (AUC 0.80, p_FDR = 0.028). No other significant associations between functional molecular changes and postoperative outcome were found. Increased relative ictal perfusion in the contralateral central region significantly predicted outcome after epilepsy surgery in patients with refractory focal epilepsy. We postulate that these relative perfusion changes could be an expression of better preoperative neuronal network integration and centralization in the contralateral central structures, which is suggested to be associated with better postoperative outcome.

Association of network connectivity via resting state functional MRI with consciousness, mortality, and outcomes in neonatal acute brain injury

BACKGROUND

An accurate and comprehensive test of integrated brain network function is needed for neonates during the acute brain injury period to inform on morbidity. This retrospective cohort study assessed whether integrated brain network function acquired by resting state functional MRI during the acute period in neonates with brain injury, is associated with acute exam, neonatal mortality, and 6-month outcomes.

METHODS

Study subjects included 40 consecutive neonates with resting state functional MRI acquired within 31 days after suspected brain insult from March 2018 to July 2019 at Phoenix Children's Hospital. Acute-period exam and test results were assigned ordinal scores based on severity as documented by respective treating specialists. Analyses (Fisher exact, Wilcoxon-rank sum test, ordinal/multinomial logistic regression) examined association of resting state networks with demographics, presentation, neurological exam, electroencephalogram, anatomical MRI, magnetic resonance spectroscopy, passive task functional MRI, and outcomes of discharge condition, outpatient development, motor tone, seizure, and mortality.

RESULTS

Subjects had a mean (standard deviation) gestational age of 37.8 (2.6) weeks, a majority were male (63%), with a diagnosis of hypoxic ischemic encephalopathy (68%). Findings at birth included mild distress (48%), moderately abnormal neurological exam (33%), and consciousness characterized as awake but irritable (40%). Significant associations after multiple testing corrections were detected for resting state networks: basal ganglia with outpatient developmental delay (odds ratio [OR], 14.5; 99.4% confidence interval [CI], 2.00-105; P < .001) and motor tone/weakness (OR, 9.98; 99.4% CI, 1.72-57.9; P < .001); language/frontoparietal network with discharge condition (OR, 5.13; 99.4% CI, 1.22-21.5; P = .002) and outpatient developmental delay (OR, 4.77; 99.4% CI, 1.21-18.7; P=.002); default mode network with discharge condition (OR, 3.72; 99.4% CI, 1.01-13.78; P=.006) and neurological exam (P = .002 (FE); OR, 11.8; 99.4% CI, 0.73-191; P = .01 (OLR)); and seizure onset zone with motor tone/weakness (OR, 3.31; 99.4% CI, 1.08-10.1; P=.003). Resting state networks were not detected in three neonates, who died prior to discharge.

CONCLUSIONS

This study provides level 3 evidence (OCEBM Levels of Evidence Working Group) demonstrating that in neonatal acute brain injury, the degree of abnormality of resting state networks is associated with acute exam and outcomes. Total lack of brain network detection was only found in patients who did not survive.

Clinical applications of magnetic resonance imaging based functional and structural connectivity

Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective.

Presurgical Evaluation Strategies for Intractable Epilepsy of Childhood

For children who continue to experience seizures despite treatment with antiseizure medications, epilepsy surgery can be considered. The goals of the presurgical evaluation are to determine the best surgical approach to render a good outcome. In patients with drug resistant focal epilepsy, the epileptogenic zone defines the minimal brain volume which must be resected for surgical success and to delineate the relationship of this region with functional cortex. A number of noninvasive tools for these tasks have emerged over the past decade, and existing technologies have been revised and improved. In this review, we examine the recent published evidence for these techniques, specifically as applied to the pediatric population. Discussed herein are the diagnostic value of methods such as video electroencephalography, magnetic resonance imaging, and supportive neuroimaging techniques including single photon emission tomography, photon emission tomography, and magnetoencephalography. Functional testing including functional magnetic resonance imaging, electrical stimulation mapping, and transcranial magnetic stimulation are considered in the context of pediatric epilepsy. The application of emerging techniques to preoperative testing such as source localization, image post-processing, and artificial intelligence is covered. We summarize the relative value of presurgical testing based on patient characteristics, including lesional or nonlesional MRI, temporal or extratemporal epilepsy, and other factors relevant in pediatric epilepsy such as pathological substrate and age.

Resting State Functional Magnetic Resonance Imaging Elucidates Neurotransmitter Deficiency in Autism Spectrum Disorder

Resting-state functional magnetic resonance imaging provides dynamic insight into the functional organization of the brains' intrinsic activity at rest. The emergence of resting-state functional magnetic resonance imaging in both the clinical and research settings may be attributed to recent advancements in statistical techniques, non-invasiveness and enhanced spatiotemporal resolution compared to other neuroimaging modalities, and the capability to identify and characterize deep brain structures and networks. In this report we describe a 16-year-old female patient with autism spectrum disorder who underwent resting-state functional magnetic resonance imaging due to late regression. Imaging revealed deactivated networks in deep brain structures involved in monoamine synthesis. Monoamine neurotransmitter deficits were confirmed by cerebrospinal fluid analysis. This case suggests that resting-state functional magnetic resonance imaging may have clinical utility as a non-invasive biomarker of central nervous system neurochemical alterations by measuring the function of neurotransmitter-driven networks. Use of this technology can accelerate and increase the accuracy of selecting appropriate therapeutic agents for patients with neurological and neurodevelopmental disorders.

Individual localization value of resting-state fMRI in epilepsy presurgical evaluation: A combined study with stereo-EEG

OBJECTIVE

To examine the individual-patient-level localization value of resting-state functional MRI (rsfMRI) metrics for the seizure onset zone (SOZ) defined by stereo-electroencephalography (SEEG) in patients with medically intractable focal epilepsies.

METHODS

We retrospectively included 19 patients who underwent SEEG implantation for epilepsy presurgical evaluation. Voxel-wise whole-brain analysis was performed on 3.0 T rsfMRI to generate clusters for amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo) and degree centrality (DC), which were co-registered with the SEEG-defined SOZ to evaluate their spatial overlap. Subgroup and correlation analyses were conducted for various clinical characteristics.

RESULTS

ALFF demonstrated concordant clusters with SEEG-defined SOZ in 73.7% of patients, with 93.3% sensitivity and 77.8% PPV. The concordance rate showed no significant difference when subgrouped by lesional/non-lesional MRI, SOZ location, interictal epileptiform discharges on scalp EEG, pathology or seizure outcomes. No significant correlation was seen between ALFF concordance rate and epilepsy duration, seizure-onset age, seizure frequency or number of antiseizure medications. ReHo and DC did not achieve favorable concordance results (10.5% and 15.8%, respectively). All concordant clusters showed regional activation, representing increased neural activities.

CONCLUSION

ALFF had high concordance rate with SEEG-defined SOZ at individual-patient level.

SIGNIFICANCE

ALFF activation on rsfMRI can add localizing information for the noninvasive presurgical workup of intractable focal epilepsies.

Locked-in Intact Functional Networks in Children with Autism Spectrum Disorder: A Case-Control Study

Resting-state functional magnetic resonance imaging (rs-fMRI) has the potential to investigate abnormalities in brain network structure and connectivity on an individual level in neurodevelopmental disorders, such as autism spectrum disorder (ASD), paving the way toward using this technology for a personalized, precision medicine approach to diagnosis and treatment. Using a case-control design, we compared five patients with severe regressive-type ASD to five patients with temporal lobe epilepsy (TLE) to examine the association between brain network characteristics and diagnosis. All children with ASD and TLE demonstrated intact motor, language, and frontoparietal (FP) networks. However, aberrant networks not usually seen in the typical brain were also found. These aberrant networks were located in the motor (40%), language (80%), and FP (100%) regions in children with ASD, while children with TLE only presented with aberrant networks in the motor (40%) and language (20%) regions, in addition to identified seizure onset zones. Fisher's exact test indicated a significant relationship between aberrant FP networks and diagnosis (p = 0.008), with ASD and atypical FP networks co-occurring more frequently than expected by chance. Despite severe cognitive delays, children with regressive-type ASD may demonstrate intact typical cortical network activation despite an inability to use these cognitive facilities. The functions of these intact cognitive networks may not be fully expressed, potentially because aberrant networks interfere with their long-range signaling, thus creating a unique "locked-in network" syndrome.

An exploratory study into the influence of laterality and location of hippocampal sclerosis on seizure prognosis and global cortical thinning

In mesial temporal lobe epilepsy (mTLE), the correlation between disease duration, seizure laterality, and rostro-caudal location of hippocampal sclerosis has not been examined in the context of seizure severity and global cortical thinning. In this retrospective study, we analyzed structural 3 T MRI from 35 mTLE subjects. Regions of FLAIR hyperintensity (as an indicator of sclerosis)—based on 2D coronal FLAIR sequences—in the hippocampus were manually segmented, independently and in duplicate; degree of segmentation agreement was confirmed using the DICE index. Segmented lesions were used for separate analyses. First, the correlation of cortical thickness with disease duration and seizure focus laterality was explored using linear model regression. Then, the relationship between the rostro-caudal location of the FLAIR hyperintense signal and seizure severity, based on the Cleveland Clinic seizure freedom score (ccSFS), was explored using probabilistic voxel-wise mapping and functional connectivity analysis from normative data. The mean DICE Index was 0.71 (range 0.60–0.81). A significant correlation between duration of epilepsy and decreased mean whole brain cortical thickness was identified, regardless of seizure laterality (p < 0.05). The slope of cortical volume loss over time, however, was greater in subjects with right seizure focus. Based on probabilistic voxel-wise mapping, FLAIR hyperintensity in the posterior hippocampus was significantly associated with lower ccSFS scores (greater seizure severity). Finally, the right hippocampus was found to have greater brain-wide connectivity, compared to the left side, based on normative connectomic data. We have demonstrated a significant correlation between duration of epilepsy and right-sided seizure focus with global cortical thinning, potentially due to greater brain-wide connectivity. Sclerosis along the posterior hippocampus was associated with greater seizure severity, potentially serving as an important biomarker of seizure outcome after surgery.

Individual variability in functional connectivity architecture of the mouse brain

In recent years precision fMRI has emerged in human brain research, demonstrating characterization of individual differences in brain organization. However, mechanistic investigations to the sources of individual variability are limited in humans and thus require animal models. Here, we used resting-state fMRI in awake mice to quantify the contribution of individual variation to the functional architecture of the mouse cortex. We found that the mouse connectome is also characterized by stable individual features that support connectivity-based identification. Unlike in humans, we found that individual variation is homogeneously distributed in sensory and association networks. Finally, connectome-based predictive modeling of motor behavior in the rotarod task revealed that individual variation in functional connectivity explained behavioral variability. Collectively, these results establish the feasibility of precision fMRI in mice and lay the foundation for future mechanistic investigations of individual brain organization and pre-clinical studies of brain disorders in the context of personalized medicine.

Brain-responsive corticothalamic stimulation in the centromedian nucleus for the treatment of regional neocortical epilepsy

OBJECTIVE

The aim of the study was to determine if corticothalamic responsive stimulation targeting the centromedian nucleus of the thalamus (CMT) is a potential treatment for neocortical epilepsies with regional onsets.

METHODS

We assessed efficacy and safety of CMT and neocortical responsive stimulation, detection, and stimulation programming, methods for implantation, and location and patterns of electrographic seizure onset and spread in 7 patients with medically intractable focal seizures with a regional neocortical onset.

RESULTS

The median follow-up duration was 17 months (average: 17 months, range: 8-28 months). The median % reduction in disabling seizures (excludes auras) in the 7 patients was 88% (mean: 80%, range: 55-100%). The median % reduction in all seizure types (disabling + auras) was 73% (mean: 67%, range: 15-94%). There were no adverse events related to implantation of the responsive neurostimulator and leads or related to the delivery of responsive stimulation. Stimulation-related contralateral paresthesias were addressed by adjusting stimulation parameters in the clinic during stimulation testing. Electrographic seizures were detected in the CMT and neocortex in all seven patients. Four patients had simultaneous or near simultaneous seizure onsets in the neocortex and CMT and three had onsets in the neocortex with spread to the CMT.

CONCLUSION

In this small series of patients with medically intractable focal seizures and regional neocortical onset, responsive neurostimulation to the neocortex and CMT improved seizure control and was well tolerated.

SIGNIFICANCE

Responsive corticothalamic neurostimulation of the CMT and neocortex is a potential treatment for patients with regional neocortical epilepsies.

Contributions of Imaging to Neuromodulatory Treatment of Drug-Refractory Epilepsy

Epilepsy affects about 1% of the world's population, and up to 30% of all patients will ultimately not achieve freedom from seizures with anticonvulsive medication alone. While surgical resection of a magnetic resonance imaging (MRI) -identifiable lesion remains the first-line treatment option for drug-refractory epilepsy, surgery cannot be offered to all. Neuromodulatory therapy targeting "seizures" instead of "epilepsy" has emerged as a valuable treatment option for these patients, including invasive procedures such as deep brain stimulation (DBS), responsive neurostimulation (RNS) and peripheral approaches such as vagus nerve stimulation (VNS). The purpose of this review is to provide in-depth information on current concepts and evidence on network-level aspects of drug-refractory epilepsy. We reviewed the current evidence gained from studies utilizing advanced imaging methodology, with a specific focus on their contributions to neuromodulatory therapy.

Identification of neural networks preferentially engaged by epileptogenic mass lesions through lesion network mapping analysis

Lesion network mapping (LNM) has been applied to true lesions (e.g., cerebrovascular lesions in stroke) to identify functionally connected brain networks. No previous studies have utilized LNM for analysis of intra-axial mass lesions. Here, we implemented LNM for identification of potentially vulnerable epileptogenic networks in mass lesions causing medically-refractory epilepsy (MRE). Intra-axial brain lesions were manually segmented in patients with MRE seen at our institution (EL_INST). These lesions were then normalized to standard space and used as seeds in a high-resolution normative resting state functional magnetic resonance imaging template. The resulting connectivity maps were first thresholded (p_{Bonferroni_cor} < 0.05) and binarized; the thresholded binarized connectivity maps were subsequently summed to produce overall group connectivity maps, which were compared with established resting-state networks to identify potential networks prone to epileptogenicity. To validate our data, this approach was also applied to an external dataset of epileptogenic lesions identified from the literature (EL_LIT). As an additional exploratory analysis, we also segmented and computed the connectivity of institutional non-epileptogenic lesions (NEL_INST), calculating voxel-wise odds ratios (VORs) to identify voxels more likely to be functionally-connected with EL_INST versus NEL_INST. To ensure connectivity results were not driven by anatomical overlap, the extent of lesion overlap between EL_INST, and EL_LIT and NEL_INST was assessed using the Dice Similarity Coefficient (DSC, lower index ~ less overlap). Twenty-eight patients from our institution were included (EL_INST: 17 patients, 17 lesions, 10 low-grade glioma, 3 cavernoma, 4 focal cortical dysplasia; NEL_INST: 11 patients, 33 lesions, all brain metastases). An additional 23 cases (25 lesions) with similar characteristics to the EL_INST data were identified from the literature (EL_LIT). Despite minimal anatomical overlap of lesions, both EL_INST and EL_LIT showed greatest functional connectivity overlap with structures in the Default Mode Network, Frontoparietal Network, Ventral Attention Network, and the Limbic Network-with percentage volume overlap of 19.5%, 19.1%, 19.1%, and 12.5%, respectively-suggesting them as networks consistently engaged by epileptogenic mass lesions. Our exploratory analysis moreover showed that the mesial frontal lobes, parahippocampal gyrus, and lateral temporal neocortex were at least twice as likely to be functionally connected with the EL_INST compared to the NEL_INST group (i.e. Peak VOR > 2.0); canonical resting-state networks preferentially engaged by EL_INSTs were the Limbic and the Frontoparietal Networks (Mean VOR > 1.5). In this proof of concept study, we demonstrate the feasibility of LNM for intra-axial mass lesions by showing that ELs have discrete functional connections and may preferentially engage in discrete resting-state networks. Thus, the underlying normative neural circuitry may, in part, explain the propensity of particular lesions toward the development of MRE. If prospectively validated, this has ramifications for patient counseling along with both approach and timing of surgery for lesions in locations prone to development of MRE.

Resting-state functional MRI connectivity impact on epilepsy surgery plan and surgical candidacy: prospective clinical work

OBJECTIVE

The authors' goal was to prospectively quantify the impact of resting-state functional MRI (rs-fMRI) on pediatric epilepsy surgery planning.

METHODS

Fifty-one consecutive patients (3 months to 20 years old) with intractable epilepsy underwent rs-fMRI for presurgical evaluation. The team reviewed the following available diagnostic data: video-electroencephalography (n = 51), structural MRI (n = 51), FDG-PET (n = 42), magnetoencephalography (n = 5), and neuropsychological testing (n = 51) results to formulate an initial surgery plan blinded to the rs-fMRI findings. Subsequent to this discussion, the connectivity results were revealed and final recommendations were established. Changes between pre- and post-rs-fMRI treatment plans were determined, and changes in surgery recommendation were compared using McNemar's test.

RESULTS

Resting-state fMRI was successfully performed in 50 (98%) of 51 cases and changed the seizure onset zone localization in 44 (88%) of 50 patients. The connectivity results prompted 6 additional studies, eliminated the ordering of 11 further diagnostic studies, and changed the intracranial monitoring plan in 10 cases. The connectivity results significantly altered surgery planning with the addition of 13 surgeries, but it did not eliminate planned surgeries (p = 0.003). Among the 38 epilepsy surgeries performed, the final surgical approach changed due to rs-fMRI findings in 22 cases (58%), including 8 (28%) of 29 in which extraoperative direct electrical stimulation mapping was averted.

CONCLUSIONS

This study demonstrates the impact of rs-fMRI connectivity results on the decision-making for pediatric epilepsy surgery by providing new information about the location of eloquent cortex and the seizure onset zone. Additionally, connectivity results may increase the proportion of patients considered eligible for surgery while optimizing the need for further testing.

Functional Networks in Epilepsy Presurgical Evaluation

Continuing advancements in neuroimaging methodology allow for increasingly detailed in vivo characterization of structural and functional brain networks, leading to the recognition of epilepsy as a disorder of large-scale networks. In surgical candidates, analysis of functional networks has proved invaluable for the identification of eloquent brain areas, such as hemispherical language dominance. More recently, connectome-based biomarkers have demonstrated potential to further inform clinical decision making in drug-refractory epilepsy. This article summarizes current evidence on epilepsy as a network disorder, emphasizing potential benefits of network analysis techniques for preoperative assessments and resection planning.

Beyond the Epileptic Focus: Functional Epileptic Networks in Focal Epilepsy

Focal epilepsy can be conceptualized as a network disorder, and the functional epileptic network can be described as a complex system of multiple brain areas that interact dynamically to generate epileptic activity. However, we still do not fully understand the functional architecture of epileptic networks. We studied a cohort of 21 patients with extratemporal focal epilepsy. We used independent component analysis of functional magnetic resonance imaging (fMRI) data. In order to identify the epilepsy-related components, we examined the general linear model-derived electroencephalography-fMRI (EEG–fMRI) time courses associated with interictal epileptic activity as intrinsic hemodynamic epileptic biomarkers. Independent component analysis revealed components related to the epileptic time courses in all 21 patients. Each epilepsy-related component described a network of spatially distributed brain areas that corresponded to the specific epileptic network in each patient. We also provided evidence for the interaction between the epileptic activity generated at the epileptic network and the physiological resting state networks. Our findings suggest that independent component analysis, guided by EEG–fMRI epileptic time courses, have the potential to define the functional architecture of the epileptic network in a noninvasive way. These data could be useful in planning invasive EEG electrode placement, guiding surgical resections, and more effective therapeutic interventions.

Lesion Network Localization of Seizure Freedom following MR-guided Laser Interstitial Thermal Ablation

Treatment-resistant epilepsy is a common and debilitating neurological condition, for which neurosurgical cure is possible. Despite undergoing nearly identical ablation procedures however, individuals with treatment-resistant epilepsy frequently exhibit heterogeneous outcomes. We hypothesized that treatment response may be related to the brain regions to which MR-guided laser ablation volumes are functionally connected. To test this, we mapped the resting-state functional connectivity of surgical ablations that either resulted in seizure freedom (N = 11) or did not result in seizure freedom (N = 16) in over 1,000 normative connectomes. There was no difference seizure outcome with respect to the anatomical location of the ablations, and very little overlap between ablation areas was identified using the Dice Index. Ablations that did not result in seizure-freedom were preferentially connected to a number of cortical and subcortical regions, as well as multiple canonical resting-state networks. In contrast, ablations that led to seizure-freedom were more functionally connected to prefrontal cortices. Here, we demonstrate that underlying normative neural circuitry may in part explain heterogenous outcomes following ablation procedures in different brain regions. These findings may ultimately inform target selection for ablative epilepsy surgery based on normative intrinsic connectivity of the targeted volume.