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Background: Portable low-field-strength magnetic resonance imaging (MRI) systems represent a promising alternative to traditional high-field-strength systems with the potential to make MR technology available at scale in lowresource settings. However, lower image quality and resolution may limit the research and clinical potential of these devices. We tested two super-resolution methods to enhance image quality in a low-field MR system and compared their correspondence with images acquired from a high-field system in a sample of young people. Methods: T1- and T2-weighted structural MR images were obtained from a low- field (64mT) Hyperfine and high-field (3T) Siemens system in N = 70 individuals (mean age = 20.39 years, range 9--26 years). We tested two super-resolution approaches to improve image correspondence between images acquired at high- and low-field: (1) processing via a convolutional neural network ('SynthSR'), and (2) multi-orientation image averaging. We extracted brain region volumes, cortical thickness, and cortical surface area estimates. We used Pearson correlations to test the correspondence between these measures, and Steiger Z tests to compare the difference in correspondence between standard imaging and super-resolution approaches. Results: Single pairs of T1- and T2-weighted images acquired at low field showed high correspondence to high-field-strength images for estimates of total intracranial volume, surface area cortical volume, subcortical volume, and total brain volume (r range = 0.60--0.88). Correspondence was lower for cerebral white matter volume (r = 0.32, p = 0.007, q = 0.009) and non-significant for mean cortical thickness (r = -0.05, p = 0.664, q = 0.664). Processing images with SynthSR yielded significant improvements in correspondence for total brain volume, white matter volume, total surface area, subcortical volume, cortical volume, and total intracranial volume (r range = 0.85--0.97), with the exception of global mean cortical thickness (r = 0.14). An alternative multi-orientation image averaging approach improved correspondence for cerebral white matter and total brain volume. Processing with SynthSR also significantly improved correspondence across widespread regions for estimates of cortical volume, surface area and subcortical volume, as well as within isolated prefrontal and temporal regions for estimates of cortical thickness. Conclusion: Applying super-resolution approaches to low-field imaging improves regional brain volume and surface area accuracy in young people. Finer-scale brain measurements, such as cortical thickness, remain challenging with the limited resolution of low-field systems. [ABSTRACT FROM AUTHOR]

Objective: Epilepsy patients are often grouped together by clinical variables. Quantitative neuroimaging metrics can provide a data‐driven alternative for grouping of patients. In this work, we leverage ultra‐high‐field 7‐T structural magnetic resonance imaging (MRI) to characterize volumetric atrophy patterns across hippocampal subfields and thalamic nuclei in drug‐resistant focal epilepsy. Methods: Forty‐two drug‐resistant epilepsy patients and 13 controls with 7‐T structural neuroimaging were included in this study. We measured hippocampal subfield and thalamic nuclei volumetry, and applied an unsupervised machine learning algorithm, Latent Dirichlet Allocation (LDA), to estimate atrophy patterns across the hippocampal subfields and thalamic nuclei of patients. We studied the association between predefined clinical groups and the estimated atrophy patterns. Additionally, we used hierarchical clustering on the LDA factors to group patients in a data‐driven approach. Results: In patients with mesial temporal sclerosis (MTS), we found a significant decrease in volume across all ipsilateral hippocampal subfields (false discovery rate‐corrected p [pFDR] <.01) as well as in some ipsilateral (pFDR <.05) and contralateral (pFDR <.01) thalamic nuclei. In left temporal lobe epilepsy (L‐TLE) we saw ipsilateral hippocampal and some bilateral thalamic atrophy (pFDR <.05), whereas in right temporal lobe epilepsy (R‐TLE) extensive bilateral hippocampal and thalamic atrophy was observed (pFDR <.05). Atrophy factors demonstrated that our MTS cohort had two atrophy phenotypes: one that affected the ipsilateral hippocampus and one that affected the ipsilateral hippocampus and bilateral anterior thalamus. Atrophy factors demonstrated posterior thalamic atrophy in R‐TLE, whereas an anterior thalamic atrophy pattern was more common in L‐TLE. Finally, hierarchical clustering of atrophy patterns recapitulated clusters with homogeneous clinical properties. Significance: Leveraging 7‐T MRI, we demonstrate widespread hippocampal and thalamic atrophy in epilepsy. Through unsupervised machine learning, we demonstrate patterns of volumetric atrophy that vary depending on disease subtype. Incorporating these atrophy patterns into clinical practice could help better stratify patients to surgical treatments and specific device implantation strategies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Since the introduction of MRI as a sustainable diagnostic modality, global accessibility to its services has revealed a wide discrepancy between populations—leaving most of the population in LMICs without access to this important imaging modality. Several factors lead to the scarcity of MRI in LMICs; for example, inadequate infrastructure and the absence of a dedicated workforce are key factors in the scarcity observed. RAD‐AID has contributed to the advancement of radiology globally by collaborating with our partners to make radiology more accessible for medically underserved communities. However, progress is slow and further investment is needed to ensure improved global access to MRI. [ABSTRACT FROM AUTHOR]

In March 2022, the first ISMRM Workshop on Low‐Field MRI was held virtually. The goals of this workshop were to discuss recent low field MRI technology including hardware and software developments, novel methodology, new contrast mechanisms, as well as the clinical translation and dissemination of these systems. The virtual Workshop was attended by 368 registrants from 24 countries, and included 34 invited talks, 100 abstract presentations, 2 panel discussions, and 2 live scanner demonstrations. Here, we report on the scientific content of the Workshop and identify the key themes that emerged. The subject matter of the Workshop reflected the ongoing developments of low‐field MRI as an accessible imaging modality that may expand the usage of MRI through cost reduction, portability, and ease of installation. Many talks in this Workshop addressed the use of computational power, efficient acquisitions, and contemporary hardware to overcome the SNR limitations associated with low field strength. Participants discussed the selection of appropriate clinical applications that leverage the unique capabilities of low‐field MRI within traditional radiology practices, other point‐of‐care settings, and the broader community. The notion of "image quality" versus "information content" was also discussed, as images from low‐field portable systems that are purpose‐built for clinical decision‐making may not replicate the current standard of clinical imaging. Speakers also described technical challenges and infrastructure challenges related to portability and widespread dissemination, and speculated about future directions for the field to improve the technology and establish clinical value. [ABSTRACT FROM AUTHOR]

Objective: Epilepsy surgery is an effective treatment for drug‐resistant patients. However, how different surgical approaches affect long‐term brain structure remains poorly characterized. Here, we present a semiautomated method for quantifying structural changes after epilepsy surgery and compare the remote structural effects of two approaches, anterior temporal lobectomy (ATL), and selective amygdalohippocampectomy (SAH). Methods: We studied 36 temporal lobe epilepsy patients who underwent resective surgery (ATL = 22, SAH = 14). All patients received same‐scanner MR imaging preoperatively and postoperatively (mean 2 years). To analyze postoperative structural changes, we segmented the resection zone and modified the Advanced Normalization Tools (ANTs) longitudinal cortical pipeline to account for resections. We compared global and regional annualized cortical thinning between surgical treatments. Results: Across procedures, there was significant cortical thinning in the ipsilateral insula, fusiform, pericalcarine, and several temporal lobe regions outside the resection zone as well as the contralateral hippocampus. Additionally, increased postoperative cortical thickness was seen in the supramarginal gyrus. Patients treated with ATL exhibited greater annualized cortical thinning compared with SAH cases (ATL: −0.08 ± 0.11 mm per year, SAH: −0.01 ± 0.02 mm per year, t = 2.99, P = 0.006). There were focal postoperative differences between the two treatment groups in the ipsilateral insula (P = 0.039, corrected). Annualized cortical thinning rates correlated with preoperative cortical thickness (r = 0.60, P < 0.001) and had weaker associations with age at surgery (r = −0.33, P = 0.051) and disease duration (r = −0.42, P = 0.058). Significance: Our evidence suggests that selective procedures are associated with less cortical thinning and that earlier surgical intervention may reduce long‐term impacts on brain structure. [ABSTRACT FROM AUTHOR]

Objective: Temporal lobe epilepsy (TLE) is the most common type of focal epilepsy. An increasingly identified subset of patients with TLE consists of those who show bilaterally independent temporal lobe seizures. The purpose of this study was to leverage network neuroscience to better understand the interictal whole brain network of bilateral TLE (BiTLE). Methods: In this study, using a multicenter resting state functional magnetic resonance imaging (rs‐fMRI) data set, we constructed whole‐brain functional networks of 19 patients with BiTLE, and compared them to those of 75 patients with unilateral TLE (UTLE). We quantified resting‐state, whole‐brain topological properties using metrics derived from network theory, including clustering coefficient, global efficiency, participation coefficient, and modularity. For each metric, we computed an average across all brain regions, and iterated this process across network densities. Curves of network density vs each network metric were compared between groups. Finally, we derived a combined metric, which we term the "integration‐segregation axis," by combining whole‐brain average clustering coefficient and global efficiency curves, and applying principal component analysis (PCA)–based dimensionality reduction. Results: Compared to UTLE, BiTLE had decreased global efficiency (p =.031), and decreased whole brain average participation coefficient across a range of network densities (p =.019). Modularity maximization yielded a larger number of smaller communities in BiTLE than in UTLE (p =.020). Differences in network properties separate BiTLE and UTLE along the integration‐segregation axis, with regions within the axis having a specificity of up to 0.87 for BiTLE. Along the integration‐segregation axis, UTLE patients with poor surgical outcomes were distributed in the same regions as BiTLE, and network metrics confirmed similar patterns of increased segregation in both BiTLE and poor outcome UTLE. Significance: Increased interictal whole‐brain network segregation, as measured by rs‐fMRI, is specific to BiTLE, as well as poor surgical outcome UTLE, and may assist in non‐invasively identifying this patient population prior to intracranial electroencephalography or device implantation. [ABSTRACT FROM AUTHOR]

Objective: Measuring cortico‐cortical evoked potentials (CCEPs) is a promising tool for mapping epileptic networks, but it is not known how variability in brain state and stimulation technique might impact the use of CCEPs for epilepsy localization. We test the hypotheses that (1) CCEPs demonstrate systematic variability across trials and (2) CCEP amplitudes depend on the timing of stimulation with respect to endogenous, low‐frequency oscillations. Methods: We studied 11 patients who underwent CCEP mapping after stereo‐electroencephalography electrode implantation for surgical evaluation of drug‐resistant epilepsy. Evoked potentials were measured from all electrodes after each pulse of a 30 s, 1 Hz bipolar stimulation train. We quantified monotonic trends, phase dependence, and standard deviation (SD) of N1 (15–50 ms post‐stimulation) and N2 (50–300 ms post‐stimulation) amplitudes across the 30 stimulation trials for each patient. We used linear regression to quantify the relationship between measures of CCEP variability and the clinical seizure‐onset zone (SOZ) or interictal spike rates. Results: We found that N1 and N2 waveforms exhibited both positive and negative monotonic trends in amplitude across trials. SOZ electrodes and electrodes with high interictal spike rates had lower N1 and N2 amplitudes with higher SD across trials. Monotonic trends of N1 and N2 amplitude were more positive when stimulating from an area with higher interictal spike rate. We also found intermittent synchronization of trial‐level N1 amplitude with low‐frequency phase in the hippocampus, which did not localize the SOZ. Significance: These findings suggest that standard approaches for CCEP mapping, which involve computing a trial‐averaged response over a.2–1 Hz stimulation train, may be masking inter‐trial variability that localizes to epileptogenic tissue. We also found that CCEP N1 amplitudes synchronize with ongoing low‐frequency oscillations in the hippocampus. Further targeted experiments are needed to determine whether phase‐locked stimulation could have a role in localizing epileptogenic tissue. [ABSTRACT FROM AUTHOR]

Temporal lobe epilepsy (TLE) is one of the most common subtypes of focal epilepsy, with mesial temporal sclerosis (MTS) being a common radiological and histopathological finding. Accurate identification of MTS during presurgical evaluation confers an increased chance of good surgical outcome. Here we propose the use of glutamate‐weighted chemical exchange saturation transfer (GluCEST) magnetic resonance imaging (MRI) at 7 Tesla for mapping hippocampal glutamate distribution in epilepsy, allowing to differentiate lesional from non‐lesional mesial TLE. We demonstrate that a directional asymmetry index, which quantifies the relative difference between GluCEST contrast in hippocampi ipsilateral and contralateral to the seizure onset zone, can differentiate between sclerotic and non‐sclerotic hippocampi, even in instances where traditional presurgical MRI assessments did not provide evidence of sclerosis. Overall, our results suggest that hippocampal glutamate mapping through GluCEST imaging is a valuable addition to the presurgical epilepsy evaluation toolbox. [ABSTRACT FROM AUTHOR]

Modern MRI scanners have trended toward higher field strengths to maximize signal and resolution while minimizing scan time. However, high‐field devices remain expensive to install and operate, making them scarce outside of high‐income countries and major population centers. Low‐field strength scanners have drawn renewed academic, industry, and philanthropic interest due to advantages that could dramatically increase imaging access, including lower cost and portability. Nevertheless, low‐field MRI still faces inherent limitations in image quality that come with decreased signal. In this article, we review advantages and disadvantages of low‐field MRI scanners, describe hardware and software innovations that accentuate advantages and mitigate disadvantages, and consider clinical applications for a new generation of low‐field devices. In our review, we explore how these devices are being or could be used for high acuity brain imaging, outpatient neuroimaging, MRI‐guided procedures, pediatric imaging, and musculoskeletal imaging. Challenges for their successful clinical translation include selecting and validating appropriate use cases, integrating with standards of care in high resource settings, expanding options with actionable information in low resource settings, and facilitating health care providers and clinical practice in new ways. By embracing both the promise and challenges of low‐field MRI, clinicians and researchers have an opportunity to transform medical care for patients around the world. Level of Evidence: 5 Technical Efficacy: Stage 6 [ABSTRACT FROM AUTHOR]

Planning surgery for patients with medically refractory epilepsy often requires recording seizures using intracranial EEG. Quantitative measures derived from interictal intracranial EEG yield potentially appealing biomarkers to guide these surgical procedures; however, their utility is limited by the sparsity of electrode implantation as well as the normal confounds of spatiotemporally varying neural activity and connectivity. We propose that comparing intracranial EEG recordings to a normative atlas of intracranial EEG activity and connectivity can reliably map abnormal regions, identify targets for invasive treatment and increase our understanding of human epilepsy. Merging data from the Penn Epilepsy Center and a public database from the Montreal Neurological Institute, we aggregated interictal intracranial EEG retrospectively across 166 subjects comprising >5000 channels. For each channel, we calculated the normalized spectral power and coherence in each canonical frequency band. We constructed an intracranial EEG atlas by mapping the distribution of each feature across the brain and tested the atlas against data from novel patients by generating a z-score for each channel. We demonstrate that for seizure onset zones within the mesial temporal lobe, measures of connectivity abnormality provide greater distinguishing value than univariate measures of abnormal neural activity. We also find that patients with a longer diagnosis of epilepsy have greater abnormalities in connectivity. By integrating measures of both single-channel activity and inter-regional functional connectivity, we find a better accuracy in predicting the seizure onset zones versus normal brain (area under the curve = 0.77) compared with either group of features alone. We propose that aggregating normative intracranial EEG data across epilepsy centres into a normative atlas provides a rigorous, quantitative method to map epileptic networks and guide invasive therapy. We publicly share our data, infrastructure and methods, and propose an international framework for leveraging big data in surgical planning for refractory epilepsy. [ABSTRACT FROM AUTHOR]

Epilepsy is among one of the most common neurologic disorders. The role of magnetic resonance imaging (MRI) in the diagnosis and management of patients with epilepsy is well established, and most patients with epilepsy are likely to undergo at least one or more MRI examinations in the course of their disease. Recent advances in high-field MRI have enabled high resolution in vivo visualization of small and intricate anatomic structures that are of great importance in the assessment of seizure disorders. Familiarity with normal anatomic variations is essential in the accurate diagnosis and image interpretation, as these variations may be mistaken for epileptogenic foci, leading to unnecessary follow-up imaging, or worse, unnecessary treatment. After a brief overview of normal imaging anatomy of the mesial temporal lobe, this article will review a few important common and uncommon anatomic variations, mimics, and pitfalls that may be encountered in the imaging evaluation of patients with epilepsy. [ABSTRACT FROM AUTHOR]

Researchers have used direct electrical brain stimulation to treat a range of neurological and psychiatric disorders. However, for brain stimulation to be maximally effective, clinicians and researchers should optimize stimulation parameters according to desired outcomes. The goal of our large-scale study was to comprehensively evaluate the effects of stimulation at different parameters and locations on neuronal activity across the human brain. To examine how different kinds of stimulation affect human brain activity, we compared the changes in neuronal activity that resulted from stimulation at a range of frequencies, amplitudes, and locations with direct human brain recordings. We recorded human brain activity directly with electrodes that were implanted in widespread regions across 106 neurosurgical epilepsy patients while systematically stimulating across a range of parameters and locations. Overall, stimulation most often had an inhibitory effect on neuronal activity, consistent with earlier work. When stimulation excited neuronal activity, it most often occurred from high-frequency stimulation. These effects were modulated by the location of the stimulating electrode, with stimulation sites near white matter more likely to cause excitation and sites near gray matter more likely to inhibit neuronal activity. By characterizing how different stimulation parameters produced specific neuronal activity patterns on a large scale, our results provide an electrophysiological framework that clinicians and researchers may consider when designing stimulation protocols to cause precisely targeted changes in human brain activity. • Direct electrical stimulation responses depend on stimulation frequency, amplitude, region, and proximity to white-matter tracts. • High-frequency stimulation is most likely to excite while low-frequency stimulation is most likely to inhibit neuronal activity. • Stimulation near white matter is more likely to excite, and stimulation near gray matter is more likely to inhibit neuronal activity. • Comprehensive characterization of parameter space can help with stimulation protocols design. [ABSTRACT FROM AUTHOR]

Background and Purpose: This study's purpose is to correlate location and metabolic activity of developmental venous anomalies (DVAs) in epilepsy patients to the seizure focus as determined by ictal/interictal encephaloelectrogram (EEG).Methods: A retrospective search was performed for epilepsy patients with DVAs who underwent brain 18 F-fluorodeoxyglucose positron emission tomography (18 F-FDG-PET) and magnetic resonance imaging (MRI). MRI exams were analyzed to characterize DVA location and associated structural findings. MRI and PET images were co-registered and assessment of 18 F-FDG uptake in the DVA territory was performed. The electronic medical record was reviewed for each subject to determine seizure semiology and site of seizure focus by ictal/interictal EEG.Results: Twenty-eight DVAs in 25 patients were included. Twelve DVAs demonstrated regional metabolic abnormality on 18 F-FDG-PET. There was no significant correlation between DVA site and seizure focus on EEG. DVA location was concordant with EEG seizure focus in three subjects, and all three demonstrated hypometabolism on 18 F-FDG-PET. This significance remains indeterminate, as one of these DVAs was associated with cavernoma, which could serve as the true seizure focus, and one of the patients underwent resection of the DVA without decrease in seizure frequency. Furthermore, there was no statistically significant relationship between DVA metabolic activity and DVA-EEG lobar or laterality concordance.Conclusions: In this sample, there is no significant correlation between location of DVA and seizure focus, and hypometabolism within the DVA territory is not predictive of EEG/DVA co-localization. As use of 18 F-FDG-PET for evaluation of epilepsy increases, knowledge of this poor correlation is important to avoid diagnostic confusion and potentially unnecessary surgery in epilepsy patients. [ABSTRACT FROM AUTHOR]

Patients with drug-resistant epilepsy often require surgery to become seizure-free. While laser ablation and implantable stimulation devices have lowered the morbidity of these procedures, seizure-free rates have not dramatically improved, particularly for patients without focal lesions. This is in part because it is often unclear where to intervene in these cases. To address this clinical need, several research groups have published methods to map epileptic networks but applying them to improve patient care remains a challenge. In this study we advance clinical translation of these methods by: (i) presenting and sharing a robust pipeline to rigorously quantify the boundaries of the resection zone and determining which intracranial EEG electrodes lie within it; (ii) validating a brain network model on a retrospective cohort of 28 patients with drug-resistant epilepsy implanted with intracranial electrodes prior to surgical resection; and (iii) sharing all neuroimaging, annotated electrophysiology, and clinical metadata to facilitate future collaboration. Our network methods accurately forecast whether patients are likely to benefit from surgical intervention based on synchronizability of intracranial EEG (area under the receiver operating characteristic curve of 0.89) and provide novel information that traditional electrographic features do not. We further report that removing synchronizing brain regions is associated with improved clinical outcome, and postulate that sparing desynchronizing regions may further be beneficial. Our findings suggest that data-driven network-based methods can identify patients likely to benefit from resective or ablative therapy, and perhaps prevent invasive interventions in those unlikely to do so. [ABSTRACT FROM AUTHOR]