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Preclinical MRI studies have been utilized for the discovery of biomarkers that predict post-traumatic epilepsy (PTE). However, these single site studies often lack statistical power due to limited and homogeneous datasets. Therefore, multisite studies, such as the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), are developed to create large, heterogeneous datasets that can lead to more statistically significant results. EpiBioS4Rx collects preclinical data internationally across sites, including the United States, Finland, and Australia. However, in doing so, there are robust normalization and harmonization processes that are required to obtain statistically significant and generalizable results. This work describes the tools and procedures used to harmonize multisite, multimodal preclinical imaging data acquired by EpiBioS4Rx. There were four main harmonization processes that were utilized, including file format harmonization, naming convention harmonization, image coordinate system harmonization, and diffusion tensor imaging (DTI) metrics harmonization. By using Python tools and bash scripts, the file formats, file names, and image coordinate systems are harmonized across all the sites. To harmonize DTI metrics, values are estimated for each voxel in an image to generate a histogram representing the whole image. Then, the Quantitative Imaging Toolkit (QIT) modules are utilized to scale the mode to a value of one and depict the subsequent harmonized histogram. The standardization of file formats, naming conventions, coordinate systems, and DTI metrics are qualitatively assessed. The histograms of the DTI metrics were generated for all the individual rodents per site. For inter-site analysis, an average of the individual scans was calculated to create a histogram that represents each site. In order to ensure the analysis can be run at the level of individual animals, the sham and TBI cohort were analyzed separately, which depicted the same harmonization factor. The results demonstrate that these processes qualitatively standardize the file formats, naming conventions, coordinate systems, and DTI metrics of the data. This assists in the ability to share data across the study, as well as disseminate tools that can help other researchers to strengthen the statistical power of their studies and analyze data more cohesively.

Traumatic brain injury (TBI) causes 10–20% of structural epilepsies and 5% of all epilepsies. The lack of prognostic biomarkers for post-traumatic epilepsy (PTE) is a major obstacle to the development of anti-epileptogenic treatments. Previous studies revealed TBI-induced alterations in blood microRNA (miRNA) levels, and patients with epilepsy exhibit dysregulation of blood miRNAs. We hypothesized that acutely altered plasma miRNAs could serve as prognostic biomarkers for brain damage severity and the development of PTE. To investigate this, epileptogenesis was induced in adult male Sprague Dawley rats by lateral fluid-percussion-induced TBI. Epilepsy was defined as the occurrence of at least one unprovoked seizure during continuous 1-month video-electroencephalography monitoring in the sixth post-TBI month. Cortical pathology was analyzed by magnetic resonance imaging on day 2 (D2), D7, and D21, and by histology 6 months post-TBI. Small RNA sequencing was performed from tail-vein plasma samples on D2 and D9 after TBI (n = 16, 7 with and 9 without epilepsy) or sham operation (n = 4). The most promising miRNA biomarker candidates were validated by droplet digital polymerase chain reaction in a validation cohort of 115 rats (8 naïve, 17 sham, and 90 TBI rats [21 with epilepsy]). These included 7 brain-enriched plasma miRNAs (miR-434-3p, miR-9a-3p, miR-136-3p, miR-323-3p, miR-124-3p, miR-212-3p, and miR-132-3p) that were upregulated on D2 post-TBI (p < 0.001 for all compared with naïve rats). The acute post-TBI plasma miRNA profile did not predict the subsequent development of PTE or PTE severity. Plasma miRNA levels, however, predicted the cortical pathology severity on D2 (Spearman ρ = 0.345–0.582, p < 0.001), D9 (ρ = 0.287–0.522, p < 0.001–0.01), D21 (ρ = 0.269–0.581, p < 0.001–0.05) and at 6 months post-TBI (ρ = 0.230–0.433, p < 0.001–0.05). We found that the levels of 6 of 7 miRNAs also reflected mild brain injury caused by the craniotomy during sham operation (ROC AUC 0.76–0.96, p < 0.001–0.05). In conclusion, our findings revealed that increased levels of neuronally enriched miRNAs in the blood circulation after TBI reflect the extent of cortical injury in the brain but do not predict PTE development. [ABSTRACT FROM AUTHOR]

Plasma neurofilament light chain (NF-L) levels were assessed as a diagnostic biomarker for traumatic brain injury (TBI) and as a prognostic biomarker for somatomotor recovery, cognitive decline, and epileptogenesis. Rats with severe TBI induced by lateral fluid-percussion injury (n = 26, 13 with and 13 without epilepsy) or sham-operation (n = 8) were studied. During a 6-month follow-up, rats underwent magnetic resonance imaging (MRI) (day (D) 2, D7, and D21), composite neuroscore (D2, D6, and D14), Morris-water maze (D35–D39), and a 1-month-long video-electroencephalogram to detect unprovoked seizures during the 6th month. Plasma NF-L levels were assessed using a single-molecule assay at baseline (i.e., naïve animals) and on D2, D9, and D178 after TBI or a sham operation. Plasma NF-L levels were 483-fold higher on D2 (5072.0 ± 2007.0 pg/mL), 89-fold higher on D9 (930.3 ± 306.4 pg/mL), and 3-fold higher on D176 32.2 ± 8.9 pg/mL after TBI compared with baseline (10.5 ± 2.6 pg/mL; all p < 0.001). Plasma NF-L levels distinguished TBI rats from naïve animals at all time-points examined (area under the curve [AUC] 1.0, p < 0.001), and from sham-operated controls on D2 (AUC 1.0, p < 0.001). Plasma NF-L increases on D2 were associated with somatomotor impairment severity (ρ = −0.480, p < 0.05) and the cortical lesion extent in MRI (ρ = 0.401, p < 0.05). Plasma NF-L increases on D2 or D9 were associated with the cortical lesion extent in histologic sections at 6 months post-injury (ρ = 0.437 for D2; ρ = 0.393 for D9, p < 0.05). Plasma NF-L levels, however, did not predict somatomotor recovery, cognitive decline, or epileptogenesis (p > 0.05). Plasma NF-L levels represent a promising noninvasive translational diagnostic biomarker for acute TBI and a prognostic biomarker for post-injury somatomotor impairment and long-term structural brain damage. [ABSTRACT FROM AUTHOR]

Brain atrophy induced by traumatic brain injury (TBI) progresses in parallel with epileptogenesis over time, and thus accurate placement of intracerebral electrodes to monitor seizure initiation and spread at the chronic postinjury phase is challenging. We evaluated in adult male Sprague Dawley rats whether adjusting atlas-based electrode coordinates on the basis of magnetic resonance imaging (MRI) increases electrode placement accuracy and the effect of chronic electrode implantations on TBI-induced brain atrophy. One group of rats (EEG cohort) was implanted with two intracortical (anterior and posterior) and a hippocampal electrode right after TBI to target coordinates calculated using a rat brain atlas. Another group (MRI cohort) was implanted with the same electrodes, but using T2-weighted MRI to adjust the planned atlas-based 3D coordinates of each electrode. Histological analysis revealed that the anterior cortical electrode was in the cortex in 83% (25% in targeted layer V) of the EEG cohort and 76% (31%) of the MRI cohort. The posterior cortical electrode was in the cortex in 40% of the EEG cohort and 60% of the MRI cohort. Without MRI-guided adjustment of electrode tip coordinates, 58% of the posterior cortical electrodes in the MRI cohort will be in the lesion cavity, as revealed by simulated electrode placement on histological images. The hippocampal electrode was accurately placed in 82% of the EEG cohort and 86% of the MRI cohort. Misplacement of intracortical electrodes related to their rostral shift due to TBI-induced cortical and hippocampal atrophy and caudal retraction of the brain, and was more severe ipsilaterally than contralaterally (p < 0.001). Total lesion area in cortical subfields targeted by the electrodes (primary somatosensory cortex, visual cortex) was similar between cohorts (p > 0.05). MRI-guided adjustment of coordinates for electrodes improved the success rate of intracortical electrode tip placement nearly to that at the acute postinjury phase (68% vs. 62%), particularly in the posterior brain, which exhibited the most severe postinjury atrophy. Overall, MRI-guided electrode implantation improved the quality and interpretation of the origin of EEG-recorded signals. [ABSTRACT FROM AUTHOR]

(1) Background: Transfer learning refers to machine learning techniques that focus on acquiring knowledge from related tasks to improve generalization in the tasks of interest. In magnetic resonance imaging (MRI), transfer learning is important for developing strategies that address the variation in MR images from different imaging protocols or scanners. Additionally, transfer learning is beneficial for reutilizing machine learning models that were trained to solve different (but related) tasks to the task of interest. The aim of this review is to identify research directions, gaps in knowledge, applications, and widely used strategies among the transfer learning approaches applied in MR brain imaging; (2) Methods: We performed a systematic literature search for articles that applied transfer learning to MR brain imaging tasks. We screened 433 studies for their relevance, and we categorized and extracted relevant information, including task type, application, availability of labels, and machine learning methods. Furthermore, we closely examined brain MRI-specific transfer learning approaches and other methods that tackled issues relevant to medical imaging, including privacy, unseen target domains, and unlabeled data; (3) Results: We found 129 articles that applied transfer learning to MR brain imaging tasks. The most frequent applications were dementia-related classification tasks and brain tumor segmentation. The majority of articles utilized transfer learning techniques based on convolutional neural networks (CNNs). Only a few approaches utilized clearly brain MRI-specific methodology, and considered privacy issues, unseen target domains, or unlabeled data. We proposed a new categorization to group specific, widely-used approaches such as pretraining and fine-tuning CNNs; (4) Discussion: There is increasing interest in transfer learning for brain MRI. Well-known public datasets have clearly contributed to the popularity of Alzheimer's diagnostics/prognostics and tumor segmentation as applications. Likewise, the availability of pretrained CNNs has promoted their utilization. Finally, the majority of the surveyed studies did not examine in detail the interpretation of their strategies after applying transfer learning, and did not compare their approach with other transfer learning approaches. [ABSTRACT FROM AUTHOR]

Highlights • Novel web based application for rapid generation of cortical unfolded maps in adult rat brain. • Can be used, for example, to unfold cortical areas in histologic sections or MRI slices. • Useful for analysis of lesion location and extent as well as mapping of the location of axon terminals. Abstract Background Both the type and severity of functional impairments caused by damage to the cerebral cortex depend on the functional cortical areas and networks affected, as well as the lesion extent and type. New method To accelerate the laborious quantitative analysis of cortical lesion location and size, we created user-friendly software that generates two-dimensional unfolded cortical maps of the lesions in adult rats. The program imports and superimposes simple user-made measurements, e.g., from histologic sections, on a template. The software then quantifies the total lesion area and the area of damage in each cortical cytoarchitechtonic region. To validate the accuracy of the software, we compared computer-generated maps with manually created unfolded maps from 32 rats with lateral fluid-percussion-induced traumatic brain injury. Results The total area of the cortical lesions varied from 7.37 to 38.45 mm2 in the automated analysis and from 7.26 to 38.97 mm2 in the manual analysis (p > 0.05). The Pearson correlation coefficient between the automated and manual analyses was 0.998 (p < 0.001). The mean difference between the automated and manual measurements was 0.50 ± 0.69 mm2 (range 0.00–3.30 mm2). The slight differences between the analyses related to human error in positioning the measurements to the anteroposterior coordinates on the template map. Comparison with existing methods Compared with the manual method, the automated method accurately and quickly generates unfolded cortical maps of lesioned cortical areas. Conclusions Application provides a novel tool for accurately positioning the lesioned area on the cortical mantle and quantifying the lesion area in histologic sections or magnetic resonance images. [ABSTRACT FROM AUTHOR]

Project 1 of the Preclinical Multicenter Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) consortium aims to identify preclinical biomarkers for antiepileptogenic therapies following traumatic brain injury (TBI). The international participating centers in Finland, Australia, and the United States have made a concerted effort to ensure protocol harmonization. Here, we evaluate the success of harmonization process by assessing the timing, coverage, and performance between the study sites. We collected data on animal housing conditions, lateral fluid-percussion injury model production, postoperative care, mortality, post-TBI physiological monitoring, timing of blood sampling and quality, MR imaging timing and protocols, and duration of video-electroencephalography (EEG) follow-up using common data elements. Learning effect in harmonization was assessed by comparing procedural accuracy between the early and late stages of the project. The animal housing conditions were comparable between the study sites but the postoperative care procedures varied. Impact pressure, duration of apnea, righting reflex, and acute mortality differed between the study sites (p < 0.001). The severity of TBI on D2 post TBI assessed using the composite neuroscore test was similar between the sites, but recovery of acute somato-motor deficits varied (p < 0.001). A total of 99% of rats included in the final cohort in UEF, 100% in Monash, and 79% in UCLA had blood samples taken at all time points. The timing of sampling differed on day (D)2 (p < 0.05) but not D9 (p > 0.05). Plasma quality was poor in 4% of the samples in UEF, 1% in Monash and 14% in UCLA. More than 97% of the final cohort were MR imaged at all timepoints in all study sites. The timing of imaging did not differ on D2 and D9 (p > 0.05), but varied at D30, 5 months, and ex vivo timepoints (p < 0.001). The percentage of rats that completed the monthly high-density video-EEG follow-up and the duration of video-EEG recording on the 7th post-injury month used for seizure detection for diagnosis of post-traumatic epilepsy differed between the sites (p < 0.001), yet the prevalence of PTE (UEF 21%, Monash 22%, UCLA 23%) was comparable between the sites (p > 0.05). A decrease in acute mortality and increase in plasma quality across time reflected a learning effect in the TBI production and blood sampling protocols. Our study is the first demonstration of the feasibility of protocol harmonization for performing powered preclinical multi-center trials for biomarker and therapy discovery of post-traumatic epilepsy. • Harmonizing procedures in multicenter epileptogenesis biomarker discovery is feasible. • Successful harmonization of procedures depends on data collection using common data elements. • Data collection and procedural variability are primarily caused by experimenter differences. • Performance of key procedures decreased over time as a result of a learning effect. [ABSTRACT FROM AUTHOR]

A biomarker is a characteristic that can be objectively measured as an indicator of normal biologic processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions. Biomarker modalities include molecular, histologic, radiographic, or physiologic characteristics. To improve the understanding and use of biomarker terminology in biomedical research, clinical practice, and medical product development, the Food and Drug Administration (FDA)–National Institutes of Health (NIH) Joint Leadership Council developed the BEST Resource (B iomarkers, E ndpoint S , and other T ools). The seven BEST biomarker categories include the following: (a) susceptibility/risk biomarkers, (b) diagnostic biomarkers, (c) monitoring biomarkers, (d) prognostic biomarkers, (e) predictive biomarkers, (f) pharmacodynamic/response biomarkers, and (g) safety biomarkers. We hypothesize some potential overlap between the reported biomarkers of traumatic brain injury (TBI), epilepsy, and posttraumatic epilepsy (PTE). Here, we tested this hypothesis by reviewing studies focusing on biomarker discovery for posttraumatic epileptogenesis and epilepsy. The biomarker modalities reviewed here include plasma/serum and cerebrospinal fluid molecular biomarkers, imaging biomarkers, and electrophysiologic biomarkers. Most of the reported biomarkers have an area under the receiver operating characteristic curve greater than 0.800, suggesting both high sensitivity and high specificity. Our results revealed little overlap in the biomarker candidates between TBI, epilepsy, and PTE. In addition to using single parameters as biomarkers, machine learning approaches have highlighted the potential for utilizing patterns of markers as biomarkers. Although published data suggest the possibility of identifying biomarkers for PTE, we are still in the early phase of the development curve. Many of the seven biomarker categories lack PTE-related biomarkers. Thus, further exploration using proper, statistically powered, and standardized study designs with validation cohorts, and by developing and applying novel analytical methods, is needed for PTE biomarker discovery. • Review of biomarker discovery-related terminology • Review of plasma micro RNA biomarkers for posttraumatic epilepsy • Review of plasma protein biomarkers for posttraumatic epilepsy • Review of imaging biomarkers for posttraumatic epilepsy • Harmonization and potential of big data analysis in biomarker discovery [ABSTRACT FROM AUTHOR]

• Harmonization of surgical techniques and standardization of epidural and intracerebral electrode placements. • HFOs were detected from deep and cortical screw electrodes. • The algorithm selected is one of the biggest sources of the high-variability in the detections. • Progress towards the evaluation of HFOs as an electrophysiological biomarker of PTE in a multi-center design. Studies of chronic epilepsy show pathological high frequency oscillations (HFOs) are associated with brain areas capable of generating epileptic seizures. Only a few of these studies have focused on HFOs during the development of epilepsy, but results suggest pathological HFOs could be a biomarker of epileptogenesis. The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy" (EpiBioS4Rx) is a multi-center project designed to identify biomarkers of epileptogenesis after a traumatic brain injury (TBI) and evaluate treatments that could modify or prevent the development of post-traumatic epilepsy. One goal of the EpiBioS4Rx project is to assess whether HFOs could be a biomarker of post-traumatic epileptogenesis. The current study describes the work towards this goal, including the development of common surgical procedures and EEG protocols, an interim analysis of the EEG for HFOs, and identifying issues that need to be addressed for a robust biomarker analysis. At three participating sites – University of Eastern Finland (UEF), Monash University in Melbourne (Melbourne) and University of California, Los Angeles (UCLA) – TBI was induced in adult male Sprague-Dawley rats by lateral fluid-percussion injury. After injury and in sham-operated controls, rats were implanted with screw and microwire electrodes positioned in neocortex and hippocampus to record EEG. A separate group of rats had serial magnetic resonance imaging after injury and then implanted with electrodes at 6 months. Recordings 28 days post-injury were available from UEF and UCLA, but not Melbourne due to technical issues with their EEG files. Analysis of recordings from 4 rats – UEF and UCLA each had one TBI and one sham-operated control – showed EEG contained evidence of HFOs. Computer-automated algorithms detected a total of 1,819 putative HFOs and of these only 40 events (2%) were detected by all three sites. Manual review of all events verified 130 events as HFO and the remainder as false positives. Review of the 40 events detected by all three sites was associated with 88% agreement. This initial report from the EpiBioS4Rx Consortium demonstrates the standardization of EEG electrode placements, recording protocol and long-term EEG monitoring, and differences in detection algorithm HFO results between sites. Additional work on detection strategy, detection algorithm performance, and training in HFO review will be performed to establish a robust, preclinical evaluation of HFOs as a biomarker of post-traumatic epileptogenesis. [ABSTRACT FROM AUTHOR]