Gene Expression Profile as a Predictor of Seizure Liability.

Analysis platforms to predict drug-induced seizure liability at an early phase of drug development would improve safety and reduce attrition and the high cost of drug development. We hypothesized that a drug-induced in vitro transcriptomics signature predicts its ictogenicity. We exposed rat cortical neuronal cultures to non-toxic concentrations of 34 compounds for 24 h; 11 were known to be ictogenic (tool compounds), 13 were associated with a high number of seizure-related adverse event reports in the clinical FDA Adverse Event Reporting System (FAERS) database and systematic literature search (FAERS-positive compounds), and 10 were known to be non-ictogenic (FAERS-negative compounds). The drug-induced gene expression profile was assessed from RNA-sequencing data. Transcriptomics profiles induced by the tool, FAERS-positive and FAERS-negative compounds, were compared using bioinformatics and machine learning. Of the 13 FAERS-positive compounds, 11 induced significant differential gene expression; 10 of the 11 showed an overall high similarity to the profile of at least one tool compound, correctly predicting the ictogenicity. Alikeness-% based on the number of the same differentially expressed genes correctly categorized 85%, the Gene Set Enrichment Analysis score correctly categorized 73%, and the machine-learning approach correctly categorized 91% of the FAERS-positive compounds with reported seizure liability currently in clinical use. Our data suggest that the drug-induced gene expression profile could be used as a predictive biomarker for seizure liability.

Discovery and Validation of Circulating microRNAs as Biomarkers for Epileptogenesis after Experimental Traumatic Brain Injury-The EPITARGET Cohort.

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.

Plasma Neurofilament Light Chain (NF-L) Is a Prognostic Biomarker for Cortical Damage Evolution but Not for Cognitive Impairment or Epileptogenesis Following Experimental TBI.

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.

Biomarkers for posttraumatic epilepsy.

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 (Biomarkers, EndpointS, and other Tools). 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.

Plasma miR-9-3p and miR-136-3p as Potential Novel Diagnostic Biomarkers for Experimental and Human Mild Traumatic Brain Injury.

Noninvasive, affordable circulating biomarkers for difficult-to-diagnose mild traumatic brain injury (mTBI) are an unmet medical need. Although blood microRNA (miRNA) levels are reportedly altered after traumatic brain injury (TBI), their diagnostic potential for mTBI remains inconclusive. We hypothesized that acutely altered plasma miRNAs could serve as diagnostic biomarkers both in the lateral fluid percussion injury (FPI) model and clinical mTBI. We performed plasma small RNA-sequencing from adult male Sprague-Dawley rats (n = 31) at 2 days post-TBI, followed by polymerase chain reaction (PCR)-based validation of selected candidates. miR-9a-3p, miR-136-3p, and miR-434-3p were identified as the most promising candidates at 2 days after lateral FPI. Digital droplet PCR (ddPCR) revealed 4.2-, 2.8-, and 4.6-fold elevations in miR-9a-3p, miR-136-3p, and miR-434-3p levels (p < 0.01 for all), respectively, distinguishing rats with mTBI from naïve rats with 100% sensitivity and specificity. DdPCR further identified a subpopulation of mTBI patients with plasma miR-9-3p (n = 7/15) and miR-136-3p (n = 5/15) levels higher than one standard deviation above the control mean at <2 days postinjury. In sTBI patients, plasma miR-9-3p levels were 6.5- and 9.2-fold in comparison to the mTBI and control groups, respectively. Thus, plasma miR-9-3p and miR-136-3p were identified as promising biomarker candidates for mTBI requiring further evaluation in a larger patient population.

In Vitro and In Vivo Pipeline for Validation of Disease-Modifying Effects of Systems Biology-Derived Network Treatments for Traumatic Brain Injury-Lessons Learned.

We developed a pipeline for the discovery of transcriptomics-derived disease-modifying therapies and used it to validate treatments in vitro and in vivo that could be repurposed for TBI treatment. Desmethylclomipramine, ionomycin, sirolimus and trimipramine, identified by in silico LINCS analysis as candidate treatments modulating the TBI-induced transcriptomics networks, were tested in neuron-BV2 microglial co-cultures, using tumour necrosis factor α as a monitoring biomarker for neuroinflammation, nitrite for nitric oxide-mediated neurotoxicity and microtubule associated protein 2-based immunostaining for neuronal survival. Based on (a) therapeutic time window in silico, (b) blood-brain barrier penetration and water solubility, (c) anti-inflammatory and neuroprotective effects in vitro (p < 0.05) and (d) target engagement of Nrf2 target genes (p < 0.05), desmethylclomipramine was validated in a lateral fluid-percussion model of TBI in rats. Despite the favourable in silico and in vitro outcomes, in vivo assessment of clomipramine, which metabolizes to desmethylclomipramine, failed to demonstrate favourable effects on motor and memory tests. In fact, clomipramine treatment worsened the composite neuroscore (p < 0.05). Weight loss (p < 0.05) and prolonged upregulation of plasma cytokines (p < 0.05) may have contributed to the worsened somatomotor outcome. Our pipeline provides a rational stepwise procedure for evaluating favourable and unfavourable effects of systems-biology discovered compounds that modulate post-TBI transcriptomics.

On-line detection of Escherichia coli intrusion in a pilot-scale drinking water distribution system.

Improvements in microbial drinking water quality monitoring are needed for the better control of drinking water distribution systems and for public health protection. Conventional water quality monitoring programmes are not always able to detect a microbial contamination of drinking water. In the drinking water production chain, in addition to the vulnerability of source waters, the distribution networks are prone to contamination. In this study, a pilot-scale drinking-water distribution network with an on-line monitoring system was utilized for detecting bacterial intrusion. During the experimental Escherichia coli intrusions, the contaminant was measured by applying a set of on-line sensors for electric conductivity (EC), pH, temperature (T), turbidity, UV-absorbance at 254 nm (UVAS SC) and with a device for particle counting. Monitored parameters were compared with the measured E. coli counts using the integral calculations of the detected peaks. EC measurement gave the strongest signal compared with the measured baseline during the E. coli intrusion. Integral calculations showed that the peaks in the EC, pH, T, turbidity and UVAS SC data were detected corresponding to the time predicted. However, the pH and temperature peaks detected were barely above the measured baseline and could easily be mixed with the background noise. The results indicate that on-line monitoring can be utilized for the rapid detection of microbial contaminants in the drinking water distribution system although the peak interpretation has to be performed carefully to avoid being mixed up with normal variations in the measurement data.

Successful harmonization in EpiBioS4Rx biomarker study on post-traumatic epilepsy paves the way towards powered preclinical multicenter studies.

OBJECTIVE: 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. METHOD: 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. RESULTS: 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. SIGNIFICANCE: 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.

Image data harmonization tools for the analysis of post-traumatic epilepsy development in preclinical multisite MRI studies.

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.

Ultrasonic vocalizations - Novel seizure-related manifestation in rats.

OBJECTIVE: Seizures of frontal or temporal lobe origin can associate with vocalizations in humans. Our objective was to assess whether rats emit specific seizure-related patterns of ultrasonic vocalizations (USVs) during seizures and epileptiform activity. METHODS: Adult male Sprague-Dawley rats were treated with a single administration of pentylenetetrazol (PTZ, 50 mg/kg, i.p.) and monitored with simultaneous USV and video-electroencephalogram recordings for up to 15 min. USVs were detected using a deep learning algorithm (DeepSqueak-Screener) and manually annotated into the 15 previously described subcategories. The number, frequency, duration, sonographic structure, and temporal relationship of the USVs to seizures and epileptiform activity were assessed. RESULTS: A total of 2147 USVs were recorded in 12 rats that expressed a total of 22 PTZ-induced seizures. Of the USVs, 77% were in the 50-kHz range (i.e., appetitive state) and 23% in the 22-kHz ( i.e., aversive state) range. More than a third (37%) of the USVs could be classified into 1 of the 15 call subcategories; the remaining 63% belonged to a novel "multiform" USV category with a complex sonographic structure. Of the 2147 USVs, 23% occurred during the PTZ-induced seizures and 77% during other types of PTZ-induced epileptiform activity. Almost all (19/22) of seizures were associated with USVs. In each rat, the first seizure was always associated with a USV. The shorter the latency to the first USV, the shorter the latency to the onset of the first electrographic seizure (r = 0.995, p < 0.001). The greater the number of USVs, the greater the number of seizures (r = 0.916, p < 0.001) and the longer the total seizure duration in a given rat (r = 0.750, p < 0.05). SIGNIFICANCE: Like in humans, vocalizations are a seizure-related behavioral feature in rats and recording USVs provides a novel noninvasive tool for detecting experimental seizures. Further studies are needed to explore USV occurrence during spontaneous seizures and their potential for screening novel anti-seizure drugs.

Seizure Susceptibility and Sleep Disturbance as Biomarkers of Epileptogenesis after Experimental TBI.

Objectives: We investigated whether seizure susceptibility increases over weeks−months after experimental traumatic brain injury (TBI), and whether seizure susceptibility in rats predicts the development of post-traumatic epilepsy (PTE) or epileptiform activity. We further investigated whether rats develop chronic sleep disturbance after TBI, and whether sleep disturbance parameters­alone or in combination with pentylenetetrazol (PTZ) test parameters­could serve as novel biomarkers for the development of post-traumatic epileptogenesis. Methods: TBI was induced in adult male Sprague-Dawley rats with lateral fluid-percussion injury. Sham-operated experimental controls underwent craniectomy without exposure to an impact force. Seizure susceptibility was tested with a PTZ test (30 mg/kg, intraperitoneally) on day (D) 30, D60, D90, and D180 after TBI (n = 28) or sham operation (n = 16) under video electroencephalogram (vEEG). In the 7th post-injury month, rats underwent continuous vEEG monitoring to detect spontaneous seizures and assess sleep disturbances. At the end of the experiments, rats were perfused for brain histology. Results: In the TBI group, the percentage of rats with PTZ-induced seizures increased over time (adjusted p < 0.05 compared with D30). Combinations of three PTZ test parameters (latency to the first epileptiform discharge (ED), number of EDs, and number of PTZ-induced seizures) survived the leave-one-out validation for differentiating rats with or without epileptiform activity, indicating an area under the receiver operating curve (AUC) of 0.743 (95% CI 0.472−0.992, p = 0.05) with a misclassification rate of 36% on D90, and an AUC of 0.752 (95% CI 0.483−0.929, p < 0.05) with a misclassification rate of 32% on D180. Sleep analysis revealed that the number of transitions to N3 or rapid eye movement (REM) sleep, along with the total number of transitions, was increased in the TBI group during the lights-on period (all p < 0.05). The sleep fragmentation index during the lights-on period was greater in the TBI rats than in sham-operated rats (p < 0.05). A combination of sleep parameters showed promise as diagnostic biomarkers of prior TBI, with an AUC of 0.792 (95% CI 0.549−0.934, p < 0.01) and a misclassification rate of 28%. Rats with epilepsy or any epileptiform activity had more transitions from N3 to the awake stage (p < 0.05), and the number of N3−awake transitions differentiated rats with or without epileptiform activity, with an AUC of 0.857 (95% CI 0.651−1.063, p < 0.01). Combining sleep parameters with PTZ parameters did not improve the biomarker performance. Significance: This is the first attempt to monitor the evolution of seizure susceptibility over months in a well-described rat model of PTE. Our data suggest that assessment of seizure susceptibility and sleep disturbance can provide diagnostic biomarkers of prior TBI and prognostic biomarkers of post-traumatic epileptogenesis.

MRI-Guided Electrode Implantation for Chronic Intracerebral Recordings in a Rat Model of Post-Traumatic Epilepsy-Challenges and Gains.

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.

Transfer Learning in Magnetic Resonance Brain Imaging: A Systematic Review.

(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.

Facing the threat: common yellowjacket wasps as indicators of heavy metal pollution.

We investigated the common wasp, Vespula vulgaris as a bioindicator and biomonitor of metals in the industrial area. Using traps, we collected 257 yellowjackets along a pollution gradient in the Harjavalta Cu-Ni smelter in Southwest Finland. Our method detected metal elements such as arsenic (As), cobalt (Co), copper (Cu), iron (Fe), nickel (Ni), lead (Pb), zinc (Zn), and mercury (Hg) in wasps. The data analyses revealed V. vulgaris can be a proper indicator for As, Cd, Co, Cu, Ni, and Pb, rather than for Fe and Zn contamination. Body burdens of As, Cd, Co, Cu, Ni, and Pb decreased with an increase in distance from smelter. Enrichment factor (EF) followed the pattern Pb ˃ Cd ˃ As ˃ Co ˃ Cu ˃ Ni. The highest bioaccumulation (BAF) values were revealed for Cd (5.9) and the lowest for Pb (0.1). Specially designed software (WaspFacer) allowed revealing body burdens of As, Cd, Co, Cu, Ni, and Pb to be associated with rather smaller than more asymmetric facial colour markings in yellowjackets. These results add to the body of literature on how heavy metal contaminants can have tangible phenotypic effects on insects and open future opportunities for using wasps as indicators of metal pollution.

Web Application for Quantification of Traumatic Brain Injury-Induced Cortical Lesions in Adult Mice.

Disabilities resulting from traumatic brain injury (TBI) strongly correlate with the cytoarchitectonic part of the brain damaged, lesion area, and type of lesion. We developed a Web application to estimate the location of the lesion on mouse cerebral cortex caused by TBI induced by lateral fluid-percussion injury. The application unfolds user-determined TBI lesion measurements, e.g., from histologic sections to a reference template, and estimates the total lesion area, including the percentage of cortex damaged in different cytoarchitectural cortical regions. The resulting lesion can be visualized on a two-dimensional map of mouse cerebral cortex. The application also visualizes the development of the lesion over time when measurements from multiple time points are available. The web application was validated by comparing its performance to the manual method. The total area of the cortical lesion was similar between the manual (9.19 ± 0.66 mm2, range 4.25-14.93 mm2) and the automated analysis (9.27 ± 0.66 mm2, range 4.50-15.10 mm2) (p = 0.938). The results of the manual and automated analyses were strongly correlated (r = 0.999, p < 0.0001, Pearson correlation). The lesion localized in the same cytoarchitectonic regions when the unfolded map from the automated method was superimposed onto the map obtained using the manual method. The Web application-automated method is faster than the manual method in generating unfolded cortical lesion maps. The accuracy of the presented automated method in determining the anteroposterior level and outlining the lesion is equal to or greater than that of the manual method. Our application provides a novel tool for accurately quantifying and visualizing TBI lesions on mouse cerebral cortex.

Acute Non-Convulsive Status Epilepticus after Experimental Traumatic Brain Injury in Rats.

Severe traumatic brain injury (TBI) induces seizures or status epilepticus (SE) in 20-30% of patients during the acute phase. We hypothesized that severe TBI induced with lateral fluid-percussion injury (FPI) triggers post-impact SE. Adult Sprague-Dawley male rats were anesthetized with isoflurane and randomized into the sham-operated experimental control or lateral FPI-induced severe TBI groups. Electrodes were implanted right after impact or sham-operation, then video-electroencephalogram (EEG) monitoring was started. In addition, video-EEG was recorded from naïve rats. During the first 72 h post-TBI, injured rats had seizures that were intermingled with other epileptiform EEG patterns typical to non-convulsive SE, including occipital intermittent rhythmic delta activity, lateralized or generalized periodic discharges, spike-and-wave complexes, poly-spikes, poly-spike-and-wave complexes, generalized continuous spiking, burst suppression, or suppression. Almost all (98%) of the electrographic seizures were recorded during 0-72 h post-TBI (23.2 ± 17.4 seizures/rat). Mean latency from the impact to the first electrographic seizure was 18.4 ± 15.1 h. Mean seizure duration was 86 ± 57 sec. Analysis of high-resolution videos indicated that only 41% of electrographic seizures associated with behavioral abnormalities, which were typically subtle (Racine scale 1-2). Fifty-nine percent of electrographic seizures did not show any behavioral manifestations. In most of the rats, epileptiform EEG patterns began to decay spontaneously on Days 5-6 after TBI. Interestingly, also a few sham-operated and naïve rats had post-operation seizures, which were not associated with EEG background patterns typical to non-convulsive SE seen in TBI rats. To summarize, our data show that lateral FPI-induced TBI results in non-convulsive SE with subtle behavioral manifestations; this explains why it has remained undiagnosed until now. The lateral FPI model provides a novel platform for assessing the mechanisms of acute symptomatic non-convulsive SE and for testing treatments to prevent post-injury SE in a clinically relevant context.

Harmonization of pipeline for detection of HFOs in a rat model of post-traumatic epilepsy in preclinical multicenter study on post-traumatic epileptogenesis.

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.

Harmonization of the pipeline for seizure detection to phenotype post-traumatic epilepsy in a preclinical multicenter study on post-traumatic epileptogenesis.

RATIONALE: The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) Centre without walls is an NIH funded multicenter consortium. One of EpiBioS4Rx projects is a preclinical post-traumatic epileptogenesis biomarker study that involves three study sites: The University of Eastern Finland, Monash University (Melbourne) and the University of California Los Angeles. Our objective is to create a platform for evaluating biomarkers and testing new antiepileptogenic treatments for post-traumatic epilepsy (PTE) using the lateral fluid percussion injury (FPI) model in rats. As only 30-50% of rats with severe lateral FPI develop PTE by 6 months post-injury, prolonged video-EEG monitoring is crucial to identify animals with PTE. Our objective is to harmonize the surgical and data collection procedures, equipment, and data analysis for chronic EEG recording in order to phenotype PTE in this rat model across the three study sites. METHODS: Traumatic brain injury (TBI) was induced using lateral FPI in adult male Sprague-Dawley rats aged 11-12 weeks. Animals were divided into two cohorts: a) the long-term video-EEG follow-up cohort (Specific Aim 1), which was implanted with EEG electrodes within 24 h after the injury; and b) the magnetic resonance imaging (MRI) follow-up cohort (Specific Aim 2), at 5 months after lateral FPI. Four cortical epidural screw electrodes (2 ipsilateral, 2 contralateral) and three intracerebral bipolar electrodes were implanted (septal CA1 and the dentate gyrus, layers II and VI of the perilesional cortex both anterior and posterior to the injury site). During the 7th post-TBI month, animals underwent 4 weeks of continuous video-EEG recordings to diagnose of PTE. RESULTS: All centers harmonized the induction of TBI and surgical procedures for the implantation of EEG recordings, utilizing 4 or more EEG recording channels to cover areas ipsilateral and contralateral to the brain injury, perilesional cortex and the hippocampus and dentate gyrus. Ground and reference screw electrodes were implanted. At all sites the minimum sampling rate was 512 Hz, utilizing a finite impulse response (FIR) and impedance below 10 KΩ through the entire recording. As part of the quality control criteria we avoided electrical noise, and monitoring changes in impedance over time and the appearance of noise on the recordings. To reduce electrical noise, we regularly checked the integrity of the cables, stability of the EEG recording cap and the appropriate connection of the electrodes with the cables. Following the pipeline presented in this article and after applying the quality control criteria to our EEG recordings all of the sites were successful to phenotype seizure in chronic EEG recordings of animals after TBI. DISCUSSION: Despite differences in video-EEG acquisition equipment used, the three centers were able to consistently phenotype seizures in the lateral fluid-percussion model applying the pipeline presented here. The harmonization of methodology will help to improve the rigor of preclinical research, improving reproducibility of pre-clinical research in the search of biomarkers and therapies to prevent antiepileptogenesis.

Informatics tools to assess the success of procedural harmonization in preclinical multicenter biomarker discovery study on post-traumatic epileptogenesis.

The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is a National Institutes for Neurological Diseases and Stoke funded Centers-Without-Walls international multidisciplinary study aimed at preventing epileptogenesis. The preclinical biomarker discovery in EpiBios4Rx applies a multicenter study design to allow the number of animals that are required for adequate statistical power for the analysis to be studied in an efficient manner. Further, the use of multiple centers mimics the clinical trial situation, and therefore potentially the chance of successful clinical translation of the outcomes of the study. Its successful implementation requires harmonization of procedures and data analyses between the three contributing centers in Finland, Australia, and USA. The objective of the present analysis was to develop metrics for analysis of the success of harmonization of procedures to guide further data analyses and plan the future multicenter preclinical studies. The interim analysis of data is based on the analysis of data from 212 rats with lateral fluid-percussion injury or sham-operation included in the biomarker discovery by April 30, 2018. The details of protocols, including production of injury, post-injury follow-up, blood sampling, electroencephalogram recording, and magnetic resonance imaging have been presented in the accompanying manuscripts in this Supplement. Implementation of protocols in EpiBios4Rx project participant centers was visualized in 2D using t-distributed stochastic neighborhood embedding (t-SNE). The protocols applied to each rat were presented as feature vectors of procedure related variables (e.g., impact pressure, anesthesia time). The total number of protocol features linked to each rat was 112. The missing data was accounted in visualization by utilizing imputation and adding the number of missing values as a third dimension to 2D t-SNE plot, resulting in a 3D overview of protocol data. Intraclass correlation coefficient (ICC) using Euclidean distances and area under receiver operating characteristic curve (AUC) of k-nearest neighbor classifier (KNN) were utilized to quantify the degree of clustering by center. Both subsets of data with incomplete protocol vectors omitted and missing protocol data imputed were assessed. Our data show that a visible clustering by center was observed in all t-SNE plots, except for day 7 neuroscores. Both ICC and AUC indicated clustering by center in all protocol variable subsets, excluding unimputed day 7 neuroscores (ICC 0.04 and AUC 0.6). ICC for imputed set of all protocol related variables was 0.1 and KNN AUC 0.92. In conclusion, both ICC and AUC indicated differences in protocol between EpiBios4Rx participating centers, which needs to be taken into account in data analysis. Importantly, the majority of observed differences are recoverable as they relate to insufficient updates in record keeping. While AUC score of KNN is a more sensitive measure for protocol harmonization than ICC for data that displays complex splintered clustering, ICC and AUC provide complementary measures to assess the degree of procedural harmonization. This experience should be helpful for other groups planning such multicenter post-traumatic epileptogenesis studies in the future.

Algorithm for automatic detection of spontaneous seizures in rats with post-traumatic epilepsy.

BACKGROUND: Labor intensive electroencephalogram (EEG) analysis is a major bottleneck to identifying anti-epileptogenic treatments in experimental models of post-traumatic epilepsy. We aimed to develop an algorithm for automated seizure detection in experimental post-traumatic epilepsy. NEW METHOD: Continuous (24/7) 1-month-long video-EEG monitoring with three epidural screw electrodes was started 154 d after lateral fluid-percussion induced traumatic brain injury (TBI; n = 97) or sham-injury (n = 29) in adult male Sprague-Dawley rats. First, an experienced researcher screened a total of 90,720 h of digitized recordings on a computer screen to annotate the occurrence of spontaneous seizures. The same files were then analyzed using an algorithm in Spike2 (ver.9), which searching for temporally linked power peaks (14-42 Hz) in all three EEG channels, and then positive events were marked as a probable seizures. Finally, an experienced researcher confirmed all seizure candidates visually on the computer screen. RESULTS: Visual analysis identified 197 seizures in 29 rats. Automatic detection identified 4346 seizure candidates in 109 rats, of which 202 in the same 29 rats were true positives, resulting in a false positive rate of 0.046/h or 1.10/d. The algorithm demonstrated 5% specificity and 100% sensitivity. The algorithm analyzed 1-month 3-channel EEG in 7 cohorts in 2 h, whereas analysis by an experienced technician took ∼500 h. COMPARISON WITH EXISTING METHODS: The algorithm had 100% sensitivity. It performed slightly better and was substantially faster than investigator-performed visual analysis. CONCLUSIONS: We present a novel seizure detection algorithm for automated detection of seizures in a rat model of post-traumatic epilepsy.