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

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

Priorities to Promote Participant Engagement in the Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network.

BACKGROUND: Engaging diverse populations in cancer genomics research is of critical importance and is a fundamental goal of the NCI Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network. Established as part of the Cancer Moonshot, PE-CGS is a consortium of stakeholders including clinicians, scientists, genetic counselors, and representatives of potential study participants and their communities. Participant engagement is an ongoing, bidirectional, and mutually beneficial interaction between study participants and researchers. PE-CGS sought to set priorities in participant engagement for conducting the network\u27s research. METHODS: PE-CGS deliberatively engaged its stakeholders in the following four-phase process to set the network\u27s research priorities in participant engagement: (i) a brainstorming exercise to elicit potential priorities; (ii) a 2-day virtual meeting to discuss priorities; (iii) recommendations from the PE-CGS External Advisory Panel to refine priorities; and (iv) a virtual meeting to set priorities. RESULTS: Nearly 150 PE-CGS stakeholders engaged in the process. Five priorities were set: (i) tailor education and communication materials for participants throughout the research process; (ii) identify measures of participant engagement; (iii) identify optimal participant engagement strategies; (iv) understand cancer disparities in the context of cancer genomics research; and (v) personalize the return of genomics findings to participants. CONCLUSIONS: PE-CGS is pursuing these priorities to meaningfully engage diverse and underrepresented patients with cancer and posttreatment cancer survivors as participants in cancer genomics research and, subsequently, generate new discoveries. IMPACT: Data from PE-CGS will be shared with the broader scientific community in a manner consistent with participant informed consent and community agreement

Dyslexia and Configural Perception of Character Sequences

Developmental dyslexia is a complex and heterogeneous disorder characterized by unexpected difficulty in learning to read. Although it is considered to be biologically based, the degree of variation has made the nature and locus of dyslexia difficult to ascertain. Hypotheses regarding the cause have ranged from low-level perceptual deficits to higher order cognitive deficits, such as phonological processing and visual-spatial attention. We applied the capacity coefficient, a measure obtained from a mathematical cognitive model of response times to measure how efficiently participants processed different classes of stimuli. The capacity coefficient was used to test the extent to which individuals with dyslexia can be distinguished from normal reading individuals based on their ability to take advantage of word, pronounceable nonword, consonant sequence or unfamiliar context when categorizing character strings. Within subject variability of the capacity coefficient across character string types was fairly regular across normal reading adults and consistent with a previous study of word perception with the capacity coefficient - words and pseudowords were processed at supercapacity and unfamiliar characters strings at limited-capacity. Two distinct patterns were observed in individuals with dyslexia. One group had a profile similar to the normal reading adults while the other group showed very little variation in capacity across conditions. It is possible that these individuals used a similar strategy for all four conditions and were able to generalize this strategy when processing unfamiliar characters. This difference across dyslexia groups may be used to identify sub-types of the disorder and suggest significant differences in word level processing among these subtypes. Therefore, this approach may be useful in further delineating among types of dyslexia, which in turn may lead to better understanding of the etiologies of dyslexia

Dyslexia and Configural Perception of Character Sequences

Developmental dyslexia is a complex and heterogeneous disorder characterized by unexpected difficulty in learning to read. Although it is considered to be biologically based, the degree of variation has made the nature and locus of dyslexia difficult to ascertain. Hypotheses regarding the cause have ranged from low-level perceptual deficits to higher order cognitive deficits, such as phonological processing and visual-spatial attention. We applied the capacity coefficient, a measure obtained from a mathematical cognitive model of response times to measure how efficiently participants processed different classes of stimuli. The capacity coefficient was used to test the extent to which individuals with dyslexia can be distinguished from normal reading individuals based on their ability to take advantage of word, pronounceable non-word, consonant sequence or unfamiliar context when categorizing character strings. Within subject variability of the capacity coefficient across character string types was fairly regular across normal reading adults and consistent with a previous study of word perception with the capacity coefficient—words and pseudowords were processed at super-capacity and unfamiliar characters strings at limited-capacity. Two distinct patterns were observed in individuals with dyslexia. One group had a profile similar to the normal reading adults while the other group showed very little variation in capacity across string-type. It is possible that these individuals used a similar strategy for all four string-types and were able to generalize this strategy when processing unfamiliar characters. This difference across dyslexia groups may be used to identify sub-types of the disorder and suggest significant differences in word level processing among these subtypes. Therefore, this approach may be useful in further delineating among types of dyslexia, which in turn may lead to better understanding of the etiologies of dyslexia

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

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

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

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

Table_2_Automated seizure onset zone locator from resting-state functional MRI in drug-resistant epilepsy.docx

ObjectiveAccurate localization of a seizure onset zone (SOZ) from independent components (IC) of resting-state functional magnetic resonance imaging (rs-fMRI) improves surgical outcomes in children with drug-resistant epilepsy (DRE). Automated IC sorting has limited success in identifying SOZ localizing ICs in adult normal rs-fMRI or uncategorized epilepsy. Children face unique challenges due to the developing brain and its associated surgical risks. This study proposes a novel SOZ localization algorithm (EPIK) for children with DRE.MethodsEPIK is developed in a phased approach, where fMRI noise-related biomarkers are used through high-fidelity image processing techniques to eliminate noise ICs. Then, the SOZ markers are used through a maximum likelihood-based classifier to determine SOZ localizing ICs. The performance of EPIK was evaluated on a unique pediatric DRE dataset (n = 52). A total of 24 children underwent surgical resection or ablation of an rs-fMRI identified SOZ, concurrently evaluated with an EEG and anatomical MRI. Two state-of-art techniques were used for comparison: (a) least squares support-vector machine and (b) convolutional neural networks. The performance was benchmarked against expert IC sorting and Engel outcomes for surgical SOZ resection or ablation. The analysis was stratified across age and sex.ResultsEPIK outperformed state-of-art techniques for SOZ localizing IC identification with a mean accuracy of 84.7% (4% higher), a precision of 74.1% (22% higher), a specificity of 81.9% (3.2% higher), and a sensitivity of 88.6% (16.5% higher). EPIK showed consistent performance across age and sex with the best performance in those SignificanceAutomated SOZ localization from rs-fMRI, validated against surgical outcomes, indicates the potential for clinical feasibility. It eliminates the need for expert sorting, outperforms prior automated methods, and is consistent across age and sex.</p

Table_3_Automated seizure onset zone locator from resting-state functional MRI in drug-resistant epilepsy.docx

ObjectiveAccurate localization of a seizure onset zone (SOZ) from independent components (IC) of resting-state functional magnetic resonance imaging (rs-fMRI) improves surgical outcomes in children with drug-resistant epilepsy (DRE). Automated IC sorting has limited success in identifying SOZ localizing ICs in adult normal rs-fMRI or uncategorized epilepsy. Children face unique challenges due to the developing brain and its associated surgical risks. This study proposes a novel SOZ localization algorithm (EPIK) for children with DRE.MethodsEPIK is developed in a phased approach, where fMRI noise-related biomarkers are used through high-fidelity image processing techniques to eliminate noise ICs. Then, the SOZ markers are used through a maximum likelihood-based classifier to determine SOZ localizing ICs. The performance of EPIK was evaluated on a unique pediatric DRE dataset (n = 52). A total of 24 children underwent surgical resection or ablation of an rs-fMRI identified SOZ, concurrently evaluated with an EEG and anatomical MRI. Two state-of-art techniques were used for comparison: (a) least squares support-vector machine and (b) convolutional neural networks. The performance was benchmarked against expert IC sorting and Engel outcomes for surgical SOZ resection or ablation. The analysis was stratified across age and sex.ResultsEPIK outperformed state-of-art techniques for SOZ localizing IC identification with a mean accuracy of 84.7% (4% higher), a precision of 74.1% (22% higher), a specificity of 81.9% (3.2% higher), and a sensitivity of 88.6% (16.5% higher). EPIK showed consistent performance across age and sex with the best performance in those SignificanceAutomated SOZ localization from rs-fMRI, validated against surgical outcomes, indicates the potential for clinical feasibility. It eliminates the need for expert sorting, outperforms prior automated methods, and is consistent across age and sex.</p

Table_4_Automated seizure onset zone locator from resting-state functional MRI in drug-resistant epilepsy.docx

ObjectiveAccurate localization of a seizure onset zone (SOZ) from independent components (IC) of resting-state functional magnetic resonance imaging (rs-fMRI) improves surgical outcomes in children with drug-resistant epilepsy (DRE). Automated IC sorting has limited success in identifying SOZ localizing ICs in adult normal rs-fMRI or uncategorized epilepsy. Children face unique challenges due to the developing brain and its associated surgical risks. This study proposes a novel SOZ localization algorithm (EPIK) for children with DRE.MethodsEPIK is developed in a phased approach, where fMRI noise-related biomarkers are used through high-fidelity image processing techniques to eliminate noise ICs. Then, the SOZ markers are used through a maximum likelihood-based classifier to determine SOZ localizing ICs. The performance of EPIK was evaluated on a unique pediatric DRE dataset (n = 52). A total of 24 children underwent surgical resection or ablation of an rs-fMRI identified SOZ, concurrently evaluated with an EEG and anatomical MRI. Two state-of-art techniques were used for comparison: (a) least squares support-vector machine and (b) convolutional neural networks. The performance was benchmarked against expert IC sorting and Engel outcomes for surgical SOZ resection or ablation. The analysis was stratified across age and sex.ResultsEPIK outperformed state-of-art techniques for SOZ localizing IC identification with a mean accuracy of 84.7% (4% higher), a precision of 74.1% (22% higher), a specificity of 81.9% (3.2% higher), and a sensitivity of 88.6% (16.5% higher). EPIK showed consistent performance across age and sex with the best performance in those SignificanceAutomated SOZ localization from rs-fMRI, validated against surgical outcomes, indicates the potential for clinical feasibility. It eliminates the need for expert sorting, outperforms prior automated methods, and is consistent across age and sex.</p

Table_1_Automated seizure onset zone locator from resting-state functional MRI in drug-resistant epilepsy.docx

ObjectiveAccurate localization of a seizure onset zone (SOZ) from independent components (IC) of resting-state functional magnetic resonance imaging (rs-fMRI) improves surgical outcomes in children with drug-resistant epilepsy (DRE). Automated IC sorting has limited success in identifying SOZ localizing ICs in adult normal rs-fMRI or uncategorized epilepsy. Children face unique challenges due to the developing brain and its associated surgical risks. This study proposes a novel SOZ localization algorithm (EPIK) for children with DRE.MethodsEPIK is developed in a phased approach, where fMRI noise-related biomarkers are used through high-fidelity image processing techniques to eliminate noise ICs. Then, the SOZ markers are used through a maximum likelihood-based classifier to determine SOZ localizing ICs. The performance of EPIK was evaluated on a unique pediatric DRE dataset (n = 52). A total of 24 children underwent surgical resection or ablation of an rs-fMRI identified SOZ, concurrently evaluated with an EEG and anatomical MRI. Two state-of-art techniques were used for comparison: (a) least squares support-vector machine and (b) convolutional neural networks. The performance was benchmarked against expert IC sorting and Engel outcomes for surgical SOZ resection or ablation. The analysis was stratified across age and sex.ResultsEPIK outperformed state-of-art techniques for SOZ localizing IC identification with a mean accuracy of 84.7% (4% higher), a precision of 74.1% (22% higher), a specificity of 81.9% (3.2% higher), and a sensitivity of 88.6% (16.5% higher). EPIK showed consistent performance across age and sex with the best performance in those SignificanceAutomated SOZ localization from rs-fMRI, validated against surgical outcomes, indicates the potential for clinical feasibility. It eliminates the need for expert sorting, outperforms prior automated methods, and is consistent across age and sex.</p