Christina Marie Patterson

Cyclin-Dependent Kinase-Like 5 (CDKL5) Deficient Disorder (CDD) is a rare X-linked developmental and epileptic encephalopathy characterized by early-onset refractory epilepsy, severe neurodevelopmental impairment, and lifelong disability. Although more than thirty anti-seizure medications are available, most CDD patients remain pharmaco-resistant. Gene-based therapies are emerging, but therapeutic development is hindered by marked clinical heterogeneity, small patient populations, and the lack of robust, translatable brain-based biomarkers for clinical trials. Genetically engineered Cdkl5 mouse models recapitulate many cognitive, behavioral, and molecular features of CDD, yet their utility is limited by the absence of overt seizures, precluding seizure-based outcome measures. Here, we establish high-definition brain network (HDBN) biomarkers using advanced diffusion MRI tractography combined with graph-theoretical analysis to quantify whole-brain network organization in Cdkl5 knockout mice. Diffusion MRI enables non-invasive mapping of axonal connectivity by leveraging anisotropic water diffusion, while high-angular-resolution acquisition overcomes key limitations of conventional diffusion tensor imaging in regions with complex fiber architecture. We demonstrate that Cdkl5 knockout mice exhibit reproducible and region-specific disruptions in brain network organization, prominently affecting the somatosensory and somatomotor cortex, hippocampus, hypothalamus, amygdala, and superior colliculus—regions implicated in cognition, learning and memory, homeostasis, anxiety, and visual–motor function. In contrast, networks within the entorhinal cortex remain largely preserved. These findings identify HDBN metrics as sensitive, non-invasive biomarkers that capture clinically relevant circuit-level abnormalities in CDD. Because diffusion MRI–based network analyses are directly translatable across species, HDBN biomarkers provide a unified framework for therapeutic evaluation in mouse models, large animals, and human clinical trials, enabling longitudinal monitoring of disease progression and treatment response.

Cyclin-Dependent Kinase-Like 5 (CDKL5) Deficient Disorder (CDD) is a rare X-linked developmental and epileptic encephalopathy characterized by early-onset refractory epilepsy, severe neurodevelopmental impairment, and lifelong disability. Although more than thirty anti-seizure medications are available, most CDD patients remain pharmaco-resistant. Gene-based therapies are emerging, but therapeutic development is hindered by marked clinical heterogeneity, small patient populations, and the lack of robust, translatable brain-based biomarkers for clinical trials. Genetically engineered Cdkl5 mouse models recapitulate many cognitive, behavioral, and molecular features of CDD, yet their utility is limited by the absence of overt seizures, precluding seizure-based outcome measures. Here, we establish high-definition brain network (HDBN) biomarkers using advanced diffusion MRI tractography combined with graph-theoretical analysis to quantify whole-brain network organization in Cdkl5 knockout mice. Diffusion MRI enables non-invasive mapping of axonal connectivity by leveraging anisotropic water diffusion, while high-angular-resolution acquisition overcomes key limitations of conventional diffusion tensor imaging in regions with complex fiber architecture. We demonstrate that Cdkl5 knockout mice exhibit reproducible and region-specific disruptions in brain network organization, prominently affecting the somatosensory and somatomotor cortex, hippocampus, hypothalamus, amygdala, and superior colliculus—regions implicated in cognition, learning and memory, homeostasis, anxiety, and visual-motor function. In contrast, networks within the entorhinal cortex remain largely preserved. These findings identify HDBN metrics as sensitive, non-invasive biomarkers that capture clinically relevant circuit-level abnormalities in CDD. Because diffusion MRI–based network analyses are directly translatable across species, HDBN biomarkers provide a unified framework for therapeutic evaluation in mouse models, large animals, and human clinical trials, enabling longitudinal monitoring of disease progression and treatment response.

Developmental and Epileptic Encephalopathies (DEEs) comprise a heterogeneous group of severe neurological disorders with early onset refractory epilepsy and profound neurodevelopmental impairments. Despite the availability of more than thirty anti-seizure medications, most individuals with DEE remain refractory, experiencing persistent seizures that impose substantial burdens on patients, caregivers, and health care systems. DEEs are predominantly genetic, largely caused by de novo mutations, although non-genetic factors also contribute. Crucially, developmental deficits in DEE arise from both the underlying etiology and the disruptive effects of epileptic activity on the developing brain, distinguishing DEE from epilepsies without major neurodevelopmental involvement. Seizures during critical developmental windows exert acute and enduring effects on neurogenesis, synaptogenesis, circuit refinement, pruning, and brain maturation, which can greatly disrupt neurodevelopment. Mitochondria emerge as central regulators at the intersection of neurodevelopment and epilepsy. Mitochondrial dysfunction, whether from primary genetic mutations or seizure-induced injury, impairs bioenergetics, calcium homeostasis, redox states, and metabolic signaling, lowers seizure thresholds, and increases neuronal vulnerability. Moreover, mitochondrial metabolism directly interfaces with epigenetic regulation, linking energy state to long-term gene expression, altering developmental programming. This review synthesizes current knowledge on mitochondrial contributions to DEE pathogenesis, clinical diagnosis, and highlights emerging mitochondrial-targeted therapeutic strategies.

Introduction: Continuous neurologic assessment in the pediatric intensive care unit is challenging. Current electroencephalography (EEG) guidelines support monitoring status epilepticus, vasospasm detection, and cardiac arrest prognostication, but the scope of brain dysfunction in critically ill patients is larger. We explore quantitative EEG in pediatric intensive care unit patients with neurologic emergencies to identify quantitative EEG changes preceding clinical detection. Methods: From 2017 to 2020, we identified pediatric intensive care unit patients at a single quaternary children's hospital with EEG recording near or during acute neurologic deterioration. Quantitative EEG analysis was performed using Persyst P14 (Persyst Development Corporation). Included features were fast Fourier transform, asymmetry, and rhythmicity spectrograms, “from-baseline” patient-specific versions of the above features, and quantitative suppression ratio. Timing of quantitative EEG changes was determined by expert review and prespecified quantitative EEG alert thresholds. Clinical detection of neurologic deterioration was defined pre hoc and determined through electronic medical record documentation of examination change or intervention. Results: Ten patients were identified, age 23 months to 27 years, and 50% were female. Of 10 patients, 6 died, 1 had new morbidity, and 3 had good recovery; the most common cause of death was cerebral edema and herniation. The fastest changes were on “from-baseline” fast Fourier transform spectrograms, whereas persistent changes on asymmetry spectrograms and suppression ratio were most associated with morbidity and mortality. Median time from first quantitative EEG change to clinical detection was 332 minutes (interquartile range: 201-456 minutes). Conclusion: Quantitative EEG is potentially useful in earlier detection of neurologic deterioration in critically ill pediatric intensive care unit patients. Further work is required to quantify the predictive value, measure improvement in outcome, and automate the process.

Despite the relative successes in the surgical treatment of pharmacoresistant epilepsy, there is rather little research on the neural (re)organization that potentially subserves behavioral compensation. Here, we examined the post-surgical functional connectivity (FC) in children and adolescents who have undergone unilateral cortical resection and, yet, display remarkably normal behavior. Conventionally, FC has been investigated in terms of the mean correlation of the BOLD time courses extracted from different brain regions. Here, we demonstrated the value of segregating the voxel-wise relationships into mutually exclusive populations that were either positively or negatively correlated. While, relative to controls, the positive correlations were largely normal, negative correlations among networks were increased. Together, our results point to reorganization in the contralesional hemisphere, possibly suggesting competition for cortical territory due to the demand for representation of function. Conceivably, the ubiquitous negative correlations enable the differentiation of function in the reduced cortical volume following a unilateral resection.