Byron Bernal’s research while affiliated with Florida International University and other places

Functional magnetic resonance imaging (fMRI) has become an important pillar in the evaluation of children’s brain function. We review the differences between fMRI in adults and children, emphasizing the technical challenges that the technique poses in noncooperative children. We also review the state of the art of the fMRI clinical applications with special attention to its role in surgical planning, and relevant cognitive pediatric conditions. A review of pharmacology-fMRI and resting-state fMRI in children is presented as the authors stress the importance of these two new branches of fMRI. We share as well our experience based on more than 20 years of experience performing clinical pediatric fMRI.KeywordsfMRIPediatricsSedationAuditoryMotorVisualCognitiveLanguageApplications

Alongside positive blood oxygenation level–dependent (BOLD) responses associated with interictal epileptic discharges, a variety of negative BOLD responses (NBRs) are typically found in epileptic patients. Previous studies suggest that, in general, up to four mechanisms might underlie the genesis of NBRs in the brain: (i) neuronal disruption of network activity, (ii) altered balance of neurometabolic/vascular couplings, (iii) arterial blood stealing, and (iv) enhanced cortical inhibition. Detecting and classifying these mechanisms from BOLD signals are pivotal for the improvement of the specificity of the electroencephalography–functional magnetic resonance imaging (EEG-fMRI) image modality to identify the seizure-onset zones in refractory local epilepsy. This requires models with physiological interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a Windkessel model with viscoelastic compliance/inductance in combination with dynamic models of both neuronal population activity and tissue/blood O2 to classify the hemodynamic response functions (HRFs) linked to the above mechanisms in the irritative zones of epileptic patients. First, we evaluated the most relevant imprints on the BOLD response caused by variations of key model parameters. Second, we demonstrated that a general linear model is enough to accurately represent the four different types of NBRs. Third, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying the BOLD signal from irritative zones. Cross-validation indicates that these four mechanisms can be classified from realistic fMRI BOLD signals. To demonstrate proof of concept, we applied our methodology to EEG-fMRI data from five epileptic patients undergoing neurosurgery, suggesting the presence of some of these mechanisms. We concluded that a proper identification and interpretation of NBR mechanisms in epilepsy can be performed by combining general linear models and biophysically inspired models.

Functional magnetic resonance imaging (fMRI) has become a broadly accepted presurgical mapping tool for pediatric populations with brain pathology. The aim of this article is to provide general guidelines on the pragmatic aspects of performing and processing fMRI, as well as interpreting its results across children of all age groups. Based on the author's accumulated experience of more than 20 years on this specific field, these guidelines consider many factors that include the particular physiology and anatomy of the child's brain, and how specific peculiarities may pose disadvantages or even certain advantages when performing fMRI procedures. The author carefully details the various challenges that the practitioner might face in dealing with limited volitional behavior and language comprehension of infants and small children and remedial strategies. The type and proper choice of task-based paradigms in keeping with the age and performance of the patient are discussed, as well as the appropriate selection and dosage of sedative agents and their inherent limitations. Recommendations about the scanner and settings for specific sequences are provided, as well as the required devices for appropriate stimulus delivery, response, and motion control. Practical aspects of fMRI postprocessing and quality control are discussed. Finally, given the relevance of resting-state-fMRI for use in noncooperative patients, a praxis-oriented guide to obtain, classify, and understand the spontaneous neural networks (utilizing independent component analysis) is also provided. The article concludes with a thorough discussion about the possible pitfalls at different stages of the fMRI process.

Treatment-resistant epilepsy is a common and debilitating neurological condition, for which neurosurgical cure is possible. Despite undergoing nearly identical ablation procedures however, individuals with treatment-resistant epilepsy frequently exhibit heterogeneous outcomes. We hypothesized that treatment response may be related to the brain regions to which MR-guided laser ablation volumes are functionally connected. To test this, we mapped the resting-state functional connectivity of surgical ablations that either resulted in seizure freedom (N= 11) or did notresult in seizure freedom (N= 16) in over 1,000 normative connectomes.There was no diference seizure outcome with respect to the anatomical location of the ablations, and very little overlap between ablation areas was identifed using the Dice Index.Ablations that did notresultin seizure-freedom were preferentially connected to a number of cortical and subcortical regions, as well as multiple canonical resting-state networks. In contrast, ablations that led to seizure-freedom were more functionally connected to prefrontal cortices. Here, we demonstrate that underlying normative neural circuitry may in part explain heterogenous outcomes following ablation procedures in diferent brain regions. These fndings may ultimately inform target selection for ablative epilepsy surgery based on normative intrinsic connectivity of the targeted volume.

Treatment-resistant epilepsy is a common and debilitating neurological condition, for which neurosurgical cure is possible. Despite undergoing nearly identical ablation procedures however, individuals with treatment-resistant epilepsy frequently exhibit heterogeneous outcomes. We hypothesized that treatment response may be related to the brain regions to which MR-guided laser ablation volumes are functionally connected. To test this, we mapped the resting-state functional connectivity of surgical ablations that either resulted in seizure freedom (N = 11) or did not result in seizure freedom (N = 16) in over 1,000 normative connectomes. There was no difference seizure outcome with respect to the anatomical location of the ablations, and very little overlap between ablation areas was identified using the Dice Index. Ablations that did not result in seizure-freedom were preferentially connected to a number of cortical and subcortical regions, as well as multiple canonical resting-state networks. In contrast, ablations that led to seizure-freedom were more functionally connected to prefrontal cortices. Here, we demonstrate that underlying normative neural circuitry may in part explain heterogenous outcomes following ablation procedures in different brain regions. These findings may ultimately inform target selection for ablative epilepsy surgery based on normative intrinsic connectivity of the targeted volume.

Objective Vagus nerve stimulation (VNS) is a common treatment for medically intractable epilepsy, but response rates are highly variable, with no preoperative means of identifying good candidates. This study aimed to predict VNS response using structural and functional connectomic profiling. Methods Fifty‐six children, comprising discovery (n = 38) and validation (n = 18) cohorts, were recruited from 3 separate institutions. Diffusion tensor imaging was used to identify group differences in white matter microstructure, which in turn informed beamforming of resting‐state magnetoencephalography recordings. The results were used to generate a support vector machine learning classifier, which was independently validated. This algorithm was compared to a second classifier generated using 31 clinical covariates. Results Treatment responders demonstrated greater fractional anisotropy in left thalamocortical, limbic, and association fibers, as well as greater connectivity in a functional network encompassing left thalamic, insular, and temporal nodes (p < 0.05). The resulting classifier demonstrated 89.5% accuracy and area under the receiver operating characteristic (ROC) curve of 0.93 on 10‐fold cross‐validation. In the external validation cohort, this model demonstrated an accuracy of 83.3%, with a sensitivity of 85.7% and specificity of 75.0%. This was significantly superior to predictions using clinical covariates alone, which exhibited an area under the ROC curve of 0.57 (p < 0.008). Interpretation This study provides the first multi‐institutional, multimodal connectomic prediction algorithm for VNS, and provides new insights into its mechanism of action. Reliable identification of VNS responders is critical to mitigate surgical risks for children who may not benefit, and to ensure cost‐effective allocation of health care resources. ANN NEUROL 2019;86:743–753

Background: fMRI of mental phenomena is quite difficult to perform because lack of patient's cooperation or because the symptoms are stable. In some exceptional cases, however, fMRI and DTI are capable to provide insights on the anatomy of organic hallucinations. Methods: In this report we describe a 14-year-old boy with a left fronto-dorsal tumor who experienced chronic complex brief, frequent and repetitive complex visual and auditory hallucinations. His clinical picture included multiple and severe social and mood problems. During a presurgical fMRI mapping the patient complained of having the visual and auditory hallucinations. A block-design FMRI paradigm was obtained from the event timecourse. Deterministic DTI of the brain was obtained seeding the lesion as ROI. The patient underwent surgery and electrocorticography of the lesional area. Results: The fMRI of the hallucinations showed activation in the left inferior frontal gyrus (IFG) and the peri-lesional area. The tractography of the tumor revealed structural aberrant connectivity to occipital and temporal areas in addition to the expected connectivity with the IFG via the aslant fasciculus and homotopic contralateral areas. Intraoperative EEG demonstrated epileptic discharges in the tumor and neighboring areas. After resection, the patient's hallucinations stopped completely. He regained his normal social life and recover his normal mood. He remained asymptomatic for 90 days. Afterwards, hallucinations reappeared but with less intensity. Conclusions: To our knowledge, this is the first reported case of combined functional and structural connectivity imaging demonstrating brain regions participating in a network involved in the generation of complex auditory and visual hallucinations.