Recovery of chronic motor neuropathy due to acute intermittent porphyria after givosiran treatment in a young boy: a case report.

BACKGROUND: We describe the first case of a pediatric patient with acute intermittent porphyria and severe chronic porphyric neuropathy treated with givosiran, a small-interfering RNA that drastically decreases delta-aminolevulinic acid production and reduces porphyric attacks' recurrence. CASE REPORT: A 12-year-old male patient with refractory acute intermittent porphyria and severe porphyric neuropathy was followed prospectively for 12 months after givosiran initiation (subcutaneous, 2.5 mg/kg monthly). Serial neurological, structural, and resting-state functional magnetic resonance imaging (MRI) evaluations were performed, including clinical scales and neurophysiological tests. Delta-aminolevulinic acid urinary levels dropped drastically during treatment. In parallel, all the administered neurological rating scales and neurophysiological assessments showed improvement in all domains. Moreover, an improvement in central motor conduction parameters and resting-state functional connectivity in the sensory-motor network was noticed. At the end of the follow-up, the patient could walk unaided after using a wheelchair for 5 years. CONCLUSIONS: A clear beneficial effect of givosiran was demonstrated in our patient with both clinical and peripheral nerve neurophysiologic outcome measures. Moreover, we first reported a potential role of givosiran in recovering central motor network impairment in acute intermittent porphyria (AIP), which was previously unknown. This study provides Class IV evidence that givosiran improves chronic porphyric neuropathy.

fMRI-Based Effective Connectivity in Surgical Remediable Epilepsies: A Pilot Study.

Simultaneous EEG-fMRI can contribute to identify the epileptogenic zone (EZ) in focal epilepsies. However, fMRI maps related to Interictal Epileptiform Discharges (IED) commonly show multiple regions of signal change rather than focal ones. Dynamic causal modeling (DCM) can estimate effective connectivity, i.e. the causal effects exerted by one brain region over another, based on fMRI data. Here, we employed DCM on fMRI data in 10 focal epilepsy patients with multiple IED-related regions of BOLD signal change, to test whether this approach can help the localization process of EZ. For each subject, a family of competing deterministic, plausible DCM models were constructed using IED as autonomous input at each node, one at time. The DCM findings were compared to the presurgical evaluation results and classified as: "Concordant" if the node identified by DCM matches the presumed focus, "Discordant" if the node is distant from the presumed focus, or "Inconclusive" (no statistically significant result). Furthermore, patients who subsequently underwent intracranial EEG recordings or surgery were considered as having an independent validation of DCM results. The effective connectivity focus identified using DCM was Concordant in 7 patients, Discordant in two cases and Inconclusive in one. In four of the 6 patients operated, the DCM findings were validated. Notably, the two Discordant and Invalidated results were found in patients with poor surgical outcome. Our findings provide preliminary evidence to support the applicability of DCM on fMRI data to investigate the epileptic networks in focal epilepsy and, particularly, to identify the EZ in complex cases.

New onset status epilepticus in influenza associated encephalopathy: The presenting manifestation of genetic generalized epilepsy.

We hereby present a case of a young woman with no history of seizures or epilepsy who experienced a de novo generalized Non Convulsive Status Epilepticus (NCSE) followed by encephalopathy lasting for several days during influenza B infection. Influenza can have a broad spectrum of presentation ranging from an uncomplicated illness to many serious conditions as is the case of influenza associated encephalitis/encephalopathy (IAE). In this context however, it is possible to observe seizures and/or status epilepticus as the presenting manifestation of a genetic generalized epilepsy.

Classification of EEG abnormalities in partial epilepsy with simultaneous EEG-fMRI recordings.

Scalp EEG recordings and the classification of interictal epileptiform discharges (IED) in patients with epilepsy provide valuable information about the epileptogenic network, particularly by defining the boundaries of the "irritative zone" (IZ), and hence are helpful during pre-surgical evaluation of patients with severe refractory epilepsies. The current detection and classification of epileptiform signals essentially rely on expert observers. This is a very time-consuming procedure, which also leads to inter-observer variability. Here, we propose a novel approach to automatically classify epileptic activity and show how this method provides critical and reliable information related to the IZ localization beyond the one provided by previous approaches. We applied Wave_clus, an automatic spike sorting algorithm, for the classification of IED visually identified from pre-surgical simultaneous Electroencephalogram-functional Magnetic Resonance Imagining (EEG-fMRI) recordings in 8 patients affected by refractory partial epilepsy candidate for surgery. For each patient, two fMRI analyses were performed: one based on the visual classification and one based on the algorithmic sorting. This novel approach successfully identified a total of 29 IED classes (compared to 26 for visual identification). The general concordance between methods was good, providing a full match of EEG patterns in 2 cases, additional EEG information in 2 other cases and, in general, covering EEG patterns of the same areas as expert classification in 7 of the 8 cases. Most notably, evaluation of the method with EEG-fMRI data analysis showed hemodynamic maps related to the majority of IED classes representing improved performance than the visual IED classification-based analysis (72% versus 50%). Furthermore, the IED-related BOLD changes revealed by using the algorithm were localized within the presumed IZ for a larger number of IED classes (9) in a greater number of patients than the expert classification (7 and 5, respectively). In contrast, in only one case presented the new algorithm resulted in fewer classes and activation areas. We propose that the use of automated spike sorting algorithms to classify IED provides an efficient tool for mapping IED-related fMRI changes and increases the EEG-fMRI clinical value for the pre-surgical assessment of patients with severe epilepsy.

An electrophysiological validation of stochastic DCM for fMRI.

In this note, we assess the predictive validity of stochastic dynamic causal modeling (sDCM) of functional magnetic resonance imaging (fMRI) data, in terms of its ability to explain changes in the frequency spectrum of concurrently acquired electroencephalography (EEG) signal. We first revisit the heuristic model proposed in Kilner et al. (2005), which suggests that fMRI activation is associated with a frequency modulation of the EEG signal (rather than an amplitude modulation within frequency bands). We propose a quantitative derivation of the underlying idea, based upon a neural field formulation of cortical activity. In brief, dense lateral connections induce a separation of time scales, whereby fast (and high spatial frequency) modes are enslaved by slow (low spatial frequency) modes. This slaving effect is such that the frequency spectrum of fast modes (which dominate EEG signals) is controlled by the amplitude of slow modes (which dominate fMRI signals). We then use conjoint empirical EEG-fMRI data-acquired in epilepsy patients-to demonstrate the electrophysiological underpinning of neural fluctuations inferred from sDCM for fMRI.

Use of levetiracetam in treating epilepsy associated with other medical conditions.

OBJECTIVE: This prospective, open-label study was conducted to evaluate the effectiveness, tolerability, and safety of levetiracetam in patients with epilepsy in whom unfavorable metabolism, complex drug interactions, or direct toxic effects of antiepileptic drugs (AEDs) had caused a worsening of comorbid conditions. METHODS: Study design included the introduction of levetiracetam, discontinuation of other AEDs, and a serial assessment comprising electroencephalograms and blood tests at baseline and 2, 6, and 12 months. Of 21 patients, 16 had partial and five generalized epilepsy. Concomitant pathologies were gastroenterological (six), vascular (four), endocrinological (four), or complex conditions including hematological (four) or dermatological (three) disease. A change of regimen was necessitated by drug-drug interactions in four patients, direct real or potential toxic effects of previous AEDs in 13, and a combination of interactions/toxic effects in four. RESULTS: After 12 months, 12 patients were seizure-free, nine had reductions in seizure frequency of 50-75%, and improvement in concomitant medical conditions was observed. No side effects were reported. CONCLUSION: Levetiracetam appears to be effective, well tolerated, and safe in patients with epilepsy and other medical conditions that are difficult to manage because of drug interactions or AED-related side effects.