Angelman syndrome: a case series assessing neurological issues in adulthood.

BACKGROUND: This study aimed to evaluate the clinical symptoms of Angelman syndrome (AS) in adults and to identify the neurological pathways affected in this disease. AS is a neurogenetic disorder resulting due to the deletion or inactivation of the ubiquitin-protein-ligase E3A gene on maternal chromosome 15. SUMMARY: A retrospective analysis of data from six adults patients with clinical, electroencephalographic and genetic confirmation of AS was performed. Movement disorders of the hands and mouth, laughing spells, severe expressive speech disorders, a happy nature, hyposomnia and anxiety are the major neurological characteristics of AS in adulthood. Cerebellar ataxia, muscle hypotonia and tremor, though constant in childhood, tend to be attenuated in adulthood. Epilepsy, one of the most frequent symptoms in childhood and in adulthood, is characterised by specific electroencephalographic patterns. Key Messages: These clinical characteristics are important to improve the clinical awareness and genetic diagnosis of AS. Clinicians must be better informed concerning the adult phenotype as it is not well described in the literature. We stress the importance of AS as one of the main causes of intractable epilepsy. The authors suggest frontal and cerebellar dysfunction. Further functional cerebral imaging studies are necessary.

Bedside Evaluation of the Functional Organization of the Auditory Cortex in Patients with Disorders of Consciousness.

To measure the level of residual cognitive function in patients with disorders of consciousness, the use of electrophysiological and neuroimaging protocols of increasing complexity is recommended. This work presents an EEG-based method capable of assessing at an individual level the integrity of the auditory cortex at the bedside of patients and can be seen as the first cortical stage of this hierarchical approach. The method is based on two features: first, the possibility of automatically detecting the presence of a N100 wave and second, in showing evidence of frequency processing in the auditory cortex with a machine learning based classification of the EEG signals associated with different frequencies and auditory stimulation modalities. In the control group of twelve healthy volunteers, cortical frequency processing was clearly demonstrated. EEG recordings from two patients with disorders of consciousness showed evidence of partially preserved cortical processing in the first patient and none in the second patient. From these results, it appears that the classification method presented here reliably detects signal differences in the encoding of frequencies and is a useful tool in the evaluation of the integrity of the auditory cortex. Even though the classification method presented in this work was designed for patients with disorders of consciousness, it can also be applied to other pathological populations.