Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates

This is the author accepted manuscript. The final version is available from IOP Publishing via the DOI in this record.OBJECTIVE: The paper investigates the presence of autism using the functional brain connectivity measures derived from electro-encephalogram (EEG) of children during face perception tasks. APPROACH: Phase synchronized patterns from 128-channel EEG signals are obtained for typical children and children with autism spectrum disorder (ASD). The phase synchronized states or synchrostates temporally switch amongst themselves as an underlying process for the completion of a particular cognitive task. We used 12 subjects in each group (ASD and typical) for analyzing their EEG while processing fearful, happy and neutral faces. The minimal and maximally occurring synchrostates for each subject are chosen for extraction of brain connectivity features, which are used for classification between these two groups of subjects. Among different supervised learning techniques, we here explored the discriminant analysis and support vector machine both with polynomial kernels for the classification task. MAIN RESULTS: The leave one out cross-validation of the classification algorithm gives 94.7% accuracy as the best performance with corresponding sensitivity and specificity values as 85.7% and 100% respectively. SIGNIFICANCE: The proposed method gives high classification accuracies and outperforms other contemporary research results. The effectiveness of the proposed method for classification of autistic and typical children suggests the possibility of using it on a larger population to validate it for clinical practice.The work presented in this paper was supported by FP7 EU funded MICHELANGELO project, Grant Agreement #288241. URL: www.michelangelo-project.eu/

Using Brain Connectivity Measure of EEG Synchrostates for Discriminating Typical and Autism Spectrum Disorder

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record.In this paper we utilized the concept of stable phase synchronization topography - synchrostates - over the scalp derived from EEG recording for formulating brain connectivity network in Autism Spectrum Disorder (ASD) and typically-growing children. A synchronization index is adapted for forming the edges of the connectivity graph capturing the stability of each of the synchrostates. Such network is formed for 11 ASD and 12 control group children. Comparative analyses of these networks using graph theoretic measures show that children with autism have a different modularity of such networks from typical children. This result could pave the way to a new modality for possible identification of ASD from non-invasively recorded EEG data

MECP2 duplication phenotype in symptomatic females: report of three further cases.

BACKGROUND: Xq28 duplications, including MECP2 (methyl CpG-binding protein 2; OMIM 300005), have been identified in approximately 140 male patients presenting with hypotonia, severe developmental delay/intellectual disability, limited or absent speech and ambulation, and recurrent respiratory infections. Female patients with Xq28 duplication have been rarely reported and are usually asymptomatic. Altogether, only fifteen symptomatic females with Xq28 duplications including MECP2 have been reported so far: six of them had interstitial duplications while the remaining had a duplication due to an unbalanced X;autosome translocation. Some of these females present with unspecific mild to moderate intellectual disability whereas a more complex phenotype is reported for females with unbalanced X;autosome translocations.FINDINGS: Here we report on the clinical features of three other adolescent to adult female patients with Xq28 interstitial duplications of variable size, all including MECP2 gene.CONCLUSIONS: Mild to moderate cognitive impairment together with learning difficulties and speech delay were evident in each of our patients. Moreover, early inadequate behavioral patterns followed by persistent difficulties in the social and communication domains, as well as the occurrence of mild psychiatric disturbances, are common features of these three patients

Idiopathic epilepsies with seizures precipitated by fever and SCN1A abnormalities.

Epilepsia. 2007 Sep;48(9):1678-85. Epub 2007 Jun 11. Idiopathic epilepsies with seizures precipitated by fever and SCN1A abnormalities. Marini C, Mei D, Temudo T, Ferrari AR, Buti D, Dravet C, Dias AI, Moreira A, Calado E, Seri S, Neville B, Narbona J, Reid E, Michelucci R, Sicca F, Cross HJ, Guerrini R. SourceEpilepsy, Neurophysiology and Neurogenetic Unit, Institute of Child Neurology and Psychiatry, IRCCS Stella Maris Foundation, Calambrone, Pisa, Italy. Abstract PURPOSE: SCN1A is the most clinically relevant epilepsy gene, most mutations lead to severe myoclonic epilepsy of infancy (SMEI) and generalized epilepsy with febrile seizures plus (GEFS+). We studied 132 patients with epilepsy syndromes with seizures precipitated by fever, and performed phenotype-genotype correlations with SCN1A alterations. METHODS: We included patients with SMEI including borderline SMEI (SMEB), GEFS+, febrile seizures (FS), or other seizure types precipitated by fever. We performed a clinical and genetic study focusing on SCN1A, using dHPLC, gene sequencing, and MLPA to detect genomic deletions/duplications on SMEI/SMEB patients. RESULTS: We classified patients as: SMEI/SMEB = 55; GEFS+= 26; and other phenotypes = 51. SCN1A analysis by dHPLC/sequencing revealed 40 mutations in 37 SMEI/SMEB (67%) and 3 GEFS+ (11.5%) probands. MLPA showed genomic deletions in 2 of 18 SMEI/SMEB. Most mutations were de novo (82%). SMEB patients carrying mutations (8) were more likely to have missense mutations (62.5%), conversely SMEI patients (31) had more truncating, splice site or genomic alterations (64.5%). SMEI/SMEB with truncating, splice site or genomic alterations had a significantly earlier age of onset of FS compared to those with missense mutations and without mutations (p = 0.00007, ANOVA test). None of the remaining patients with seizures precipitated by fever carried SCN1A mutations. CONCLUSION: We obtained a frequency of 71%SCN1A abnormalities in SMEI/SMEB and of 11.5% in GEFS+ probands. MLPA complements DNA sequencing of SCN1A increasing the mutation detection rate. SMEI/SMEB with truncating, splice site or genomic alterations had a significantly earlier age of onset of FS. This study confirms the high sensitivity of SCN1A for SMEI/SMEB phenotypes

Clinical and genetic factors predicting Dravet syndrome in infants with SCN1A mutations

OBJECTIVE: To explore the prognostic value of initial clinical and mutational findings in infants with SCN1A mutations. METHODS: Combining sex, age/fever at first seizure, family history of epilepsy, EEG, and mutation type, we analyzed the accuracy of significant associations in predicting Dravet syndrome vs milder outcomes in 182 mutation carriers ascertained after seizure onset. To assess the diagnostic accuracy of all parameters, we calculated sensitivity, specificity, receiver operating characteristic (ROC) curves, diagnostic odds ratios, and positive and negative predictive values and the accuracy of combined information. We also included in the study demographic and mutational data of the healthy relatives of mutation carrier patients. RESULTS: Ninety-seven individuals (48.5%) had Dravet syndrome, 49 (23.8%) had generalized/genetic epilepsy with febrile seizures plus, 30 (14.8%) had febrile seizures, 6 (3.5%) had focal epilepsy, and 18 (8.9%) were healthy relatives. The association study indicated that age at first seizure and frameshift mutations were associated with Dravet syndrome. The risk of Dravet syndrome was 85% in the 0- to 6-month group, 51% in the 6- to 12-month range, and 0% after the 12th month. ROC analysis identified onset within the sixth month as the diagnostic cutoff for progression to Dravet syndrome (sensitivity = 83.3%, specificity = 76.6%). CONCLUSIONS: In individuals with SCN1A mutations, age at seizure onset appears to predict outcome better than mutation type. Because outcome is not predetermined by genetic factors only, early recognition and treatment that mitigates prolonged/repeated seizures in the first year of life might also limit the progression to epileptic encephalopathy

Expresión del factor de crecimiento del endotelio vascular en carcinomas renales y su relación con la microdensidad vascular, la embolia tumoral y las metástasis a distancia

Los carcinomas renales de células claras derivan de las células epiteliales renales originadas en los túbulos contorneados proximales de las nefronas y se caracterizan por presentar una profusa vascularización. El funcionamiento aberrante del gen del VHL presente en gran parte de estos tumores, se traduce en la liberación de una serie de factores de crecimiento, entre ellos del factor de crecimiento del endotelio vascular (VEGF), implicado en el crecimiento y proliferación de las células tumorales, así como en el proceso de angiogénesis necesaria para el desarrollo de metástasis por vía hematógena. Varios trabajos han sostenido la hipótesis de que la marcación con VEGF podría ser de importancia como factor pronóstico. El objetivo del presente trabajo es determinar la distribución e intensidad de la inmunomarcación con VEGF en tumores renales de células calaras y su relación con la microdensidad vascular (MDV), la presencia de embolias tumorales y las metástasis a distancia.Facultad de Ciencias Médica

SYNGAP1 encephalopathy:A distinctive generalized developmental and epileptic encephalopathy

Objective To delineate the epileptology, a key part of the SYNGAP1 phenotypic spectrum, in a large patient cohort. Methods Patients were recruited via investigators' practices or social media. We included patients with (likely) pathogenic SYNGAP1 variants or chromosome 6p21.32 microdeletions incorporating SYNGAP1. We analyzed patients' phenotypes using a standardized epilepsy questionnaire, medical records, EEG, MRI, and seizure videos. Results We included 57 patients (53% male, median age 8 years) with SYNGAP1 mutations (n = 53) or microdeletions (n = 4). Of the 57 patients, 56 had epilepsy: generalized in 55, with focal seizures in 7 and infantile spasms in 1. Median seizure onset age was 2 years. A novel type of drop attack was identified comprising eyelid myoclonia evolving to a myoclonic-atonic (n = 5) or atonic (n = 8) seizure. Seizure types included eyelid myoclonia with absences (65%), myoclonic seizures (34%), atypical (20%) and typical (18%) absences, and atonic seizures (14%), triggered by eating in 25%. Developmental delay preceded seizure onset in 54 of 56 (96%) patients for whom early developmental history was available. Developmental plateauing or regression occurred with seizures in 56 in the context of a developmental and epileptic encephalopathy (DEE). Fifty-five of 57 patients had intellectual disability, which was moderate to severe in 50. Other common features included behavioral problems (73%); high pain threshold (72%); eating problems, including oral aversion (68%); hypotonia (67%); sleeping problems (62%); autism spectrum disorder (54%); and ataxia or gait abnormalities (51%). Conclusions SYNGAP1 mutations cause a generalized DEE with a distinctive syndrome combining epilepsy with eyelid myoclonia with absences and myoclonic-atonic seizures, as well as a predilection to seizures triggered by eating.</p

Building on Progress Towards Fair Access : annual report 2019

Supplementary Table 3: Anti-epileptic drug use in patients with SYNGAP1 mutations or deletion