Identifying epileptogenic abnormalities through spatial clustering of MEG interictal band power

Successful epilepsy surgery depends on localising and resecting cerebral abnormalities and networks that generate seizures. Abnormalities, however, may be widely distributed across multiple discontiguous areas. We propose spatially constrained clusters as candidate areas for further investigation, and potential resection. We quantified the spatial overlap between the abnormality cluster and subsequent resection, hypothesising a greater overlap in seizure-free patients. Thirty-four individuals with refractory focal epilepsy underwent pre-surgical resting-state interictal MEG recording. Fourteen individuals were totally seizure free (ILAE 1) after surgery and 20 continued to have some seizures post-operatively (ILAE 2+). Band power abnormality maps were derived using controls as a baseline. Patient abnormalities were spatially clustered using the k-means algorithm. The tissue within the cluster containing the most abnormal region was compared with the resection volume using the dice score. The proposed abnormality cluster overlapped with the resection in 71% of ILAE 1 patients. Conversely, an overlap only occurred in 15% of ILAE 2+ patients. This effect discriminated outcome groups well (AUC=0.82). Our novel approach identifies clusters of spatially similar tissue with high abnormality. This is clinically valuable, providing (i) a data-driven framework to validate current hypotheses of the epileptogenic zone localisation or (ii) to guide further investigation.Comment: 16 pages, 3 figure

Identifying epileptogenic abnormalities through spatial clustering of MEG interictal band power

Successful epilepsy surgery depends on localising and resecting cerebral abnormalities and networks that generate seizures. Abnormalities, however, may be widely distributed across multiple discontiguous areas. We propose spatially constrained clusters as candidate areas for further investigation, and potential resection. We quantified the spatial overlap between the abnormality cluster and subsequent resection, hypothesising a greater overlap in seizure-free patients. Thirty-four individuals with refractory focal epilepsy underwent pre-surgical resting-state interictal MEG recording. Fourteen individuals were totally seizure free (ILAE 1) after surgery and 20 continued to have some seizures post-operatively (ILAE 2+). Band power abnormality maps were derived using controls as a baseline. Patient abnormalities were spatially clustered using the k-means algorithm. The tissue within the cluster containing the most abnormal region was compared with the resection volume using the dice score. The proposed abnormality cluster overlapped with the resection in 71% of ILAE 1 patients. Conversely, an overlap only occurred in 15% of ILAE 2+ patients. This effect discriminated outcome groups well (AUC=0.82). Our novel approach identifies clusters of spatially similar tissue with high abnormality. This is clinically valuable, providing (i) a data-driven framework to validate current hypotheses of the epileptogenic zone localisation or (ii) to guide further investigation

Common risk factor approach to address socioeconomic inequality in the oral health of preschool children – a prospective cohort study

Background: Dental caries remains the most prevalent chronic condition in children and a major contributor to poor general health. There is ample evidence of a skewed distribution of oral health, with a small proportion of children in the population bearing the majority of the burden of the disease. This minority group is comprised disproportionately of socioeconomically disadvantaged children. An in-depth longitudinal study is needed to better understand the determinants of child oral health, in order to support effective evidence-based policies and interventions in improving child oral health. The aim of the Study of Mothers’ and Infants’ Life Events Affecting Oral Health (SMILE) project is to identify and evaluate the relative importance and timing of critical factors that shape the oral health of young children and then to seek to evaluate those factors in their inter-relationship with socioeconomic influences.Methods/Design: This investigation will apply an observational prospective study design to a cohort ofsocioeconomically-diverse South Australian newborns and their mothers, intensively following these dyads as the children grow to toddler age. Mothers of newborn children will be invited to participate in the study in the early post-partum period. At enrolment, data will be collected on parental socioeconomic status, mothers’ general and dental health conditions, details of the pregnancy, infant feeding practice and parental health behaviours and practices. Data on diet and feeding practices, oral health behaviours and practices, and dental visiting patterns will be collected at 3, 6, 12 and 24 months of age. When children turn 24-30 months, the children and their mothers/primary care givers will be invited to an oral examination to record oral health status. Anthropometric assessment will also be conducted.Discussion: This prospective cohort study will examine a wide range of determinants influencing child oral health and related general conditions such as overweight. It will lead to the evaluation of the inter-relationship among main influences and their relative effect on child oral health. The study findings will provide high level evidence of pathways through which socio-environmental factors impact child oral health. It will also provide an opportunity to examine the relationship between oral health and childhood overweight.Discussion: This prospective cohort study will examine a wide range of determinants influencing child oral health and related general conditions such as overweight. It will lead to the evaluation of the inter-relationship among main influences and their relative effect on child oral health. The study findings will provide high level evidence of pathways through which socio-environmental factors impact child oral health. It will also provide an to examine the relationship between oral health and childhood overweight

Comparison of methods to estimate non-milk extrinsic sugars and their application to sugars in the diet of young adolescents

Consistent information on the non-milk extrinsic sugars (NMES) content of foods and the NMES intake by the population is required in order to allow comparisons between dietary surveys. A critical appraisal of methods of NMES estimation was conducted to investigate whether the different published methods for estimating the NMES content of foods lead to significantly different values for the dietary intake of NMES by children and to consider the relative practicality of each method. NMES values of foods were calculated using three different published descriptions of methods of NMES estimation, and the values were compared within food groups. Dietary intake values for English children aged 11–12 years were calculated using each method and compared in terms of overall NMES intake and the contribution of different food groups to NMES intake. There was no significant difference in the dietary intake of NMES in children between the method used in the National Diet and Nutrition Surveys (NDNS) (81·9 g/d; 95 % CI 79·0, 84·7) and a method developed by the Human Nutrition Research Centre (84·3 g/d; 95 % CI 81·4, 87·2) at Newcastle University, UK, although the latter gave slightly higher values. An earlier method used by the Ministry of Agriculture, Food and Fisheries gave significantly higher values than the other two methods (102·5 g/d; 95 % CI 99·3, 105·6; P<0·05). The method used in the NDNS surveys and the method used by the Human Nutrition Research Centre at Newcastle University are both thorough and detailed methods that give consistent results. However, the method used in the NDNS surveys was more straightforward to apply in practice and is the best method for a single uniform approach to the estimation of NMES

Healthy eating:Perceptions and practice (the ASH30 study)

Perceptions of healthy eating may influence food intake. Anthropometric and dietary data were collected from 197 respondents (average age 32.5 years: 2000/2001) in Northumberland (78%) and elsewhere in the UK (22%). A questionnaire and two 3-day food diaries were completed. Foods consumed were assigned to one of five food categories from The Balance of Good Health. This paper explores respondents' concepts of 'healthy eating' and responses to the statement, 'My eating patterns are healthy' and compares responses with measured intakes for each of the five food categories. Fifty-three respondents disagreed, 62 neither agreed nor disagreed and 82 agreed with the statement. Intakes of foods containing fat and/or sugar, fruit and vegetables and meat, fish and alternatives were significantly different between the three response groups. The 'agree' group had the highest intake of fruit and vegetables and the lowest intake of foods containing fat and/or sugar and meat, fish and alternatives. A significantly higher proportion of individuals from the highest socio-economic group agreed with the statement. Significantly more individuals with Body Mass Indexes in the two lower quartiles agreed with the statement. This paper shows a relationship between perceptions of eating patterns and socio-economic status, adiposity and measured food intake.</p