Application and comparison of classification algorithms for recognition of Alzheimer's disease in electrical brain activity (EEG)

The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.

The syndromology of anorectal malformations revisited: from patterns of associated malformations to the recognition of syndromes

ackground: Although the frequency of associated malformation is high, the incidence of inheritable syndromes is widely underestimated in children with anorectal malformation (ARM). Data sources: OMIM database, patient records and charts of the Department of Pediatric Surgery, Johannes Gutenberg-University, Mainz, Germany. Methods: We analyzed all associations, sequences and syndromes listed in the OMIM database that can be accompanied by ARM. A large cohort of children born with ARM was then retrospectively investigated as to the type of ARM, presence of additional malformations and possible categorization as a syndrome, sequence or association. For this process a syndrome finder was developed and employed. This simplistic tool allows for a rapid first check of possible syndromes before a more complex analysis is started using the OMIM database and consulting specialists. Results: Among 317 children with ARM, associated malformations were present in 77.7% of 127 children with high ARM, in 68.7% of 32 with intermediate ARM, and in 25.3% of 158 with a low type ARM. Three or more organ systems were involved in 29.1% children with high type ARM and 25% with intermediate ARM and 8.2% with a low type ARM. An association of the vertebral anal tracheo-esophageal renal (VATER) and vertebral anal cardiac tracheo-esophageal renal limb (VACTERL) type was found in a total of 35 patients. Before analysis, 11 syndromes and 35 associations which were not clear previously in this patient cohort were described. In other 17 patients, 14 syndromes and 3 associations were identified. Conclusions: The high number of only retrospectively identified syndromes suggests that a routine search is necessary in every patient with ARM and additional malformations.

The development and evaluation of a computer-based program to test and to teach the recognition of facial affect

Autism is a chronic pervasive neurodevelopmental disorder characterized by the early onset of social and communicative impairments as well as restricted, ritualized, stereotypic behavior. The endophenotype of autism includes neuropsychological deficits, for instance a lack of "Theory of Mind" and problems recognizing facial affect. In this study, we report the development and evaluation of a computer-based program to teach and test the ability to identify basic facially expressed emotions. 10 adolescent or adult subjects with high-functioning autism or Asperger-syndrome were included in the investigation. A priori the facial affect recognition test had shown good psychometric properties in a normative sample (internal consistency: rtt=.91-.95; retest reliability: rtt=.89-.92). In a prepost design, one half of the sample was randomly assigned to receive computer treatment while the other half of the sample served as control group. The training was conducted for five weeks, consisting of two hours training a week. The trained individuals improved significantly on the affect recognition task, but not on any other measure. Results support the usefulness of the program to teach the detection of facial affect. However, the improvement found is limited to a circumscribed area of social-communicative function and generalization is not ensured.

Moving from sustainable management to sustainable governance of natural resources: The role of social learning processes in rural India, Bolivia and Mali

The present paper discusses a conceptual, methodological and practical framework within which the limitations of the conventional notion of natural resource management (NRM) can be overcome. NRM is understood as the application of scientific ecological knowledge to resource management. By including a consideration of the normative imperatives that arise from scientific ecological knowledge and submitting them to public scrutiny, ‘sustainable management of natural resources’ can be recontextualised as ‘sustainable governance of natural resources’. This in turn makes it possible to place the politically neutralising discourse of ‘management’ in a space for wider societal debate, in which the different actors involved can deliberate and negotiate the norms, rules and power relations related to natural resource use and sustainable development. The transformation of sustainable management into sustainable governance of natural resources can be conceptualised as a social learning process involving scientists, experts, politicians and local actors, and their corresponding scientific and non-scientific knowledges. The social learning process is the result of what Habermas has described as ‘communicative action’, in contrast to ‘strategic action’. Sustainable governance of natural resources thus requires a new space for communicative action aiming at shared, intersubjectively validated definitions of actual situations and the goals and means required for transforming current norms, rules and power relations in order to achieve sustainable development. Case studies from rural India, Bolivia and Mali explore the potentials and limitations for broadening communicative action through an intensification of social learning processes at the interface of local and external knowledge. Key factors that enable or hinder the transformation of sustainable management into sustainable governance of natural resources through social learning processes and communicative action are discussed.