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Abstract: This paper reports our work in the context of the neuGRID project in the development of intelligent services for a robust and efficient Neuroimaging analysis environment. neuGRID is an EC-funded project driven by the needs of the Alzheimer's disease research community that aims to facilitate the collection and archiving of large amounts of imaging data coupled with a set of services and algorithms. By taking Alzheimer's disease as an exemplar, the neuGRID project has developed a set of intelligent services and a Grid infrastructure to enable the European neuroscience community to carry out research required for the study of degenerative brain diseases. We have investigated the use of machine learning approaches, especially evolutionary multi-objective meta-heuristics for optimising scientific analysis on distributed infrastructures. The salient features of the services and the functionality of a planning and execution architecture based on an evolutionary multi-objective meta-heuristics to achieve analysis efficiency are presented. We also describe implementation details of the services that will form an intelligent analysis environment and present results on the optimisation that has been achieved as a result of this investigation. [Copyright &y& Elsevier]

Highlights: [•] The need for traceability is identified in neuroscience Alzheimer's studies. [•] We propose a software solution that supports provenance data management. [•] We follow a service-oriented architecture approach in its implementation. [•] Analyses are evaluated for imaging in the neuGRID/N4U Alzheimer's projects. [•] We consider the use of an ‘Analysis Service’ in the context of medical research. [ABSTRACT FROM AUTHOR]