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Neuroimaging research is increasingly shifting towards distributed computing architectures for the processing of ever growing neuroimaging datasets. At present compute and data intensive neuroimaging workflows often use cluster-based resources to analyse datasets. For increased scalability however, distributed grid-based analysis platforms may be required. Such an analysis infrastructure necessitates robust methods of grid-aware planning and optimisation in order to efficiently execute often highly complex workflows. This paper presents the approaches used in neuGRID to plan the workflow gridification and enactment for neuroimaging research. Experiments show that grid-aware workflow planning techniques can achieve significant performance gains. Turn-around time of a typical neuroimaging workflow reduces by 30% compared to the same workflow enacted without grid-aware planning. Data efficiency also increases by more than 25%. The use of workflow planning techniques in the neuGRID infrastructure may enable it to process larger neuroimaging datasets and therefore allow researchers to carry out more statistically significant analysis.