Comprehensive studies in the field of brain pathology require strong information support for the
consolidation of data from different sources. The heterogeneity of data sources and the resourceintensive nature of preprocessing make it difficult to conduct comprehensive interdisciplinary
research. To solve this problem for brain studies, an information system with unified access to
heterogeneous data is required. Effective implementation of such a system requires adapting
preprocessing methods and creating a model for combining disparate data into a single information
environment. We analyze the possibilities and methods of consolidation of clinical and biological data,
build a model for the consolidation and interaction of heterogeneous data sources for brain research,
implement the model as a cloud service, and provide a data interface in a format encapsulating a
complex architecture from the user. We present the design and implementation of an information
system; we show and discuss the results of the application of cluster analysis methods to differentiate
various types of dementia with MRI data. Our results show that a study of the properties of cluster
analysis data can significantly help neurophysiologists in the study of cognitive disorders such as
Alzheimer’s disease, especially with the possibilities provided by the proposed information system.