title: Memory-Efficient Analysis of Dense Functional Connectomes.
abstract: The functioning of the human brain relies on the interplay and integration of numerous individual units within a complex network. To identify network configurations characteristic of specific cognitive tasks or mental illnesses, functional connectomes can be constructed based on the assessment of synchronous fMRI activity at separate brain sites, and then analyzed using graph-theoretical concepts. In most previous studies, relatively coarse parcellations of the brain were used to define regions as graphical nodes. Such parcellated connectomes are highly dependent on parcellation quality because regional and functional boundaries need to be relatively consistent for the results to be interpretable. In contrast, dense connectomes are not subject to this limitation, since the parcellation inherent to the data is used to define graphical nodes, also allowing for a more detailed spatial mapping of connectivity patterns. However, dense connectomes are associated with considerable computational demands in terms of both time and memory requirements. The memory required to explicitly store dense connectomes in main memory can render their analysis infeasible, especially when considering high-resolution data or analyses across multiple subjects or conditions. Here, we present an object-based matrix representation that achieves a very low memory footprint by computing matrix elements on demand instead of explicitly storing them. In doing so, memory required for a dense connectome is reduced to the amount needed to store the underlying time series data. Based on theoretical considerations and benchmarks, different matrix object implementations and additional programs (based on available Matlab functions and Matlab-based third-party software) are compared with regard to their computational efficiency. The matrix implementation based on on-demand computations has very low memory requirements, thus enabling analyses that would be otherwise infeasible to conduct due to insufficient memory. An open source software package containing the created programs is available for download.
authors.affiliation: Department of Neurology, Otto-von-Guericke UniversityMagdeburg, Germany; Department of Computer Science, Otto-von-Guericke UniversityMagdeburg, Germany; Leibniz Institute for NeurobiologyMagdeburg, Germany.
authors.forename: Kristian
authors.initials: K
authors.lastname: Loewe
authors.affiliation: Department of Neurology, Otto-von-Guericke UniversityMagdeburg, Germany; Leibniz Institute for NeurobiologyMagdeburg, Germany; Center for Cognitive Neuroscience, Duke UniversityDurham, NC, USA.
authors.forename: Sarah E
authors.initials: SE
authors.lastname: Donohue
authors.affiliation: Department of Neurology, Otto-von-Guericke UniversityMagdeburg, Germany; Leibniz Institute for NeurobiologyMagdeburg, Germany; Kliniken SchmiederAllensbach, Germany.
authors.forename: Mircea A
authors.initials: MA
authors.lastname: Schoenfeld
authors.affiliation: Department of Computer Science, Otto-von-Guericke University Magdeburg, Germany.
authors.forename: Rudolf
authors.initials: R
authors.lastname: Kruse
authors.affiliation: Department of Computer Science, Otto-von-Guericke University Magdeburg, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Borgelt
keywords: big data; dense connectome analysis; functional connectivity; graph theoretical analysis; resting-state fMRI