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.
Highlights
Graph-based analysis of dense connectomes allows for spatially precise mapping of fMRI-based functional connectivity patterns but is associated with considerable computational demands (van den Heuvel et al, 2008; Hayasaka and Laurienti, 2010; de Reus and Van den Heuvel, 2013; Fornito et al, 2013)
To assess the performance of the different functional connectivity matrix representation (FCMAT) variants, we used the computation of node degrees based on nodal time series data as an example application
In an attempt to address this issue, we presented an object-based functional connectivity matrix representation (FCMAT) and corresponding implementation variants, tailored for the analysis of dense functional connectomes
Summary
Graph-based analysis of dense connectomes allows for spatially precise mapping of fMRI-based functional connectivity patterns but is associated with considerable computational demands (van den Heuvel et al, 2008; Hayasaka and Laurienti, 2010; de Reus and Van den Heuvel, 2013; Fornito et al, 2013). While the analysis of such parcellated connectomes offers many advantages and led to impactful findings, their spatial sensitivity is rather limited, as the use of region-level nodes typically involves the aggregation of fMRI time series from the incorporated voxels, at the cost of more detailed spatial information (Wang et al, 2010; Scheinost et al, 2012; Stanley et al, 2013) Such analyes are highly dependent on parcellation quality because regional and functional boundaries need to be relatively consistent to obtain meaningful results (Smith et al, 2011, 2013; Zuo and Xing, 2014; Jiang et al, 2015; Jiang and Zuo, 2016). The suitability of a given parcellation depends on the application because functional boundaries may vary between individuals (Biswal et al, 2010; Kelly et al, 2012), in the context of different tasks (Lohmann et al, 2016; Mišicand Sporns, 2016), or in association with dysfunction and disease (Matthews and Hampshire, 2016)
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