Abstract
Large-scale structural brain networks encode white matter connectivity patterns among distributed brain areas. These connection patterns are believed to support cognitive processes and, when compromised, can lead to neurocognitive deficits and maladaptive behavior. A powerful approach for studying the organizing principles of brain networks is to construct group-representative networks from multisubject cohorts. Doing so amplifies signal to noise ratios and provides a clearer picture of brain network organization. Here, we show that current approaches for generating sparse group-representative networks overestimate the proportion of short-range connections present in a network and, as a result, fail to match subject-level networks along a wide range of network statistics. We present an alternative approach that preserves the connection-length distribution of individual subjects. We have used this method in previous papers to generate group-representative networks, though to date its performance has not been appropriately benchmarked and compared against other methods. As a result of this simple modification, the networks generated using this approach successfully recapitulate subject-level properties, outperforming similar approaches by better preserving features that promote integrative brain function rather than segregative. The method developed here holds promise for future studies investigating basic organizational principles and features of large-scale structural brain networks.
Highlights
The human brain is a network composed of neural elements—neurons, populations, and areas—interconnected to one another via synapses, axonal projections, and myelinated fiber tracts, depending on the scale considered (Sporns, Tononi, & Kötter, 2005)
Distance-dependent consensus thresholding: Same as consensus thresholding, but where the threshold for connection retention varies with distance
Connections are retained if they appear in at least τAvg subjects, where τAvg is the consensus threshold that results in a binary density equal to that of the typical subject
Summary
The human brain is a network composed of neural elements—neurons, populations, and areas—interconnected to one another via synapses, axonal projections, and myelinated fiber tracts, depending on the scale considered (Sporns, Tononi, & Kötter, 2005) These connections shape neural elements’ patterns of input and output and play an important role in determining any given element’s functional properties (Passingham, Stephan, & Kötter, 2002). It is often the case that group-representative networks are generated by aggregating network data from many subjects while preserving those properties that are consistently expressed at the subject level (de Reus & van den Heuvel, 2013; Roberts, Perry, Roberts, Mitchell, & Breakspear, 2017; Zalesky et al, 2016) This approach, when performed carefully, can theoretically enhance signal while suppressing noise and artifacts, affording a clearer view of the brain’s network organization
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