Table 1.
Demographic and clinical characteristics of all study participants.
Figure 1.
Overview of the network selection procedure and the Network Based Statistics (NBS).
(a) Using the DTI data, white matter tracts of the brain were reconstructed. (b) Cortical and sub-cortical brain regions were selected by automatic parcellation of the cerebrum. (c) An individual brain network was defined, consisting of nodes (i.e. the parcellated brain regions) and connections between nodes i and j that were connected by a white matter pathway. (d) Repeating this for each region i and j in the collection of parcellated brain regions, resulted in a (weighted) connectivity matrix M. Connections were weighted by their FA value, as determined from the DTI measurement. Next, using Network Based Statistics (NBS), the connectivity matrices of ALS patients and controls were compared. (e1) First, each connection between region i and j was tested between patients and controls using t-statistics. (e2) This resulted in a binary difference matrix, with 1s for those connections that showed a (absolute) t-value between controls and patients higher than a set T-threshold T, and 0 otherwise. Third, the sizes of the (largest) connected components in the difference matrix was computed, revealing sub-networks of regions showing affected connectivity in patients. Fourth, permutation testing was used to define a distribution of (largest) component size that could occur under the null-hypothesis (i.e. no difference between patient and controls). 5000 permutations, permuting group assignment, were computed. Finally, the original observed component size (i.e. difference between patients and controls) was given a p-value based on the computed null-distribution, by defining the percentage of the null-distribution that exceeded the size of the observed impaired network in patients.
Table 2.
Global graph metrics.
Figure 2.
Cortical brain regions with impaired structural connectivity in ALS.
(a) The NBS procedure revealed a sub-network of brain regions showing significantly reduced structural connectivity in ALS patients, compared to the healthy controls. Figure shows the set of involved parcellated cortical regions (p = 0.0075, see materials and methods). (b) Using an NBS threshold of p = 1/N (N being the number of nodes of the network), a similar but more extended network was revealed. This model-free approach revealed a sub-network consistent with known motor regions, including precentral and paracentral gyri (primary motor), caudal middle frontal and superior frontal gyri (supplemental motor areas, BA6). The subcortical structures found with the NBS procedure were not included in this figure. Right = Right; Left = Left.
Figure 3.
Overlap between motor network and impaired NBS network.
(a) Direct cortical connections of the primary motor network. The direct connections of the left and right precentral gyri in the group of healthy controls are shown. Figure illustrates (per region) the percentage of healthy control subjects that showed a direct structural white matter connection to the left or right precentral gyrus. The primary motor network was selected as those regions that were connected to the primary motor regions in the majority of healthy controls (>75%). (b) Figure shows the overlap (right column) between the exploratory NBS network (left column, NBSb network, Figure 2b) and the regions of the healthy motor network (middle column, showing regions of Figure 3a). The impaired NBS network was found to strongly overlap the motor network (p<0.0001, Fisher’s Exact test). Right = Right; Left = Left.
Table 3.
Overlap between the impaired sub-network in ALS and the motor network.
Figure 4.
Network of impaired structural connectivity in ALS.
(a) The nodes and interconnections of the NBS network (NBSa network, Figure 2a) as viewed from an anatomical perspective. (b) The NBS network from a network perspective (optimizing free Kamada-Kawai energy, constructed with pajek http://vlado.fmf.uni-lj.si/pub/networks/pajek/). Nodes and connections showing significantly reduced efficiency in patients are highlighted.