Simulating the impact of white matter connectivity on processing time scales using brain network models

Abstract

The capacity of the brain to process input across temporal scales is exemplified in human narrative, which requires integration of information ranging from words, over sentences to long paragraphs. It has been shown that this processing is distributed in a hierarchy across multiple areas in the brain with areas close to the sensory cortex, processing on a faster time scale than areas in associative cortex. In this study we used reservoir computing with human derived connectivity to investigate the effect of the structural connectivity on time scales across brain regions during a narrative task paradigm. We systematically tested the effect of removal of selected fibre bundles (IFO, ILF, MLF, SLF I/II/III, UF, AF) on the processing time scales across brain regions. We show that long distance pathways such as the IFO provide a form of shortcut whereby input driven activation in the visual cortex can directly impact distant frontal areas. To validate our model we demonstrated significant correlation of our predicted time scale ordering with empirical results from the intact/scrambled narrative fMRI task paradigm. This study emphasizes structural connectivity's role in brain temporal processing hierarchies, providing a framework for future research on structure and neural dynamics across cognitive tasks.

Fig. 1. Image processing and analysis workflow.

A We processed diffusion and T1w MR images from 100 subjects of the HCP dataset to construct a full brain connectome. The fibre orientation distribution images were used together with TractSeg to estimate volumetric masks for major fibre bundles and to extract bundle specific connectomes. B We constructed two reservoir networks from full brain connectomes, where the second network was lesioned by removing a specific fibre bundle bilaterally. The reservoir pair was then used for the intact/scrambled narrative task, where a narrative input signal is projected to one of the 7 Networks of the Schaefer parcellation. In the task, each reservoir receives subsequently the input twice, while the order of the first input is intact, the midsection of the second input is scrambled. For each brain region of the reservoir we obtained simulated differences in activity due to these different inputs. The last segment of the input contains the intact order again, thus one can measure the alignment time for each brain region of the network to show similar activity. Finally, we compared the difference in alignment time due to lesions.

Fig. 2. Alignment time change in an EDR based connectome due to long-range pathway insertion.

A We built a reservoir using the EDR and projected the intact/scrambled narrative input to the first 300 neurons to measure the alignment time difference caused by the insertion of an additional long-range pathway (black rectangle in the connectome matrix). The pathway connects neurons 200–299 and 800–899 bidirectionally and causes a speed up (blue stars in the box plot), especially for neurons 800–899. Boxes indicate quartiles, with whiskers extending to the farthest datapoint within 1.5 times the inter-quartile range. B Same as (A) but inserting the extra pathway at positions 350–449 and 850–949, which leads to a slowdown of neurons in the range 300–499 (red stars in the box plot). C Systematic exploration of the effect of bidirectional pathway insertion at positions 300 neurons off the diagonal of the connectome matrix. The effect of either significant speed up or slowdown is shown for the neurons grouped at 0–99, 300–399 and 600–699. The positions of the specific connectomes from (A) and (B) are marked by the star and pentagon, respectively.

Fig. 3. Alignment time change in a full brain connectome due to IFO fibre bundle removal.

A Spatial distribution of alignment times when projecting the intact/scrambled narrative input to the visual network. B Alignment times (from panel A) increase with increasing Euclidean distance of a brain region to the input visual network. The black line indicates a linear model fit. Observations marked with a black “+” are indicating the regions of the OFC and PFC. C Same as panel B, but after IFO bundle removal. D Depiction of the IFO bundle. E Alignment time changes across the cortex when introducing a bilateral lesion of the IFO. The same regions as in (B) and (C) are marked with a black “+”. F Boxplot of alignment times of marked “+” regions pre- and post-lesion. Boxes indicate quartiles, with whiskers extending to the farthest datapoint within 1.5 times the inter-quartile range. All pre- and post-lesion distribution were tested against each other with a paired t-test and resulted in a p value < 0.05.

Fig. 4. Alignment time change in a full brain connectome due to fibre bundle removal.

A Change in alignment times across the brain after bilateral removal of the ILF bundle. Within the transparent cortical surface, the geometry of the bundle if demonstrated. B Same as (A) but for the UF. C Same as (A) but for the SLF_II.

Fig. 5. Alignment time changes due to AF lesion as a function of the input network.

A 3D rendering of the AF in both hemispheres. B Overview of the 7 Networks from the Schaefer parcellation. C Alignment time changes due to AF lesion using each of the 7 networks as input.

Fig. 6. Global effect of fibre bundle removal for each of the 7 input networks.

A Each box indicates the distribution of positive alignment time change (ATC) summed across all brain regions for all subjects, as an overall measure of the effect of a specific fibre bundle removal. The network, to which the intact/scrambled narrative was projected to, is indicated by the colour of the box. Boxes indicate quartiles, with whiskers extending to the farthest datapoint within 1.5 times the inter-quartile range. B Same as (A) but for negative ATCs. C The ratio of positive over negative ATCs. If <1 ( >1) indicates that a specific fibre bundle removal has an overall speed up (slow down) effect.

Fig. 7. Comparison of the alignment time hierarchy between reservoir network and Chien and Honey, 2020.

A Mean alignment times across subjects when projecting the intact/scrambled narrative to the 5 fastest regions (of Chien and Honey, 2020) in the temporal lobe of the full brain connectome. B Ordering the ROIs of the connectome according to their alignment time from fastest to slowest and comparing the ordering with the 70 regions of Chien and Honey, 2020. Each dot represents a brain region. Spearman’s rank correlation coefficient ρ = 0.685.

Fig. 8. Impact of bilateral lesions on alignment times within the language network (black and white inflated cortex) when the input timeseries is projected onto the auditory regions from Fig. 7(A).

A The boxplot shows positive alignment time change (ATC) summed across all brain regions for all subjects, as an overall measure of the effect of a specific fibre bundle removal. Boxes indicate quartiles, with whiskers extending to the farthest datapoint within 1.5 times the inter-quartile range. B Same as (A) but for negative ATCs.