Contributions of network structure, chemoarchitecture and diagnostic categories to transitions between cognitive topographies

The mechanisms linking the brain’s network structure to cognitively relevant activation patterns remain largely unknown. Here, by leveraging principles of network control, we show how the architecture of the human connectome shapes transitions between 123 experimentally defined cognitive activation maps (cognitive topographies) from the NeuroSynth meta-analytic database. Specifically, we systematically integrated large-scale multimodal neuroimaging data from functional magnetic resonance imaging, diffusion tractography, cortical morphometry and positron emission tomography to simulate how anatomically guided transitions between cognitive states can be reshaped by neurotransmitter engagement or by changes in cortical thickness. Our model incorporates neurotransmitter-receptor density maps (18 receptors and transporters) and maps of cortical thickness pertaining to a wide range of mental health, neurodegenerative, psychiatric and neurodevelopmental diagnostic categories (17,000 patients and 22,000 controls). The results provide a comprehensive look-up table charting how brain network organization and chemoarchitecture interact to manifest different cognitive topographies, and establish a principled foundation for the systematic identification of ways to promote selective transitions between cognitive topographies.


nature portfolio | reporting summary
April 2023 Reporting for specific materials, systems and methods We require information from authors about some types of materials, experimental systems and methods used in many studies.Here, indicate whether each material, system or method listed is relevant to your study.If you are not sure if a list item applies to your research, read the appropriate section before selecting a response.Design specifications HCP dataset: The diffusion MRI scan was conducted on a Siemens 3T Skyra scanner using a 2D spin-echo singleshotmultiband EPI sequence with a multi-band factor of 3 and monopolar gradient pulse.The spatial resolution was1.25 mm isotropic.TR=5500 ms, TE=89.50ms.The b-values were 1000, 2000, and 3000 s/mm2.The totalnumber of diffusion sampling directions was 90, 90, and S0 for each of the shells in addition to 6 bO images.Functional data: gradient-echo EPI, TR= 720 ms, TE= 33.1 ms, flip angle = 52°, FOV= 208 × 180, voxel size = 2 mm isotropic.Resting-state data were collected, as well as task-based data pertaining to 7 tasks.Details can be found in Van Essen et al (2013).
Behavioral performance measures HCP dataset: behavioural measures were collected by the HCP consortium, but not used in this study.For the in-scanner task data, we did not look at task performance.
Lausanne dataset:no behavioural measures were recorded during scanning.

Acquisition Imaging type(s)
Diffusion and functional (resting-state, task-based) Field strength

3T
Sequence & imaging parameters HCP dataset: The diffusion MRI scan was conducted on a Siemens 3T Skyra scanner using a 2D spin-echo singleshotmultiband EPI sequence with a multi-band factor of 3 and monopolar gradient pulse.The spatial resolution was1.25 mm isotropic.TR=5500 ms, TE=89.50ms.The b-values were 1000, 2000, and 3000 s/mm2.The totalnumber of diffusion sampling directions was 90, 90, and 90 for each of the shells in addition to 6 b0 images.

Area of acquisition
Whole brain (both datasets).

Diffusion MRI Used Not used
Parameters HCP: The b-values were 1,000, 2,000, and 3,000 s/mm2.The total number of diffusion sampling directions was 90, 90 and 90 for each of the shells in addition to 6 bO images.1.25 mm isotropic resolution.Lausanne: 128 diffusion-weighted volumes and a single bO volume, maximum b-value 8,000 s/mm2, 2.2 x 2.2 x 3.0 mm voxel size.

April 2023
submodel, across all 2*p -1 submodels.The sum of the dominance of all input variables is equal to the total adjusted R2 of the complete model, making the percentage of relative importance an intuitive method that partitions the total effect size across predictors.