With the HCP, nonetheless, came hope that we could begin to quantify and alphabetise the regions of the brain by function, not anatomy. By combining digital tractography and fMRI imaging, it is now possible to create individualised connectomic brain maps and draw forth millions more data points than we ever had available.
This, however, also generated more data than could practically be handled in many research settings and most clinical environments. There is now tension that using connectomic data to better understand and treat an individual’s brain is possible, but not practical.
Indeed, a growing number of ‘computational’ neuroscientists is a telling sign that the brain is better treated as a data problem in addition to a biological one, and for this approach most hospitals and neuroscience centers are ill-equipped.
T1 Anatomical MRI images are visualised to co-register analysed data.
Traditionally, these images provided little insight into brain function. Note, this subject has a high grade butterfly glioma.
The structural connections of the brain are visualized through white matter tracts mapped with constrained spherical deconvolution (CSD) tractography.
Compared to standard diffusion tensor imaging (DTI), the use of CSD mitigates the impact of intra-voxel crossing of fibres.
Using in-house technologies, the ‘parcellations’1 or functional areas of the cortex discovered and described through the Human Connectome Project are mapped out for this specific subject, even around the glioma.
1. Doyen, S., Nicholas, P., Poologaindran, A., Crawford, L., Young, I. M., Romero-Garcia, R., & Sughrue, M. E. (2021). Connectivity-based parcellation of normal and anatomically distorted human cerebral cortex. Human Brain Mapping, 1– 12. https://doi.org/10.1002/hbm.25728
The connectivity between parcellations are measured, representing over 100,000 data points.
Through a simple selection, a brain network can be selected and viewed.
Pictured here, the subject’s Central Executive Network (CEN) in 3D.
The connectivity analysis is then focussed on relevant areas.
Red - positively correlated
Blue - negatively correlated
Next we use machine learning to determine areas of brain activity deviating from normal brain connectivity (as determined by user defined levels of standard deviations from the normal brain connectivity.)
Hypoconnectivity in blue
Hyperconnectivity in red
By selecting a region of interest, the software automatically co-registers this back onto the anatomical view.
In a few simple steps, we have now found a region of both functional and structural interest that may be exported and further investigated.
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