Connectomics is the big data approach to constructing and analyzing a computer-generated map of the brain's functional connectivity. The definition of connectomics stems from the word “genomics,” which is the use of big data to study genetics in organisms. Connectomics borrows the “-omics” suffix from genomics because it applies a similar approach to analyzing the massive datasets required to model the human connectome. Ongoing modelling of the brain has quickly become one of the most complicated, but most exciting big data challenges of our time.
The Human Connectome Project (HCP) began using sophisticated imaging and machine learning techniques to segment the brain into functional areas. The HCP constructed a parcellation with 180 areas per hemisphere, based on cortical architecture, function, connectivity, and/or topography (Glasser, et al., 2016) By defining cortical areas by multiple features of brain function and structure, the HCP parcellation provided greater opportunity for accurate data acquisition and analysis than the original Brodmann areas.
Connectomics studies connectivity in the brain in two ways:
1. Through its functional connections: what parcellations fire together
2. Through its structural connections: what is wired together
The HCP parcellation has provided a valuable tool for segmenting the brain into simple, understandable components. Viewing its interconnected areas enables us to dissect the brain’s architecture and determine which areas work together to transfer information between interconnected—yet remote—areas of the brain. This parcellation has since been augmented to include an additional 19 areas, for a total of 379 cortical areas. Since different areas of the brain play simultaneous roles in task processing, these 379 areas are then grouped into networks based on their function and interconnectivity.
Choosing Targets for Non-invasive Brain Stimulation
What can connectomics offer as a technique?
The successful use of non-invasive brain stimulation (NIBS) invariably relies on the correct use of a target within the brain for therapeutic or experimental intervention. If the choice of target is incorrect or the equipment is misplaced, an effect of NIBS will be reduced or even absent. There are a number of different ways to choose a target for non-invasive brain stimulation. The simplest way is to rely on 10-20 EEG coordinate positions. It is also possible to locate a small number of areas using transcranial magnetic stimulation (TMS). In many experiments however 10-20 co-ordinates or functionally defined positions are not sufficient to guide NIBS interventions.
The locations of the motor cortex, early visual cortex or frontal eye field (FEF) can be identified by delivering pulses of transcranial magnetic stimulation (TMS) from the scalp location nearest to the sites, and finding the optimal location for motor evoked potentials (MEPs) (Mills et al., 1992), phosphenes (Kammer et al., 2001) or saccades (Ro et al., 2010), respectively. However, in many cases, a physiological read-out in response to TMS pulses is not possible. As a result, an additional means of choosing targets for NIBS must be identified. For some experiments, it is sufficient to choose 10-20 electroencephalography (EEG) co-ordinates (e.g. C3 - motor cortex; F3 - left DLPFC). This has been demonstrated to be a successful way of quickly placing a TMS coil in the right place. However, it ignores variability in the exact position of these sites between participants. An alternative is to identify specific coordinates for each individual undergoing NIBS. This usually requires the high-spatial-resolution of functional magnetic resonance imaging (fMRI) or even a simpler structural MRI, which can be used to identify subject-specific coordinates that can become the basis for NIBS targets.
MRI can be used in two ways to produce targets. The first method is to use the desired target using basic anatomy from a T1 structural MRI. One step further is to use functional magnetic resonance imaging, which is particularly powerful when participants complete a task in the fMRI scanner. When the task recruits a cognitive process of interest, blood-oxygen-level-dependent (BOLD) coordinates can be obtained for each participant within a target of interest. These individual targets can then be used to guide the positioning and calibration of NIBS equipment for each participant. An example of this can be found in Rahnev et al. (2016) who had participants complete a basic visual detection task where different conditions were emphasised when making a response. This revealed subject-specific BOLD coordinates of right DLPFC and FEF, which were then subjected to intermittent theta burst stimulation (iTBS). The application of iTBS then revealed that rostral regions of the frontal lobe support the later stages of perceptual decision-making (such as criterion setting and confidence).
However, it might not be feasible to devise a task that recruits a cognitive process of interest. Moreover, it may consume too many resources to have participants complete a task in an fMRI scanner. An alternative is to rely on the human connectome, which relies on structural MRI, diffusion-weighted imaging and fMRI to parcellate the human brain into distinct regions using functional and structural MRI data. Infinitome software can be used to automatically parcellate the individual’s MRI into the Glasser atlas (Glasser et al., 2016). The Glasser atlas can be used to reveal fine differences in the location of brain areas within individuals. In addition to this, the Glasser atlas can also be used to create functional connectivity matrices, which can be used to locate brain networks.
How can I use connectomics in my non-invasive brain stimulation research?
The Infinitome software package creates an initial parcellation of the Glasser and then verifies this initial parcellation using white matter tracts identified using DWI. This stage of verification is completed by comparing whether the initial parcellation matches the physical connections measured using DWI. The initial overlay is then altered so the parcellation of the Glasser atlas matches the physical connections between brain regions using DWI. Once the parcellation has been completed, the extent to which functional connectivity exists between regions can be examined.
Examination of the functional connectivity between brain regions enables brain networks to be identified. For example, if you wish to apply TMS to DLPFC but your network of interest is one involving DLPFC and the intraparietal sulcus (IPS). Infinitome can be used to identify voxels within specific areas within DLPFC that exhibit functional connectivity with IPS (or any other regions within a broader network of interest). This enables NIBS to be used to not only target specific regions but also to choose regions that are situated within broader networks of interest to non-invasive brain stimulation.
- Connectivity-based parcellation of normal and anatomically distorted human cerebral cortex. Stephane Doyen, Peter Nicholas, Anujan Poologaindran, Lewis Crawford, Isabella M. Young, Rafeael Romero-Garcia, Michael E. Sughrue. Human Brain Mapping. November 2021
- A multi-modal parcellation of human cerebral cortex. Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., ... & Smith, S. M. , Van Essen, D. C.. Nature. August 2016
- The influence of current direction on phosphene thresholds evoked by transcranial magnetic stimulation.. Kammer, T., Beck, S., Erb, M. & Grodd, W.. Clinical Neurophysiology. November 2001
- Magnetic brain stimulation with a double coil: the importance of coil orientation
. Mills, K. R., Boniface, S. J., & Schubert, M. Electroencephalography and Clinical Neurophysiology. February 1992
- Causal evidence for frontal cortex organization for perceptual decision making.. Rahnev, D., Nee, D. E., Riddle, J., Larson, A. S., & D’Esposito, M.. Proceedings of the National Academy of Sciences. May 2016
- Locating the Human Frontal Eye Fields With Transcranial Magnetic Stimulation. Ro, T. Farnè , A. & Chang, E.. Journal of Clinical and Experimental Neuropsychology. November 2002
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