The title SeeElephants refers, on the one hand, to the roundworm C. Elegans, whose connectome has been fully mapped for some time [1]. On the other hand, it is a reference to the parable of the wise blind men and the elephant, a commonly-used metaphor when describing humankind's investigations of our own brain and mind. The implication of both references is that we can gather many facts and yet remain blind, and yet the hope is always there that we may one day 'see the elephant'.
I hope this explanation meets the requirement for being creative!
I'm here to advance my line of research on the neural correlates of high performance cognition. Although brain networks themselves are not my focus, they are part of the necessary empirical basis for interpreting observations, which I aim to make in multimodal data: behaviour, EEG/MEG (as I require high temporal resolution), physiology, phenomenology.
Looking at the reading list, Table 1 (ranked according to my personal preference), I see that most papers seem to deal with (f)MRI data. This is fine since I need to learn basics, but I hope to also learn about what I can do with encephalography-type data.
| No. | Author(s) | Title | Highlight |
|---|---|---|---|
| 2 | Cole et al | Intrinsic and Task-Evoked Network Architectures of the Human Brain | "Tasks modify the intrinsic architecture to produce “evoked” network architectures" |
| 7 | Margulies et al | Situating the default-mode network along a principal gradient of macroscale cortical organization | "We describe an overarching organization of large-scale connectivity that situates the default-mode network at the opposite end of a spectrum from primary sensory and motor regions." |
| 5 | Drysdale et al | Resting-state connectivity biomarkers define neurophysiological subtypes of depression | "...we show here that patients with depression can be subdivided into four neurophysiological subtypes ('biotypes') defined by distinct patterns of dysfunctional connectivity in limbic and frontostriatal networks" |
| 8 | Power et al | Functional network organization of the human brain | "Functional systems are patterned across the cortex with spatial regularities" |
| 4 | Finn et al | Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity | "...individual variability is both robust and reliable...functional connectivity profiles act as a 'fingerprint'" |
| 6 | Mueller et al | Individual variability in functional connectivity architecture of the human brain | "Brain regions of high connectivity variability predict behavioral differences" |
| 3 | Smith et al | A positive-negative mode of population covariation links brain connectivity, demographics and behavior | "...subjects were predominantly spread along a single 'positive-negative' axis linking lifestyle, demographic and psychometric measures to each other and to a specific pattern of brain connectivity" |
| 10 | Smith et al | Network modelling methods for FMRI | "...we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds" |
| 1 | Chai et al | Functional network dynamics of the language system | "We observe the presence of a stable core [and] more flexible periphery of brain regions" |
| 9 | Poldrack et al | Long-term neural and physiological phenotyping of a single human | "An intensive phenome-wide assessment of a single human was performed over a period of 18 months, including functional and structural brain connectivity using magnetic resonance imaging, psychological function and physical health, gene expression and metabolomics" |
I would like to learn practical methods for working in source space in M/EEG, using tools suited to my background (Computer Science) and skills, e.g. Matlab, R, or Python.