Abstract
Neuroscience and neuroimaging have been providing new challenges for statisticians and quantitative researchers in general. As datasets of increasing complexity and dimension become available, the need for statistical techniques to analyze brain related phenomena becomes prominent. In this paper, we delve into data coming from functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI). The aim is to combine information from both sources in order to learn possible patterns of dependencies among regions of interest (ROIs) of the brain. First, we infer positions of these regions in a latent space, using the observed structural connectivity provided by the DTI data, to understand if physical spatial coordinates suitably reflect how ROIs are effectively interconnected. Secondly, we inspect Granger causality in the fMRI data in order to capture patterns of activations between ROIs. Then, we compare results from the analysis on these datasets, to find a link between functional and structural connectivity. Preliminary findings show that latent space positions well reflect hemisphere separation of the brain but are not perfectly connected to all the other structural partitions (that is, lobe, cortex, etc.); furthermore, activations of ROIs inferred from fMRI data are tied to observed structural connections derived from DTI scans.
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Notes
- 1.
Handedness is the dominance of one hand over the other, or the unequal distribution of fine motor skills between the left and right hands.
- 2.
Procrustes correlation, \(\rho (S_1,S_2)\), is a measure of similarity among two spaces, \(S_1, S_2\). In particular, it measures up to which degree space \(S_2\) was generated by a transformation (rotation, translation or scaling) of space \(S_1\). It is bounded in [0, 1].
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Acknowledgements
Data were provided by Greg Kiar and Eric Bridgeford from NeuroData at Johns Hopkins University, who graciously preprocessed the raw DTI and R-fMRI imaging data available at http://fcon_1000.projects.nitrc.org/indi/CoRR/html/nki_1.html. We would like to thank Ritabrata Dutta for initial discussions during ‘StartUp Research’ and for comments to the final version of the manuscript. Also, we would like to thank the organizers of ‘StartUp Research’ event, www.congressi.unisi.it/startupresearch/, for creating the opportunity for this research contribution and the other working groups present at the event for fruitful discussions.
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Appendices
6 A. Desikan Atlas Codes
Code | Left/Right | Code | Left/Right | Region name |
|---|---|---|---|---|
1 | L | 35 | R | bank of the superior temporal sulcus |
2 | L | 36 | R | caudal anterior cingulate |
3 | L | 37 | R | caudal middle frontal gyrus |
4 | L | 38 | R | cuneus |
5 | L | 39 | R | entorhinal |
6 | L | 40 | R | fusiform |
7 | L | 41 | R | inferior parietal lobule |
8 | L | 42 | R | inferior temporal gyrus |
9 | L | 43 | R | isthmus cingulate cortex |
10 | L | 44 | R | lateral occipital gyrus |
11 | L | 45 | R | lateral orbitofrontal |
12 | L | 46 | R | lingual |
13 | L | 47 | R | medial orbitofrontal |
14 | L | 48 | R | middle temporal gyrus |
15 | L | 49 | R | parahippocampal |
16 | L | 50 | R | paracentral |
17 | L | 51 | R | pars opercularis |
18 | L | 52 | R | pars orbitalis |
19 | L | 53 | R | pars triangularis |
20 | L | 54 | R | pericalcarine |
21 | L | 55 | R | postcentral |
22 | L | 56 | R | posterior cingulate cortex |
23 | L | 57 | R | precentral |
24 | L | 58 | R | precuneus |
25 | L | 59 | R | rostral anterior cingulate cortex |
26 | L | 60 | R | rostral middle frontal gyrus |
27 | L | 61 | R | superior frontal gyrus |
28 | L | 62 | R | superior parietal lobule |
29 | L | 63 | R | superior temporal gyrus |
30 | L | 64 | R | supramarginal gyrus |
31 | L | 65 | R | frontal pole |
32 | L | 66 | R | temporal pole |
33 | L | 67 | R | transverse temporal |
34 | L | 68 | R | insula |
7 B. MCMC Diagnostics of Intercept Parameters of the Latent Space Model
See Fig. 9.
Trace plots and histograms of the posterior distributions of the intercept parameters \(\beta \) in the latent space model, for subjects 8, 9 and 24
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Crispino, M., D’Angelo, S., Ranciati, S., Mira, A. (2018). Understanding Dependency Patterns in Structural and Functional Brain Connectivity Through fMRI and DTI Data. In: Canale, A., Durante, D., Paci, L., Scarpa, B. (eds) Studies in Neural Data Science. START UP RESEARCH 2017. Springer Proceedings in Mathematics & Statistics, vol 257. Springer, Cham. https://doi.org/10.1007/978-3-030-00039-4_1
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