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Improved analysis framework for MS connectomes.

Authors:

Abstract

Structural connectivity matrices are a powerful tool largely used in many areas of neuroscience. In multiple sclerosis (MS) the presence of lesions in the white matter (WM) prevents proper streamline tracking, thus affecting metrics of the connectivity matrices. Our aim is to assess this effect with an enhanced representation of the connectivity that will provide researchers with better ways to understand and compare connectomes. MRI acquisitions were performed on 10 healthy volunteers and 5 MS patients using a 3T Siemens scanner. The MRI protocol included a 3D structural T1-MPRAGE and a HARDI. We have calculated the anatomical atlas for each subject and computed the whole brain tracking using deterministic tracking from tensors and from 6th and 8th order spherical harmonics. Instead of representing the connectome as an adjacency matrix of the normalized connectivity, we put all the edges in a single row and defined this as the connectivity spectrum. This representation shows the connectome as a linear combination of elemental connectomes, which are defined as single streamline between two particular ROI. To enhance visualization, we calculated the spectrum amplitude in a logarithmic way. Then, we averaged the connectivity spectrums of the 10 controls to create templates for each tracking technique. After comparing a single control's connectivity spectrum, we saw that the peaks were shape consistent but had different amplitudes. The one based on tensors was the most different in amplitude while the two based on spherical deconvolution were quite similar. Comparing the healthy spectrums with those of MS patients, we saw the same shape consistency as described before and low amplitudes when comparing the same technique. Comparisons between connectivity matrices can be tricky due to the similarity of the color scales and the huge differences among connectivity orders. This method provides an improved framework for connectivity analysis, facilitating human interpretation.
M. Andorr๠, M. Ramos² , E. Martinez¹ ,
E. Lampert¹ , P. Rodrigues² ,
P. Villoslada¹ , V. Prckovska¹
¹ Institut d ́Investigacions Biomèdiques August Pi i Sunyer,
Barcelona, Spain
² MintLabs, S.L., Barcelona, Spain
Improved analysis
framework for MS
connectomes
17/04/15
Verona, Italy
Limitations of Connectivity Matrices
Motivation
The classical representation of connectivity among ROI pairs is an N by N
square simetric matrix.
It has some human interpretation limitations:
Elemental Conectome
Methods
We defined an elemental connectome as the most simple possible
connectome representing one single track between two particular ROIs.
ROI
1
ROI
2
This way, any connectome can be understood as a lineal combination of this
elemental connectomes. For an N ROIs connectome we can decompose as:
Connectivity Spectrum
Methods
If we consider the scalar weights αifor each elemental connectome we
have what we call the connectivity spectrum. Its amplitude is given by:
Each number of tracks has been normalized by the maximum connectivity
order and then, represented in logaritmic terms to enhance the
representation.
Cohort: 10 healthy controls, 5 MS patients
Data: 3T Siemens MRI (0.9mm T1-MPRAGE; 60 directions HARDI)
Processing:
Deterministic tracking (MRtrix3): DTI, CSD6 and CSD8.1
T1 Segmentation: 'Desikan-Killiany’ (Freesurfer 5.3)2
Healthy template spectrum
Results
One healthy control spectrum with DTI, CSD6 and CSD8.
MS patient spectrum comparison
Results
Healthy averaged template spectrum and one MS patient spectrum.
Discussion
Connectivity spectrum is a 2D plot easier to analyze than connectivity
matrices plots to human eyes.
Peaks and valleys are consistent. Amplitude has subtle changes.
• Moving from CSD of order 6 to 8 doesn’t change a lot the results.
MS patients has the same shape of controls but lower amplitude as a
global analysis.
• Overlaying MS patient to control’s template give us a quick first idea of
which connections are highly affected by WM lesions.
This method can be used also to make patients template for those
diseases that are anatomically lesion consistent.
References
1. An automated labeling system for subdividing the human cerebral cortex on
MRI scans into gyral based regions of interest. Desikan et al. 2006.
2. Fibre-tracking was performed using the MRtrix package. Tournier et al.
2012.
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Article
In this study, we have assessed the validity and reliability of an automated labeling system that we have developed for subdividing the human cerebral cortex on magnetic resonance images into gyral based regions of interest (ROIs). Using a dataset of 40 MRI scans we manually identified 34 cortical ROIs in each of the individual hemispheres. This information was then encoded in the form of an atlas that was utilized to automatically label ROIs. To examine the validity, as well as the intra- and inter-rater reliability of the automated system, we used both intraclass correlation coefficients (ICC), and a new method known as mean distance maps, to assess the degree of mismatch between the manual and the automated sets of ROIs. When compared with the manual ROIs, the automated ROIs were highly accurate, with an average ICC of 0.835 across all of the ROIs, and a mean distance error of less than 1 mm. Intra- and inter-rater comparisons yielded little to no difference between the sets of ROIs. These findings suggest that the automated method we have developed for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable. This method may be useful for both morphometric and functional studies of the cerebral cortex as well as for clinical investigations aimed at tracking the evolution of disease-induced changes over time, including clinical trials in which MRI-based measures are used to examine response to treatment.
Fibre-tracking was performed using the MRtrix package
  • Tournier
Fibre-tracking was performed using the MRtrix package. Tournier et al. 2012.