Tractometry of the subcortical motor
network using SHORE-based indices
‡ S.
Obertino, ‡ M. Zucchelli, † A. Daducci, ┴ C. Granziera*, ‡ Gloria Menegaz*
‡ Dept. of Computer Science, University of Verona. † École Polytechnique Fédérale de Lausanne. ┴ Centre hospitalier universitaire Vaudois. * equal contribuEon [email protected], [email protected] Figure 1 Introduc*on Putamen Subcor*cal Networks: In this work we aim at investigating the 3D Simple
Harmonic Oscillator based Reconstruction and
Estimation3 (3D-SHORE) derived numerical indices for
quantitative tractography. In particular, we target the
cortical motor network (SC-MN) of a cohort of ten
healthy subjects. Using diffusion spectrum imaging
(DSI) we reconstructed the network connections and
compared the resulting information about white matter
(WM) density and structure to that provided by
Generalized Fractional Anisotropy (GFA) and
Magnetization Transfer Ratio (MTR). The SC-MN
gathers the connections between the cortical motor
area, the basal ganglia and the thalamus, and it
essentially consists of three major subcortical networks
(Figure 1).
Caudatus Globus Pallidus Sensory Motor loop Thalamus M1 Thalamus Premotor loop vPM/dPM Putamen Thalamus GPi Putamen SMA loop GPi SMA Thalamus Caudatus Putamen Methods M1 = primary motor area PMd = premotor dorsal area PMv = premotor ventral area SMA = supplementary motor area GPi = globus pallidus interna GPi Caudatus Ten healthy subjects (age 56.1±17.8 years old, mean±SD)
went through a DSI scan twice one month
apart
(± 1 week, tp1c and tp2c, see [1] for more details).
The Ensemble Average Propagator (EAP) was reconstructed using the SHORE model [6] and the orientational (ODF) and microstructural indices
were derived including Return to zero (RTOP), Return to axis (RTAP) and Return to plan (RTPP) probability and propagator anisotropy (PA) as in
[6]. From RTAP, an estimation of the mean ensemble value of the axons’ radius (R) can be inferred [5].
Tractometry was performed as in [2] and SHORE indices were extracted for each fiber bundle. The repeatability of the measurements across timepoints was defined in terms of the stability of the respective probability density functions across time points. This was assessed by measuring the
percent absolute changes across time of parametric histogram features (mean, variance, skewness, kurtosis and peak hight) and by calculating
the distances between histograms (Mean Square Error, Kullbach-Leibler, Hausdorff). The reported measures represent the ensemble values
across region pairs and subjects.
Results and Discussion Table 1 %
Mean Variance Skewness Kurtosis Peak height
4±1
7±2
17±5
21±6
14±3
GFA
5±1
46±14
13±4
22±5
20±4
MTR
4±2
9±2
8±10
13±24
19±5
RTAP
11±5
17±9
31±25
15±3
Radius 2±1
4±1
8±2
12±5
20±9
14±5
PA
Figure 2 Mean
MSE
KLD
Hausdorff
GFA-R
GFA-PA
KLD (e-2)
Hausdorff (e-2)
3,0±2,9
7,2±6,5
5,0±2,1
46,3±44,1
40,9±34,1
19,2±9,8
7,2±13,8
6,5±7,3
6,8±5,2
4,5±4,3
8,1±7,7
6,2±2,6
3,7±3,5
6,1±5,5
5,6±2,4
Comparative analysis revealed that
1)  the indicies are not normally distributed along the bundles for any
pair of regions (Jarque-Bera, p<0.001);
2)  histograms are stable across time points for all indices (GFA,
MTR,RTAP, R, PA) (Table 1);
3)  RTAP, R and PA absolute percent changes on mean is highly
correlated with those of GFA as well as histogram distances;
4)  PA has the highest correlation with GFA, while R has the highest
correlation with MTR and is highly correlated with GFA;
5)  the distributions of the estimated axons radius is consistent across
the fiber bundles
and the values reported in literature [7,8]
(Figure 2: average distribution across subjects).
Table 2 GFA-RTAP
MSE (e-5)
GFA-MTR
MTR-RTAP
MTR-R
MTR-PA
Pearson
Spearman
Pearson
Spearman
Pearson
Spearman
Pearson
Spearman
Pearson
Spearman
Pearson
Spearman
Pearson
Spearman
86,44
48,18
86,25
47,27
87,67
81,82
10
22,73
-11,15
-12,73
-27,08
-21,82
21,82
17,27
55,18
32,73
76,19
42,73
97,79
85,46
39,25
27,27
-26,93
-41,82
68,64
59,09
36,98
23,64
81,89
70
93,85
88,18
97,51
95,46
66,17
40,91
23,75
10
49
22,73
61,98
32,73
49,07
40,91
79,46
52,73
96,45
84,55
46,44
34,55
-36,71
-40
71,58
78,18
39,45
35,46
Conclusions Our results confirm that all the indices are reproducible over time in the SC-MN of normal subjects. Last, our
data show that 3D-SHORE indices are highly correlated to anisotropy measures like GFA and much less with
measures of myelin presence (i.e. MTR) with the exception of R. This might suggests that the applied 3DSHORE can be sensitive enough to detect the effect of myelin on axonal diameter.
References.
[1] S. Obertino, et al, ISMRM Workshop 2013.
[2] C. Granziera, et al, Neurology, 2012.
[3] P. Hagmann, et al, PLoS ONE, 2007.
[4] J. Wedeen, et al, NeuroImage, 2008.
[5] A. Daducci, et al, PLoS ONE, 2012.
[6] E. Ozarslan, et al, NeuroImage, 2013.
[7] F. Aboitz, et al, Brain Res.,1992.
[8] D. Alexander, et al, NeuroImage, 2010.