Apurva Shah1, Apoorva Safai1, Veeramani Preethish Kumar2, Atchayaram Nalini2, Jitender Saini3, and Madhura Ingalhalikar1
1Symbiosis Center for Medical Image Analysis, Symbiosis International University, Pune, India, 2Department of Neurology, National Institute of Mental Health and Neurosciences, Bengaluru, India, 3Department of Radiology, National Institute of Mental Health and Neurosciences, Bengaluru, India
Synopsis
Duchenne
Muscular Dystrophy (DMD) is a genetic neuromuscular disorder,
characterized by muscle weakness and cognitive deficits due to
mutation in DMD gene. Dp140+ and Dp140- are DMD subtypes derived
based on promoter site of isoform Dp140 in DMD gene. Our work
investigated the structural connectivity in DMD and its sub-types and
demonstrated widespread and global reduction in connectivity across
whole brain in DMD compared to controls. Higher dysconnectivity was
observed in Dp140- subtype especially in cerebellar and frontal
regions compared to Dp140+ implying that the promotor site of Dp140
isoform plays a crucial role in terms of impaired information
processing.
Introduction
Duchenne
Muscular Dystrophy (DMD) is a X-linked recessive neuromuscular
disorder caused by loss of dystrophin due to mutations in DMD gene1.
In addition to severe and progressive muscle weakness in young boys,
significant cognitive manifestations such as low IQ, reading
difficulties and neuropsychiatric illnesses like ADHD, ASD, OCD,
epilepsy are prevalent in DMD patients2-4.
The DMD gene comprises of multiple independent tissue specific
promoters that produce isoforms of various lengths and splicing
patterns. Isoforms from variants of downstream promoter Dp140, are
derived based on the promoter site and are found to influence
cognitive functioning. However, the impact on brain structure in
terms of connectivity is not known. The investigation of such
genotype-brain structure relationship is therefore crucial and our
study focuses on comprehensively characterizing the diffusion based
structural connectivity and evaluating changes in global and nodal
network measures in a moderately large cohort of DMD patients and its
Dp140 subtypes.Method
In
this study, 57 DMD patients (age: 8.1 ± 1.2 years) and 38 (age: 8.4
± 1.4 years) healthy controls (HC) were recruited, who underwent
detailed clinical and neurological examination, genetic testing (MLPA
testing) and MRI scanning. Disease severity was assessed using
Muscular Dystrophy Functional Rating Scale (MDFRS). Among DMD
patients (MDFRS: 98.96±10.31), 23 were identified as Dp+140 (age:
7.8 ± 1.1 years; MDFRS: 104.13±7.65) and were identified as Dp140-
34 (age: 8.3± 1.2 years; MDFRS: 95.47±10.5) using MLPA testing. MRI
images of all subjects were acquired using 3T Philips Achieva with 32
channel head coil. T1 weighted images were acquired with
TR/TE=9.8ms/4.6ms, FOV=240x240, voxel size=1x1x1mm. Diffusion MRI
data was acquired in 15 diffusion directions with b value=1000s/mm2
and
a single b value=0 s/mm2
using single-shot spin-echo, echo planar axial plane, SENSE reduction
factor =2.5, TR/TE=5000ms/65ms, FOV=128x128,voxel size=1.75x1.75x2mm.
Processing of T1 weighted images included skull stripping, bias
correction, tissue segmentation and parcellation of brain into 86
regions was done using Freesurfer5.
Preprocessing of diffusion images included eddy current and motion
correction followed by brain extraction. Structural connectome (SC)
was constructed by performing probabilistic fiber tracking in FSL6
as shown in Figure 1. Global and nodal network measures were
constructed using Brain Connectivity Toolbox [ref]. Multivariate
Analysis of Covariance (MANCOVA) was performed to compare global and
nodal measures between HC and DMD group with age as covariate and
were corrected for multiple comparison with FDR p<0.05. To
evaluate differences in global and nodal measures between DMD
subtypes, a t-test was applied between Dp140+ and Dp140- groups, with
threshold of p<0.05. Edge-wise
analysis was performed between HC and DMD and within DMD sub groups
using NBS with 10000 permutation and p-value<0.05.Results
Global
measures such as global efficiency (HC:
0.077 ± 0.01, DMD:0.072 ± 0.01, p=0.046),
transitivity (HC:
1.642 ± 0.43, DMD:1.409 ± 0.42,p=0.015) and
clustering coefficient (CC) (HC:
1.627 ± 0.44, DMD:1.393 ± 0.43,p=0.017)
were significantly reduced in DMD patients compared to HC, whereas
characteristic path length (HC:
0.041 ± 0.01, DMD:0.046 ± 0.01,p=0.004)
was significantly increased. A trend for higher density and lower
characteristic path length was seen in Dp140+ compared to Dp140-
groups (p<0.05,
uncorrected).
In comparison to HC group, DMD patients showed significantly lower
nodal strength and lower local efficiency in multiple cortical
regions and
left hippocampus as
shown in figure 2. No significant differences in global or nodal
measures were found between Dp140+ and Dp140-. Network analysis using
NBS showed significantly higher connectivity in 128 connections in HC
group compared to DMD group as shown in figure 3. Higher connectivity
was observed in
13 connections including cerebellar and frontal regions in Dp140+
group as compared to Dp140- group as shown in figure 4.Conclusion
Structural
connectivity was widely disrupted in DMD patients, while the impaired
connectivity in cerebellar and frontal region was found to be majorly
driven by patients with Dp140- sub type than Dp140+ sub type. The
altered global network measures in DMD demonstrated weakened
information processing and poor network segregation whereas abnormal
nodal measures indicated lower overall connectivity and less
resistance to network failure.
In conclusion, our results establish a genotype-brain connectivity
relationship and characterize the structural network based
abnormalities in DMD that are more peculiar to Dp140- sub-type.Acknowledgements
No acknowledgement found.References
-
Bushby
K, Finkel R, Birnkrant DJ, et al. Diagnosis and management of
Duchenne muscular dystrophy, part 1: diagnosis, and pharmacological
and psychosocial management. Lancet Neurol 2010;9:77–93.
- Pane
M, Lombardo ME, Alfieri P, et al. Attention deficit hyperactivity
disorder and cognitive function in Duchenne muscular dystrophy:
phenotype-genotype correlation. J Pediatr 2012;161:705-709.e1.
- D’Angelo
MG, Lorusso ML, Civati F, et al. Neurocognitive profiles in Duchenne
muscular dystrophy and gene mutation site. Pediatr Neurol
2011;45:292–9.
- Banihani
R, Smile S, Yoon G, et al. Cognitive and Neurobehavioral Profile in
Boys With Duchenne Muscular Dystrophy. J Child Neurol
2015;30:1472–82.
- http://surfer.nmr.mgh.harvard.edu
- http://fsl.fmrib.ox.ac.uk