Jitender Saini1, Madhura Ingalhalikar2, Veeramani Preethish-Kumar3, Apurva Shah4, Kiran Polavarapu3, Manoj Kumar5, Apoorva Safai4, Seena Vengalil3, Saraswati Nashi3, Sekar Deepha6, Periyasamy Govindaraj6, Mohammad Afsar7, Jamuna Rajeswaran7, Atchayaram Nalini3. 1. Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India. jsaini76@gmail.com. 2. Symbiosis Centre for Medical Image Analysis, Symbiosis International University, Mulshi, Pune, Maharashtra, India. mingalhalikar@gmail.com. 3. Department of Neurology, National Institute of Mental Health and Neurosciences, Bangalore, India. 4. Symbiosis Centre for Medical Image Analysis, Symbiosis International University, Mulshi, Pune, Maharashtra, India. 5. Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neurosciences, Bangalore, India. 6. Neuromuscular Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neurosciences, Bangalore, India. 7. Department of Neuropsychology, National Institute of Mental Health and Neurosciences, Bangalore, India.
Abstract
OBJECTIVE: Neurocognitive disabilities in Duchenne muscular dystrophy (DMD) children beginning in early childhood and distal DMD gene deletions involving disruption of Dp140 isoform are more likely to manifest significant neurocognitive impairments. MRI data analysis techniques like brain-network metrics can provide information on microstructural integrity and underlying pathophysiology. METHODS: A prospective study on 95 participants [DMD = 57, and healthy controls (HC) = 38]. The muscular dystrophy functional rating scale (MDFRS) scores, neuropsychology batteries, and multiplex ligand-dependent probe amplification (MLPA) testing were used for clinical assessment, IQ estimation, and genotypic classification. Diffusion MRI and network-based statistics were used to analyze structural connectomes at various levels and correlate with clinical markers. RESULTS: Motor and executive sub-networks were extracted and analyzed. Out of 57 DMD children, 23 belong to Dp140 + and 34 to Dp140- subgroup. Motor disabilities are pronounced in Dp140- subgroup as reflected by lower MDFRS scores. IQ parameters are significantly low in all-DMD cases; however, the Dp140- has specifically lowest scores. Significant differences were observed in global efficiency, transitivity, and characteristic path length between HC and DMD. Subgroup analysis demonstrates that the significance is mainly driven by participants with Dp140- than Dp140 + isoform. Finally, a random forest classifier model illustrated an accuracy of 79% between HC and DMD and 90% between DMD- subgroups. CONCLUSIONS: Current findings demonstrate structural network-based characterization of abnormalities in DMD, especially prominent in Dp140-. Our observations suggest that participants with Dp140 + have relatively intact connectivity while Dp140- show widespread connectivity alterations at global, nodal, and edge levels. This study provides valuable insights supporting the genotype-phenotype correlation of brain-behavior involvement in DMD children.
OBJECTIVE: Neurocognitive disabilities in Duchenne muscular dystrophy (DMD) children beginning in early childhood and distal DMD gene deletions involving disruption of Dp140 isoform are more likely to manifest significant neurocognitive impairments. MRI data analysis techniques like brain-network metrics can provide information on microstructural integrity and underlying pathophysiology. METHODS: A prospective study on 95 participants [DMD = 57, and healthy controls (HC) = 38]. The muscular dystrophy functional rating scale (MDFRS) scores, neuropsychology batteries, and multiplex ligand-dependent probe amplification (MLPA) testing were used for clinical assessment, IQ estimation, and genotypic classification. Diffusion MRI and network-based statistics were used to analyze structural connectomes at various levels and correlate with clinical markers. RESULTS: Motor and executive sub-networks were extracted and analyzed. Out of 57 DMD children, 23 belong to Dp140 + and 34 to Dp140- subgroup. Motor disabilities are pronounced in Dp140- subgroup as reflected by lower MDFRS scores. IQ parameters are significantly low in all-DMD cases; however, the Dp140- has specifically lowest scores. Significant differences were observed in global efficiency, transitivity, and characteristic path length between HC and DMD. Subgroup analysis demonstrates that the significance is mainly driven by participants with Dp140- than Dp140 + isoform. Finally, a random forest classifier model illustrated an accuracy of 79% between HC and DMD and 90% between DMD- subgroups. CONCLUSIONS: Current findings demonstrate structural network-based characterization of abnormalities in DMD, especially prominent in Dp140-. Our observations suggest that participants with Dp140 + have relatively intact connectivity while Dp140- show widespread connectivity alterations at global, nodal, and edge levels. This study provides valuable insights supporting the genotype-phenotype correlation of brain-behavior involvement in DMD children.
Authors: Marco Orsini; Ana Carolina; Andorinho de F Ferreira; Anna Carolina Damm de Assis; Thais Magalhães; Silmar Teixeira; Victor Hugo Bastos; Victor Marinho; Thomaz Oliveira; Rossano Fiorelli; Acary Bulle Oliveira; Marcos R G de Freitas Journal: Neurol Int Date: 2018-07-04