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Sequential processing ability was estimated by combining performance on tasks for Melody, Standard Rhythm, Embedded Rhythm, and Accent subscores while sensory processing ability was ascertained via tasks of Tempo, Pitch, Timbre, and Tuning. Controlling for musical training, gender, and years of training, network\u2010based statistics revealed positive linear associations between total PROMS\u2010S scores and increased interhemispheric fronto\u2010temporal and parieto\u2010frontal white matter connectivity, suggesting a distinct segregated structural network for music perception. Secondary analysis revealed two subnetworks for sequential processing ability, one comprising ventral fronto\u2010temporal and subcortical regions and the other comprising dorsal fronto\u2010temporo\u2010parietal regions. A graph\u2010theoretic analysis to characterize the structural network revealed a positive association of modularity of the whole brain structural connectome with the d\u2032 total score. In addition, the nodal degree of the right posterior cingulate cortex also showed a significant positive correlation with the total d\u2032 score. Our results suggest that a distinct structural network of connectivity across fronto\u2010temporal, cerebellar, and cerebro\u2010subcortical regions is associated with music processing abilities and the right posterior cingulate cortex mediates the connectivity of this network.<\/jats:p>","DOI":"10.1111\/ejn.15407","type":"journal-article","created":{"date-parts":[[2021,8,2]],"date-time":"2021-08-02T18:15:35Z","timestamp":1627928135000},"page":"6093-6103","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Structural connectivity predicts sequential processing differences in music perception ability"],"prefix":"10.1111","volume":"54","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6549-2198","authenticated-orcid":false,"given":"Archith","family":"Rajan","sequence":"first","affiliation":[{"name":"Symbiosis Centre for Medical Image Analysis Symbiosis International (Deemed University)  Pune India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Apurva","family":"Shah","sequence":"additional","affiliation":[{"name":"Symbiosis Centre for Medical Image Analysis Symbiosis International (Deemed University)  Pune India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Madhura","family":"Ingalhalikar","sequence":"additional","affiliation":[{"name":"Symbiosis Centre for Medical Image Analysis Symbiosis International (Deemed University)  Pune India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nandini Chatterjee","family":"Singh","sequence":"additional","affiliation":[{"name":"Language Literacy and Music Laboratory National Brain Research Centre (Deemed University)  Manesar India"},{"name":"Science of Learning UNESCO Mahatma Gandhi Institute of Education for Peace and Sustainable Development  New Delhi India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,8,11]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0006-8993(98)00851-8"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.10.019"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0006-3495(94)80775-1"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116384"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cortex.2014.11.019"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2019.116513"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2020.116590"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/cercor\/bhn042"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.01.021"},{"key":"e_1_2_10_11_1","unstructured":"Dhollander T. &Connelly A.(2016).A novel iterative approach to reap the benefits of multi\u2010tissue CSD from just single\u2010shell (+ b= 0) diffusion MRI data. 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