Network diffusion model predicts neurodegeneration in limb-onset Amyotrophic Lateral Sclerosis
Objective Emerging evidences suggest that the trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of sp...
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| description | Objective Emerging evidences suggest that the trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of spread of pathology via the brain connectome, could capture the severity and progression of neurodegeneration (atrophy) in ALS. Methods We measured degeneration in limb-onset ALS patients (n = 14 at baseline, 12 at 6-months, and 9 at 12 months) and controls (n = 12 at baseline) using FreeSurfer analysis on the structural T1-weighted Magnetic Resonance Imaging (MRI) data. The NDM was simulated on the canonical structural connectome from the IIT Human Brain Atlas. To determine whether NDM could predict the atrophy pattern in ALS, the accumulation of pathology modelled by NDM was correlated against atrophy measured using MRI. In order to investigate whether network spread on the brain connectome derived from healthy individuals were significant findings, we compared our findings against network spread simulated on random networks. Results The cross-sectional analyses revealed that the network diffusion seeded from the inferior frontal gyrus (pars triangularis and pars orbitalis) significantly predicts the atrophy pattern in ALS compared to controls. Whereas, atrophy over time with-in the ALS group was best predicted by seeding the network diffusion process from the inferior temporal gyrus at 6-month and caudal middle frontal gyrus at 12-month. Network spread simulated on the random networks showed that the findings using healthy brain connectomes are significantly different from null models. Interpretation Our findings suggest the involvement of extra-motor regions in seeding the spread of pathology in ALS. Importantly, NDM was able to recapitulate the dynamics of pathological progression in ALS. Understanding the spatial shifts in the seeds of degeneration over time can potentially inform further research in the design of disease modifying therapeutic interventions in ALS. |
| doi_str_mv | 10.1371/journal.pone.0272736 |
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M. Sarah ; Lie, Yenni ; Poudel, Govinda R ; Egan, Gary F</creator><creatorcontrib>Bhattarai, Anjan ; Chen, Zhaolin ; Chua, Phyllis ; Talman, Paul ; Mathers, Susan ; Chapman, Caron ; Howe, James ; Lee, C. M. Sarah ; Lie, Yenni ; Poudel, Govinda R ; Egan, Gary F</creatorcontrib><description>Objective Emerging evidences suggest that the trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of spread of pathology via the brain connectome, could capture the severity and progression of neurodegeneration (atrophy) in ALS. Methods We measured degeneration in limb-onset ALS patients (n = 14 at baseline, 12 at 6-months, and 9 at 12 months) and controls (n = 12 at baseline) using FreeSurfer analysis on the structural T1-weighted Magnetic Resonance Imaging (MRI) data. The NDM was simulated on the canonical structural connectome from the IIT Human Brain Atlas. To determine whether NDM could predict the atrophy pattern in ALS, the accumulation of pathology modelled by NDM was correlated against atrophy measured using MRI. In order to investigate whether network spread on the brain connectome derived from healthy individuals were significant findings, we compared our findings against network spread simulated on random networks. Results The cross-sectional analyses revealed that the network diffusion seeded from the inferior frontal gyrus (pars triangularis and pars orbitalis) significantly predicts the atrophy pattern in ALS compared to controls. Whereas, atrophy over time with-in the ALS group was best predicted by seeding the network diffusion process from the inferior temporal gyrus at 6-month and caudal middle frontal gyrus at 12-month. Network spread simulated on the random networks showed that the findings using healthy brain connectomes are significantly different from null models. Interpretation Our findings suggest the involvement of extra-motor regions in seeding the spread of pathology in ALS. Importantly, NDM was able to recapitulate the dynamics of pathological progression in ALS. Understanding the spatial shifts in the seeds of degeneration over time can potentially inform further research in the design of disease modifying therapeutic interventions in ALS.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0272736</identifier><identifier>PMID: 35951510</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Alzheimer's disease ; Amyotrophic lateral sclerosis ; Analysis ; Atrophy ; Biology and Life Sciences ; Brain ; Computer and Information Sciences ; Degeneration ; Dementia ; Deoxyribonucleic acid ; Design modifications ; Diagnosis ; Diffusion ; Diffusion models ; DNA ; DNA-binding protein ; Frontal gyrus ; Genetic aspects ; Genotype & phenotype ; Hypotheses ; Magnetic resonance ; Magnetic resonance imaging ; Medicine and Health Sciences ; Modelling ; Neurodegeneration ; Neuroimaging ; Neuropathology ; Pathology ; Patients ; Propagation ; Protein binding ; Protein seeding ; Proteins ; Research and Analysis Methods ; Seeds ; Simulation ; Spinal cord ; Temporal gyrus ; Therapeutic applications</subject><ispartof>PloS one, 2022-08, Vol.17 (8), p.e0272736-e0272736</ispartof><rights>COPYRIGHT 2022 Public Library of Science</rights><rights>2022 Bhattarai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2022 Bhattarai et al 2022 Bhattarai et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c669t-fc332877f7fb3657df2ae24a6fa57bf523ce43bde2a2ed78aa653736c91fa9af3</citedby><cites>FETCH-LOGICAL-c669t-fc332877f7fb3657df2ae24a6fa57bf523ce43bde2a2ed78aa653736c91fa9af3</cites><orcidid>0000-0002-9178-6509</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371353/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9371353/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids></links><search><creatorcontrib>Bhattarai, Anjan</creatorcontrib><creatorcontrib>Chen, Zhaolin</creatorcontrib><creatorcontrib>Chua, Phyllis</creatorcontrib><creatorcontrib>Talman, Paul</creatorcontrib><creatorcontrib>Mathers, Susan</creatorcontrib><creatorcontrib>Chapman, Caron</creatorcontrib><creatorcontrib>Howe, James</creatorcontrib><creatorcontrib>Lee, C. M. Sarah</creatorcontrib><creatorcontrib>Lie, Yenni</creatorcontrib><creatorcontrib>Poudel, Govinda R</creatorcontrib><creatorcontrib>Egan, Gary F</creatorcontrib><title>Network diffusion model predicts neurodegeneration in limb-onset Amyotrophic Lateral Sclerosis</title><title>PloS one</title><description>Objective Emerging evidences suggest that the trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of spread of pathology via the brain connectome, could capture the severity and progression of neurodegeneration (atrophy) in ALS. Methods We measured degeneration in limb-onset ALS patients (n = 14 at baseline, 12 at 6-months, and 9 at 12 months) and controls (n = 12 at baseline) using FreeSurfer analysis on the structural T1-weighted Magnetic Resonance Imaging (MRI) data. The NDM was simulated on the canonical structural connectome from the IIT Human Brain Atlas. To determine whether NDM could predict the atrophy pattern in ALS, the accumulation of pathology modelled by NDM was correlated against atrophy measured using MRI. In order to investigate whether network spread on the brain connectome derived from healthy individuals were significant findings, we compared our findings against network spread simulated on random networks. Results The cross-sectional analyses revealed that the network diffusion seeded from the inferior frontal gyrus (pars triangularis and pars orbitalis) significantly predicts the atrophy pattern in ALS compared to controls. Whereas, atrophy over time with-in the ALS group was best predicted by seeding the network diffusion process from the inferior temporal gyrus at 6-month and caudal middle frontal gyrus at 12-month. Network spread simulated on the random networks showed that the findings using healthy brain connectomes are significantly different from null models. Interpretation Our findings suggest the involvement of extra-motor regions in seeding the spread of pathology in ALS. Importantly, NDM was able to recapitulate the dynamics of pathological progression in ALS. Understanding the spatial shifts in the seeds of degeneration over time can potentially inform further research in the design of disease modifying therapeutic interventions in ALS.</description><subject>Alzheimer's disease</subject><subject>Amyotrophic lateral sclerosis</subject><subject>Analysis</subject><subject>Atrophy</subject><subject>Biology and Life Sciences</subject><subject>Brain</subject><subject>Computer and Information Sciences</subject><subject>Degeneration</subject><subject>Dementia</subject><subject>Deoxyribonucleic acid</subject><subject>Design modifications</subject><subject>Diagnosis</subject><subject>Diffusion</subject><subject>Diffusion models</subject><subject>DNA</subject><subject>DNA-binding protein</subject><subject>Frontal gyrus</subject><subject>Genetic aspects</subject><subject>Genotype & phenotype</subject><subject>Hypotheses</subject><subject>Magnetic resonance</subject><subject>Magnetic resonance imaging</subject><subject>Medicine and Health Sciences</subject><subject>Modelling</subject><subject>Neurodegeneration</subject><subject>Neuroimaging</subject><subject>Neuropathology</subject><subject>Pathology</subject><subject>Patients</subject><subject>Propagation</subject><subject>Protein binding</subject><subject>Protein seeding</subject><subject>Proteins</subject><subject>Research and Analysis Methods</subject><subject>Seeds</subject><subject>Simulation</subject><subject>Spinal cord</subject><subject>Temporal gyrus</subject><subject>Therapeutic applications</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNk1tv0zAUxyMEYlvhGyARCQmNhxZfEid5mVRNXCpVTGLAI9aJc9y6JHGxHWDfHncNaEF7QH6wdfzz_1x8TpI8o2RBeUFf7-zgemgXe9vjgrCCFVw8SE5pxdlcMMIf3jmfJGfe7wjJeSnE4-SE51VOc0pOk68fMPy07lvaGK0Hb2yfdrbBNt07bIwKPu1xcNGywR4dhANg-rQ1XT23vceQLrsbG5zdb41K1xAi1KbXqkVnvfFPkkcaWo9Px32WfH775tPl-_n66t3qcrmeKyGqMNeKc1YWhS50zUVeNJoBsgyEhryodc64wozXDTJg2BQlgMh5zFdVVEMFms-S50fdfWu9HEvjJSsIJTSjpYjE6kg0FnZy70wH7kZaMPLWYN1GggsmBi6VVoCKizqqZwgZkKrMWV1CLaAiFKLWxehtqDtsFPYhZj0Rnd70Zis39oes4sfxnEeB81HA2e8D-iA74xW2LfRoh2PclFf5bdwv_kHvz26kNhATML2OXwLqICqX0aXghMQ2mCWLe6i4GuyMin2kTbRPHryaPIhMwF9hA4P3cnX98f_Zqy9T9uUddovQhq237XBoLz8FsyOoYjt5h_pvkSmRhzH4Uw15GAM5jgH_Dd8l-qo</recordid><startdate>20220811</startdate><enddate>20220811</enddate><creator>Bhattarai, Anjan</creator><creator>Chen, Zhaolin</creator><creator>Chua, Phyllis</creator><creator>Talman, Paul</creator><creator>Mathers, Susan</creator><creator>Chapman, Caron</creator><creator>Howe, James</creator><creator>Lee, C. 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M. Sarah</au><au>Lie, Yenni</au><au>Poudel, Govinda R</au><au>Egan, Gary F</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Network diffusion model predicts neurodegeneration in limb-onset Amyotrophic Lateral Sclerosis</atitle><jtitle>PloS one</jtitle><date>2022-08-11</date><risdate>2022</risdate><volume>17</volume><issue>8</issue><spage>e0272736</spage><epage>e0272736</epage><pages>e0272736-e0272736</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Objective Emerging evidences suggest that the trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of spread of pathology via the brain connectome, could capture the severity and progression of neurodegeneration (atrophy) in ALS. Methods We measured degeneration in limb-onset ALS patients (n = 14 at baseline, 12 at 6-months, and 9 at 12 months) and controls (n = 12 at baseline) using FreeSurfer analysis on the structural T1-weighted Magnetic Resonance Imaging (MRI) data. The NDM was simulated on the canonical structural connectome from the IIT Human Brain Atlas. To determine whether NDM could predict the atrophy pattern in ALS, the accumulation of pathology modelled by NDM was correlated against atrophy measured using MRI. In order to investigate whether network spread on the brain connectome derived from healthy individuals were significant findings, we compared our findings against network spread simulated on random networks. Results The cross-sectional analyses revealed that the network diffusion seeded from the inferior frontal gyrus (pars triangularis and pars orbitalis) significantly predicts the atrophy pattern in ALS compared to controls. Whereas, atrophy over time with-in the ALS group was best predicted by seeding the network diffusion process from the inferior temporal gyrus at 6-month and caudal middle frontal gyrus at 12-month. Network spread simulated on the random networks showed that the findings using healthy brain connectomes are significantly different from null models. Interpretation Our findings suggest the involvement of extra-motor regions in seeding the spread of pathology in ALS. Importantly, NDM was able to recapitulate the dynamics of pathological progression in ALS. Understanding the spatial shifts in the seeds of degeneration over time can potentially inform further research in the design of disease modifying therapeutic interventions in ALS.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>35951510</pmid><doi>10.1371/journal.pone.0272736</doi><tpages>e0272736</tpages><orcidid>https://orcid.org/0000-0002-9178-6509</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Alzheimer's disease Amyotrophic lateral sclerosis Analysis Atrophy Biology and Life Sciences Brain Computer and Information Sciences Degeneration Dementia Deoxyribonucleic acid Design modifications Diagnosis Diffusion Diffusion models DNA DNA-binding protein Frontal gyrus Genetic aspects Genotype & phenotype Hypotheses Magnetic resonance Magnetic resonance imaging Medicine and Health Sciences Modelling Neurodegeneration Neuroimaging Neuropathology Pathology Patients Propagation Protein binding Protein seeding Proteins Research and Analysis Methods Seeds Simulation Spinal cord Temporal gyrus Therapeutic applications |
| title | Network diffusion model predicts neurodegeneration in limb-onset Amyotrophic Lateral Sclerosis |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-09T02%3A03%3A26IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Network%20diffusion%20model%20predicts%20neurodegeneration%20in%20limb-onset%20Amyotrophic%20Lateral%20Sclerosis&rft.jtitle=PloS%20one&rft.au=Bhattarai,%20Anjan&rft.date=2022-08-11&rft.volume=17&rft.issue=8&rft.spage=e0272736&rft.epage=e0272736&rft.pages=e0272736-e0272736&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0272736&rft_dat=%3Cgale_plos_%3EA713630020%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2701014186&rft_id=info:pmid/35951510&rft_galeid=A713630020&rft_doaj_id=oai_doaj_org_article_cfcaec36b91f4ea4a09852b8ab6a901a&rfr_iscdi=true |