Age-related microstructural and physiological changes in normal brain measured by MRI γ-metrics derived from anomalous diffusion signal representation
Nowadays, increasing longevity associated with declining cerebral nervous system functions, suggests the need for continued development of new imaging contrast mechanisms to support the differential diagnosis of age-related decline. In our previous papers, we developed a new imaging contrast metrics...
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description | Nowadays, increasing longevity associated with declining cerebral nervous system functions, suggests the need for continued development of new imaging contrast mechanisms to support the differential diagnosis of age-related decline.
In our previous papers, we developed a new imaging contrast metrics derived from anomalous diffusion signal representation and obtained from diffusion-weighted (DW) data collected by varying diffusion gradient strengths. Recently, we highlighted that the new metrics, named γ-metrics, depended on the local inhomogeneity due to differences in magnetic susceptibility between tissues and diffusion compartments in young healthy subjects, thus providing information about myelin orientation and iron content within cerebral regions. The major structural modifications occurring in brain aging are myelinated fibers damage in nerve fibers and iron accumulation in gray matter nuclei. Therefore, we investigated the potential of γ-metrics in relation to other conventional diffusion metrics such as DTI, DKI and NODDI in detecting age-related structural changes in white matter (WM) and subcortical gray matter (scGM). DW-images were acquired in 32 healthy subjects, adults and elderly (age range 20–77 years) using 3.0T and 12 b-values up to 5000 s/mm2. Association between diffusion metrics and subjects’ age was assessed using linear regression. A decline in mean γ (Mγ) in the scGM and a complementary increase in radial γ (γ⊥) in frontal WM, genu of corpus callosum and anterior corona radiata with advancing age were found. We suggested that the increase in γ⊥ might reflect declined myelin density, and Mγ decrease might mirror iron accumulation. An increase in D// and a decrease in the orientation dispersion index (ODI) were associated with axonal loss in the pyramidal tracts, while their inverted trends within the thalamus were thought to be linked to reduced architectural complexity of nerve fibers. γ-metrics together with conventional diffusion-metrics can more comprehensively characterize the complex mechanisms underlining age-related changes than conventional diffusion techniques alone.
•γ metrics provide complementary information compared to conventional diffusion metrics.•This study shows the added value of γ-metrics to assess brain alterations due to aging.•Axial γ increase in white matter may reflect breakdown of myelin and axonal damage.•Mean γ decrease in subcortical gray matter may mirror iron deposit accumulation. |
doi_str_mv | 10.1016/j.neuroimage.2018.12.044 |
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In our previous papers, we developed a new imaging contrast metrics derived from anomalous diffusion signal representation and obtained from diffusion-weighted (DW) data collected by varying diffusion gradient strengths. Recently, we highlighted that the new metrics, named γ-metrics, depended on the local inhomogeneity due to differences in magnetic susceptibility between tissues and diffusion compartments in young healthy subjects, thus providing information about myelin orientation and iron content within cerebral regions. The major structural modifications occurring in brain aging are myelinated fibers damage in nerve fibers and iron accumulation in gray matter nuclei. Therefore, we investigated the potential of γ-metrics in relation to other conventional diffusion metrics such as DTI, DKI and NODDI in detecting age-related structural changes in white matter (WM) and subcortical gray matter (scGM). DW-images were acquired in 32 healthy subjects, adults and elderly (age range 20–77 years) using 3.0T and 12 b-values up to 5000 s/mm2. Association between diffusion metrics and subjects’ age was assessed using linear regression. A decline in mean γ (Mγ) in the scGM and a complementary increase in radial γ (γ⊥) in frontal WM, genu of corpus callosum and anterior corona radiata with advancing age were found. We suggested that the increase in γ⊥ might reflect declined myelin density, and Mγ decrease might mirror iron accumulation. An increase in D// and a decrease in the orientation dispersion index (ODI) were associated with axonal loss in the pyramidal tracts, while their inverted trends within the thalamus were thought to be linked to reduced architectural complexity of nerve fibers. γ-metrics together with conventional diffusion-metrics can more comprehensively characterize the complex mechanisms underlining age-related changes than conventional diffusion techniques alone.
•γ metrics provide complementary information compared to conventional diffusion metrics.•This study shows the added value of γ-metrics to assess brain alterations due to aging.•Axial γ increase in white matter may reflect breakdown of myelin and axonal damage.•Mean γ decrease in subcortical gray matter may mirror iron deposit accumulation.</description><identifier>ISSN: 1053-8119</identifier><identifier>EISSN: 1095-9572</identifier><identifier>DOI: 10.1016/j.neuroimage.2018.12.044</identifier><identifier>PMID: 30583064</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adult ; Age ; Age Factors ; Aged ; Aging ; Anomalous diffusion ; Brain ; Brain research ; Corpus callosum ; Differential diagnosis ; Diffusion ; Diffusion Magnetic Resonance Imaging - methods ; DKI ; DTI ; Female ; Fibers ; Geriatrics ; Gray Matter - diagnostic imaging ; Humans ; Iron ; Iron deposition ; Magnetic resonance imaging ; Magnetic susceptibility ; Male ; Middle Aged ; Myelin ; Nervous system ; Neuroimaging ; NODDI ; Normal aging ; Physiology ; Pyramidal tracts ; Substantia alba ; Substantia grisea ; Thalamus ; White Matter - diagnostic imaging ; Young Adult</subject><ispartof>NeuroImage (Orlando, Fla.), 2019-03, Vol.188, p.654-667</ispartof><rights>2018 Elsevier Inc.</rights><rights>Copyright © 2018 Elsevier Inc. All rights reserved.</rights><rights>2018. Elsevier Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c452t-2e4c024cc4fce220e96e62c88645adf802898c0c2e446b77480ab94b3a73d5063</citedby><cites>FETCH-LOGICAL-c452t-2e4c024cc4fce220e96e62c88645adf802898c0c2e446b77480ab94b3a73d5063</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2187132592?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995,64385,64387,64389,72469</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30583064$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Guerreri, Michele</creatorcontrib><creatorcontrib>Palombo, Marco</creatorcontrib><creatorcontrib>Caporale, Alessandra</creatorcontrib><creatorcontrib>Fasano, Fabrizio</creatorcontrib><creatorcontrib>Macaluso, Emiliano</creatorcontrib><creatorcontrib>Bozzali, Marco</creatorcontrib><creatorcontrib>Capuani, Silvia</creatorcontrib><title>Age-related microstructural and physiological changes in normal brain measured by MRI γ-metrics derived from anomalous diffusion signal representation</title><title>NeuroImage (Orlando, Fla.)</title><addtitle>Neuroimage</addtitle><description>Nowadays, increasing longevity associated with declining cerebral nervous system functions, suggests the need for continued development of new imaging contrast mechanisms to support the differential diagnosis of age-related decline.
In our previous papers, we developed a new imaging contrast metrics derived from anomalous diffusion signal representation and obtained from diffusion-weighted (DW) data collected by varying diffusion gradient strengths. Recently, we highlighted that the new metrics, named γ-metrics, depended on the local inhomogeneity due to differences in magnetic susceptibility between tissues and diffusion compartments in young healthy subjects, thus providing information about myelin orientation and iron content within cerebral regions. The major structural modifications occurring in brain aging are myelinated fibers damage in nerve fibers and iron accumulation in gray matter nuclei. Therefore, we investigated the potential of γ-metrics in relation to other conventional diffusion metrics such as DTI, DKI and NODDI in detecting age-related structural changes in white matter (WM) and subcortical gray matter (scGM). DW-images were acquired in 32 healthy subjects, adults and elderly (age range 20–77 years) using 3.0T and 12 b-values up to 5000 s/mm2. Association between diffusion metrics and subjects’ age was assessed using linear regression. A decline in mean γ (Mγ) in the scGM and a complementary increase in radial γ (γ⊥) in frontal WM, genu of corpus callosum and anterior corona radiata with advancing age were found. We suggested that the increase in γ⊥ might reflect declined myelin density, and Mγ decrease might mirror iron accumulation. An increase in D// and a decrease in the orientation dispersion index (ODI) were associated with axonal loss in the pyramidal tracts, while their inverted trends within the thalamus were thought to be linked to reduced architectural complexity of nerve fibers. γ-metrics together with conventional diffusion-metrics can more comprehensively characterize the complex mechanisms underlining age-related changes than conventional diffusion techniques alone.
•γ metrics provide complementary information compared to conventional diffusion metrics.•This study shows the added value of γ-metrics to assess brain alterations due to aging.•Axial γ increase in white matter may reflect breakdown of myelin and axonal damage.•Mean γ decrease in subcortical gray matter may mirror iron deposit accumulation.</description><subject>Adult</subject><subject>Age</subject><subject>Age Factors</subject><subject>Aged</subject><subject>Aging</subject><subject>Anomalous diffusion</subject><subject>Brain</subject><subject>Brain research</subject><subject>Corpus callosum</subject><subject>Differential diagnosis</subject><subject>Diffusion</subject><subject>Diffusion Magnetic Resonance Imaging - methods</subject><subject>DKI</subject><subject>DTI</subject><subject>Female</subject><subject>Fibers</subject><subject>Geriatrics</subject><subject>Gray Matter - diagnostic imaging</subject><subject>Humans</subject><subject>Iron</subject><subject>Iron deposition</subject><subject>Magnetic resonance imaging</subject><subject>Magnetic susceptibility</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Myelin</subject><subject>Nervous system</subject><subject>Neuroimaging</subject><subject>NODDI</subject><subject>Normal aging</subject><subject>Physiology</subject><subject>Pyramidal tracts</subject><subject>Substantia alba</subject><subject>Substantia grisea</subject><subject>Thalamus</subject><subject>White Matter - 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methods</topic><topic>DKI</topic><topic>DTI</topic><topic>Female</topic><topic>Fibers</topic><topic>Geriatrics</topic><topic>Gray Matter - diagnostic imaging</topic><topic>Humans</topic><topic>Iron</topic><topic>Iron deposition</topic><topic>Magnetic resonance imaging</topic><topic>Magnetic susceptibility</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Myelin</topic><topic>Nervous system</topic><topic>Neuroimaging</topic><topic>NODDI</topic><topic>Normal aging</topic><topic>Physiology</topic><topic>Pyramidal tracts</topic><topic>Substantia alba</topic><topic>Substantia grisea</topic><topic>Thalamus</topic><topic>White Matter - diagnostic imaging</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Guerreri, Michele</creatorcontrib><creatorcontrib>Palombo, Marco</creatorcontrib><creatorcontrib>Caporale, Alessandra</creatorcontrib><creatorcontrib>Fasano, Fabrizio</creatorcontrib><creatorcontrib>Macaluso, Emiliano</creatorcontrib><creatorcontrib>Bozzali, Marco</creatorcontrib><creatorcontrib>Capuani, Silvia</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Psychology Database (ProQuest)</collection><collection>Biological Science Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest One Psychology</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>NeuroImage (Orlando, Fla.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Guerreri, Michele</au><au>Palombo, Marco</au><au>Caporale, Alessandra</au><au>Fasano, Fabrizio</au><au>Macaluso, Emiliano</au><au>Bozzali, Marco</au><au>Capuani, Silvia</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Age-related microstructural and physiological changes in normal brain measured by MRI γ-metrics derived from anomalous diffusion signal representation</atitle><jtitle>NeuroImage (Orlando, Fla.)</jtitle><addtitle>Neuroimage</addtitle><date>2019-03</date><risdate>2019</risdate><volume>188</volume><spage>654</spage><epage>667</epage><pages>654-667</pages><issn>1053-8119</issn><eissn>1095-9572</eissn><abstract>Nowadays, increasing longevity associated with declining cerebral nervous system functions, suggests the need for continued development of new imaging contrast mechanisms to support the differential diagnosis of age-related decline.
In our previous papers, we developed a new imaging contrast metrics derived from anomalous diffusion signal representation and obtained from diffusion-weighted (DW) data collected by varying diffusion gradient strengths. Recently, we highlighted that the new metrics, named γ-metrics, depended on the local inhomogeneity due to differences in magnetic susceptibility between tissues and diffusion compartments in young healthy subjects, thus providing information about myelin orientation and iron content within cerebral regions. The major structural modifications occurring in brain aging are myelinated fibers damage in nerve fibers and iron accumulation in gray matter nuclei. Therefore, we investigated the potential of γ-metrics in relation to other conventional diffusion metrics such as DTI, DKI and NODDI in detecting age-related structural changes in white matter (WM) and subcortical gray matter (scGM). DW-images were acquired in 32 healthy subjects, adults and elderly (age range 20–77 years) using 3.0T and 12 b-values up to 5000 s/mm2. Association between diffusion metrics and subjects’ age was assessed using linear regression. A decline in mean γ (Mγ) in the scGM and a complementary increase in radial γ (γ⊥) in frontal WM, genu of corpus callosum and anterior corona radiata with advancing age were found. We suggested that the increase in γ⊥ might reflect declined myelin density, and Mγ decrease might mirror iron accumulation. An increase in D// and a decrease in the orientation dispersion index (ODI) were associated with axonal loss in the pyramidal tracts, while their inverted trends within the thalamus were thought to be linked to reduced architectural complexity of nerve fibers. γ-metrics together with conventional diffusion-metrics can more comprehensively characterize the complex mechanisms underlining age-related changes than conventional diffusion techniques alone.
•γ metrics provide complementary information compared to conventional diffusion metrics.•This study shows the added value of γ-metrics to assess brain alterations due to aging.•Axial γ increase in white matter may reflect breakdown of myelin and axonal damage.•Mean γ decrease in subcortical gray matter may mirror iron deposit accumulation.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>30583064</pmid><doi>10.1016/j.neuroimage.2018.12.044</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Adult Age Age Factors Aged Aging Anomalous diffusion Brain Brain research Corpus callosum Differential diagnosis Diffusion Diffusion Magnetic Resonance Imaging - methods DKI DTI Female Fibers Geriatrics Gray Matter - diagnostic imaging Humans Iron Iron deposition Magnetic resonance imaging Magnetic susceptibility Male Middle Aged Myelin Nervous system Neuroimaging NODDI Normal aging Physiology Pyramidal tracts Substantia alba Substantia grisea Thalamus White Matter - diagnostic imaging Young Adult |
title | Age-related microstructural and physiological changes in normal brain measured by MRI γ-metrics derived from anomalous diffusion signal representation |
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