{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1361981469736139520.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.1016/j.dadm.2016.12.007"}},{"identifier":{"@type":"URI","@value":"https://api.elsevier.com/content/article/PII:S2352872916300707?httpAccept=text/xml"}},{"identifier":{"@type":"URI","@value":"https://api.elsevier.com/content/article/PII:S2352872916300707?httpAccept=text/plain"}},{"identifier":{"@type":"URI","@value":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1016%2Fj.dadm.2016.12.007"}},{"identifier":{"@type":"URI","@value":"https://onlinelibrary.wiley.com/doi/pdf/10.1016/j.dadm.2016.12.007"}},{"identifier":{"@type":"URI","@value":"https://onlinelibrary.wiley.com/doi/full-xml/10.1016/j.dadm.2016.12.007"}},{"identifier":{"@type":"URI","@value":"https://alz-journals.onlinelibrary.wiley.com/doi/pdf/10.1016/j.dadm.2016.12.007"}}],"dc:title":[{"@value":"Alzheimer's disease: The influence of age on clinical heterogeneity through the human brain connectome"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:title>Abstract</jats:title><jats:sec><jats:title>Introduction</jats:title><jats:p>One major factor that influences the heterogeneity of Alzheimer's disease (AD) is age: younger AD patients more frequently exhibit atypical forms of AD. We propose that this age‐related heterogeneity can be understood better by considering age‐related differences in atrophy in the context of large‐scale brain networks subserving cognitive functions that contribute to memory.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We examined data from 75 patients with mild AD dementia from Alzheimer's Disease Neuroimaging Initiative. These individuals were chosen because they have cerebrospinal fluid amyloid and p‐tau levels in the range suggesting the presence of AD neuropathology, and because they were either younger than age 65 years early‐onset AD (EOAD) or age 80 years or older late‐onset AD (LOAD).</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>In the EOAD group, the most prominent atrophy was present in the posterior cingulate cortex, whereas in the LOAD group, atrophy was most prominent in the medial temporal lobe. Structural covariance analysis showed that the magnitude of atrophy in these epicenters is strongly correlated with a distributed atrophy pattern similar to distinct intrinsic connectivity networks in the healthy brain. An examination of memory performance in EOAD dementia versus LOAD dementia demonstrated relatively more prominent impairment in encoding in the EOAD group than in the LOAD group, with similar performance in memory storage in LOAD and EOAD but greater impairment in semantic memory in LOAD than in EOAD.</jats:p></jats:sec><jats:sec><jats:title>Discussion</jats:title><jats:p>The observations provide novel insights about age as a major factor contributing to the heterogeneity in the topography of AD‐related cortical atrophy.</jats:p></jats:sec>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1381981469736139522","@type":"Researcher","foaf:name":[{"@value":"Bradford C. Dickerson"}],"jpcoar:affiliationName":[{"@value":"Department of Neurology Massachusetts General Hospital Boston MA USA"},{"@value":"Department of Neurology Harvard Medical School Boston MA USA"}]},{"@id":"https://cir.nii.ac.jp/crid/1381981469736139523","@type":"Researcher","foaf:name":[{"@value":"Michael Brickhouse"}],"jpcoar:affiliationName":[{"@value":"Department of Neurology Massachusetts General Hospital Boston MA USA"},{"@value":"Department of Neurology Harvard Medical School Boston MA USA"}]},{"@id":"https://cir.nii.ac.jp/crid/1381981469736139520","@type":"Researcher","foaf:name":[{"@value":"Scott McGinnis"}],"jpcoar:affiliationName":[{"@value":"Department of Neurology Massachusetts General Hospital Boston MA USA"},{"@value":"Department of Neurology Brigham and Women's Hospital Boston MA USA"}]},{"@id":"https://cir.nii.ac.jp/crid/1381981469736139521","@type":"Researcher","foaf:name":[{"@value":"David A. Wolk"}],"jpcoar:affiliationName":[{"@value":"Department of Neurology University of Pennsylvania Philadelphia PA USA"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"23528729"},{"@type":"EISSN","@value":"23528729"}],"prism:publicationName":[{"@value":"Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring"}],"dc:publisher":[{"@value":"Wiley"}],"prism:publicationDate":"2016-12-22","prism:volume":"6","prism:number":"1","prism:startingPage":"122","prism:endingPage":"135"},"reviewed":"false","dc:rights":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"url":[{"@id":"https://api.elsevier.com/content/article/PII:S2352872916300707?httpAccept=text/xml"},{"@id":"https://api.elsevier.com/content/article/PII:S2352872916300707?httpAccept=text/plain"},{"@id":"https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1016%2Fj.dadm.2016.12.007"},{"@id":"https://onlinelibrary.wiley.com/doi/pdf/10.1016/j.dadm.2016.12.007"},{"@id":"https://onlinelibrary.wiley.com/doi/full-xml/10.1016/j.dadm.2016.12.007"},{"@id":"https://alz-journals.onlinelibrary.wiley.com/doi/pdf/10.1016/j.dadm.2016.12.007"}],"createdAt":"2016-12-22","modifiedAt":"2025-10-13","relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1360572092804972672","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Utility of Easy <b><i>Z</i></b>-Score Imaging System-Assisted SPECT in Detecting Onset Age-Dependent Decreases in Cerebral Blood Flow in the Posterior Cingulate Cortex, Precuneus, and Parietal Lobe in Alzheimer’s Disease with Amyloid Accumulation"}]},{"@id":"https://cir.nii.ac.jp/crid/1360848657494246528","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Development of a risk score for the prediction of incident dementia in older adults using a frailty index and health checkup data: The JAGES longitudinal study"}]},{"@id":"https://cir.nii.ac.jp/crid/1360853567778462208","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["isReferencedBy"],"jpcoar:relatedTitle":[{"@value":"Comparison of Alzheimer’s disease patients and healthy controls in the easy Z-score imaging system with differential image reconstruction methods using SPECT/CT: verification using normal database of our institution"}]}],"dataSourceIdentifier":[{"@type":"CROSSREF","@value":"10.1016/j.dadm.2016.12.007"},{"@type":"CROSSREF","@value":"10.1016/j.ypmed.2018.04.004_references_DOI_7pbw3HO6vTe86tcSo6jFPOD7pRV"},{"@type":"CROSSREF","@value":"10.1159/000507654_references_DOI_7pbw3HO6vTe86tcSo6jFPOD7pRV"},{"@type":"CROSSREF","@value":"10.1007/s12149-020-01562-8_references_DOI_7pbw3HO6vTe86tcSo6jFPOD7pRV"}]}