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Mahzarnia A, Stout JA, Anderson RJ, Moon HS, Yar Han Z, Beck K, Browndyke JN, Dunson DB, Johnson KG, O’Brien RJ, Badea A. Identifying vulnerable brain networks associated with Alzheimer's disease risk. Cereb Cortex 2023; 33:5307-5322. [PMID: 36320163 PMCID: PMC10399292 DOI: 10.1093/cercor/bhac419] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 12/23/2022] Open
Abstract
The selective vulnerability of brain networks in individuals at risk for Alzheimer's disease (AD) may help differentiate pathological from normal aging at asymptomatic stages, allowing the implementation of more effective interventions. We used a sample of 72 people across the age span, enriched for the APOE4 genotype to reveal vulnerable networks associated with a composite AD risk factor including age, genotype, and sex. Sparse canonical correlation analysis (CCA) revealed a high weight associated with genotype, and subgraphs involving the cuneus, temporal, cingulate cortices, and cerebellum. Adding cognitive metrics to the risk factor revealed the highest cumulative degree of connectivity for the pericalcarine cortex, insula, banks of the superior sulcus, and the cerebellum. To enable scaling up our approach, we extended tensor network principal component analysis, introducing CCA components. We developed sparse regression predictive models with errors of 17% for genotype, 24% for family risk factor for AD, and 5 years for age. Age prediction in groups including cognitively impaired subjects revealed regions not found using only normal subjects, i.e. middle and transverse temporal, paracentral and superior banks of temporal sulcus, as well as the amygdala and parahippocampal gyrus. These modeling approaches represent stepping stones towards single subject prediction.
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Affiliation(s)
- Ali Mahzarnia
- Radiology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Jacques A Stout
- Brain Imaging and Analysis Center, Duke University Medical School, Durham, 27710 NC, USA
| | - Robert J Anderson
- Radiology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Hae Sol Moon
- Biomedical Engineering Department, Pratt School of Engineering, Duke University, Durham, 27710 NC, USA
| | - Zay Yar Han
- Radiology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Kate Beck
- Neurology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Jeffrey N Browndyke
- Psychiatry and Behavioral Sciences Department, Duke University Medical School, Durham, 27710 NC, USA
| | - David B Dunson
- Statistical Sciences, Trinity College, Duke University, Durham, 27710 NC, USA
| | - Kim G Johnson
- Neurology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Richard J O’Brien
- Neurology Department, Duke University Medical School, Durham, 27710 NC, USA
| | - Alexandra Badea
- Radiology Department, Duke University Medical School, Durham, 27710 NC, USA
- Brain Imaging and Analysis Center, Duke University Medical School, Durham, 27710 NC, USA
- Biomedical Engineering Department, Pratt School of Engineering, Duke University, Durham, 27710 NC, USA
- Neurology Department, Duke University Medical School, Durham, 27710 NC, USA
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Green ZD, Vidoni ED, Swerdlow RH, Burns JM, Morris JK, Honea RA. Increased Functional Connectivity of the Precuneus in Individuals with a Family History of Alzheimer's Disease. J Alzheimers Dis 2023; 91:559-571. [PMID: 36463439 PMCID: PMC9912732 DOI: 10.3233/jad-210326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
BACKGROUND First-degree relatives of individuals with late-onset Alzheimer's disease (AD) have increased risk for AD, with children of affected parents at an especially high risk. OBJECTIVE We aimed to investigate default mode network connectivity, medial temporal cortex volume, and cognition in cognitively healthy (CH) individuals with (FH+) and without (FH-) a family history of AD, alongside amnestic mild cognitive impairment (aMCI) and AD individuals, to determine the context and directionality of dysfunction in at-risk individuals. Our primary hypothesis was that there would be a linear decline (CH FH- > CH FH+ > aMCI > AD) within the risk groups on all measures of AD risk. METHODS We used MRI and fMRI to study cognitively healthy individuals (n = 28) with and without AD family history (FH+ and FH-, respectively), those with aMCI (n = 31) and early-stage AD (n = 25). We tested connectivity within the default mode network, as well as measures of volume and thickness within the medial temporal cortex and selected seed regions. RESULTS As expected, we identified decreased medial temporal cortex volumes in the aMCI and AD groups compared to cognitively healthy groups. We also observed patterns of connectivity across risk groups that suggest a nonlinear relationship of change, such that the FH+ group showed increased connectivity compared to the FH- and AD groups (CH FH+ > CH FH- > aMCI > AD). This pattern emerged primarily in connectivity between the precuneus and frontal regions. CONCLUSION These results add to a growing literature that suggests compensatory brain function in otherwise cognitively healthy individuals with a family history of AD.
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Affiliation(s)
- Zachary D. Green
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Eric D. Vidoni
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Russell H. Swerdlow
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Jeffrey M. Burns
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Jill K. Morris
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Robyn A. Honea
- University of Kansas Alzheimer’s Disease Research Center, University of Kansas School of Medicine, Kansas City, KS, USA,
Department of Neurology, University of Kansas School of Medicine, Kansas City, KS, USA,Correspondence to: Robyn A. Honea, University of Kansas School of Medicine, Department of Neurology, University of Kansas Alzheimer’s Disease Research Center, 4350 Shawnee Mission Parkway, Fairway, KS, 66205, USA. Tel.: +1 913 588 5514; E-mail:
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Piersson AD, Mohamad M, Suppiah S, Rajab NF. Topographical patterns of whole-brain structural alterations in association with genetic risk, cerebrospinal fluid, positron emission tomography biomarkers of Alzheimer’s disease, and neuropsychological measures. Clin Transl Imaging 2021. [DOI: 10.1007/s40336-021-00440-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Guo S, Lai C, Wu C, Cen G. Conversion Discriminative Analysis on Mild Cognitive Impairment Using Multiple Cortical Features from MR Images. Front Aging Neurosci 2017; 9:146. [PMID: 28572766 PMCID: PMC5435825 DOI: 10.3389/fnagi.2017.00146] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2016] [Accepted: 05/01/2017] [Indexed: 01/18/2023] Open
Abstract
Neuroimaging measurements derived from magnetic resonance imaging provide important information required for detecting changes related to the progression of mild cognitive impairment (MCI). Cortical features and changes play a crucial role in revealing unique anatomical patterns of brain regions, and further differentiate MCI patients from normal states. Four cortical features, namely, gray matter volume, cortical thickness, surface area, and mean curvature, were explored for discriminative analysis among three groups including the stable MCI (sMCI), the converted MCI (cMCI), and the normal control (NC) groups. In this study, 158 subjects (72 NC, 46 sMCI, and 40 cMCI) were selected from the Alzheimer's Disease Neuroimaging Initiative. A sparse-constrained regression model based on the l2-1-norm was introduced to reduce the feature dimensionality and retrieve essential features for the discrimination of the three groups by using a support vector machine (SVM). An optimized strategy of feature addition based on the weight of each feature was adopted for the SVM classifier in order to achieve the best classification performance. The baseline cortical features combined with the longitudinal measurements for 2 years of follow-up data yielded prominent classification results. In particular, the cortical thickness produced a classification with 98.84% accuracy, 97.5% sensitivity, and 100% specificity for the sMCI–cMCI comparison; 92.37% accuracy, 84.78% sensitivity, and 97.22% specificity for the cMCI–NC comparison; and 93.75% accuracy, 92.5% sensitivity, and 94.44% specificity for the sMCI–NC comparison. The best performances obtained by the SVM classifier using the essential features were 5–40% more than those using all of the retained features. The feasibility of the cortical features for the recognition of anatomical patterns was certified; thus, the proposed method has the potential to improve the clinical diagnosis of sub-types of MCI and predict the risk of its conversion to Alzheimer's disease.
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Affiliation(s)
- Shengwen Guo
- Department of Biomedical Engineering, South China University of TechnologyGuangzhou, China
| | - Chunren Lai
- Department of Biomedical Engineering, South China University of TechnologyGuangzhou, China
| | - Congling Wu
- Department of Biomedical Engineering, South China University of TechnologyGuangzhou, China
| | - Guiyin Cen
- Guangdong General HospitalGuangzhou, China
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Weiner MW, Veitch DP, Aisen PS, Beckett LA, Cairns NJ, Green RC, Harvey D, Jack CR, Jagust W, Morris JC, Petersen RC, Saykin AJ, Shaw LM, Toga AW, Trojanowski JQ. Recent publications from the Alzheimer's Disease Neuroimaging Initiative: Reviewing progress toward improved AD clinical trials. Alzheimers Dement 2017; 13:e1-e85. [PMID: 28342697 DOI: 10.1016/j.jalz.2016.11.007] [Citation(s) in RCA: 170] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Revised: 11/21/2016] [Accepted: 11/28/2016] [Indexed: 01/31/2023]
Abstract
INTRODUCTION The Alzheimer's Disease Neuroimaging Initiative (ADNI) has continued development and standardization of methodologies for biomarkers and has provided an increased depth and breadth of data available to qualified researchers. This review summarizes the over 400 publications using ADNI data during 2014 and 2015. METHODS We used standard searches to find publications using ADNI data. RESULTS (1) Structural and functional changes, including subtle changes to hippocampal shape and texture, atrophy in areas outside of hippocampus, and disruption to functional networks, are detectable in presymptomatic subjects before hippocampal atrophy; (2) In subjects with abnormal β-amyloid deposition (Aβ+), biomarkers become abnormal in the order predicted by the amyloid cascade hypothesis; (3) Cognitive decline is more closely linked to tau than Aβ deposition; (4) Cerebrovascular risk factors may interact with Aβ to increase white-matter (WM) abnormalities which may accelerate Alzheimer's disease (AD) progression in conjunction with tau abnormalities; (5) Different patterns of atrophy are associated with impairment of memory and executive function and may underlie psychiatric symptoms; (6) Structural, functional, and metabolic network connectivities are disrupted as AD progresses. Models of prion-like spreading of Aβ pathology along WM tracts predict known patterns of cortical Aβ deposition and declines in glucose metabolism; (7) New AD risk and protective gene loci have been identified using biologically informed approaches; (8) Cognitively normal and mild cognitive impairment (MCI) subjects are heterogeneous and include groups typified not only by "classic" AD pathology but also by normal biomarkers, accelerated decline, and suspected non-Alzheimer's pathology; (9) Selection of subjects at risk of imminent decline on the basis of one or more pathologies improves the power of clinical trials; (10) Sensitivity of cognitive outcome measures to early changes in cognition has been improved and surrogate outcome measures using longitudinal structural magnetic resonance imaging may further reduce clinical trial cost and duration; (11) Advances in machine learning techniques such as neural networks have improved diagnostic and prognostic accuracy especially in challenges involving MCI subjects; and (12) Network connectivity measures and genetic variants show promise in multimodal classification and some classifiers using single modalities are rivaling multimodal classifiers. DISCUSSION Taken together, these studies fundamentally deepen our understanding of AD progression and its underlying genetic basis, which in turn informs and improves clinical trial design.
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Affiliation(s)
- Michael W Weiner
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA; Department of Radiology, University of California, San Francisco, CA, USA; Department of Medicine, University of California, San Francisco, CA, USA; Department of Psychiatry, University of California, San Francisco, CA, USA; Department of Neurology, University of California, San Francisco, CA, USA.
| | - Dallas P Veitch
- Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, CA, USA
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | - Laurel A Beckett
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | - Nigel J Cairns
- Knight Alzheimer's Disease Research Center, Washington University School of Medicine, Saint Louis, MO, USA; Department of Neurology, Washington University School of Medicine, Saint Louis, MO, USA
| | - Robert C Green
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Danielle Harvey
- Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, CA, USA
| | | | - William Jagust
- Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, USA
| | - John C Morris
- Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA
| | | | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Leslie M Shaw
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Arthur W Toga
- Laboratory of Neuroimaging, Institute of Neuroimaging and Informatics, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - John Q Trojanowski
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Institute on Aging, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Alzheimer's Disease Core Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Udall Parkinson's Research Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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Foley SF, Tansey KE, Caseras X, Lancaster T, Bracht T, Parker G, Hall J, Williams J, Linden DEJ. Multimodal Brain Imaging Reveals Structural Differences in Alzheimer's Disease Polygenic Risk Carriers: A Study in Healthy Young Adults. Biol Psychiatry 2017; 81:154-161. [PMID: 27157680 PMCID: PMC5177726 DOI: 10.1016/j.biopsych.2016.02.033] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Revised: 02/08/2016] [Accepted: 02/29/2016] [Indexed: 01/02/2023]
Abstract
BACKGROUND Recent genome-wide association studies have identified genetic loci that jointly make a considerable contribution to risk of developing Alzheimer's disease (AD). Because neuropathological features of AD can be present several decades before disease onset, we investigated whether effects of polygenic risk are detectable by neuroimaging in young adults. We hypothesized that higher polygenic risk scores (PRSs) for AD would be associated with reduced volume of the hippocampus and other limbic and paralimbic areas. We further hypothesized that AD PRSs would affect the microstructure of fiber tracts connecting the hippocampus with other brain areas. METHODS We analyzed the association between AD PRSs and brain imaging parameters using T1-weighted structural (n = 272) and diffusion-weighted scans (n = 197). RESULTS We found a significant association between AD PRSs and left hippocampal volume, with higher risk associated with lower left hippocampal volume (p = .001). This effect remained when the APOE gene was excluded (p = .031), suggesting that the relationship between hippocampal volume and AD is the result of multiple genetic factors and not exclusively variability in the APOE gene. The diffusion tensor imaging analysis revealed that fractional anisotropy of the right cingulum was inversely correlated with AD PRSs (p = .009). We thus show that polygenic effects of AD risk variants on brain structure can already be detected in young adults. CONCLUSIONS This finding paves the way for further investigation of the effects of AD risk variants and may become useful for efforts to combine genotypic and phenotypic data for risk prediction and to enrich future prevention trials of AD.
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Affiliation(s)
- Sonya F Foley
- Cardiff University Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Wales, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Wales, United Kingdom; Central Biotechnology Services, TIME Institute, Wales, United Kingdom.
| | - Katherine E Tansey
- Cardiff University Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Wales, United Kingdom; Medical Research Council Integrative Epidemiology Unit, School of Social and Community Medicine, Faculty of Medicine & Dentistry, University of Bristol, Bristol, United Kingdom
| | - Xavier Caseras
- Cardiff University Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Wales, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Wales, United Kingdom
| | - Thomas Lancaster
- Cardiff University Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Wales, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Wales, United Kingdom
| | - Tobias Bracht
- Cardiff University Brain Research Imaging Centre, School of Psychology, Wales, United Kingdom
| | - Greg Parker
- Cardiff University Brain Research Imaging Centre, School of Psychology, Wales, United Kingdom
| | - Jeremy Hall
- Cardiff University Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Wales, United Kingdom; Neuroscience and Mental Health Research Institute, Wales, United Kingdom
| | - Julie Williams
- Cardiff University Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Wales, United Kingdom
| | - David E J Linden
- Cardiff University Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Wales, United Kingdom; Cardiff University Brain Research Imaging Centre, School of Psychology, Wales, United Kingdom
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Pini L, Pievani M, Bocchetta M, Altomare D, Bosco P, Cavedo E, Galluzzi S, Marizzoni M, Frisoni GB. Brain atrophy in Alzheimer's Disease and aging. Ageing Res Rev 2016; 30:25-48. [PMID: 26827786 DOI: 10.1016/j.arr.2016.01.002] [Citation(s) in RCA: 445] [Impact Index Per Article: 55.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2015] [Revised: 01/15/2016] [Accepted: 01/20/2016] [Indexed: 01/22/2023]
Abstract
Thanks to its safety and accessibility, magnetic resonance imaging (MRI) is extensively used in clinical routine and research field, largely contributing to our understanding of the pathophysiology of neurodegenerative disorders such as Alzheimer's disease (AD). This review aims to provide a comprehensive overview of the main findings in AD and normal aging over the past twenty years, focusing on the patterns of gray and white matter changes assessed in vivo using MRI. Major progresses in the field concern the segmentation of the hippocampus with novel manual and automatic segmentation approaches, which might soon enable to assess also hippocampal subfields. Advancements in quantification of hippocampal volumetry might pave the way to its broader use as outcome marker in AD clinical trials. Patterns of cortical atrophy have been shown to accurately track disease progression and seem promising in distinguishing among AD subtypes. Disease progression has also been associated with changes in white matter tracts. Recent studies have investigated two areas often overlooked in AD, such as the striatum and basal forebrain, reporting significant atrophy, although the impact of these changes on cognition is still unclear. Future integration of different MRI modalities may further advance the field by providing more powerful biomarkers of disease onset and progression.
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Affiliation(s)
- Lorenzo Pini
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Michela Pievani
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Martina Bocchetta
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London, UK
| | - Daniele Altomare
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Paolo Bosco
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Enrica Cavedo
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Sorbonne Universités, Université Pierre et Marie Curie, Paris 06, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A) Hôpital de la Pitié-Salpétrière & Institut du Cerveau et de la Moelle épinière (ICM), UMR S 1127, Hôpital de la Pitié-Salpétrière Paris & CATI Multicenter Neuroimaging Platform, France
| | - Samantha Galluzzi
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Moira Marizzoni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy
| | - Giovanni B Frisoni
- Laboratory Alzheimer's Neuroimaging & Epidemiology, IRCCS Fatebenefratelli, Brescia, Italy; Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, Geneva, Switzerland.
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Ganske S, Haussmann R, Gruschwitz A, Werner A, Osterrath A, Baumgaertel J, Lange J, Donix KL, Linn J, Donix M. Family History of Alzheimer's Disease and Cortical Thickness in Patients With Dementia. Am J Alzheimers Dis Other Demen 2016; 31:450-6. [PMID: 27303063 PMCID: PMC10852676 DOI: 10.1177/1533317516653827] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2024]
Abstract
A first-degree family history of Alzheimer's disease reflects genetic risks for the neurodegenerative disorder. Recent imaging data suggest localized effects of genetic risks on brain structure in healthy people. It is unknown whether this association can also be found in patients who already have dementia. Our aim was to investigate whether family history risk modulates regional medial temporal lobe cortical thickness in patients with Alzheimer's disease. We performed high-resolution magnetic resonance imaging and cortical unfolding data analysis on 54 patients and 53 nondemented individuals. A first-degree family history of Alzheimer's disease was associated with left hemispheric cortical thinning in the subiculum among patients and controls. The contribution of Alzheimer's disease family history to regional brain anatomy changes independent of cognitive impairment may reflect genetic risks that modulate onset and clinical course of the disease.
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Affiliation(s)
- Steffi Ganske
- Department of Psychiatry, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Robert Haussmann
- Department of Psychiatry, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Antonia Gruschwitz
- Department of Psychiatry, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Annett Werner
- Department of Neuroradiology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Antje Osterrath
- Department of Psychiatry, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany DZNE, German Center for Neurodegenerative Diseases, Dresden, Germany
| | - Johanna Baumgaertel
- Department of Psychiatry, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Jan Lange
- Department of Psychiatry, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Katharina L Donix
- Department of Psychiatry, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Jennifer Linn
- Department of Neuroradiology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany
| | - Markus Donix
- Department of Psychiatry, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany DZNE, German Center for Neurodegenerative Diseases, Dresden, Germany
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