Alzheimer's and cerebrovascular disease: the twin towers of dementia.
Joel Ramireza-b, Melissa F. Holmesa, Fuqiang Gaoa-b, and Sandra E. Blacka-c
With dementia prevalence on the rise, it is imperative to develop novel therapies and treatments to address the increasing recognition of the clinical and pathological overlap of Alzheimer’s and cerebrovascular disease - the top two leading causes of dementia. Although the research methods currently employed have made great advances towards our understanding of comorbid neurovascular and neurodegenerative diseases, these knowledge-based silos have had a tendency to operate in relative isolation. As our cumulative body of knowledge within each platform increases, so should the coordination of research. By examining current findings in neuroimaging, neuropsychology, genetics, neuropathology, and molecular neurobiology, this blanket-level mini-review will examine the spectrum of research findings that contributes to our understanding of Alzheimer’s and vascular contributions to dementia.
Given our aging population, dementia was recently recognized by the World Health Organization as a public health priority. With Alzheimer’s disease (AD) and vascular dementia (VaD) as the top two leading causes of dementia, the need to develop novel treatment targets and more aggressive management strategies has never been greater. Although AD and VaD frequently co-occur and share common risk factors, they present and progress heterogeneously, encompassing a broad range of complex neurovascular and neurodegenerative pathological processes and etiologies. Recent advances in neuroimaging, neuropsychology, genetics, neuropathology, and molecular neurobiology have led to the development of promising early biomarkers that have improved the diagnosis and prognostication of these co-contributors to dementia. However, despite these advances, research efforts often face challenges in bridging the interdisciplinary divide, often acting independently within cultural silos. To overcome these knowledge-translation obstacles the identification of future treatment targets may arise more effectively if our efforts turn towards the synthesis of findings across these knowledge platforms.
Advances in MRI segmentation techniques have yielded numerous useful biomarkers for measuring neurodegenerative and neurovascular burden in clinical and normal populations. The various combinations of these markers suggest that the vascular contributions to cognitive decline and AD neuropathology may be more closely related than previously thought. For example, the well-established AD markers of hippocampal volume1 and global atrophy were also recently acknowledged as significant MRI correlates of the cognitive dysfunctions outlined by the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network (NINDS-CSN) for vascular cognitive impairment2.
Additionally, recent findings suggest that small vessel disease, manifested as white matter hyperintensities of presumed vascular origin (WMH), can interact with beta amyloid (Aβ) pathology to negatively impact hippocampal volume in non-demented elderly3. A similar association with hippocampal atrophy was also demonstrated in AD patients with cholinergic hyperintensities4, an interesting relationship as cholinesterase deficits have also been demonstrated in VaD independent of concomitant AD pathology5. Advances in cortical thickness measurement that led to the identification of a cortical signature for AD6 have also yielded findings which suggest that vascular risk factors7 and WMH8,9 may also influence cortical thinning in AD and MCI signature regions, where watershed regions of vascular supply and rich club hubs of functional connectivity coincide10. Indeed, recent attention regarding the presence of WMH and lacunar infarcts in AD and MCI clinical populations, have led numerous international groups to acknowledge vasculopathy as a core feature that needs to be addressed if we are to move forward with the improvement of clinical outcomes in dementia11–14.
Although WMH and lacunar infarcts are the most commonly correlated markers of small vessel disease, measurements of other MRI-based small vessel disease markers have gained some recent attention15. As differential markers commonly associated with hypertensive arteriopathy and cerebral amyloid angiopathy (CAA), MRI-visible perivascular spaces (PVS)16, cerebral microbleeds17, superficial siderosis18, and cortical microinfarcts19, are commonly assessed using visual rating scales; although some recent progress has been shown towards the automatic segmentation PVS16,20. Given the significant overlap between CAA and AD, as well as the increased risk for stroke and intracerebral hemorrhage21,22, the importance of cerebral microbleeds and superficial siderosis has justifiably garnered significant attention23,24. Moreover, the modified Boston criteria for the clinicoradiological diagnosis of sporadic possible/probable CAA is partially based on the burden of lobar microbleeds and superficial siderosis observed on MRI25.
Moving beyond basic structural imaging markers, advanced neuroimaging techniques have also yielded novel findings that inform us on the overlap between neurodegenerative and neurovascular burden in clinical and elderly populations. Diffusion tensor imaging (DTI), an MRI-based measure of white matter structural integrity26, is increasingly being utilized in combination with markers of white matter small vessel disease burden and focal gray matter atrophy to assess structural and functional brain networks observed in cognitive impairment and AD27,28. Recently described as a cascading network failure in AD progression29, assessment of vascular disruptions along specific white matter tracts of the default mode network involves the use of ‘task-free’ functional MRI (fMRI), often in combination with structural imaging metrics30,31. Blood perfusion changes assessed by single photon emission computed tomography (SPECT), dynamic susceptibility contrast, and more recently, non-invasive arterial spin labelling (ASL) MRI, have also produced findings that further confirm the cerebral hypoperfusion and microvascular disease observed in AD32–35. Moreover, changes in the blood oxygen level-dependent (BOLD) signal in response to changes in end-tidal partial pressure of carbon dioxide (PETCO2) as a vasoactive stimulus has been used to assess cerebrovascular reactivity (CVR)36, providing additional insight to the progression of small vessel disease in dementia. Additionally, although still relatively understudied, as a literal ‘eye’ into the underlying Aβ, tauopathy, and vascular burden observed in the brain, recent findings have proposed the use of ocular and retinal abnormalities as novel non-invasive biomarkers in the study of AD and cerebrovascular disease37–39.
Moreover, many of these studies have included analyses that examine the associations between these imaging biomarkers and the presence of the apolipoprotein E epsilon 4 (APOE4) allele on chromosome 19, a strong genetic marker of AD40, which has recently been implicated to influence poor gait41, post-stroke cognitive decline42, and VaD43. Large scale genome wide association studies (GWAS) analysis on 74,046 individuals of European ancestry have identified at least 20 loci (including APOE) associated with late-onset AD44, although several rare genetic variants not detected by GWAS have also been suggested45–47. Genome wide meta-analyses of small vessel disease, stroke, and their shared genetic contributions with AD have also reported significant findings48,49; however, a full understanding of these genetic associations, the underlying mechanisms they represent, and how this information translates into therapeutics advances is still underway.
Several autopsy studies also report on the common comorbidity between cerebrovascular disease and AD pathology50–56, with some studies suggesting that vascular pathology may lower the threshold for dementia. Further analyses suggest several pathophysiological mechanisms including atherosclerosis in the Circle of Willis54,57, arteriosclerosis13, blood brain barrier dysfunction, pericyte loss58,59, hypoperfusion60, clasmatodendrosis61–63, and venous collagenosis64,65. Moreover, the common overlap of CAA and AD has been reported in several postmortem studies66–69, with one study combining two different longitudinal clinicopathological studies reporting CAA to be commonly (present in 79% of all cases), associated with increased odds of AD dementia, and an increased rate of decline in global cognition, perceptual speed, episodic and semantic memory70.
Finally, recent attention has focused on understanding the systems of the brain which are responsible for fluid circulation and the clearance of waste and neurotoxic proteins, such as the Aβ, α-synuclein, and hyperphosphorylated tau within neurofibrillary tangles, found in AD and other dementia pathologies71. Of particular interest is the glymphatic system72, a complex system of perivascular tunnels surrounding cerebral veins and arteries, where parenchymal waste and interstitial solute clearance is primarily driven by the astroglial water channel aquaporin-473. Interestingly, preclinical models have demonstrated that the glymphatic system is primarily engaged during sleep74, suggesting that poor sleep may be associated with poor waste clearance, which was recently supported by a small proof-of-concept study on cerebrovascular disease patients using MRI-visible perivascular spaces and polysomnography-derived sleep parameters75.
Parallel research based around the concept of protein elimination failure angiopathy (PEFA) in CAA and AD focuses on a cerebral waste clearance system driven by physiological functions around vascular basement membrane pathways76. Recent experiments using biotinylated and fluorescent Aβ injected into the hippocampus and tracers injected into the cerebrospinal fluid of mice suggest several basement membrane layer clearance pathways, possibly influenced by size, rigidity, and charge (positive/negative/neutral) of the particles being transported77.
As evidenced by this blanket-level mini-review of the findings from clinical, neuroimaging, genetics, pathology, and basic science research, it is hopefully evident that 100 more papers correlating WMH with another cognitive test will no longer be sufficient to contribute to our understanding of the overlap between neurovascular and neurodegenerative disease. While there are some gaps within each of these knowledge-based platforms, it would also be important to consider bridging the interdisciplinary gaps between them as well. In order to provide a more comprehensive understanding of the complex neurological disease processes resulting in dementia, future work should focus on international research collaborations with multi-modal, multi-platform, big data analytics. As a scientific community, we are now at a stage where effective progress towards therapeutic advances will likely arise from the analysis of progression data synthesized from numerous interdisciplinary platforms.
Acknowledgements & Funding
We gratefully acknowledge financial and salary support (JR, MFH, FG) from the Canadian Institutes of Health Research (#125740 & #13129), Heart & Stroke Foundation Canadian Partnership for Stroke Recovery, Hurvitz Brain Sciences Research program at Sunnybrook Research Institute and the Linda C. Campbell Foundation. JR additionally received partial funding from the Canadian Vascular Network and the Ontario Brain Institute’s Ontario Neurodegenerative Disease Research Initiative. Furthermore, we would like to graciously thank the Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Dept. of Medicine, and the Brill Chair Neurology, SHSC and Dept. of Medicine, University of Toronto for financial and salary support (SEB). Finally, we would like to thank Sabrina Adamo (Junior Imaging Analyst, Sunnybrook Research Institute) for her assistance with the literature review.
Conflict of Interest
JR, MFH and FG report no conflicts of interest. SEB reports institutional grants from GE Healthcare, Transition Therapeutics, Cognoptix, and Biogen Idec.
Duchesne S, Valdivia F, Robitaille N, et al. Manual segmentation qualification platform for the EADC-ADNI harmonized protocol for hippocampal segmentation project. Alzheimers Dement [online serial]. Epub 2015 Jan 21. Accessed at: http://www.ncbi.nlm.nih.gov/pubmed/25617509. Accessed February 17, 2015.
Wong A, Wang D, Black SE, et al. Volumetric magnetic resonance imaging correlates of the National Institute of Neurological Disorders and Stroke Canadian Stroke Network vascular cognitive impairment neuropsychology protocols. J Clin Exp Neuropsychol [online serial] Routledge. 2015; 37: 1004–1012. Accessed at: http://www.tandfonline.com/doi/full/10.1080/13803395.2015.1038983.
Freeze WM, Jacobs HIL, Gronenschild EH, et al. White Matter Hyperintensities Potentiate Hippocampal Volume Reduction in Non-Demented Older Individuals with Abnormal Amyloid-β. Ramirez J, editor. J Alzheimer’s Dis [online serial]. 2016;55:333–342. Accessed at: http://www.medra.org/servlet/aliasResolver?alias=iospress&doi=10.3233/JAD-160474. Accessed November 29, 2016.
McNeely AA, Ramirez J, Nestor SM, et al. Cholinergic Subcortical Hyperintensities in Alzheimer’s Disease Patients from the Sunnybrook Dementia Study Relationships with Cognitive Dysfunction and Hippocampal Atrophy. J Alzheimers Dis. 2015; 43: 785–796.
Roman GC. Facts myths and controversies in vascular dementia. J Neurol Sci. 2004; 226: 49–52.
Dickerson BC, Bakkour A, Salat DH, et al. The cortical signature of Alzheimer’s disease regionally specific cortical thinning relates to symptom severity in very mild to mild AD dementia and is detectable in asymptomatic amyloid positive individuals Cereb Cortex.
Tchistiakova E, MacIntosh BJ. Summative effects of vascular risk factors on cortical thickness in mild cognitive impairment. Neurobiol Aging [online serial]. 2016; 45: 98–106. Accessed at: http://linkinghub.elsevier.com/retrieve/pii/S019745801630080X. Accessed November 29, 2016.
Kim YJ, Kwon HK, Lee JM, et al. Gray and white matter changes linking cerebral small vessel disease to gait disturbances. Neurology [online serial]. Epub 2016; 1–10. Accessed at: http://www.neurology.org/cgi/doi/10.1212/WNL.0000000000002516.
Jacobs HIL, Clerx L, Gronenschild EHBM,et al. White matter hyperintensities are positively associated with cortical thickness in Alzheimer’s disease. J Alzheimer’s Dis. 2014; 39: 409–422.
Villeneuve S, Reed BR, Madison CM, et al. Vascular risk and Aβ interact to reduce cortical thickness in AD vulnerable brain regions. Neurology [online serial]. 2014; 83: 40–47. Accessed at: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4114172&tool=pmcentrez&rendertype=abstract.
Snyder HM, Corriveau RA, Craft S, et al. Vascular Contributions to Cognitive Impairment and Dementia Including Alzheimer’s Disease. Alzheimer’s Dement. 2015; 11: 710–717.
Lee S, Viqar F, Zimmerman ME, et al. White matter hyperintensities are a core feature of Alzheimer’s disease Evidence from the dominantly inherited Alzheimer network. Ann Neurol [online serial]. 2016; 79: 929–939. Accessed at: http://www.ncbi.nlm.nih.gov/pubmed/27016429. Accessed July 11, 2016.
Arvanitakis Z, Capuano AW, Leurgans SE, et al. Relation of cerebral vessel disease to Alzheimer’s disease dementia and cognitive function in elderly people: a cross-sectional study. Lancet Neurol [online serial]. 2016; 0: 1–10. Elsevier Ltd; Accessed at: http://dx.doi.org/10.1016/S1474-4422(16)30029-1.
Iturria Medina Y, Sotero RC, Toussaint PJ, et al. Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data driven analysis. Nat Commun [online serial]. 2016; 7: 11934. Accessed at: http://dx.doi.org/10.1038/ncomms11934.
Wardlaw JM, Smith EE, Biessels GJ, et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013; 12: 822–838.
Ramirez J, Berezuk C, McNeely AA, et al. Visible virchow-robin spaces on magnetic resonance imaging of Alzheimer’s disease patients and normal elderly from the sunnybrook dementia study. J Alzheimers Dis [online serial]. 2015; 43: 415–424. Accessed at: http://www.ncbi.nlm.nih.gov/pubmed/25096616. Accessed December 19, 2014.
Greenberg SM, Vernooij MW, Cordonnier C, et al. Cerebral microbleeds a guide to detection and interpretation. Lancet Neurol [online serial]. 2009; 8: 165–174. Elsevier Ltd;. Accessed at: http://dx.doi.org/10.1016/S1474-4422(09)70013-4.
Charidimou A, Jäger RH, Peeters A, et al. White matter perivascular spaces are related to cortical superficial siderosis in cerebral amyloid angiopathy. Stroke. 2014; 45: 2930–2935.
van Veluw SJ, Hilal S, Kuijf HJ, et al. Cortical microinfarcts on 3T MRI Clinical correlates in memory-clinic patients. Alzheimer’s Dement [online serial]. Epub 2015; 1–10. Accessed at: http://linkinghub.elsevier.com/retrieve/pii/S1552526015001235.
Hernandez MD, Piper RJ, Wang X, et al. Towards the automatic computational assessment of enlarged perivascular spaces on brain magnetic resonance images A systematic review. J Magn Reson Imaging. Brain Research Imaging. 2013; 38: 774-85.
Akoudad S, Portegies MLP, Koudstaal PJ, et al. Cerebral Microbleeds Are Associated with an Increased Risk of Stroke The Rotterdam Study. Circulation. 2015; 132: 509–516.
Charidimou A, Peeters A, Jager HR, et al. Cortical superficial siderosis and bleeding risk in cerebral amyloid angiopathy Multicentre MRI cohort study. Neurology. 2013; 81: 1666–1673.
Cordonnier C, van der Flier WM. Brain microbleeds and Alzheimer’s disease innocent observation or key player. Brain. 2011; 134: 335–344.
Charidimou A, Linn J, Vernooij MW, et al. Cortical superficial siderosis Detection and clinical significance in cerebral amyloid angiopathy and related conditions. Brain. 2015; 138: 2126–2139.
Boulouis G, Charidimou A, Greenberg SM. Sporadic Cerebral Amyloid Angiopathy Pathophysiology Neuroimaging Features and Clinical Implications. Semin Neurol. 2016; 36: 233–243.
Mori S, Zhang J. Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron. 2006; 51: 527–539.
Kim H-J, Im K, Kwon H, et al. Effects of amyloid and small vessel disease on white matter network disruption. J Alzheimers Dis [online serial]. 2015; 44: 963–975. Accessed at: http://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=25374100&retmode=ref&cmd=prlinks\npapers3://publication/doi/10.3233/JAD-141623.
Dyrba M, Grothe M, Kirste T, et al. Multimodal analysis of functional and structural disconnection in Alzheimer’s disease using multiple kernel SVM. Hum Brain Mapp [online serial]. 2015; 36: 2118–2131. Accessed at: http://doi.wiley.com/10.1002/hbm.22759. Accessed November 29, 2016.
Jones DT, Knopman DS, Gunter JL, et al. Cascading network failure across the Alzheimer’s disease spectrum. Brain. 2016; 139: 547–562.
Taylor ANW, Kambeitz Ilankovic L, Gesierich B, et al. Tract specific white matter hyperintensities disrupt neural network function in Alzheimer’s disease. Alzheimer’s Dement [online serial]. Epub 2016 Jul. Accessed at: http://linkinghub.elsevier.com/retrieve/pii/S1552526016326607. Accessed November 29, 2016.
Nestor SM, Miši? B, Ramirez J, et al. Small vessel disease is linked to disrupted structural network covariance in Alzheimer’s disease. Alzheimer’s Dement. 2017. E-Pub ahead of print.
Makedonov I, Black SE, Macintosh BJ. Cerebral small vessel disease in aging and Alzheimer’s disease a comparative study using MRI and SPECT. EurJNeurol. 2013; 20: 243–250.
Zhang Q, Stafford RB, Wang Z, et al. Microvascular perfusion based on arterial spin labeled perfusion MRI as a measure of vascular risk in Alzheimer’s disease. J Alzheimer’s Dis. 2012; 32: 677–687.
Love S, Miners JS. Cerebral Hypoperfusion and the Energy Deficit in Alzheimer’s Disease. Brain Pathol [online serial]. 2016; 26: 607–617. Accessed at: http://doi.wiley.com/10.1111/bpa.12401. Accessed November 29, 2016.
de la Torre JC. Cerebral Perfusion Enhancing Interventions A New Strategy for the Prevention of Alzheimer Dementia. Brain Pathol [online serial]. 2016; 26: 618–631. Accessed at: http://doi.wiley.com/10.1111/bpa.12405. Accessed November 29, 2016.
Sam K, Peltenburg B, Conklin J, et al. Cerebrovascular reactivity and white matter integrity. Neurology. 2016; 87: 2333–2339.
Javaid FZ, Brenton J, Guo L,et al. Visual and ocular manifestations of Alzheimer’s disease and their use as biomarkers for diagnosis and progression. Front Neurol. 2016; 7.
Hart NJ, Koronyo Y, Black KL, et al. Ocular indicators of Alzheimer’s exploring disease in the retina. Acta Neuropathol [online serial]. 2016; 132: 767–787. Springer Berlin Heidelberg; Accessed at: http://link.springer.com/10.1007/s00401-016-1613-6.
Taylor AM, MacGillivray TJ, Henderson RD, et al. Retinal Vascular Fractal Dimension, Childhood IQ and Cognitive Ability in Old Age The Lothian Birth Cohort Study 1936. Shiels PG editor. PLoS One [online serial]. 2015; 10: e0121119. Accessed at: http://dx.plos.org/10.1371/journal.pone.0121119. Accessed November 29, 2016.
Saykin AJ, Shen L, Yao X, et al. Genetic studies of quantitative MCI and AD phenotypes in ADNI Progress opportunities and plans. Alzheimer’s Dement [online serial]. 2015; 11: 792–814. Accessed at: http://linkinghub.elsevier.com/retrieve/pii/S1552526015001776. Accessed November 29, 2016.
Nadkarni NK, Perera S, Snitz BE, et al. Association of Brain Amyloid-β With Slow Gait in Elderly Individuals Without Dementia. JAMA Neurol [online serial]. 2016; 56: 1244–1251. Accessed at: http://archneur.jamanetwork.com/article.aspx?doi=10.1001/jamaneurol.2016.3474. Accessed November 29, 2016.
Rajan KB, Aggarwal NT, Schneider JA, et al. Role of APOE ε4 Allele and Incident Stroke on Cognitive Decline and Mortality. Alzheimer Dis Assoc Disord [online serial]. 2016; 30: 318–323. Accessed at: http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage&an=00002093-201610000-00005. Accessed November 29, 2016.
Skrobot OA, McKnight AJ, Passmore PA, et al. A Validation Study of Vascular Cognitive Impairment Genetics Meta Analysis Findings in an Independent Collaborative Cohort. J Alzheimer’s Dis. 2016; 53: 981–989.
Lambert JC, Ibrahim Verbaas CA, Harold D, et al. Meta analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer’s disease. Nat Genet [online serial]. 2013; 45: 1452–1458. Accessed at: http://www.nature.com/doifinder/10.1038/ng.2802. Accessed November 30, 2016.
Cruchaga C, Karch CM, Jin SC, et al. Rare coding variants in the phospholipase D3 gene confer risk for Alzheimer’s disease. Nature [online serial]. 2014; 505: 550–554. Accessed at: http://www.nature.com/doifinder/10.1038/nature12825. Accessed November 30, 2016.
Le Guennec K, Nicolas G, Quenez O, et al. ABCA7 rare variants and Alzheimer disease risk. Neurology [online serial]. 2016; 86: 2134–2137. Accessed at: http://www.neurology.org/lookup/doi/10.1212/WNL.0000000000002627. Accessed November 30, 2016.
Colonna M, Wang Y. TREM2 variants: new keys to decipher Alzheimer disease pathogenesis. Nat Rev Neurosci [online serial]. 2016; 17: 201–207. Accessed at: http://www.nature.com/doifinder/10.1038/nrn.2016.7. Accessed November 30, 2016.
Neurology Working Group of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, Stroke Genetics Network (SiGN), International Stroke Genetics Consortium (ISGC). Identification of additional risk loci for stroke and small vessel disease: a meta-analysis of genome wide association studies. Lancet Neurol [online serial]. 2016; 15: 695–707. Accessed at: http://www.ncbi.nlm.nih.gov/pubmed/27068588. Accessed December 5, 2016.
Traylor M, Adib-Samii P, Harold D, et al. Shared genetic contribution to ischemic stroke and Alzheimer’s disease. Ann Neurol [online serial]. 2016; 79: 739–747. Accessed at: http://www.ncbi.nlm.nih.gov/pubmed/26913989. Accessed December 5, 2016.
Snowdon DA, Greiner LH, Mortimer JA, et al. Brain infarction and the clinical expression of Alzheimer disease. The Nun Study. J Am Med Assoc. 1997; 277: 813–817.
Lim A, Tsuang D, Kukull W, et al. Clinico neuropathological correlation of Alzheimer’s disease in a community based case series. J Am Geriatr Soc. 1999; 47: 564–569. Geriatric Research, Education, and Clinical Center, VA Puget Sound Health Care System, Seattle, Washington 98108, USA;
Toledo JB, Arnold SE, Raible K, et al. Contribution of cerebrovascular disease in autopsy confirmed neurodegenerative disease cases in the National Alzheimer’s Coordinating Centre. Brain. 2013; 136: 2697–2706.
Fernando MS, Ince PG. Vascular pathologies and cognition in a population-based cohort of elderly people. J Neurol Sci [online serial]. 2004; 226: 13–17. Accessed at: http://www.ncbi.nlm.nih.gov/pubmed/15537512. Accessed July 21, 2014.
Yarchoan M, Xie SX, Kling MA, et al. Cerebrovascular atherosclerosis correlates with Alzheimer pathology in neurodegenerative dementias. Brain. 2012; 135: 3749–3756.
Attems J, Jellinger KA. The overlap between vascular disease and Alzheimer’s disease lessons from pathology. BMC Med [online serial]. 2014; 12: 206. Accessed at: http://www.biomedcentral.com/1741-7015/12/206.
Schneider JA, Arvanitakis Z, Bang W, et al. Mixed brain pathologies account for most dementia cases in community-dwelling older persons. Neurology. 2007; 69: 2197–2204. From Rush Alzheimer’s Disease Center and Rush Institute for Healthy Aging (J.A.S., Z.A., W.B., D.A.B.), Department of Neurological Sciences (J.A.S., Z.A., D.A.B.), and Department of Pathology (J.A.S.), Rush University Medical Center, Chicago, IL;
Roher AE, Tyas SL, Maarouf CL, et al. Intracranial atherosclerosis as a contributing factor to Alzheimer’s disease dementia. Alzheimer’s Dement [online serial]. 2011; 7: 436–444. Accessed at: http://linkinghub.elsevier.com/retrieve/pii/S1552526010024210. Accessed November 30, 2016.
Simpson JE, Hosny O, Wharton SB, et al. Microarray RNA expression analysis of cerebral white matter lesions reveals changes in multiple functional pathways. Stroke. 2009; 40: 369–375.
Halliday MR, Rege SV, Ma Q, et al. Accelerated pericyte degeneration and blood brain barrier breakdown in apolipoprotein E4 carriers with Alzheimer’s disease. J Cereb Blood Flow Metab [online serial]. Epub 2015 Mar 11. Accessed at: http://jcb.sagepub.com/lookup/doi/10.1038/jcbfm.2015.44. Accessed November 30, 2016.
Fernando MS, Simpson JE, Matthews F, et al. White matter lesions in an unselected cohort of the elderly molecular pathology suggests origin from chronic hypoperfusion injury. Stroke. 2006; 37: 1391–1398.
Chen A, Akinyemi RO, Hase Y, et al. Frontal white matter hyperintensities clasmatodendrosis and gliovascular abnormalities in ageing and post-stroke dementia. Brain. Epub 2016; 1–17.
Sahlas DJ, Bilbao JM, Swartz RH, et al. Clasmatodendrosis correlating with periventricular hyperintensity in mixed dementia. Ann Neurol. 2002; 52: 378–381.
Simpson JE, Fernando MS, Clark L, et al. White matter lesions in an unselected cohort of the elderly astrocytic microglial and oligodendrocyte precursor cell responses. Neuropathol Appl Neurobiol. 2007; 33: 410–419.
Moody DM, Brown WR, Challa VR, et al. Periventricular venous collagenosis association with leukoaraiosis. Radiology. 1995; 194: 469–476.
Black SE, Gao FQ, Bilbao J. Understanding white matter disease Imaging-pathological correlations in vascular cognitive impairment. Stroke. 2009; 40: S48–S52.
Schrag M, McAuley G, Pomakian J, et al. Correlation of hypointensities in susceptibility weighted images to tissue histology in dementia patients with cerebral amyloid angiopathy a postmortem MRI study. Acta Neuropathol. 2010; 119: 291–302.
Zabel M, Schrag M, Crofton A, et al. A Shift in Microglial β-Amyloid Binding in Alzheimer’s Disease Is Associated with Cerebral Amyloid Angiopathy. Brain Pathol [online serial]. 2013; 23: 390–401. Accessed at: http://doi.wiley.com/10.1111/bpa.12005. Accessed November 30, 2016.
Brenowitz WD, Nelson PT, Besser LM, et al. Cerebral amyloid angiopathy and its co occurrence with Alzheimer’s disease and other cerebrovascular neuropathologic changes. Neurobiol Aging [online serial]. 2015; 36: 2702–2708. Elsevier Inc; Accessed at: http://dx.doi.org/10.1016/j.neurobiolaging.2015.06.028.
Attems J, Quass M, Jellinger KA, et al. Topographical distribution of cerebral amyloid angiopathy and its effect on cognitive decline are influenced by Alzheimer disease pathology. J Neurol Sci. 2007; 257: 49–55.
Boyle PA, Yu L, Nag S, et al. Cerebral amyloid angiopathy and cognitive outcomes in community based older persons. Neurology. 2015; 85: 1930–1936.
Tarasoff Conway JM, Carare RO, Osorio RS, et al. Clearance systems in the brain-implications for Alzheimer disease. Nat Rev Neurol [online serial]. 2015; 11: 457–470. Accessed at: http://www.ncbi.nlm.nih.gov/pubmed/26195256. Accessed November 18, 2015.
Jessen NA, Munk ASF, Lundgaard I, et al. The Glymphatic System: A Beginner’s Guide. Neurochem Res [online serial]. Epub 2015; 1–17. Springer US; Accessed at: http://link.springer.com/article/10.1007/s11064-015-1581-6.
Zeppenfeld DM, Simon M, Haswell JD, et al. Association of Perivascular Localization of Aquaporin-4 With Cognition and Alzheimer Disease in Aging Brains. JAMA Neurol [online serial]. 2016; Epub ahead: 1–9. Accessed at: http://archneur.jamanetwork.com/article.aspx?doi=10.1001/jamaneurol.2016.4370.
Xie L, Kang H, Xu Q, et al. Sleep drives metabolite clearance from the adult brain. Science (80- ) [online serial]. 2013; 342: 373–377. Accessed at: http://www.ncbi.nlm.nih.gov/pubmed/24136970.
Berezuk C, Ramirez J, Gao F, et al. Virchow Robin Spaces? Correlations with Polysomnography Derived Sleep Parameters. Sleep. 2015; 38: 853–858.
Carare RO, Kalaria R. Cerebrovascular pathology the dark side of neurodegeneration. Acta Neuropathol [online serial]. 2016; 131: 641–643. Springer Berlin Heidelberg; Accessed at: "http://dx.doi.org/10.1007/s00401-016-1573-x.
Morris AWJ, Sharp MM, Albargothy NJ, et al. Vascular basement membranes as pathways for the passage of fluid into and out of the brain. Acta Neuropathol [online serial]. 2016; 131: 1–12. Springer Berlin Heidelberg; Accessed at: "http://dx.doi.org/10.1007/s00401-016-1555-z.