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1.  Defining the Domain of Geriatric Medicine in an Urban Public Health System Affiliated with an Academic Medical Center 
The American Geriatrics Society has recommended a reexamination of the roles and deployment of providers with expertise in geriatric medicine. Healthcare systems use a variety of strategies to maximize their geriatric expertise. In general, these health systems tend to focus geriatric medicine resources on a group of older adults that are locally defined as the most in need. This article describes a model of care within an academic urban public health system and describes how local characteristics interact to define the domain of geriatric medicine. This domain is defined using 4 years of data from an electronic medical record combined with data collected from clinical trials.
From January 2002 to December 2005, 31,443 adults aged 65 and older were seen at any clinical site within this healthcare system. The mean age was 75 (range 65–105); 61% were women; 35% African American, and 2% Hispanic. The payer mix was 80% Medicare and 17% Medicaid. The local geriatric medicine program includes sites of care in inpatient, ambulatory, nursing home, and home-based settings.
By design, this geriatric medicine clinical practice complements the care provided to older adults by the primary care practice. Primary care physicians tend to cede care to geriatric medicine for older adults with advanced disability or geriatric syndromes. This is most apparent for older adults in nursing facilities or those requiring home-based care. There is a dynamic interplay between design features, reputation, and capacity that modulates volume, location, and type of patients seen by geriatrics.
PMCID: PMC4569869  PMID: 18795983
geriatric medicine; healthcare system; physician manpower
2.  Voxel-wise co-analysis of macro- and microstructural brain alteration in Mild Cognitive Impairment and Alzheimer’s disease using anatomical and diffusion MRI 
Background and Purpose
To determine if a voxel-wise “co-analysis” of structural and diffusion tensor magnetic resonance imaging (MRI) together reveals additional brain regions affected in mild cognitive impairment (MCI) and Alzheimer’s Disease (AD) than voxel-wise analysis of the individual MRI modalities alone.
Twenty-one patients with MCI, 21 patients with AD, and 21 cognitively normal healthy elderly were studied with MRI. Maps of deformation and fractional anisotropy (FA) were computed and used as dependent variables in univariate and multivariate statistical models.
Univariate voxel-wise analysis of macrostructural changes in MCI showed atrophy in the right anterior temporal lobe, left posterior parietal/precuneus region, WM adjacent to the cingulate gyrus, and dorsolateral prefrontal regions, consistent with prior research. Univariate voxel-wise analysis of microstructural changes in MCI showed reduced FA in the left posterior parietal region extending into the corpus callosum, consistent with previous work. The multivariate analysis, which provides more information than univariate tests when structural and FA measures are correlated, revealed additional MCI-related changes in corpus callosum and temporal lobe.
These results suggest that in corpus callosum and temporal regions macro- and microstructural variations in MCI can be congruent, providing potentially new insight into the mechanisms of brain tissue degeneration.
PMCID: PMC3951683  PMID: 23421601
MANOVA; deformation morphometry; fractional anisotropy; multimodality imaging; multivariate statistics; univariate statistics
3.  Brain Amyloid-β Burden Is Associated with Disruption of Intrinsic Functional Connectivity within the Medial Temporal Lobe in Cognitively Normal Elderly 
The Journal of Neuroscience  2015;35(7):3240-3247.
The medial temporal lobe is implicated as a key brain region involved in the pathogenesis of Alzheimer's disease (AD) and consequent memory loss. Tau tangle aggregation in this region may develop concurrently with cortical Aβ deposition in preclinical AD, but the pathological relationship between tau and Aβ remains unclear. We used task-free fMRI with a focus on the medical temporal lobe, together with Aβ PET imaging, in cognitively normal elderly human participants. We found that cortical Aβ load was related to disrupted intrinsic functional connectivity of the perirhinal cortex, which is typically the first brain region affected by tauopathies in AD. There was no concurrent association of cortical Aβ load with cognitive performance or brain atrophy. These findings suggest that dysfunction in the medial temporal lobe may represent a very early sign of preclinical AD and may predict future memory loss.
PMCID: PMC4331637  PMID: 25698758
Alzheimer's disease; amyloid; hippocampus; perirhinal cortex
4.  Errors in Completion of Referrals among Urban Older Adults in Ambulatory Care 
Clinical care often requires referrals, but many referrals never result in completed evaluations. We determined the extent to which referral-based consultations were completed in a U.S. medical institution. Factors associated with completion were identified.
In cross-sectional analysis, we analyzed billing records and electronic and paper-based medical records, for patients 65 or more years of age receiving healthcare between July 2000 and June 2002 in an integrated, urban, tax-supported medical institution on an academic campus. All referrals in ambulatory care, scheduling of consultation within 180 days, and completion were assessed. We conducted multivariate survival analysis to identify factors associated with completion.
We identified 6,785 patients with encounters. Mean age was 72 years, with 66% women, 55% African-American, and 32% Medicaid-eligible. Of 81% with at least one primary-care visit in ambulatory care, 63% had at least one referral. About 8% of referrals required multiple orders before an appointment was scheduled. Among 7,819 orders for specialty consultation in ambulatory care, 71% led to appointments, and 70% of appointments were kept (completed = 0.71*0.70 or 50%). Scheduling of consultations varied (12% to 90%) by specialty. Medicare, singular orders, location of referral, and lack of hospitalization were independently significantly associated scheduling of appointments.
Among older adults studied, half of medical specialty referrals were not completed. Multiple process errors likely contribute to these results, including missing information, misguided referrals, and faulty communications. Information systems offer important opportunities to improve the referrals process.
PMCID: PMC4469338  PMID: 20367818
Referral and Consultation; Geriatrics; Medical Errors; Scheduling; Primary Care
5.  Accelerated vs. unaccelerated serial MRI based TBM-SyN measurements for Clinical Trials in Alzheimer’s disease 
NeuroImage  2015;113:61-69.
Our primary objective was to compare the performance of unaccelerated vs. accelerated structural MRI for measuring disease progression using serial scans in Alzheimer’s disease (AD).
We identified cognitively normal (CN), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) and AD subjects from all available Alzheimer’s Disease Neuroimaging Initiative (ADNI) subjects with usable pairs of accelerated and unaccelerated scans. There were a total of 696 subjects with baseline and 3 month scans, 628 subjects with baseline and 6 month scans and 464 subjects with baseline and 12 month scans available. We employed the Symmetric Diffeomorphic Image Normalization method (SyN) for normalization of the serial scans to obtain Tensor Based Morphometry (TBM) maps which indicate the structural changes between pairs of scans. We computed a TBM-SyN summary score of annualized structural changes over 31 regions of interest (ROIs) that are characteristically affected in AD. TBM-SyN scores were computed using accelerated and unaccelerated scan pairs and compared in terms of agreement, group-wise discrimination, and sample size estimates for a hypothetical therapeutic trial.
We observed a number of systematic differences between TBM-SyN scores computed from accelerated and unaccelerated pairs of scans. TBM-SyN scores computed from accelerated scans tended to have overall higher estimated values than those from unaccelerated scans. However, the performance of accelerated scans was comparable to unaccelerated scans in terms of discrimination between clinical groups and sample sizes required in each clinical group for a therapeutic trial. We also found that the quality of both accelerated vs. unaccelerated scans were similar.
Accelerated scanning protocols reduce scan time considerably. Their group-wise discrimination and sample size estimates were comparable to those obtained with unaccelerated scans. The two protocols did not produce interchangeable TBM-SyN estimates, so it is arguably important to use either accelerated pairs of scans or unaccelerated pairs of scans throughout the study duration.
PMCID: PMC4456670  PMID: 25797830
6.  Smoking and increased Alzheimer’s disease risk: A review of potential mechanisms 
Cigarette smoking has been linked with both increased and decreased risk for Alzheimer’s disease (AD). This is relevant for the US military because the prevalence of smoking in the military is approximately 11% higher than in civilians.
Systematic review of published studies on the association between smoking and increased risk for AD, and preclinical and human literature on the relationships between smoking, nicotine exposure and AD-related neuropathology. Original data from comparisons of smoking and never-smoking cognitively normal elders on in vivo amyloid imaging are also presented.
Overall, the literature indicates that former/active smoking is related to a significantly increased risk for AD. Cigarette smoke/smoking is associated with AD neuropathology in preclinical models and humans. Smoking-related cerebral oxidative stress is a potential mechanism promoting AD pathophysiology and increased risk for AD.
A reduction in the incidence of smoking will likely reduce the future prevalence of AD.
PMCID: PMC4098701  PMID: 24924665
7.  Are hippocampal size differences in posttraumatic stress disorder mediated by sleep pathology? 
Posttraumatic stress disorder (PTSD) is associated with smaller volumes of the hippocampus, as has been demonstrated by meta-analyses. Proposed mechanistic relationships are reviewed briefly, including the hypothesis that sleep disturbances mediate the effects of PTSD on hippocampal volume. Evidence for this includes findings that insomnia and restricted sleep are associated with changes in hippocampal cell regulation and impairments in cognition. We present results of a new study of 187 subjects in whom neither PTSD nor poor sleep was associated with lower hippocampal volume. We outline a broad research agenda centered on the hypothesis that sleep changes mediate the relationship between PTSD and hippocampal volume.
PMCID: PMC4111235  PMID: 24924666
Neurogenesis; Neuroimaging; Dementia; Veterans; Sleep; PTSD
8.  The Preclinical Alzheimer Cognitive Composite 
JAMA neurology  2014;71(8):961-970.
As Alzheimer disease (AD) research moves to intervene in presymptomatic phases of the disease, we must develop outcome measures sensitive to the earliest disease-related changes.
To demonstrate the feasibility of a cognitive composite outcome for clinically normal elderly participants with evidence of AD pathology using the ADCS Preclinical Alzheimer Cognitive Composite (ADCS-PACC). The ADCS-PACC combines tests that assess episodic memory, timed executive function, and global cognition. The ADCS-PACC is the primary outcome measure for the first clinical trial in preclinical AD (ie, the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s study).
With the ADCS-PACC, we derive pilot estimates of amyloid-related decline using data from 2 observational studies conducted in North America and another conducted in Australia. The participants analyzed had normal cognition and mean ages of 75.81, 71.37, and 79.42 years across the 3 studies.
For the 2 studies that collected data on Aβ levels (ADNI and AIBL), we estimate decline in a preclinical AD “Aβ-positive” placebo group and compare them with an “Aβ-negative” group. For the study that did not include data on Aβ levels (the ADCS Prevention Instrument [ADCS-PI] study), we grouped participants by the presence of APOE-ɛ4 and by clinical progression.
In ADNI, Aβ-positive participants showed more decline than did Aβ-negative participants with regard to the ADCS-PACC score at 24 months (mean [SE] difference, −1.239 [0.522] [95% CI, −2.263 to −0.215]; P = .02). In AIBL, the mean (SE) difference is significant at both 18 months (−1.009 [0.406] [95% CI, −1.805 to −0.213]; P = .01) and 36 months (−1.404 [0.452] [95% CI, −2.290 to −0.519]; P = .002). In the ADCS-PI study, APOE-ɛ4 allele carriers performed significantly worse on the ADCS-PACC at 24 months (mean [SE] score, −0.742 [0.294] [95% CI, −1.318 to −0.165]; P = .01) and 36 months (−1.531 [0.469] [95% CI, −2.450 to −0.612]; P = .001). In the ADCS-PI study, cognitively normal participants who progress from a global Clinical Dementia Rating score of 0 are significantly worse on the ADCS-PACC than cognitively normal participants who are stable with a global Clinical Dementia Rating score of 0 at months 12, 24, and 36 (mean [SE] ADCS-PACC score, −4.471 [0.702] [95% CI, −5.848 to −3.094]; P < .001). Using pilot estimates of variance and assuming 500 participants per group with 30% attrition and a 5% α level, we project 80% power to detect effects in the range of Δ = 0.467 to 0.733 on the ADCS-PACC.
Analyses of at-risk cognitively normal populations suggest that we can reliably measure the first signs of cognitive decline with the ADCS-PACC. These analyses also suggest the feasibility of secondary prevention trials.
PMCID: PMC4439182  PMID: 24886908
9.  CSF Apo-E levels associate with cognitive decline and MRI changes 
Acta neuropathologica  2014;127(5):621-632.
Apolipoprotein E (APOE) ε4 allele is the most important genetic risk factor for Alzheimer’s disease (AD) and it is thought to do so by modulating levels of the its product, apolipoprotein E (Apo-E), and regulating amyloid-β (Aβ) clearance. However, information on clinical and biomarker correlates of Apo-E proteins is scarce. We examined the relationship of cerebrospinal fluid (CSF) and plasma Apo-E protein levels, and APOE genotype to cognition and AD biomarker changes in 311 AD Neuroimaging Initiative (ADNI) subjects with CSF Apo-E measurements and 565 subjects with plasma Apo-E measurements. At baseline, higher CSF Apo-E levels were associated with higher total and phosphorylated CSF tau levels. CSF Apo-E levels were associated with longitudinal cognitive decline, MCI conversion to dementia, and grey matter atrophy rate in total tau/Aβ1–42 ratio and APOE genotype adjusted analyses. In analyses stratified by APOE genotype, our results were only significant in the group without the ε4 allele. Baseline CSF Apo-E levels did not predict longitudinal CSF Aβ or tau changes. Plasma Apo-E levels show a mild correlation with CSF Apo-E levels, but were not associated with longitudinal cognitive and MRI changes. Based on our analyses, we speculate that increased CSF Apo-E2 or -E3 levels might represent a protective response to injury in AD and may have neuroprotective effects by decreasing neuronal damage independent of tau and amyloid deposition in addition to its effects on amyloid clearance.
PMCID: PMC3988233  PMID: 24385135
cerebrospinal fluid; plasma; dementia; beta amyloid; tau; MRI; dementia; neurodegeneration; Alzheimer’s Disease; APOE
10.  Association of brain amyloid-β with cerebral perfusion and structure in Alzheimer’s disease and mild cognitive impairment 
Brain  2014;137(5):1550-1561.
Patients with Alzheimer’s disease show reduced cerebral blood flow, but it is unclear how this relates to β-amyloid pathology. By comparing patients with Alzheimer’s dementia, mild cognitive impairment, and controls, Mattsson et al. show that high β-amyloid load is associated with increased atrophy and reduced perfusion, independent of diagnosis.
Patients with Alzheimer’s disease have reduced cerebral blood flow measured by arterial spin labelling magnetic resonance imaging, but it is unclear how this is related to amyloid-β pathology. Using 182 subjects from the Alzheimer’s Disease Neuroimaging Initiative we tested associations of amyloid-β with regional cerebral blood flow in healthy controls (n = 51), early (n = 66) and late (n = 41) mild cognitive impairment, and Alzheimer’s disease with dementia (n = 24). Based on the theory that Alzheimer’s disease starts with amyloid-β accumulation and progresses with symptoms and secondary pathologies in different trajectories, we tested if cerebral blood flow differed between amyloid-β-negative controls and -positive subjects in different diagnostic groups, and if amyloid-β had different associations with cerebral blood flow and grey matter volume. Global amyloid-β load was measured by florbetapir positron emission tomography, and regional blood flow and volume were measured in eight a priori defined regions of interest. Cerebral blood flow was reduced in patients with dementia in most brain regions. Higher amyloid-β load was related to lower cerebral blood flow in several regions, independent of diagnostic group. When comparing amyloid-β-positive subjects with -negative controls, we found reductions of cerebral blood flow in several diagnostic groups, including in precuneus, entorhinal cortex and hippocampus (dementia), inferior parietal cortex (late mild cognitive impairment and dementia), and inferior temporal cortex (early and late mild cognitive impairment and dementia). The associations of amyloid-β with cerebral blood flow and volume differed across the disease spectrum, with high amyloid-β being associated with greater cerebral blood flow reduction in controls and greater volume reduction in late mild cognitive impairment and dementia. In addition to disease stage, amyloid-β pathology affects cerebral blood flow across the span from controls to dementia patients. Amyloid-β pathology has different associations with cerebral blood flow and volume, and may cause more loss of blood flow in early stages, whereas volume loss dominates in late disease stages.
PMCID: PMC3999717  PMID: 24625697
Alzheimer’s disease; beta-amyloid; PET imaging; perfusion imaging; magnetic resonance imaging
11.  Emerging β-Amyloid Pathology and Accelerated Cortical Atrophy 
JAMA neurology  2014;71(6):725-734.
The effect of β-amyloid (Aβ) accumulation on regional structural brain changes in early stages of Alzheimer disease (AD) is not well understood.
To test the hypothesis that the development of Aβ pathology is related to increased regional atrophy in the brains of cognitively normal (CN) persons.
Design, Setting, and Participants
Longitudinal clinicobiomarker cohort study involving 47 CN control subjects and 15 patients with AD dementia. All participants underwent repeated cerebrospinal fluid Aβ42 and structural magnetic resonance imaging measurements for up to 4 years. Cognitively normal controls were classified using the longitudinal cerebrospinal fluid Aβ42 data and included 13 stable Aβ negative (normal baseline Aβ42 levels, with less than the median reduction over time), 13 declining Aβ negative (normal baseline Aβ42 levels, with greater than the median reduction over time), and 21 Aβ positive (pathologic baseline Aβ42 levels). All 15 patients with AD dementia were Aβ positive.
Main Outcomes and Measures
Group effects on regional gray matter volumes at baseline and over time, tested by linear mixed-effects models.
Baseline gray matter volumes were similar among the CN Aβ groups, but atrophy rates were increased in frontoparietal regions in the declining Aβ-negative and Aβ-positive groups and in amygdala and temporal regions in the Aβ-positive group. Aβ-positive patients with AD dementia had further increased atrophy rates in hippocampus and temporal and cingulate regions.
Conclusions and Relevance
Emerging Aβ pathology is coupled to increased frontoparietal (but not temporal) atrophy rates. Atrophy rates peak early in frontoparietal regions but accelerate in hippocampus, temporal, and cingulate regions as the disease progresses to dementia. Early-stage Aβ pathology may have mild effects on local frontoparietal cortical integrity while effects in temporal regions appear later and accelerate, leading to the atrophy pattern typically seen in AD.
PMCID: PMC4410966  PMID: 24781145
12.  Effects of traumatic brain injury and posttraumatic stress disorder on Alzheimer’s disease in veterans, using the Alzheimer’s Disease Neuroimaging Initiative 
Both traumatic brain injury (TBI) and posttraumatic stress disorder (PTSD) are common problems resulting from military service, and both have been associated with increased risk of cognitive decline and dementia resulting from Alzheimer’s disease (AD) or other causes. This study aims to use imaging techniques and biomarker analysis to determine whether traumatic brain injury (TBI) and/or PTSD resulting from combat or other traumas increase the risk for AD and decrease cognitive reserve in Veteran subjects, after accounting for age. Using military and Department of Veterans Affairs records, 65 Vietnam War veterans with a history of moderate or severe TBI with or without PTSD, 65 with ongoing PTSD without TBI, and 65 control subjects are being enrolled in this study at 19 sites. The study aims to select subject groups that are comparable in age, gender, ethnicity, and education. Subjects with mild cognitive impairment (MCI) or dementia are being excluded. However, a new study just beginning, and similar in size, will study subjects with TBI, subjects with PTSD, and control subjects with MCI. Baseline measurements of cognition, function, blood, and cerebrospinal fluid bio-markers; magnetic resonance images (structural, diffusion tensor, and resting state blood-level oxygen dependent (BOLD) functional magnetic resonance imaging); and amyloid positron emission tomographic (PET) images with florbetapir are being obtained. One-year follow-up measurements will be collected for most of the baseline procedures, with the exception of the lumbar puncture, the PET imaging, and apolipoprotein E genotyping. To date, 19 subjects with TBI only, 46 with PTSD only, and 15 with TBI and PTSD have been recruited and referred to 13 clinics to undergo the study protocol. It is expected that cohorts will be fully recruited by October 2014. This study is a first step toward the design and statistical powering of an AD prevention trial using at-risk veterans as subjects, and provides the basis for a larger, more comprehensive study of dementia risk factors in veterans.
PMCID: PMC4392759  PMID: 24924673
Traumatic brain injury; Posttraumatic stress disorder; Alzheimer’s disease; Veterans; Neuroimaging
13.  Common genetic variants influence human subcortical brain structures 
Hibar, Derrek P. | Stein, Jason L. | Renteria, Miguel E. | Arias-Vasquez, Alejandro | Desrivières, Sylvane | Jahanshad, Neda | Toro, Roberto | Wittfeld, Katharina | Abramovic, Lucija | Andersson, Micael | Aribisala, Benjamin S. | Armstrong, Nicola J. | Bernard, Manon | Bohlken, Marc M. | Boks, Marco P. | Bralten, Janita | Brown, Andrew A. | Chakravarty, M. Mallar | Chen, Qiang | Ching, Christopher R. K. | Cuellar-Partida, Gabriel | den Braber, Anouk | Giddaluru, Sudheer | Goldman, Aaron L. | Grimm, Oliver | Guadalupe, Tulio | Hass, Johanna | Woldehawariat, Girma | Holmes, Avram J. | Hoogman, Martine | Janowitz, Deborah | Jia, Tianye | Kim, Sungeun | Klein, Marieke | Kraemer, Bernd | Lee, Phil H. | Olde Loohuis, Loes M. | Luciano, Michelle | Macare, Christine | Mather, Karen A. | Mattheisen, Manuel | Milaneschi, Yuri | Nho, Kwangsik | Papmeyer, Martina | Ramasamy, Adaikalavan | Risacher, Shannon L. | Roiz-Santiañez, Roberto | Rose, Emma J. | Salami, Alireza | Sämann, Philipp G. | Schmaal, Lianne | Schork, Andrew J. | Shin, Jean | Strike, Lachlan T. | Teumer, Alexander | van Donkelaar, Marjolein M. J. | van Eijk, Kristel R. | Walters, Raymond K. | Westlye, Lars T. | Whelan, Christopher D. | Winkler, Anderson M. | Zwiers, Marcel P. | Alhusaini, Saud | Athanasiu, Lavinia | Ehrlich, Stefan | Hakobjan, Marina M. H. | Hartberg, Cecilie B. | Haukvik, Unn K. | Heister, Angelien J. G. A. M. | Hoehn, David | Kasperaviciute, Dalia | Liewald, David C. M. | Lopez, Lorna M. | Makkinje, Remco R. R. | Matarin, Mar | Naber, Marlies A. M. | McKay, D. Reese | Needham, Margaret | Nugent, Allison C. | Pütz, Benno | Royle, Natalie A. | Shen, Li | Sprooten, Emma | Trabzuni, Daniah | van der Marel, Saskia S. L. | van Hulzen, Kimm J. E. | Walton, Esther | Wolf, Christiane | Almasy, Laura | Ames, David | Arepalli, Sampath | Assareh, Amelia A. | Bastin, Mark E. | Brodaty, Henry | Bulayeva, Kazima B. | Carless, Melanie A. | Cichon, Sven | Corvin, Aiden | Curran, Joanne E. | Czisch, Michael | de Zubicaray, Greig I. | Dillman, Allissa | Duggirala, Ravi | Dyer, Thomas D. | Erk, Susanne | Fedko, Iryna O. | Ferrucci, Luigi | Foroud, Tatiana M. | Fox, Peter T. | Fukunaga, Masaki | Gibbs, J. Raphael | Göring, Harald H. H. | Green, Robert C. | Guelfi, Sebastian | Hansell, Narelle K. | Hartman, Catharina A. | Hegenscheid, Katrin | Heinz, Andreas | Hernandez, Dena G. | Heslenfeld, Dirk J. | Hoekstra, Pieter J. | Holsboer, Florian | Homuth, Georg | Hottenga, Jouke-Jan | Ikeda, Masashi | Jack, Clifford R. | Jenkinson, Mark | Johnson, Robert | Kanai, Ryota | Keil, Maria | Kent, Jack W. | Kochunov, Peter | Kwok, John B. | Lawrie, Stephen M. | Liu, Xinmin | Longo, Dan L. | McMahon, Katie L. | Meisenzahl, Eva | Melle, Ingrid | Mohnke, Sebastian | Montgomery, Grant W. | Mostert, Jeanette C. | Mühleisen, Thomas W. | Nalls, Michael A. | Nichols, Thomas E. | Nilsson, Lars G. | Nöthen, Markus M. | Ohi, Kazutaka | Olvera, Rene L. | Perez-Iglesias, Rocio | Pike, G. Bruce | Potkin, Steven G. | Reinvang, Ivar | Reppermund, Simone | Rietschel, Marcella | Romanczuk-Seiferth, Nina | Rosen, Glenn D. | Rujescu, Dan | Schnell, Knut | Schofield, Peter R. | Smith, Colin | Steen, Vidar M. | Sussmann, Jessika E. | Thalamuthu, Anbupalam | Toga, Arthur W. | Traynor, Bryan J. | Troncoso, Juan | Turner, Jessica A. | Valdés Hernández, Maria C. | van ’t Ent, Dennis | van der Brug, Marcel | van der Wee, Nic J. A. | van Tol, Marie-Jose | Veltman, Dick J. | Wassink, Thomas H. | Westman, Eric | Zielke, Ronald H. | Zonderman, Alan B. | Ashbrook, David G. | Hager, Reinmar | Lu, Lu | McMahon, Francis J. | Morris, Derek W. | Williams, Robert W. | Brunner, Han G. | Buckner, Randy L. | Buitelaar, Jan K. | Cahn, Wiepke | Calhoun, Vince D. | Cavalleri, Gianpiero L. | Crespo-Facorro, Benedicto | Dale, Anders M. | Davies, Gareth E. | Delanty, Norman | Depondt, Chantal | Djurovic, Srdjan | Drevets, Wayne C. | Espeseth, Thomas | Gollub, Randy L. | Ho, Beng-Choon | Hoffmann, Wolfgang | Hosten, Norbert | Kahn, René S. | Le Hellard, Stephanie | Meyer-Lindenberg, Andreas | Müller-Myhsok, Bertram | Nauck, Matthias | Nyberg, Lars | Pandolfo, Massimo | Penninx, Brenda W. J. H. | Roffman, Joshua L. | Sisodiya, Sanjay M. | Smoller, Jordan W. | van Bokhoven, Hans | van Haren, Neeltje E. M. | Völzke, Henry | Walter, Henrik | Weiner, Michael W. | Wen, Wei | White, Tonya | Agartz, Ingrid | Andreassen, Ole A. | Blangero, John | Boomsma, Dorret I. | Brouwer, Rachel M. | Cannon, Dara M. | Cookson, Mark R. | de Geus, Eco J. C. | Deary, Ian J. | Donohoe, Gary | Fernández, Guillén | Fisher, Simon E. | Francks, Clyde | Glahn, David C. | Grabe, Hans J. | Gruber, Oliver | Hardy, John | Hashimoto, Ryota | Hulshoff Pol, Hilleke E. | Jönsson, Erik G. | Kloszewska, Iwona | Lovestone, Simon | Mattay, Venkata S. | Mecocci, Patrizia | McDonald, Colm | McIntosh, Andrew M. | Ophoff, Roel A. | Paus, Tomas | Pausova, Zdenka | Ryten, Mina | Sachdev, Perminder S. | Saykin, Andrew J. | Simmons, Andy | Singleton, Andrew | Soininen, Hilkka | Wardlaw, Joanna M. | Weale, Michael E. | Weinberger, Daniel R. | Adams, Hieab H. H. | Launer, Lenore J. | Seiler, Stephan | Schmidt, Reinhold | Chauhan, Ganesh | Satizabal, Claudia L. | Becker, James T. | Yanek, Lisa | van der Lee, Sven J. | Ebling, Maritza | Fischl, Bruce | Longstreth, W. T. | Greve, Douglas | Schmidt, Helena | Nyquist, Paul | Vinke, Louis N. | van Duijn, Cornelia M. | Xue, Luting | Mazoyer, Bernard | Bis, Joshua C. | Gudnason, Vilmundur | Seshadri, Sudha | Ikram, M. Arfan | Martin, Nicholas G. | Wright, Margaret J. | Schumann, Gunter | Franke, Barbara | Thompson, Paul M. | Medland, Sarah E.
Nature  2015;520(7546):224-229.
The highly complex structure of the human brain is strongly shaped by genetic influences1. Subcortical brain regions form circuits with cortical areas to coordinate movement2, learning, memory3 and motivation4, and altered circuits can lead to abnormal behaviour and disease2. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume5 and intracranial volume6. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10−33; 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability inhuman brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.
PMCID: PMC4393366  PMID: 25607358
14.  Biomarkers and cognitive endpoints to optimize trials in Alzheimer's disease 
To find the combination of candidate biomarkers and cognitive endpoints to maximize statistical power and minimize cost of clinical trials of healthy elders at risk for cognitive decline due to Alzheimer's disease.
Four-hundred and twelve cognitively normal participants were followed over 7 years. Nonlinear methods were used to estimate the longitudinal trajectories of several cognitive outcomes including delayed memory recall, executive function, processing speed, and several cognitive composites by subgroups selected on the basis of biomarkers, including APOE-ε4 allele carriers, cerebrospinal fluid biomarkers (Aβ42, total tau, and phosphorylated tau), and those with small hippocampi.
Derived cognitive composites combining Alzheimer's Disease Assessment Scale (ADAS)-cog scores with additional delayed memory recall and executive function components captured decline more robustly across biomarker groups than any measure of a single cognitive domain or ADAS-cog alone. Substantial increases in power resulted when including only participants positive for three or more biomarkers in simulations of clinical trials.
Clinical trial power may be improved by selecting participants on the basis of amyloid and neurodegeneration biomarkers and carefully tailoring primary cognitive endpoints to reflect the expected decline specific to these individuals.
PMCID: PMC4435707  PMID: 26000325
15.  Neuroimaging abnormalities in adults with sickle cell anemia 
Neurology  2014;82(10):835-841.
This study was conducted to determine the relationship of frontal lobe cortical thickness and basal ganglia volumes to measures of cognition in adults with sickle cell anemia (SCA).
Participants included 120 adults with SCA with no history of neurologic dysfunction and 33 healthy controls (HCs). Participants were enrolled at 12 medical center sites, and raters were blinded to diagnostic group. We hypothesized that individuals with SCA would exhibit reductions in frontal lobe cortex thickness and reduced basal ganglia and thalamus volumes compared with HCs and that these structural brain abnormalities would be associated with measures of cognitive functioning (Wechsler Adult Intelligence Scale, 3rd edition).
After adjusting for age, sex, education level, and intracranial volume, participants with SCA exhibited thinner frontal lobe cortex (t = −2.99, p = 0.003) and reduced basal ganglia and thalamus volumes compared with HCs (t = −3.95, p < 0.001). Reduced volume of the basal ganglia and thalamus was significantly associated with lower Performance IQ (model estimate = 3.75, p = 0.004) as well as lower Perceptual Organization (model estimate = 1.44, p = 0.007) and Working Memory scores (model estimate = 1.37, p = 0.015). Frontal lobe cortex thickness was not significantly associated with any cognitive measures.
Our findings suggest that basal ganglia and thalamus abnormalities may represent a particularly salient contributor to cognitive dysfunction in adults with SCA.
PMCID: PMC3959758  PMID: 24523480
16.  Cerebrospinal Fluid α-Synuclein and Lewy Body-Like Symptoms in Normal Controls, Mild Cognitive Impairment, and Alzheimer’s Disease 
Journal of Alzheimer's disease : JAD  2015;43(3):1007-1016.
Reduced cerebrospinal fluid (CSF) α-synuclein has been described in synucleinopathies, including dementia with Lewy bodies (DLB). Common symptoms of DLB include visual hallucinations and visuospatial and executive deficits. Co-occurrence of Lewy body pathology is common in Alzheimer’s disease (AD) patients, but it is unknown if reduced CSF α-synuclein is associated with Lewy body-like symptomatology in AD.
Determine associations between CSF α-synuclein and Lewy body-like symptomatology.
We included 73 controls (NC), 121 mild cognitive impairment (MCI) patients, and 61 AD patients (median follow-up 3.5 years, range 0.6–7.8). We tested associations between baseline CSF α-synuclein and visual hallucinations and (longitudinal) cognition. Models were tested with and without co-varying for CSF total tau (T-tau), which is elevated in AD patients, and believed to reflect neurodegeneration.
Hallucinations were reported in 20% of AD patients, 13% of MCI patients, and 8% of NC. In AD, low CSF α-synuclein was associated with hallucinations. When adjusting for CSF T-tau, low CSF α-synuclein was associated with accelerated decline of executive function (NC, MCI, and AD), memory (MCI and AD), and language (MCI).
The associations of low CSF α-synuclein with hallucinations and poor executive function, which are hallmarks of DLB, indirectly suggest that this biomarker may reflect underlying synuclein pathology. The associations with memory and language in MCI and AD suggests either that reduced CSF α-synuclein also partly reflects global impaired neuronal/synaptic function, or that non-specific overall cognitive deterioration is accelerated in the presence of synuclein related pathology. The findings will require autopsy verification.
PMCID: PMC4350922  PMID: 25125463
Alpha-synuclein; Alzheimer’s disease; cerebrospinal fluid; cognition; hallucinations; tau
17.  Apolipoprotein ε4 Is Associated with Lower Brain Volume in Cognitively Normal Chinese but Not White Older Adults 
PLoS ONE  2015;10(3):e0118338.
Studying ethnically diverse groups is important for furthering our understanding of biological mechanisms of disease that may vary across human populations. The ε4 allele of apolipoprotein E (APOE ε4) is a well-established risk factor for Alzheimer’s disease (AD), and may confer anatomic and functional effects years before clinical signs of cognitive decline are observed. The allele frequency of APOE ε4 varies both across and within populations, and the size of the effect it confers for dementia risk may be affected by other factors. Our objective was to investigate the role APOE ε4 plays in moderating brain volume in cognitively normal Chinese older adults, compared to older white Americans. We hypothesized that carrying APOE ε4 would be associated with reduced brain volume and that the magnitude of this effect would be different between ethnic groups. We performed whole brain analysis of structural MRIs from Chinese living in America (n = 41) and Shanghai (n = 30) and compared them to white Americans (n = 71). We found a significant interaction effect of carrying APOE ε4 and being Chinese. The APOE ε4xChinese interaction was associated with lower volume in bilateral cuneus and left middle frontal gyrus (Puncorrected<0.001), with suggestive findings in right entorhinal cortex and left hippocampus (Puncorrected<0.01), all regions that are associated with neurodegeneration in AD. After correction for multiple testing, the left cuneus remained significantly associated with the interaction effect (PFWE = 0.05). Our study suggests there is a differential effect of APOE ε4 on brain volume in Chinese versus white cognitively normal elderly adults. This represents a novel finding that, if verified in larger studies, has implications for how biological, environmental and/or lifestyle factors may modify APOE ε4 effects on the brain in diverse populations.
PMCID: PMC4349764  PMID: 25738563
18.  Effects of CSF proteins on brain atrophy rates in cognitively healthy older adults 
Neurobiology of aging  2013;35(3):10.1016/j.neurobiolaging.2013.08.027.
Biomarkers associated with Alzheimer’s disease (AD)-like brain atrophy in healthy people may identify mechanisms involved in early stage AD. Aside from cerebrospinal fluid (CSF) β-amyloid42 (Aβ42) and tau, no studies have tested associations between CSF proteins and AD-like brain atrophy. We studied 90 healthy elders, who underwent lumbar puncture at baseline, and serial magnetic resonance imaging scans for up to 4 years. We tested statistical effects of baseline CSF proteins (N=70 proteins related to Aβ42-metabolism, microglial activity and synaptic/neuronal function) on atrophy rates in 7 AD-related regions. Besides effects of Aβ42 and phosphorylated tau (P-tau) that were seen in several regions, novel CSF proteins were found to have effects in inferior and middle temporal cortex (including Apolipoprotein CIII, Apolipoprotein D and Apolipoprotein H). Several proteins (including S100β and Matrix Metalloproteinase-3) had effects that depended on the presence of brain Aβ pathology, as measured by CSF Aβ42. Other proteins (including P-tau and Apolipoprotein D) had effects even after adjusting for CSF Aβ42. The statistical effects in this exploratory study were mild and not significant after correction for multiple comparisons, but some of the identified proteins may be associated with brain atrophy in healthy people. Proteins interacting with CSF Aβ42 may be related to Aβ brain pathology, while proteins associated with atrophy even after adjusting for CSF Aβ42 may be related to Aβ-independent mechanisms.
PMCID: PMC3864623  PMID: 24094581
cerebrospinal fluid; biomarkers; atrophy; longitudinal; Alzheimer’s disease
19.  Multimodal MRI-based Imputation of the Aβ+ in Early Mild Cognitive Impairment 
To identify brain atrophy from structural-MRI and cerebral blood flow(CBF) patterns from arterial spin labeling perfusion-MRI that are best predictors of the Aβ-burden, measured as composite 18F-AV45-PET uptake, in individuals with early mild cognitive impairment(MCI). Furthermore, to assess the relative importance of imaging modalities in classification of Aβ+/Aβ− early mild cognitive impairment.
Sixty-seven ADNI-GO/2 participants with early-MCI were included. Voxel-wise anatomical shape variation measures were computed by estimating the initial diffeomorphic mapping momenta from an unbiased control template. CBF measures normalized to average motor cortex CBF were mapped onto the template space. Using partial least squares regression, we identified the structural and CBF signatures of Aβ after accounting for normal cofounding effects of age, sex, and education.
18F-AV45-positive early-MCIs could be identified with 83% classification accuracy, 87% positive predictive value, and 84% negative predictive value by multidisciplinary classifiers combining demographics data, ApoE ε4-genotype, and a multimodal MRI-based Aβ score.
Multimodal-MRI can be used to predict the amyloid status of early-MCI individuals. MRI is a very attractive candidate for the identification of inexpensive and non-invasive surrogate biomarkers of Aβ deposition. Our approach is expected to have value for the identification of individuals likely to be Aβ+ in circumstances where cost or logistical problems prevent Aβ detection using cerebrospinal fluid analysis or Aβ-PET. This can also be used in clinical settings and clinical trials, aiding subject recruitment and evaluation of treatment efficacy. Imputation of the Aβ-positivity status could also complement Aβ-PET by identifying individuals who would benefit the most from this assessment.
PMCID: PMC3981105  PMID: 24729983
20.  Voxelwise multivariate analysis of multimodality magnetic resonance imaging 
Human brain mapping  2013;35(3):831-846.
Most brain magnetic resonance imaging (MRI) studies concentrate on a single MRI contrast or modality, frequently structural MRI. By performing an integrated analysis of several modalities, such as structural, perfusion-weighted, and diffusion-weighted MRI, new insights may be attained to better understand the underlying processes of brain diseases. We compare two voxelwise approaches: (1) fitting multiple univariate models, one for each outcome and then adjusting for multiple comparisons among the outcomes and (2) fitting a multivariate model. In both cases, adjustment for multiple comparisons is performed over all voxels jointly to account for the search over the brain. The multivariate model is able to account for the multiple comparisons over outcomes without assuming independence because the covariance structure between modalities is estimated. Simulations show that the multivariate approach is more powerful when the outcomes are correlated and, even when the outcomes are independent, the multivariate approach is just as powerful or more powerful when at least two outcomes are dependent on predictors in the model. However, multiple univariate regressions with Bonferroni correction remains a desirable alternative in some circumstances. To illustrate the power of each approach, we analyze a case control study of Alzheimer's disease, in which data from three MRI modalities are available.
PMCID: PMC4324330  PMID: 23408378
multivariate analysis; multiple comparisons; multimodality imaging; diffusion tensor imaging; structural magnetic resonance imaging; perfusion weighted magnetic resonance imaging; Alzheimer's disease
21.  The future of blood-based biomarkers for Alzheimer’s disease 
Treatment of Alzheimer’s disease (AD) is significantly hampered by the lack of easily accessible biomarkers that can detect disease presence and predict disease risk reliably. Fluid biomarkers of AD currently provide indications of disease stage; however, they are not robust predictors of disease progression or treatment response, and most are measured in cerebrospinal fluid, which limits their applicability. With these aspects in mind, the aim of this article is to underscore the concerted efforts of the Blood-Based Biomarker Interest Group, an international working group of experts in the field. The points addressed include: (1) the major challenges in the development of blood-based biomarkers of AD, including patient heterogeneity, inclusion of the “right” control population, and the blood– brain barrier; (2) the need for a clear definition of the purpose of the individual markers (e.g., prognostic, diagnostic, or monitoring therapeutic efficacy); (3) a critical evaluation of the ongoing biomarker approaches; and (4) highlighting the need for standardization of preanalytical variables and analytical methodologies used by the field.
PMCID: PMC4128378  PMID: 23850333
22.  Association of plasma and cortical beta-amyloid is modulated by APOE ε4 status 
APOE ε4’s role as a modulator of the relationship between soluble plasma beta-amyloid (Aβ) and fibrillar brain Aβ measured by Pittsburgh Compound-B positron emission tomography ([11C]PiB PET) has not been assessed.
Ninety-six Alzheimer’s Disease Neuroimaging Initiative participants with [11C]PiB scans and plasma Aβ1-40 and Aβ1-42 measurements at time of scan were included. Regional and voxel-wise analyses of [11C]PiB data were used to determine the influence of APOE ε4 on association of plasma Aβ1-40, Aβ1-42, and Aβ1-40/Aβ1-42 with [11C]PiB uptake.
In APOE ε4− but not ε4+ participants, positive relationships between plasma Aβ1-40/Aβ1-42 and [11C]PiB uptake were observed. Modeling the interaction of APOE and plasma Aβ1-40/Aβ1-42 improved the explained variance in [11C]PiB binding compared to using APOE and plasma Aβ1-40/Aβ1-42 as separate terms.
The results suggest that plasma Aβ is a potential Alzheimer’s disease biomarker and highlight the importance of genetic variation in interpretation of plasma Aβ levels.
PMCID: PMC3750076  PMID: 23541187
Alzheimer’s disease (AD); mild cognitive impairment (MCI); Alzheimer’s Disease Neuroimaging Initiative (ADNI); beta-amyloid (Aβ); plasma beta-amyloid; positron emission tomography (PET); Pittsburgh Compound-B ([11C]PiB); Apolipoprotein E (APOE)
23.  Vascular risk and Aβ interact to reduce cortical thickness in AD vulnerable brain regions 
Neurology  2014;83(1):40-47.
The objective of this study was to define whether vascular risk factors interact with β-amyloid (Aβ) in producing changes in brain structure that could underlie the increased risk of Alzheimer disease (AD).
Sixty-six cognitively normal and mildly impaired older individuals with a wide range of vascular risk factors were included in this study. The presence of Aβ was assessed using [11C]Pittsburgh compound B–PET imaging, and cortical thickness was measured using 3-tesla MRI. Vascular risk was measured with the Framingham Coronary Risk Profile Index.
Individuals with high levels of vascular risk factors have thinner frontotemporal cortex independent of Aβ. These frontotemporal regions are also affected in individuals with Aβ deposition, but the latter show additional thinning in parietal cortices. Aβ and vascular risk were found to interact in posterior (especially in parietal) brain regions, where Aβ has its greatest effect. In this way, the negative effect of Aβ in posterior regions is increased by the presence of vascular risk.
Aβ and vascular risk interact to enhance cortical thinning in posterior brain regions that are particularly vulnerable to AD. These findings give insight concerning the mechanisms whereby vascular risk increases the likelihood of developing AD and supports the therapeutic intervention of controlling vascular risk for the prevention of AD.
PMCID: PMC4114172  PMID: 24907234
24.  In-vivo investigation of the human cingulum bundle using the optimization of MR diffusion spectrum imaging 
European journal of radiology  2009;75(1):e29-e36.
Diffusion spectrum imaging (DSI) is a generalization of diffusion tensor imaging to map fibrous structure of white matter and potentially very sensitive to alterations of the cingulum bundles in dementia. In this in-vivo 4T study, DSI parameters especially spatial resolution and diffusion encoding bandwidth were optimized on humans to segment the cingulum bundles for tract level measurements of diffusion. The careful tailoring of the DSI acquisitions in conjunction with fiber tracking provided an optimal DSI setting for a reliable quantification of the cingulum bundle tracts. The optimization of tracking the cingulum bundle was verified using fiber tract quantifications, including coefficients of variability of DSI measurements along the fibers between and within healthy subjects in back-to-back studies and variogram analysis of spatial correlations between diffusion orientation distribution functions (ODF) along the cingulum bundle tracts. The results demonstrate identification of the cingulum bundle in human brain is reproducible using an optimized DSI parameter for maximum b-value and high spatial resolution of the DSI acquisition with a feasible acquisition time of whole brain in clinical practice. This optimized DSI setting should be useful for detecting alterations along the cingulum bundle in Alzheimer disease and related neurodegenerative disorders.
PMCID: PMC4266695  PMID: 19615838
MR diffusion; Diffusion spectrum imaging; optimization; cingulum bundle; Alzheimer
25.  Locally Linear Embedding (LLE) for MRI based Alzheimer’s Disease Classification 
NeuroImage  2013;83:10.1016/j.neuroimage.2013.06.033.
Modern machine learning algorithms are increasingly being used in neuroimaging studies, such as the prediction of Alzheimer’s disease (AD) from structural MRI. However, finding a good representation for multivariate brain MRI features in which their essential structure is revealed and easily extractable has been difficult. We report a successful application of a machine learning framework that significantly improved the use of brain MRI for predictions. Specifically, we used the unsupervised learning algorithm of locally linear embedding (LLE) to transform multivariate MRI data of regional brain volume and cortical thickness to a locally linear space with fewer dimensions, while also utilizing the global nonlinear data structure. The embedded brain features were then used to train a classifier for predicting future conversion to AD based on a baseline MRI. We tested the approach on 413 individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) who had baseline MRI scans and complete clinical follow-ups over 3 years with following diagnoses: Cognitive normal (CN; n= 137), stable mild cognitive impairment (s-MCI; n=93), MCI converters to AD (c-MCI, n=97), and AD (n=86). We found classifications using embedded MRI features generally outperformed (p < 0.05) classifications using the original features directly. Moreover, the improvement from LLE was not limited to a particular classifier but worked equally well for regularized logistic regressions, support vector machines, and linear discriminant analysis. Most strikingly, using LLE significantly improved (p = 0.007) predictions of MCI subjects who converted to AD and those who remained stable (accuracy/sensitivity/specificity: = 0.68/0.80/0.56). In contrast, predictions using the original features performed not better than by chance (accuracy/sensitivity/specificity: = 0.56/0.65/0.46). In conclusion, LLE is a very effective tool for classification studies of AD using multivariate MRI data. The improvement in predicting conversion to AD in MCI could have important implications for health management and for powering therapeutic trials by targeting non-demented subjects who later convert to AD.
PMCID: PMC3815961  PMID: 23792982
Alzheimer’s disease; locally linear embedding; statistical learning; classification of AD; MRI

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