Browsing by Subject "Mild cognitive impairment"
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Item 2014 Update of the Alzheimer's Disease Neuroimaging Initiative: A review of papers published since its inception(Elsevier, 2016-06-01) Weiner, Michael W.; Veitch, Dallas P.; Aisen, Paul S.; Beckett, Laurel A.; Cairns, Nigel J.; Cedarbaum, Jesse; Green, Robert C.; Harvey, Danielle; Jack, Clifford R.; Jagust, William; Luthman, Johan; Morris, John C.; Petersen, Ronald C.; Saykin, Andrew J.; Shaw, Leslie; Shen, Li; Schwarz, Adam; Toga, Arthur W.; Trojanowski, John Q.; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineThe Alzheimer's Disease Neuroimaging Initiative (ADNI) is an ongoing, longitudinal, multicenter study designed to develop clinical, imaging, genetic, and biochemical biomarkers for the early detection and tracking of Alzheimer's disease (AD). The initial study, ADNI-1, enrolled 400 subjects with early mild cognitive impairment (MCI), 200 with early AD, and 200 cognitively normal elderly controls. ADNI-1 was extended by a 2-year Grand Opportunities grant in 2009 and by a competitive renewal, ADNI-2, which enrolled an additional 550 participants and will run until 2015. This article reviews all papers published since the inception of the initiative and summarizes the results to the end of 2013. The major accomplishments of ADNI have been as follows: (1) the development of standardized methods for clinical tests, magnetic resonance imaging (MRI), positron emission tomography (PET), and cerebrospinal fluid (CSF) biomarkers in a multicenter setting; (2) elucidation of the patterns and rates of change of imaging and CSF biomarker measurements in control subjects, MCI patients, and AD patients. CSF biomarkers are largely consistent with disease trajectories predicted by β-amyloid cascade (Hardy, J Alzheimer's Dis 2006;9(Suppl 3):151-3) and tau-mediated neurodegeneration hypotheses for AD, whereas brain atrophy and hypometabolism levels show predicted patterns but exhibit differing rates of change depending on region and disease severity; (3) the assessment of alternative methods of diagnostic categorization. Currently, the best classifiers select and combine optimum features from multiple modalities, including MRI, [(18)F]-fluorodeoxyglucose-PET, amyloid PET, CSF biomarkers, and clinical tests; (4) the development of blood biomarkers for AD as potentially noninvasive and low-cost alternatives to CSF biomarkers for AD diagnosis and the assessment of α-syn as an additional biomarker; (5) the development of methods for the early detection of AD. CSF biomarkers, β-amyloid 42 and tau, as well as amyloid PET may reflect the earliest steps in AD pathology in mildly symptomatic or even nonsymptomatic subjects and are leading candidates for the detection of AD in its preclinical stages; (6) the improvement of clinical trial efficiency through the identification of subjects most likely to undergo imminent future clinical decline and the use of more sensitive outcome measures to reduce sample sizes. Multimodal methods incorporating APOE status and longitudinal MRI proved most highly predictive of future decline. Refinements of clinical tests used as outcome measures such as clinical dementia rating-sum of boxes further reduced sample sizes; (7) the pioneering of genome-wide association studies that leverage quantitative imaging and biomarker phenotypes, including longitudinal data, to confirm recently identified loci, CR1, CLU, and PICALM and to identify novel AD risk loci; (8) worldwide impact through the establishment of ADNI-like programs in Japan, Australia, Argentina, Taiwan, China, Korea, Europe, and Italy; (9) understanding the biology and pathobiology of normal aging, MCI, and AD through integration of ADNI biomarker and clinical data to stimulate research that will resolve controversies about competing hypotheses on the etiopathogenesis of AD, thereby advancing efforts to find disease-modifying drugs for AD; and (10) the establishment of infrastructure to allow sharing of all raw and processed data without embargo to interested scientific investigators throughout the world.Item Altered Cerebral Blood Flow in Older Adults with Alzheimer’s Disease: A Systematic Review(Springer, 2023) Swinford, Cecily G.; Risacher, Shannon L.; Wu, Yu-Chien; Apostolova, Liana G.; Gao, Sujuan; Bice, Paula J.; Saykin, Andrew J.; Radiology and Imaging Sciences, School of MedicineThe prevalence of Alzheimer’s disease is projected to reach 13 million in the U.S. by 2050. Although major efforts have been made to avoid this outcome, so far there are no treatments that can stop or reverse the progressive cognitive decline that defines Alzheimer’s disease. The utilization of preventative treatment before significant cognitive decline has occurred may ultimately be the solution, necessitating a reliable biomarker of preclinical/prodromal disease stages to determine which older adults are most at risk. Quantitative cerebral blood flow is a promising potential early biomarker for Alzheimer’s disease, but the spatiotemporal patterns of altered cerebral blood flow in Alzheimer’s disease are not fully understood. The current systematic review compiles the findings of 81 original studies that compared resting gray matter cerebral blood flow in older adults with mild cognitive impairment or Alzheimer’s disease and that of cognitively normal older adults and/or assessed the relationship between cerebral blood flow and objective cognitive function. Individuals with Alzheimer’s disease had relatively decreased cerebral blood flow in all brain regions investigated, especially the temporoparietal and posterior cingulate, while individuals with mild cognitive impairment had consistent results of decreased cerebral blood flow in the posterior cingulate but more mixed results in other regions, especially the frontal lobe. Most papers reported a positive correlation between regional cerebral blood flow and cognitive function. This review highlights the need for more studies assessing cerebral blood flow changes both spatially and temporally over the course of Alzheimer’s disease, as well as the importance of including potential confounding factors in these analyses.Item Amyloid and Tau Pathology are Associated with Cerebral Blood Flow in a Mixed Sample of Nondemented Older Adults with and without Vascular Risk Factors for Alzheimer’s Disease(Elsevier, 2023) Swinford, Cecily G.; Risacher, Shannon L.; Vosmeier, Aaron; Deardorff, Rachael; Chumin, Evgeny J.; Dzemidzic, Mario; Wu, Yu-Chien; Gao, Sujuan; McDonald, Brenna C.; Yoder, Karmen K.; Unverzagt, Frederick W.; Wang, Sophia; Farlow, Martin R.; Brosch, Jared R.; Clark, David G.; Apostolova, Liana G.; Sims, Justin; Wang, Danny J.; Saykin, Andrew J.; Radiology and Imaging Sciences, School of MedicineIdentification of biomarkers for the early stages of Alzheimer's disease (AD) is an imperative step in developing effective treatments. Cerebral blood flow (CBF) is a potential early biomarker for AD; generally, older adults with AD have decreased CBF compared to normally aging peers. CBF deviates as the disease process and symptoms progress. However, further characterization of the relationships between CBF and AD risk factors and pathologies is still needed. We assessed the relationships between CBF quantified by arterial spin-labeled magnetic resonance imaging, hypertension, APOEε4, and tau and amyloid positron emission tomography in 77 older adults: cognitively normal, subjective cognitive decline, and mild cognitive impairment. Tau and amyloid aggregation were related to altered CBF, and some of these relationships were dependent on hypertension or APOEε4 status. Our findings suggest a complex relationship between risk factors, AD pathologies, and CBF that warrants future studies of CBF as a potential early biomarker for AD.Item An interpretable Alzheimer's disease oligogenic risk score informed by neuroimaging biomarkers improves risk prediction and stratification(Frontiers Media, 2023-10-26) Suh, Erica H.; Lee, Garam; Jung, Sang-Hyuk; Wen, Zixuan; Bao, Jingxuan; Nho, Kwangsik; Huang, Heng; Davatzikos, Christos; Saykin, Andrew J.; Thompson, Paul M.; Shen, Li; Kim, Dokyoon; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineIntroduction: Stratification of Alzheimer's disease (AD) patients into risk subgroups using Polygenic Risk Scores (PRS) presents novel opportunities for the development of clinical trials and disease-modifying therapies. However, the heterogeneous nature of AD continues to pose significant challenges for the clinical broadscale use of PRS. PRS remains unfit in demonstrating sufficient accuracy in risk prediction, particularly for individuals with mild cognitive impairment (MCI), and in allowing feasible interpretation of specific genes or SNPs contributing to disease risk. We propose adORS, a novel oligogenic risk score for AD, to better predict risk of disease by using an optimized list of relevant genetic risk factors. Methods: Using whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort (n = 1,545), we selected 20 genes that exhibited the strongest correlations with FDG-PET and AV45-PET, recognized neuroimaging biomarkers that detect functional brain changes in AD. This subset of genes was incorporated into adORS to assess, in comparison to PRS, the prediction accuracy of CN vs. AD classification and MCI conversion prediction, risk stratification of the ADNI cohort, and interpretability of the genetic information included in the scores. Results: adORS improved AUC scores over PRS in both CN vs. AD classification and MCI conversion prediction. The oligogenic model also refined risk-based stratification, even without the assistance of APOE, thus reflecting the true prevalence rate of the ADNI cohort compared to PRS. Interpretation analysis shows that genes included in adORS, such as ATF6, EFCAB11, ING5, SIK3, and CD46, have been observed in similar neurodegenerative disorders and/or are supported by AD-related literature. Discussion: Compared to conventional PRS, adORS may prove to be a more appropriate choice of differentiating patients into high or low genetic risk of AD in clinical studies or settings. Additionally, the ability to interpret specific genetic information allows the focus to be shifted from general relative risk based on a given population to the information that adORS can provide for a single individual, thus permitting the possibility of personalized treatments for AD.Item Application of Different Standard Error Estimates in Reliable Change Methods(Oxford University Press, 2021) Hammers, Dustin B.; Duff, Kevin; Neurology, School of MedicineObjective: This study attempted to clarify the applicability of standard error (SE) terms in clinical research when examining the impact of short-term practice effects on cognitive performance via reliable change methodology. Method: This study compared McSweeney's SE of the estimate (SEest) to Crawford and Howell's SE for prediction of the regression (SEpred) using a developmental sample of 167 participants with either normal cognition or mild cognitive impairment (MCI) assessed twice over 1 week. One-week practice effects in older adults: Tools for assessing cognitive change. Using these SEs, previously published standardized regression-based (SRB) reliable change prediction equations were then applied to an independent sample of 143 participants with MCI. Results: This clinical developmental sample yielded nearly identical SE values (e.g., 3.697 vs. 3.719 for HVLT-R Total Recall SEest and SEpred, respectively), and the resultant SRB-based discrepancy z scores were comparable and strongly correlated (r = 1.0, p < .001). Consequently, observed follow-up scores for our sample with MCI were consistently below expectation compared to predictions based on Duff's SRB algorithms. Conclusions: These results appear to replicate and extend previous work showing that the calculation of the SEest and SEpred from a clinical sample of cognitively intact and MCI participants yields similar values and can be incorporated into SRB reliable change statistics with comparable results. As a result, neuropsychologists utilizing reliable change methods in research investigation (or clinical practice) should carefully balance mathematical accuracy and ease of use, among other factors, when determining which SE metric to use.Item Application of Neuropsychological Criteria to Classify Mild Cognitive Impairment in the ACTIVE Study(American Psychological Association, 2020-11) Thomas, Kelsey R.; Cook, Sarah E.; Bondi, Mark W.; Unverzagt, Frederick W.; Gross, Alden L.; Willis, Sherry L.; Marsiske, Michael; Psychiatry, School of MedicineObjective: Comprehensive neuropsychological criteria (NP criteria) for mild cognitive impairment (MCI) has reduced diagnostic errors and better predicted progression to dementia than conventional MCI criteria that rely on a single impaired score and/or subjective report. This study aimed to implement an actuarial approach to classifying MCI in the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study. Method: ACTIVE study participants (N = 2,755) were classified as cognitively normal (CN) or as having MCI using NP criteria. Estimated proportion of MCI participants and reversion rates were examined as well as baseline characteristics by MCI subtype. Mixed effect models examined associations of MCI subtype with 10-year trajectories of self-reported independence and difficulty performing instrumental activities of daily living (IADLs). Results: The proportion of MCI participants was estimated to be 18.8%. Of those with MCI at baseline, 19.2% reverted to CN status for all subsequent visits. At baseline, the multidomain-amnestic MCI group generally had the greatest breadth and depth of cognitive impairment and reported the most IADL difficulty. Longitudinally, MCI participants showed faster IADL decline than CN participants (multidomain-amnestic MCI > single domain-amnestic MCI > nonamnestic MCI). Conclusion: NP criteria identified a proportion of MCI and reversion rate within ACTIVE that is consistent with prior studies involving community-dwelling samples. The pattern of everyday functioning change suggests that being classified as MCI, particularly amnestic MCI, is predictive of future loss of independence. Future work will apply these classifications in ACTIVE to better understand the relationships between MCI and health, social, and cognitive intervention-related factors.Item Associations of circulating saturated long-chain fatty acids with risk of mild cognitive impairment and Alzheimer’s disease in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort(Elsevier, 2023) Fan, Lei; Borenstein, Amy R.; Wang, Sophia; Nho, Kwangsik; Zhu, Xiangzhu; Wen, Wanqing; Huang, Xiang; Mortimer, James A.; Shrubsole, Martha J.; Dai, Qi; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineBackground: No study has examined the associations between peripheral saturated long-chain fatty acids (LCFAs) and conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD). This study aimed to examine whether circulating saturated LCFAs are associated with both risks of incident MCI from cognitively normal (CN) participants and incident AD progressed from MCI in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Methods: We conducted analysis of data from older adults aged 55-90 years who were recruited at 63 sites across the USA and Canada. We examined associations between circulating saturated LCFAs (i.e., C14:0, C16:0, C18:0, C20:0) and risk for incident MCI in CN participants, and incident AD progressed from MCI. Findings: 829 participants who were enrolled in ADNI-1 had data on plasma saturated LCFAs, of which 618 AD-free participants were included in our analysis (226 with normal cognition and 392 with MCI; 60.2% were men). Cox proportional-hazards models were used to account for time-to-event/censor with a 48-month follow-up period for the primary analysis. Other than C20:0, saturated LCFAs were associated with an increased risk for AD among participants with MCI at baseline (Hazard ratios (HRs) = 1.3 to 2.2, P = 0.0005 to 0.003 in fully-adjusted models). No association of C20:0 with risk of AD among participants with MCI was observed. No associations were observed between saturated LCFAs and risk for MCI among participants with normal cognition. Interpretation: Saturated LCFAs are associated with increased risk of progressing from MCI to AD. This finding holds the potential to facilitate precision prevention of AD among patients with MCI.Item Automated and manual hippocampal segmentation techniques: Comparison of results, reproducibility and clinical applicability(Elsevier, 2019) Hurtz, Sona; Chow, Nicole; Watson, Amity E.; Somme, Johanne H.; Goukasian, Naira; Hwang, Kristy S.; Morra, John; Elashoff, David; Gao, Sujuan; Petersen, Ronald C.; Aisen, Paul S.; Thompson, Paul M.; Apostolova, Liana G.; Biostatistics, School of Public HealthBACKGROUND: Imaging techniques used to measure hippocampal atrophy are key to understanding the clinical progression of Alzheimer's disease (AD). Various semi-automated hippocampal segmentation techniques are available and require human expert input to learn how to accurately segment new data. Our goal was to compare 1) the performance of our automated hippocampal segmentation technique relative to manual segmentations, and 2) the performance of our automated technique when provided with a training set from two different raters. We also explored the ability of hippocampal volumes obtained using manual and automated hippocampal segmentations to predict conversion from MCI to AD. METHODS: We analyzed 161 1.5 T T1-weighted brain magnetic resonance images (MRI) from the ADCS Donepezil/Vitamin E clinical study. All subjects carried a diagnosis of mild cognitive impairment (MCI). Three different segmentation outputs (one produced by manual tracing and two produced by a semi-automated algorithm trained with training sets developed by two raters) were compared using single measure intraclass correlation statistics (smICC). The radial distance method was used to assess each segmentation technique's ability to detect hippocampal atrophy in 3D. We then compared how well each segmentation method detected baseline hippocampal differences between MCI subjects who remained stable (MCInc) and those who converted to AD (MCIc) during the trial. Our statistical maps were corrected for multiple comparisons using permutation-based statistics with a threshold of p < .01. RESULTS: Our smICC analyses showed significant agreement between the manual and automated hippocampal segmentations from rater 1 [right smICC = 0.78 (95%CI 0.72-0.84); left smICC = 0.79 (95%CI 0.72-0.85)], the manual segmentations from rater 1 versus the automated segmentations from rater 2 [right smICC = 0.78 (95%CI 0.7-0.84); left smICC = 0.78 (95%CI 0.71-0.84)], and the automated segmentations of rater 1 versus rater 2 [right smICC = 0.97 (95%CI 0.96-0.98); left smICC = 0.97 (95%CI 0.96-0.98)]. All three segmentation methods detected significant CA1 and subicular atrophy in MCIc compared to MCInc at baseline (manual: right pcorrected = 0.0112, left pcorrected = 0.0006; automated rater 1: right pcorrected = 0.0318, left pcorrected = 0.0302; automated rater 2: right pcorrected = 0.0029, left pcorrected = 0.0166). CONCLUSIONS: The hippocampal volumes obtained with a fast semi-automated segmentation method were highly comparable to the ones obtained with the labor-intensive manual segmentation method. The AdaBoost automated hippocampal segmentation technique is highly reliable allowing the efficient analysis of large data sets.Item Baseline neuropsychiatric symptoms and psychotropic medication use midway through data collection of the Longitudinal Early-Onset Alzheimer's Disease Study (LEADS) cohort(Wiley, 2023) Polsinelli, Angelina J.; Wonderlin, Ryan J.; Hammers, Dustin B.; Pena Garcia, Alex; Eloyan, Anii; Taurone, Alexander; Thangarajah, Maryanne; Beckett, Laurel; Gao, Sujuan; Wang, Sophia; Kirby, Kala; Logan, Paige E.; Aisen, Paul; Dage, Jeffrey L.; Foroud, Tatiana; Griffin, Percy; Iaccarino, Leonardo; Kramer, Joel H.; Koeppe, Robert; Kukull, Walter A.; La Joie, Renaud; Mundada, Nidhi S.; Murray, Melissa E.; Nudelman, Kelly; Soleimani-Meigooni, David N.; Rumbaugh, Malia; Toga, Arthur W.; Touroutoglou, Alexandra; Vemuri, Prashanthi; Atri, Alireza; Day, Gregory S.; Duara, Ranjan; Graff-Radford, Neill R.; Honig, Lawrence S.; Jones, David T.; Masdeu, Joseph; Mendez, Mario F.; Womack, Kyle; Musiek, Erik; Onyike, Chiadi U.; Riddle, Meghan; Rogalski, Emily; Salloway, Steven; Sha, Sharon J.; Turner, Raymond S.; Wingo, Thomas S.; Wolk, David A.; Carrillo, Maria C.; Dickerson, Bradford C.; Rabinovici, Gil D.; Apostolova, Liana G.; LEADS Consortium; Neurology, School of MedicineIntroduction: We examined neuropsychiatric symptoms (NPS) and psychotropic medication use in a large sample of individuals with early-onset Alzheimer's disease (EOAD; onset 40-64 years) at the midway point of data collection for the Longitudinal Early-onset Alzheimer's Disease Study (LEADS). Methods: Baseline NPS (Neuropsychiatric Inventory - Questionnaire; Geriatric Depression Scale) and psychotropic medication use from 282 participants enrolled in LEADS were compared across diagnostic groups - amyloid-positive EOAD (n = 212) and amyloid negative early-onset non-Alzheimer's disease (EOnonAD; n = 70). Results: Affective behaviors were the most common NPS in EOAD at similar frequencies to EOnonAD. Tension and impulse control behaviors were more common in EOnonAD. A minority of participants were using psychotropic medications, and use was higher in EOnonAD. Discussion: Overall NPS burden and psychotropic medication use were higher in EOnonAD than EOAD participants. Future research will investigate moderators and etiological drivers of NPS, and NPS differences in EOAD versus late-onset AD. Keywords: early-onset Alzheimer's disease; early-onset dementia; mild cognitive impairment; neuropharmacology; neuropsychiatric symptoms; psychotropic medications.Item Biomarker disclosure protocols in prodromal Alzheimer’s disease clinical trials(Wiley, 2023) Rahman-Filipiak, Annalise; Bolton, Corey; Grill, Joshua D.; Rostamzadeh, Ayda; Chin, Nathaniel; Heidebrink, Judith; Getz, Sarah; Fowler, Nicole R.; Rosen, Allyson; Lingler, Jennifer; Wijsman, Ellen; Clark, Lindsay; Advisory Group on Risk Evidence Education in Dementia (AGREED); Medicine, School of MedicineIntroduction: The development of biomarkers for Alzheimer's disease (AD) has allowed researchers to increase sample homogeneity and test candidate treatments earlier in the disease. The integration of biomarker "screening" criteria should be met with a parallel implementation of standardized methods to disclose biomarker testing results to research participants; however, the extent to which protocolized disclosure occurs in trials is unknown. Methods: We reviewed the literature to identify prodromal AD trials published in the past 10 years. From these, we quantified the frequency of biomarker disclosure reporting and the depth of descriptions provided. Results: Of 30 published trials using positron emission tomography or cerebrospinal fluid-based amyloid positivity as an eligibility criterion, only one mentioned disclosure, with no details on methods. Discussion: Possible reasons for and implications of this information gap are discussed. Recommendations are provided for trialists considering biomarker screening as part of intervention trials focused on prodromal AD. Highlights: Few prodromal Alzheimer's disease (AD) trial papers discuss biomarker disclosure. Disclosure has implications for participants, family members, and trial success. Disclosure must be consistently integrated and reported in prodromal AD trials. Best practice guidelines and training resources for disclosure are needed.