Browsing by Subject "MCI"

Now showing 1 - 6 of 6
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    Brain health: the importance of recognizing cognitive impairment: an IAGG consensus conference
    (Elsevier, 2015-09-01) Morley, John E.; Morris, John C.; Berg-Weger, Marla; Borson, Soo; Carpenter, Brian D.; del Campo, Natalia; Dubois, Bruno; Fargo, Keith; Fitten, L. Jaime; Flaherty, Joseph H.; Ganguli, Mary; Grossberg, George T.; Malmstrom, Theodore K.; Petersen, Ronald D.; Rodriguez, Carroll; Saykin, Andrew J.; Scheltens, Philip; Tangalos, Eric G.; Verghese, Joe; Wilcock, Gordon; Winblad, Bengt; Woo, Jean; Vellas, Bruno; Department of Radiology and Imaging Sciences, IU School of Medicine
    Cognitive impairment creates significant challenges for patients, their families and friends, and clinicians who provide their health care. Early recognition allows for diagnosis and appropriate treatment, education, psychosocial support, and engagement in shared decision-making regarding life planning, health care, involvement in research, and financial matters. An IAGG-GARN consensus panel examined the importance of early recognition of impaired cognitive health. Their major conclusion was that case-finding by physicians and health professionals is an important step toward enhancing brain health for aging populations throughout the world. This conclusion is in keeping with the position of the United States' Centers for Medicare and Medicaid Services that reimburses for detection of cognitive impairment as part the of Medicare Annual Wellness Visit and with the international call for early detection of cognitive impairment as a patient's right. The panel agreed on the following specific findings: (1) validated screening tests are available that take 3 to 7 minutes to administer; (2) a combination of patient- and informant-based screens is the most appropriate approach for identifying early cognitive impairment; (3) early cognitive impairment may have treatable components; and (4) emerging data support a combination of medical and lifestyle interventions as a potential way to delay or reduce cognitive decline.
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    Enrichment of clinical trials in MCI due to AD using markers of amyloid and neurodegeneration
    (American Academy of Neurology (AAN), 2016-09-20) Wolz, Robin; Schwarz, Adam J.; Gray, Katherine R.; Yu, Peng; Hill, Derek L.G.; Radiology and Imaging Sciences, School of Medicine
    Objective: To investigate the effect of enriching mild cognitive impairment (MCI) clinical trials using combined markers of amyloid pathology and neurodegeneration. Methods: We evaluate an implementation of the recent National Institute for Aging–Alzheimer's Association (NIA-AA) diagnostic criteria for MCI due to Alzheimer disease (AD) as inclusion criteria in clinical trials and assess the effect of enrichment with amyloid (A+), neurodegeneration (N+), and their combination (A+N+) on the rate of clinical progression, required sample sizes, and estimates of trial time and cost. Results: Enrichment based on an individual marker (A+ or N+) substantially improves all assessed trial characteristics. Combined enrichment (A+N+) further improves these results with a reduction in required sample sizes by 45% to 60%, depending on the endpoint. Conclusions: Operationalizing the NIA-AA diagnostic criteria for clinical trial screening has the potential to substantially improve the statistical power of trials in MCI due to AD by identifying a more rapidly progressing patient population.
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    Experience and participation implications of daily enhancement meaningful activity in persons with mild cognitive impairment
    (2016-04-01) Ellis, Jennifer L.; Arnold, Brent Lee; Lu, Yvonne Yueh-Feng; Altenburger, Peter Andrew; Munk, Niki
    Background: Persons with Mild Cognitive Impairment (PwMCI) battle progressive disengagement from personally meaningful activities that results in functional decline. Little is known about PwMCI experience of engaging in meaningful activities and relationships among MCI stage, confidence, depressive symptoms, and function. Daily Engagement of Meaningful Activity (DEMA) is a multicomponent, family-focused, tailored intervention designed to benefit PwMCI and their caregivers by facilitating goal identification, preserve engagement, and support adjustments to cognitive and functional changes. Objectives: The aims of this secondary analysis were to: (i) describe PwMCI experience of engagement in DEMA, (ii) evaluate for potential relationship among MCI stage, confidence, depressive symptoms, activity type, activity performance, physical function and (iii) evaluate ability of select outcomes to predict change in depressive symptoms and physical function, (iv) determine difference between participants when sub-grouped by ICF level. Methods: Mixed methodology was used to conduct a secondary analysis from the parent study. The parent study used a two-group randomized trial involving PwMCI and informal caregivers participating in the Indiana Alzheimer Disease Center DEMA program. Quantitative analysis (dyads: DEMA N=20, Information Support N = 20) examined outcomes at baseline, posttest and follow-up. Analysis employed: (i) Colaizzi's Method of empirical phenomenology to describe PwMCI experience of engagement in activity intervention related to perceptions of changes in confidence, activity performance, and physical function; (ii) Pearson's and Spearman's correlation to ascertain relationship; (iii) Linear regression to model the relationship between explanatory and dependent variables; (iv) Independent t-test to determine significant difference in activities and physical function. Results: Qualitative themes confirm improved awareness, adjustment, problem-solving, confidence and optimized function. Significant correlations were found at baseline and posttest for MCI stage, depressive symptoms, activity type and physical function. At posttest, change in self-rated performance predicted change in depressive symptoms. Additionally, those who engaged in activity at the ICF level of participation demonstrated a significant increase in confidence and physical function. Conclusion: Qualitative themes and quantitative results clearly indicate the positive impact of DEMA. Future research should employ a larger, randomized controlled longitudinal trial to ascertain DEMA impact on physical function, reduction of participation restriction and improved QOL.
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    Identifying Neuroimaging and Proteomic Biomarkers for MCI and AD via the Elastic Net
    (Springer-Verlag, 2011-09) Shen, Li; Kim, Sungeun; Qi, Yuan; Inlow, Mark; Swaminathan, Shanker; Nho, Kwangsik; Wan, Jing; Risacher, Shannon L.; Shaw, Leslie M.; Trojanowski, John Q.; Weiner, Michael W.; Saykin, Andrew J.; Department of Radiology and Imaging Sciences, IU School of Medicine
    Multi-modal neuroimaging and biomarker data provide exciting opportunities to enhance our understanding of phenotypic characteristics associated with complex disorders. This study focuses on integrative analysis of structural MRI data and proteomic data from an RBM panel to examine their predictive power and identify relevant biomarkers in a large MCI/AD cohort. MRI data included volume and thickness measures of 98 regions estimated by FreeSurfer. RBM data included 146 proteomic analytes extracted from plasma and serum. A sparse learning model, elastic net logistic regression, was proposed to classify AD and MCI, and select disease-relevant biomarkers. A linear support vector machine coupled with feature selection was employed for comparison. Combining RBM and MRI data yielded improved prediction rates: HC vs AD (91.9%), HC vs MCI (90.5%) and MCI vs AD (86.5%). Elastic net identified a small set of meaningful imaging and proteomic biomarkers. The elastic net has great power to optimize the sparsity of feature selection while maintaining high predictive power. Its application to multi-modal imaging and biomarker data has considerable potential for discovering biomarkers and enhancing mechanistic understanding of AD and MCI.
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    Identifying Neuroimaging and Proteomic Biomarkers for MCI and AD via the Elastic Net
    (Office of the Vice Chancellor for Research, 2012-04-13) Shen, Li; Kim, Sungeun; Qi, Yuan; Inlow, Mark; Swaminathan, Shanker; Nho, Kwangsik; Wan, Jing; Risacher, Shannon L.; Shaw, Leslie M.; Trojanowski, John Q.; Weiner, Michael W.; Saykin, Andrew J.; ADNI
    Abstract Multi-modal neuroimaging and biomarker data provide exciting opportunities to enhance our understanding of phenotypic characteristics associated with complex disorders. This study focuses on integrative analysis of structural MRI data and proteomic data from an RBM panel to examine their predictive power and identify relevant biomarkers in a large MCI/AD cohort. MRI data included volume and thickness measures of 98 regions estimated by FreeSurfer. RBM data included 146 proteomic analytes extracted from plasma and serum. A sparse learning model, elastic net logistic regression, was proposed to classify AD and MCI, and select disease-relevant biomarkers. A linear support vector machine coupled with feature selection was employed for comparison. Combining RBM and MRI data yielded improved prediction rates: HC vs AD (91.9%), HC vs MCI (90.5%) and MCI vs AD (86.5%). Elastic net identified a small set of meaningful imaging and proteomic biomarkers. The elastic net has great power to optimize the sparsity of feature selection while maintaining high predictive power. Its application to multi-modal imaging and biomarker data has considerable potential for discovering biomarkers and enhancing mechanistic understanding of AD and MCI.
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    White matter alterations in early-stage Alzheimer's disease: A tract-specific study
    (Elsevier, 2019-08-21) Wen, Qiuting; Mustafi, Sourajit M.; Li, Junjie; Risacher, Shannon L.; Tallman, Eileen; Brown, Steven A.; West, John D.; Harezlak, Jaroslaw; Farlow, Martin R.; Unverzagt, Frederick W.; Gao, Sujuan; Apostolova, Liana G.; Saykin, Andrew J.; Wu, Yu-Chien; Radiology and Imaging Sciences, School of Medicine
    Introduction: Diffusion magnetic resonance imaging may allow for microscopic characterization of white matter degeneration in early stages of Alzheimer's disease. Methods: Multishell Diffusion magnetic resonance imaging data were acquired from 100 participants (40 cognitively normal, 38 with subjective cognitive decline, and 22 with mild cognitive impairment [MCI]). White matter microscopic degeneration in 27 major tracts of interest was assessed using diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging, and q-space imaging. Results: Lower DTI fractional anisotropy and higher radial diffusivity were observed in the cingulum, thalamic radiation, and forceps major of participants with MCI. These tracts of interest also had the highest predictive power to discriminate groups. Diffusion metrics were associated with cognitive performance, particularly Rey Auditory Verbal Learning Test immediate recall, with the highest association observed in participants with MCI. Discussion: While DTI was the most sensitive, neurite orientation dispersion and density imaging and q-space imaging complementarily characterized reduced axonal density accompanied with dispersed and less restricted white matter microstructures.