There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Jaime Mondragon |
| Institution: | University Medical Center Groningen |
| Department: | Neurology |
| Country: | |
| Proposed Analysis: | I. Motivation The Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a multicenter collaboration with the common goal of collecting, validating and utilizing data such as MRI and PET images, genetics, cognitive tests, CSF and blood biomarkers as predictors to define the progression of Alzheimer’s disease (Mueller et al., 2005). ADNI is an ideal large multicenter longitudinal study that can provide supporting evidence for resting state connectivity (rs-fMRI) changes associated with late-onset Alzheimer’s disease (LOAD). Assessment of rs-fMRI in mild cognitive impairment (MCI) and LOAD is possible through the functional connectivity analysis of arterial spin-labeled (ASL) and blood oxygen level dependent (BOLD) weighted contrasts. In the ADNI, cognitive reserve as a protective factor for conversion of MCI to LOAD can be studied since education can be used as a proxy for cognitive reserve (Stern Y, 2009) and multiple cognitive evaluations, MRI scans and biobank materials have been collected (Mueller et al., 2005). Recently, a study for the ADNI, evaluated anosognosia, using ECog, as an independent predictor of conversion from MCI to LOAD (Gerretsen et al., 2017). Our group has previous experience with network analysis in relation to cognitive ageing using 1) node centrality (Geerligs et al., 2014b) 2) cluster analysis (Geerligs et al., 2014a, Saliasi et al., 2015); 3) modularity (Geerligs et al., 2015); 4) local efficiency (Geerligs et al., 2015); 5) global efficiency (Geerligs et al., 2015) and 6) functional connectivity between networks (Geerligs et al., 2014b). In addition, our group has experience in both independent component analysis and region of interest analysis of brain networks for both BOLD and ASL contrasts. II. Summary Background The research and clinical consensus suggest the division of the cognitive decline continuum in LOAD into three stages, a preclinical, a prodromal and a clinical (i.e. mild, moderate and severe; Sperling et al., 2011). MCI is the transitional cognitive state between normal aging and mild dementia (Petersen et al., 2001). Of particular interest is amnestic mild cognitive impairment (aMCI) due to its emphasis on memory loss. LOAD is a progressive cerebral disease defined by a clinical and a pathological component; clinically, this disease involves anterograde memory impairment and deficit in one or several of the following cognitive domains: language, visuospatial ability, praxis and executive functioning (Boughey et al., 2007). The National Institute on Aging and the Alzheimer’s Association revised criteria for LOAD proposes criteria for all-cause dementia or core clinical criteria for dementia and criteria for LOAD dementia (McKhann et al., 2011). The terminology proposed by the NIA-AA for classifying LOAD patients are, “probable AD dementia”, “possible AD dementia”, and “probable or possible LOAD dementia with evidence of the LOAD pathophysiological process” (McKhann et al., 2011). With the first two designations intended all clinical settings use, while the third designation intended for research purposes (McKhann et al., 2011). Anosognosia is the unawareness or denial of a neurological deficit (Langer and Levine, 2014). Impaired self-awareness alludes to the partial loss of knowledge; correspondingly, anosognosia involves complete loss of knowledge of one’s impaired neurological or neuropsychological functioning (Prigatano, 2014). Anosognosia incidence and prevalence have reported large variability across dementia populations. Anosognosia for activities of daily living (ADLs) deficits can be present from an early stage of LOAD dementia and have a reported frequency between 20% and 80%; having a great degree of variability due to the use of diverse diagnostic methods, sampling bias (i.e. community versus memory clinic samples) and the use of heterogeneous sample sizes (Starkstein, 2014). Patients with mild or moderate LOAD have a reported incidence between 21.0 and 38.3% and a prevalence between 3.5 and 71.0% for anosognosia (Starkstein et al., 2010; Castrillo-Sanz et al. 2016; Turró-Garriga et al., 2016). Cross-cultural assessment of the differences in unawareness of memory deficits in a large community-based study, report regional differences in the frequency of anosognosia, 81.2% in India, 72.0% in Latin America and 63.5% in China (Mograbi et al., 2012). Recently, in an analysis of ADNI data, anosognosia has been associated as an independent predictor of conversion from MCI to LOAD, represented by hypometabolism in the PCC and right angular gyrus (Gerretsen et al., 2017). Assessment of anosognosia in patients with cognitive decline is possible with the Everyday Cognition scale (ECog; Gerretsen et al., 2017). Cognitive reserve increases an individual's ability to sustain high levels of neuronal injury without the onset of clinical symptoms (Sperling et al., 2011; Steffner et al., 2012; Skup et al., 2011). This concept provides an explanation to understand the susceptibility differences to pathological changes between individuals; while some are capable of withstanding these changes maintaining an adequate function, others are not (Steffner et al., 2012; Skup et al., 2011). Genetic and epigenetic factors, such as environment and education, modulate cognitive reserve and neuroplasticity (Vasile C, 2013); however, further evaluation of these associations is needed. The concept of cognitive reserve represents the ability to activate alternate neural networks or cognitive strategies to cope with this besieging pathology (Sperling et al., 2011). Cognitive reserve has positive effects in patients with amyloid-β positive biomarkers (e.g. CSF or PET), having greater effects on attention and executive function in preclinical stages of the LOAD continuum (Groot et al., 2018). Even though patients with greater cognitive reserve can tolerate greater pathological burden, they deteriorate more quickly when cognitive impairment symptoms manifest (Steffner et al., 2012). While the concept of cognitive reserve as a biomarker is under investigation (Steffner et al., 2012), the future of cognitive decline assessment steers to tests that measure functional injury rather than structural damage; nonetheless, functional and structural diagnostic tools have a complementary value. Efforts to describe the normal changes in brain connectivity between younger and older populations are underway; showing that aging has a large impact, not only on connectivity within functional networks but also on connectivity between networks (Geerligs et al., 2015). Connectivity within networks significantly decreases with progression of age (Sombataro et al., 2010; Grady et al., 2010). These changes include decreased modularity and decreased local efficiency, especially in networks supporting higher-level cognitive functions like the DMN, cingulo-opercular and frontoparietal control networks (Geerligs et al., 2015). The DMN is a set of functionally connected brain regions, which shows task-induced deactivation during a cognitive task (Sambataro et al., 2010). Most rs-fMRI studies have found reduced DMN connectivity in LOAD and various other diseases of cognitive impairment (Barkhof et al., 2014). Scientific evidence of brain connectivity in LOAD points toward decreased connectivity between the DMN, the hippocampus and the posterior cingulate when compared to age-matched subjects (Greicius et al. 2004; Sorg et al., 2007). PET and fMRI are helpful to delineate the DMN and through connectivity efficiency, measurements permit a qualitative measure of cognitive reserve (Steffner et al., 2012; Greicius et al., 2004). The DMN is closely associated with episodic memory processing (Shin et al., 2011). A multivariate analysis combining single DMN region ICA and single correlation of time courses between 2 DMN regions yielded a diagnostic accuracy of 97% (sensitivity and specificity are 100% and 95%) (Koch et al., 2012). Overall connectivity decreases in LOAD patients, exemplified by reduced global clustering as well as regional differences in clustering (Sporns O, 2011). Recent attempts from the Alzheimer’s Imaging Consortium to establish a robust biomarker of large-scale network failure in Alzheimer’s disease have yielded the novel network quotient, a biomarker for the cascading failure model of LOAD (Jones et al., 2016, Wiepert et al., 2017). The cascading failure model of LOAD is a hypothetical characterization of the progressive connectivity changes across the entire disease spectrum (Jones et al., 2016). This model proposes that the posterior DMN fails before finding measurable PET amyloid plaques, sparking a connectivity cascade failure reminiscent of a power outage on a power grid. In addition, the model poses the association of amyloid accumulation and the existence of high connectivity between the posterior DMN and frontal lobe hubs (Jones et al., 2016). Vascular dysregulation compromises hemodynamic and metabolic neuroimaging biomarkers. Vascular dysregulation has been proposed as the initial pathologic event leading to LOAD with the multifactorial causal model of brain (dis)organization recently contesting the most cited model (i.e. hypothetical model of dynamic biomarkers) which tries to explain the temporal appearance of pathophysiological biomarkers associated to LOAD (Iturria-Medina et al., 2017). While the hypothetical model of dynamic biomarkers (Jack et al., 2013) introduces a possible temporal biomarker incidence model, it fails to reflect the multifactorial and interactive nature of biomarkers with different spatiotemporal scales (Iturria-Medina et al., 2016). ASL perfusion is a non-invasive MRI sequence that allows quantification of blood flow. Extensive validation of ASL is available against other exogenous contrasts, such as 15O-PET (Fan et al., 2016). Local changes in cerebral perfusion using ASL in neurodegenerative diseases, such as MCI, LOAD, frontotemporal dementia, vascular dementia, and dementia of mixed etiology have been identified (Alsop et al., 2015; Ferré et al., 2013; Wolk and Detre, 2012). ASL is another MRI contrast used to a lesser extent than blood oxygen level dependent (BOLD) to assess rs-fMRI. BOLD and ASL contrast complement each other. While BOLD signal has a higher temporal resolution, ASL provides a quantitative and direct measure of the physiology of specific networks (Jann et al., 2015). BOLD and ASL have a moderated to a high level of spatial overlap, the combination both rs-fMRI provides a powerful tool for characterizing the spatiotemporal and quantitative properties of resting brain networks (Jann et al., 2015). Graph analysis of rs-fMRI ASL perfusion is capable of mapping the brain connectome (Liang et al., 2014) and can serve as an alternative to the BOLD technique to assess resting-state functional connectivity (Chen et al., 2015). Network connectivity has the potential to become a biomarker for MCI as there have been changes reported in the network topology in aMCI patients that progress to LOAD (Wang L. et al., 2013, Wang J. et al., 2013, Franzmeier et al., 2017). Patients with aMCI compared to healthy subjects have reduced rs-fMRI activity, including regions such as posterior cingulate cortex, right angular gyrus, parahippocampal gyrus, left fusiform gyrus, left supramarginal gyrus and bilateral middle temporal gyri (Lau et al., 2016). Patients with aMCI who convert to LOAD display a topological reorganization of the functional connectome (Wang J. et al., 2013). Nonetheless, prospective longitudinal studies with strong statistical power have yet to explore the concept of cognitive reserve and its association to other biomarkers (such as APOE genotype, CSF biomarkers and structural MRI measurements in patients with aMCI that progress to LOAD over time). III. Cooperation with other participants, projects/organizations Post-acquisition image processing of BOLD sequences will be performed in cooperation with the Neuroimaging center (NiC), Neuroscience Department, UMC Groningen. While ASL image processing will be performed with the assistance of Dr. Sánchez-Catasús. IV. Research questions What is the rs-fMRI within and between brain networks such as the default mode network (DMN) and the frontoparietal network (FPN) (could be obtained from rs-fMRI data in the ADNI Study) in MCI patients and how is it related to conversion to LOAD in these patients? Our hypotheses are that: 1. MCI patients with lower GFC values will convert to LOAD at a higher rate and will have a higher rate of abnormal network metrics (modularity and hub connectivity) than MCI patients who do not convert to LOAD. 2. MCI patients that convert to LOAD will have increased path length, decreased nodal strength in the DMN and other networks and impaired functional connectivity between the different networks. How is the cognitive reserve (represented by years of education in the ADNI Study) related to (changes in) network connectivity and conversion to LOAD in MCI patients? Our hypotheses are that: 1. MCI patients with more years of education will show higher rs-fMRI global functional connectivity (GFC) values (Franzmeier et al., 2017) within the cognitive control network compared with subjects with less education. 2. MCI patients with more years of education will show less of a decrease in GFC values through time than patients with fewer years of education. 3. MCI patients with more years of education will have lower conversion rates to LOAD and this will be related to higher GFC values and smaller GFC changes over time. What is the association between cognitive decline as represented by changes in intra- and internetwork connectivity and by psychometric tests? Our hypothesis is that: 1. MCI patients that have large-scale topological reorganization of the DMN and other brain networks at baseline and between measurements will perform more poorly on the baseline cognitive assessments and will show stronger decline in performance over time What is the relationship between brain connectivity measured through changes in BOLD signal and cerebral perfusion using arterial spin-labeled contrasts and anosognosia in patients with LOAD and MCI? Our hypotheses are that: 1. In MCI-nonconverters, anosognosia will be associated with hypoactivation (i.e. measured through ASL and BOLD contrasts) of Cortical Midline Structures (i.e. posterior cingulate cortex and right angular gyrus). 2. In MCI-converters, anosognosia will be associated with hypoactivation (i.e. measured through ASL and BOLD contrasts) of Cortical Midline Structures (i.e. posterior cingulate cortex and right angular gyrus). 3. In LOAD, anosognosia will be associated with hypoactivation and within network connectivity of the DMN (i.e. measured through ASL and BOLD contrasts). V. Aims 1. To understand network dynamics of MCI patients who do or do not convert to LOAD. 2. To determine the associations between network connectivity (functional) and topological changes versus possible protective mechanisms such as cognitive reserve, and independent predictors such as APOE genotype, sex, and (changes in) cognitive performance. 3. To determine whether rs-fMRI connectivity within and between networks can be used as a biomarker for cognitive decline. 4. To compare activation patterns in patients with anosognosia and with different cognitive reserve through ASL and BOLD. 5. To calculate and show differences in risk of LOAD progression at 12 months. VI. Plan of action Design Retrospective longitudinal study Number of required patients N: 120 (minimum) However, as many subjects available would be recommended for inclusion. Power analysis The statistical power of the mean 2-sided equality test with significance level 0.05 is calculated based on the group means, standard deviations and sample sizes, using online power calculator (“Power and Sample Size, Free Online Calculators”). Data from a previous analysis (Geerligs et al., 2015) was used to calculate the sample size which yielded n=65; using the comparison of global versus local efficiency in healthy elders as the variable of interest. The sample size was adjusted to comply with the multisite and multi-scanner nature of the data acquisition, to n=120 (Dansereau et al., 2017). Recently, a study for the ADNI, evaluated anosognosia, using ECog, as an independent predictor of conversion from MCI to LOAD (Gerretsen et al., 2017). This group examined the relationship between brain glucose metabolism and anosognosia, reporting acceptable effect sizes with a sample size of 191 for LOAD, 499 for MCI and 372 for healthy subjects. Inclusion/Exclusion criteria Inclusion: participants included in the ADNI database; patients must be classified as having subjective cognitive complaints, aMCI, multiple domain MCI, or LOAD at baseline; all subjects must have a Sagittal 3D T1-weighted gradient-echo sequence, and 2D rs-fMRI (ASL and BOLD) at baseline. Exclusion: as defined by the Alzheimer’s Disease Neuroimaging Initiative study protocol (Mueller et al., 2005). Exact list of desired clinical variables from ADNI Clinical data: clinical status or diagnostic category (LOAD, MCI or Normal) at baseline 12 months and available at subsequent follow-up, demographic data such as gender, race, ethnicity, age, mean age for scans, education, APOE4 status. Scales and questionnaires: CDR score will be used to assess disease progression on LOAD converters; addressing the first aim of this project. Cognitive assessment: MMSE (global cognition), Local Memory (Immediate and Delayed Recall), Digit-Span forward and backward (working memory), Verbal Fluency tests (semantic memory), Trail Making parts A and B (visual search speed, scanning, speed of processing, mental flexibility, as well as executive functioning), ECog (Anosognosia), and any other ADNI cognitive measure available in the Uniform Data Set (UDS) or non-UDS cognitive measures. These scores will be used to assess cognitive decline to elucidate correlations between cognitive decline and network changes; addressing the third aim of this project. Cerebrospinal fluid: CSF to Aβ1-42 levels will be used to quantify brain β-amyloid. CSF t-tau and p-tau levels will be used as neuronal injury screening biomarkers. MRI: Sagittal 3D T1-weighted gradient-echo sequence to establish stereotactic map. Structural magnetic resonance imaging (i.e.T1-weighted and 3D SPGR or similar volumetric sequences) will be used for partial volume correction as well as neuronal injury biomarkers. 2D rs-fMRI for activation and connectivity analysis of BOLD sequence and 2D T2-weighted FLAIR turbo/fast spin-echo for coregistration purposes. ASL perfusion images (i.e. continuous, pseudpcontinous or pulsed ASL) will be used for activation and for connectivity analysis. Positron emission tomography: PET imaging for β-amyloid (i.e. PiB or Florbetapir) will be used to quantify brain β-amyloid and for amyloid burden classification. While FDG-PET activation will be used as a metabolism biomarker; additionally, these regions will be compared to the results obtained through the ASL and BOLD activation analysis. Statistical analysis plan All data processing and analysis steps will performed in Matlab (Mathworks, Natick, MA), using functionality provided by the SPM12 software package (University College London, UK) as well as customized scripts and functions. Statistical analysis will be performed SPSS 24 (SPSS Inc., Chicago, IL). Clinical characteristics including age, gender, years of education, diagnosis, neuropsychological performance scores, ECog scores will be analyzed using Chi-squared and Fisher’s test when appropriate. ECog scores and cognitive test scores will be compared between sites and diagnostic groups using analysis of covariance (ANCOVA) adjusted for age, education, gender, and diagnosis. Associations between ECog scores and years of education with objective cognitive test scores will be assessed using Pearson’s correlation coefficients. Kruskal-Wallis test will be used analysis of variance on non-parametric variables. Multivariate-adjusted Cox proportional hazard regression models will be used to study the relationship between MCI groups at baseline and conversion rate to LOAD. Adjusted Cox regression models will be used to control the influence of sociodemographic, neuropsychiatric, neuropsychological and genetic variables. Kaplan-Meir plots will be used to show differences in risk of LOAD progression at 12 months. VII. Timeline and Deliverables Once all imaging data has been collected from the ADNI database, image post-processing will take 4 months, image analysis will take 4 months and statistical analysis of the data will take an additional 4 months. We expect that it will take 3 months to write the first draft of a manuscript. In total, we expect this project to take 15 months from start to the first draft of the manuscript, once post-processing of the images begins. References Alsop DC, Detre JA, Golay X, et al. (2015). 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| Additional Investigators | |
| Investigator's Name: | Natasha Maurits |
| Proposed Analysis: | Will be responsible for the post-acquisition image processing of BOLD sequences. Will assist in the analysis of the results (activation, connectivity, and metabolism), as well as be part of the overall manuscript preparation. |
