Serial PIB and MRI in normal, mild cognitive impairment and Alzheimers disease: Implications for sequence of pathological events in Alzheimers disease

Clifford R. Jack, Mayo Clinic, Diagnostic Radiology, 200 First Street SW, Rochester, MN 55905, USA. .
Brain (Impact Factor: 10.23). 03/2009; 132(Pt 5):1355-65. DOI: 10.1093/brain/awp062
Source: PubMed

ABSTRACT The purpose of this study was to use serial imaging to gain insight into the sequence of pathologic events in Alzheimer's disease, and the clinical features associated with this sequence. We measured change in amyloid deposition over time using serial (11)C Pittsburgh compound B (PIB) positron emission tomography and progression of neurodegeneration using serial structural magnetic resonance imaging. We studied 21 healthy cognitively normal subjects, 32 with amnestic mild cognitive impairment and 8 with Alzheimer's disease. Subjects were drawn from two sources--ongoing longitudinal registries at Mayo Clinic, and the Alzheimer's disease Neuroimaging Initiative (ADNI). All subjects underwent clinical assessments, MRI and PIB studies at two time points, approximately one year apart. PIB retention was quantified in global cortical to cerebellar ratio units and brain atrophy in units of cm(3) by measuring ventricular expansion. The annual change in global PIB retention did not differ by clinical group (P = 0.90), and although small (median 0.042 ratio units/year overall) was greater than zero among all subjects (P < 0.001). Ventricular expansion rates differed by clinical group (P < 0.001) and increased in the following order: cognitively normal (1.3 cm(3)/year) < amnestic mild cognitive impairment (2.5 cm(3)/year) < Alzheimer's disease (7.7 cm(3)/year). Among all subjects there was no correlation between PIB change and concurrent change on CDR-SB (r = -0.01, P = 0.97) but some evidence of a weak correlation with MMSE (r =-0.22, P = 0.09). In contrast, greater rates of ventricular expansion were clearly correlated with worsening concurrent change on CDR-SB (r = 0.42, P < 0.01) and MMSE (r =-0.52, P < 0.01). Our data are consistent with a model of typical late onset Alzheimer's disease that has two main features: (i) dissociation between the rate of amyloid deposition and the rate of neurodegeneration late in life, with amyloid deposition proceeding at a constant slow rate while neurodegeneration accelerates and (ii) clinical symptoms are coupled to neurodegeneration not amyloid deposition. Significant plaque deposition occurs prior to clinical decline. The presence of brain amyloidosis alone is not sufficient to produce cognitive decline, rather, the neurodegenerative component of Alzheimer's disease pathology is the direct substrate of cognitive impairment and the rate of cognitive decline is driven by the rate of neurodegeneration. Neurodegeneration (atrophy on MRI) both precedes and parallels cognitive decline. This model implies a complimentary role for MRI and PIB imaging in Alzheimer's disease, with each reflecting one of the major pathologies, amyloid dysmetabolism and neurodegeneration.

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Available from: Maria Shiung, Aug 14, 2015
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    • "In this way, experiments employing the most suitable animal model could be designed to measure similar, or ideally exact, endpoints/outcomes that are used in patients, for the most reliable predictions to be made. This has been adopted in certain respects, with the use of characterized biomarkers between patients and animal models for studying neurodegeneration (Delatour, Guegan, Volk, & Dhenain, 2006; Jack et al., 2009), such as in PD with in vivo SPECT imaging measurement of [99mTc]-TRODAT-1 binding (Fernagut et al., 2010; Meissner et al., 2003; Prunier et al., 2003), or in AD research, with levels of amyloid-b and phosphorylated tau proteins in the CSF (Buerger et al., 2006; Hampel et al., 2004; Head et al., 2010; Zilka, Korenova, Kovacech, Iqbal, & Novak, 2010). "
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    • "Since the subject's morphometric information is already built into the estimate of b via Equation 6, our result might simply imply that CSF biomarkers lack incremental power to predict rate of progression, beyond what is explained by imaging. This is in line with converging understanding based on the early deposition and subsequent plateauing of amyloid (Villemagne et al., 2013), that while CSF biomarkers are good predictors of conversion risk, neurodegenerative markers like MRI are more sensitive predictors of current disease state and its rate of decline (Da et al., 2014; Dickerson and Wolk, 2013; Fjell and Walhovd, 2011; Jack et al., 2009; Vemuri et al., 2009). Our dichotomized CSF results support this interpretation, such that CSF biomarker levels appear to exert an effect on rate of progression only beyond the pathologic threshold (Fjell et al., 2010; Mattsson et al., 2014; Schott et al., 2010). "
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    • "It is also important to investigate if and how neuroimaging measures might relate to cognitive decline. Studies have reported dissociations between amyloid deposition and cognitive decline in healthy controls and subjects with MCI and AD (Jack et al., 2009) while others have found associations between amyloid burden and poorer cognitive performance (Kennedy et al., 2012; Rodrigue et al., 2012) and even changing associations based on the disease stage (Chetelat, 2013; Jack et al., 2009; Naslund et al., 2000; Villemagne et al., 2013). The potential predictive power of DTI on cognition has been less explored, but there is evidence that DTI values correlate with decline in working memory in healthy elderly (Bendlin et al., 2010a; Charlton et al., 2006; Pfefferbaum et al., 2000; Raz et al., 2010), and significant correlations between measures of white matter and AD symptom severity have been found, suggesting that A C C E P T E D M A N U S C R I P T "
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