Neuroimaging Biomarkers for Alzheimer's Disease

Recent advances in Alzheimer's disease imaging: positron emission tomography, structural, and functional MRI.

With an aging population and a prevalence of about 10% in the population older than 65 years old (Alzheimer’s Association, 2013), Alzheimer's disease is a severe public health issue and the most common cause of dementia.

Difficulties remembering names and recent events, apathy, and depression are often early clinical symptoms. Later on, the symptoms include impaired judgment, disorientation, confusion, behavior changes, and difficulty speaking, swallowing, and walking (Alzheimer’s Association, 2013).

Amyloid and Tau Imaging

Amyloid PET in a patient with Alzheimer's disease, loaded in BrainMagix and fused with a T1-weighted MRI image. Images from Klunk et al. Alzheimer's ||| Dementia 2015.
The disease is a neurodegenerative one, characterized by the accumulation of insoluble protein aggregates such as amyloid-β (Aβ) plaques and neurofibrillary tangles of hyperphosphorylated tau in the brain, resulting in brain atrophy and progressive loss of cognitive function (Nordberg, 2015). However, the origin and mechanisms of the disease are not totally understood. The amyloid hypothesis posits that an abnormal accumulation of the beta-amyloid protein in the brain is the primary influence in Alzheimer's disease pathogenesis, the rest of the process, including tangle deposition and neurodegeneration, being considered as downstream effects of an imbalance between Aβ production and clearance.

Positron emission tomography (PET) radioligand 11C-Pittsburgh compound B (PiB) has been developed more than a decade ago. It binds to fibrillar amyloid, the form of amyloid that is found in plaques, making it possible to image, in vivo, the accumulation of the amyloid protein in the brain (Klunk et al., 2004). The advent of new 18F-labeled ligands (Florbetapir, Flutemetamol, and Florbetaben), with a longer half-life, have made clinical amyloid imaging possible (Petrella, 2013). The accumulation of amyloid in the brain has been identified as an early biomarker of Alzheimer’s disease (Bateman et al., 2012). However, the interpretation of the examination, and especially of the cut-off points, is still debated (Murray et al., 2015). At this stage, its use in clinical practice is still limited because of practical, medical, and ethical considerations (Johnson et al., 2013).

In addition, the amyloid hypothesis is debated, and several recent clinical trials of anti-amyloid drugs have failed (Grainger, 2014; Nordberg, 2015), casting doubt on the usefulness of clearing amyloid at the late stage of the disease, when the brain is already severely damaged (Nordberg, 2015). A recent study identified the accumulation of the tau protein as the main driver of cognitive decline and memory loss (Murray et al., 2015), suggesting that amyloid may not be the main culprit.

As for beta-amyloid, PET tracers have been developed that specifically bind to the tau protein. However, because of Idiosyncrasies of the tau aggregation and because of a more complex tracer design, in vivo tau imaging is still limited to research purposes (Villemagne et al., 2015).

Structural MRI in Alzheimer’s Disease

MRI-based measurements of brain atrophy are regarded as valid neuroimaging biomarkers of the state and progression of Alzheimer's disease (Frisoni et al., 2010). Rates of whole-brain atrophy have been estimated at 1.4–2.2% per year in Alzheimer patients (Frisoni et al., 2010), whereas the rates of atrophy during normal aging usually do not exceed 0.7% per year. This atrophy can be quantified, via a segmentation of the brain parenchyma, in BrainMagix’s SurferMagix module (Hermoye et al., 2014).

Hippocampal volumetry in a patient with Alzheimer's disease, visualized in BrainMagix's SurferMagix Module. Hippocampal atrophy can be observed.
Atrophy of the medial temporal lobe (and especially, of the hippocampus and the entorhinal cortex) is also a valid biomarker of mild cognitive impairment (MCI) and Alzheimer’s disease progression. The rate of atrophy varies from 3 to 6% per year in Alzheimer’s disease, whereas it is limited to 0.3–2.2% per year in normal aging (Frisoni et al., 2010). Although visual rating scales or the manual outlining of the hippocampus can be used, automated software programs, such as BrainMagix's SurferMagix module, reduce the interaction time and increase the reliability of the measurement. A harmonized protocol for hippocampal volumetry has been defined in order to reduce the variability between the studies (Frisoni and Jack, 2011).

FDG-PET Imaging in Alzheimer’s Disease

A progressive reduction in glucose metabolism, as measured by 18F-FDG PET, has been reported to occur years in advance of clinical symptoms in patients with Alzheimer’s disease. This decrease is especially apparent in the parieto-temporal, frontal and posterior cingulate cortices, and is correlated with the severity of dementia. It is, therefore, a good biomarker to monitor the clinical progression of Alzheimer’s disease (Nordberg et al., 2010).

Functional MRI in Alzheimer’s Disease

Functional MRI and, especially, resting-state fMRI, in which the cooperation of the patient is not necessary, are promising tools in the diagnosis and the follow-up of patients with Alzheimer's disease (Hampel et al., 2011). However, their use is still limited to research studies. Other MRI-based techniques, such as perfusion imaging or arterial spin labeling (ASL) also show promise as diagnostic markers, but have not yet been validated as clinical biomarkers (Frisoni et al., 2010).

Time Course of Alzheimer’s Disease and Biomarkers

Hippocampal volumetry in a patient with Alzheimer disease, visualized in BrainMagix's SurferMagix Module. Hippocampal atrophy can be observed.
The curves of the disease’s progression show that amyloid deposition in the brain, decreased concentration of Aβ in the cerebrospinal fluid (CSF), tau-mediated neuronal injury, increased concentration of the tau protein in the CSF, brain atrophy, and decreased glucose metabolism can be observed at least one decade before the onset of the symptoms (Jack et al., 2010; Bateman et al., 2012; Petrella, 2013).

The diagnosis of Alzheimer’s disease, and especially of the progression from its pre-symptomatic phase, to its prodromal phase (mild cognitive impairment - MCI) to its clinical phase (dementia) is challenging. An international working group (IWG-2) has recently updated the diagnosis criteria for the disease (Dubois et al., 2014). In addition to specific clinical phenotype (i.e. MCI or dementia), decreased Aβ in the CSF, increased tau in the CSF and/or increased tracer retention on amyloid PET are defined as diagnostic biomarkers. Volumetric MRI and FDG-PET can serve as biomarkers of the disease’s progression (Dubois et al., 2014).

These early biomarkers pave the way for early detection of the disease and, potentially, for better prevention strategies (Nordberg, 2015). The development of new treatments targeting the pre-symptomatic stage of the disease is one of the most promising avenues in order to manage this disease, as well as avoid a pandemic in the aging population.

Differential Diagnosis with Non-Alzheimer's Causes of Dementia

Alzheimer’s disease is not the only cause of dementia. Distinctive features, such as strategic infarct or extensive white matter changes (in vascular dementia), focal frontal or temporal atrophy (in frontotemporal degeneration), dementia with preserved medial temporal lobes (in dementia with Lewy bodies), atrophy of putamen, middle cerebellar peduncle, pons and cerebellum (in multiple system atrophy), or high signal in the basal ganglia or in the pulvinar thalamic nuclei on FLAIR images, as well as changes in the striatum or cortical ribbon on diffusion images (in Creutzfeldt–Jakob disease) can help to discriminate Alzheimer’s disease from other causes of dementia (Frisoni et al., 2010).

References

  • Alzheimer's Association. 2013 Alzheimer's Disease Facts and Figures.
  • Bateman et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med. 2012, 367:795-804.
  • Dubois et al. Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol. 2014, 13: 614–629.
  • Frisoni et al. The clinical use of structural MRI in Alzheimer disease. Nat Rev Neurol 2010, 6:67–77.
  • Frisoni & Jack. Harmonization of magnetic resonance-based manual hippocampal segmentation: a mandatory step for wide clinical use. Alzheimers Dement. 2011, 7:171-174.
  • Grainger. Beta-amyloid: The emperor really does have no clothes. Forbes 2014.
  • Hampel et al. Recent developments of functional magnetic resonance imaging research for drug development in Alzheimer’s disease. Prog. Neurobiol. 2011, 95:570–578.
  • Hermoye et al. Quantitative Imaging Biomarker Software for Neurological Disorders. RSNA, Chicago 2014.
  • Jack et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 2010, 9: 119–128.
  • Johnson et al. Appropriate use criteria for amyloid PET: a report of the Amyloid Imaging Task Force, the Society of Nuclear Medicine and Molecular Imaging, and the Alzheimer’s Association. Alzheimers Dement. J. Alzheimers Assoc. 2013, 9: 1–16.
  • Klunk et al. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann. Neurol. 2004, 55: 306–319.
  • Klunk et al. The Centiloid Project: standardizing quantitative amyloid plaque estimation by PET. Alzheimers Dement. J. Alzheimers Assoc. 2015, 11: 1–15.
  • Murray et al. Clinicopathologic and 11C-Pittsburgh compound B implications of Thal amyloid phase across the Alzheimer’s disease spectrum. Brain 2015, 138: 1370–1381.
  • Nordberg Dementia in 2014: Towards early diagnosis in Alzheimer disease. Nat. Rev. Neurol. 2015, 11: 69–70.
  • Nordberg et al. The use of PET in Alzheimer disease. Nat. Rev. Neurol. 2010, 6: 78–87.
  • Petrella. Neuroimaging and the search for a cure for Alzheimer Disease. Radiology 2013, 269:671-691.
  • Villemagne et al. Tau imaging: early progress and future directions. Lancet Neurol. 2015, 14: 114–124.

Last updated on November 1, 2015. Revision #1

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