As neuroimaging advances paint ever more detailed pictures of the brain’s wear and tear during Alzheimer’s disease, a glaring problem emerges: The pictures don’t always match. In a study published in this week’s Journal of Neuroscience, Gaël Chételat, INSERM-EPHE-University of Caen, France, and colleagues compared three types of neuroimaging scans in the same AD patients. The analysis revealed distinct patterns of atrophy, hypometabolism, and amyloid accumulation across different brain areas; amyloid profiles in particular differed sharply from the other two. Besides offering a new method for comparing imaging techniques, the work suggests that non-amyloid mechanisms may underlie regional differences in pathology in AD. Chételat presented some of these data at meetings earlier this year (see ARF conference story and ARF Webinar).

In another multimodal imaging analysis reported in the same journal issue, researchers led by Dorene Rentz and Trey Hedden of Massachusetts General Hospital, Boston, asked if neuropathologies common to age-associated conditions—brain Aβ in AD, white matter abnormalities in cerebrovascular disease (CVD)—affect different cognitive skills in normal elderly. The team found no correlation between these two, suggesting that they reflect different biological pathways with distinct effects on cognition.

Research dating back more than a decade has shown inconsistent patterns of atrophy and hypometabolism in the brains of AD patients. To assess these patterns, Chételat and colleagues developed a statistical method to directly compare magnetic resonance imaging (MRI) and fluorodeoxyglucose (FDG) positron emission tomography (PET) data in the same subject. As they previously reported, certain brain areas showed similar degrees of atrophy and hypometabolism, whereas most did not (Chételat et al., 2008).

Now, first author Renaud La Joie and colleagues added a third measurement to the mix—brain Aβ assessed by florbetapir PET. Applying the same statistical method, they found that brain areas were differentially vulnerable to atrophy, hypometabolism, and amyloid buildup. For example, in the hippocampus, atrophy exceeded hypometabolism and Aβ load was low. In contrast, frontal regions had high amyloid, but minimal atrophy and hypometabolism. Posterior association areas were amyloid laden, with notable hypometabolism and moderate atrophy.

“The study presents an approach to systematically put the multimodal imaging data in the same units, which is important when comparing across modalities and investigating regional differences between them,” noted Prashanthi Vemuri of Mayo Clinic, Rochester, Minnesota, in an e-mail to Alzforum (see full comment below).

Furthermore, the findings expand possibilities for exploring non-amyloid mechanisms, suggested Elizabeth Mormino, a postdoc working with Reisa Sperling of Massachusetts General Hospital in Boston. Determining why regional discrepancies exist has important implications, Mormino noted, “For instance, if anterior regions are, in fact, resistant to amyloid-related toxicity, understanding how these regions avoid deleterious effects may reveal ways in which amyloid might be combated.”

Chételat said the work seems consistent with recent studies suggesting that the order of AD biomarker changes during disease progression plays out slightly differently in familial AD cohorts compared to what current models propose. For example, the Alzheimer’s Prevention Initiative recently reported that young presenilin 1 mutation carriers had grey matter atrophy, even though this is usually a late symptom detected after amyloid and tau deposition (ARF related news story; see also Bateman et al., 2012; ARF Webinar on AD biomarkers), Chételat said.

In the other multimodal imaging study, the MGH researchers examined both brain Aβ and white matter hyperintensities (WMH) in the same cognitively normal elderly people. These are pathological hallmarks of AD and cerebrovascular disease, respectively, which appear to share common risk factors based on epidemiological studies (see de la Torre, 2010). However, other research suggests the biomarkers may affect cognition differently, with amyloid buildup primarily degrading memory and white matter changes weakening executive control (see Hedden et al., 2012).

Hedden and colleagues analyzed 168 healthy seniors who had PET amyloid imaging, MRI measures of white matter degradation, and neuropsychological testing as participants in the Harvard Aging Brain longitudinal study. The researchers developed a statistical algorithm—similar to the one used by Chételat’s team—that assigns global values to amyloid and WMH measurements across defined brain areas, and compares them on a common scale. They looked at each biomarker’s relation to several cognitive domains—episodic memory, executive function, and processing speed. As hypothesized, amyloid burden distinctly affected episodic memory, whereas WMH primarily influenced executive function with milder effect on other cognitive domains. The two biomarkers did not correlate with each other. The results suggest that “even before clinical impairment, amyloid burden and WMH likely represent neuropathological cascades with distinct etiologies and dissociable influences on cognition,” the authors wrote.

Adam Brickman of Columbia University, New York, praised the study’s “beautiful statistics and careful methodology.” However, he added, the effect sizes are small, and the data do not disprove the idea that amyloid and WMH might interact in some way. The authors agree, noting that selection bias could have accounted in part for the failure to detect a relationship between the two biomarkers. Seniors with a “double hit” might be more likely to suffer cognitive loss that would have excluded them from the study. In essence, this would remove the subjects who might have a correlation between amyloid and white matter changes.

Recent analyses from the Dominantly Inherited Alzheimer Network (DIAN) found unusually high white matter damage. This suggests that amyloid burden and WMH may indeed be linked in people with familial AD mutations (see ARF related news story)—unlike what Hedden’s team saw in elderly normals. Hedden thinks cerebral amyloid angiopathy (CAA)—a process by which amyloid clogs the walls of blood vessels—could account for the apparent association between brain Aβ and white matter abnormalities in mutation carriers. “It is quite possible that mutation carriers have CAA as well as the cerebral amyloidosis that is more typical of AD,” Hedden said. In advanced stages of sporadic AD, he added, it is possible that amyloid could spread to blood vessels and affect white matter change through a CAA route.—Esther Landhuis.

References:
Hedden T, Mormino EC, Amariglio RE, Younger AP, Schultz AP, Becker JA, Buckner RL, Johnson KA, Sperling RA, Rentz DM. Cognitive Profile of Amyloid Burden and White Matter Hyperintensities in Cognitively Normal Older Adults. J Neurosci. 14 Nov 2012;32(46):16233-42. Abstract

La Joie R, Perrotin A, Barré L, Hommet C, Mézenge F, Ibazizene M, Camus V, Abbas A, Landeau B, Guilloteau D, de La Sayette V, Eustache F, Desgranges B, Chételat G. Region-Specific Hierarchy Between Atrophy, Hypometabolism, and β-Amyloid (Aβ) Load in Alzheimer’s Disease Dementia. 14 Nov 2012;32(46):16265-73. Abstract

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Comments on News and Primary Papers

  1. In this study, multimodal imaging data from the same subjects provide the authors with a unique opportunity to investigate the regional specificity of different Alzheimer’s disease pathologies. The study presents an approach to systematically put the multimodal imaging data on the same scale so that comparisons between scans are valid. This is important when comparing across modalities, and allowed the authors to investigate the regional differences between modalities. The results neatly tie together the findings in the existing literature and propose a new methodology to investigate regional hierarchy of AD pathologies. It is, however, important to remember that the modality-specific regional differences they found may be due to the differences in the specificity of the imaging technology in detecting the pathological signal. Moving forward, there is a need to employ such methodologies in subjects with longitudinal follow-up as well as in subjects covering the entire cognitive spectrum to improve our understanding of the disease.

  2. In this paper, La Joie et al. provide convincing evidence for distinct patterns of brain changes (atrophy, hypometabolism, and amyloid deposition) across different brain regions in Alzheimer’s disease.

    Although the presence of regional discrepancies requires more complicated explanations, an understanding of why these discrepancies exist has important implications. For instance, if anterior regions are, in fact, resistant to amyloid-related toxicity, an understanding of how anterior regions avoid deleterious effects may reveal ways in which amyloid might be combated. These regional discrepancies also highlight the potential relevance of non-amyloid etiologies, such as neurofibrillary tangles, comorbidities, etc., which are important considerations for a field that is highly focused on amyloid.

    View all comments by Elizabeth Mormino

References

News Citations

  1. Paris: Beyond Genomics, French Science Draws on Populations, Patients
  2. API Echoes DIAN: Biomarker Changes Precede Symptoms by 20 Years
  3. Expanding the Network, DIAN Starts Showing Longitudinal Data

Webinar Citations

  1. Adding Dynamics and Nuance to Alzheimer’s Staging
  2. Together at Last, Top Five Biomarkers Model Stages of AD

Paper Citations

  1. . Direct voxel-based comparison between grey matter hypometabolism and atrophy in Alzheimer's disease. Brain. 2008 Jan;131(Pt 1):60-71. PubMed.
  2. . Clinical and biomarker changes in dominantly inherited Alzheimer's disease. N Engl J Med. 2012 Aug 30;367(9):795-804. PubMed.
  3. . Vascular risk factor detection and control may prevent Alzheimer's disease. Ageing Res Rev. 2010 Jul;9(3):218-25. PubMed.
  4. . Failure to modulate attentional control in advanced aging linked to white matter pathology. Cereb Cortex. 2012 May;22(5):1038-51. PubMed.
  5. . Cognitive profile of amyloid burden and white matter hyperintensities in cognitively normal older adults. J Neurosci. 2012 Nov 14;32(46):16233-42. PubMed.
  6. . Region-specific hierarchy between atrophy, hypometabolism, and β-amyloid (Aβ) load in Alzheimer's disease dementia. J Neurosci. 2012 Nov 14;32(46):16265-73. PubMed.

External Citations

  1. Alzheimer’s Prevention Initiative
  2. Dominantly Inherited Alzheimer Network

Further Reading

Papers

  1. . Cognitive profile of amyloid burden and white matter hyperintensities in cognitively normal older adults. J Neurosci. 2012 Nov 14;32(46):16233-42. PubMed.
  2. . Region-specific hierarchy between atrophy, hypometabolism, and β-amyloid (Aβ) load in Alzheimer's disease dementia. J Neurosci. 2012 Nov 14;32(46):16265-73. PubMed.
  3. . Direct voxel-based comparison between grey matter hypometabolism and atrophy in Alzheimer's disease. Brain. 2008 Jan;131(Pt 1):60-71. PubMed.
  4. . Clinical and biomarker changes in dominantly inherited Alzheimer's disease. N Engl J Med. 2012 Aug 30;367(9):795-804. PubMed.

Primary Papers

  1. . Cognitive profile of amyloid burden and white matter hyperintensities in cognitively normal older adults. J Neurosci. 2012 Nov 14;32(46):16233-42. PubMed.
  2. . Region-specific hierarchy between atrophy, hypometabolism, and β-amyloid (Aβ) load in Alzheimer's disease dementia. J Neurosci. 2012 Nov 14;32(46):16265-73. PubMed.