Caroli webinar 05 01 2011_v2
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webinar presented on AlzForum January 5, 2012.
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Caroli webinar 05 01 2011_v2
- 1. Adding Dynamics and Nuance to Alzheimer’s Staging AlzForum webinar – 05.01.2012 Anna Caroli Laboratory of Epidemiology and Neuroimaging IRCCS Fatebenefratelli, Brescia, Italy Medical Imaging Unit Mario Negri Institute, Bergamo, Italy
- 2. Multimodal Imaging AD definitively diagnosed only by histopathology Initiating mechanisms not fully understood, yet Several neuroimaging biomarkers for AD Multimodal imaging: Small et al. Nat Neurosci 2005 - relationship between individual cerebral damages (overall cerebral damage) - increase in prognostic power and diagnostic accuracy
- 3. Compensatory and depression mechanisms Hypoperfusion and atrophy patterns do not overlap (MCI due to AD) Caroli et al. J Neurol 2007 9 MCI due to AD and 17 NC with SPECT and MRI GM atrophy and hypoperfusion (SPM) Structural/perfusional deficit relationship assessed voxel-based Caroli et al. Neurobiol Aging 2010 (epub 2008) 25 AD and 21 NC with FDG-PET and MRI GM atrophy and hypometabolism (SPM) Structural/metabolic deficit relationship assessed voxel-based (BPM) Caroli et al. Dement Geriatr Cogn Disord 2010 GM structural and functional deficit: similar regions, different degrees of severity Regional functional compensatory and depression mechanisms
- 4. Amyloid toxicity 23 AD and 17 NC with PIB-PET and MRI GM atrophy and PIB uptake assessed voxel-based GM atrophy Pearson’s correlation PIB uptake Significance Medial temporal lobe highly susceptible to amyloid toxicity, neocortical areas more resilient (AD) Frisoni et al. Neurology 2009
- 5. Multimodal imaging: still much to do… Structural damage, functional alterations, and protein build-up: interrelated but not concurrent nor co-localised phenomena - assess region-specific relationship - assess compensatory mechanisms at different stage - compare biomarker dynamics
- 6. Biomarker dynamics: theoretical model Frisoni et al. Nat Rev Neurol 2010
- 7. Biomarker dynamics: first evidence 229 NC, 154 MCI due to AD, 95 early AD, 98 late AD from ADNI Aβ 1-42, tau, hippocampal volume, and FDG-PET Linear and sigmoid fits in comparison Sigmoid better than linear fit (all but FDG-PET) - Aβ 1– 42: steep, early stabilizing time course - Tau/hippocampal volume: later change, reflecting disease progression - FDG-PET: early change, likely follows a linear decline Caroli et al. Neurobiol Aging 2010
- 8. Open issues - Biomarker dynamics from healthy cognition - Effect of co-occurring diseases on biomarker dynamics - Biomarker dynamics in disease-specific cerebral regions Clinical issues: -Which is the “best” biomarker? Stage-specific choice? - Biomarkers combination in clinics?
- 9. Thank you Epidemiology and Neuroimaging Lab Medical Imaging Unit IRCCS Fatebenefratelli Mario Negri Institute Brescia, Italy Bergamo, Italy Giovanni B Frisoni Andrea Remuzzi Marina Boccardi Luca Antiga Martina Bocchetta Katia Passera Jenny Borsci Simone Manini Enrica Cavedo Samantha Galluzzi Marco Lorenzi Moira Marizzoni Cristina Muscio Donata Paternico’ Michela Pievani Annapaola Prestia acaroli@fatebenefratelli.it
Editor's Notes
- Thanks Laura, and good morning to everybody. In this presentation I will focus on a couple of aspects our group has been working on during the last years, which are 1) the investigation of the relationship between AD biomarkers through multimodal imaging techniques, and 2) the investigation and comparison of biomarkers dynamics. I will first show some results and then share with you some ideas about potential future directions and open issues
- As we know, at present AD can be definitively diagnosed only by histopathology, and initiating mechanisms are not fully understood, yet. In her presentation, Gael has already given an overview of available biomarkers, showing that neuroimaging is playing a more and more relevant role in the identification and quantification of AD in vivo, especially in preclinical stages As individual imaging techniques enable to study single cerebral alterations, they need to be combined to assess the overall cerebral damage. Multimodal imaging enables to investigate the relationship between different imaging biomarkers in the regions affected by AD neuropathology in order to have a more complete picture, and could enable to increase prognostic power and diagnostic accuracy of each individual imaging technique.
- A first application of multimodal imaging on our side was the investigation of compensatory and depression mechanisms in AD. In a past study about MCI due to AD we showed that hypoperfusion and atrophy patterns do not overlap, suggesting that perfusional deficit is not entirely due to structural loss. We then went on investigating the voxel-wise relationship between structural and perfusional deficit in the same group: we assessed perfusional compensatory and depression patterns, in terms of relative preserved perfusion and hypoperfusion exceeding atrophy, respectively, and we found the presence of a perfusional compensatory mechanism taking place in the neocortex, trying and counteracting the pathological changes of AD We performed a similar study to investigate metabolic compensatory and depression patterns (using FDG-PET) in a different group of AD patients, and we found the presence of both compensatory mechanism (likely reflecting spared synaptic plasticity of the surviving neurons) and exceeding hypometabolism, likely due to distant effects of atrophy and additional factors (e.g. amyloid) Despite differences, both studies suggested that GM structural and functional deficit map to similar regions, with different degrees of severity; and both regional functional compensatory and depression mechanisms take place since preclinical stage
- Another application of multimodal imaging was the investigation of amyloid toxicity in AD. We considered a group of AD patients who underwent both MR and PIB-PET imaging, we assessed GM atrophy and PIB uptake patterns, and we then investigated voxel-wise Person’s correlation between the two phenomena. What we found was that amyloid deposition was significantly associated with atrophy only in the MTL, suggesting that such region could be highly susceptible to amyloid toxicity while neocortical areas are more resilient
- Our results, together with those from other groups (including those previously shown by Gael), suggest that structural damage, functional alterations and protein build-up are interrelated but not concurrent nor co-localised phenomena. This first relevant finding opens the way to several future directions: 1 – the region-specific relationship should be assessed in order to increase early diagnostic accuracy and to identify novel targets for pharmaceutical intervention Secondly, compensatory mechanisms should be investigated at different disease stage, in order to understand the mechanisms that trigger the disease onset and drive its progression And to map the time course of functional brain failure Last, biomarkers dynamics should be investigated and compared to express the disease process in terms of a series of testable biological indicators Let me now focus on the latter point
- Gael has already given an overview of the theoretical models used to describe and compare the natural progression of AD biomarkers. Let me recall in particular the model originally proposed by Jack and colleagues, and further supported by our group, according to which AD biomarkers follow a sigmoid trend Amyloid markers represent the earliest detectable changes in the AD disease course, reaching a plateau already by the MCI stage Functional and metabolic markers are abnormal by the MCI stage, and continue to change well into the dementia stage Structural changes come later, following a temporal pattern mirroring tau pathology deposition.
- What we tried to do was to provide first evidence to this theoretical model, using real data from the ADNI cohort. Among all available biomarkers, we considered Aβ 1-42, tau, hippocampal volume, and FDG-PET. For each biomarker, we transformed individual values into Z-scores and we plotted them against ADAS-cog scores (clinical variable used to set patients along the disease stage continuum) And we finally compared sigmoid and linear fits. As shown in the picture, for most biomarkers (namely all but FDG-PET) the sigmoid model fitted data significantly better than the linear one. Amyloid time course followed a steep curve, stabilizing early in the disease course. CSF tau and hippocampal volume changed later showing similar monotonous trends, reflecting disease progression. FDG-PET started changing early in time and likely followed a linear decline. These results thus provided first evidence in favor of Jack theoretical model
- Our studies, together with several studies from other groups, have moved a first step forward, but many issues are still to be faced: - First of all, biomarker dynamics should be investigated throughout the whole course of the disease, ideally following healthy people in time and modeling the thresholds where clinical symptoms occur. - the effect of co-occurring diseases and conditions on biomarker dynamics should be investigated - Finally, as structural loss and synaptic dysfunction do not occur at the same time throughout the brain, it will be interesting to investigate the structural and functional variations in disease-specific cerebral regions (e.g. posterior cingulate, medial temporal, lateral temporal and frontal) during the whole disease time course. All these issues point to the following clinical ones: - Which is the best biomarker to be used to assess disease stage and monitor disease progression? Should the choice be stage-specific? - And again, how to use different biomarkers together in a clinical setting? Which combination is to be preferred?
- Finally, I would like to thank all colleagues from my two labs, and all of you for attention.