Parsing Local and Distal Aβ Shows Links to Metabolism, Cognition
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For decades, researchers have wondered why Aβ deposits in the brain correlate poorly with local neural activity and cognition. Perhaps the correlation was not that weak, after all. A study in the June 4 Nature Communications suggests that it may all come down to where you look. Researchers led by Pedro Rosa-Neto at McGill University, Montreal, report that the presence of Aβ deposits in certain regions of the brain correlates tightly with hypometabolism in distal, connected regions. They conclude that hypometabolism, in turn, renders those distant neurons vulnerable to stress, from, lo and behold, local Aβ. The results suggest an Aβ double hit of sorts, where distal deposits prime neurons to succumb to local ones.
- Amyloid does not affect local neuron metabolism …
- … but it slows the metabolism of distant, connected neurons.
- This hypometabolism combines with nearby Aβ to hasten cognitive decline.
“I really liked how the authors used the regional data to ask whether amyloid had a local, global, or distant effect,” wrote Bernard Hanseeuw, Massachusetts General Hospital, Boston. “[They] show that Aβ burden globally affects brain metabolism, and that cognition depends on the synergy between Aβ and brain hypometabolism in specific brain regions, mainly the posterior midline.” (See comment below.)
David Jones, Mayo Clinic, Rochester, Minnesota, found the study interesting, but questioned the interpretation. “The authors largely rely on small-scale molecular brain physiology when interpreting their results and largely ignore large-scale brain physiology,” he wrote (see comment below).
Long-Distance Relationship. Aβ in regions of the default mode network, including the precuneus (green dot), posterior cingulate (yellow), inferior parietal (blue), medial prefrontal (pink), and lateral temporal (red) correlates with hypometabolism at distal, but connected sites in the DMN (orange lines). Aβ in other regions, including the anterior cingulate (light blue) and paracentral cortex (orange) poorly associated with hypometabolism, and only outside the DMN. [Courtesy of Pascoal et al., Nature Communications.]
First author Tharick Pascoal wondered why researchers had never consistently tied regional Aβ to local hypometabolism. On a global level, the amount of amyloid in the brain predicts the degree of overall hypometabolism, but locally this association breaks down (Lowe et al., 2014; Altmann et al., 2015). This disconnect would seem to fly in the face of the amyloid hypothesis, the authors reasoned, since it predicts that Aβ causes neurodegeneration.
What if regional hypometabolism results from Aβ at distal but connected sites? To test this, Pascoal compared florbetapir and FDG PET scans taken from 152 cognitively normal older adults and 170 people with mild cognitive impairment. All the MCI volunteers were amyloid-positive, as were 53 of the healthy controls. Looking voxel-wise across both sets of scans, Pascoal confirmed that in both MCI and normal controls, no correlation existed between local Aβ and local hypometabolism in many areas of the brain, including the posterior cingulate, precuneus, lateral temporal, and inferior parietal cortices. These are part of the default mode network (DMN), a series of interconnected brain regions that are both important for cognitive processing and a hotbed for amyloid deposition (Mar 2004 news; Sep 2005 news).
A different picture emerged when Pascoal looked for correlations further afield. In this analysis, amyloid in the posterior cingulate, precuneus, lateral temporal, and inferior parietal cortices did correlate with hypometabolism, but in distal regions within the DMN (see image above). Aβ in other regions either did not correlate with glucose metabolism or did so with regions outside the DMN.
Does this long-distance correlation have anything to do with cognition? Apparently not, but, in a surprising twist, local correlations do. When the authors tracked how Aβ/hypometabolism correlations in the DMN correlated with cognitive decline in the MCI group, it was synergism between local Aβ and local hypometabolism that predicted a decline in MMSE scores over 5.6 years.
“We see amyloid affecting the brain in two stages,” said Rosa-Neto (see image below). “In the first stage we see an effect on the network, that is, it causes hypometabolism in distant areas. The second strike is when amyloid starts to impose deleterious effects on localized tissue. This impacts cognition.”
Double Hit? The model predicts that Aβ from one neuron can make distant but connected neurons more vulnerable to stress. When local Aβ or other toxins compound that vulnerability, dementia follows. [Courtesy of Pascoal et al., Nature Communications.]
Marcus Raichle, Washington University, St. Louis, found the paper intriguing. “This type of network analysis is a very nice way of framing the pathology of AD, and opens up lines of thinking that broaden our horizons as to the pathophysiology,” he wrote (see comment below).
Is it Aβ that drives these correlations? Why not tau, which itself correlates with hypometabolism? To answer this, the authors repeated the analysis in transgenic rats that express human APP carrying the Swedish and Indiana mutations (Do Carmo and Cuello, 2013). These animals accumulate amyloid deposits but have no tau pathology. Here, too, Aβ correlated with hypometabolism in distal regions, and local synergism between Aβ and hypometabolism predicted poorer performance in a water maze. “We are not saying that tau may not be important. In fact, we think it will exacerbate the situation,” said Pascoal. “The point was to show this [cognitive effect] can be attributed to amyloid alone.”
Could these PET correlations help predict or track a person’s cognitive decline? “Possibly,” said Rosa-Neto. The authors did not conduct this analysis here, but published a machine-learning algorithm based on this data that predicted progression to AD over two years (Mathotaarachchi et al., 2017). More importantly, Rosa-Neto thinks that this two-hit hypothesis helps explain why clinical trials targeting amyloid have failed. “We see already in MCI that the network is vulnerable,” he said. “If you clean up Aβ later, that vulnerability is still there.” He agrees with the idea that Aβ must be targeted much earlier in disease to benefit cognition.—Tom Fagan
References
News Citations
- Network Diagnostics: "Default-Mode" Brain Areas Identify Early AD
- Tracing Alzheimer Disease Back to Source
Paper Citations
- Lowe VJ, Weigand SD, Senjem ML, Vemuri P, Jordan L, Kantarci K, Boeve B, Jack CR Jr, Knopman D, Petersen RC. Association of hypometabolism and amyloid levels in aging, normal subjects. Neurology. 2014 Jun 3;82(22):1959-67. Epub 2014 May 2 PubMed.
- Altmann A, Ng B, Landau SM, Jagust WJ, Greicius MD, Alzheimer’s Disease Neuroimaging Initiative. Regional brain hypometabolism is unrelated to regional amyloid plaque burden. Brain. 2015 Dec;138(Pt 12):3734-46. Epub 2015 Sep 29 PubMed.
- Do Carmo S, Cuello AC. Modeling Alzheimer's disease in transgenic rats. Mol Neurodegener. 2013 Oct 25;8:37. PubMed.
- Mathotaarachchi S, Pascoal TA, Shin M, Benedet AL, Kang MS, Beaudry T, Fonov VS, Gauthier S, Rosa-Neto P, Alzheimer's Disease Neuroimaging Initiative. Identifying incipient dementia individuals using machine learning and amyloid imaging. Neurobiol Aging. 2017 Nov;59:80-90. Epub 2017 Jul 11 PubMed.
Further Reading
No Available Further Reading
Primary Papers
- Pascoal TA, Mathotaarachchi S, Kang MS, Mohaddes S, Shin M, Park AY, Parent MJ, Benedet AL, Chamoun M, Therriault J, Hwang H, Cuello AC, Misic B, Soucy JP, Aston JA, Gauthier S, Rosa-Neto P. Aβ-induced vulnerability propagates via the brain's default mode network. Nat Commun. 2019 Jun 4;10(1):2353. PubMed.
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Comments
Washington University
This is a very interesting paper. One of the intriguing features of AD is the anatomy of the human brain that is vulnerable. I believe that a reasonable consensus is that it involves the hippocampus and the default mode network, which has been characterized by Randy Buckner and others as a hippocampal-cortical memory system. Thus, for neurons, it matters who’s talking to whom. This article advances that idea by introducing the notion of propagation, presumably from the hippocampus via the DMN to the cortex. This type of network analysis is a very nice way of framing the pathology of AD and opens up lines of thinking that broaden our horizons as to the pathophysiology.
How Aβ is acting in this context is complicated. What we have learned from work in the Holtzman lab at Washington University is that tissue Aβ is a remarkably sensitive marker of neuronal activity. Attenuating activity by shaving whiskers on one side of the face in transgenic mice actually retards plaque growth in the contralateral hemisphere. This article does not pursue that idea but, rather, suggests that Aβ is toxic. That, too, may be possible but one should keep in mind that too much activity is, itself, potentially toxic.
The bottom line is that this article is an important addition to the literature. Understanding large-scale network organization is critical in framing our understanding of a disease like AD.
Mayo Clinic
This is an interesting study that is trying to address an important apparent spatiotemporal paradox that arises when researchers try to reconcile large-scale spatial and temporal patterns associated with AD physiology and small-scale molecular events occurring regionally. However, the authors largely rely on small-scale molecular brain physiology when interpreting their results and largely ignore large-scale brain physiology, in particular homeostatic forces in large-scale network dynamics that drive a balance between network segregation and integration to optimize global network efficiency at the cost of local/regional efficiency (van den Heuvel and Sporns, 2019).
The cascading network failure theory of AD incorporates this large-scale network physiology (Jones et al., 2016) and relates it to spatial and temporal patterns of amyloid globally and neurodegeneration regionally (Jones et al., 2017). We have also found that a biomarker for these AD-associated homeostatic shifts in network physiology, the network failure quotient, is correlated with higher levels of global network efficiency (Wiepert et al., 2017) in line with recent theoretical models regarding homeostatic tradeoffs between global network integration and local/regional segregation (van den Heuvel and Sporns, 2019).
References:
Jones DT, Graff-Radford J, Lowe VJ, Wiste HJ, Gunter JL, Senjem ML, Botha H, Kantarci K, Boeve BF, Knopman DS, Petersen RC, Jack CR Jr. Tau, amyloid, and cascading network failure across the Alzheimer's disease spectrum. Cortex. 2017 Dec;97:143-159. Epub 2017 Oct 3 PubMed.
Jones DT, Knopman DS, Gunter JL, Graff-Radford J, Vemuri P, Boeve BF, Petersen RC, Weiner MW, Jack CR Jr, Alzheimer’s Disease Neuroimaging Initiative. Cascading network failure across the Alzheimer's disease spectrum. Brain. 2016 Feb;139(Pt 2):547-62. Epub 2015 Nov 19 PubMed.
van den Heuvel MP, Sporns O. A cross-disorder connectome landscape of brain dysconnectivity. Nat Rev Neurosci. 2019 May 24; PubMed.
Wiepert DA, Lowe VJ, Knopman DS, Boeve BF, Graff-Radford J, Petersen RC, Jack CR Jr, Jones DT. A robust biomarker of large-scale network failure in Alzheimer's disease. Alzheimers Dement (Amst). 2017;6:152-161. Epub 2017 Jan 25 PubMed.
Cliniques Universitaires Saint-Luc and Massachusetts General Hospital
I really liked how the authors used the regional data to ask whether amyloid had a local, global, or distant effect.
The data convincingly confirm the absence of local toxicity of fibrillar Aβ, as detected using PET. It also shows that Aβ burden globally affects brain metabolism and that cognition depends on the synergy between Aβ and brain hypometabolism in specific brain regions, mainly the posterior midline.
The parallel between the animal data and the human data is beautiful and confirms the distant effects of Aβ. Now the one thing that I miss, as acknowledged as a limitation of the study by the authors, is tau-PET data in humans:
Rats do not develop tangles, unless tau is mutated or injected in the animal’s brain. The fact that amyloid in the rats was associated with hypometabolism in distant regions by tau-independent mechanisms doesn’t necessarily mean that the same observation in humans is tau-independent. Indeed the amyloid burden in rats (with multiple amyloid mutations) is way higher than in humans and it could trigger mechanisms that are different than those observed in people with a sporadic form of the disease. In previous papers, FDG signals were more closely related to tau than to amyloid in AD patients and older adults (Ossenkoppele et al., 2016; Hanseeuw et al., 2017). Demonstrating that the amyloid-related hypometabolism was not tau-related in humans would definitely be worthwhile. However, such large data sets (n > 300 Aβ+) with tau-PET are still rare. We thus need to wait for more data to conduct detailed regional analyses of Aβ, tau, and FDG uptake.
References:
Ossenkoppele R, Schonhaut DR, Schöll M, Lockhart SN, Ayakta N, Baker SL, O'Neil JP, Janabi M, Lazaris A, Cantwell A, Vogel J, Santos M, Miller ZA, Bettcher BM, Vossel KA, Kramer JH, Gorno-Tempini ML, Miller BL, Jagust WJ, Rabinovici GD. Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer's disease. Brain. 2016 May;139(Pt 5):1551-67. Epub 2016 Mar 8 PubMed.
Hanseeuw BJ, Betensky RA, Schultz AP, Papp KV, Mormino EC, Sepulcre J, Bark JS, Cosio DM, LaPoint M, Chhatwal JP, Rentz DM, Sperling RA, Johnson KA. Fluorodeoxyglucose metabolism associated with tau-amyloid interaction predicts memory decline. Ann Neurol. 2017 Apr;81(4):583-596. Epub 2017 Apr 6 PubMed.
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