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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  1. 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.

    View all comments by Marcus Raichle

  2. 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.

    View all comments by David Jones

  3. 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.

    View all comments by Bernard Hanseeuw

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