Giorgio J, Adams JN, Maass A, Jagust WJ, Breakspear M. Amyloid induced hyperexcitability in default mode network drives medial temporal hyperactivity and early tau accumulation. Neuron. 2024 Feb 21;112(4):676-686.e4. Epub 2023 Dec 13 PubMed.

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  1. Activity and communication across large-scale brain networks, spanning multiple regions, are known to be disrupted in AD (Harris et al., 2020). However, it is not clear whether these changes are a cause of disease progression and phenotype or merely an expression of dysfunction on the level of individual cells. Similarly, Aβ and tau proteins are thought to cause AD, but how and why they accumulate throughout the brain and manifest their effects is unknown.

    This elegant study by Giorgio et al. posits that network dysfunction is both a cause of the clinical manifestation of AD and drives the accumulation of Aβ and tau. This is crucial work that reinforces the notion that large-scale networks drive the propagation of pathological proteins in AD, with important implications for therapeutic intervention.

    The authors perform fMRI in young and older adults performing a novelty detection task. This task is known to drive or suppress activity in large-scale brain networks, including the default mode network (DMN) and medial temporal lobe (MTL), known to be disrupted in AD (Sperling et al., 2009; Palmqvist et al., 2017). The brain is thought to solve this task through repetition suppression, whereby repeated presentation of the same stimulus causes a progressive reduction in neural responses, meaning novel stimuli can be detected through a large amplitude response. Repetition suppression is known to be disrupted in AD (Pihlajamaki et al., 2011). 

    In young adults, reciprocal inhibitory connectivity between the MTL and the DMN drives repetition suppression during the task. However, in older adults, the sign of this connectivity is flipped resulting in excitatory drive between the two networks. Crucially, the magnitude of excitation correlated with levels of Aβ and tau expression in the DMN and MTL respectively. Intriguingly, the authors used a clever modeling approach to show that this excitatory connectivity likely drives further expression of Aβ and tau; implying that network dysfunction plays a causal role in AD progression.

    While this is exciting work that will likely inspire future lines of AD research, questions remain. First, this task is simple and does not recapitulate the complexity of experience and neural processing during aging. Second, the analysis approach employed here was specifically designed to extract predefined distinct networks, but these networks do not operate in isolation; they are dynamic and are part of a global network of connectivity (Cohen and D'Esposito, 2016). Finally, fMRI does not permit causal intervention to test whether experimental manipulation of these networks drives pathology, an insight that would be required to conclusively demonstrate their involvement in AD progression.

    References:

    Harris SS, Wolf F, De Strooper B, Busche MA. Tipping the Scales: Peptide-Dependent Dysregulation of Neural Circuit Dynamics in Alzheimer's Disease. Neuron. 2020 Aug 5;107(3):417-435. Epub 2020 Jun 23 PubMed.

    Sperling RA, Laviolette PS, O'keefe K, O'brien J, Rentz DM, Pihlajamaki M, Marshall G, Hyman BT, Selkoe DJ, Hedden T, Buckner RL, Becker JA, Johnson KA. Amyloid deposition is associated with impaired default network function in older persons without dementia. Neuron. 2009 Jul 30;63(2):178-88. PubMed.

    Palmqvist S, Schöll M, Strandberg O, Mattsson N, Stomrud E, Zetterberg H, Blennow K, Landau S, Jagust W, Hansson O. Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity. Nat Commun. 2017 Oct 31;8(1):1214. PubMed.

    Pihlajamäki M, O'keefe K, O'brien J, Blacker D, Sperling RA. Failure of repetition suppression and memory encoding in aging and Alzheimer's disease. Brain Imaging Behav. 2011 Mar;5(1):36-44. PubMed.

    Cohen JR, D'Esposito M. The Segregation and Integration of Distinct Brain Networks and Their Relationship to Cognition. J Neurosci. 2016 Nov 30;36(48):12083-12094. PubMed.

    View all comments by Marc Aurel Busche

  2. In Alzheimer’s disease research, a key lingering question regards how, precisely, remote neocortical Aβ influences medial temporal tau neurofibrillary pathology. This question can and should be examined at multiple levels to provide a comprehensive mechanistic picture. Giorgio et al tackled this question using a systems physiology approach. Their elegant work adds to a growing body of literature suggesting that hyperexcitability in the neocortex, where Aβ first deposits, may drive hyperexcitability in the medial temporal lobe and influence its local tau accumulation.

    How do these new findings relate to connectional accounts of the Aβ-tau interaction (Lee et al., 2022)? Structural connectivity may provide the anatomical infrastructure enabling both long-range cellular/molecular and physiological interactions, like the ones described by Giogio et al. Physiological changes, in turn, may induce, enhance, or reflect the connectionally mediated cellular/molecular Abeta-tau interactions.

    These are exciting times in AD research, when emerging new tools and data sets make it possible to integrate across levels of analysis to refine AD pathogenesis models.

    References:

    Lee WJ, Brown JA, Kim HR, La Joie R, Cho H, Lyoo CH, Rabinovici GD, Seong JK, Seeley WW, Alzheimer’s Disease Neuroimaging Initiative. Regional Aβ-tau interactions promote onset and acceleration of Alzheimer's disease tau spreading. Neuron. 2022 Jun 15;110(12):1932-1943.e5. Epub 2022 Apr 19 PubMed.

    View all comments by William W. Seeley

  3. Georgio et. al. provide support for a model where Aβ in the DMN drives hyperexcitability leading to hyperexcitability in the medial temporal lobe MTL and accumulation of tau.

    The study uses fMRI BOLD signal while subjects perform a mnemonic discrimination task where novel and similar visual stimuli are presented at random intervals. In normal adults, the cortical activity is reduced when a stimulus is repeated (repetition suppression) but not so in adults with AD. Using dynamic causal modeling (DCM) the authors could infer how DMN and MTL connectivity influenced each other during task conditions. The parametric empirical Bayes (PEB) analyses showed that when stimuli were repeated, Aβ was increasing the gain in DMN which was overstimulating the MTL network only in Aβ-positive individuals (PiB-PET) and transition from inhibition to excitation in MTL with increasing levels of tau in the EC (FTP-PET).

    The analysis also showed that the transition of DMN from inhibition to excitation was not a feature of aging but of aging to AD status. The authors also performed cross-validation analysis to show that Aβ-positive individuals showed greater association of increased tau burden in EC due to MTL excitation from DMN.  

    While the paper convincingly shows that hyperexcitability in the DMN drives hyperexcitability in the MTL and tau accumulation in EC in AD, the authors caution that the mechanism in atypical AD might be different. The study also uses one specific task, so confirming the finding with other tasks might be important to validate the overall hypothesis that hyperexcitability from DMN to MTL could lead to increased tau accumulation in EC.

    View all comments by Abid Hussaini

  4. Giorgio and colleagues investigate changes in neuronal activity as a potential link between Aβ and tau accumulation. The study appears motivated by previous work showing that Aβ can induce a shift of neuronal activity toward hyperexcitability (e.g., via abnormal glutamate release and impaired GABAergic function) (Busche et al., 2008; Ren et al., 2018) and that neuronal hyperexcitability may drive faster tau accumulation (Wu et al., 2016). 

    By combining PET and task-based fMRI data in a sample of cognitively normal older adults with varying degrees of fibrillar Aβ and tau levels, they therefore tested the hypothesis that Aβ triggers tau accumulation by altering the excitatory/inhibitory (E/I) balance toward excitation. They quantified neuronal hyperexcitability using a complex dynamic-causal modelling-based task fMRI analysis, in which they determined the remote interactions between the DMN, vulnerable to early Aβ pathology (Palmqvist et al.,, 2017). and the early tau-vulnerable MTL (Schöll et al., 2016). Their results showed that higher DMN activity suppressed MTL activity in Aβ-negative subjects during a cognitive task, but that this association flipped in Aβ-positive individuals, suggesting that DMN activation is linked to MTL activation once sufficient Aβ is present in the brain.

    Based on their dynamic causal modelling framework, the authors conclude that these processes are not simply parallel, but that the DMN actively suppresses or excites the MTL depending on the overall Aβ load. Importantly, they also show that DMN-associated activation rather than suppression of the MTL is linked to faster tau accumulation in the entorhinal cortex, a key site of earliest tau accumulation (Schöll et al., 2016). 

    Principally, these results suggest that a change in brain network interactions toward excitability may lead to faster activity-dependent tau accumulation and constitute a missing link between Aβ and tau aggregation. These are novel and highly interesting findings, yet several limitations should be noted.

    First, the sample size is relatively small, e.g., only 32 subjects with longitudinal tau-PET, and only cognitively normal individuals with earliest AD pathology were included who showed a low rate of tau accumulation over time (~1.8 percent annual increase). Therefore, it will be critical to extend and replicate these findings in larger cohorts spanning the entire AD spectrum. However, we acknowledge that conducting task fMRI experiments in cognitively impaired populations can be challenging, so alternative approaches to investigate excitatory/inhibitory shifts in neuronal activity should be investigated.

    Second, it will be important to determine the spatial correspondence between Aβ deposition and hyperexcitatory shifts in neuronal activity, since Aβ accumulation is pronounced early, yet not limited to the DMN, with substantial inter-individual heterogeneity in Aβ deposition patterns (Collij et al., 2022). Similarly, tau deposition patterns are spatially heterogeneous and not necessarily restricted to the entorhinal cortex, even in early stage AD (Franzmeier et al., 2020). 

    Finally, with regard to the clinical relevance of the findings, it has consistently been shown that symptoms typically emerge when tau has spread from the entorhinal cortex to the neocortex. Thus, to better account for the vast spatio-temporal heterogeneity of Aβ and tau in Alzheimer’s disease, pathogenesis will be of high interest in the future.

    Overall, we think that this is an important and timely study that aims to embed Aβ and tau accumulation in a comprehensive mechanistic pathophysiological disease model. A strength of this model is that it goes beyond a mere spatio-temporal description of biomarker dynamics, thereby providing a multidimensional model that actually allows formal hypothesis testing including interactions between Aβ, tau and functional brain changes.

    From a pathophysiological point of view, developing this line of research may further uncover potential new avenues for developing interventions that specifically target the Aβ-tau axis, e.g., by targeting aberrant neuronal activity via pharmacological interventions or noninvasive brain stimulation.  

    References:

    Busche MA, Eichhoff G, Adelsberger H, Abramowski D, Wiederhold KH, Haass C, Staufenbiel M, Konnerth A, Garaschuk O. Clusters of hyperactive neurons near amyloid plaques in a mouse model of Alzheimer's disease. Science. 2008 Sep 19;321(5896):1686-9. PubMed.

    Ren SQ, Yao W, Yan JZ, Jin C, Yin JJ, Yuan J, Yu S, Cheng Z. Amyloid β causes excitation/inhibition imbalance through dopamine receptor 1-dependent disruption of fast-spiking GABAergic input in anterior cingulate cortex. Sci Rep. 2018 Jan 10;8(1):302. PubMed.

    Wu JW, Hussaini SA, Bastille IM, Rodriguez GA, Mrejeru A, Rilett K, Sanders DW, Cook C, Fu H, Boonen RA, Herman M, Nahmani E, Emrani S, Figueroa YH, Diamond MI, Clelland CL, Wray S, Duff KE. Neuronal activity enhances tau propagation and tau pathology in vivo. Nat Neurosci. 2016 Aug;19(8):1085-92. Epub 2016 Jun 20 PubMed.

    Palmqvist S, Schöll M, Strandberg O, Mattsson N, Stomrud E, Zetterberg H, Blennow K, Landau S, Jagust W, Hansson O. Earliest accumulation of β-amyloid occurs within the default-mode network and concurrently affects brain connectivity. Nat Commun. 2017 Oct 31;8(1):1214. PubMed.

    Schöll M, Lockhart SN, Schonhaut DR, O'Neil JP, Janabi M, Ossenkoppele R, Baker SL, Vogel JW, Faria J, Schwimmer HD, Rabinovici GD, Jagust WJ. PET Imaging of Tau Deposition in the Aging Human Brain. Neuron. 2016 Mar 2;89(5):971-82. PubMed.

    Collij LE, Salvadó G, Wottschel V, Mastenbroek SE, Schoenmakers P, Heeman F, Aksman L, Wink AM, Berckel BN, van de Flier WM, Scheltens P, Visser PJ, Barkhof F, Haller S, Gispert JD, Lopes Alves I, Alzheimer's Disease Neuroimaging Initiative; for the ALFA study. Spatial-Temporal Patterns of β-Amyloid Accumulation: A Subtype and Stage Inference Model Analysis. Neurology. 2022 Apr 26;98(17):e1692-e1703. Epub 2022 Mar 15 PubMed.

    Franzmeier N, Dewenter A, Frontzkowski L, Dichgans M, Rubinski A, Neitzel J, Smith R, Strandberg O, Ossenkoppele R, Buerger K, Duering M, Hansson O, Ewers M. Patient-centered connectivity-based prediction of tau pathology spread in Alzheimer's disease. Sci Adv. 2020 Nov;6(48) Print 2020 Nov PubMed.

    View all comments by Rik Ossenkoppele

  5. This study utilizes task fMRI to look at repetition suppression in 42 older adults, of whom 21 had evidence of abnormal amyloid pathology. Task-evoked paradigms are a powerful tool to understand complex cognitive constructs, and they are underutilized in studying Alzheimer disease and other dementias.

    The current work presents a number of important findings in understanding AD-related changes in the brain, but also has limitations that should be noted. While DCM can assign directional associations between correlated brain regions, which the authors here interpret as "excitatory" and "inhibitory," the BOLD signal is not a proxy of inhibition or excitation as would be envisioned through something like GABAergic interneurons (Logothetis, 2008). As a result, extending the data to such interpretations can be problematic and should be done with caution. This is particularly true as DCM is drastically impacted by sample size (Silchenko et al., 2023) and is unreliable in small samples. Overall, though, very thought-provoking work.

    References:

    Logothetis NK. What we can do and what we cannot do with fMRI. Nature. 2008 Jun 12;453(7197):869-78. PubMed.

    Silchenko AN, Hoffstaedter F, Eickhoff SB. Impact of sample size and regression of tissue-specific signals on effective connectivity within the core default mode network. Hum Brain Mapp. 2023 Dec 1;44(17):5858-5870. Epub 2023 Sep 15 PubMed.

    View all comments by Brian Gordon

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