. Neurophysiological signatures in Alzheimer's disease are distinctly associated with TAU, amyloid-β accumulation, and cognitive decline. Sci Transl Med. 2020 Mar 11;12(534) PubMed.


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  1. This paper is a significant step forward in our understanding of how AD pathology quantitatively manifests at the circuit level. Not only does it confirm and extend recent evidence of altered circuit function in early AD, it also, critically, supports related data obtained by us and others in translational models. Importantly, the data presented by Ranasinghe et al. suggests that circuit-based functional readouts can be leveraged to differentially profile AD phenotypes and acts as biomarkers of underlying pathological changes.

    It will be of great interest to the field whether these results are also applicable to larger cohorts, and in particular to other related neurodegenerative disorders. For example, different tauopathies exhibit distinctive pathoprogression properties, and it would be fascinating to determine whether alpha band hyposynchrony, which co-localised to areas of increased tau in the current paper, is also able to discriminate between these variants. A related question pertains to the, as yet unclear, mechanism(s) by which tau engenders hyposynchrony. Tau-related neuronal hypoactivity, as reported by us and others, likely plays a key role in destabilizing the overlying circuit, and further work will elucidate whether nicotinic acetylcholine receptors, which have been linked to altered alpha oscillations and tau pathology, may also be candidate foci.

    In contrast, the current paper reports that delta/theta hypersynchrony was unable to differentiate between AD phenotypes and co-localized to both Ab and tau deposition. Importantly, voxel-wise general linear model analysis revealed that differential uptake of both peptides was associated with modulation of delta/theta synchrony, such that areas with both high Ab and tau uptake were associated with hyposynchrony, while others in which Ab or tau uptake were high when tau or Ab were low, respectively, were linked to hypersynchrony. This suggests that delta/theta synchrony reflects an interaction between Ab and tau pathologies, and it will thus be fascinating to confirm in longitudinal studies whether delta/theta hypersynchrony at early stages of AD transitions to hyposynchrony at late stages, and if synchrony may be a biomarker of disease progression. It will also be interesting to determine whether the recently reported improvements in Ab peptide burden evoked by gamma-frequency stimulation reflects a rectification of altered low-frequency synchronization (such as in the frequency bands reported here), given known cross-frequency coupling processes.

    In summary, the data presented by Ranasinghe et al. provide further support to the notion that the cellular and molecular changes in AD converge at the level of circuits. This study is an important advance in a growing body of literature describing circuit level changes in AD, and it signposts new horizons for novel diagnostic and therapeutic approaches.

    View all comments by Marc Aurel Busche
  2. Ranasinghe et al. report that different clinical phenotypes of Alzheimer’s disease (AD) are associated with different patterns of EEG synchronization abnormalities.

    The magnetoencephalographic recordings, molecular imaging, and multimodal analyses in this paper are elegant. The results confirm previous findings that AD has clinical variations and that these are linked to concordant anatomical differences in the distribution of neurofibrillary tauopathy (Gefen et al., 2012). In keeping with this heterogeneity, perturbations of network connectivity in AD have been shown to be more profound in the hippocampus when the phenotype is amnestic, and in the inferior frontal gyrus (Broca’s area) when the phenotype is aphasic (Martersteck et al., 2020). 

    Together with the findings of Ranasinghe et al., we can now conclude that the anatomy of AD pathology and its clinical manifestations are not entirely determined by the cellular and molecular biology of the disease and that there are critical interactions with patient-specific factors and co-morbidities that remain to be identified.  


    . Clinically concordant variations of Alzheimer pathology in aphasic versus amnestic dementia. Brain. 2012 May;135(Pt 5):1554-65. PubMed.

    . Differential neurocognitive network perturbation in amnestic and aphasic Alzheimer disease. Neurology. 2020 Feb 18;94(7):e699-e704. Epub 2020 Jan 22 PubMed.

    View all comments by Emily Rogalski
  3. Based on the findings that theta/alpha (4-12 Hz) coherence is enhanced between brain areas during spatial memory tasks in rodents, one could hypothesize that disruptions in theta/alpha synchrony or coherence may occur in situations of memory dysfunction (Jones and Wilson, 2005). The current study nicely shows that reduced 8-12 Hz synchrony correlates not only with cognitive dysfunction, but also in pathological tau deposition in AD. In addition, the authors report aberrant 2-8 Hz synchrony in AD. Future longitudinal studies may reveal how and why disruptions in network synchrony evolve as AD pathogenesis progress. It is worth noting that a previous longitudinal study has already shown slow-wave oscillations are also affected in AD (Lucey et al., 2019). Overall, these findings point to the idea that targeting network synchrony early on in disease progression may offer benefits.


    . Theta rhythms coordinate hippocampal-prefrontal interactions in a spatial memory task. PLoS Biol. 2005 Dec;3(12):e402. Epub 2005 Nov 15 PubMed.

    . Reduced non-rapid eye movement sleep is associated with tau pathology in early Alzheimer's disease. Sci Transl Med. 2019 Jan 9;11(474) PubMed.

    View all comments by Li-Huei Tsai
  4. This is an elegantly designed study using multidomain imaging to better understand network dysfunction in Alzheimer’s disease and their relationships to phenotype. At a general level, this study adds to the literature on the value of neurophysiological biomarkers on top of other more established biomarkers, e.g. Aβ, tau, and neurodegeneration biomarkers, in better diagnosing, classifying, and predicting trajectories in Alzheimer’s disease.

    Their multidomain imaging approach allowed the authors to discover some interesting findings. The specificity of co-localization and association between alpha hyposynchrony and tau, but not Aβ, deposition is very interesting and points toward specific mechanisms underlying these different neurophysiological markers. This idea is also supported by the fact that the authors found an association and co-localization between delta-theta hypersychrony and tau and Aβ deposition.

    Another exciting finding is that different AD phenotypes had different anatomical profiles of alpha hyposynchrony but a common delta-theta hypersynchrony profile. This finding supports the potential use of these neurophysiological markers in identifying different aspects of the AD syndromes for clinical classification and treatment interventions.

    The work reported in this paper is very promising for the field of neurophysiology in AD. It warrants attempts of replication with larger sample sizes and across multiple sites. It also advances further the agenda for using these neurophysiological markers to personalize interventions that can be moderated by these markers or that can target them directly.

    View all comments by Tarek Rajji
  5. The general message that a noninvasive neurophysiological technique can provide that much relevant information on molecular AD pathophysiology based on just one minute of data is quite baffling, and illustrates the growing relevance of MEG for clinical and research purposes in AD. Alpha (8–12 Hz) hyposynchrony has been described before in AD in EEG and MEG studies, but we haven’t seen such an evident spatial relation with specific AD-variants yet.

    The fact that resting-state, source-space (i.e., sensor space signals are used to reconstruct the activity sources, including deeper regions) MEG data is used, is in line with literature and our own experience that this approach produces robust and relevant results. So there seems to be no need for specific cognitive tasks or evoked potentials, which can be difficult to assess and interpret in this patient group. In addition, this study supports the view that functional connectivity analysis seems to add new information to the more established, local analysis (e.g., power spectral density).

    We agree with the suggestion that modulating spontaneous brain rhythms may open up new therapeutic options (and are currently pursuing this aim); oscillatory behavior may be more “downstream” from AD pathology than synapse function, but that does not automatically make it a less effective potential treatment target. Also, involving not just local activity but connectivity patterns as well will lead to more rational strategies when modulating highly interconnected human brain networks.

    While we consider this an important study, there are some limitations. For functional connectivity analysis, the “imaginary coherence” coupling measure was used. There are many connectivity measures, and all of them have different strong and weak points, so this particular choice may have had consequences for the interpretation of the results. A confirmation of these results with other coupling measures could strengthen the message. A further limitation is that the set of patients that received all modalities (MEG, tau-PET and Aβ-PET) is rather small (n=12), as the authors argue themselves. One final remark on the interpretation of the results: the overlapping spatial patterns of abnormal synchrony and amyloid/tau deposition are explained as a modulation of the latter on the former. However, since there are also reports of neuronal firing rates actually driving protein deposition, the causal chain in this relationship is still open for debate: e.g., a bidirectional, pathological influence is conceivable.

    View all comments by Willem de Haan
  6. This paper highlights some striking observations:

    1) Alpha rhythm hyposynchrony is specifically related to both Tau burden and cognitive state;

    2) The left hemisphere is the main affected brain region in Alzheimer’s disease;

    3) Delta/theta synchrony has a double, counterintuitive, behavior: earlier hyper-, later hyposynchrony, suggesting an inverse relationship with Aβ/Tau burden.

    It should be considered that the so-called alpha rhythm (8-12 Hz) is not a unitary phenomenon. Rather, it encompasses different oscillatory components, so that we can describe at least slow and high alpha rhythms, both with different functional tasks and neural generators. If we could detect a different synchrony of the sub-alpha components, computed in an individual way, we could shed light on the subtle connectivity damage in AD network and find a specific biomarker of the disease.

    View all comments by Davide Vito Moretti

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