Altered excitatory and inhibitory neuronal subpopulation parameters are distinctly associated with tau and amyloid in Alzheimer’s disease

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1. 

   Version published to 10.7554/elife.77850 on eLife

   May 26, 2022
2. 

   eLife

   May 12, 2022

   Author Response

   Reviewer #1 (Public Review):

   This manuscript is filling an important gap in the literature which is the association between the excitation/inhibition unbalance found in animal models and findings in human neurophysiology. Thus, the idea of using computational models as a linkage between those two levels of analysis help to reconcile many of the previous results. In addition, it provides a reinforcement of the strong relationship between neurophysiological findings with MEG and protein imaging by PET. Therefore, I have found this work of great interest for the literature on the neurophysiology of dementia. I have some comments that are mainly trying to have a better understanding of the findings and aim to get potential associations with previous stages of the disease.

   1. If the patients involved in the study are …

   Author Response

   Reviewer #1 (Public Review):

   This manuscript is filling an important gap in the literature which is the association between the excitation/inhibition unbalance found in animal models and findings in human neurophysiology. Thus, the idea of using computational models as a linkage between those two levels of analysis help to reconcile many of the previous results. In addition, it provides a reinforcement of the strong relationship between neurophysiological findings with MEG and protein imaging by PET. Therefore, I have found this work of great interest for the literature on the neurophysiology of dementia. I have some comments that are mainly trying to have a better understanding of the findings and aim to get potential associations with previous stages of the disease.

   1. If the patients involved in the study are already with a diagnosis of dementia (MMSE <24; CDR 0.72), why they are still presenting hyperexcitability? Typically, amyloid hyperexcitability starts years before in earlier stages of the disease. The continuous hyper calcium neuronal intake should induce toxic effects leading to neuronal death and accelerating degeneration. Furthermore, in the AD stage, Tau is typically dominating inducing neuronal silence. If this process is true, why at the stage of dementia patients still show hyperexcitable activity instead of showing a more global reduced neuronal activity?

   Indeed, Aβ accumulation starts decades before the clinical symptoms and contributes to hyperexcitability in the early stages of the disease. Furthermore, it is well accepted tau induces neuronal silencing. However, the opposing effects of Aβ contributing to hyperexcitability and tau contributing to neuronal silencing are more complicated and don’t seem to simply cancel out each other in AD progression. There are several reasons. First, the spatial patterns of Aβ and Tau distributions are distinct and lead to complex effects on neuronal hyperexcitability (see response to point 2 below). Second, tau appears to have an important enabling role for network hyperexcitability in AD mice.8-10 While the amount of wild-type tau corelates with the degree of network hyperexcitability, genetic reductions in tau expression block epileptiform manifestations and stabilize networks.11-13 Specifically, tau ablation modulates the baseline excitatory neuronal activity and excitability of inhibitory neuronal activity, counteracting the network hyperexcitability.11 Collectively, hyperexcitability is expected to be observed throughout the course of AD progression and not just during the early stages of the disease. (Discussion: page 18, lines 18-27; page 19, lines 1-4; Figure 5 and Discussion section 4.2)

   1. About the role of Aβ in the modulation of alpha and beta. As indicated in the manuscript alpha and beta bands tend to be enhanced in power due to the presence of Aβ. However, the final effect is a reduction of power in comparison with the control group leading to the idea that the Tau effect is stronger in these particular frequency bands. Because tau and Aβ distribution across the cortex is differential I wonder whether in regions with fewer tau deposits alpha and beta power increased. This could be a good validation/test for the model proposed in this study.

   We completely agree with the reviewer that the associations between frequency specific oscillatory signatures and the protein accumulations are region-specific. Furthermore, as correctly predicted by the reviewer the alpha and beta oscillatory changes in the frontal cortices, where there is a relatively higher amyloid accumulation than tau, show an increased pattern. We have included an additional supplementary figure illustrating these changes and also briefly discuss these additional findings in the revised manuscript. (Appendix figure.1)

   1. In some previous studies, increased excitatory activity, due to loss of inhibition, leads to effects in the gamma band. This was shown in both animal models (Palop and Mucke, 2016) and in humans (Rammp et al, 2020; Cuesta et al, 2022). Is there any reason for not finding effects in this frequency band?

   We completely agree with the reviewer that gamma band activity is crucially important in studying abnormal excitatory-to-inhibitory activity in AD. However, accurate reconstruction of regional power spectrum from resting-state MEG data in the gamma band, which has extremely low signal-to-noise ratio is more challenging. Application of neural mass model to compare against empirical spectra based on such low values may lead to spurious associations and potentially inaccurate conclusions. Therefore, in this study, where our main goal was to examine the abnormal excitatory-to-inhibitory activity in AD patients using the neural mass model and empirical power spectrum, we excluded gamma band from our analysis. Notwithstanding the low signal-to-noise ratio, methodologies that incorporate cross frequency phase-amplitude coupling and transfer entropy measures may be better suited to examine the changes in gamma rhythms. This indeed is our current work-in-progress and we expect to present these findings in future manuscripts.

   1. The readers, and I, could need an explanation of the association between slow waves and hyperexcitability. In data from human patients with brain damage and atrophy, a typical finding is delta to theta activity. Therefore, white and grey matter damage explains better the appearance of these rhythms. Again, in epilepsy, a seizure could lead to high-frequency oscillations and it is after a seizure when slow waves show up (when inhibition bit excitation). I perfectly understand that in the data presented in this work amyloid modulates this rhythm, but explanations such as amyloid induce neurodegeneration and consequently, slow waves, could not be ruled out. The explanations already indicated in the discussion section are perfectly fine and could inspire future work, but more traditional ones could be indicated as well. Honestly, I was expecting having Tau more associated with slow waves, as tau has been linked to brain atrophy. Is true that Tau is affecting the reduction of more rapid frequencies such as alpha and beta, but its association with neurodegeneration should not lead to the increase of slow waves?

   We thank the reviewer for raising this important point. Considering the complexity of cellular, molecular, and ionic components involved in generating oscillatory rhythms, it is likely that more than one underlying mechanism may contribute to abnormal oscillations. The extensive literature on brain damage ranging from acute perinatal hypoxic injuries to chronic traumatic brain damage report various electrophysiological phenotypes including increased delta power, reduced alpha power, abnormal non rapid eye movement sleep rhythms as well as epileptiform manifestations.14,15 However, the causal relationships between electrophysiological phenotypes and neuronal loss remain unknown.

   Diverse investigations in AD have demonstrated that neuronal loss is not an immediate functional consequence of Aβ accumulation.16,17 In contrast, as the reviewer correctly pointed out, neuronal loss is tightly coupled to tau accumulation in vulnerable networks.18 Neither do we have compelling evidence to support the hypothesis that Aβ directly causes loss of neurons first which in turn is followed by network hyperexcitability, nor to support that neuronal loss in AD causes directly contributes to increased slow wave activity. On the contrary, there is much evidence from electrophysiological and fMRI studies to support the hypothesis that functional changes occur much earlier in the time course of Aβ accumulation.19,20 Our current findings are consistent with such prior observations. Moreover, many patients in the current study had only mild cognitive deficits with minimal atrophy. Together, these findings suggest that spectral power increases associated with Aβ, as well as decreases associated with tau, represent early functional abnormalities, independent of atrophy.

   We completely agree with the important observation pointed out by the reviewer that focal and generalized slowing of brain rhythms are characteristic patterns of electrophysiological abnormalities in epilepsy, especially during the interictal period. In fact, recent studies from our group as well as from others studying epileptiform activity in patients with AD identified focal and generalized slow waves as a strong indicator of subclinical epileptic activity in patients with AD21,22. The current findings of slowed oscillatory spectra in AD patients who harbor network hyperexcitability is therefore in complete agreement with the fundamental knowledge base in epilepsy literature. The exact mechanisms how increased delta-theta and reduced alpha may contribute to network hyperexcitability remains to be elucidated. Our working framework for potential interactions of molecular and network mechanisms leading to altered excitatory and inhibitory network activity in AD are now included in our revised discussion. (Page 20, Discussion section 4.2; Figure 5)

   1. Previous work has found a strong association between amyloid and alpha rhythm in the frontal regions. Here authors found this association with delta to theta in the same brain regions. However, delta is associated with local hyperexcitability and, in Nakamura et al (2018,) they associate alpha with the same phenomena. How this could be justified?

   In fact, the findings in this study is completely consistent with what was reported in Nakamura et. al. 20185 : (1) A main finding in the current study is that compared to controls, individuals in AD neuropathological spectrum (Aβ+ MCI/mild-AD) have increased delta-theta power correlated with higher Aβ. Nakamura et. al. 2018 results show consistent findings in their Aβ+MCI patients compared to controls (Aβ+ and Aβ-). For example, as depicted in Figure-1A of Nakamura et. al. 2018, in all 10 brain regions examined, delta-theta range power is higher and alpha power is lower in Aβ+ MCI compared to controls (either Aβ+ or Aβ-). (2) In our analysis we found a positive correlation between Aβ and alpha and beta band spectral power, although the absolute power value is reduced in patients compared to controls. This is also a consistent finding in Nakamura et. al. 2018 paper, as illustrated in their Figure 1A where Aβ+ MCI patients consistently showed reduced values of alpha power compared to controls either Aβ+ or Aβ- and a positive main effect of Aβ on alpha power. (3) Our spectral analysis highlights the signature spectral change as high delta-theta and low alpha and beta in AD patients, while the neural mass model application considering the full spectrum demonstrates the abnormal excitatory-to-inhibitory activity in patients with AD. Nakamura et. al.2018, on the other hand, reports the same signature spectral change in their Aβ+MCI patients, and speculate about the potential mechanism of abnormal excitatory-to-inhibitory activity albeit without the application of neural mass model.

   Having pointed out the strong, consistent message about the signature changes between these two studies we would also like to draw attention to some particular details. First, 40% of the patients considered as MCI in Nakamura et. al 2018 are Aβ-, and as such are not in the AD neuropathological spectrum. It is likely that these patients either belong to frontotemporal lobar degeneration type dementia or other rear non-AD neurodegenerative conditions. This is a strong confound which influenced the conclusion of regional associations of Aβ and spectral changes and any other conclusions derived from that study (e.g., Figure 1B, in Nakamura 2018 et. al). Second, Nakamura et. al. 2018 did not use tau imaging in their study and were not in a position to make associations between reduced spectral power and tau accumulation or speculate about its effects on abnormal excitatory-to-inhibitory activity. We discuss the Nakamura 2018 et. al. findings explicitly in our revised discussion. (Discussion: page 21, 17-26)

   1. I fully agree that findings in oscillatory activity, and its associations with pathological proteins, are stage-specific rather than disease-specific. However, why alpha and delta increases can be associated with the same neuronal mechanism (hyperexcitability) at different stages of the disease?

   The reviewer is absolutely correct here. The key to the answer here lies in the fact that AD is progressive in nature and the relative effects of Aβ and tau are indeed different at different stages of disease. Therefore, despite the mechanistic effects of these proteins having an invariant pathological effect their manifestations may vary along the temporal timeline of AD (early Aβ, followed by tau). What our results suggest (and consistent with basic science data) is that Aβ strongly affects inhibitory neurons while tau affects excitatory neurons. An important observation here therefore is that not only Aβ associated inhibitory neuronal changes but also tau associated excitatory changes are contributors to hyperexcitability. (Discussion: section 4.2 & 4.3)

   Reviewer #2 (Public Review):

   Ranasinghe and co-authors explored the relationship between amyloid-beta and tau deposition and neural oscillatory behaviour in Alzheimer's disease (AD) by using a computational neural mass model that can generate neurophysiological power spectra comparable to EEG- or MEG-like, macroscopic brain activity assessments. The model parameters that represent neuronal excitation and inhibition were tuned to optimally resemble the empirical MEG data from AD patients in different relevant frequency bands, and subsequently, the different parameter changes in all 68 cortical neural masses, representing local neuronal excitation or inhibition, were compared with the local amyloid and tau deposition rates. This comparison was used to demonstrate the different, frequency-specific effects of these two proteins, to form an integrated, multimodal/-scale explanation of the molecular/neurophysiological AD disease mechanism.

   The role of neurophysiology in AD pathophysiology is underestimated in the AD research community, as for many it appears to be a more 'downstream' aspect than protein deposition, inflammation or genetic predisposition. However, given the tight relation between cognition and brain activity, the clear involvement of neurophysiology at micro and macro levels, and reports that neuronal activity can influence structural pathology in AD, its central role is evident. It is very laudable that this author group aims to focus on the combination of neurophysiology and computational modelling to further explore how AD pathology actually leads to cognitive impairment. As multi-scale, simultaneous, longitudinal recordings in humans are too burdensome, computational modeling represents a very flexible and powerful new instrument to bridge different levels of detail and predict developments over time. However, the pitfall of using models is the endless options for designing the model, as they will ultimately affect the results and interpretation. However, by constraining the model with biologically plausible effects and parameters, and by validating it with empirical data, it can not only serve to unravel mechanistic principles of disease but also predict successful interventions. Currently, it is not known what model simplifications can be accepted, and which elements need more detail, and this probably also depends on the specific research question and hypotheses. The novel, well-described approach makes the present study a valuable addition to a research field that is under development.

   The conclusions of this paper are mostly well supported by data, but some methodological aspects, as described below, limit the power of the study, or rather provide a valuable perspective on the proposed neurophysiological mechanisms, but not the only valid one.

   Strengths:

   • The group is a high-profile team, known for many influential publications.

   • The use of state-of-the-art techniques like tau-PET and source-space MEG combined with computational modeling may currently be the most powerful approach available for this purpose.

   • AD patient diagnosis is pathology-supported and conforms to NIAAA.

   • The modeling and empirical data are processed and analyzed rigorously; statistical analysis is sound.

   • The methods and result sections are well-written and presented in a logical order.

   We thank the reviewer for highlighting many strengths in our work.

   Weaknesses:

   1. The chosen AD patient cohort is relevant and well-defined, but broad: it includes both persons at the predementia and dementia levels of AD. Since brain changes during the AD disease course are gradual and variable, this heterogeneous group may have limited the observed changes and interpretation. Changes in brain activity are frequently reported to be non-linear, involving transient increases in activity in the early, predementia phase. Also, the effect of amyloid-beta may depend on deposition load (see for example Gaubert ea, Brain 2019). The group heterogeneity in the present study may have obscured distinct activity patterns in different phases of the disease.

   We agree with the reviewer that group heterogeneity in the present study and the dynamicity of AD disease could contribute to variability in our observations. Indeed, an ideal study design to capture such dynamic phenomena is a true longitudinal study where we follow each subject from high-risk AD stage through pre-clinical and then clinical stages of the disease, which requires extensive resources for patient follow-up on all aspects including clinical and imaging facilities. Although not complete substitutes to such longitudinal design, cross-sectional models, such as ours, do provide valuable information to understand the mechanistic relationships along the biological progression of AD. While our cohort includes patients with mild cognitive impairment (MCI) whose Clinical Dementia Rating (CDR)=0.5 and those with mild dementia (CDR=1) and moderate (CDR=2), our cohort predominantly represents MCI (15 out of 20 patients are CDR=0.5; 75%). Among the 5 patients who were identified as CDR1 and 2, only two had low values in mini mental score exam (MMSE) ranging below 19 points. Recent studies using in-vivo tau-PET imaging have clearly demonstrated that patients with AD and at CDR 0.5 have almost saturated amyloid accumulation and tau accumulation up to mid-Braak stages. As such, our cohort although heterogenous, is clearly representative of early-stage AD or pre-clinical AD. Together, our findings suggest that oscillatory changes in the earliest stages in the biological progression of AD is robust and provide clear indices of underlying pathological manifestations. Nevertheless, we acknowledge that these results need to be replicated in larger cohorts within homogenous CDR categories and in longitudinal studies. We address this in our revised ‘limitations’ section. (Page 23, lines 15-17)

   1. As the authors state, PET is sensitive to aggregates of proteins. However, soluble oligomers in early phases are toxic as well but cannot be assessed with the current approach. This may have led to a misinterpretation of local toxic effects which is hard to quantify, limiting the power of the current approach. As the authors state, the neural gain parameter might be more sensitive to early, soluble protein toxicity, but how can this be supported?

   We agree with the reviewer that soluble oligomers that may be important biomarkers of AD. However, they cannot be measured clearly with the current assays. Although the PET signal is incomplete as it is only capturing the deposited proteins, it has also been shown in basic science models that soluble amyloid oligomers are concentrated around plaques.23 Therefore, it is reasonable to conclude that while the full strength of the association may not be evaluated in our analyses, that regional effects are well captured. It is possible that neural gain parameters which were abnormal in patients with AD, are influenced more by soluble oligomers than by deposited proteins where the latter did not show significant associations with the gain parameters. However, testing this hypothesis is beyond the scope of the current study. We acknowledge this issue in the revised ‘limitations’ section and aim to address these important molecular associations raised by the reviewer in our future investigations using cerebral organoids with AD pathology. (Page 23, lines 7-10)

   1. Pathological deposition in subcortical regions and its effect on large-scale oscillatory behaviour is not considered in this study, while early subcortical (e.g. entorhinal) changes are a key feature of the disease. As the authors used source space MEG, involving subcortical structures is technically feasible (e.g. AAL atlas), and may have given a more accurate view.

   We thank the reviewer for this important point and allowing us to clarify. Our regions indeed included entorhinal cortex. The terminology used as ‘cortical’ included both neocortex and the allocortex, the latter which include the entorhinal cortex. The subcortical regions we have excluded in this study only included the basal ganglia. We have now clarified these details in our methods section. (Page 6, lines 6-8)

   1. The authors use a recently developed spectral graph neural mass model, which has several theoretical advantages over more complex, biophysically realistic models. However, there are also disadvantages. Since the model does not generate oscillatory output that can be assessed visually, it is unclear whether the parameter changes required to match the empirical MEG spectra are still within a range that would produce realistic oscillatory behaviour, that also visually resembles AD patient data. Also, since model parameters are less directly linked to neuronal properties as in for example a Jansen-Rit model, the meaning of parameter changes is more difficult to grasp. For example, it seems logical that increased time constants in the model lead to spectral slowing, but how would time-constant abnormalities translate to (inter)neuron dysfunction? Also, since no simulations are required, the contribution of coupled neural masses that influence each other's behaviour during a neurodegenerative process is not captured.

   We thank the reviewer for raising important questions about our spectral graph model. In the current study, we focus on capturing the steady state frequency response of local neural oscillators. The model is certainly capable of producing oscillatory output (characterized by strong peaks in the spectra). Although our model was constructed in the frequency domain, its oscillatory output can be examined in the time-domain using inverse Laplace transforms. We have demonstrated this in one of our most recent studies.24 In that study, we found that the transient impulse response of this model is either a decaying oscillation, limit cycle, or an unstable oscillation. We respectfully disagree that our model parameters are any less interpretable than comparable non-linear models like the Jansen-Rit models. They are both neural field models with differences only in implementation details and inference procedures. For instance, while our model does not require simulations because it allows for a closed-form solution, it does indeed capture coupled neural subpopulation interactions.25

   1. In the discussion section, the associations between a-beta and tau and neuronal hyper/hypoactivity are adequately compared to recent basic science literature, but since the authors state that the observed effects indicate an overall, net balance between underlying excitatory and inhibitory dysfunction, it is not clear how the model could help to further determine the exact link between -for example- a-beta-induced glutamate toxicity and neuronal behaviour. This less specific link makes it easier but also more ambiguous to explain the directions of the observed effects.

   We completely agree with the reviewer that increased amyloid in basic science models have been shown to correlate with abnormal inhibitory neuronal activity as well as with excitatory neuronal activity. Consistent with these observations, we found that increased amyloid accumulation of associated with increased inhibitory time-constants. However, we did not find any association between higher amyloid and excitatory neuronal parameter deficits. NMM estimations are derived for the level of local neuronal subpopulations. It is important to reiterate that the current findings indicate an overall inhibitory functional deficit at the level of local networks which in turn may be contributed by abnormal inhibitory as well as excitatory deficits at cellular level. We have included these points to our discussion and also refer the reviewer to figure 5 in the revised manuscript which summarizes our findings and posits a framework to examine these interactions. (Page 18, lines 13-17; Figure 5 and section 4.2 in Discussion)

   Read the original source
3. 

   eLife

   May 6, 2022

   Evaluation Summary:

   The authors explored the relationship between amyloid-beta and tau deposition and neural oscillations in Alzheimer's disease (AD) by using a computational neural mass model that can generate neurophysiological power spectra comparable to EEG- or MEG-like, macroscopic brain activity assessments. This analysis demonstrates the different, frequency-specific effects of amyloid-beta and tau proteins on excitation and inhibition, providing an integrated, multimodal explanation of the AD pathogenesis.

   (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

   Read the original source
4. 

   eLife

   May 6, 2022

   Reviewer #1 (Public Review):

   This manuscript is filling an important gap in the literature which is the association between the excitation/inhibition unbalance found in animal models and findings in human neurophysiology. Thus, the idea of using computational models as a linkage between those two levels of analysis help to reconcile many of the previous results. In addition, it provides a reinforcement of the strong relationship between neurophysiological findings with MEG and protein imaging by PET. Therefore, I have found this work of great interest for the literature on the neurophysiology of dementia. I have some comments that are mainly trying to have a better understanding of the findings and aim to get potential associations with previous stages of the disease.

   If the patients involved in the study are already with a diagnosis of …

   Reviewer #1 (Public Review):

   This manuscript is filling an important gap in the literature which is the association between the excitation/inhibition unbalance found in animal models and findings in human neurophysiology. Thus, the idea of using computational models as a linkage between those two levels of analysis help to reconcile many of the previous results. In addition, it provides a reinforcement of the strong relationship between neurophysiological findings with MEG and protein imaging by PET. Therefore, I have found this work of great interest for the literature on the neurophysiology of dementia. I have some comments that are mainly trying to have a better understanding of the findings and aim to get potential associations with previous stages of the disease.

   If the patients involved in the study are already with a diagnosis of dementia (MMSE <24; CDR 0.72), why they are still presenting hyperexcitability? Typically, amyloid hyperexcitability starts years before in earlier stages of the disease. The continuous hyper calcium neuronal intake should induce toxic effects leading to neuronal death and accelerating degeneration. Furthermore, in the AD stage, Tau is typically dominating inducing neuronal silence. If this process is true, why at the stage of dementia patients still show hyperexcitable activity instead of showing a more global reduced neuronal activity?

   About the role of Aβ in the modulation of alpha and beta. As indicated in the manuscript alpha and beta bands tend to be enhanced in power due to the presence of Aβ. However, the final effect is a reduction of power in comparison with the control group leading to the idea that the Tau effect is stronger in these particular frequency bands. Because tau and Aβ distribution across the cortex is differential I wonder whether in regions with fewer tau deposits alpha and beta power increased. This could be a good validation/test for the model proposed in this study.

   In some previous studies, increased excitatory activity, due to loss of inhibition, leads to effects in the gamma band. This was shown in both animal models (Palop and Mucke, 2016) and in humans (Rammp et al, 2020; Cuesta et al, 2022). Is there any reason for not finding effects in this frequency band?
   The readers, and I, could need an explanation of the association between slow waves and hyperexcitability. In data from human patients with brain damage and atrophy, a typical finding is delta to theta activity. Therefore, white and grey matter damage explains better the appearance of these rhythms. Again, in epilepsy, a seizure could lead to high-frequency oscillations and it is after a seizure when slow waves show up (when inhibition bit excitation). I perfectly understand that in the data presented in this work amyloid modulates this rhythm, but explanations such as amyloid induce neurodegeneration and consequently, slow waves, could not be ruled out. The explanations already indicated in the discussion section are perfectly fine and could inspire future work, but more traditional ones could be indicated as well. Honestly, I was expecting having Tau more associated with slow waves, as tau has been linked to brain atrophy. Is true that Tau is affecting the reduction of more rapid frequencies such as alpha and beta, but its association with neurodegeneration should not lead to the increase of slow waves?

   Previous work has found a strong association between amyloid and alpha rhythm in the frontal regions. Here authors found this association with delta to theta in the same brain regions. However, delta is associated with local hyperexcitability and, in Nakamura et al (2018,) they associate alpha with the same phenomena. How this could be justified? I fully agree that findings in oscillatory activity, and its associations with pathological proteins, are stage-specific rather than disease-specific. However, why alpha and delta increases can be associated with the same neuronal mechanism (hyperexcitability) at different stages of the disease?

   Read the original source
5. 

   eLife

   May 6, 2022

   Reviewer #2 (Public Review):

   Ranasinghe and co-authors explored the relationship between amyloid-beta and tau deposition and neural oscillatory behaviour in Alzheimer's disease (AD) by using a computational neural mass model that can generate neurophysiological power spectra comparable to EEG- or MEG-like, macroscopic brain activity assessments. The model parameters that represent neuronal excitation and inhibition were tuned to optimally resemble the empirical MEG data from AD patients in different relevant frequency bands, and subsequently, the different parameter changes in all 68 cortical neural masses, representing local neuronal excitation or inhibition, were compared with the local amyloid and tau deposition rates. This comparison was used to demonstrate the different, frequency-specific effects of these two proteins, to form an …

   Reviewer #2 (Public Review):

   Ranasinghe and co-authors explored the relationship between amyloid-beta and tau deposition and neural oscillatory behaviour in Alzheimer's disease (AD) by using a computational neural mass model that can generate neurophysiological power spectra comparable to EEG- or MEG-like, macroscopic brain activity assessments. The model parameters that represent neuronal excitation and inhibition were tuned to optimally resemble the empirical MEG data from AD patients in different relevant frequency bands, and subsequently, the different parameter changes in all 68 cortical neural masses, representing local neuronal excitation or inhibition, were compared with the local amyloid and tau deposition rates. This comparison was used to demonstrate the different, frequency-specific effects of these two proteins, to form an integrated, multimodal/-scale explanation of the molecular/neurophysiological AD disease mechanism.

   The role of neurophysiology in AD pathophysiology is underestimated in the AD research community, as for many it appears to be a more 'downstream' aspect than protein deposition, inflammation or genetic predisposition. However, given the tight relation between cognition and brain activity, the clear involvement of neurophysiology at micro and macro levels, and reports that neuronal activity can influence structural pathology in AD, its central role is evident. It is very laudable that this author group aims to focus on the combination of neurophysiology and computational modelling to further explore how AD pathology actually leads to cognitive impairment. As multi-scale, simultaneous, longitudinal recordings in humans are too burdensome, computational modeling represents a very flexible and powerful new instrument to bridge different levels of detail and predict developments over time. However, the pitfall of using models is the endless options for designing the model, as they will ultimately affect the results and interpretation. However, by constraining the model with biologically plausible effects and parameters, and by validating it with empirical data, it can not only serve to unravel mechanistic principles of disease but also predict successful interventions. Currently, it is not known what model simplifications can be accepted, and which elements need more detail, and this probably also depends on the specific research question and hypotheses. The novel, well-described approach makes the present study a valuable addition to a research field that is under development.

   The conclusions of this paper are mostly well supported by data, but some methodological aspects, as described below, limit the power of the study, or rather provide a valuable perspective on the proposed neurophysiological mechanisms, but not the only valid one.

   Strengths:

   • The group is a high-profile team, known for many influential publications.
   • The use of state-of-the-art techniques like tau-PET and source-space MEG combined with computational modeling may currently be the most powerful approach available for this purpose.
   • AD patient diagnosis is pathology-supported and conforms to NIAAA.
   • The modeling and empirical data are processed and analyzed rigorously; statistical analysis is sound.
   • The methods and result sections are well-written and presented in a logical order.

   Weaknesses:

   • The chosen AD patient cohort is relevant and well-defined, but broad: it includes both persons at the predementia and dementia levels of AD. Since brain changes during the AD disease course are gradual and variable, this heterogeneous group may have limited the observed changes and interpretation. Changes in brain activity are frequently reported to be non-linear, involving transient increases in activity in the early, predementia phase. Also, the effect of amyloid-beta may depend on deposition load (see for example Gaubert ea, Brain 2019). The group heterogeneity in the present study may have obscured distinct activity patterns in different phases of the disease.
   • As the authors state, PET is sensitive to aggregates of proteins. However, soluble oligomers in early phases are toxic as well but cannot be assessed with the current approach. This may have led to a misinterpretation of local toxic effects which is hard to quantify, limiting the power of the current approach. As the authors state, the neural gain parameter might be more sensitive to early, soluble protein toxicity, but how can this be supported?
   • Pathological deposition in subcortical regions and its effect on large-scale oscillatory behaviour is not considered in this study, while early subcortical (e.g. entorhinal) changes are a key feature of the disease. As the authors used source space MEG, involving subcortical structures is technically feasible (e.g. AAL atlas), and may have given a more accurate view.
   • The authors use a recently developed spectral graph neural mass model, which has several theoretical advantages over more complex, biophysically realistic models. However, there are also disadvantages. Since the model does not generate oscillatory output that can be assessed visually, it is unclear whether the parameter changes required to match the empirical MEG spectra are still within a range that would produce realistic oscillatory behaviour, that also visually resembles AD patient data. Also, since model parameters are less directly linked to neuronal properties as in for example a Jansen-Rit model, the meaning of parameter changes is more difficult to grasp. For example, it seems logical that increased time constants in the model lead to spectral slowing, but how would time-constant abnormalities translate to (inter)neuron dysfunction? Also, since no simulations are required, the contribution of coupled neural masses that influence each other's behaviour during a neurodegenerative process is not captured.
   • In the discussion section, the associations between a-beta and tau and neuronal hyper/hypoactivity are adequately compared to recent basic science literature, but since the authors state that the observed effects indicate an overall, net balance between underlying excitatory and inhibitory dysfunction, it is not clear how the model could help to further determine the exact link between -for example- a-beta-induced glutamate toxicity and neuronal behaviour. This less specific link makes it easier but also more ambiguous to explain the directions of the observed effects.

   In conclusion, the aim to unravel the multiscale pathophysiological mechanism of AD is one of the current top priorities. Various groups are pursuing similar multimodal modelling approaches to explain AD pathophysiology, with different methodologies. Considering its strengths and weaknesses, this study can become a valuable contribution to the AD neurophysiology computational modeling field, and may also help to interest and unite researchers coming primarily from clinical backgrounds (PET, MEG, pharma). Further use of spectral graph models should be investigated, and replication of current results with a different type of model may strengthen the conclusions. In general, a comparison between the various neural mass models with their specific strengths and weaknesses for representing AD pathophysiology will help the field forward.

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6. 

   Version published to 10.1101/2022.03.09.483594v1 on bioRxiv

   Mar 10, 2022