Neurodegenerative diseases and dementia represent an increasing social and economic burden to worldwide health (GBD 2019 Dementia Forecasting Collaborators, 2022), with rising prevalence and incidence in countries with an aging population and specially across underrepresented populations from the developing world, where classical biomarkers and treatments are not yet massively available (Hou et al., 2019; Mukadam et al., 2019; Parra et al., 2018; Parra et al., 2023). Alzheimer’s disease (AD) and behavioral variant frontotemporal dementia (bvFTD) are among the most prevalent neurodegenerative diseases, each linked to specific pathophysiology and to highly heterogeneous and atypical manifestation, hindering detection and diagnosis (Musa et al., 2020; Parra et al., 2018). Neuroimaging methods such as positron emission tomography (PET) (Engler et al., 2008; Foster et al., 2007; Jack et al., 2018; Mosconi et al., 2008; Nordberg, 2004), functional magnetic resonance imaging (fMRI) (Hafkemeijer et al., 2015; Jalilianhasanpour et al., 2019; Moguilner et al., 2021), and electroencephalography (EEG) Dottori et al., 2017; Lindau et al., 2003; Nishida et al., 2013; Nishida et al., 2011; Cruzat et al., 2023 have been employed extensively to profile these diseases based on their underlying whole-brain activity patterns, and to develop automated classifiers capable of assisting clinical decision-making (Kim et al., 2019; Moguilner et al., 2021; Herzog et al., 2022). In spite of these advances, the limited knowledge of the large-scale network mechanisms of neurodegenerative dementias hinders the development of interventions oriented to improve brain health and subjective well-being. Moreover, without addressing the underlying neurobiological mechanisms, purely data-driven metrics can be heterogeneous and difficult to interpret, in particular when obtained from large-scale studies which are capable of picking up small differences that arise due to confounding factors (Deco and Kringelbach, 2014).

Several methods capable of externally modulating brain activity have been proposed to treat AD and bvFTD, including transcranial magnetic stimulation (TMS) (Antczak et al., 2018; Nardone et al., 2012), direct and alternating electrical current stimulation (tDCS and tACS) (Benussi et al., 2021; Benussi et al., 2020a; Bréchet et al., 2021; Ferrucci et al., 2008; Prehn and Flöel, 2015; Sprugnoli et al., 2021; Zhou et al., 2022; Pini et al., 2022; Birba et al., 2017), and ultrasound pulse stimulation (Liu et al., 2021), among others. Overall, the use of transcranial stimulation techniques resulted in mixed results, with only some of these studies reporting promising results in terms of the restoration of disease-specific cognitive impairments and functional abnormalities. A frequent limitation is given by their exploratory nature, highlighting the need for principled methods to determine the brain regions to be stimulated, as well as relevant parameters such as the scalp location of the stimulation devices (montage), and the intensity and frequency of the delivered perturbation. These limitations could be addressed using whole-brain models of brain activity that incorporate multiple sources of experimental data to reproduce realistic estimates of brain dynamics and functional connectivity (Menardi et al., 2022; Deco et al., 2019; Wang et al., 2015). After parameter optimization, models can be used as a sandbox to test the outcome of different forms of external perturbation (Cofré et al., 2020). Previous research supports the feasibility of fitting whole-brain phenomenological models to fMRI data acquired from AD patients, suggesting mechanistic explanations for reduced whole-brain synchronization and small-worldness of functional networks in the patients (Demirtaş et al., 2017). However, the use of whole-brain models to infer and reverse the large-scale network mechanisms underlying neurodegenerative dementias (AD and bvFTD) remains largely unexplored.

Computational models of brain activity present a trade-off between interpretability and accuracy to represent empirical data, the latter being closely related to the number of parameters that are optimized during the fitting procedure (Cofré et al., 2020; Ipina et al., 2020). Increasing the size of parameter space is necessary to model brain states characterized by spatially heterogeneous changes in neural dynamics, as is the case of neurodegenerative disorders, where atrophy has been linked to alterations in the local excitation/inhibition (E/I) balance (Mehta et al., 2013; Lopatina et al., 2019; Maestú et al., 2021; Palop and Mucke, 2016). Neurodegenerative diseases such AD and bvFTD are characterized by heterogeneity across different brain levels involving both structure and dynamics (Mehta et al., 2013; Peet et al., 2021; Seelaar et al., 2011; Verdi et al., 2021; Prado et al., 2023), challenging the assumptions of spatially homogeneous biomarker models. Moreover, the issue of interpretability manifests when the impact of simulated stimulation is considered. In which ways whole-brain activity can be modified? How do these changes depend on the selected stimulation site? What is the relationship between the perturbed dynamics and those observed in target healthy brain states? Providing answers to these questions (and thus exploring the landscape of perturbation-induced states, i.e., the perturbational landscape) becomes increasingly difficult as the number of model parameters is increased.

We implemented a two-stage procedure to tackle these issues by means of whole-brain activity models. First, we introduced empirical structural connectivity data to couple the local dynamics, as well as priors to modulate local model parameters based on atrophy maps of different neurodegenerative diseases, such as AD, bvFTD, and Parkison’s disease (PD), with the hypothesis that disease-specific maps are capable of improving the model fit to the fMRI data of the corresponding disease. Next, we fitted these models to the empirical data, which allowed us to identify potential mechanisms underlying the changes observed in AD and bvFTD, relative to the relationship between the dynamical regime of local activity and whole-brain connectivity patterns. Finally, we used variational autoencoders (VAE) (Perl et al., 2020) to obtain a low-dimensional representation of brain activity, investigating how positioning in this latent space is related to the diagnosis and the severity of the disease. Our analysis concluded with the systematic exploration of different stimulation protocols, visualizing the outcome as trajectories in latent space, and thus allowing us to interpret the effects of the stimulation in terms of their capacity to restore healthy whole-brain activity patterns.

The outline of the procedure followed in this work is shown in Figure 1. First, we implemented a whole-brain model with local dynamics (one per node in the Automated Anatomical Labeling atlas [AAL]) (Tzourio-Mazoyer et al., 2002) given by the normal form of a Hopf bifurcation (Deco et al., 2017). Depending on the bifurcation parameter a (related to the excitation/inhibition ratio) these dynamics present two qualitatively different behaviors: fixed-point dynamics (a < 0) and oscillations around a limit cycle (a > 0). When noise is added to the model, dynamics close to the bifurcation ($\mathrm{a}\approx 0$) change stochastically between both regimes, giving rise to oscillations with complex amplitude modulations. Regions were coupled by the anatomical connectivity matrix obtained from diffusion tensor imaging (DTI) measurements. The model was used to simulate phenomenological time series (i.e., the output of the model is interpreted as the measured BOLD signal, without the need to apply biophysical transformations). Then, the whole-brain functional connectivity (FC) matrices computed from the simulated time series by means of Pearson’s correlation were encoded into a two-dimensional space using a deep learning architecture known as VAE (Perl et al., 2020).

Figure 1

Download asset Open asset

Briefly, VAEs are deep neural networks with autoencoder (AE) architecture (Kingma and Welling, 2013), which are trained to map inputs to probability distributions in latent space by minimizing the error between the input and the output. This output corresponds to the input as reproduced from the latent space values. Moreover, VAEs can be regularized during the training process to produce meaningful outputs after the decoding step, as well as to ensure continuity between the outputs and the corresponding choice of latent space values. The most common architecture of this network can be subdivided into three parts: the encoder network, the middle variational layer (with units corresponding to the latent space), and decoder network. The encoder transforms the input into the latent space, which is typically of much lower dimension than the input and output layers. On the other hand, the decoder converts the values of the units in the latent space to the output space.

The whole-brain model had different bifurcation parameters for each AAL region, constrained by the spatial maps of the following anatomical priors: resting state networks (RSN), regions with different brain atrophy values, random assignment of regions to the spatial maps, and maps corresponding to an equipartition of neighboring regions. For each region in the prior, an independent parameter (Δ) was fitted, then these parameters were combined to obtain the local bifurcation parameters of the model (note that regions were potentially overlapping) (Ipina et al., 2020). Following Ipina et al., 2020, these free parameters were optimized using a genetic algorithm to optimize the agreement with fMRI FC from an elderly healthy control (CNT), AD, and bvFTD patients. We explored different forms of in silico external perturbations that emulate specific empirical perturbation protocols. Wave stimulation (periodic perturbation delivered at the dominant frequency of the local BOLD fluctuations) imitates the characteristics of tACS, considering that both approaches are based on an external periodical driver applied to the brain. The specific application of the nodal natural oscillatory frequency is based on reports that suggest electrophysiological oscillations can be synchronized by in-phase tACS stimulation (Helfrich et al., 2014), even though this mechanism has been recently disputed (Lafon et al., 2017) and further research is required for its validation. On the other hand, the simulated Sync/Noise stimulation increases/decreases the overall value of the bifurcation parameter underlying the switching of the dynamical regime of a specific brain region. This can be associated with a direct alteration in nodal neural excitability, which resembles the impact created by tDCS stimulation (Nitsche and Paulus, 2000). After systematically applying these perturbations to all pairs of homotopic nodes and encoding the resulting FC matrices, we obtained low-dimensional perturbational landscapes, consisting of trajectories in latent space parametrized by the stimulation intensity. In turn, these trajectories were classified depending on how closely they brought the dynamics to a predefined target state, in this case, that of the healthy control group.

Fitting the whole-brain model

The results of model fitting are shown in Figure 2. The violin plots display 1 – goodness of fit (GoF) values for CNT, AD, and bvFTD using the following anatomical priors: RSN (Beckmann and Smith, 2004), AD, and bvFTD atrophy maps (AD + FTD), Parkinson’s disease atrophy map (PD), random assignment (Random), and equally sized groups of nodes defined by anatomical proximity (Equal). Note that we combined the anatomical priors (i.e., atrophy maps) from AD and FTD due to their high level of spatial correlation (R = 0.75, p<0.0001, see Figure 2—figure supplements 1 and 2). These values were obtained for 300 independent realizations of parameter optimization with a genetic algorithm (Ipina et al., 2020). Here, the use of maps from a different neurodegenerative disease (PD, without dementia and less characterized by atrophy) was implemented as an additional control to compare the resulting GoF with the values obtained using disease-specific atrophy maps. We found that the best fits were obtained using RSN for CNT, AD + FTD atrophy maps or RSN for AD, and AD + FTD atrophy maps for bvFTD (in all cases with 1 − GoF ≈ 0.6, similar to values reported in previous publications based on different datasets) (Deco et al., 2017; Demirtaş et al., 2017; Sanz Perl et al., 2021), showing that atrophy maps contain meaningful spatial heterogeneities that improve the fit to the empirical whole-brain FC matrix. We repeated the same procedure but considering separately the AD and FTD maps, and evaluated the goodness of fit for each condition, as shown in supplementary material Figure 2—figure supplement 3. We found that the best fits were obtained using RSN for CNT, RSN, AD, or FTD atrophy maps for AD, and the FTD atrophy map for FTD (with 1 – GoF≈0.6) (see supplementary material).

Figure 2 with 3 supplements see all

Download asset Open asset

The insets show the empirical (‘emp,’ below diagonal) and simulated (‘sim,’ above diagonal) FC matrices. While good correspondence between empirical and simulated data can be observed in the off-diagonal blocks of the FC matrix, a comparatively lower homotopic FC was obtained for the simulated dynamics, as can be expected from known limitations of the anatomical connectome (Reveley et al., 2015). Due to the use of SSIM as a target optimization function (Ipina et al., 2020), the optimal simulated matrices captured both the average FC and the relative differences between matrix entries, which jointly minimize the Euclidean and correlation distances, respectively.

Finally, to facilitate further comparison, we used the same prior (RSN) to fit all the groups. The panels in Figure 2 show that the GoF obtained using the RSN prior was similar to that obtained using disease-specific atrophy maps. Moreover, after fitting the model and converting the $\mathrm{\Delta }$ to bifurcation parameters, we correlated the regional parameters obtained for the RSN and AD + FTD atrophy maps. Even though the RSN prior resulted in significantly lower GoF for the bvFTD group, we found an acceptable level of similarity (R ≈ 0.5) between the local bifurcation parameters obtained using the RSN and AD + FTD atrophy map priors. Based on this result, in the rest of this work we adopted the necessary approximation of using the RSN parcellation to constrain local bifurcation parameters, in order to obtain models based on the same prior that could be compared across the different pathologies.

Comparison of model parameters between groups

Here and in the following analysis, we divided the AD group into two subgroups of different severity (AD- and AD+), as determined using non-atrophy measures of brain disease obtained from the patients. For this purpose, we used the median white-matter hyperintensity (WMHI) scores, a marker of small vessel cerebrovascular damage associated with AD disease severity, scored using fluid-attenuated inversion recovery (FLAIR) images (Lee et al., 2016). Vascular compromise has been recently identified as a critical selective vulnerability to AD in vascular subpopulations, transcriptomic perturbations, and expression of genes identified in AD genome-wide association studies (Yang et al., 2022). We chose to use an anatomical marker of disease severity instead of a cognitive/behavioral one since it is more closely related to the empirical neuroimaging data used to train the model. We compared the optimal model parameters (Δ and bifurcation parameters) between CNT, AD-, AD+, and bvFTD across 300 independent runs of the optimization procedure (Figure 3A). Comparing the resulting distributions in terms of the mean and standard deviation, we observed increased/decreased Δ values for the auditory-salience RSN (Aud +Sal) for AD/bvFTD, respectively, and the converse result for the sensorimotor network.

Figure 3

Download asset Open asset

To facilitate the interpretation of these findings, we combined the local Δ values for each AAL, yielding the local bifurcation parameters; next, we visualized brain regions with increased or decreased values relative to control (defined as those regions with $\left|\mathrm{d}\right|$ > 0.8 between both distributions). The corresponding results are shown in Figure 3B. For AD patients with low WMHI scores (AD-), we only observed a shift towards dynamical instability (i.e., closer to the bifurcation) in the posterior cingulate cortex and the right insula. These results were maintained for the AD+ group, but we also observed a shift away from the bifurcation towards stable fixed-point dynamics in the bilateral hippocampus and regions of the frontal cortex. Finally, the bvFTD group was characterized by a shift away from the bifurcation but in the opposite sense, that is, towards the synchronized regime, located in the bilateral insular cortex.

Overall, these results show that the changes in FC observed in AD and bvFTD can be related to region-specific shifts away from the complex and flexible dynamics that manifest in the proximity of the bifurcation point, with shifts towards stable noisy dynamics in the bilateral hippocampus for AD (but only for the subgroup with high WMHI scores) and shift towards stable oscillations in the bilateral insula for bvFTD. Note that these results concern the bifurcation parameters, which are obtained as a combination of the Δ values optimized using the genetic algorithm (Figure 3A).

Latent space encoding

After investigating the differences between groups in the six-dimensional parameter space spanned by the Δ values, we obtained two-dimensional representations of these states by encoding the simulated FC using a VAE. Figure 4A presents a comparison between the original (above diagonal) and reconstructed (below diagonal) FC matrices; clearly, the reconstructed matrices closely resemble the originals. SSIM values between original and reconstructed FC matrices were 0.86 (CNT), 0.71 (AD+), 0.82 (AD-), and 0.76 (bvFTD). Panel B illustrates the structure of the FC matrix decoded at each point of the two-dimensional latent space spanned by the two hidden units of the VAE. Panel C shows the encoding of 300 independent runs of the model fitted to CNT, AD-, AD+, and bvFTD, with the larger colored circles indicating the average positioning of all runs in latent space. The two different neurodegenerative diseases, AD and bvFTD, were encoded in different directions of the latent space, indicating their qualitatively different effects on whole-brain connectivity and dynamics. Moreover, we observed that AD- and AD+ are within the same line in latent space, with AD- being closer to CNT than AD+, as expected in terms of the WHMI scores (see Figure 4D). These results highlight that the organization of the latent space is suggestive of different pathophysiological mechanisms and that it is sensitive to disease severity, which can be inferred from the relative placement of the encoded FC matrices.

Figure 4

Download asset Open asset

Perturbational landscapes of neurodegenerative diseases

The latent space encoding facilitates the visualization of complex manipulations applied to the model. We leveraged this method to investigate how each disease group responded to three different perturbations: periodic forcing at the peak BOLD frequency of each node (Wave stimulation), shift of the bifurcation parameter towards the synchronized regime (Sync perturbation), and shift towards the noisy regime (Noise perturbation). These perturbations can be applied with different intensities and at different pairs of homotopic brain regions, resulting in a series of FC matrices that depend on these two variables. After encoding, these FC matrices appear as a set of continuous trajectories in latent space that start from the state under perturbation (perturbational landscape), as can be seen in Figure 5. Interestingly, all perturbation protocols presented an asymptotic behavior where further increases of the parameter failed to keep displacing the encoded state. Here, the trajectories in dark gray tones are the closest to CNT, with lighter colors indicating less proximity. Both the Wave and Sync stimulations resulted in a similar perturbational landscape, as expected from the similar effects of these perturbations on the nodal dynamics. As shown in the right column of Figure 5, while AD- could be displaced to reach CNT, this became progressively more difficult for AD+ and bvFTD (for all pairs of distributions shown in this panel, $\left|\mathrm{d}\right|$ > 0.8). For bvFTD, in particular, very few trajectories approached the region corresponding to CNT.

Figure 5 with 1 supplement see all

Download asset Open asset

Brain regions that increased similarity to controls when stimulated

Finally, we ranked regions according to the proximity of their corresponding trajectories to CNT. It is important to note that the 2D location in the embedding and its proximity to CNT provides more information than one-dimensional metrics such as the GOF between the perturbed FCs and the CNT FC, including the trajectory of the perturbation (see Figure 5—figure supplement 1 in the supplementary material). Figure 6 presents this information, showing the top decile of regions for each disease subgroup and stimulation type. We found that Wave and Sync stimulation applied to regions in the visual and temporal lobes (mainly in the hippocampus) displaced both AD- and AD+ towards CNT. In the case of bvFTD, frontal regions involved with social cognition were highlighted. For the Noise protocol, regions of the sensorimotor cortex were the most important to displace the brain state from AD to CNT, while frontal regions were again the most relevant for the bvFTD group. Finally, for AD the temporo-posterior involvement was more systematic across different types of stimulation compared to bvFTD.

Figure 6

Download asset Open asset

We used a phenomenological model of fMRI dynamics informed by disease-specific priors to investigate the mechanisms underlying whole-brain FC changes in AD and bvFTD, the most common neurodegenerative diseases leading to dementia (Bang et al., 2015; Scheltens et al., 2021). The use of VAE for dimensionality reduction allowed us to explore the global effects of simulated stimulation in the model, yielding the perturbational landscape of AD and bvFTD whole-brain dynamics, from which we identified key regions to induce transitions towards healthy brain states. In the following, we discuss our findings in light of the multiscale pathophysiology of neurodegenerative diseases and the different interventions that have been proposed for its treatment.

Contrary to previous implementations of the Hopf normal mode as a whole-brain activity model, we allowed regional variations constrained by different spatial heterogeneity maps. We obtained the best fits using priors based on RSN and disease-specific atrophy maps, with lower GoF when using maps obtained from a different neurodegenerative disease (PD), prompting the need to discuss the relationship between atrophy and local dynamics. AD is linked to altered cellular energy metabolism (Gu et al., 2012), excitation/inhibition ratio (Lopatina et al., 2019; Maestú et al., 2021) and neurotrophic factor release (Murer et al., 2001), impairing neural microcircuit function (Palop and Mucke, 2016). Using TMS, it was shown that imbalances between inhibition and excitation correlate with negative and positive symptoms in bvFTD patients (Benussi et al., 2020b), and distinguish between bvFTD and AD patients (Benussi et al., 2017). Considering the relationship between the Hopf bifurcation parameter and the local excitation/inhibition ratio (which can be inferred by studying the bifurcation diagram of more biophysically realistic models, such as the Wilson–Cowan model) (Cowan et al., 2016), it is likely that this feature of cortical dynamics is being captured during local parameter optimization. The methodological framework developed here could be applied to investigate the dynamical consequences of other spatial heterogeneities, which in the case of AD and bvFTD include glucose metabolism, tau and amyloid PET imaging, and multimodal allostatic markers (Engler et al., 2008; Foster et al., 2007; Jack et al., 2018; Mosconi et al., 2008; Nordberg, 2004; Migeot et al., 2022).

The regions with the most prominent alterations in the local bifurcation parameters are the most affected by AD and bvFTD pathology. Temporo-posterior atrophy, including the hippocampus in the medial temporal lobe (MTL), is indicative of AD and predicts disease onset and progression (Pini et al., 2016). Moreover, these regions also show altered FC profiles (Liu et al., 2008) and hemodynamic responses in spatial navigation (Vlček and Laczó, 2014) and memory-related tasks (Golby et al., 2005), both functions compromised in AD. The expansion of temporo-posterior compromise to more frontal regions in AD+ is also consistent with multimodal models of disease progression (Raj et al., 2015). The shift towards stable fixed-points hippocampal dynamics is consistent with reports of functional uncoupling of the MTL in AD (Berron et al., 2020), as well as with increased cortical inhibition (Maestú et al., 2021). On the other hand, bvFTD presents disruptions in a fronto-insular-temporal network implied in social cognition, among other functions (Beeldman et al., 2018; Ibáñez, 2018; Legaz et al., 2022; Salamone et al., 2021; Santamaría-García et al., 2017). In particular, the breakdown of the salience network includes degeneration of the anterior insula, impacting emotional, social, and interoceptive deficits in bvFTD (Garcia-Cordero et al., 2021; Ibañez and Manes, 2012; Migeot et al., 2022). Consistent with our results (i.e., shift away from the bifurcation towards synchronous dynamics in the bilateral insula), these abnormalities manifest in the fMRI signal as increases in the power of low-frequency oscillations (Day et al., 2013). Interestingly, using the RSN prior resulted in GoF values comparable to those obtained using AD atrophy maps, a result compatible with the observation that disrupted whole-brain dynamics and FC in AD patients follows the general outline of the major RSN (Brier et al., 2012).

While previous modeling studies addressed the effect of localized perturbations (Deco et al., 2019; Ipina et al., 2020; Leonardo et al., 2017), the high dimensionality of whole-brain FC has hampered their global analysis and visualization. For this purpose, we adopted a framework based on VAE, a deep learning method for nonlinear dimensionality reduction, capable of representing the simulated FC matrices in a bidimensional latent space with low reconstruction error (Perl et al., 2020). Within this latent space, we distinguished controls, AD and bvFTD patients, with different directions encoding the severity of each disease. Despite the distinctive clinical patterns that characterize these two diseases, their overlap in terms of cognition and behavior can complicate their differential diagnosis (Musa et al., 2020; Parra et al., 2018). Our model was capable of learning FC patterns that mapped into clearly separated latent space regions, suggesting that future modeling efforts at the single subject level could significantly contribute to the problem of classification between AD and bvFTD, assisting the development of mechanistic biomarkers (Deco and Kringelbach, 2014). Moreover, the latent space encoding also captured the severity of AD in terms of WMHI (note that the same analysis could not be conducted for the bvFTD dataset due to insufficient number of participants). Also, while WMHI are highly prevalent and associated with disease severity, such association is weak in the case of bvFTD patients (Hu et al., 2021). As documented in the literature, the severity of small-vessel cerebrovascular disease (visualized using T2-weighted MRI) is increased in AD patients, serves as a predictor of AD future development and rate of cognitive decline, and correlates with atrophy progression (Hu et al., 2021; Kim et al., 2020; Lee et al., 2016; Liu et al., 2018). Moreover, our results support recent critical evidence unveiling a major vascular contribution to AD (Yang et al., 2022). The low (AD-) and high (AD+) severity groups were encoded within the same direction, but at different distances from the control group, suggesting that disease progression could be described in terms of a one-dimensional manifold.

We explored three different forms of simulated stimulation, finding one trajectory in latent space per pair of homotopic AAL regions (where stimulation was delivered), parameterized by Δa (for Noise and Sync stimulation), and ${\mathrm{F}}_0$ (for Wave stimulation). As expected, the Wave stimulation delivered at the dominant frequency of each node displaced the dynamics towards the oscillatory regime, resulting in effects very similar to those obtained with the Sync stimulation. For these two types of stimulation, key nodes to transition from AD towards the healthy state included the hippocampus as well as temporo-posterior regions. In the case of bvFTD, these regions comprised frontal areas involved with social cognition, which is known to be compromised by this pathology (Bang et al., 2015; Garcia-Cordero et al., 2021; Ibañez and Manes, 2012; Migeot et al., 2022). Interestingly, the brain regions important to revert abnormal brain FC did not match with those presenting different bifurcation parameters (see Figure 3), implying that the simulated perturbation is not limited to the normalization of these differences, but also causes complex downstream effects displacing the dynamics in the desired direction.

Our study presents some limitations to be acknowledged, partly stemming from the model and the sources of empirical data necessary for its construction. Phenomenological models are not directly interpretable in terms of biophysical variables, and they only allow exploring mechanisms associated with general aspects of the dynamics. With respect to model fitting, as in previous studies (Ipina et al., 2020; Sanz Perl et al., 2021; Perl et al., 2020), we observed low inter-hemispheric simulated FC, which is related to the known underestimation of homotopic fiber tracts by DTI (Reveley et al., 2015). These analyses should be revisited when more accurate whole-brain connectivity estimates become available, expecting a better fit to the empirical data that could strengthen the main conclusions of our work. We conducted group-level analyses, which constitute a valuable proof of principle for applying whole-brain modeling to neurodegenerative diseases, but fall short of providing information at the single subject level, which would be required to correlate the results of the model with behavioral outcomes, especially given results showing that the therapeutic effect of transcranial stimulation depends on individual characteristics of brain anatomy (Unal et al., 2020; Tsapkini et al., 2018; de Aguiar et al., 2020; Pytel et al., 2021). For this purpose, models tailored to data from individual patients will be required (Menardi et al., 2022). Another limitation of this work is related to the diagnosis of the patients, which was based on current clinical criteria. Mainstream dementia diagnosis may also include biomarkers such as deposition of amyloid-β and tau proteins. These are quantified through positron emission tomography (PET) or plasma, especially in AD (Agrawal and Biswas, 2015). However, it is currently common for dementia research to use the standard clinical criteria as reported in this paper (Birba et al., 2022; Herzog et al., 2022; Legaz et al., 2022; Maito et al., 2023; Parra et al., 2023). Moreover, PET/plasma biomarkers present caveats for global settings. PET is not widely available, that is, the percentage of PET access for patients in global settings is less than 1% (Parra et al., 2023; Parra et al., 2018) nor cost-effective (Parra et al., 2023). It does not provide a conclusive diagnosis (Brown et al., 2014) nor discriminates well between FTD variants (Ntymenou et al., 2021). Fluid biomarkers (i.e., plasma) are very promising, but are not yet massively accessible. More importantly, plasma biomarkers currently lack systematic validation in diverse and non-stereotypical populations (Parra et al., 2023) like the current sample. However, future works should (a) combine clinical and biomarkers criteria to model whole brain dynamics and (b) use metabolic maps of tau and amyloid maps as priors to improve the fit of the whole brain modeling. Finally, despite our choice of the AAL parcellation being based on its widespread adoption in dementia research (Agosta et al., 2013; Lord et al., 2016; Reyes et al., 2018; Sedeño et al., 2017; Whitwell et al., 2011), future research may require a systematic exploration of other brain parcellations applied to the dementia population. In that direction, to compare the differences across parcellations, a recent study by Gonzalez et al. used the AAL and HCP atlas (Glasser et al., 2016) parcellations on a dementia subsample (graph connectivity and graph multifeature in both modalities) and did not find significant differences (Gonzalez-Gomez et al., 2023).

In conclusion, we developed a computational model capable of capturing the most salient differences in whole-brain dynamics between controls and patients. In doing so, the model informed potential underlying dynamical mechanisms that could be traceable to biophysical observables, such as the local excitation/inhibition ratio. We also introduced a novel methodological framework to visualize complex global manipulations of brain activity, leveraging it to investigate the relationship between bvFTD and AD of different severity, as well as their perturbational landscape. Whole-brain models of neurodegeneration may bring together innovative combinations of multimodal brain biomarkers (atrophy, structural and functional connectivity, vascularity, metabolism) while assessing the critical challenges of dimensionality, heterogeneity, and differential diagnosis. While the implemented model facilitated interpretation in dynamical terms, future efforts should expand our work towards more biophysically realistic models, allowing the assessment of pharmacological modulation, among other simulated interventions to enhance cognitive function and slow down its decline in patients suffering from neurodegeneration.

Data and codes required to reproduce the findings of this manuscript are freely available at the following OSF webpage: https://tinyurl.com/27652jkz.

1. 1. Tagliazucchi E

   (2022) Open Science Framework

   ID wunzt. Model-based whole-brain perturbational landscape of neurodegenerative diseases.

   https://osf.io/wunzt/

1. 1. Agosta F
   2. Sala S
   3. Valsasina P
   4. Meani A
   5. Canu E
   6. Magnani G
   7. Cappa SF
   8. Scola E
   9. Quatto P
   10. Horsfield MA
   11. Falini A
   12. Comi G
   13. Filippi M

   (2013) Brain network connectivity assessed using graph theory in frontotemporal dementia

   Neurology 81:134–143.

   https://doi.org/10.1212/WNL.0b013e31829a33f8

   • PubMed
   • Google Scholar
2. 1. Agrawal M
   2. Biswas A

   (2015) Molecular diagnostics of neurodegenerative disorders

   Frontiers in Molecular Biosciences 2:54.

   https://doi.org/10.3389/fmolb.2015.00054

   • PubMed
   • Google Scholar
3. 1. Antczak J
   2. Kowalska K
   3. Klimkowicz-Mrowiec A
   4. Wach B
   5. Kasprzyk K
   6. Banach M
   7. Rzeźnicka-Brzegowy K
   8. Kubica J
   9. Słowik A

   (2018) Repetitive transcranial magnetic stimulation for the treatment of cognitive impairment in frontotemporal dementia: an open-label pilot study

   Neuropsychiatric Disease and Treatment 14:749–755.

   https://doi.org/10.2147/NDT.S153213

   • PubMed
   • Google Scholar
4. 1. Baez S
   2. Couto B
   3. Torralva T
   4. Sposato LA
   5. Huepe D
   6. Montañes P
   7. Reyes P
   8. Matallana D
   9. Vigliecca NS
   10. Slachevsky A
   11. Manes F
   12. Ibanez A

   (2014) Comparing moral judgments of patients with frontotemporal dementia and frontal stroke

   JAMA Neurology 71:1172–1176.

   https://doi.org/10.1001/jamaneurol.2014.347

   • PubMed
   • Google Scholar
5. 1. Bang J
   2. Spina S
   3. Miller BL

   (2015) Frontotemporal dementia

   Lancet 386:1672–1682.

   https://doi.org/10.1016/S0140-6736(15)00461-4

   • PubMed
   • Google Scholar
6. 1. Beckmann CF
   2. Smith SM

   (2004) Probabilistic independent component analysis for functional magnetic resonance imaging

   IEEE Transactions on Medical Imaging 23:137–152.

   https://doi.org/10.1109/TMI.2003.822821

   • PubMed
   • Google Scholar
7. 1. Beeldman E
   2. Raaphorst J
   3. Klein Twennaar M
   4. Govaarts R
   5. Pijnenburg YAL
   6. de Haan RJ
   7. de Visser M
   8. Schmand BA

   (2018) The cognitive profile of behavioural variant FTD and its similarities with ALS: a systematic review and meta-analysis

   Journal of Neurology, Neurosurgery, and Psychiatry 89:995–1002.

   https://doi.org/10.1136/jnnp-2017-317459

   • PubMed
   • Google Scholar
8. 1. Benussi A
   2. Di Lorenzo F
   3. Dell’Era V
   4. Cosseddu M
   5. Alberici A
   6. Caratozzolo S
   7. Cotelli MS
   8. Micheli A
   9. Rozzini L
   10. Depari A
   11. Flammini A
   12. Ponzo V
   13. Martorana A
   14. Caltagirone C
   15. Padovani A
   16. Koch G
   17. Borroni B

   (2017) Transcranial magnetic stimulation distinguishes alzheimer disease from frontotemporal dementia

   Neurology 89:665–672.

   https://doi.org/10.1212/WNL.0000000000004232

   • PubMed
   • Google Scholar
9. 1. Benussi A
   2. Dell’Era V
   3. Cantoni V
   4. Cotelli MS
   5. Cosseddu M
   6. Spallazzi M
   7. Alberici A
   8. Padovani A
   9. Borroni B

   (2020a) Neurophysiological correlates of positive and negative symptoms in frontotemporal dementia

   Journal of Alzheimer’s Disease 73:1133–1142.

   https://doi.org/10.3233/JAD-190986

   • PubMed
   • Google Scholar
10. 1. Benussi A
    2. Dell’Era V
    3. Cosseddu M
    4. Cantoni V
    5. Cotelli MS
    6. Cotelli M
    7. Manenti R
    8. Benussi L
    9. Brattini C
    10. Alberici A
    11. Borroni B

    (2020b) Transcranial stimulation in frontotemporal dementia: A randomized, double-blind, sham-controlled trial

    Alzheimer’s & Dementia 6:e12033.

    https://doi.org/10.1002/trc2.12033

    • PubMed
    • Google Scholar
11. 1. Benussi A
    2. Cantoni V
    3. Cotelli MS
    4. Cotelli M
    5. Brattini C
    6. Datta A
    7. Thomas C
    8. Santarnecchi E
    9. Pascual-Leone A
    10. Borroni B

    (2021) Exposure to gamma tacs in Alzheimer’s disease: a randomized, double-blind, sham-controlled, crossover, pilot study

    Brain Stimulation 14:531–540.

    https://doi.org/10.1016/j.brs.2021.03.007

    • PubMed
    • Google Scholar
12. 1. Berron D
    2. van Westen D
    3. Ossenkoppele R
    4. Strandberg O
    5. Hansson O

    (2020) Medial temporal lobe connectivity and its associations with cognition in early Alzheimer’s disease

    Brain 143:1233–1248.

    https://doi.org/10.1093/brain/awaa068

    • PubMed
    • Google Scholar
13. 1. Birba A
    2. Ibáñez A
    3. Sedeño L
    4. Ferrari J
    5. García AM
    6. Zimerman M

    (2017) Non-invasive brain stimulation: a new strategy in mild cognitive impairment?

    Frontiers in Aging Neuroscience 9:16.

    https://doi.org/10.3389/fnagi.2017.00016

    • PubMed
    • Google Scholar
14. 1. Birba A
    2. Santamaría-García H
    3. Prado P
    4. Cruzat J
    5. Ballesteros AS
    6. Legaz A
    7. Fittipaldi S
    8. Duran-Aniotz C
    9. Slachevsky A
    10. Santibañez R
    11. Sigman M
    12. García AM
    13. Whelan R
    14. Moguilner S
    15. Ibáñez A

    (2022) Allostatic-interoceptive overload in frontotemporal dementia

    Biological Psychiatry 92:54–67.

    https://doi.org/10.1016/j.biopsych.2022.02.955

    • PubMed
    • Google Scholar
15. 1. Bréchet L
    2. Michel CM
    3. Schacter DL
    4. Pascual-Leone A

    (2021) Improving autobiographical memory in Alzheimer’s disease by transcranial alternating current stimulation

    Current Opinion in Behavioral Sciences 40:64–71.

    https://doi.org/10.1016/j.cobeha.2021.01.003

    • PubMed
    • Google Scholar
16. 1. Brier MR
    2. Thomas JB
    3. Snyder AZ
    4. Benzinger TL
    5. Zhang D
    6. Raichle ME
    7. Holtzman DM
    8. Morris JC
    9. Ances BM

    (2012) Loss of intranetwork and internetwork resting state functional connections with Alzheimer’s disease progression

    The Journal of Neuroscience 32:8890–8899.

    https://doi.org/10.1523/JNEUROSCI.5698-11.2012

    • PubMed
    • Google Scholar
17. 1. Brown RKJ
    2. Bohnen NI
    3. Wong KK
    4. Minoshima S
    5. Frey KA

    (2014) Brain PET in suspected dementia: patterns of altered FDG metabolism

    Radiographics 34:684–701.

    https://doi.org/10.1148/rg.343135065

    • PubMed
    • Google Scholar
18. 1. Chao-Gan Y
    2. Yu-Feng Z

    (2010) DPARSF: a matlab toolbox for pipeline data analysis of resting-state fmri

    Frontiers in Systems Neuroscience 4:13.

    https://doi.org/10.3389/fnsys.2010.00013

    • PubMed
    • Google Scholar
19. 1. Cofré R
    2. Herzog R
    3. Mediano PAM
    4. Piccinini J
    5. Rosas FE
    6. Sanz Perl Y
    7. Tagliazucchi E

    (2020) Whole-Brain models to explore altered states of consciousness from the bottom up

    Brain Sciences 10:626.

    https://doi.org/10.3390/brainsci10090626

    • PubMed
    • Google Scholar
20. 1. Cordes D
    2. Haughton VM
    3. Arfanakis K
    4. Carew JD
    5. Turski PA
    6. Moritz CH
    7. Quigley MA
    8. Meyerand ME

    (2001)

    Frequencies contributing to functional connectivity in the cerebral cortex in resting-state data

    AJNR. American Journal of Neuroradiology 22:1326–1333.

    • PubMed
    • Google Scholar
21. 1. Cowan JD
    2. Neuman J
    3. van Drongelen W

    (2016) Wilson-cowan equations for neocortical dynamics

    Journal of Mathematical Neuroscience 6:1.

    https://doi.org/10.1186/s13408-015-0034-5

    • PubMed
    • Google Scholar
22. 1. Cruzat J
    2. Herzog R
    3. Prado P
    4. Sanz-Perl Y
    5. Gonzalez-Gomez R
    6. Moguilner S
    7. Kringelbach ML
    8. Deco G
    9. Tagliazucchi E
    10. Ibañez A

    (2023) Temporal irreversibility of large-scale brain dynamics in Alzheimer’s disease

    The Journal of Neuroscience 43:1643–1656.

    https://doi.org/10.1523/JNEUROSCI.1312-22.2022

    • PubMed
    • Google Scholar
23. 1. Day GS
    2. Farb NAS
    3. Tang-Wai DF
    4. Masellis M
    5. Black SE
    6. Freedman M
    7. Pollock BG
    8. Chow TW

    (2013) Salience network resting-state activity: prediction of frontotemporal dementia progression

    JAMA Neurology 70:1249–1253.

    https://doi.org/10.1001/jamaneurol.2013.3258

    • PubMed
    • Google Scholar
24. 1. de Aguiar V
    2. Zhao Y
    3. Faria A
    4. Ficek B
    5. Webster KT
    6. Wendt H
    7. Wang Z
    8. Hillis AE
    9. Onyike CU
    10. Frangakis C
    11. Caffo B
    12. Tsapkini K

    (2020) Brain volumes as predictors of tdcs effects in primary progressive aphasia

    Brain and Language 200:104707.

    https://doi.org/10.1016/j.bandl.2019.104707

    • PubMed
    • Google Scholar
25. 1. Deco G
    2. Kringelbach ML

    (2014) Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders

    Neuron 84:892–905.

    https://doi.org/10.1016/j.neuron.2014.08.034

    • PubMed
    • Google Scholar
26. 1. Deco G
    2. Kringelbach ML
    3. Jirsa VK
    4. Ritter P

    (2017) The dynamics of resting fluctuations in the brain: metastability and its dynamical cortical core

    Scientific Reports 7:3095.

    https://doi.org/10.1038/s41598-017-03073-5

    • PubMed
    • Google Scholar
27. 1. Deco G
    2. Cruzat J
    3. Cabral J
    4. Knudsen GM
    5. Carhart-Harris RL
    6. Whybrow PC
    7. Logothetis NK
    8. Kringelbach ML

    (2018) Whole-Brain multimodal neuroimaging model using serotonin receptor maps explains non-linear functional effects of LSD

    Current Biology 28:3065–3074.

    https://doi.org/10.1016/j.cub.2018.07.083

    • PubMed
    • Google Scholar
28. 1. Deco G
    2. Cruzat J
    3. Cabral J
    4. Tagliazucchi E
    5. Laufs H
    6. Logothetis NK
    7. Kringelbach ML

    (2019) Awakening: predicting external stimulation to force transitions between different brain states

    PNAS 116:18088–18097.

    https://doi.org/10.1073/pnas.1905534116

    • PubMed
    • Google Scholar
29. 1. Demirtaş M
    2. Falcon C
    3. Tucholka A
    4. Gispert JD
    5. Molinuevo JL
    6. Deco G

    (2017) A whole-brain computational modeling approach to explain the alterations in resting-state functional connectivity during progression of Alzheimer’s disease

    NeuroImage. Clinical 16:343–354.

    https://doi.org/10.1016/j.nicl.2017.08.006

    • PubMed
    • Google Scholar
30. 1. Donnelly-Kehoe PA
    2. Pascariello GO
    3. Garcia AM
    4. Hodges JR
    5. Miller B
    6. Rosen H
    7. Manes F
    8. Landin-Romero R
    9. Matallana D
    10. Serrano C
    11. Herrera E
    12. Reyes P
    13. Santamaria-Garcia H
    14. Kumfor F
    15. Piguet O
    16. Ibanez A
    17. Sedeno L

    (2019) Robust automated computational approach for classifying frontotemporal neurodegeneration: multimodal/multicenter neuroimaging

    Alzheimers Dement 11:588–598.

    https://doi.org/10.1016/j.dadm.2019.06.002

    • Google Scholar
31. 1. Dottori M
    2. Sedeño L
    3. Martorell Caro M
    4. Alifano F
    5. Hesse E
    6. Mikulan E
    7. García AM
    8. Ruiz-Tagle A
    9. Lillo P
    10. Slachevsky A
    11. Serrano C
    12. Fraiman D
    13. Ibanez A

    (2017) Towards affordable biomarkers of frontotemporal dementia: a classification study via network’s information sharing

    Scientific Reports 7:3822.

    https://doi.org/10.1038/s41598-017-04204-8

    • PubMed
    • Google Scholar
32. 1. Dubois B
    2. Feldman HH
    3. Jacova C
    4. Dekosky ST
    5. Barberger-Gateau P
    6. Cummings J
    7. Delacourte A
    8. Galasko D
    9. Gauthier S
    10. Jicha G
    11. Meguro K
    12. O’brien J
    13. Pasquier F
    14. Robert P
    15. Rossor M
    16. Salloway S
    17. Stern Y
    18. Visser PJ
    19. Scheltens P

    (2007) Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS-ADRDA criteria

    The Lancet. Neurology 6:734–746.

    https://doi.org/10.1016/S1474-4422(07)70178-3

    • PubMed
    • Google Scholar
33. 1. Engler H
    2. Santillo AF
    3. Wang SX
    4. Lindau M
    5. Savitcheva I
    6. Nordberg A
    7. Lannfelt L
    8. Långström B
    9. Kilander L

    (2008) In vivo amyloid imaging with PET in frontotemporal dementia

    European Journal of Nuclear Medicine and Molecular Imaging 35:100–106.

    https://doi.org/10.1007/s00259-007-0523-1

    • PubMed
    • Google Scholar
34. 1. Ferrucci R
    2. Mameli F
    3. Guidi I
    4. Mrakic-Sposta S
    5. Vergari M
    6. Marceglia S
    7. Cogiamanian F
    8. Barbieri S
    9. Scarpini E
    10. Priori A

    (2008) Transcranial direct current stimulation improves recognition memory in alzheimer disease

    Neurology 71:493–498.

    https://doi.org/10.1212/01.wnl.0000317060.43722.a3

    • PubMed
    • Google Scholar
35. 1. Foster NL
    2. Heidebrink JL
    3. Clark CM
    4. Jagust WJ
    5. Arnold SE
    6. Barbas NR
    7. DeCarli CS
    8. Scott Turner R
    9. Koeppe RA
    10. Higdon R
    11. Minoshima S

    (2007) Fdg-Pet improves accuracy in distinguishing frontotemporal dementia and Alzheimer’s disease

    Brain 130:2616–2635.

    https://doi.org/10.1093/brain/awm177

    • PubMed
    • Google Scholar
36. 1. Garcia-Cordero I
    2. Migeot J
    3. Fittipaldi S
    4. Aquino A
    5. Campo CG
    6. García A
    7. Ibáñez A

    (2021) Metacognition of emotion recognition across neurodegenerative diseases

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 137:93–107.

    https://doi.org/10.1016/j.cortex.2020.12.023

    • PubMed
    • Google Scholar
37. 1. GBD 2019 Dementia Forecasting Collaborators

    (2022) Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the global burden of disease study 2019

    The Lancet. Public Health 7:e105–e125.

    https://doi.org/10.1016/S2468-2667(21)00249-8

    • PubMed
    • Google Scholar
38. 1. Glasser MF
    2. Smith SM
    3. Marcus DS
    4. Andersson JLR
    5. Auerbach EJ
    6. Behrens TEJ
    7. Coalson TS
    8. Harms MP
    9. Jenkinson M
    10. Moeller S
    11. Robinson EC
    12. Sotiropoulos SN
    13. Xu J
    14. Yacoub E
    15. Ugurbil K
    16. Van Essen DC

    (2016) The human connectome project’s neuroimaging approach

    Nature Neuroscience 19:1175–1187.

    https://doi.org/10.1038/nn.4361

    • PubMed
    • Google Scholar
39. 1. Golby A
    2. Silverberg G
    3. Race E
    4. Gabrieli S
    5. O’Shea J
    6. Knierim K
    7. Stebbins G
    8. Gabrieli J

    (2005) Memory encoding in Alzheimer’s disease: an fMRI study of explicit and implicit memory

    Brain 128:773–787.

    https://doi.org/10.1093/brain/awh400

    • PubMed
    • Google Scholar
40. 1. Gonzalez-Gomez R
    2. Ibañez A
    3. Moguilner S
    4. Rubinov M

    (2023) Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference

    Network Neuroscience 7:322–350.

    https://doi.org/10.1162/netn_a_00285

    • Google Scholar
41. 1. Gu XM
    2. Huang HC
    3. Jiang ZF

    (2012) Mitochondrial dysfunction and cellular metabolic deficiency in Alzheimer’s disease

    Neuroscience Bulletin 28:631–640.

    https://doi.org/10.1007/s12264-012-1270-2

    • PubMed
    • Google Scholar
42. 1. Hafkemeijer A
    2. Möller C
    3. Dopper EGP
    4. Jiskoot LC
    5. Schouten TM
    6. van Swieten JC
    7. van der Flier WM
    8. Vrenken H
    9. Pijnenburg YAL
    10. Barkhof F
    11. Scheltens P
    12. van der Grond J
    13. Rombouts S

    (2015) Resting state functional connectivity differences between behavioral variant frontotemporal dementia and alzheimer’s disease

    Frontiers in Human Neuroscience 9:474.

    https://doi.org/10.3389/fnhum.2015.00474

    • PubMed
    • Google Scholar
43. 1. Helfrich RF
    2. Schneider TR
    3. Rach S
    4. Trautmann-Lengsfeld SA
    5. Engel AK
    6. Herrmann CS

    (2014) Entrainment of brain oscillations by transcranial alternating current stimulation

    Current Biology 24:333–339.

    https://doi.org/10.1016/j.cub.2013.12.041

    • PubMed
    • Google Scholar
44. 1. Herzog R
    2. Rosas FE
    3. Whelan R
    4. Fittipaldi S
    5. Santamaria-Garcia H
    6. Cruzat J
    7. Birba A
    8. Moguilner S
    9. Tagliazucchi E
    10. Prado P
    11. Ibanez A

    (2022) Genuine high-order interactions in brain networks and neurodegeneration

    Neurobiology of Disease 175:105918.

    https://doi.org/10.1016/j.nbd.2022.105918

    • PubMed
    • Google Scholar
45. 1. Hou Y
    2. Dan X
    3. Babbar M
    4. Wei Y
    5. Hasselbalch SG
    6. Croteau DL
    7. Bohr VA

    (2019) Ageing as a risk factor for neurodegenerative disease

    Nature Reviews. Neurology 15:565–581.

    https://doi.org/10.1038/s41582-019-0244-7

    • PubMed
    • Google Scholar
46. 1. Hu HY
    2. Ou YN
    3. Shen XN
    4. Qu Y
    5. Ma YH
    6. Wang ZT
    7. Dong Q
    8. Tan L
    9. Yu JT

    (2021) White matter hyperintensities and risks of cognitive impairment and dementia: a systematic review and meta-analysis of 36 prospective studies

    Neuroscience and Biobehavioral Reviews 120:16–27.

    https://doi.org/10.1016/j.neubiorev.2020.11.007

    • PubMed
    • Google Scholar
47. 1. Ibañez A
    2. Manes F

    (2012) Contextual social cognition and the behavioral variant of frontotemporal dementia

    Neurology 78:1354–1362.

    https://doi.org/10.1212/WNL.0b013e3182518375

    • PubMed
    • Google Scholar
48. 1. Ibáñez A

    (2018) Brain oscillations, inhibition and social inappropriateness in frontotemporal degeneration

    Brain 141:e73.

    https://doi.org/10.1093/brain/awy233

    • PubMed
    • Google Scholar
49. 1. Ibanez A
    2. Parra MA
    3. Butler C

    (2021a) The latin america and the caribbean consortium on dementia (LAC-CD): from networking to research to implementation science

    Journal of Alzheimer’s Disease 82:S379–S394.

    https://doi.org/10.3233/JAD-201384

    • PubMed
    • Google Scholar
50. 1. Ibanez A
    2. Yokoyama JS
    3. Possin KL
    4. Matallana D
    5. Lopera F
    6. Nitrini R
    7. Takada LT
    8. Custodio N
    9. Sosa Ortiz AL
    10. Avila-Funes JA
    11. Behrens MI
    12. Slachevsky A
    13. Myers RM
    14. Cochran JN
    15. Brusco LI
    16. Bruno MA
    17. Brucki SMD
    18. Pina-Escudero SD
    19. Okada de Oliveira M
    20. Donnelly Kehoe P
    21. Garcia AM
    22. Cardona JF
    23. Santamaria-Garcia H
    24. Moguilner S
    25. Duran-Aniotz C
    26. Tagliazucchi E
    27. Maito M
    28. Longoria Ibarrola EM
    29. Pintado-Caipa M
    30. Godoy ME
    31. Bakman V
    32. Javandel S
    33. Kosik KS
    34. Valcour V
    35. Miller BL

    (2021b) The multi-partner consortium to expand dementia research in latin america (redlat): driving multicentric research and implementation science

    Frontiers in Neurology 12:631722.

    https://doi.org/10.3389/fneur.2021.631722

    • PubMed
    • Google Scholar
51. 1. Ipina IP
    2. Kehoe PD
    3. Kringelbach M
    4. Laufs H
    5. Ibanez A
    6. Deco G
    7. Perl YS
    8. Tagliazucchi E

    (2020) Modeling regional changes in dynamic stability during sleep and wakefulness

    Neuroimage 215:116833.

    https://doi.org/10.1016/j.neuroimage.2020.116833

    • Google Scholar
52. 1. Jack CR
    2. Wiste HJ
    3. Schwarz CG
    4. Lowe VJ
    5. Senjem ML
    6. Vemuri P
    7. Weigand SD
    8. Therneau TM
    9. Knopman DS
    10. Gunter JL
    11. Jones DT
    12. Graff-Radford J
    13. Kantarci K
    14. Roberts RO
    15. Mielke MM
    16. Machulda MM
    17. Petersen RC

    (2018) Longitudinal tau PET in ageing and Alzheimer’s disease

    Brain 141:1517–1528.

    https://doi.org/10.1093/brain/awy059

    • PubMed
    • Google Scholar
53. 1. Jalilianhasanpour R
    2. Beheshtian E
    3. Sherbaf G
    4. Sahraian S
    5. Sair HI

    (2019) Functional connectivity in neurodegenerative disorders: alzheimer’s disease and frontotemporal dementia

    Topics in Magnetic Resonance Imaging 28:317–324.

    https://doi.org/10.1097/RMR.0000000000000223

    • PubMed
    • Google Scholar
54. 1. Kim JP
    2. Kim J
    3. Park YH
    4. Park SB
    5. Lee JS
    6. Yoo S
    7. Kim EJ
    8. Kim HJ
    9. Na DL
    10. Brown JA
    11. Lockhart SN
    12. Seo SW
    13. Seong JK

    (2019) Machine learning based hierarchical classification of frontotemporal dementia and alzheimer’s disease

    NeuroImage. Clinical 23:101811.

    https://doi.org/10.1016/j.nicl.2019.101811

    • PubMed
    • Google Scholar
55. 1. Kim HW
    2. Hong J
    3. Jeon JC

    (2020) Cerebral small vessel disease and alzheimer’s disease: a review

    Frontiers in Neurology 11:927.

    https://doi.org/10.3389/fneur.2020.00927

    • PubMed
    • Google Scholar
56. Conference

    1. Kingma DP
    2. Welling M

    (2013) Auto-Encoding Variational Bayes

    2nd International Conference on Learning Representations, ICLR 2014 - Conference Track Proceedings.

    https://doi.org/10.48550/arXiv.1312.6114

    • Google Scholar
57. 1. Lafon B
    2. Henin S
    3. Huang Y
    4. Friedman D
    5. Melloni L
    6. Thesen T
    7. Doyle W
    8. Buzsáki G
    9. Devinsky O
    10. Parra LC
    11. A Liu A

    (2017) Low frequency transcranial electrical stimulation does not entrain sleep rhythms measured by human intracranial recordings

    Nature Communications 8:1199.

    https://doi.org/10.1038/s41467-017-01045-x

    • PubMed
    • Google Scholar
58. 1. Lee S
    2. Viqar F
    3. Zimmerman ME
    4. Narkhede A
    5. Tosto G
    6. Benzinger TLS
    7. Marcus DS
    8. Fagan AM
    9. Goate A
    10. Fox NC
    11. Cairns NJ
    12. Holtzman DM
    13. Buckles V
    14. Ghetti B
    15. McDade E
    16. Martins RN
    17. Saykin AJ
    18. Masters CL
    19. Ringman JM
    20. Ryan NS
    21. Förster S
    22. Laske C
    23. Schofield PR
    24. Sperling RA
    25. Salloway S
    26. Correia S
    27. Jack C Jr
    28. Weiner M
    29. Bateman RJ
    30. Morris JC
    31. Mayeux R
    32. Brickman AM
    33. Dominantly Inherited Alzheimer Network

    (2016) White matter hyperintensities are a core feature of Alzheimer’s disease: evidence from the dominantly inherited Alzheimer network

    Annals of Neurology 79:929–939.

    https://doi.org/10.1002/ana.24647

    • PubMed
    • Google Scholar
59. 1. Legaz A
    2. Abrevaya S
    3. Dottori M
    4. González Campo C
    5. Birba A
    6. Martorell Caro M
    7. Aguirre J
    8. Slachevsky A
    9. Aranguiz R
    10. Serrano C
    11. Gillan CM
    12. Leroi I
    13. García AM
    14. Fittipaldi S
    15. Ibañez A

    (2022) Multimodal mechanisms of human socially reinforced learning across neurodegenerative diseases

    Brain 145:1052–1068.

    https://doi.org/10.1093/brain/awab345

    • PubMed
    • Google Scholar
60. 1. Leonardo GL
    2. Roberts JA
    3. Cocchi L

    (2017) Mapping how local perturbations influence systems-level brain dynamics

    NeuroImage 160:97–112.

    https://doi.org/10.1016/j.neuroimage.2017.01.057

    • PubMed
    • Google Scholar
61. 1. Lindau M
    2. Jelic V
    3. Johansson SE
    4. Andersen C
    5. Wahlund LO
    6. Almkvist O

    (2003) Quantitative EEG abnormalities and cognitive dysfunctions in frontotemporal dementia and Alzheimer’s disease

    Dementia and Geriatric Cognitive Disorders 15:106–114.

    https://doi.org/10.1159/000067973

    • PubMed
    • Google Scholar
62. 1. Liu Y
    2. Wang K
    3. Yu C
    4. He Y
    5. Zhou Y
    6. Liang M
    7. Wang L
    8. Jiang T

    (2008) Regional homogeneity, functional connectivity and imaging markers of Alzheimer’s disease: a review of resting-state fMRI studies

    Neuropsychologia 46:1648–1656.

    https://doi.org/10.1016/j.neuropsychologia.2008.01.027

    • PubMed
    • Google Scholar
63. 1. Liu Y
    2. Braidy N
    3. Poljak A
    4. Chan DKY
    5. Sachdev P

    (2018) Cerebral small vessel disease and the risk of Alzheimer’s disease: a systematic review

    Ageing Research Reviews 47:41–48.

    https://doi.org/10.1016/j.arr.2018.06.002

    • PubMed
    • Google Scholar
64. 1. Liu X
    2. Naomi SSM
    3. Sharon WL
    4. Russell EJ

    (2021) The applications of focused ultrasound (FUS) in alzheimer’s disease treatment: A systematic review on both animal and human studies

    Aging and Disease 12:1977–2002.

    https://doi.org/10.14336/AD.2021.0510

    • PubMed
    • Google Scholar
65. 1. Lopatina OL
    2. Malinovskaya NA
    3. Komleva YK
    4. Gorina YV
    5. Shuvaev AN
    6. Olovyannikova RY
    7. Belozor OS
    8. Belova OA
    9. Higashida H
    10. Salmina AB

    (2019) Excitation/inhibition imbalance and impaired neurogenesis in neurodevelopmental and neurodegenerative disorders

    Reviews in the Neurosciences 30:807–820.

    https://doi.org/10.1515/revneuro-2019-0014

    • PubMed
    • Google Scholar
66. 1. Lord A
    2. Ehrlich S
    3. Borchardt V
    4. Geisler D
    5. Seidel M
    6. Huber S
    7. Murr J
    8. Walter M

    (2016) Brain parcellation choice affects disease-related topology differences increasingly from global to local network levels

    Psychiatry Research. Neuroimaging 249:12–19.

    https://doi.org/10.1016/j.pscychresns.2016.02.001

    • PubMed
    • Google Scholar
67. 1. Maestú F
    2. de Haan W
    3. Busche MA
    4. DeFelipe J

    (2021) Neuronal excitation/inhibition imbalance: core element of a translational perspective on alzheimer pathophysiology

    Ageing Research Reviews 69:101372.

    https://doi.org/10.1016/j.arr.2021.101372

    • PubMed
    • Google Scholar
68. 1. Maito MA
    2. Santamaría-García H
    3. Moguilner S
    4. Possin KL
    5. Godoy ME
    6. Avila-Funes JA
    7. Behrens MI
    8. Brusco IL
    9. Bruno MA
    10. Cardona JF
    11. Custodio N
    12. García AM
    13. Javandel S
    14. Lopera F
    15. Matallana DL
    16. Miller B
    17. de Oliveira MO
    18. Pina-Escudero SD
    19. Slachevsky A
    20. Ortiz ALS
    21. Takada LT
    22. Tagliazuchi E
    23. Valcour V
    24. Yokoyama JS
    25. Ibañez A

    (2023) Classification of Alzheimer’s disease and frontotemporal dementia using routine clinical and cognitive measures across multicentric underrepresented samples: a cross sectional observational study

    Lancet Regional Health. Americas 17:100387.

    https://doi.org/10.1016/j.lana.2022.100387

    • PubMed
    • Google Scholar
69. 1. McKhann G
    2. Drachman D
    3. Folstein M
    4. Katzman R
    5. Price D
    6. Stadlan EM

    (1984) Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA work group under the auspices of department of health and human services Task force on Alzheimer’s disease

    Neurology 34:939–944.

    https://doi.org/10.1212/wnl.34.7.939

    • PubMed
    • Google Scholar
70. 1. McKhann GM
    2. Knopman DS
    3. Chertkow H
    4. Hyman BT
    5. Jack CR Jr
    6. Kawas CH
    7. Klunk WE
    8. Koroshetz WJ
    9. Manly JJ
    10. Mayeux R
    11. Mohs RC
    12. Morris JC
    13. Rossor MN
    14. Scheltens P
    15. Carrillo MC
    16. Thies B
    17. Weintraub S
    18. Phelps CH

    (2011) The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on aging-alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease

    Alzheimer’s & Dementia 7:263–269.

    https://doi.org/10.1016/j.jalz.2011.03.005

    • PubMed
    • Google Scholar
71. 1. Mehta A
    2. Prabhakar M
    3. Kumar P
    4. Deshmukh R
    5. Sharma PL

    (2013) Excitotoxicity: bridge to various triggers in neurodegenerative disorders

    European Journal of Pharmacology 698:6–18.

    https://doi.org/10.1016/j.ejphar.2012.10.032

    • PubMed
    • Google Scholar
72. 1. Melloni M
    2. Billeke P
    3. Baez S
    4. Hesse E
    5. de la Fuente L
    6. Forno G
    7. Birba A
    8. García-Cordero I
    9. Serrano C
    10. Plastino A
    11. Slachevsky A
    12. Huepe D
    13. Sigman M
    14. Manes F
    15. García AM
    16. Sedeño L
    17. Ibáñez A

    (2016) Your perspective and my benefit: multiple lesion models of self-other integration strategies during social bargaining

    Brain 139:3022–3040.

    https://doi.org/10.1093/brain/aww231

    • PubMed
    • Google Scholar
73. 1. Menardi A
    2. Rossi S
    3. Koch G
    4. Hampel H
    5. Vergallo A
    6. Nitsche MA
    7. Stern Y
    8. Borroni B
    9. Cappa SF
    10. Cotelli M
    11. Ruffini G
    12. El-Fakhri G
    13. Rossini PM
    14. Dickerson B
    15. Antal A
    16. Babiloni C
    17. Lefaucheur J-P
    18. Dubois B
    19. Deco G
    20. Ziemann U
    21. Pascual-Leone A
    22. Santarnecchi E

    (2022) Toward noninvasive brain stimulation 2.0 in alzheimer’s disease

    Ageing Research Reviews 75:101555.

    https://doi.org/10.1016/j.arr.2021.101555

    • PubMed
    • Google Scholar
74. 1. Migeot JA
    2. Duran-Aniotz CA
    3. Signorelli CM
    4. Piguet O
    5. Ibáñez A

    (2022) A predictive coding framework of allostatic-interoceptive overload in frontotemporal dementia

    Trends in Neurosciences 45:838–853.

    https://doi.org/10.1016/j.tins.2022.08.005

    • PubMed
    • Google Scholar
75. 1. Moguilner S
    2. García AM
    3. Perl YS
    4. Tagliazucchi E
    5. Piguet O
    6. Kumfor F
    7. Reyes P
    8. Matallana D
    9. Sedeño L
    10. Ibáñez A

    (2021) Dynamic brain fluctuations outperform connectivity measures and mirror pathophysiological profiles across dementia subtypes: a multicenter study

    NeuroImage 225:117522.

    https://doi.org/10.1016/j.neuroimage.2020.117522

    • PubMed
    • Google Scholar
76. 1. Mosconi L
    2. Tsui WH
    3. Herholz K
    4. Pupi A
    5. Drzezga A
    6. Lucignani G
    7. Reiman EM
    8. Holthoff V
    9. Kalbe E
    10. Sorbi S
    11. Diehl-Schmid J
    12. Perneczky R
    13. Clerici F
    14. Caselli R
    15. Beuthien-Baumann B
    16. Kurz A
    17. Minoshima S
    18. de Leon MJ

    (2008) Multicenter standardized 18F-FDG PET diagnosis of mild cognitive impairment, Alzheimer’s disease, and other dementias

    Journal of Nuclear Medicine 49:390–398.

    https://doi.org/10.2967/jnumed.107.045385

    • PubMed
    • Google Scholar
77. 1. Mukadam N
    2. Sommerlad A
    3. Huntley J
    4. Livingston G

    (2019) Population attributable fractions for risk factors for dementia in low-income and middle-income countries: an analysis using cross-sectional survey data

    The Lancet. Global Health 7:e596–e603.

    https://doi.org/10.1016/S2214-109X(19)30074-9

    • PubMed
    • Google Scholar
78. 1. Murer MG
    2. Yan Q
    3. Raisman-Vozari R

    (2001) Brain-derived neurotrophic factor in the control human brain, and in alzheimer’s disease and parkinson’s disease

    Progress in Neurobiology 63:71–124.

    https://doi.org/10.1016/s0301-0082(00)00014-9

    • PubMed
    • Google Scholar
79. 1. Musa G
    2. Slachevsky A
    3. Muñoz-Neira C
    4. Méndez-Orellana C
    5. Villagra R
    6. González-Billault C
    7. Ibáñez A
    8. Hornberger M
    9. Lillo P

    (2020) Alzheimer’s disease or behavioral variant frontotemporal dementia? review of key points toward an accurate clinical and neuropsychological diagnosis

    Journal of Alzheimer’s Disease 73:833–848.

    https://doi.org/10.3233/JAD-190924

    • PubMed
    • Google Scholar
80. 1. Nardone R
    2. Bergmann J
    3. Christova M
    4. Caleri F
    5. Tezzon F
    6. Ladurner G
    7. Trinka E
    8. Golaszewski S

    (2012) Effect of transcranial brain stimulation for the treatment of alzheimer disease: a review

    International Journal of Alzheimer’s Disease 2012:687909.

    https://doi.org/10.1155/2012/687909

    • PubMed
    • Google Scholar
81. 1. Nishida K
    2. Yoshimura M
    3. Isotani T
    4. Yoshida T
    5. Kitaura Y
    6. Saito A
    7. Mii H
    8. Kato M
    9. Takekita Y
    10. Suwa A
    11. Morita S
    12. Kinoshita T

    (2011) Differences in quantitative EEG between frontotemporal dementia and alzheimer’s disease as revealed by LORETA

    Clinical Neurophysiology 122:1718–1725.

    https://doi.org/10.1016/j.clinph.2011.02.011

    • PubMed
    • Google Scholar
82. 1. Nishida K
    2. Morishima Y
    3. Yoshimura M
    4. Isotani T
    5. Irisawa S
    6. Jann K
    7. Dierks T
    8. Strik W
    9. Kinoshita T
    10. Koenig T

    (2013) EEG microstates associated with salience and frontoparietal networks in frontotemporal dementia, schizophrenia and alzheimer’s disease

    Clinical Neurophysiology 124:1106–1114.

    https://doi.org/10.1016/j.clinph.2013.01.005

    • PubMed
    • Google Scholar
83. 1. Nitsche MA
    2. Paulus W

    (2000) Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation

    The Journal of Physiology 527:633–639.

    https://doi.org/10.1111/j.1469-7793.2000.t01-1-00633.x

    • PubMed
    • Google Scholar
84. 1. Nordberg A

    (2004) Pet imaging of amyloid in Alzheimer’s disease

    The Lancet Neurology 3:519–527.

    https://doi.org/10.1016/S1474-4422(04)00853-1

    • PubMed
    • Google Scholar
85. 1. Ntymenou S
    2. Tsantzali I
    3. Kalamatianos T
    4. Voumvourakis KI
    5. Kapaki E
    6. Tsivgoulis G
    7. Stranjalis G
    8. Paraskevas GP

    (2021) Blood biomarkers in frontotemporal dementia: review and meta-analysis

    Brain Sciences 11:244.

    https://doi.org/10.3390/brainsci11020244

    • PubMed
    • Google Scholar
86. 1. Palop JJ
    2. Mucke L

    (2016) Network abnormalities and interneuron dysfunction in alzheimer disease

    Nature Reviews. Neuroscience 17:777–792.

    https://doi.org/10.1038/nrn.2016.141

    • PubMed
    • Google Scholar
87. 1. Parra MA
    2. Baez S
    3. Allegri R
    4. Nitrini R
    5. Lopera F
    6. Slachevsky A
    7. Custodio N
    8. Lira D
    9. Piguet O
    10. Kumfor F
    11. Huepe D
    12. Cogram P
    13. Bak T
    14. Manes F
    15. Ibanez A

    (2018) Dementia in latin america: assessing the present and envisioning the future

    Neurology 90:222–231.

    https://doi.org/10.1212/WNL.0000000000004897

    • PubMed
    • Google Scholar
88. 1. Parra MA
    2. Orellana P
    3. Leon T
    4. Victoria CG
    5. Henriquez F
    6. Gomez R
    7. Avalos C
    8. Damian A
    9. Slachevsky A
    10. Ibanez A
    11. Zetterberg H
    12. Tijms BM
    13. Yokoyama JS
    14. Pina-Escudero SD
    15. Cochran JN
    16. Matallana DL
    17. Acosta D
    18. Allegri R
    19. Arias-Suarez BP
    20. Barra B
    21. Behrens MI
    22. Brucki SMD
    23. Busatto G
    24. Caramelli P
    25. Castro-Suarez S
    26. Contreras V
    27. Custodio N
    28. Dansilio S
    29. Diaz MM
    30. Duque L
    31. Farias GA
    32. Ferreira ST
    33. Guimet NM
    34. Kmaid A
    35. Lira D
    36. Lopera F
    37. Meza BM
    38. Miotto EC
    39. Nitrini R
    40. Nunez A
    41. O’Neill S
    42. Ochoa J
    43. Pintado-Caipa M
    44. Resende EPF
    45. Risacher S
    46. Rojas LA
    47. Sabaj V
    48. Schilling L
    49. Sellek AF
    50. Sosa A
    51. Takada LT
    52. Teixeira AL
    53. Unaucho-Pilalumbo M
    54. Duran-Aniotz C

    (2023) Biomarkers for dementia in latin american countries: gaps and opportunities

    Alzheimers Dement 19:721–735.

    https://doi.org/10.1002/alz.12757

    • Google Scholar
89. 1. Peet BT
    2. Spina S
    3. Mundada N
    4. La Joie R

    (2021) Neuroimaging in frontotemporal dementia: heterogeneity and relationships with underlying neuropathology

    Neurotherapeutics 18:728–752.

    https://doi.org/10.1007/s13311-021-01101-x

    • PubMed
    • Google Scholar
90. 1. Perl YS
    2. Bocaccio H
    3. Pérez-Ipiña I
    4. Zamberlán F
    5. Piccinini J
    6. Laufs H
    7. Kringelbach M
    8. Deco G
    9. Tagliazucchi E

    (2020) Generative embeddings of brain collective dynamics using variational autoencoders

    Physical Review Letters 125:238101.

    https://doi.org/10.1103/PhysRevLett.125.238101

    • PubMed
    • Google Scholar
91. 1. Piguet O
    2. Hornberger M
    3. Mioshi E
    4. Hodges JR

    (2011) Behavioural-variant frontotemporal dementia: diagnosis, clinical staging, and management

    The Lancet Neurology 10:162–172.

    https://doi.org/10.1016/S1474-4422(10)70299-4

    • PubMed
    • Google Scholar
92. 1. Pini L.
    2. Pievani M
    3. Bocchetta M
    4. Altomare D
    5. Bosco P
    6. Cavedo E
    7. Galluzzi S
    8. Marizzoni M
    9. Frisoni GB

    (2016) Brain atrophy in Alzheimer’s disease and aging

    Ageing Research Reviews 30:25–48.

    https://doi.org/10.1016/j.arr.2016.01.002

    • PubMed
    • Google Scholar
93. 1. Pini L
    2. Pizzini FB
    3. Boscolo-Galazzo I
    4. Ferrari C
    5. Galluzzi S
    6. Cotelli M
    7. Gobbi E
    8. Cattaneo A
    9. Cotelli MS
    10. Geroldi C
    11. Zanetti O
    12. Corbetta M
    13. van den Heuvel M
    14. Frisoni GB
    15. Manenti R
    16. Pievani M

    (2022) Brain network modulation in alzheimer’s and frontotemporal dementia with transcranial electrical stimulation

    Neurobiology of Aging 111:24–34.

    https://doi.org/10.1016/j.neurobiolaging.2021.11.005

    • PubMed
    • Google Scholar
94. 1. Prado P
    2. Moguilner S
    3. Mejía JA
    4. Sainz-Ballesteros A
    5. Otero M
    6. Birba A
    7. Santamaria-Garcia H
    8. Legaz A
    9. Fittipaldi S
    10. Cruzat J
    11. Tagliazucchi E
    12. Parra M
    13. Herzog R
    14. Ibáñez A

    (2023) Source space connectomics of neurodegeneration: one-metric approach does not fit all

    Neurobiology of Disease 179:106047.

    https://doi.org/10.1016/j.nbd.2023.106047

    • PubMed
    • Google Scholar
95. 1. Prehn K
    2. Flöel A

    (2015) Potentials and limits to enhance cognitive functions in healthy and pathological aging by tdcs

    Frontiers in Cellular Neuroscience 9:355.

    https://doi.org/10.3389/fncel.2015.00355

    • PubMed
    • Google Scholar
96. 1. Pytel V
    2. Cabrera-Martín MN
    3. Delgado-Álvarez A
    4. Ayala JL
    5. Balugo P
    6. Delgado-Alonso C
    7. Yus M
    8. Carreras MT
    9. Carreras JL
    10. Matías-Guiu J
    11. Matías-Guiu JA

    (2021) Personalized repetitive transcranial magnetic stimulation for primary progressive aphasia

    Journal of Alzheimer’s Disease 84:151–167.

    https://doi.org/10.3233/JAD-210566

    • PubMed
    • Google Scholar
97. 1. Raj A
    2. LoCastro E
    3. Kuceyeski A
    4. Tosun D
    5. Relkin N
    6. Weiner M
    7. Alzheimer’s Disease Neuroimaging Initiative

    (2015) Network diffusion model of progression predicts longitudinal patterns of atrophy and metabolism in alzheimer’s disease

    Cell Reports 10:359–369.

    https://doi.org/10.1016/j.celrep.2014.12.034

    • PubMed
    • Google Scholar
98. 1. Rascovsky K
    2. Hodges JR
    3. Knopman D
    4. Mendez MF
    5. Kramer JH
    6. Neuhaus J
    7. van Swieten JC
    8. Seelaar H
    9. Dopper EGP
    10. Onyike CU
    11. Hillis AE
    12. Josephs KA
    13. Boeve BF
    14. Kertesz A
    15. Seeley WW
    16. Rankin KP
    17. Johnson JK
    18. Gorno-Tempini M-L
    19. Rosen H
    20. Prioleau-Latham CE
    21. Lee A
    22. Kipps CM
    23. Lillo P
    24. Piguet O
    25. Rohrer JD
    26. Rossor MN
    27. Warren JD
    28. Fox NC
    29. Galasko D
    30. Salmon DP
    31. Black SE
    32. Mesulam M
    33. Weintraub S
    34. Dickerson BC
    35. Diehl-Schmid J
    36. Pasquier F
    37. Deramecourt V
    38. Lebert F
    39. Pijnenburg Y
    40. Chow TW
    41. Manes F
    42. Grafman J
    43. Cappa SF
    44. Freedman M
    45. Grossman M
    46. Miller BL

    (2011) Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia

    Brain 134:2456–2477.

    https://doi.org/10.1093/brain/awr179

    • PubMed
    • Google Scholar
99. 1. Reveley C
    2. Seth AK
    3. Pierpaoli C
    4. Silva AC
    5. Yu D
    6. Saunders RC
    7. Leopold DA
    8. Ye FQ

    (2015) Superficial white matter fiber systems impede detection of long-range cortical connections in diffusion mr tractography

    PNAS 112:E2820–E2828.

    https://doi.org/10.1073/pnas.1418198112

    • PubMed
    • Google Scholar
100. 1. Reyes P
     2. Ortega-Merchan MP
     3. Rueda A
     4. Uriza F
     5. Santamaria-García H
     6. Rojas-Serrano N
     7. Rodriguez-Santos J
     8. Velasco-Leon MC
     9. Rodriguez-Parra JD
     10. Mora-Diaz DE
     11. Matallana D

     (2018) Functional connectivity changes in behavioral, semantic, and nonfluent variants of frontotemporal dementia

     Behavioural Neurology 2018:9684129.

     https://doi.org/10.1155/2018/9684129

     • PubMed
     • Google Scholar
101. 1. Salamone PC
     2. Legaz A
     3. Sedeño L
     4. Moguilner S
     5. Fraile-Vazquez M
     6. Campo CG
     7. Fittipaldi S
     8. Yoris A
     9. Miranda M
     10. Birba A
     11. Galiani A
     12. Abrevaya S
     13. Neely A
     14. Caro MM
     15. Alifano F
     16. Villagra R
     17. Anunziata F
     18. Okada de Oliveira M
     19. Pautassi RM
     20. Slachevsky A
     21. Serrano C
     22. García AM
     23. Ibañez A

     (2021) Interoception primes emotional processing: multimodal evidence from neurodegeneration

     The Journal of Neuroscience 41:4276–4292.

     https://doi.org/10.1523/JNEUROSCI.2578-20.2021

     • PubMed
     • Google Scholar
102. 1. Santamaría-García H
     2. Baez S
     3. Reyes P
     4. Santamaría-García JA
     5. Santacruz-Escudero JM
     6. Matallana D
     7. Arévalo A
     8. Sigman M
     9. García AM
     10. Ibáñez A

     (2017) A lesion model of envy and schadenfreude: legal, deservingness and moral dimensions as revealed by neurodegeneration

     Brain 140:3357–3377.

     https://doi.org/10.1093/brain/awx269

     • PubMed
     • Google Scholar
103. 1. Sanz Perl Y
     2. Pallavicini C
     3. Pérez Ipiña I
     4. Demertzi A
     5. Bonhomme V
     6. Martial C
     7. Panda R
     8. Annen J
     9. Ibañez A
     10. Kringelbach M
     11. Deco G
     12. Laufs H
     13. Sitt J
     14. Laureys S
     15. Tagliazucchi E

     (2021) Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness

     PLOS Computational Biology 17:e1009139.

     https://doi.org/10.1371/journal.pcbi.1009139

     • PubMed
     • Google Scholar
104. 1. Scheltens P
     2. De Strooper B
     3. Kivipelto M
     4. Holstege H
     5. Chételat G
     6. Teunissen CE
     7. Cummings J
     8. van der Flier WM

     (2021) Alzheimer’s disease

     Lancet 397:1577–1590.

     https://doi.org/10.1016/S0140-6736(20)32205-4

     • PubMed
     • Google Scholar
105. 1. Sedeño L
     2. Piguet O
     3. Abrevaya S
     4. Desmaras H
     5. García-Cordero I
     6. Baez S
     7. Alethia de la Fuente L
     8. Reyes P
     9. Tu S
     10. Moguilner S
     11. Lori N
     12. Landin-Romero R
     13. Matallana D
     14. Slachevsky A
     15. Torralva T
     16. Chialvo D
     17. Kumfor F
     18. García AM
     19. Manes F
     20. Hodges JR
     21. Ibanez A

     (2017) Tackling variability: a multicenter study to provide a gold-standard network approach for frontotemporal dementia

     Human Brain Mapping 38:3804–3822.

     https://doi.org/10.1002/hbm.23627

     • PubMed
     • Google Scholar
106. 1. Seelaar H
     2. Rohrer JD
     3. Pijnenburg YAL
     4. Fox NC
     5. van Swieten JC

     (2011) Clinical, genetic and pathological heterogeneity of frontotemporal dementia: a review

     Journal of Neurology, Neurosurgery, and Psychiatry 82:476–486.

     https://doi.org/10.1136/jnnp.2010.212225

     • PubMed
     • Google Scholar
107. 1. Song XW
     2. Dong ZY
     3. Long XY
     4. Li SF
     5. Zuo XN
     6. Zhu CZ
     7. He Y
     8. Yan CG
     9. Zang YF

     (2011) Rest: a toolkit for resting-state functional magnetic resonance imaging data processing

     PLOS ONE 6:e25031.

     https://doi.org/10.1371/journal.pone.0025031

     • PubMed
     • Google Scholar
108. 1. Sprugnoli G
     2. Munsch F
     3. Cappon D
     4. Paciorek R
     5. Macone J
     6. Connor A
     7. El Fakhri G
     8. Salvador R
     9. Ruffini G
     10. Donohoe K
     11. Shafi MM
     12. Press D
     13. Alsop DC
     14. Pascual Leone A
     15. Santarnecchi E

     (2021) Impact of multisession 40hz tacs on hippocampal perfusion in patients with alzheimer’s disease

     Alzheimer’s Research & Therapy 13:203.

     https://doi.org/10.1186/s13195-021-00922-4

     • PubMed
     • Google Scholar
109. 1. Tsapkini K
     2. Webster KT
     3. Ficek BN
     4. Desmond JE
     5. Onyike CU
     6. Rapp B
     7. Frangakis CE
     8. Hillis AE

     (2018) Electrical brain stimulation in different variants of primary progressive aphasia: a randomized clinical trial

     Alzheimer’s & Dementia 4:461–472.

     https://doi.org/10.1016/j.trci.2018.08.002

     • Google Scholar
110. 1. Tzourio-Mazoyer N
     2. Landeau B
     3. Papathanassiou D
     4. Crivello F
     5. Etard O
     6. Delcroix N
     7. Mazoyer B
     8. Joliot M

     (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain

     NeuroImage 15:273–289.

     https://doi.org/10.1006/nimg.2001.0978

     • PubMed
     • Google Scholar
111. 1. Unal G
     2. Ficek B
     3. Webster K
     4. Shahabuddin S
     5. Truong D
     6. Hampstead B
     7. Bikson M
     8. Tsapkini K

     (2020) Impact of brain atrophy on tDCS and HD-tdcs current flow: a modeling study in three variants of primary progressive aphasia

     Neurological Sciences 41:1781–1789.

     https://doi.org/10.1007/s10072-019-04229-z

     • PubMed
     • Google Scholar
112. 1. Verdi S
     2. Marquand AF
     3. Schott JM
     4. Cole JH

     (2021) Beyond the average patient: how neuroimaging models can address heterogeneity in dementia

     Brain 144:2946–2953.

     https://doi.org/10.1093/brain/awab165

     • PubMed
     • Google Scholar
113. 1. Vlček K
     2. Laczó J

     (2014) Neural correlates of spatial navigation changes in mild cognitive impairment and alzheimer’s disease

     Frontiers in Behavioral Neuroscience 8:89.

     https://doi.org/10.3389/fnbeh.2014.00089

     • PubMed
     • Google Scholar
114. 1. Wang Z
     2. Bovik AC
     3. Sheikh HR
     4. Simoncelli EP

     (2004) Image quality assessment: from error visibility to structural similarity

     IEEE Transactions on Image Processing 13:600–612.

     https://doi.org/10.1109/tip.2003.819861

     • PubMed
     • Google Scholar
115. 1. Wang Y
     2. Hutchings F
     3. Kaiser M

     (2015) Computational modeling of neurostimulation in brain diseases

     Progress in Brain Research 222:191–228.

     https://doi.org/10.1016/bs.pbr.2015.06.012

     • PubMed
     • Google Scholar
116. 1. Whitwell JL
     2. Josephs KA
     3. Avula R
     4. Tosakulwong N
     5. Weigand SD
     6. Senjem ML
     7. Vemuri P
     8. Jones DT
     9. Gunter JL
     10. Baker M
     11. Wszolek ZK
     12. Knopman DS
     13. Rademakers R
     14. Petersen RC
     15. Boeve BF
     16. Jack CR

     (2011) Altered functional connectivity in asymptomatic MAPT subjects: a comparison to bvFTD

     Neurology 77:866–874.

     https://doi.org/10.1212/WNL.0b013e31822c61f2

     • PubMed
     • Google Scholar
117. Preprint

     1. Yang AC
     2. Vest RT
     3. Kern F
     4. Lee DP
     5. Maat CA
     6. Losada PM
     7. Chen MB
     8. Agam M
     9. Schaum N
     10. Khoury N
     11. Calcuttawala K
     12. Pálovics R
     13. Shin A
     14. Wang EY
     15. Luo J
     16. Gate D
     17. Siegenthaler JA
     18. McNerney MW
     19. Keller A
     20. Wyss-Coray T

     (2022) A Human Brain Vascular Atlas Reveals Diverse Cell Mediators of Alzheimer’s Disease Risk

     bioRxiv.

     https://doi.org/10.1101/2021.04.26.441262

     • Google Scholar
118. 1. Zhou D
     2. Li A
     3. Li X
     4. Zhuang W
     5. Liang Y
     6. Zheng CY
     7. Zheng H
     8. Yuan TF

     (2022) Effects of 40 Hz transcranial alternating current stimulation (tacs) on cognitive functions of patients with Alzheimer’s disease: a randomised, double-blind, sham-controlled clinical trial

     Journal of Neurology, Neurosurgery & Psychiatry 93:568–570.

     https://doi.org/10.1136/jnnp-2021-326885

     • Google Scholar

Author details

1. Yonatan Sanz Perl

   1. Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
   2. National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina
   3. Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
   4. Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain

   Contribution

   Conceptualization, Software, Visualization, Methodology, Writing - original draft

   For correspondence

   yonatan.sanz@upf.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1270-5564

2. Sol Fittipaldi

   1. National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina
   2. Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina

   Contribution

   Methodology

   Competing interests

   No competing interests declared

3. Cecilia Gonzalez Campo

   1. National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina
   2. Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina

   Contribution

   Methodology

   Competing interests

   No competing interests declared

4. Sebastián Moguilner

   1. Global Brain Health Institute, University of California, San Francisco, San Francisco, United States
   2. Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

5. Josephine Cruzat

   1. Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
   2. Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

6. Matias E Fraile-Vazquez

   Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina

   Contribution

   Data curation

   Competing interests

   No competing interests declared

7. Rubén Herzog

   Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile

   Contribution

   Writing – review and editing

   Competing interests

   No competing interests declared

8. Morten L Kringelbach

   1. Department of Psychiatry, University of Oxford, Oxford, United Kingdom
   2. Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Århus, Denmark
   3. Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
   4. Centre for Eudaimonia and Human Flourishing, University of Oxford, Oxford, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

9. Gustavo Deco

   1. Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
   2. Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain
   3. Institució Catalana de la Recerca i Estudis Avancats (ICREA), Barcelona, Spain
   4. Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
   5. School of Psychological Sciences, Monash University, Clayton, Australia

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8995-7583

10. Pavel Prado

    Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile

    Contribution

    Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-1324-6400

11. Agustin Ibanez

    1. National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina
    2. Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
    3. Global Brain Health Institute, University of California, San Francisco, San Francisco, United States
    4. Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile
    5. Trinity College Institute of Neuroscience (TCIN), Trinity College Dublin, Dublin, Ireland

    Contribution

    Conceptualization, Supervision, Writing - original draft

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6758-5101

12. Enzo Tagliazucchi

    1. Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
    2. National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina
    3. Cognitive Neuroscience Center (CNC), Universidad de San Andrés, Buenos Aires, Argentina
    4. Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibáñez, Santiago, Chile

    Contribution

    Conceptualization, Supervision, Visualization, Writing - original draft

    Competing interests

    No competing interests declared

• 1,792

  views

• 246

  downloads

• 15

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.