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A cerebral arteriovenous malformation (AVM) is an abnormal set of direct connections between the arteries and veins in the brain. These can cause a variety of neurologic symptoms, especially if they are large, and especially if they rupture.

Vein_of_galen_ax_direct_AV

arteriovenous malformation in the great cerebral vein of Galen; from Wikipedia user Filip em via Dr Laughlin Dawes

Mohr et al. recently published the result of the ARUBA trial, which compared medical (i.e., medical treatment for symptoms as needed) to interventional (i.e., surgical) treatment of this condition.

Their intention-to-treat analysis favored event-free survival in the medical management (MM; red) group:

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Mohr et al 2017; doi: 10.1212/WNL.0000000000004532

The actually-treated analysis favored event-free survival in the medical management (MM) group even more strongly:

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Mohr et al 2017; doi: 10.1212/WNL.0000000000004532

The authors suggest on the basis of this data that a reasonable management approach for unruptured cerebral AVM is to wait to see if a hemorrhage occurs, which may be mild if it does occur, and only then consider surgical intervention.

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Many articles include the premise that Alzheimer’s disease (AD) neuropathology is unique to humans. However, there is a large body of literature suggesting that the characteristic neuropathology of AD, including diffuse amyloid plaques, neuritic amyloid plaques, and abnormally phosphorylated tau, are also seen in some non-human primates.

One of the only exceptions where AD pathology has not been commonly reported is coexisting amyloid plaques and neurofibrillary tangles, although even this has been reported in one chimpanzee.

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Coexisting tau and amyloid immunoreactivity in the PFC of a 41-year old chimpanzee; PMC2573460

On the genetic level, tau is identical between chimps and humans, while APP is 99% identical.

It is not that surprising that chimps would have the most similar neuropathology as humans, because chimps (and bonobos) are among the most similar non-human primates to humans.

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cladogram based on morphology and genetics from http://anthro.palomar.edu/primate/prim_8.htm

Now, a nice article from Edler et al examines neuropathology from 20 chimpanzees aged 37-62 to directly interrogate the presence of AD neuropathology in a large sample.

The authors scored neuropathology in all 20 chimpanzees in 4 brain regions (PFC, MTG, CA1, CA3) using the following scoring system:

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Here were some of their findings:

  • All of the chimps had APP/Aβ and Aβ-positive blood vessels, while only 2/3rds had plaques not associated with vessels, suggested that Aβ accumulation near blood vessels may be an early or precursor lesion in chimps.
  • Cerebral amyloid angiopathy, which is seen in 80% of AD patients, had a strong association with tau pathology in their chimps, especially pretangle density:
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CAA = Cerebral Amyloid Angiopathy; Fig 7B; PMID: 28888720

  • On the other hand, Aβ42 levels were not correlated with tau pathology.
  • Pretangle and NFT staining in chimps followed the pattern of Braak staging seen in humans.
  • In reviewing the literature, they note that only subtle, but not profound, age-related memory decline has been demonstrated in chimps. This may be because chimps have differences in APOE and other factors, but it is also the case that very few studies have directly addressed this question.

Overall, the most important finding from this study confirmed the previous 2008 report from a single chimp that amyloid and tau can coexist species other than humans.

These non-human primate studies shine an important light on the true biology of AD, which is especially important to consider when evolutionary or environmental explanations are invoked to explain the disease.

When I was reviewing the current clinical trials for Alzheimer’s disease (AD) a few months ago, I noted that the phase II trial of levetiracetam was particularly exciting — because if it works, it would require a massive reconceptualization in AD pathogenesis.

That’s mainly because levetiracetam is an anti-seizure medication, and focal seizures haven’t been traditionally associated with AD.

But our available tools for measuring focal seizures are quite poor — for example, they only cover a very small portion of the brain, mostly at the surface. So it is quite possible that AD pathogenesis could involve localized seizures early in the disease process, especially in a “buried” brain region like the hippocampus.

Now, Lam et al have provided some more evidence for the microseizure/focal memory-associated seizure hypothesis in AD, with a case report on two patients, both of whom were experiencing early stages of memory loss.

For both of these patients, the team used a minimally invasive surgical technique to place medial temporal electrodes for monitoring, which has been described in several previous studies including a 2005 study from Zumsteg et al that has a nice diagram:

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Localization of medial temporal lobe/foramen ovale (FO) electrode; https://doi.org/10.1016/j.clinph.2005.08.009

(Note that the hippocampus, that old memory-associated stalwart, is in the medial temporal lobe.)

One of the patients that Lam et al were treating had evidence of recurrent subclinical seizures in the medial temporal lobe (MTL), which were more prevalent during sleep.

Remarkably, treating her with 1500 mg/d of levetiracetam led to abrogation of the seizures, and she had only “mild progression” of her cognitive deficits.

Intriguingly, when she missed several doses of levetiracetam, she noticed a confusion spell. But like anything, this could just be correlation — maybe she missed doses because of worsening AD pathophysiology in that time period, and the confusion spell was an exaggerated symptom of this. Missing doses is certainly not a formal crossover or washout study. Still, intriguing.

The other patient that Lam et al were treating also had frequent MTL seizure activity that was exacerbated during sleep, as well as classic symptoms of early onset AD (eg, CSF pTau levels of 73.2 pg/ml).

Unfortunately, levetiracetam was not tolerated in this second patient, which the authors state was “owing to worsening mood.”

It would be great news if early AD pathophysiology were related to seizures, even in a subset of patients, because we already have drugs to treat seizures. So I agree with the authors that more research is certainly needed in this area.

That said, the evidence so far is certainly interesting, but far from definitive. Something like this study, but with a sample size of 10-20 and good correlations of MTL seizure activity with cognitive decline and/or confusion episodes, would be a great next step.

It would also be nice to know how common MTL seizure activity is during sleep in asymptomatic elderly people.

Over the last few years researchers have figured out how to transform iPS cells into dopamine-producing neurons, raising the possibility of transplanting dopaminergic cells into the brains of patients with Parkinson’s disease (PD).

Kikuchi et al. looked at the effect of dopaminergic cell transplantation into the putamen on PD symptoms in monkeys treated with MPTP, which is a model of PD.

Compared to placebo injections, the stem cell transplantation improved symptoms. Notably, it did so somewhat less well than L-DOPA, but it seems plausible that this therapy could be eventually used once L-DOPA has failed, as L-DOPA tends to do over time in PD.

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Extended Data Fig 2K/I; doi:10.1038/nature23664

Perhaps the best news from this study is that they identified no markers of cancer formation in the transplanted brains after more than a year post-transplant. It’s always good news when your proposed therapy turns out to be less likely to cause brain cancer as a side effect.

Clinical trials will apparently start soon — from which we will have much to learn, and hopefully some good news.

Recently I took a bit of a dive into the literature surrounding controlled hypothermia for recovery from return of spontaneous circulation post-cardiac arrest.

I started with this recent editorial, which describes some of the RCT evidence showing improved neurologic outcome if you cool following revival (and possibly even mid-arrest), as well as the recent trial demonstrating equivalent outcomes between cooling to 33C and 36C. Here’s their takeaway:

We concur with the AAN experts that less is not more and cooling should be harder, better, faster, stronger, in the sense that neurologists should be hardliners who embrace cooling as a default mode for nearly all cardiac arrest survivors, making it harder to exclude patients, while using cooling techniques that are the better ones, starting as quickly as possible after ROSC, and that 338C is stronger than 368C.

I then watched this nice lecture by Nicola Robertson, who describes her work on cooling to prevent brain damage prevention in perinatal asphyxia. As far as I can tell, the research here has been extensive and explains more of the mechanisms of why cooling can be an effective treatment for hypoxia.

She includes in her talk a lovely image from Gunn et al. 2016, which is worth a thousand words:

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Gunn et al. 2016; NTP/EPP = markers of mitochondrial metabolism; Lactate = marker of cell death

The idea is that there are two phases to neurotoxicity in the presence of hypoxia. The first phase involves a primary energy failure. This is when cells die because they don’t have enough oxygen/energy. But surprisingly, this is often not that all that large effect in terms of long-term brain damage.

Often a larger factor comes in the second phase of neurotoxicity — the activation of molecular signaling cascades which tell the cells to die. This involves an evolutionary mechanism by which the body clears out poorly functioning cells if they aren’t working properly.

As it turns out, by cooling brain cells during the latent phase molecular signaling cascade, clinicians can slow down and mitigate the secondary activation damage, thereby improving long-term neurologic outcome.

Applying this in adults following cardiac arrest is still a rapidly evolving field, and it’ll be interesting to see how the field and evidence base evolves over time.

One of the major problems with applying cooling for neurologic injury in more diverse clinical settings is that cooling to low temperatures, such as 33C, often leads to excessive amounts of shivering. If the shivering problem can be overcome, it may receive more widespread use in different conditions.

After reading Phil Tetlock and Dan Gardner’s book Superforecasters, I’ve decided to try to make prospective, quantitative predictions about AD therapies currently in clinical trials with an endpoint of decreasing cognitive decline.

Disclosures

I have investments in S&P index funds but no individual stocks. I’m funded with an NIH training grant for AD. However, everything in this post is based on public information.

My (in-progress) thesis is on one aspect of the basic biology of AD, but I still don’t know feel that I know all that much about AD, which is such a broad topic. I certainly don’t want to make it seem like I’m calling myself an expert. Still, that seems like a good reason to make predictions: to create an incentive to learn more and hold myself accountable.

Preamble

Most AD drugs fail, and the hardest barrier to entry is Phase III clinical trials. From 2002-2012, 1/54 drugs that were tested in phase III clinical trials were approved by the FDA (memantine was the only approval; data from here).

ad_drug_devo

data: 10.1186/s13195-016-0207-9

Since 2012, there have been several additional high-profile phase III failures, including the amyloid immunotherapy drug Solanezumab and the BACE inhibitor Verubecestat, and no additional FDA approvals.

This is our reference class: we should expect that ~1.85% of the drugs in Phase III clinical trials are likely to be approved.

Maybe we can raise the probability of a generic AD drug approval a little bit now, since we presumably know more now about science, medicine, and AD in particular now than we did in past decades.

On the other hand, if our current theories driving AD drug development (such as the amyloid hypothesis) happen to be particularly misguided, then the probability of approval might be lower accurate than they were in the past. Plus, it’s plausible that the FDA has more experience and will make the evidence necessary for approval more rigorous now than they might have in the past.

Overall, I think 1-4% is a reasonable prior for an new therapy in a phase III AD clinical trial today.

You might be asking: shouldn’t there be more AD drugs approved by the FDA by chance? If all you need is two-tailed p < 0.05 and this should happen 2.5% of the time, why have only 1.85% of AD drugs been approved? Part of the reason is that FDA approval criteria is more strict than simply p < 0.05, and requires “independent substantiation.” That said, it’s daunting that the probability of phase III AD therapy approval is close to chance levels.

Note about my motivations

I, like most people, urgently hope that all of these drugs work. I’m not being critical about their chances because I want to them to fail, I’m doing it so that I can more build more accurate models of how the AD field operates.

Choosing drugs for evaluation

The amazing AlzForum has a great page where they list 22 therapeutics currently in phase III clinical trials. Of these, here are some that I will not be evaluating:

1. LMTM: has already failed in clinical trials.

2. Vitamin E: has already completed a clinical trial with some mild success.

3. AVP-786: this DXM-containing compound is primarily for treating disinhibition in AD, not cognition.

4. Aripiprazole: for psychosis in AD.

5. Brexpiprazole: for agitation in AD.

6. ITI-007: for agitation in AD.

7. Masitinib: A mast cell inhibitor, but the clinical trial for AD doesn’t seem to be updating anymore and I can’t find information about it online.

8. CPAP: A prevention trial with an unclear path (to me) towards FDA approval.

9. Crenezumab: I’m confused by the possible path to approval for this passive Aβ immunotherapy in the context of late-onset AD, since it has already failed in trials of mild to moderate AD. I’m not saying that it’s impossible, of course, just that I don’t understand well enough how it would work to assign a probability.

10. Gantenerumab: This drug also failed in a Phase III clinical trial already in early stage symptomatic patients, so I am confused by its path to FDA approval.

11. Solanezumab. In late 2016, this Aβ immunotherapy was found to have failed in clinical drugs.

12. Verubecestat. Merck’s BACE inhibitor just had its Phase III clinical trial stopped a few weeks ago.

13. Idalopirdine: Already failed in one Phase III clinical trial.

That leaves 9.

To cover my bases, I also did a search for “alzheimer | Open Studies | Phase 3” at clinicaltrials.gov, where these studies are registered.

Through this search, I found some others I’m not going to consider, because I only want to consider drugs that are going to fail due to lack of efficacy, instead of lack of interest.

1. Coconut oil: specifically the Fuel for Thought version. I will not make a prediction on its FDA approval status since according to the company’s website it is already “generally recognized as safe” by the FDA.

2. tCDS: This modality has one trial that I will not be considering since I don’t know if it has a large enough sample size to achieve FDA approval with only n = 100 in a Phase II/III study.

3. There’s a phase III trial of purified EPA, but I’m not going to consider that because it seems that it seems similar to coconut oil in terms of FDA approval.

4. Albumin/IVIg: There’s a study of albumin and IVIG plasmapharesis in AD, but it’s unclear if this trial is still ongoing, as it hasn’t been updated in almost two years. IVIg has previously been unsuccessful in AD.

5. Nasal insulin: I’m also confused about the path to monetization and FDA approval of this drug, so I’m not going to evaluate it.

However, I did find sodium oligomannurarate and JNJ-54861911 using this search, and I’m adding them to the list. Another 2 makes 11 total therapies for predictions.

Efficacy predictions 

1. Levetiracetam (a widely used, FDA-approved anti-epileptic drug). AFAIK this is not yet in phase III: there’s one trial in phase II, although there is a phase III trial planned. Let’s assume for the purposes of prediction that the MCI phase III trial happens, with a primary endpoint of decreasing the rate of cognitive decline.

The idea that AD might be related to circuit/neuronal network dysfunction is very much in the air right now, eg following the report last month that flashing light on the retinas at particular frequencies to induce gamma rhythms leads to dramatic cognitive improvements in the 5xFAD mouse model of AD.

Levetiracetam is already widely used in AD patients with seizures, making it likely to be safe.

It could be true that Levetiracetam really does affect Aβ processing on the cellular level and decrease Aβ levels. I don’t really buy this, even before one of the papers on which this was based was retracted.

There has been one study evaluating the effect of Levetiracetam in humans in a within-study design, but the effect is pretty weak and non-existent at the highest dose, for reasons that are unclear to me.

One could imagine that Levetiracetam were successful in reducing cognitive decline in individuals without overt seizures, it might make sense to reconceptualize one aspect of AD around something like microseizures. We know that seizures are much more likely following strokes and other brain injuries, making this a plausible hypothesis.

Probability of FDA approval by the end of 2023: 2%

2. ALZT-OP1 (combination of inhaled cromolyn and oral ibuprofen). Currently in a 600-early AD patient clinical trial. Trials of NSAIDs and ibuprofen have failed in phase III trials multiple times, despite epidemiologic evidence suggesting that they should be beneficial.

By adding inhaled cromolyn, another anti-inflammatory drug that is approved as an asthma prophylactic, the funders are hoping that their trial will be different. A nice 2015 study showed that IP cromolyn administration decreases soluble monomeric Aβ-42 by about half in the APPswe/PS1dE9 mouse model of AD. Otherwise, there’s not much else published.

If this works, it would really emphasize the importance of systemic (as opposed to brain-specific) inflammation in AD and maybe mast cells in particular. But given the previous failures of systemic anti-inflammatory treatments, it seems pretty unlikely.

Probability of FDA approval by the end of 2023: 1.5%

3. AZD3293 (Lilly’s BACE inhibitor). BACE is a critical part of the amyloid processing pathway, and this small molecule inhibits it. AZD3293 has been shown in human studies to robustly decrease plasma and CSF Aβ42 and soluble AβPP β.

AZD3293 is in two large clinical trials, NCT02245737 with estimated n = 2202 and NCT02783573 with estimated n = 1899. The trials are for relatively early AD, with requirements of MMSE > 21 and > 20, respectively.

What is great about Eli Lilly’s large investment in AZD3293 is that we will have a very good sense of whether and how effective this drug is, as well as the extent to which decreasing CSF/brain amyloid leads to improved cognition. Assuming they release the data that they generate for analysis (which they probably will), they deserve a lot of credit for this undertaking.

Unfortunately, the negative Verubecestat (Merck’s BACE inhibitor) trial results that came out a few weeks ago are disheartening for the prospect of BACE inhibition in AD. It’s also possible that off-target effects of BACE inhibition, such as myelination, may decrease cognitive decline to an equivalent or greater degree as the benefits from decreased Aβ production.

However, it’s still totally plausible that this drug will work where the Merck BACE inhibitor did not, since there will clearly be differences in their effects, including pharmacokinetics and off-target effects.

Overall this feels like another clear of the amyloid hypothesis: if you lower Aβ levels, will you reduce the rate of cognitive decline? If this drug also doesn’t work while reducing Aβ, it seems that it will really be time for the field to go back to the basics.

Probability of FDA approval by the end of 2023: 5%

4. Aducanumab (Aβ passive immunotherapy). This is a monoclonal antibody that preferentially binds parenchymal, aggregated forms of Aβ.

It was derived from healthy, older donors who were cognitively healthy, with the assumption that it may have helped prevent them from developing AD.

It is probably the most promising drug in AD right now and its dose-dependent amyloid and cognitive effects in humans were described in a Nature paper in September 2016.

This seems to be the consensus “most likely to work” of the current drugs in clinical trials. There is still some reason for skepticism, though.

First, it’s not entirely clear to me why this amyloid reduction technique works when so many other Aβ therapies have failed.

And while Fig 3 of the Nature shows some nice dose-dependent effects, the error bars are still pretty high.

There are currently two phase III clinical trials for the drug, each with n = 1350 participants, requiring a positive amyloid PET scan and CDR = 0.5 and MMSE 24-30, which is early in the disease process. Results in 2019 and 2020.

Probability of FDA approval by the end of 2023: 20%

5. Azeliragon (small molecule RAGE inhibitor). This drug failed clinical trials in the mid-2000s, but the lower dose may have shown an effect, and now it has been taken back to clinical trials at the lower dose for a phase III trials in participants with MMSE 21-26 and an MRI scan showing a diagnosis of probable AD.

If this drug works at the lower dose, it suggests that astrocyte and microglia inflammation are a particularly strong targets in AD.

Probability of FDA approval by the end of 2023: 2%

6. E2609 (Biogen’s BACE inhibitor). This small molecule has been shown to reduce Aβ in the CSF and serum of non-human primates. This is being tested in a large (n = 1330) phase III clinical trial. Results expected by 2020.

As with the other BACE inhibitors, it’s plausible but not clear to me why this drug should succeed where Verubecestat failed, so I will give it the same probability as AZD3293. However, it also has only one trial as opposed to AZD3293’s two, so it seems slightly less well powered to detect a small effect on improving cognition.

Probability of FDA approval by the end of 2023: 4%

7. Nilvadipine (L-type Ca channel blocker). This anti-hypertensive drug was previously considered a possible therapy for AD in the context of reducing blood pressure, which can decrease the rate of cognitive decline. It made a splash in 2011 when it was found to decrease Aβ in vitro, and it is currently in a Phase III trial that should report results by the end of this year.

Given the spate of Aβ therapy failures, the Aβ reduction is not as promising as it was 6 years ago, although the drug may have other effects and may also reduce cognitive decline through its effects on blood pressure.

Probability of FDA approval by the end of 2023: 2.5%

8. Sodium oligomannurarate. There is not much info about this drug online, besides the clinical trial notice and this phase II trial report from Medscape which notes that it did not meet the primary cognitive endpoint (ADAS-cog/12) in its phase II trial. Not much info to go on here.

Probability of FDA approval by the end of 2023: 0.75%

9. JNJ-54861911 (Janssen BACE inhibitor). Another BACE inhibitor that is currently in a phase II/III trial.

Probability of FDA approval by the end of 2023: 4%

10. CAD106 (Active Aβ immunotherapy) + CNP520 (BACE inhibitor). This is an active vaccination strategy for Aβ, which would be fantastic for the field if it worked, since it is likely to be much cheaper than passive Aβ immunotherapy.

These drugs are currently being tested in a large Phase 2 trial (n = 1340).

Overall, the probability here feels similar to the probability of the other Aβ therapies. The combination of the active immunotherapy alongside BACE inhibition makes this trial intriguing.

Probability of FDA approval by the end of 2023: 4.5% (that at least one of the two or the combination will be approved)

11. Pioglitazone (PPARγ agonist, insulin sensitizing, small molecule). This drug is approved to treat type 2 diabetes. PPARγ agonism has been shown to play a role in inflammatory processes in the brain.

It is being studied in an extremely large study (n = 3494) that is coupled with a genetic risk model that includes APOE e4 and TOMM40.

I think that this trial has some potential, based in part on mouse model data as well as a variety of data suggesting an interplay between hyperglycemia-associated toxicity and risk of AD.

However, most of the early phase human data has been negative, including a study by the NIA (n = 25) and a study by the University of Colorado (n = 68).

Another problem with this trial is that — to the best of my knowledge — TOMM40 variants are no longer thought to be strongly associated with the risk of AD.

That said, there are some really interesting possible mechanistic angles here, including a possible role for pioglitazone in regulating myelin phagocytosis by immune cells, which may interact with AD.

Probability of FDA approval by the end of 2023: 4% (prediction)

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Overall probability that at least one drug will be approved for cognition in late-onset AD within the next 6 years (not necessarily one of the above): 35%

Obviously, these predictions are highly correlated.

For example, if one of the remaining BACE inhibitors works, then that makes it more likely that others will too.

As another example, if any of the amyloid therapies finally work, then that makes it more likely that the others will.

If any of the drugs work, that makes it more likely that all of the others will, because maybe clinical trial strategies (eg, enrolling patients earlier in the disease process) are generally more apt than they were previously.

There’s also some uncertainty around how the FDA will work over the next 6 years. I’m talking about cognitive efficacy approvals, not biomarker approvals.

To be explicit: if a drug is approved preliminarily based on biomarkers but not cognitive efficacy, I’m not going to count it as an approval for the purposes of these predictions. I

‘ll note that I’m a bit nervous in making these predictions public. What if they are all horribly wrong?

But I hope that we will move towards a world where people make more quantitative public predictions and are incentivized to do so. Of course, I plan to evaluate these predictions in 6 years and hold myself accountable.

A really nice article from Tyssowski et al. The authors did RNAseq on neurons that either were or were not stimulated with neural activity. They found that a subset of proteins (251) that have been previously described as “[neuronal] activity regulated genes” were able to predict the stimulation state of those neurons well above chance. Specifically: 92% of the time using nearest neighbor classification as measured by leave one out cross validation.

I’m interested in the broad question of “which RNAs/proteins are important for neuronal activity” and this set of activity regulated genes is pretty clearly within that set. Interestingly, it seems that the expression of these genes is pretty highly correlated (very similar chromatin states, transcription factors, etc), so I don’t think you would have to perfectly preserve ALL of them in order to allow for a high-fidelity preservation of information.

On that note, it’d be interesting if someone were to use this data to try to predict neuron stimulation state using the smallest set of activity regulated genes as necessary. For example, the 19 rapidly-induced activity regulated genes, including the non-transcription factors Arc and Amigo3, seem like they would punch above their weight in terms of predicting neuronal activation state.

Figure 6 from the paper

Arc expression and enhancer acetylation is stimulated after only 10 minutes of neuronal activity; http://biorxiv.org/content/early/2017/06/05/146282.full.pdf+html

It also suggests an experiment for any brain preservation procedure that purports to preserve gene expression important for neural activity: stimulate neural activity on a subset of neurons (probably in vitro, since it’s easier and should yield the same result), perform your brain preservation processing steps, attempt to measure the expression of these genes, and then see if you can distinguish between which neurons were stimulated or not on the basis of those measurements.