mental-health

The Path to Scalable Psychiatric Gene Therapy and a Future of Cures for Widespread Mental Illnesses — Restoring Joy to a Billion Lives

January 31, 2026By logancollins No Comments

PDF version: The Path to Scalable Psychiatric Gene Therapy and a Future of Cures for Widespread Mental Illnesses – By Logan Thrasher Collins

The Promise of Psychiatric Gene Therapy

Current psychiatric interventions remain insufficient to address the highly prevalent mental illnesses which plague more than a billion people (1 in 7) across the world.¹ Widespread and debilitating diseases like major depressive disorder (MDD),² anxiety disorders,³ schizophrenia,⁴ bipolar disorders,⁵ post-traumatic stress disorder (PTSD),⁶ substance abuse disorders,^7,8 and personality disorders^9,10 pose an enormous global health burden and are one of the most central causes of human suffering. Patients frequently do not respond to pharmacological interventions for these conditions, resulting in vast numbers of people struggling through life without options for proper management. As a result, at least 800K people die by suicide annually.¹¹ Today’s neuropharmacology industry employs small molecule drugs which modulate neurochemical states, utilizing strategies like neurotransmitter reuptake inhibition and receptor agonism. Mechanistic underpinnings of many neuropharmacological treatments are not well understood.^2,12 Furthermore, small molecules suffer from extensive off-target binding¹³ and frequently come with side effects, many of which can be extremely debilitating and/or dangerous. Though it has been cemented into place by partial successes,¹² the way we currently treat mental illnesses is woefully inadequate.

Why might gene therapy solutions eventually offer better treatment options for common psychiatric disorders compared to traditional small molecule pharmaceuticals? Gene therapies possess a number of intrinsic advantages like cell-type-specific targetability, direct in situ expression of therapeutic proteins or RNAs, and the capacity to dynamically respond to environmental conditions. They also have potential for spatiotemporal programmability via emerging sonogenetics^14–17 and chemogenetics¹⁸ approaches. (Sonogenetics has particular promise, which will be discussed in more detail later). In addition, gene therapies may be engineered to downregulate (RNAi or CRISPRi)¹⁹ or even ablate (CRISPR knockout)²⁰ expression of almost any gene in the genome, offering an unprecedented array of new therapeutic targets. CRISPRa might also be employed to upregulate target genes without altering the genome.¹⁹ Engineering gene therapies which express multiple proteins or RNAs at once may synergistically improve efficacy.^21,22 Importantly, gene therapies can be engineered to persist for long periods of time^23,24 or to only provide a burst of short term expression. Depending on the genetic payload, one or the other of these durations may represent the most optimal choice. Gene therapies altogether provide a much larger space of possibilities for precision alteration of brain states than has been possible for small molecule treatments. I would argue that this space’s capabilities remain severely underexplored primarily because of a lack of delivery system capabilities.

Better Delivery Systems are Needed

Gene therapy promises to open a new world of precision psychiatric treatments, yet its potential has gone unrealized. This makes sense as there are a number of obstacles which render psychiatric gene therapy a difficult target. Among these, the challenges of brain delivery, safety, and manufacturing scalability represent particularly recalcitrant bottlenecks. Adeno-associated virus (AAV) gene therapies have progressed furthest in the brain delivery field. Industry players like 4DMT, Dyno Therapeutics, Apertura Therapeutics, and Capsida Biotherapeutics have made efforts via directed evolution, rational design, and machine learning towards optimizing AAV capsids for blood-brain-barrier (BBB) crossing efficiency. However, safety concerns stemming from several patient deaths in systemically administered AAV therapies over the past few years have slowed this progress. Additionally, limitations in AAV manufacturing capacity pose a problem for scaling the vector to populations of 1M+ patients.²⁵ This will be discussed in more detail further on. It should be noted as well that AAVs are limited by their small DNA packaging capacity of 4.7 kb. To unlock the potential of psychiatric gene therapy, we need safer and more scalable delivery systems.

Although there exist multiple obstacles to overcome before gene therapy can realize its potential as a psychiatric modality, I propose a lack of delivery systems represents a foundational missing piece. Without strong delivery vehicles to feasibilize solutions, the field of psychiatric gene therapy will not be credible enough to receive substantial investment. It is a “tools problem”. As mentioned earlier, there is a particular need for vectors which at once possess high safety, scalability, and efficacy. I strongly suspect that the emergence of vectors with these qualities would seed an explosion of efforts towards gene therapies for brain diseases, which would eventually allow us to tackle psychiatric ailments. While psychiatric diseases are unlikely to represent the initial targets of brain gene therapies, opening the door to brain delivery will in my view be necessary to take steps in the direction of modernizing psychiatry through precision genetic medicines.

Examination of the Current Landscape

As mentioned earlier, AAV gene therapies are the current frontrunner for brain delivery yet possess both scalability and safety limitations. I will explain the scalability issue using publicly available information on AAV manufacturing: Final yields (after purification) of AAVs have been reported or modeled in scientific literature with values ranging from around 7.5×10¹² vg/L to 7.5×10¹³ vg/L.^26–28 I will thus assume here that 5×10¹³ vg/L is a typical yield. For this rough calculation, I will also assume that the yield scales linearly with bioreactor volume. As such, a 2,000 L bioreactor would make batches of around 10¹⁷ vg and a 200 L bioreactor would make batches of around 10¹⁶ vg. According to a 2024 report, a 200 L AAV production run at cGMP quality costs about $2M (including analytics).²⁹A 2022 meta-analysis study of clinical AAV doses states that per patient systemic delivery amounts range from 3.5×10¹³ vg total to 1.5×10¹⁷ vg total.³⁰ Huang et al.’s highly promising AAV BIhTFR1 capsid (the basis for Apertura Therapeutics) was originally administered to mice at a higher dose of 10¹⁴ vg/kg and a lower dose of 5×10¹² vg/kg.³¹ Generously (perhaps too generously) assuming that the lower dose is sufficient, this would equate to about 4×10¹⁴ vg total in an average 80.7 kg North American adult human.³² Dividing 10¹⁶ vg from a $2M (200 L) batch by 4×10¹⁴ vg per dose, this means each batch would provide 25 doses for about $80,000 each. Even if substantial improvements in manufacturing yield and in lowering required dosage happen, I am skeptical that systemic AAV approaches will scale to disease indications with 1M+ patients. Despite this, AAVs remain still a central point in the gene therapy industry for a reason and paradigm-shifting approaches to manufacturing²⁵ and/or efficacy might still change the current scalability challenges.

Another existing modality is transient focused ultrasound BBB opening (BBBO). I would argue that BBBO is extremely promising for some applications but not a universal solution. Treatment of many brain diseases necessitates brain-wide delivery. By contrast, BBBO is generally a localized delivery technique.³³ Although some common psychiatric diseases fit these parameters, most common ailments with clean clinical endpoints do not. Some work has been done to extend BBBO ultrasound to larger-volume delivery through multiple sonication^34,35 or raster scanning,³⁶ but this remains much less well-developed by comparison to localized BBBO approaches and may exhibit greater safety concerns. Indeed, while single-site BBBO possesses a fairly strong safety profile, there is still evidence it can cause problematic inflammatory responses and occasional microhemorrhages.^37–39 Also, the level of risk may increase if delivery of vectors with large diameters (e.g. 100 nm) is needed.^40,41 As BBBO involves a device, an injection of microbubbles, a procedure, and its own set of safety concerns, it adds complexity which can increase regulatory burden. But I do not think BBBO should be discounted. In some situations, it possesses enormous advantages. These situations may indeed include potential treatments for certain psychiatric disorders. Though BBBO does not universally solve the problem of safe and scalable delivery, I expect it may still play a major role in the field of brain gene therapy.

Intranasal delivery represents a highly promising alternative to intravenous injections which maintains minimal invasiveness. It circumvents the BBB by allowing delivery vectors to migrate through the olfactory (and to a lesser degree trigeminal) nerves into the brain.^42,43 This minimizes toxicity by vastly reducing exposure of peripheral organs to the delivery vector. Additionally, much lower doses of delivery vector can be used for intranasal delivery, which might bring AAVs back into the equation as a potentially scalable option. The main drawback of the intranasal route is that the vast majority of delivered DNA accumulates in the olfactory bulb and adjacent brain regions.^42,44 Roughly, as the distance from these regions increases, the amount of DNA delivered decreases.⁴⁴ In a study by Chukwu et al., intranasal delivery of AAV9 was shown to achieve 15% transduction efficiency and 9% gene expression efficiency on average across the brain compared to intravenous delivery.⁴⁴ Remarkably, this intranasal delivery decreased exposure of peripheral organs by a factor of 13,400 compared to intravenous injection. It should be noted that AAV9 has limited BBB crossing efficiency compared to optimized capsids like AAV BIhTFR1.³¹ Indeed, intravenous AAV BIhTFR1 transduces brain 40-50 times more efficiently than intravenous AAV9 in humanized TfR1 mice. Yet overall, I would speculate that novel intranasal delivery systems have strong potential for safer and more scalable gene therapies. The intranasal route deserves serious consideration.

Translational Strategies

The path to psychiatric gene therapy may require a detour focusing on “easier” high-prevalence brain disease indications with more clearly defined clinical endpoints. This detour will allow the field to consolidate, cultivating enough successes to justify the financial risk of pursuing psychiatric diseases. Additionally, manufacturing, regulatory, and clinical infrastructure for brain gene therapy in large patient populations may establish itself in this way. That said, I do think it would be beneficial for companies to pursue psychiatric indications in parallel. Even if these initial attempts do not pan out, they may help strengthen the field’s knowledge base and infrastructure. What are some high-prevalence brain indications with clear clinical endpoints which represent strong potential targets for early brain delivery? I will start by nominating stroke, Parkinson’s disease (PD), and epilepsy (open to suggestions here). Though it will by no means be easy to develop efficacious gene therapies for such conditions, I remain optimistic that this foundation of successes in brain treatment is attainable.

In my view, sonogenetic genes have immense potential as payloads for psychiatric gene therapy. Sonogenetics broadly speaking involves delivery of genes encoding mechanosensitive proteins, often ion channels.⁴⁵ The mechanosensitive proteins change state (e.g. channel opening) in response to ultrasound waves, allowing neuromodulation through transcranial focused ultrasound (tFUS). Sonogenetic gene therapy should thus enable both millimeter-scale spatial resolution and cell-type-specific targeting for neurostimulation, offering unprecedented possibilities for treatment of psychiatric diseases.^14–17 This technological convergence could radically transform how mental illness is treated. But delivery nonetheless remains among the most central challenges which must be overcome before sonogenetics reaches clinical feasibility. Neuromedicine cannot explore sonogenetic therapies without a strong foundation of enabling delivery systems.

From a translational perspective, one of the greatest strengths of sonogenetics is that the different effects of stimulating a chosen neuronal cell type across numerous human brain regions may rapidly be tested. While this could have benefits at the preclinical level as well, the most important outcomes will likely occur during clinical stage testing. As an example, consider an anxiety disorder patient who has received a gene therapy which expresses mechanosensitive ion channels in a brain-wide fashion across GABAergic neurons. A clinician might leverage tFUS stimulation in the patient’s lateral amygdala for a few weeks.^46,47 If that strategy did not improve the patient’s symptoms, the clinician may easily switch the tFUS stimulation to the central amygdala region⁴⁶ or the bed nucleus of the stria terminalis (BNST)⁴⁸ or the lateral septum.⁴⁹ Many distinct brain regions could be explored without needing to develop a new therapeutic. This would allow fast clinical screening of strategies for modulating neural circuits towards better mental health. Since the challenges of clinical trials represent a massive limiting factor for therapeutics in general, the ability to quickly explore this space of possibilities could dramatically accelerate discovery. Combining the incredible precision of sonogenetic tFUS with such a rapid screening strategy may reveal superior therapeutic targets for combating mental illness.

Conclusion

As I have discussed in this essay, a lack of scalable delivery systems is a central roadblock to the promise of psychiatric gene therapy. Because of this, I have begun developing strategies for overcoming the scalable delivery problem through a number of ideas. I have done early-stage experiments towards a couple of these ideas while others remain in the ideation stage. (I cannot publicly describe the details of my ideas on the internet because public disclosure of IP precludes patentability). I should note that I am currently a graduate student and expect to complete my PhD in around six months. This article represents part of my efforts to lay the groundwork for my upcoming scalable brain delivery plans.

Mental illness represents one of the most profound challenges facing humanity. It affects how we live our lives and interact with the world. It affects people we love. It takes away precious time from people who otherwise could have been experiencing the extraordinary beauty of life and the universe. I believe that positive emotional experiences represent the most fundamental form of value in the cosmos. Mental illness blocks people off from experiencing joy, which is in my view an immeasurable tragedy that needs to be righted. It is time to do the science needed to reset our brains to live life to the fullest.

If you are interested in discussing anything related to this space, please reach out to: logan (dot) phospholipid (at) gmail (dot) com!

If you would like to read more about me and my scientific and entrepreneurial background, please check out my bio at: https://logancollinsblog.com/

References:

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13. Roth, B. L. Molecular pharmacology of metabotropic receptors targeted by neuropsychiatric drugs. Nat. Struct. Mol. Biol. 26, 535–544 (2019).

14. Liu, T. et al. Sonogenetics: Recent advances and future directions. Brain Stimul. 15, 1308–1317 (2022).

15. Xian, Q. et al. Modulation of deep neural circuits with sonogenetics. Proc. Natl. Acad. Sci. 120, e2220575120 (2023).

16. Hahmann, J., Ishaqat, A., Lammers, T. & Herrmann, A. Sonogenetics for Monitoring and Modulating Biomolecular Function by Ultrasound. Angew. Chemie Int. Ed. n/a, e202317112 (2024).

17. Tang, J., Feng, M., Wang, D., Zhang, L. & Yang, K. Recent advancement of sonogenetics: A promising noninvasive cellular manipulation by ultrasound. Genes Dis. 11, 101112 (2024).

18. Miyakawa, N. et al. Chemogenetic attenuation of cortical seizures in nonhuman primates. Nat. Commun. 14, 971 (2023).

19. Bendixen, L., Jensen, T. I. & Bak, R. O. CRISPR-Cas-mediated transcriptional modulation: The therapeutic promises of CRISPRa and CRISPRi. Mol. Ther. 31, 1920–1937 (2023).

20. Doudna, J. A. The promise and challenge of therapeutic genome editing. Nature 578, 229–236 (2020).

21. Zhao, M., Zhao, Z., Koh, J.-T., Jin, T. & Franceschi, R. T. Combinatorial gene therapy for bone regeneration: Cooperative interactions between adenovirus vectors expressing bone morphogenetic proteins 2, 4, and 7. J. Cell. Biochem. 95, 1–16 (2005).

22. Won, Y.-W. et al. Synergistically Combined Gene Delivery for Enhanced VEGF Secretion and Antiapoptosis. Mol. Pharm. 10, 3676–3683 (2013).

23. Wang, J. & Vos, J.-M. H. Infectious Epstein-Barr virus vectors for episomal gene therapy. in Gene Therapy Methods (ed. Phillips, M. I. B. T.-M. in E.) vol. 346 649–660 (Academic Press, 2002).

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25. Collins, L. T., Ponnazhagan, S. & Curiel, D. T. Synthetic Biology Design as a Paradigm Shift toward Manufacturing Affordable Adeno-Associated Virus Gene Therapies. ACS Synth. Biol. 12, 17–26 (2023).

26. Reid, C. A., Hörer, M. & Mandegar, M. A. Advancing AAV production with high-throughput screening and transcriptomics. Cell Gene Ther. Insights (2024).

27. Cameau, E., Glover, C. & Pedregal, A. Cost modelling comparison of adherent multi-trays with suspension and fixed-bed bioreactors for the manufacturing of gene therapy products. Cell Gene Ther. Insights (2020).

28. Smith, J., Grieger, J. & Samulski, J. Overcoming bottlenecks in AAV manufacturing for gene therapy. Immuno-oncology Insights (2018).

29. Gangurde, R. & Winitsky, S. Gene therapy: are high costs and manufacturing complexities impeding progress? https://www.parexel.com/insights/blog/gene-therapy-are-high-costs-and-manufacturing-complexities-impeding-progress (2024).

30. Au, H. K. E., Isalan, M. & Mielcarek, M. Gene Therapy Advances: A Meta-Analysis of AAV Usage in Clinical Settings. Front. Med. Volume 8-, (2022).

31. Huang, Q. et al. An AAV capsid reprogrammed to bind human transferrin receptor mediates brain-wide gene delivery. Science (80-. ). 384, 1220–1227 (2024).

32. Walpole, S. C. et al. The weight of nations: an estimation of adult human biomass. BMC Public Health 12, 439 (2012).

33. Gorick, C. M. et al. Applications of focused ultrasound-mediated blood-brain barrier opening. Adv. Drug Deliv. Rev. 191, 114583 (2022).

34. Batts, A. J. et al. A multifunctional theranostic ultrasound platform for remote magnetogenetics and expanded blood-brain barrier opening. Brain Stimul. Basic, Transl. Clin. Res. Neuromodulation 18, 1939–1951 (2025).

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40. Chen, Hong & Konofagou, Elisa E. The Size of Blood–Brain Barrier Opening Induced by Focused Ultrasound is Dictated by the Acoustic Pressure. J. Cereb. Blood Flow Metab. 34, 1197–1204 (2014).

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Amygdala Structure, Function, and Clinically Relevant Pathways

January 2, 2026January 2, 2026By logancollins No Comments

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Amygdala Structure, Function, and Clinically Relevant Pathways – by Logan Thrasher Collins

Anatomy

The amygdala consists of nuclei which can be grouped into (i) the basolateral nuclear group (BLA), (ii) the superficial cortex-like laminated region (sCLR) which contains the cortical nuclei (Co), and (iii) the centromedial nuclear group.¹ The BLA consists of the lateral nucleus (LA) and basal nucleus (BA). In turn, the BA consists of the basolateral nucleus and the basomedial nucleus. The centromedial nuclear group consists of the central nucleus (Ce), medial nucleus (Me), and intercalate cell mass (IC). In turn, Ce consists of a lateral (CeL) subdivision and a medial (CeM) subdivision. The centromedial nuclear group (Ce, Me, and IC) along with the bed nucleus of the stria terminalis (BNST) and sublenticular substantia innominata together comprise the centromedial extended amygdala.

The cellular composition of the BLA nuclei and the sCLR’s Co nuclei resembles that of the cerebral cortex in that the majority of the neurons are pyramidal-like glutamatergic cells while the rest are local GABAergic inhibitory interneurons.¹ The inhibitory interneurons include parvalbumin-containing neurons which mainly synapse on the soma and proximal dendrites of the pyramidal cells and somatostatin-containing neurons which mainly synapse on the distal dendrites of the pyramidal neurons. By contrast, the composition of the Ce and Me nuclei resembles the striatum in that many of the neurons are similar to GABAergic medium spiny neurons.

Overview of Amygdala Connectivity

Signals flow into the amygdala primarily via synapses in the BLA. Inputs from cortical sensory areas and from the thalamus (relaying subcortical signals) synapse on neurons in the LA.¹ These inputs in the LA facilitate coincidence detection and associative learning tying together the sensory cortical representations of the world with subcortical information coming in via the thalamus. The LA pyramidal-like neurons send excitatory signals to the BA’s projection neurons and to the BA’s interneurons. (It should be noted that the subnuclei of the BA are also interconnected with the prefrontal cortex, hippocampus, and striatum). Next, the BA projects glutamatergic inputs to the CeM’s GABAergic projection neurons. These BA glutamatergic projections additionally synapse on the inhibitory interneurons of the IC and the CeL, both of which regulate the CeM neurons. (An additional layer of complexity comes from further inhibitory interneuron circuits within the LA, BA, IC, CeL, and CeM). Finally, the CeM’s GABAergic projection neurons send output signals to the hypothalamus and brainstem.

Sensory inputs to the amygdala’s LA come from several sources.¹ Sensory association areas of the temporal cortex carry visual and auditory information. These areas are part of the ventral stream of sensory processing, which encodes analyses of complex features to facilitate face recognition and auditory recognition. The insular cortex, which encodes somatosensory and visceral sensations, also sends inputs to the LA. Subcortical sensory inputs to the LA come via the thalamus. In addition to LA inputs, the CeM receives visceral and nociceptive inputs directly from the pons. The sCLR receives olfactory input from the olfactory bulb as well as from higher olfactory areas.

Interestingly, the amygdala sends outputs back to cortical sensory association areas as well as primary sensory areas.¹ These modulate the valence of specific sensory stimuli, which can be thought of as a way to assign emotional value to particular stimuli.

The amygdala has strong bidirectional interactions with the orbitofrontal cortex (OFC).¹ In particular, the OFC receives strong inputs from the BA and targets the IC’s GABAergic neurons. The amygdala also interacts with the dorsal anterior cingulate cortex (dACC) and ventral anterior cingulate cortex (vAAC). The BA sends outputs to the dACC while the vACC projects back to the BA. The BA projects to the entorhinal cortex and receives inputs from the hippocampus as well, which may help tie emotional significance of particular events undergoing processing to associated memories. Finally, the amygdala receives subcortical inputs from arousal systems, including basal forebrain cholinergic inputs, ventral tegmental area (VTA) dopaminergic inputs, noradrenergic locus coeruleus inputs, and rostral raphe serotonergic inputs. The amygdala also projects back to all of these neuromodulatory regions and can influence the arousal systems.

CeM outputs to the hypothalamus and brainstem facilitate visceral behavioral responses to fear. These projections trigger various endocrine and autonomic peripheral nervous system responses such as secretion of adrenocorticotropic hormone (ACTH) into the blood and increased activation of the sympathetic nervous system.

Fear Learning

As mentioned earlier, the convergence of cortical inputs and subcortical inputs onto LA neurons facilitates associative learning between neutral stimuli and unpleasant stimuli. The neutral stimulus is often referred to as the “conditioned stimulus” (CS) while the unpleasant stimulus is referred to as the “unconditioned stimulus” (UC). In classical animal studies, the CS might take the form of a neutral sound (e.g. a tone) while the UC is often an electrical shock to the feet. It is variable as to which input pathways carry information about the CS and which input pathways carry information about the UC.²

In auditory fear learning, both the subcortical thalamic pathway afferents and the auditory cortex afferents have been shown to carry sensory CS information into the LA.² When an animal must discriminate between two distinct CS sounds to learn which sound is associated with a foot shock UC, the auditory cortical pathway is thought to be necessary because plasticity in the auditory cortex facilitates the discriminative learning. Interestingly, the primary auditory cortex has been shown to carry information about complex multifrequency sounds into the LA while the more ventral associative areas of the auditory cortex bring information about simpler tone sounds.

Both the cortical and subcortical pathways have also been shown to carry parts of the UC. In particular, the parabrachial nucleus of the brainstem has been shown to encode nociceptive UC information. To transfer this information, the parabrachial nucleus projects to the CeM and CeL nuclei of the amygdala.^2–4 However, the parabrachial nucleus does not project to the LA.² It remains unknown if there is a separate (probably glutamatergic) input to the LA which carries aversive information for associative fear learning.

It should also be noted that evidence implicates neuromodulatory systems as carrying part of the UC signal during fear learning. Locus coeruleus noradrenergic projections have been shown to contribute about half of the strength of the fear learning signal.⁵ That is, when silenced during fear learning in rats, a 50% decrease in learned fear occurred. Additionally, a subpopulation of dopaminergic neurons from the VTA which projects to the BA has been shown to contribute about 30% of the strength of the fear learning signal.⁶ That is, when silenced during fear learning in mice, a 30% decrease in learned fear occurred. Acetylcholine inputs from the basal forebrain into the BLA have also been demonstrated to be necessary for efficient fear learning.⁷ These neuromodulators may also be released, though to a lesser degree, during fear memory recall. Finally, serotoninergic neurons (especially from the raphe nuclei) have been implicated to sometimes act on the 5-HT_1A receptors of GABAergic interneurons of the LA to inhibit fear learning in LA pyramidal cells.¹ That said, serotonin can have other effects in the BLA and its influence is not fully understood.⁸

Amygdala, Emotion, and Anxiety

The amygdala represents a central part of circuits relating to fear and anxiety as well as of circuits of general emotional valence. Elevated amygdala activity with decreased top-down regulation from the vmPFC has been shown in people with higher anxiety.^1,9 It is important to note that the vmPFC overlaps with the ACC and OFC, which were discussed earlier. The vmPFC can facilitate the process of fear extinction: the decline of a learned fear via repeated exposure of a neutral CS without the associated aversive UC. As such, decreased functional connectivity between the amygdala and vmPFC is common in people with anxiety disorders.

The vmPFC facilitates fear extinction by sending excitatory input from the OFC to GABAergic neurons in the IC, which then inhibit the BA’s inputs to the CeM. The BA itself also sends excitatory projections up to the vmPFC which can induce the vmPFC’s fear extinction circuits. A distinct group of excitatory neurons in the BA target the dACC, which then sends excitatory projections back to the amygdala’s BLA to facilitate fear learning (in contrast to the vmPFC projections).^10,11 Indeed, LTP occurring via this circuit within the dACC contributes to the formation and maintenance of fear memory. In this way, the dACC is a direct part of the learning network which creates fear memories.

Inhibitory interneurons within the amygdala act as important regulators of anxiety responses.¹² In the BLA, inhibitory interneurons can suppress the magnitude of anxiety by releasing GABA onto the pyramidal projection neurons. Inhibitory interneurons in the CeL can also constrain the activity of amygdala output projection neurons of the CeM, which leads to decreased fear behavior. But it should be noted that the BLA can receive sensory input associated with either threatening or rewarding stimuli. Because of this, its projection neurons trigger different behavioral responses (threat or reward behaviors) depending on the nature of the stimulus.

There exist non-overlapping populations of putative projection neurons in the BLA which are thought to fire in response to threat and reward stimuli separately.^12,13 These populations are thought to develop via the Hebbian associative learning described previously, which leads to formation of fear pathways for some stimuli, but can also promote association of rewarding stimuli with neutral stimuli and thus form learned emotional pathways of positive valence.¹³ Additionally, inhibitory interneurons of the BLA suppress threat-related projection neurons when reward-related projection neurons are active and vice versa. With anxiety disorders, these interneuron circuits are frequently dysregulated in that negative valence is assigned to neutral or reward stimuli, leading to activation of only the threat-related projection pathway.

Extended Networks of the Amygdala and Anxiety

As has been discussed to some degree so far, the amygdala does not function in isolation. It makes numerous reciprocal connections with other brain areas to facilitate its operation. Some of the most important of these include cortical regions like the vmPFC, OFC, and ACC, which were discussed earlier. But extended subcortical structures like the BNST and hippocampus (which have so far only been mentioned briefly) also play major roles.

The BNST is a collection of nuclei nearby to the amygdala which is recruited during sustained fear and anxiety responses.¹³ It is thought that the BNST specifically activates during prolonged stressful periods of greater than 10 minutes in duration.¹⁴ The BLA sends glutamatergic projections into the BNST’s anterodorsal (ad) nucleus. Interestingly, these excitatory inputs to the BNST ad nucleus promote anxiolytic outcomes. Additionally, local inhibition of the ad nucleus from the BNST’s oval (ov) nucleus promotes anxiogenic outcomes. The BNST’s ad nucleus facilitates anxiolytic states by sending its own (predominantly) GABAergic projections to the VTA to increase positive emotional valence, to the lateral hypothalamus (LH) to decrease risk avoidance, and to the parabrachial nucleus of the brainstem to decrease respiration rate.¹⁵

BNST ad projections to the VTA are mostly GABAergic neurons synapsing onto VTA inhibitory interneurons.¹⁶ It should be noted that there are also ventral BNST (vBNST) GABAergic and glutamatergic projections which synapse onto different populations of VTA inhibitory interneurons, triggering anxiogenic phenotypes and anxiolytic phenotypes respectively.¹⁷ A major population of BNST ad projections to the lateral hypothalamus are GABAergic neurons preferentially synapsing onto GABAergic target neurons. Among these is a subpopulation of GABAergic projection neurons targeting GABAergic lateral hypothalamus neurons which also produce orexin (a neuropeptide which stimulates food intake behaviors and promotes wakefulness).¹⁶ BNST ad GABAergic projections to the parabrachial nucleus probably inhibit glutamatergic neurons which themselves would otherwise signal for increased respiratory rate.¹⁸

The amygdala also interacts with the hippocampus. As mentioned earlier, the BLA sends excitatory inputs to the hippocampal formation by first synapsing at the entorhinal cortex (EC), which then sends its own excitatory inputs to the hippocampus.¹³ These inputs are necessary for acquisition of contextual fear memories, likely mediated by the BLA amygdala’s fear learning mechanism in combination with hippocampal memory representations.

In addition, the BLA sends glutamatergic synapses directly onto pyramidal cells in the ventral hippocampus (vHPC) CA1 region, increasing anxiety-like behavior when these BLA projections are active. In part, the vHPC mediates its effects on anxiety through glutamatergic projections to the lateral septum, which sends its own projections onwards to the hypothalamus. The vHPC glutamatergic projections stimulate activation of corticotropin releasing factor receptor 2 (CRFR2) expressing GABAergic projection neurons in the lateral septum through a mechanism which is not fully understood.^13,19 These GABAergic projection neurons inhibit the anterior hypothalamic area (AHA), which itself inhibits the paraventricular nucleus (PVN) of the hypothalamus as well as the periaqueductal gray (PAG). In this way, the lateral septum disinhibits the paraventricular nucleus and the periaqueductal gray, which leads to neuroendocrine and behavioral outcomes associated with persistent anxiety.

Conclusion

While this writeup serves as an initial primer on the amygdala, there remain a plethora of relevant neural circuits to explore beyond what has been described here. Nonetheless, I hope that the information provided will offer a useful starting point for learning about the amygdala’s structure, function, and effects on mammalian emotions. As further reading, I specifically recommend references #1, #9, #12, and #13.

References

1. Benarroch, E. E. The amygdala: Functional organization and involvement in neurologic disorders. Neurology 84, 313–324 (2015).

2. Palchaudhuri, Shriya, Osypenko, Denys & Schneggenburger, Ralf. Fear Learning: An Evolving Picture for Plasticity at Synaptic Afferents to the Amygdala. Neurosci. 30, 87–104 (2022).

3. Han, S., Soleiman, M. T., Soden, M. E., Zweifel, L. S. & Palmiter, R. D. Elucidating an Affective Pain Circuit that Creates a Threat Memory. Cell 162, 363–374 (2015).

4. Herry, C. & Johansen, J. P. Encoding of fear learning and memory in distributed neuronal circuits. Nat. Neurosci. 17, 1644–1654 (2014).

5. Uematsu, A. et al. Modular organization of the brainstem noradrenaline system coordinates opposing learning states. Nat. Neurosci. 20, 1602–1611 (2017).

6. Tang, W., Kochubey, O., Kintscher, M. & Schneggenburger, R. A VTA to Basal Amygdala Dopamine Projection Contributes to Signal Salient Somatosensory Events during Fear Learning. J. Neurosci. 40, 3969 LP – 3980 (2020).

7. Jiang, L. et al. Cholinergic Signaling Controls Conditioned Fear Behaviors and Enhances Plasticity of Cortical-Amygdala Circuits. Neuron 90, 1057–1070 (2016).

8. Bocchio, M., McHugh, S. B., Bannerman, D. M., Sharp, T. & Capogna, M. Serotonin, Amygdala and Fear: Assembling the Puzzle. Front. Neural Circuits Volume 10–2016, (2016).

9. Zhang, W.-H., Zhang, J.-Y., Holmes, A. & Pan, B.-X. Amygdala Circuit Substrates for Stress Adaptation and Adversity. Biol. Psychiatry 89, 847–856 (2021).

10. Toyoda, H. et al. Interplay of Amygdala and Cingulate Plasticity in Emotional Fear. Neural Plast. 2011, 813749 (2011).

11. Jhang, J. et al. Anterior cingulate cortex and its input to the basolateral amygdala control innate fear response. Nat. Commun. 9, 2744 (2018).

12. Babaev, O., Piletti Chatain, C. & Krueger-Burg, D. Inhibition in the amygdala anxiety circuitry. Exp. Mol. Med. 50, 1–16 (2018).

13. Calhoon, G. G. & Tye, K. M. Resolving the neural circuits of anxiety. Nat. Neurosci. 18, 1394–1404 (2015).

14. Hammack, S. E., Todd, T. P., Kocho-Schellenberg, M. & Bouton, M. E. Role of the bed nucleus of the stria terminalis in the acquisition of contextual fear at long or short context-shock intervals. Behavioral Neuroscience vol. 129 673–678 at https://doi.org/10.1037/bne0000088 (2015).

15. Kim, S.-Y. et al. Diverging neural pathways assemble a behavioural state from separable features in anxiety. Nature 496, 219–223 (2013).

16. Giardino, W. J. & Pomrenze, M. B. Extended Amygdala Neuropeptide Circuitry of Emotional Arousal: Waking Up on the Wrong Side of the Bed Nuclei of Stria Terminalis. Front. Behav. Neurosci. Volume 15–2021, (2021).

17. Jennings, J. H. et al. Distinct extended amygdala circuits for divergent motivational states. Nature 496, 224–228 (2013).

18. Kaur, S. et al. Glutamatergic Signaling from the Parabrachial Nucleus Plays a Critical Role in Hypercapnic Arousal. J. Neurosci. 33, 7627 LP – 7640 (2013).

19. Anthony, T. E. et al. Control of Stress-Induced Persistent Anxiety by an Extra-Amygdala Septohypothalamic Circuit. Cell 156, 522–536 (2014).

Dopaminergic Neurons in the Ventral Tegmental Area as a Target for Treatment-Resistant Depression

August 28, 2025By logancollins No Comments

Prelude

This essay is intended as an intuitive examination of a reward system neural circuit which may serve as a useful target for new therapies aimed at fighting treatment-resistant depression (TRD). My purpose here is not to introduce an entirely novel concept, but rather to compile in one place a set of important explanations on how information flow in the reward system relates to TRD and how these reward system mechanisms may have clinical relevance.

Treatment Resistant Depression

Treatment resistant depression (TRD) is a widespread and debilitating condition. Patients with TRD are defined to have failed to adequately respond to two or more treatments for depression.¹ As a broader category, depression affects about 280 million people worldwide.² TRD affects roughly 30% of these patients³ (~84 million). In the USA, it has been estimated that about 2.8 million adults suffer from TRD.¹ A common symptom associated with TRD is anhedonia, the inability to feel positive emotions. It is thought that defects in the brain’s reward pathway are central to the neurobiology of TRD since this pathway contains the neural circuitry necessary to encode positive emotional experiences.

Reward Circuits

When sensory recognition of a potential reward occurs, various pathways inhibit activity of the lateral habenula (LHb), which in turn inhibits the rostromedial tegmental nucleus (RMTg). This disinhibits the ventral tegmental area (VTA).⁴ The VTA’s dopaminergic projections then spike in phasic bursts, sending dopamine to the nucleus accumbens (NAc) (mesolimbic pathway, a part of the medial forebrain bundle or MFB) and prefrontal cortex (PFC) (mesocortical pathway).⁵ NAc GABAergic medium spiny neurons (MSNs) generally express either the dopamine 1 receptor (D1R) or express the dopamine 2 receptor (D2R). D1R MSNs are excited by dopamine while D2R MSNs are inhibited by dopamine. Mesolimbic inputs bias the NAc to output from the D1R MSNs, which stimulate the direct motor pathway to respond to the reward. The GABAergic MSN activity furthermore inhibits the ventral pallidum (VP), which in turn lifts its own GABAergic inhibition on targets such as the mediodorsal thalamus, lateral hypothalamus, and VTA.⁶ This increases arousal and helps with motor processes. The mediodorsal thalamus projects to the PFC and triggers circuits that represent the value of the reward.⁷

These circuits facilitate reward learning by a comparative mechanism called reward prediction error (RPE). The pedunculopontine tegmental nucleus (PPTg) receives inputs about the actual reward from brainstem sensory signals (and other brain areas) and projects glutamatergic and cholinergic synapses into the VTA (and elsewhere) to activate the dopaminergic neurons.⁸ If the reward is less valuable than expected, the LHb activates, which triggers firing of GABAergic neurons in the RMTg onto the VTA dopamine neurons, shutting down the mesolimbic activity.⁴ If the actual reward remains valuable, then the mesolimbic activity continues. This process where the inhibitory LHb-RMTg signal is “subtracted” from the stimulatory PPTg signal determines the RPE comparison’s outcome and whether the VTA continues its dopaminergic signals.⁹ All of this facilitates reward learning, where mesolimbic long-term potentiation (LTP) occurs if the reward is as strong as expected (or stronger) and mesolimbic long-term depression (LTD) (not the same as psychiatric depression) occurs if the reward is not as strong as expected.

Dopaminergic Neurons and Treatment Resistant Depression

As a central driver within the reward system, dopaminergic VTA neurons have high potential as a target for combatting TRD. Activation of these neurons may alleviate anhedonia and increase motivation. There already exists clinical evidence that stimulation of VTA dopaminergic neurons has significant benefits. As mentioned, the mesolimbic pathway projections of VTA dopaminergic neurons make up a major part of the MFB. Multiple clinical studies on deep brain stimulation (DBS) of the MFB (specifically the supero-lateral MFB or slMFB) have shown long-term beneficial effects for patients with TRD.^10–12 Functional imaging evidence suggests this works indirectly through activation of descending glutamatergic fibers from the PFC which activate the VTA’s dopamine neurons.¹⁰ Dopamine axons themselves are small in diameter, which make them not as responsive to conventional DBS. It should be noted that the VTA is a highly heterogeneous structure with dopaminergic, GABAergic, and glutamatergic neurons,¹³ so DBS of the VTA in general might have off-target effects and/or partially mitigate the benefits of the stimulation. Activation of the VTA’s GABAergic and glutamatergic neurons can have markedly different effects compared to activation of only its dopaminergic neurons.¹⁴ In mice, GABAergic VTA neuronal activity particularly has been found to occur in response to aversive stimuli and stimuli predicting the absence of reward.^15,16 In rats, optogenetic stimulation of VTA dopamine neurons promotes motivated behavior while optogenetic stimulation of VTA GABA neurons disrupts reward and promotes aversion.¹⁷ Clinical and animal model evidence thus supports the idea that selective activation of VTA dopamine neurons might act as a potent therapy for TRD.

Conclusion

Based on the literature, raising the basal level of VTA dopaminergic neuron activity might demonstrate a strong ameliorative effect on TRD. Extensive preclinical and clinical testing will of course be crucial to establish safety. Possible addictiveness of treatments which activate this circuit will need careful examination in particular. Depending on the modality of treatment, different forms of neurological adaptation may occur, so ways of mitigating this issue should be explored. VTA dopaminergic neurons represent a promising target for next-generation therapies aimed at overcoming TRD.

References

1. Zhdanava, M. et al. The Prevalence and National Burden of Treatment-Resistant Depression and Major Depressive Disorder in the United States. J. Clin. Psychiatry 82, (2021).

2. World Health Organization – Depressive disorder (depression). https://www.who.int/news-room/fact-sheets/detail/depression (2023).

3. McIntyre, R. S. et al. Treatment-resistant depression: definition, prevalence, detection, management, and investigational interventions. World Psychiatry 22, 394–412 (2023).

4. Hong, S., Jhou, T. C., Smith, M., Saleem, K. S. & Hikosaka, O. Negative Reward Signals from the Lateral Habenula to Dopamine Neurons Are Mediated by Rostromedial Tegmental Nucleus in Primates. J. Neurosci. 31, 11457 LP – 11471 (2011).

5. Juarez, B. & Han, M.-H. Diversity of Dopaminergic Neural Circuits in Response to Drug Exposure. Neuropsychopharmacology 41, 2424–2446 (2016).

6. Root, D. H., Melendez, R. I., Zaborszky, L. & Napier, T. C. The ventral pallidum: Subregion-specific functional anatomy and roles in motivated behaviors. Prog. Neurobiol. 130, 29–70 (2015).

7. Haber, S. N. & Knutson, B. The Reward Circuit: Linking Primate Anatomy and Human Imaging. Neuropsychopharmacology 35, 4–26 (2010).

8. Skvortsova, V. et al. A Causal Role for the Pedunculopontine Nucleus in Human Instrumental Learning. Curr. Biol. 31, 943-954.e5 (2021).

9. Eshel, N. et al. Arithmetic and local circuitry underlying dopamine prediction errors. Nature 525, 243–246 (2015).

10. Fenoy, A. J. et al. Deep brain stimulation of the “medial forebrain bundle”: sustained efficacy of antidepressant effect over years. Mol. Psychiatry 27, 2546–2553 (2022).

11. Schlaepfer, T. E., Bewernick, B. H., Kayser, S., Mädler, B. & Coenen, V. A. Rapid Effects of Deep Brain Stimulation for Treatment-Resistant Major Depression. Biol. Psychiatry 73, 1204–1212 (2013).

12. Fenoy, A. J., Quevedo, J. & Soares, J. C. Deep brain stimulation of the “medial forebrain bundle”: a strategy to modulate the reward system and manage treatment-resistant depression. Mol. Psychiatry 27, 574–592 (2022).

13. Faget, L. et al. Afferent Inputs to Neurotransmitter-Defined Cell Types in the Ventral Tegmental Area. Cell Rep. 15, 2796–2808 (2016).

14. Root, D. H. et al. Distinct Signaling by Ventral Tegmental Area Glutamate, GABA, and Combinatorial Glutamate-GABA Neurons in Motivated Behavior. Cell Rep. 32, (2020).

15. van Zessen, R., Phillips, J. L., Budygin, E. A. & Stuber, G. D. Activation of VTA GABA Neurons Disrupts Reward Consumption. Neuron 73, 1184–1194 (2012).

16. Tan, K. R. et al. GABA Neurons of the VTA Drive Conditioned Place Aversion. Neuron 73, 1173–1183 (2012).

17. Tong, Y., Pfeiffer, L., Serchov, T., Coenen, V. A. & Döbrössy, M. D. Optogenetic stimulation of ventral tegmental area dopaminergic neurons in a female rodent model of depression: The effect of different stimulation patterns. J. Neurosci. Res. 100, 897–911 (2022).