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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).

Global Highlights in Neuroengineering 2005-2018

January 14, 2019January 24, 2019By logancollins No Comments

PDF version: global highlights in neuroengineering 2005-2018 – logan thrasher collins

Optogenetic stimulation using ChR2

(Boyden, Zhang, Bamberg, Nagel, & Deisseroth, 2005)

Ed Boyden, Karl Deisseroth, and colleagues developed optogenetics, a revolutionary technique for stimulating neural activity.
Optogenetics involves engineering neurons to express light-gated ion channels. The first channel used for this purpose was ChR2 (a protein originally found in bacteria which responds to blue light). In this way, a neuron exposed to an appropriate wavelength of light will be stimulated.
Over time, optogenetics has gained a place as an essential experimental tool for neuroscientists across the world. It has been expanded upon and improved in numerous ways and has even allowed control of animal behavior via implanted fiber optics and other light sources. Optogenetics may eventually be used in the development of improved brain-computer interfaces.

optogenetics

Blue Brain Project cortical column simulation

(Markram, 2006)

In the early stages of the Blue Brain Project, neuronal cell types from the layers of the rat neocortex were reconstructed. Furthermore, their electrophysiology was experimentally characterized.
Next, a virtual neocortical column with about 10,000 multicompartmental Hodgkin-Huxley-type neurons and over ten million synapses was built. Its connectivity was defined according the patterns of connectivity found in biological rats, (though this involved the numbers of inputs and outputs quantified for given cell types rather than explicit wiring). In addition, the spatial distributions of boutons forming synaptic terminals upon target cells reflected biological data.
The cortical column was emulated using the Blue Gene/L supercomputer and the dynamics of the emulation reflected its biological counterpart.

Optogenetic silencing using halorhodopsin

(Han & Boyden, 2007)

Ed Boyden continued developing optogenetic tools to manipulate neural activity. Along with Xue Han, he expressed a codon-optimized version of a bacterial halorhodopsin (along with the ChR2 protein) in neurons.
Upon exposure to yellow light, halorhodopsin pumps chloride ions into the cell, hyperpolarizing the membrane and inhibiting neural activity.
Using halorhodopsin and ChR2, neurons could be easily activated and inhibited using yellow and blue light respectively.

halorhodopsin and chr2 wavelengths

Brainbow

(Livet et al., 2007)

Lichtman and colleagues used Cre/Lox recombination tools to create genes which express a randomized set of three or more differently-colored fluorescent proteins (XFPs) in a given neuron, labeling the neuron with a unique combination of colors. About ninety distinct colors were emitted across a population of genetically modified neurons.
The detailed structures within neural tissue equipped with the Brainbow system can be imaged much more easily since neurons can be distinguished via color contrast.
As a proof-of-concept, hundreds of synaptic contacts and axonal processes were reconstructed in a selected volume of the cerebellum. Several other neural structures were also imaged using Brainbow.
The fluorescent proteins expressed by the Brainbow system are usable in vivo.

High temporal precision optogenetics

(Gunaydin et al., 2010)

Karl Deisseroth, Peter Hegemann, and colleagues used protein engineering to improve the temporal resolution of optogenetic stimulation.
Glutamic acid at position 123 in ChR2 was mutated to threonine, producing a new ion channel protein (dubbed ChETA).
The ChETA protein allows for induction of spike trains with frequencies up to 200 Hz and greatly decreases the incidence of unintended spikes. Furthermore, ChETA eliminates plateau potentials (a phenomenon which interferes with precise control of neural activity).

ultrafast optogenetics

Hippocampal prosthesis in rats

(Berger et al., 2012)

Theodore Berger and his team developed an artificial replacement for neurons which transmit information from the CA3 region to the CA1 region of the hippocampus.
This cognitive prosthesis employs recording and stimulation electrodes along with a multi-input multi-output (MIMO) model to encode the information in CA3 and transfer it to CA1.
The hippocampal prosthesis was shown to restore and enhance memory in rats as evaluated by behavioral testing and brain imaging.

In vivo superresolution microscopy for neuroimaging

(Berning, Willig, Steffens, Dibaj, & Hell, 2012)

Stefan Hell (2014 Nobel laureate in chemistry) developed stimulated emission depletion microscopy (STED), a type of superresolution fluorescence microscopy which allows imaging of synapses and dendritic spines.
STED microscopy uses a torus-shaped de-excitation laser that interferes with the excitation laser to deplete fluorescence except in a very small spot. In this way, the diffraction limit is surpassed since the resulting light illuminates extremely small regions of the sample.
Neurons in transgenic mice (equipped with glass-sealed holes in their skulls) were imaged using STED. Synapses and dendritic spines were observed up to fifteen nanometers below the surface of the brain tissue.

In vivo three-photon microscopy

(Horton et al., 2013)

Multi-photon excitation uses pulsed lasers to excite fluorophores with two or more photons of light with long wavelengths. During the excitation, the photons undergo a nonlinear recombination process, yielding a single emitted photon with a much shorter wavelength. Because the excitation photons possess long wavelengths, they can penetrate tissue much more deeply than traditional microscopy allows.
Horton and colleagues developed a three-photon excitation method to facilitate even deeper tissue penetration than the commonly used two-photon microscopic techniques.
Since three photons were involved per excitation event, even longer excitation wavelengths (about 1,700 nm) were usable, allowing the construction of a 3-dimensional image stack that reached a depth of up to 1.4 mm within the living mouse brain.
Blood vessels and RFP-labeled neurons were imaged using this approach. Furthermore, the depth was sufficient to enable imaging of neurons within the mouse hippocampus.

3-photon microscopy

Whole-brain functional recording from larval zebrafish

(Ahrens, Orger, Robson, Li, & Keller, 2013)

Laser-scanning light-sheet microscopy was used to volumetrically image the entire brains of larval zebrafish (an optically transparent organism).
The genetically encoded calcium sensor GCaMP5G facilitated functional recording at single-cell resolution from about 80% of the total neurons in the larval zebrafish brains. Computational methods were used to distinguish between individual neurons.
Populations of neurons that underwent correlated activity patterns were identifiedto show the technique’s utility for uncovering the dynamics of neural circuits. These populations included hindbrain neurons that were functionally linked to neural activity in the spinal cord and a population of neurons which showed coupled oscillations on the left and right halves.

whole-brain recording from larval zebrafish

Eyewire: crowdsourcing method for retina mapping

(Marx, 2013)

The Eyewire project was created by Sebastian Seung’s research group. It is a crowdsourcing initiative for connectomic mapping within the retina towards uncovering neural circuits involved in visual processing.
Laboratories first collect data via serial electron microscopy as well as functional data from two-photon microscopy.
In the Eyewire game, images of tissue slices are provided to players who then help reconstruct neural morphologies and circuits by “coloring in” the parts of the images which correspond to cells and stacking many images on top of each other to generate 3D maps. Artificial intelligence tools help provide initial “best guesses” and guide the players, but the people ultimately perform the task of reconstruction.
By November 2013, around 82,000 participants had played the game. Its popularity continues to grow.

eyewire

The BRAIN Initiative

(“Fact Sheet: BRAIN Initiative,” 2013)

The BRAIN Initiative (Brain Research through Advancing Innovative Technologies) provided neuroscientists with $110 million in governmental funding and $122 million in funding from private sources such as the Howard Hughes Medical Institute and the Allen Institute for Brain Science.
The BRAIN Initiative focused on funding research which develops and utilizes new technologies for functional connectomics. It helped to accelerate research on tools for decoding the mechanisms of neural circuits in order to understand and treat mental illness, neurodegenerative diseases, and traumatic brain injury.
The BRAIN Initiative emphasized collaboration between neuroscientists and physicists. It also pushed forward nanotechnology-based methods to image neural tissue, record from neurons, and otherwise collect neurobiological data.

The CLARITY method for making brains translucent

(Chung & Deisseroth, 2013)

Karl Deisseroth and colleagues developed a method called CLARITY to make samples of neural tissue optically translucent without damaging the fine cellular structures in the tissue. Using CLARITY, entire mouse brains have been turned transparent.
Mouse brains were infused with hydrogel monomers (acrylamide and bisacrylamide) as well as formaldehyde and some other compounds for facilitating crosslinking. Next, the hydrogel monomers were crosslinked by incubating the brains at 37°C. Lipids in the hydrogel-stabilized mouse brains were extracted using hydrophobic organic solvents and electrophoresis.
CLARITY allows antibody labeling, fluorescence microscopy, and other optically-dependent techniques to be used for imaging entire brains. In addition, it renders the tissue permeable to macromolecules, which broadens the types of experimental techniques that these samples can undergo (i.e. macromolecule-based stains, etc.)

X-ray microtomography used to reconstruct Drosophila brain hemisphere

(Mizutani, Saiga, Takeuchi, Uesugi, & Suzuki, 2013)

Mizutani and colleagues stained Drosophila brains with silver nitrate and tetrachloroaurate (a gold-containing compound), facilitating 3-dimensional imaging using X-ray microtomography at a voxel size of 220 × 328 × 314 nm.
To generate the X-rays, a synchrotron source was used. It should be noted that synchrotron sources require large facilities to operate.
Neuronal tracing was performed manually on the 3-dimensional X-ray images of the fly brain, a process which took about 1,700 person-hours. Some neuronal processes were too dense to be resolved, so they were “fused” into unified structures. Furthermore, some neuronal traces were fragmented and most of the cell bodies were not considered. This decreased the number of traces to one third of the estimated number of actual processes in the hemisphere.
Mizutani’s investigation represents an early effort at large-scale connectomics that sets the stage for further initiatives as neuronal tracing, sample preparation, and X-ray microtomography technologies continue to improve.

traced drosophila brain hemisphere

Telepathic rats engineered using hippocampal prosthesis

(S. Deadwyler et al., 2013)

Berger’s hippocampal prosthesis was implanted in pairs of rats. When “donor” rats were trained to perform a task, they developed neural representations (memories) which were recorded by their hippocampal prostheses.
The donor rat memories were run through the MIMO model and transmitted to the stimulation electrodes of the hippocampal prostheses implanted in untrained “recipient” rats. After receiving the memories, the recipient rats showed significant improvements on the task that they had not been trained to perform.

Integrated Information Theory 3.0

(Oizumi, Albantakis, & Tononi, 2014)

Integrated information theory (IIT) was originally proposed by Giulio Tononi in 2004. IIT is a quantitative theory of consciousness which may help explain the hard problem of consciousness.
IIT begins by assuming the following phenomenological axioms; each experience is characterized by how it differs from other experiences, an experience cannot be reduced to interdependent parts, and the boundaries which distinguish individual experiences are describable as having defined “spatiotemporal grains.”
From these phenomenological axioms and the assumption of causality, IIT identifies maximally irreducible conceptual structures (MICS) associated with individual experiences. MICS represent particular patterns of qualia that form unified percepts.
IIT also outlines a mathematical measure of an experience’s quantity. This measure is called integrated information or ϕ.

Openworm

(Szigeti et al., 2014)

The anatomical elegans connectome was originally mapped in 1976 by Albertson and Thomson. More data has since been collected on neurotransmitters, electrophysiology, cell morphology, and other characteristics.
Szigeti, Larson, and their colleagues made an online platform for crowdsourcing research on elegans computational neuroscience, with the goal of completing an entire “simulated worm.”
The group also released software called Geppetto, a program that allows users to manipulate both multicompartmental Hodgkin-Huxley models and highly efficient soft-body physics simulations (for modeling the worm’s electrophysiology and anatomy).

Expansion microscopy

(F. Chen, Tillberg, & Boyden, 2015)

The Boyden group developed expansion microscopy, a method which enlarges neural tissue samples (including entire brains) with minimal structural distortions and so facilitates superior optical visualization of the scaled-up neural microanatomy. Furthermore, expansion microscopy greatly increases the optical translucency of treated samples.
Expansion microscopy operates by infusing a swellable polymer network into brain tissue samples along with several chemical treatments to facilitate polymerization and crosslinking and then triggering expansion via dialysis in water. With 4.5-fold enlargement, expansion microscopy only distorts the tissue by about 1% (computed using a comparison between control superresolution microscopy of easily-resolvable cellular features and the expanded version).
Before expansion, samples can express various fluorescent proteins to facilitate superresolution microscopy of the enlarged tissue once the process is complete. Furthermore, expanded tissue is highly amenable to fluorescent stains and antibody-based labels.

expansion microscopy

Japan’s Brain/MINDS project

(Okano, Miyawaki, & Kasai, 2015)

In 2014, the Brain/MINDS (Brain Mapping by Integrated Neurotechnologies for Disease Studies) project was initiated to further neuroscientific understanding of the brain. This project received nearly $30 million in funding for its first year alone.
Brain/MINDS focuses on studying the brain of the common marmoset (a non-human primate abundant in Japan), developing new technologies for brain mapping, and understanding the human brain with the goal of finding new treatments for brain diseases.

The TrueNorth chip from DARPA and IBM

(Akopyan et al., 2015)

The TrueNorth neuromorphic computing chip was constructed and validated by DARPA and IBM. TrueNorth uses circuit modules which mimic neurons. Inputs to these fundamental circuit modules must overcome a threshold in order to trigger “firing.”
The chip can emulate up to a million neurons with over 250 million synapses while requiring far less power than traditional computing devices.

Human Brain Project cortical mesocircuit reconstruction and simulation

(Markram et al., 2015)

The Human Brain Project reconstructed a 0.29 mm³ region of rat cortical tissue including about 31,000 neurons and 37 million synapses based on morphological data, statistical connectivity rules (rather than exact connectivity), and other datasets. The cortical mesocircuit was emulated using the Blue Gene/Q supercomputer.
This emulation was sufficiently accurate to reproduce emergent neurological processes and yield insights on the mechanisms of their computations.

Recording from C. elegans neurons reveals motor operations

(Kato et al., 2015)

Live elegans worms were immobilized in microfluidic devices and the neurons in their head ganglia as well as some of their motor systems were imaged and recorded from using the calcium indicator GCaMP. As the C. elegans connectome is well-characterized, Kato and colleagues were able to determine the identities of most of the cells that underwent imaging (with the help of computational segmentation techniques).
Principal component analysis was used to reduce the dimensionality of the neural activity datasets since over 100 neurons per worm were recorded from simultaneously.
Next, phase space analysis was utilized to visualize the patterns formed by the recording data. Motor behaviors including dorsal turns, ventral turns, forward movements, and backward movements were found to correspond to specific sequences of neural events as uncovered by examining the patterns found in the phase plots. Further analyses revealed various insights about these brain dynamics and their relationship to motor actions.

c. elegans brain dynamics

Neural lace

(Liu et al., 2015)

Charles Lieber’s group developed a syringe-injectable electronic mesh made of submicrometer-thick wiring for neural interfacing.
The meshes were constructed using novel soft electronics for biocompatibility. Upon injection, the neural lace expands to cover and record from centimeter-scale regions of tissue.
Neural lace may allow for “invasive” brain-computer interfaces to circumvent the need for surgical implantation. Lieber has continued to develop this technology towards clinical application.

BigNeuron initiative towards standardized neuronal morphology acquisition

(Peng et al., 2015)

Because of the inconsistencies between neuronal reconstruction methods and lack of standardization found in neuronal morphology databases, BigNeuron was established as a community effort to improve the situation.
BigNeuron tests as many automated neuronal reconstruction algorithms as possible using large-scale microscopy datasets (from several types of light microscopy). It uses the Vaa3D neuronal reconstruction software as a central platform. Reconstruction algorithms are added to Vaa3D as plugins. These computational tests are performed on supercomputers.
BigNeuron aims to create a superior community-oriented neuronal morphology database, a set of greatly improved tools for neuronal reconstruction, a standardized protocol for future neuronal reconstructions, and a library of morphological feature definitions to facilitate classification.

Human telepathy during a 20 questions game

(Stocco et al., 2015)

Using an interactive question-and-answer setup, Stocco and colleagues demonstrated real-time telepathic communication between pairs of individuals via EEG and transcranial magnetic stimulation. Five pairs of participants played games of 20 questions and attempted to identify unknown objects.
EEG data were recorded from the respondent, computationally processed, and transmitted as transcranial magnetic stimulation signals into the mind (occipital lobe stimulation) of a respondent. The respondent’s answers were translated into higher-intensity transcranial magnetic stimulation pulses corresponding to “yes” answers or lower-intensity transcranial magnetic stimulation pulses corresponding to “no” answers.
When compared to control trials in which sham interfaces were used, the people using the brain-brain interfaces were significantly more successful at playing 20 questions games.

Expansion FISH

(F. Chen et al., 2016)

Boyden, Chen, Marblestone, Church, and colleagues combined fluorescent in situ hybridization (FISH) with expansion microscopy to image the spatial localization of RNA in neural tissue.
The group developed a chemical linker to covalently attach intracellular RNA to the infused polymer network used in expansion microscopy. This allowed for RNAs to maintain their relative spatial locations within each cell post-expansion.
After the tissue was enlarged, FISH was used to fluorescently label targeted RNA molecules. In this way, RNA localization was more effectively resolved.
As a proof-of-concept, expansion FISH was used to reveal the nanoscale distribution of long noncoding RNAs in nuclei as well as the locations of RNAs within dendritic spines.

Neural dust

(Seo et al., 2016)

Michel Maharbiz’s group invented implantable, ~ 1 mm biosensors for wireless neural recording and tested them in rats.
This neural dust could be miniaturized to less than 0.5 mm or even to microscale dimensions using customized electronic components.
Neural dust motes consist of two recording electrodes, a transistor, and a piezoelectric crystal.
The neural dust received external power from ultrasound. Neural signals were recorded by measuring disruptions to the piezoelectric crystal’s reflection of the ultrasound waves. Signal processing mathematics allowed precise detection of activity.

The China Brain Project

(Poo et al., 2016)

The China Brain Project was launched to help understand the neural mechanisms of cognition, develop brain research technology platforms, develop preventative and diagnostic interventions for brain disorders, and to improve brain-inspired artificial intelligence technologies.
This project will be take place from 2016 until 2030 with the goal of completing mesoscopic brain circuit maps.
China’s population of non-human primates and preexisting non-human primate research facilities give the China Brain Project an advantage. The project will focus on studying rhesus macaques.

Somatosensory cortex stimulation for spinal cord injuries

(Flesher et al., 2016)

Gaunt, Flesher, and colleagues found that microstimulation of the primary somatosensory cortex (S1) partially restored tactile sensations to a patient with a spinal cord injury.
Electrode arrays were implanted into the S1 regions of a patient with a spinal cord injury. The array performed intracortical microstimulation over a period of six months.
The patient reported locations and perceptual qualities of the sensations elicited by microstimulation. The patient did not experience pain or “pins and needles” from any of the stimulus trains. Overall, 93% of the stimulus trains were reported as “possibly natural.”
Results from this study might be used to engineer upper-limb neuroprostheses which provide somatosensory feedback.

Simulation of rat CA1 region

(Bezaire, Raikov, Burk, Vyas, & Soltesz, 2016)

Detailed computational models of 338,740 neurons (including pyramidal cells and various types of interneurons) were equipped with connectivity patterns based on data from the biological CA1 region. External inputs were also estimated using biological data and incorporated into the simulation. It is important to note that these connectivity patterns described the typical convergence and divergence of neurites to and from particular cell types rather than explicitly representing the exact connections found in the biological rat.
Each neuron was simulated using a multicompartmental Hodgkin-Huxley-type model with its morphological structure based on biological data from the given cell type. Furthermore, different cell types received different numbers of presynaptic terminals at specified distances from the soma. In total, over five billion synapses were present within the CA1 model.
The simulation was implemented on several different supercomputers. Due to the model’s complexity, a four second simulation took about four hours to complete.
As with the biological CA1 region, the simulation gave rise to gamma oscillations and theta oscillations as well as other biologically consistent phenomena. In addition, parvalbumin-expressing interneurons and neurogliaform cells were identified as drivers of the theta oscillations, demonstrating the utility of detailed neuronal simulations for uncovering biological insights.

ca1 simulation

UltraTracer enhances existing neuronal tracing software

(Peng et al., 2017)

UltraTracer is an algorithm that can improve the efficiency of existing neuronal tracing software for handling large datasets while maintaining accuracy.
Datasets with hundreds of billions of voxels were utilized to test UltraTracer. Ten existing tracing algorithms were augmented.
For most of the existing algorithms, the performance improvements were around 3-6 times, though a few showed improvements of 10-30 times. Even when using computers with smaller memory, UltraTracer was consistently able to enhance conventional software.
UltraTracer was made opensource and is available as a plugin for the Vaa3D tracing software suite.

Whole-brain electron microscopy in larval zebrafish

(Hildebrand et al., 2017)

Serial electron microscopy facilitated imaging of the entire brain of a larval zebrafish at 5.5 days post-fertilization.
Neuronal tracing software (a modified version of the CATMAID software) was used to reconstruct all the myelinated axons found in the larval zebrafish brain.
The reconstructed dataset included 2,589 myelinated axon segments along with some of the associated soma and dendrites. It should be noted that only 834 of the myelinated axons were successfully traced back to their cell bodies.

Hippocampal prosthesis in monkeys

(S. A. Deadwyler et al., 2017)

Theodore Berger continued developing his cognitive prosthesis and tested it in Rhesus Macaques.
As with the rats, monkeys with the implant showed substantially improved performance on memory tasks.

The $100 billion Softbank Vision Fund

(Lomas, 2017)

Masayoshi Son, the CEO of Softbank (a Japanese telecommunications corporation), announced a plan to raise $100 billion in venture capital to invest in artificial intelligence. This plan involved partnering with multiple large companies in order to raise this enormous amount of capital.
By the end of 2017, the Vision Fund successfully reached its $100 billion goal. Masayoshi Son has since announced further plans to continue raising money with a new goal of over $800 billion.
Masayoshi Son’s reason for these massive investments is the Technological Singularity. He agrees with Kurzweil that the Singularity will likely occur at around 2045 and he hopes to help bring the Singularity to fruition. Though Son is aware of the risks posed by artificial superintelligence, he feels that superintelligent AI’s potential to tackle some of humanity’s greatest challenges (such as climate change and the threat of nuclear war) outweighs those risks.

Bryan Johnson launches Kernel

(Regalado, 2017)

Entrepreneur Bryan Johnson invested $100 million to start Kernel, a neurotechnology company.
Kernel plans to develop implants that allow for recording and stimulation of large numbers of neurons at once. The company’s initial goal is to develop treatments for mental illnesses and neurodegenerative diseases. Its long-term goal is to enhance human intelligence.
Kernel originally partnered with Theodore Berger and intended to utilize his hippocampal prosthesis. Unfortunately, Berger and Kernel parted ways after about six months because Berger’s vision was reportedly too long-range to support a financially viable company (at least for now).
Kernel was originally a company called Kendall Research Systems. This company was started by a former member of the Boyden lab. In total, four members of Kernel’s team are former Boyden lab members.

Elon Musk launches NeuraLink

(Etherington, 2017)

Elon Musk (CEO of Tesla, SpaceX, and a number of other successful companies) initiated a neuroengineering venture called NeuraLink.
NeuraLink will begin by developing brain-computer interfaces (BCIs) for clinical applications, but the ultimate goal of the company is to enhance human cognitive abilities in order to keep up with artificial intelligence.
Though many of the details around NeuraLink’s research are not yet open to the public, it has been rumored that injectable electronics similar to Lieber’s neural lace might be involved.

Facebook announces effort to build brain-computer interfaces

(Constine, 2017)

Facebook revealed research on constructing non-invasive brain-computer interfaces (BCIs) at a company-run conference in 2017. The initiative is run by Regina Dugan, Facebook’s head of R&D at division building 8.
Facebook’s researchers are working on a non-invasive BCI which may eventually enable users to type one hundred words per minute with their thoughts alone. This effort builds on past investigations which have been used to help paralyzed patients.
The building 8 group is also developing a wearable device for “skin hearing.” Using just a series of vibrating actuators which mimic the cochlea, test subjects have so far been able to recognize up to nine words. Facebook intends to vastly expand this device’s capabilities.

DARPA funds research to develop improved brain-computer interfaces

(Hatmaker, 2017)

The U.S. government agency DARPA awarded $65 million in total funding to six research groups.
The recipients of this grant included five academic laboratories (headed by Arto Nurmikko, Ken Shepard, Jose-Alain Sahel and Serge Picaud, Vicent Pieribone, and Ehud Isacoff) and one small company called Paradromics Inc.
DARPA’s goal for this initiative is to develop a nickel-sized bidirectional brain-computer interface (BCI) which can record from and stimulate up to one million individual neurons at once.

Human Brain Project analyzes brain computations using algebraic topology

(Reimann et al., 2017)

Investigators at the Human Brain Project utilized algebraic topology to analyze the reconstructed ~ 31,000 neuron cortical microcircuit from their earlier work.
The analysis involved representing the cortical network as a digraph, finding directed cliques (complete directed subgraphs belonging to a digraph), and determining the net directionality of information flow (by computing the sum of the squares of the differences between in-degree and out-degree for all the neurons in a clique). In algebraic topology, directed cliques of n neurons are called directed simplices of dimension n-1.
Vast numbers of high-dimensional directed cliques were found in the cortical microcircuit (as compared to null models and other controls). Spike correlations between pairs of neurons within a clique were found to increase with the clique’s dimension and with the proximity of the neurons to the clique’s sink. Furthermore, topological metrics allowed insights into the flow of neural information among multiple cliques.
Experimental patch-clamp data supported the significance of the findings. In addition, similar patterns were found within the elegans connectome, suggesting that the results may generalize to nervous systems across species.

hbp algebraic topology

Early testing of hippocampal prosthesis algorithm in humans

(Song, She, Hampson, Deadwyler, & Berger, 2017)

Dong Song (who was working alongside Berger) tested the MIMO algorithm on human epilepsy patients using implanted recording and stimulation electrodes. The full hippocampal prosthesis was not implanted, but the electrodes acted similarly, though in a temporary capacity. Although only two patients were tested in this study, many trials were performed to compensate for the small sample size.
Hippocampal spike trains from individual cells in CA1 and CA3 were recorded from the patients during a delayed match-to-sample task. The patients were shown various images while neural activity data were recorded by the electrodes and processed by the MIMO model. The patients were then asked to recall which image they had been shown previously by picking it from a group of “distractor” images. Memories encoded by the MIMO model were used to stimulate hippocampal cells during the recall phase.
In comparison to controls in which the same two epilepsy patients were not assisted by the algorithm and stimulation, the experimental trials demonstrated a significant increase in successful pattern matching.

Brain imaging factory in China

(Cyranoski, 2017)

Qingming Luo started the HUST-Suzhou Institute for Brainsmatics, a brain imaging “factory.” Each of the numerous machines in Luo’s facility performs automated processing and imaging of tissue samples. The devices make ultrathin slices of brain tissue using diamond blades, treat the samples with fluorescent stains or other contrast-enhancing chemicals, and image then using fluorescence microscopy.
The institute has already demonstrated its potential by mapping the morphology of a previously unknown neuron which “wraps around” the entire mouse brain.

china brain mapping image

Automated patch-clamp robot for in vivo neural recording

(Suk et al., 2017)

Ed Boyden and colleagues developed a robotic system to automate patch-clamp recordings from individual neurons. The robot was tested in vivo using mice and achieved a data collection yield similar to that of skilled human experimenters.
By continuously imaging neural tissue using two-photon microscopy, the robot can adapt to a target cell’s movement and shift the pipette to compensate. This adaptation is facilitated by a novel algorithm called an imagepatching algorithm. As the pipette approaches its target, the algorithm adjusts the pipette’s trajectory based on the real-time two-photon microscopy.
The robot can be used in vivo so long as the target cells express a fluorescent marker or otherwise fluoresce corresponding to their size and position.

automated patch clamp system

Genome editing in the mammalian brain

(Nishiyama, Mikuni, & Yasuda, 2017)

Precise genome editing in the brain has historically been challenging because most neurons are postmitotic (non-dividing) and the postmitotic state prevents homology-directed repair (HDR) from occurring. HDR is a mechanism of DNA repair which allows for targeted insertions of DNA fragments with overhangs homologous to the region of interest (by contrast, non-homologous end-joining is highly unpredictable).
Nishiyama, Mikuni, and Yasuda developed a technique which allows genome editing in postmitotic mammalian neurons using adeno-associated viruses (AAVs) and CRISPR-Cas9.
The AAVs delivered ssDNA sequences encoding a single guide RNA (sgRNA) and an insert. Inserts encoding a hemagglutinin tag (HA) and inserts encoding EGFP were both tested. Cas9 was encoded endogenously by transgenic host cells and in transgenic host animals.
The technique achieved precise genome editing in vitro and in vivo with a low rate of off-target effects. Inserts did not cause deletion of nearby endogenous sequences for 98.1% of infected neurons.

Neuropixels probe

(Jun et al., 2017)

Jun and colleagues created the Neuropixels probe to facilitate simultaneous recording from hundreds of individual neurons with high spatiotemporal resolution. Previous extracellular probes were only able to record from a few dozen individual neurons.
The Neuropixels recording shank is one centimeter long and includes 384 recording channels. Due to the small size of the accompanying apparatus (a 6×9 mm base and a data transmission cable), it enables high-throughput recording in freely moving animals. Because the shank is quite long, Neuropixels can record from multiple brain regions at once.
Voltage signals are processed directly on the base of the Neuropixels apparatus, allowing for noise-free data transmission along the cable for further analysis.

neuropixels

EEG-based facial image reconstruction

(Nemrodov, Niemeier, Patel, & Nestor, 2018)

EEG data associated with viewing images of faces was collected and used to determine the neural correlates of facial processing. In this way, the images were computationally reconstructed in a fashion resembling “mind reading.”
It should be noted that the images reconstructed using data taken from multiple people were more accurate than the images reconstructed using single individuals. Nonetheless, the single individual data still yielded statistically significant accuracy.
In addition to reconstructing the images themselves, the process gave insights on the cognitive steps involved in perceiving faces.

eeg reconstructions of faces

Near-infrared light and upconversion nanoparticles for optogenetic stimulation

(S. Chen et al., 2018)

Upconversion nanoparticles absorb two or more low-energy photons and emit a higher energy photon. For instance, multiple near-infrared photons can be converted into a single visible spectrum photon.
Shuo Chen and colleagues injected upconversion nanoparticles into the brains of mice and used them to convert externally applied near-infrared (NIR) light into visible light within the brain tissue. In this way, optogenetic stimulation was performed without the need for surgical implantation of fiber optics or similarly invasive procedures.
The authors demonstrated stimulation via upconversion of NIR to blue light (to activate ChR2) and inhibition via upconversion of NIR to green light (to activate a rhodopsin called Arch).
As a proof-of-concept, this technology was used to alter the behavior of the mice by activating hippocampally-encoded fear memories.

upconversion nanoparticles and nir

Map of all neuronal cell bodies within mouse brain

(Murakami et al., 2018)

Ueda, Murakami, and colleagues combined methods from expansion microscopy and CLARITY to develop a protocol called CUBIC-X which both expands and clears entire brains. Light-sheet fluorescence microscopy was used to image the treated brains and a novel algorithm was developed to detect individual nuclei.
Although expansion microscopy causes some increased tissue transparency on its own, CUBIC-X greatly improved this property in the enlarged tissues, facilitating more detailed whole-brain imaging.
Using CUBIC-X, the spatial locations of all the cell bodies (but not dendrites, axons, or synapses) within the mouse brain were mapped. This process was performed upon several adult mouse brains as well as several developing mouse brains to allow for comparative analysis.
The authors made the spatial atlas publicly available in order to facilitate global cooperation towards annotating connectivity among the neural cell bodies within the atlas.

cubic-x

Clinical testing of hippocampal prosthesis algorithm in humans

(Hampson et al., 2018)

Further clinical tests of Berger’s hippocampal prosthesis were performed. Twenty-one patients took part in the experiments. Seventeen patients underwent CA3 recording so as to facilitate training and optimization of the MIMO model. Eight patients received CA1 stimulation so as to improve their memories.
Electrodes with the ability to record from single neurons (10-24 single-neuron recording sites) and via EEG (4-6 EEG recording sites) were implanted such that recording and stimulation could occur at CA3 and CA1 respectively.
Patients performed behavioral memory tasks. Both short-term and long-term memory showed an average improvement of 35% across the patients who underwent stimulation.

Precise optogenetic manipulation of fifty neurons

(Mardinly et al., 2018)

Mardinly and colleagues engineered a novel excitatory optogenetic ion channel called ST-ChroME and a novel inhibitory optogenetic ion channel called IRES-ST-eGtACR1. The channels were localized to the somas of host neurons and generated stronger photocurrents over shorter timescales than previously existing opsins, allowing for powerful and precise optogenetic stimulation and inhibition.
3D-SHOT is an optical technique in which light is tuned by a device called a spatial light modulator along with several other optical components. Using 3D-SHOT, light was precisely projected upon targeted neurons within a volume of 550×550×100 μm³.
By combining novel optogenetic ion channels and the 3D-SHOT technique, complex patterns of neural activity were created in vivo with high spatial and temporal precision.
Simultaneously, calcium imaging allowed measurement of the induced neural activity. More custom optoelectronic components helped avoid optical crosstalk of the fluorescent calcium markers with the photostimulating laser.

Whole-brain Drosophila connectome data acquired via serial electron microscopy

(Zheng et al., 2018)

Zheng, Bock, and colleagues collected serial electron microscopy data on the entire adult Drosophila connectome, providing the data necessary to reconstruct a complete structural map of the fly’s brain at the resolution of individual synapses, dendritic spines, and axonal processes.
The data are in the form of 7050 transmission electron microscopy images (187500 x 87500 pixels and 16 GB per image), each representing a 40nm-thin slice of the fly’s brain. In total the dataset requires 106 TB of storage.
Although much of the the data still must be processed to reconstruct a 3-dimensional map of the Drosophila brain, the authors did create 3-dimensional reconstructions of selected areas in the olfactory pathway of the fly. In doing so, they discovered a new cell type as well as several other previously unrealized insights about the organization of Drosophila’s olfactory biology.

Human telepathy using BrainNet

(Jiang et al., 2018)

EEG recordings were taken from two individuals (termed senders) while they played a Tetris-like game. Next, the recordings were converted into transcranial magnetic stimulation signals that acted to provide a third individual (called a receiver) with the necessary information to make decisions in the game without seeing the screen. The occipital cortex was stimulated. Fifteen people (five groups of three) took part in the study.
To convey their information, the senders were told to focus upon either a higher or a lower intensity light corresponding to commands within the game (the two lights were placed on different sides of the computer screen). In the receiver’s mind, this translated to perceiving a flash of light. The receiver was able to distinguish the intensities and implement the correct command within the game.
Using only the telepathically provided stimulation, the receiver made the correct game-playing decisions 81% of the time.

Transcriptomic cell type classification across mouse neocortex

(Tasic et al., 2018)

Single-cell RNA sequencing was used to characterize gene expression across 23,822 cells from the primary visual cortex and the anterior lateral motor cortex of mice.
Using dimensionality reduction and clustering methods, the resulting data were used to classify the neurons into 133 transcriptomic cell types.
Injections of adeno associated viruses (engineered to express fluorescent markers) facilitated retrograde tracing of neuronal projections within a subset of the sequenced cells. In this way, correspondences between projection patterns and transcriptomic identities were established.

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