top of page

Neuroscience Breakthroughs Since Graduate School - Part 5: Depression

Updated: Mar 2


Depressed person


Since major depressive disorder (MDD) is a heterogeneous disorder, researchers study its diverse phenotypes. Heritability ranges from 26% to 39% in twins (van Calker et al., 2021). Traditional antidepressants (ADs) exhibit a delayed onset of effect. For example, serotonin-selective reuptake inhibitors (SSRIs) are associated with clinical improvement in the first week, with decreasing gains over 6 weeks (Taylor et al., 2005). About 30-40% of patients respond to their first antidepressant trial with reduced symptom severity but not remission. The monoamine deficiency hypothesis is poorly supported by research findings, especially for serotonin. Current research has targeted seven key areas: the interaction of multiple neurotransmitter (NT) systems, decreased neurogenesis and repair, structural abnormalities, functional abnormalities, inflammation, hypothalamic-pituitary-adrenal (HPA) axis dysfunction, and reduced heart rate variability (HRV). This installment concludes with a summary of biofeedback and neurofeedback efficacy in MDD.

Click on our narrator icon to listen to this post.





Neurotransmitters in Major Depressive Disorder


Neuroscientists are increasingly adopting a systems approach, studying how NTs interact in MDD. They have mainly focused on dopamine, endocannabinoids, GABA, glutamate, norepinephrine, and serotonin. These NTs operate in concert, not in isolation (El Mansari et al., 2010).


The monoaminergic deficiency hypothesis has been poorly supported by research findings.


Advokat and colleagues (2019) provided a valuable overview in Julien's Primer of Drug Action (14th ed.):

Historically, depression was conceptualized as a deficiency in the levels of various neurotransmitters, particularly the monoamines serotonin, norepinephrine, and dopamine. It was thought that restoring the levels of these neurotransmitters to 'normal,' usually by sustaining their presence in the synaptic cleft by blocking their degradation and/or presynaptic reuptake, was responsible for their efficacy in restoring a normal mood state. These proposed physiological theories of depression and the proposed effects of various antidepressants on these transmitter systems have not held up and have been largely discarded (p. 435).

Stahl (2021) reinforced this conclusion in Stahl's Essential Psychopharmacology (5th ed.):

Thus, there is no clear and convincing evidence that monoamine deficiency accounts for depression: i.e., there is no 'real' monoamine deficit. Likewise, there is no clear and convincing evidence that abnormalities in monoamine receptors account for depression even though all the classic drugs to treat depression raise monoamine levels (p. 264).

Moncrieff and colleagues' (2022) systematic umbrella review of serotonin's role in depression likewise found:


The main areas of serotonin research provide no consistent evidence of there being an association between serotonin and depression, and no support for the hypothesis that depression is caused by lowered serotonin activity or concentrations.

Erittzoe and colleagues' (2020) finding of reduced serotonin release in response to a d-amphetamine challenge was not direct evidence of a monoamine deficit and requires replication.


Glutamate's Role in Depression


Jaso and colleagues (2017) emphasized the importance of glutamate in CNS communication and MDD pathophysiology:


It should be noted that glutamate is the major excitatory neurotransmitter in the central nervous system (CNS); it is estimated that up to 50% of CNS neurons use glutamate as their primary neurotransmitter in contrast to only 10-20% of monoaminergic neurons. In addition, both clinical and preclinical studies support the notion that glutamatergic dysfunction plays a key role in the pathophysiology of MDD, suggesting that a subsidiary role for glutamate in ketamine’s antidepressant response is unlikely.

The rapid response of treatment-resistant patients to ketamine infusion (Ketalar) and nasal spray (esketamine, Spravato) has intensified research into glutamate's role in MDD.


AMPA and NMDA receptors are two types of fast excitatory glutamate receptors. Illustration 193046428 © Juan Gaertner | Dreamstime.com


AMPA, NMDA, and GABA receptors

Modified caption: From left to right: the NMDA and AMPA receptors transport calcium cations into neurons after being activated by the neurotransmitter glutamate, and the GABA receptor right transports chloride anions after the activation by gamma-aminobutyric acid.



Glutamate binding to NMDA receptors on GABA interneurons increases GABA release and inhibits glutamate release by neurons projecting to the medial prefrontal cortex (mPFC).


Ketamine and esketamine interfere with GABA interneuron NMDA receptors. Calcium and sodium ions cannot enter the NMDA receptor and depolarize the neuron when esketamine or ketamine blocks their channel.


This reduces GABA release and increases glutamate availability in the mPFC. Researchers theorize that increased glutamate binding at mPFC AMPA receptors may mediate rapid improvement in about 70% of treatment-resistant MDD patients. ID 181293078 © Juan Gaertner | Dreamstime.com.


AMPA receptor

Modified note. Glutamate receptors are located on the membranes of neurons. The neurotransmitter glutamate (orange) activates the receptor to transport cations (red) into the neuron. This postsynaptic excitation is important for neural communication, memory formation, learning, and regulation.


Increased glutamate binding at hippocampal AMPA receptors is theorized to increase BDNF expression, promote neurogenesis, and activate newly created hippocampal neurons. Rawat and colleagues (2022) reported that ketamine activates adult-born immature granule neurons (ABINs) in mice.



Decreased Neurogenesis and Repair


Advokat et al. (2019) explained how the failure of the monoamine deficiency hypothesis led to the neurogenic theory of depression:


Another weakness of this model is that the neurotransmitter changes occur soon after drug administration, but the clinical antidepressant effect develops more slowly, often during several weeks of continuous treatment. This delay was hypothesized to be due to changes in receptor sensitivity caused by the chronic increase in synaptic levels of neurotransmitter. In the past few years, however, this view has broadened, and attention has shifted to the study of the long-term actions of antidepressant treatments on intracellular processes, such as second messengers, and their functions in the neuron.
Two of these second-messenger functions are (1) to protect neurons from damage due to injury or trauma; and (2) to promote and maintain the health and stability of newly formed neurons. Research into these processes has led to a new way of thinking about depression (and the effect of antidepressant treatment) called the neurogenic theory of depression.


Neurogenic Theory of Depression


The neurogenic theory of depression proposes that damaged neurons can repair themselves and the adult brain creates new functional neurons in the hippocampus and frontal cortex. The hippocampus participates in functions like attention, concentration, and memory, which are often compromised in depression (Advokat et al., 2019).


When depression, hypoglycemia, infection, and stress damage adult neurons in the hippocampus, neurotrophins like brain-derived neurotrophic factor (BDNF) participate in their repair (Advokat et al., 2019; Miranda et al., 2019). Hippocampus graphic © SciePro/Shutterstock.com.


Hippocampus


We summarized the evidence for neurogenesis in Neuroscience Breakthroughs Since Graduate School - Part 4: Neurons. For a refresher, although there is a consensus on adult human hippocampal neurogenesis (Eriksson et al., 1998; Jurkowski et al., 2020; Planchez et al., 2020), cortical neurogenesis remains controversial.


Advokat and colleagues (2023) view depression as a neurodegenerative disorder. They observed that 50% of depressed patients exhibit abnormal physiological stress responses. Hippocampal neurons are highly vulnerable to changes like chronically elevated glucocorticoid levels due to their high density of cortisol receptors (Kim et al., 2015). BDNF levels are depressed in depressed patients (Neto et al., 2011).


Antidepressants appear to increase serotonin and norepinephrine levels, increasing BDNF expression (Launay et al., 2011; Molteni et al., 2006; Neto et al., 2011). There is evidence that SSRIs bind to specialized BDNF (TRK) receptors that promote BDNF signaling.


Stress reduction can reduce glucocorticoids, increasing the growth of neurites (e.g., dendrites and axons), cell survival, and hippocampal volumes (Advokat et al., 2023).


Structural Abnormalities


There is neuroimaging and postmortem evidence of neuron and glial volume reductions in the cingulate cortex, hippocampus, and prefrontal cortex (Durman et al., 2016; Elbejjani et al., 2015; Sarawagi et al., 2021). Shrinkage may be due to neuronal insults, elevated cortisol, decreased BDNF, and reduced neurogenesis in the hippocampus.


Functional Abnormalities


The right prefrontal cortex is more activated than the left in MDD (Kaya & McCabe, 2019). Studies of amygdala blood flow have been inconclusive (Peluso et al., 2009).


Regions Targeted By Neurofeedback Protocols


Clinicians have evaluated alpha-asymmetry and functional MRI (fMRI) protocols to treat MDD. Since alpha is an "idling frequency," this asymmetry is seen when the alpha amplitude is greater in the left (F3) than in the right frontal lobe (F4). Alpha-asymmetry neurofeedback protocols for depression attempt to correct this imbalance, decreasing left frontal alpha with respect to right frontal alpha (Choi et al., 2011). Functional MRI protocols target the ventromedial prefrontal cortex, insula, dorsolateral prefrontal cortex, medial temporal lobe, or orbitofrontal cortex (Linden et al., 2012).



Regions Targeted By Transcranial Magnetic Stimulation


The Stanford accelerated intelligent neuromodulation therapy (SAINT) system uses a resting-state functional connectivity MRI (fcMRI) to pinpoint the optimal left dorsolateral prefrontal cortex (DLPFC) region for transcranial magnetic stimulation. DLPFC graphic © Songkram Chotik-anuchit/Shutterstock.com.

DLPFC

They target the left DLPFC subregion most anti-correlated with each participant's subgenual anterior cingulate cortex (sgACC).


The goal is to increase left DLPFC control of the subgenual cingulate cortex, which is over-activated in MDD (Cole et al., 2020).




Inflammation


Rege (2022) concluded that there is a bidirectional relationship between depression and inflammation. There are increased rates of depression in patients diagnosed with autoimmune disorders. Moreover, patients with inflammatory conditions have an increased depression risk.


Pasco and colleagues (2010) stated:

Systemic immune activation has been documented in major depression, with reports of increased levels of pro-inflammatory cytokines and changes in the acute phase protein response, notably enhancement of positive and diminution of negative acute phase proteins. C-reactive protein (CRP) is a positive acute response protein that marks systemic inflammation. Elevated levels of circulating CRP have been found in depression in both clinical and population studies (p. 372).

Serotonin and glutamate regulate inflammation. Antidepressant (i.e., SSRI and SNRI) modulation of neuroinflammation may contribute to their efficacy (Dionisie et al., 2021).



Hypothalamic-Pituitary-Adrenal (HPA) Axis Dysfunction


The hypothalamic-pituitary-adrenal (HPA) axis is the second stage of the body's defense. The HPA axis is the foundation of allostasis and its failure, allostatic overload (McEwen, 2002). The HPA axis releases the hormones CRH, ACTH (corticotropin), and cortisol. This cascade starts with signals from the amygdala to the hypothalamus and ultimately targets the adrenal glands located at the top of each kidney.


The adrenal cortex, the adrenal gland's outer region, produces the hormones aldosterone and cortisol. Cortisol is the most important glucocorticoid. Cortisol levels peak from 20-40 minutes following a stressor (Brannon et al., 2022). Adrenal gland graphic © Designua/Shutterstock.com.

adrenal gland

This pathway is regulated by negative feedback as rising cortisol levels inhibit hormone secretion by the hypothalamus and anterior pituitary. Graphic © Designua/Shutterstock.com.

stress response system

Sustained elevated cortisol levels can affect mood and produce system-wide damage. It is hypothesized that cortisol can activate peripheral immune cells whose inflammatory signaling can stimulate their counterparts in the brain. This could disrupt neurotransmitter action and threaten neuronal (and glial?) survival through excitotoxicity (Afridi & Suk, 2021; Otte et al., 2016). Graphic © medicalstocks/Shutterstock.com.

Cortisol effects


HPA axis dysfunction is not observed in all MDD patients. In some MDD patients, stressors may trigger a cascade that begins with excessive hypothalamic CRH release, resulting in elevated cortisol levels and a breakdown in the negative feedback mechanism that regulates CRH (Dean & Keshavan, 2017; Rege, 2022). In the neurogenic theory of depression, we previously discussed, sustained high cortisol levels suppress BDNF expression and neurogenesis.


Reduced Heart Rate Variability


HRV is the organized fluctuation of time intervals between successive heartbeats defined as interbeat intervals (Shaffer, Meehan, & Zerr, 2020).

Interbeat interval graphic © arka38/Shutterstock.com.


HRV

Depressed patients are twice as likely as non-depressed individuals to have lower HRV (16% vs. 7%). Lower HRV is a strong independent predictor of post-MI death (Carney et al., 2001). HRVB might reduce anxiety and depression, which are associated with low vagal activity because it increases vagal tone. From Friedman’s (2007) perspective, the problem is not “a sticky accelerator.” HRVB may fix “bad brakes” (p. 186).


Reduced HRV may predict disease and mortality because it indexes reduced regulatory capacity, which is the ability to surmount challenges like exercise and stressors adaptively. Patient age may be an essential link between reduced HRV and regulatory capacity since HRV and nervous system function decline with age (Shaffer, McCraty, & Zerr, 2014).


Reduced HRV is also seen in disorders with autonomic dysregulation, including anxiety and depressive disorders, asthma, and vulnerability to sudden infant death (Agelink et al., 2002; Carney et al., 2001; Cohen & Benjamin, 2006; Giardino, Chan, & Borson, 2004; Kazuma, Otsuka, Matsuoka, & Murata, 1997). Lehrer (2007) believes that HRV indexes adaptability and marshals evidence that increased RSA represents more efficient regulation of BP, HR, and gas exchange by synergistic control systems.



Biofeedback and Neurofeedback Efficacy in Major Depressive Disorder


Biofeedback and neurofeedback are evidence-based treatments for MDD. Meehan, Shaffer, and Zerr (in press) assigned a level-5 rating of efficacious and specific.


HRV biofeedback was supported by randomized controlled trials (RCTs) for participants diagnosed with MDD following heart surgery (Patron et al., 2013) and those with unipolar depression (Caldwell & Steffen, 2018).


RCTs validated alpha-asymmetry (Choi et al., 2011) and fMRI (Jaeckle et al., 2019; Young et al., 2014) neurofeedback protocols for MDD patients.


NF and BF were superior to bona fide treatments in at least two RCTs, meeting LaVaque and colleagues’ (2002) criteria for Level 5 – Efficacious and Specific (Meehan, Shaffer, & Zerr, in press).

Evidence-Based Practice in Biofeedback and Neurofeedback (4th ed.)


Summary


The monoamine deficiency hypothesis is poorly supported by research findings, especially for serotonin. Current research has targeted seven key areas: the interaction of multiple neurotransmitter (NT) systems, decreased neurogenesis and repair, structural abnormalities, functional abnormalities, inflammation, and reduced heart rate variability (HRV). Depression is a neurodegenerative disorder, characterized by progressive damage to neurons and glial cells. Multiple processes, including stress, elevated glucocorticoid levels, and inflammation, interfere with cellular repair and hippocampal neurogenesis. Hypothalamic-pituitary-adrenal (HPA) axis dysfunction may be observed in a subset of MDD patients. Reduced vagal tone may compromise MDD patients' ability to cope with stressors, reduce inflammation, and maintain homeostasis. The Stanford accelerated intelligent neuromodulation therapy (SAINT) system has received FDA approval for patients diagnosed with treatment-resistant depression. In addition, HRV biofeedback and alpha-asymmetry, and fMRI neurofeedback have earned level-5 ratings of efficacious and specific.



Quiz


Take a five-question exam on Quiz Maker to test your mastery.



Glossary

alpha-asymmetry neurofeedback: a protocol that trains depressed clients to relax and warm their hands using diaphragmatic breathing and autogenic phrases and then decrease left frontal alpha with respect to the right frontal alpha.

AMPA receptor: fast excitatory glutamate receptor.

anterior cingulate cortex (ACC): a part of the prefrontal cortex that plays an important role in drug craving and the Go system that predominates during the preoccupation/anticipation stage of addiction. The ACC plays an important role in attention and working memory and mediates emotional and physical pain.

apoptosis: programmed cell death.

brain-derived neurotrophic factor (BDNF): a member of the neurotrophin family of growth factors that supports neuronal survival and growth.


C-reactive protein: a systemic inflammation that is elevated in depression.


corticotropin-releasing hormone (CRH): the hypothalamic hormone that triggers adrenal cortex glucocorticoid release.

cortisol: a glucocorticoid produced by the adrenal cortex that helps convert fat and protein to glucose, reduces inflammation, and participates in apoptosis.

craving: intense motivation to acquire and ingest a drug, often triggered by cues.


dopamine: monoamine neurotransmitter released by the ventral tegmental area in response to natural reinforcers (e.g., food or sex) or self-administering a drug.

dorsolateral prefrontal cortex (DLPFC): the DLPFC is concerned with executive functioning and regulating cognitive processes. DLPFC activation promotes craving by intensifying the response to drug-related cues.

esketamine: an S(+) ketamine enantiomer marketed as SPRAVATO®. Esketamine is an NMDA (N-methyl-D-aspartate) receptor antagonist used to treat adults with treatment-resistant depression.


executive functions: the cognitive processes that control attention, inhibition of behavior, working memory, cognitive flexibility, reasoning, problem-solving, and planning.


functional MRI (fMRI) neurofeedback: real-time functional MRI neurofeedback training to increase the metabolism of brain regions that mediate positive affect.

GABA: the primary inhibitory amino acid neurotransmitter in the nervous system.

GABA receptor: ionotropic and metabotropic receptors for GABA.


glutamate: an amino acid neurotransmitter that promotes craving when released by descending prefrontal cortex pathways targeting the nucleus accumbens and ventral tegmental area.


heart rate variability (HRV): the organized fluctuation of time intervals between successive heartbeats defined as interbeat intervals.

hippocampus (HPC): a limbic structure in the medial temporal lobe involved in 4-7 Hz theta activity, control of the endocrine system’s response to stressors, formation of explicit memories, and navigation. The HPC influences craving by providing affective state, context, and stress information.


hypothalamic-pituitary-adrenal (HPA) axis: a hormonal cascade that starts with signals from the amygdala to the hypothalamus and ultimately targets the adrenal glands, releasing the hormones CRH, ACTH (corticotropin), and cortisol.

ketamine: a dissociative anesthetic marketed as Ketalar and an NMDA (N-methyl-D-aspartate) receptor antagonist used to treat adults with treatment-resistant depression.

medial orbitofrontal cortex (mOFC): a prefrontal cortex subdivision that monitors the reward value of reinforcers and mediates learning and memory about our reinforcement experiences.


medial prefrontal cortex: a midline prefrontal cortex region that exercises top-down control of attention, behavioral inhibition, habits, and working, spatial, and long-term memory; a possible target of ketamine and esketamine antidepressant treatment.


monoamine: the catecholamines dopamine, adrenaline, noradrenaline, and indoleamine serotonin.

monoaminergic deficiency hypothesis: the proposition that depression is caused by monoamine deficiency or imbalance.

neural stem cells: multipotent cells that can differentiate into neurons, astrocytes, and oligodendrocytes.


neurodegenerative disorders: diseases in which neurons and glial cells progressively lose their functions and structural integrity, and die.

neurogenesis: creating new neurons from stem cells in the cortex and hippocampus.


neurogenic theory of depression: the position that impaired hippocampal neurogenesis contributes to depression. NMDA receptor: a fast excitatory glutamate receptor.

nucleus accumbens (NAC): a ventral striatal structure targeted by dopamine released by the mesolimbic pathway. The NAC is involved in acute pleasure, reward, and the Go system.

orbitofrontal cortex (OFC): a frontal lobe subdivision that integrates sensory information, modulates visceral reactions, and participates in learning, predicting outcomes, and making choices regarding emotional and reward-related behaviors. the frontal lobe subdivision involved in the Stop system and craving. The OFC decodes stimuli's punishment and reward value and helps inhibit inappropriate behavior.


resting-state functional connectivity MRI (fcMRI): functional magnetic resonance imaging (fMRI) method used to evaluate regional interactions.


subgenual anterior cingulate cortex (sgACC): the frontal aspect of the cingulate cortex, corresponding to Brodmann area 25, that is over-activated in MDD.


transcranial magnetic stimulation (TMS): a noninvasive treatment that delivers brief magnetic pulses to stimulate neurons in targeted brain areas (e.g., the SAINT system).


vagal tone: parasympathetic nervous system regulation of cardiac conduction.

ventromedial prefrontal cortex (vmPFC): a frontal lobe subdivision involved in emotional control, inhibition of action, empathy, episodic and semantic memory, and economic decision-making. Hypofrontality of the vmPFC renders the brain more vulnerable to cue-induced craving and drug use.



References


Advokat, Claire, D., Comaty, J. E., & Julien, R. M. (2019). Julien's primer of drug action (14th ed.). Macmillan Higher Education.


Afridi, R., & Suk, K. (2021). Neuroinflammatory basis of depression: Learning from experimental models. Frontiers in Cellular Neuroscience, 15, 691067. https://doi.org/10.3389/fncel.2021.691067


Agelink, M., Boz, C., Ullrich, H., & Andrich, J. (2002). Relationship between major depression and heart rate variability. Clinical consequences and implications for antidepressive treatment. Psychiatry Research, 113(1), 139-149. https://doi.org/10.1016/s0165-1781(02)00225-1


Brannon, L., Feist, J., & Updegraff, J. A. (2022). Health psychology (10th ed.). Cengage Learning.

Caldwell, Y. T., & Steffen, P. R. (2018). Adding HRV biofeedback to psychotherapy increases heart rate variability and improves the treatment of major depressive disorder. International Journal of Psychophysiology, 131, 96-101. https://doi.org/10.1016/j.ijpsycho.2018.01.001


Carney, R. M., Blumenthal, J. A., Stein, P. K., Watkins, L., Catellier, D., Berkman, L. F., Czajkowskim S. M., O'Connor, C., Stone, P. H., & Freedland, K. E. (2001). Depression, heart rate variability, and acute myocardial infarction. Circulation, 104(17), 2024-2028. https://doi.org/10.1161/hc4201.097834

Casarotto, P. C., Girych, M., Fred, S. M., Kovaleva, V., Moliner, R., Enkavi, G., Biojone, C., Cannarozzo, C., Sahu, M. P., Kaurinkoski, K., Brunello, C. A., Steinzeig, A., Winkel, F., Patil, S., Vestring, S., Serchov, T., Diniz, C. R. A. F., Laukkanen, L., Cardon, I., Antila, H., … Castrén, E. (2021). Antidepressant drugs act by directly binding to TRKB neurotrophin receptors. Cell, 184(5), 1299–1313.e19. https://doi.org/10.1016/j.cell.2021.01.034


Choi, S. W., Chi, S. E., Chung, S. Y., Kim, J. W., Ahn, C. Y., & Kim, H. T. (2011). Is alpha wave neurofeedback effective with randomized clinical trials in depression? A pilot study. Neuropsychobiology, 63(1), 43–51. https://doi.org/10.1159/000322290


Cohen, H., & Benjamin, J. (2006). Power spectrum analysis and cardiovascular morbidity in anxiety disorders. Autonomic Neuroscience: Basic and Clinical, 128, 1–8. https://doi.org/10.1016/j.autneu.2005.06.007

Cole, E. J., Stimpson, K. H., Bentzley, B. S., Gulser, M., Cherian, K., Tischler, C., Nejad, R., Pankow, H., Choi, E., Aaron, H., Espil, F. M., Pannu, J., Xiao, X., Duvio, D., Solvason, H. B., Hawkins, J., Guerra, A., Jo, B., Raj, K. S., Phillips, A. L., … Williams, N. R. (2020). Stanford Accelerated Intelligent Neuromodulation Therapy for treatment-resistant depression. The American Journal of Psychiatry, 177(8), 716–726. https://doi.org/10.1176/appi.ajp.2019.19070720

Das, J. (2020). Repurposing of drugs-The ketamine story. Journal of Medicinal Chemistry, 63(22), 13514–13525. https://doi.org/10.1021/acs.jmedchem.0c01193

Dean, J., & Keshavan, M. (2017). The neurobiology of depression: An integrated view. Asian Journal of Psychiatry, 27, 101–111. https://doi.org/10.1016/j.ajp.2017.01.025


Dionisie, V., Filip, G. A., Manea, M. C., Manea, M., & Riga, S. (2021). The anti-inflammatory role of SSRI and SNRI in the treatment of depression: A review of human and rodent research studies. Inflammopharmacology, 29(1), 75–90. https://doi.org/10.1007/s10787-020-00777-5


Duman, R. S., Aghajanian, G. K., Sanacora, G., & Krystal, J. H. (2016). Synaptic plasticity and depression: new insights from stress and rapid-acting antidepressants. Nature Medicine, 22(3), 238–249. https://doi.org/10.1038/nm.4050

Elbejjani, M., Fuhrer, R., Abrahamowicz, M., Mazoyer, B., Crivello, F., Tzourio, C., & Dufouil, C. (2015). Depression, depressive symptoms, and rate of hippocampal atrophy in a longitudinal cohort of older men and women. Psychological medicine, 45(9), 1931–1944. https://doi.org/10.1017/S0033291714003055

El Mansari, M., Guiard, B. P., Chernoloz, O., Ghanbari, R., Katz, N., & Blier, P. (2010). Relevance of norepinephrine-dopamine interactions in the treatment of major depressive disorder. CNS Neuroscience & Therapeutics, 16(3), e1–e17. https://doi.org/10.1111/j.1755-5949.2010.00146.x


Eriksson, P. S., Perfilieva, E., Björk-Eriksson, T., Alborn, A. M., Nordborg, C., Peterson, D. A., & Gage, F. H. (1998). Neurogenesis in the adult human hippocampus. Nature Medicine, 4(11), 1313–1317. https://doi.org/10.1038/3305


Erritzoe, D., Ashok, A. H., Searle, G. E., Colasanti, A., Turton, S., Lewis, Y., Huiban, M., Moz, S., Passchier, J., Saleem, A., Beaver, J., Lingford-Hughes, A., Nutt, D. J., Howes, O. D., Gunn, R. N., Knudsen, G. M., & Rabiner, E. A. (2020). Serotonin release measured in the human brain: a PET study with [11C]CIMBI-36 and d-amphetamine challenge. Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology, 45(5), 804–810. https://doi.org/10.1038/s41386-019-0567-5

Friedman, B. H. (2007). An autonomic flexibility-neurovisceral integration model of anxiety and cardiac vagal tone. Biol Psychol. 74, 185–199. https://doi.org/10.1016/j.biopsycho.2005.08.009


Giardino, N. D., Chan, L., & Borson, S. (2004). Combined heart rate variability and pulse oximetry biofeedback for chronic obstructive pulmonary disease. Applied Psychophysiology and Biofeedback, 29(2), 121-133. https://doi.org/10.1023/b:apbi.0000026638.64386.89


Jaeckle, T., Williams, S. C., Barker, G. J., Basilio, R., Carr, E., Goldsmith, K., Colasanti, A., Giampietro, V., Cleare, A., Young, A., Moll, J., & Zahn, R. (2019). Self-blaming emotions in major depression: A randomised pilot trial comparing fMRI neurofeedback training with self-guided psychological strategies (NeuroMooD). medRxiv, 19004309. https://doi.org/10.1101/19004309


Jaso, B. A., Niciu, M. J., Iadarola, N. D., Lally, N., Richards, E. M., Park, M., Ballard, E. D., Nugent, A. C., & Machado-Vieira, R. (2017). Therapeutic modulation of glutamate receptors in Major Depressive Disorder. Current Neuropharmacology, 15(1), 57-70. https://doi.org/10.2174/1570159X14666160321123221


Jurkowski, M. P., Bettio, L., K Woo, E., Patten, A., Yau, S. Y., & Gil-Mohapel, J. (2020). Beyond the hippocampus and the SVZ: Adult neurogenesis throughout the brain. Frontiers in Cellular Neuroscience, 14, 576444. https://doi.org/10.3389/fncel.2020.576444 Kazuma, N., Otsuka, K., Matsuoka, I., & Murata, M. (1997). Heart rate variability during 24 hours in asthmatic children. Chronobiol Int, 14, 597–606. https://doi.org/10.3109/07420529709001450

Kaya, S., & McCabe, C. (2019). What role does the prefrontal cortex play in the processing of negative and positive stimuli in adolescent depression? Brain Sciences, 9(5), 104. https://doi.org/10.3390/brainsci9050104

Kim, E. J., Pellman, B., & Kim, J. J. (2015). Stress effects on the hippocampus: A critical review. Learning & Memory, 22(9), 411–416. https://doi.org/10.1101/lm.037291.114


LaVaque, T. J., Hammond, D. C., Trudeau, D., Monastra, V., Perry, J., Lehrer, P., Matheson, D., & Sherman, R. (2002). Template for developing guidelines for the evaluation of the clinical efficacy of psychophysiological evaluations. Applied Psychophysiology and Biofeedback, 27(4), 273-281. https://dx.doi.org/10.1023/A:1021061318355

Lehrer, P. M. (2007). Biofeedback training to increase heart rate variability. In P. M. Lehrer, R. M. Woolfolk, & W. E. Sime (Eds.). Principles and practice of stress management (3rd ed.). The Guilford Press.

Linden, D. E., Habes, I., Johnston, S. J., Linden, S., Tatineni, R., Subramanian, L., Sorger, B., Healy, D., & Goebel, R. (2012). Real-time self-regulation of emotion networks in patients with depression. PloS one, 7(6), e38115. https://doi.org/10.1371/journal.pone.0038115

McEwen, B. S. (2002). The end of stress as we know it. Joseph Henry Press.


Molteni, R., Calabrese, F., Bedogni, F., Tongiorgi, E., Fumagalli, F., Racagni, G., & Riva, M. A. (2006). Chronic treatment with fluoxetine up-regulates cellular BDNF mRNA expression in rat dopaminergic regions. The International Journal of Neuropsychopharmacology, 9(3), 307–317. https://doi.org/10.1017/S1461145705005766

Moncrieff, J., Cooper, R. E., Stockmann, T., Amendola, S., Hengartner, M. P., & Horowitz, M. A. (2022). The serotonin theory of depression: A systematic umbrella review of the evidence. Mol Psychiatry. https://doi.org/10.1038/s41380-022-01661-0


Neto, F. L., Borges, G., Torres-Sanchez, S., Mico, J. A., & Berrocoso, E. (2011). Neurotrophins role in depression neurobiology: A review of basic and clinical evidence. Current Neuropharmacology, 9(4), 530–552. https://doi.org/10.2174/157015911798376262


Otte, C., Gold, S. M., Penninx, B. W., Pariante, C. M., Etkin, A., Fava, M., Mohr, D. C., & Schatzberg, A. F. (2016). Major depressive disorder. Nature Reviews. Disease Primers, 2, 16065. https://doi.org/10.1038/nrdp.2016.65


Pasco, J. A., Nicholson, G. C., Williams, L. J., Jacka, F. N., Henry, M. J., Kotowicz, M. A., Schneider, H. G., Leonard, B. E., & Berk, M. (2010). Association of high-sensitivity C-reactive protein with de novo major depression. The British Journal of Psychiatry: The Journal of Mental Science, 197(5), 372–377. https://doi.org/10.1192/bjp.bp.109.076430

Patron, E., Messerotti Benvenuti, S., Favretto, G., Valfre, C., Bonfa, C., Gasparotto, R., & Palomba, D. (2013). Biofeedback assisted control of respiratory sinus arrhythmia as a biobehavioral intervention for depressive symptoms in patients after cardiac surgery: A preliminary study. Applied Psychophysiology and Biofeedback, 38, 1-9. https://doi.org/10.1007/s10484-012-9202-5


Peluso, M. A., Glahn, D. C., Matsuo, K., Monkul, E. S., Najt, P., Zamarripa, F., Li, J., Lancaster, J. L., Fox, P. T., Gao, J. H., & Soares, J. C. (2009). Amygdala hyperactivation in untreated depressed individuals. Psychiatry Research, 173(2), 158–161. https://doi.org/10.1016/j.pscychresns.2009.03.006

Pitsillou, E., Bresnehan, S. M., Kagarakis, E. A., Wijoyo, S. J., Liang, J., Hung, A., & Karagiannis, T. C. (2020). The cellular and molecular basis of major depressive disorder: Towards a unified model for understanding clinical depression. Molecular Biology Reports, 47(1), 753–770. https://doi.org/10.1007/s11033-019-05129-3


Planchez, B., Surget, A., & Belzung, C. (2020). Adult hippocampal neurogenesis and antidepressants effects. Current Opinion in Pharmacology, 50, 88–95. https://doi.org/10.1016/j.coph.2019.11.009

Rawat, R., Tunc-Ozcan, E., McGuire, T. L., Peng, C. Y., & Kessler, J. A. (2022). Ketamine activates adult-born immature granule neurons to rapidly alleviate depression-like behaviors in mice. Nature Communications, 13(1), 2650. https://doi.org/10.1038/s41467-022-30386-5


Rege, S. (2022). Neurobiology of depression – A simplified guide. psych scene hub.


Sarawagi, A., Soni, N. D., & Patel, A. B. (2021). Glutamate and GABA homeostasis and neurometabolism in Major Depressive Disorder. Frontiers in Psychiatry, 12, 637863. https://doi.org/10.3389/fpsyt.2021.637863

Shaffer, F., McCraty, R., & Zerr, C. L. (2014). A healthy heart is not a metronome: An integrative review of the heart’s anatomy and heart rate variability. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2014.01040

Shaffer, F., Meehan, Z. M., & Zerr, C. L. (2020). A critical review of ultra-short-term heart rate variability norms research. Frontiers in Neuroscience.


Taylor, M. J., Freemantle, N., Geddes, J. R., & Bhagwagar, Z. (2006). Early onset of selective serotonin reuptake inhibitor antidepressant action: Systematic review and meta-analysis. Archives of General Psychiatry, 63(11), 1217–1223. https://doi.org/10.1001/archpsyc.63.11.1217


van Calker, D., & Serchov, T. (2021). The "missing heritability"-Problem in psychiatry: Is the interaction of genetics, epigenetics and transposable elements a potential solution? Neuroscience and Biobehavioral Reviews, 126, 23–42. https://doi.org/10.1016/j.neubiorev.2021.03.019


van Noordt, S. J., & Segalowitz, S. J. (2012). Performance monitoring and the medial prefrontal cortex: A review of individual differences and context effects as a window on self-regulation. Frontiers in Human Neuroscience, 6, 197. https://doi.org/10.3389/fnhum.2012.00197


Yang, T., Nie, Z., Shu, H., Kuang, Y., Chen, X., Cheng, J., Yu, S., & Liu, H. (2020). The role of BDNF on neural plasticity in depression. Frontiers in Cellular Neuroscience, 14, 82. https://doi.org/10.3389/fncel.2020.00082 Young, K. D., Zotev, V., Phillips, R., Misaki, M., Yuan, H., Drevets, W. C., & Bodurka, J. (2014). Real-time fMRI neurofeedback training of amygdala activity in patients with Major Depressive Disorder. PLoS One, https://doi.org/0.1371/journal.pone.0088785



Feedback



We value your feedback because we produce these posts for you. Please complete our brief survey to help us improve this service.



Learn More


A Review of the Networks


Neurofeedback Tutor


qEEG100


Physiological Psychology



2. loss of time anchors (e.g., commuting to work)

Comments

Rated 0 out of 5 stars.
No ratings yet

Add a rating
bottom of page