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Normal Physiology

Normal physiology of patient’s mood, perception, emotion and behavior focuses majorly on neurotransmitters in the brain. There are over 46 neurotransmitters in the brain and many have more than one function. Neurotransmitters are chemical messengers that are released and received by synapses of neurons to mediate intracellular communication in the nervous system. They use electrical signals to stimulate messages along the neurons where it affects ion channels and eventually performs a specific mechanism at a site of action (McCance & Huether, 2014).

Serotonin is involved with mood, happiness, anxiety, and sleep induction. Raphe-Serotonin System normally modulates homeostasis, emotionality, and tolerance to aversive experiences. Norepinephrine in the brain helps regulate alertness, mood, functions in dream sleep, and maintains arousal. It also can help in the response to stressful situations. The locus ceruleus has a group of norepinephrine containing cells implicated in global psychologic processes including attention, vigilance and orientation to stimuli. Dopamine in the brain regulates reward and motivation which could explain the loss of interest in patients with depression. Dopamine motivates people to take action toward goals, desires, and needs, and issues a surge of reinforcing pleasure once they’ve been accomplished (Garcia-Arocena). Sufficient levels are needed for the brain to function properly and decreased levels have been found in patients with depression (McCance & Huether, 2014).

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Major Depressive Disorder

Major depression is classified as a unipolar mood disorder. Further, major depression can be classified as when an emotional state, such as sadness, becomes chronic and uncontrollable. It is the most common mood disorder (McCance & Huether, 2014). Mood disorders are still being studied due to the unclear nature of how they occur due to the difficult availability of human brain tissue for neurochemical measurement until patients are post-mortem. Each of the dysfunctions below focuses on what are thought to be the causes of major depressive disorder.

Pathophysiology: Genetic Predisposition and Environmental Influences

There is a genetic predisposition in major depression that runs in families. However, due to the large variance in symptoms, developmental and environmental factors also must be evaluated in the contributing factors to major depression. One view of mood disorders includes the connection between susceptible genes and environmental influence. The combination of life stressors and a potentially dysfunctional serotonin (5-HT) system. The serotonin transporter serves in the reuptake of serotonin at the synapse and may moderate the serotonergic response to stress. Individuals with 2 copies of the s allele were more likely to develop major depression and have suicidal thoughts in response to stressors than individuals homozygous for the l allele. As well as, individuals with 2 s alleles increased their risk for major depression episodes by twofold after experiencing 4 or more stressful events (McCance & Huether, 2014).

Pathophysiology: Neurochemical dysregulation

There are antidepressant drugs that can increase neurotransmitters in the body leading to another theory called the monoamine hypothesis of depression. In this hypothesis, there is a deficit in the concentration of the brain norepinephrine, dopamine, and/or serotonin resulting in depression. Antidepressant therapies focus on increasing the monoamine neurotransmitter levels within the synapses (McCance & Huether, 2014).

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Pathophysiology: Neuroendocrine Dysregulation

There are 2 theories in the pathophysiology of depression that involve dysregulation of the neuroendocrine system. The first one focuses on stress and the hypothalamic-Pituitary-Adrenal system. The hypothalamic-pituitary-adrenal system (HPA) plays an essential role in an individual’s ability to cope with stress. Chronic activation of the HPA system and chronic glucocorticoid secretion are found in 30-70% of individuals with major depression suggesting the correlation between the dysfunctional system and depression. Chronic cortisol release in the body results in secretion of pro-inflammatory cytokines which causes immunosuppression and inflammation. Also, there is a Neurotrophic Hypothesis of depression. It is thought to focus on neuronal atrophy of the hippocampus resulting in no cell growth consequently causing in a reduction of the hippocampal brain derived neurotrophic factor (BDNF) and has been proposed as an extension of the monoamine hypothesis of depression.

The second neuroendocrine dysregulation is in the hypothalamic-pituitary-thyroid system. While this dysfunction is not completely understood, 20-30% cases of major depression have shown to have an altered hypothalamic-pituitary-thyroid (HPT) system. There is an increase in thyrotropin releasing hormone, blunted thyroid stimulating hormone in response to TRH challenge and decreased nocturnal rise in TSH level that normally occur. This all increases risk for relapse (McCance & Huether, 2014).

Pathophysiology: Neuroanatomic and Function Abnormalities

Depressed individuals post-mortem brains have shown widespread decrease in serotonin 5-HT1a receptor subtype binding in the frontal, temporal, and limbic cortex as well as serotonin transporter binding in the cerebral cortex and hippocampus, reflecting a dysfunction in the raphe-serotonin system. The activation of the locus ceruleus-norepinephrine system is capable of inhibiting the raphe-serotonin system. This suggests an indirect role in the modulation of serotonin function. Norepinephrine receptor alterations are found in the frontal cortex of some suicide victims with depression. Alterations in norepinephrine systems may be linked to attention or concentration difficulties as well as sleep and arousal disturbances in depression

Alterations in frontal and limbic regions (such as the amygdala) have shown a decreased number of glial cells in people with unipolar disorders. As well as, a decreased prefrontal cortex functioning and decreased frontal lobe volume.

Depressed individuals have also been found to have abnormalities in Cerebral blood flow and glucose metabolism. Dorsolateral prefrontal abnormalities in depression may be responsible for the retardation in cognitive processing and speech deficits similar to those found in schizophrenia. Dorsomedial frontal dysfunction may be associated with mnemonic and attentional impairments that accompany mood disorders. The frontal brain has increased blood flow and metabolism. It is positively related to negative affect in depressed individuals (McCance & Huether, 2014).

Clinical Manifestations/Diagnostic Criteria

To diagnosis depression, symptoms must be present for at least two weeks. There are unremitting feelings of sadness and despair. Depressive episodes may occur or recur suddenly, gradually or continue from a few weeks to months. Twenty percent of all people with depression exhibit chronic forms of depression. Symptoms vary widely depending on the individual. The timing and length of the depression also varies.

To be diagnosed with Major Depressive Disorder, patients have to have several, usually five or more, symptoms including low mood that is present for at least two weeks (Depression, 2018). Other symptoms of major depressive disorder include:

  • Depressed or irritable mood
  • Loss of interests and pleasures – this includes interpersonal relationships
  • Significant weight gain or loss (5%) in a month
  • Sleep Disturbances: Insomnia/Hypersomnia
  • Psychomotor agitation or retardation: Restlessness or agitation can occur
  • Fatigue or loss of energy
  • Feelings of worthlessness or excessive guilt: Pessimistic/Negative outcomes are perceived
  • Poor concentration or indecisiveness
  • Recent thoughts of suicide/death: Suicidal risk increases with depression

Garcia-Arocena, D. (n.d.). Happy or SAD: The chemistry behind depression. Retrieved October 29, 2018, from https://www.jax.org/news-and-insights/jax-blog/2015/december/happy-or-sad-the-chemistry-behind-depression

LA NEUROSCIENZA DEL CERVELLO CON ADHD. (2018, September 17). Retrieved from https://mondoadhd.blog/2018/09/17/la-neuroscienza-del-cervello-adhd/

(n.d.). Retrieved October 29, 2018, from https://www.nature.com/articles/nrdp201665/figures/3

What are neurotransmitters? (2017, November 09). Retrieved from https://qbi.uq.edu.au/brain/brain-physiology/what-are-neurotransmitters

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Case Study: Karen's Story of Major Depressive Disorder

Profile image of Nakia Melecio

Major depression seems to be reaching epidemic proportions with the alarming capability for affecting a person's quality of life. Whether it’s work, home, or somewhere in between, if not managed well, depression can actually be quite debilitating, even destructive, depending on its scope and severity (Posternak & Miller, 2001). The cause of major depression is multifaceted, which includes but is not limited to the result of a diagnosis of a serious illness, loss of a loved one, or job loss. In any case, this is a serious condition that should not be taken lightly, considering all implications. It is also known to be hereditary in some cases, while in others where a constant, pervasive low mood persists, this disabling condition is observed and clinically diagnosed as major depressive disorder, which often entails loss of interest or pleasure in normal functioning and adversely impacts overall health and wellness (Posternak & Miller, 2001).

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Depression also known as Major Depressive Disorder or Clinical Depression, “refers not just to a syndrome, with all of its technical definitions, but also to an affective state that we have each experienced: a state of sadness, depletion, deflation, emptiness, hopelessness, boredom” (Berzoff & Mendez, 2011, 373). With this disorder being a very common mental illness, it often places a stigma on ethnicities, gender, sexuality, and religion. This stigma may also lead to the misdiagnosis or lack of diagnosis to certain individuals. With the feelings, thoughts, and behavior that contribute to this disorder; it is important that depressive symptoms are adequately treated.

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Depression can be characterized as a state of mental and physical lethargy in which sufferers report irritability, lack of focus, little or no motivation, lack of interest in previously enjoyed activities, sleep and appetite disturbances, hopelessness, and desired social isolation [1]. It is the second leading of cause of disability worldwide and #1 in the United States of America (USA). In the USA, there is an estimated $83 billion in economic burden due to depression yearly; 31 % direct medical costs, 7 % suicide-related mortality costs, and 62 % workplace associated costs [2]. While the economic costs to society are staggering, the personal cost of depression to the individual and on one’s social network is dramatic. If untreated, depression can result in substantial impairment in daily functioning. Depression, even at subclinical levels, can lead to substance use and/or abuse, failed or abusive relationships, or loss of employment. Children exposed to depressed parent(s) have an...

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Depression is one of the most common psychological diseases, and as it happens for many other, the most widely used form of therapy is drugs: about one in every 10 Americans takes an antidepressant and the percent is more than double among women, especially in their 40s and 50s. Although the most widespread this treatment is not at all the better, for both its side effects and the fact it acts only on the symptoms, without in any way solving the causes, thereby forcing the patient to become drug-dependent for years, and sometimes for life. Counseling and psychotherapy treatment can lead instead to the identification and resolution of the causes but have limitations as well, including an apparently higher cost than drugs and a level of effectiveness not always optimal regarding the traditional psychological treatments but significantly increasing if integrative (or better holistic) approaches are applied. From a holistic perspective every disease, especially if chronic, is a message whose purpose is to inform our consciousness that something in our life is not going in the right direction. Depending on the type of disease, this "something" can relate to diet, lifestyle, relationships, work, identity etc. Depression is an emotional disease and as such is not about feeding the body but the soul; in fact, it can be considered a form of chronic sorrow, informing us that something in our lives makes us sad. In some cases this " something " is a traumatic event (job loss, death of a loved one, a serious chronic disease, etc.) and the main goal of the therapy is to help the patient to overcome the event and move on. In much more cases depression is not related to a specific event but to an unsatisfactory way of life: a work not enough rewarding, an excess of duties with respect to the pleasures, the lack of a life purpose, loneliness, poor relationship with the partner (or with yourself) etc. In this article I will focus on this second kind of depression, whose purpose, although in a painful way, is to encourage our evolution, making us aware of what in our lives should be changed, improved or reduced, and pushing us to do so.

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Navigating the Complexity of Major Depressive Disorder

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case study about major depressive disorder

P300 Parameters in Major Depressive Disorder: A Systematic Review and Meta-Analysis


  • 1 Prof. Dr. Mehmet Kemal Arıkan Psychiatry Clinic, Istanbul, Turkey.
  • 2 Department of Psychiatry, Cerrahpasa Medical School, Istanbul University, Cerrahpasa, Istanbul, Turkey.
  • 3 Department of Mental Health and Disease, MERAM School of Medicine, Necmettin Erbakan University, Konya, Turkey.
  • 4 Department of Psychiatry, Division of Clinical Neurophysiology, Ludwig-Maximilians-University of Munich, Munich, Germany.
  • 5 Department of Neurology, Medical Faculty, Uskudar University, Istanbul Turkey.
  • PMID: 38493361
  • DOI: 10.1080/15622975.2024.2321554

Objectives: Event-related potential measures have been extensively studied in mental disorders. Among them, P300 amplitude and latency reflect impaired cognitive abilities in major depressive disorder (MDD). The present systematic review and meta-analysis was conducted to investigate whether patients with MDD differ from healthy controls (HCs) with respect to P300 amplitude and latency.

Methods: PubMed and Web of Science databases were searched from inception to 15 January 2023 for case-control studies comparing P300 amplitude and latency in patients with MDD and HCs. The primary outcome was the standard mean difference. A total of 13 articles on P300 amplitude and latency were included in the meta-analysis.

Results: Random effect models indicated that MDD patients had decreased P300 amplitude, but similar latency compared to healthy controls. According to regression analysis, the effect size increased with the severity of depression and decreased with the proportion of women in the MDD samples. Funnel plot asymmetry was not significant for publication bias.

Conclusions: Decreased P300 amplitude may be a candidate diagnostic biomarker for MDD. However, prospective studies testing P300 amplitude as a monitoring biomarker for MDD are needed.

Keywords: P300; event-related potential; major depressive disorder; meta-analysis; oddball paradigm.

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This article is part of the research topic.

Rehabilitation and Alternative Medicine in the Healthcare for Chronic Rheumatic Pain Disorders

Graded Exercise with Motion Style Acupuncture Therapy for A Patient with Failed Back Surgery Syndrome and Major Depressive Disorder: A Case Report and Literature Review Provisionally Accepted

  • 1 Jaseng Hospital of Korean Medicine, Republic of Korea
  • 2 Jaseng Spine and Joint Research Institute, Jaseng Medical Foundation, Republic of Korea

The final, formatted version of the article will be published soon.

Effective treatment of failed back surgery syndrome (FBSS) remains challenging despite urgent medical attention requirements. Depression is a contributing factor to the development and poor prognosis of FBSS, and vice versa. We report the case of a patient with FBSS and major depressive disorder (MDD) treated with graded exercise combined with motion-style acupuncture therapy (MSAT). A 53-year-old male veteran who had undergone lumbar discectomy and laminectomy with instrumented fusion was admitted to the hospital with re-current back pain and radiative pain in the left leg. The effects of failed surgery triggered MDD as a comorbidity. After a six-week routine treatment without remarkable improvement, a three-week program of graded exercise with MSAT was applied. The numeric rating scale (NRS) and short form-36 (SF-36) were used to assess low back pain with radiating leg pain, and daily functioning levels, respectively. The voluntary walking distance of the patients was measured. To analyze the therapeutic effects and other applications of the intervention, we surveyed clinical trials using MSAT or graded exercise therapy (GET). Three weeks of graded exercise with MSAT reduced physical and mental functional disabilities (SF-36, physical component: 15.0 to 37.2, mental component: 21.9 to 30.1) as well as the intensity of low back pain and/or radiative leg pain (NRS: 50 to 30). Furthermore, as the therapeutic intensity gradually increased, there was a significant corresponding increase in daily walking distance (mean daily walking distance, the first week vs. baseline, second, and third week, 3.05±0.56 : 2.07±0.79, 4.27±0.96, and 4.72±1.04 km, P=0.04, P=0.02, and P=0.003, respectively). Three randomized controlled trials of GET were included, all showing statistically significant antidepressant effects in the diseased population. Graded exercise with MSAT may be an effective rehabilitative therapy for patients with FBSS and MDD who have impaired daily routines.

Keywords: Failed Back Surgery Syndrome, Major Depressive Disorder, rehabilitation Therapy, Motion-style acupuncture therapy, Graded exercise therapy

Received: 26 Jan 2024; Accepted: 19 Mar 2024.

Copyright: © 2024 Kim, Ha and Kim. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Ju-Yeon Kim, Jaseng Spine and Joint Research Institute, Jaseng Medical Foundation, Seoul, Republic of Korea

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Major depressive disorder.

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Last Update: April 10, 2023 .

  • Continuing Education Activity

Major depressive disorder (MDD) has been ranked as the third cause of the burden of disease worldwide in 2008 by WHO, which has projected that this disease will rank first by 2030. It is diagnosed when an individual has a persistently low or depressed mood, anhedonia or decreased interest in pleasurable activities, feelings of guilt or worthlessness, lack of energy, poor concentration, appetite changes, psychomotor retardation or agitation, sleep disturbances, or suicidal thoughts. This activity reviews the evaluation and management of major depressive disorder which is one of the main causes of disability in the world and highlights the role of the interprofessional team.

  • Identify the etiology of major depressive disorder.
  • Review the appropriate management of major depressive disorder.
  • Outline the typical presentation of a patient with major depressive disorder.
  • Review the importance of improving care coordination among interprofessional team members to improve outcomes for patients affected by major depressive disorder.
  • Introduction

Major depressive disorder (MDD) has been ranked as the third cause of the burden of disease worldwide in 2008 by WHO, which has projected that this disease will rank first by 2030. [1] It is diagnosed when an individual has a persistently low or depressed mood, anhedonia or decreased interest in pleasurable activities, feelings of guilt or worthlessness, lack of energy, poor concentration, appetite changes, psychomotor retardation or agitation, sleep disturbances, or suicidal thoughts. Per the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5), an individual must have five of the above-mentioned symptoms, of which one must be a depressed mood or anhedonia causing social or occupational impairment, to be diagnosed with MDD. History of a manic or hypomanic episode must be ruled out to make a diagnosis of MDD. Children and adolescents with MDD may present with irritable mood.

Per DSM-5, other types of depression falling under the category of depressive disorders are:

  • Persistent depressive disorder, formerly known as dysthymia
  • Disruptive mood dysregulation disorder 
  • Premenstrual dysphoric disorder
  • Substance/medication-induced depressive disorder
  • Depressive disorder due to another medical condition
  • Unspecified depressive disorder

The etiology of Major depressive disorder is believed to be multifactorial, including biological, genetic, environmental, and psychosocial factors. MDD was earlier considered to be mainly due to abnormalities in neurotransmitters, especially serotonin, norepinephrine, and dopamine. This has been evidenced by the use of different antidepressants such as selective serotonin receptor inhibitors, serotonin-norepinephrine receptor inhibitors, dopamine-norepinephrine receptor inhibitors in the treatment of depression. People with suicidal ideations have been found to have low levels of serotonin metabolites. However, recent theories indicate that it is associated primarily with more complex neuroregulatory systems and neural circuits, causing secondary disturbances of neurotransmitter systems.

GABA, an inhibitory neurotransmitter, and glutamate and glycine, both of which are major excitatory neurotransmitters are found to play a role in the etiology of depression as well. Depressed patients have been found to have lower plasma, CSF, and brain GABA levels. GABA is considered to exert its antidepressant effect by inhibiting the ascending monoamine pathways, including mesocortical and mesolimbic systems. Drugs that antagonize NMDA receptors have been researched to have antidepressant properties. Thyroid and growth hormonal abnormalities have also been implicated in the etiology of mood disorders. Multiple adverse childhood experiences and trauma are associated with the development of depression later in life. [2] [3]

Severe early stress can result in drastic alterations in neuroendocrine and behavioral responses, which can cause structural changes in the cerebral cortex, leading to severe depression later in life. Structural and functional brain imaging of depressed individuals has shown increased hyperintensities in the subcortical regions, and reduced anterior brain metabolism on the left side, respectively. Family, adoption, and twin studies have indicated the role of genes in the susceptibility of depression. Genetic studies show a very high concordance rate for twins to have MDD, particularly monozygotic twins. [4]  Life events and personality traits have shown to play an important role, as well. The learned helplessness theory has associated the occurrence of depression with the experience of uncontrollable events. Per cognitive theory, depression occurs as a result of cognitive distortions in persons who are susceptible to depression.

  • Epidemiology

Major depressive disorder is a highly prevalent psychiatric disorder. It has a lifetime prevalence of about 5 to 17 percent, with the average being 12 percent. The prevalence rate is almost double in women than in men. [5]  This difference has been considered to be due to the hormonal differences, childbirth effects, different psychosocial stressors in men and women, and behavioral model of learned helplessness. Though the mean age of onset is about 40 years, recent surveys show trends of increasing incidence in younger population due to the use of alcohol and other drugs of abuse.

MDD is more common in people without close interpersonal relationships, and who are divorced or separated, or widowed. No difference in the prevalence of MDD has been found among races and socioeconomic status. Individuals with MDD often have comorbid disorders such as substance use disorders, panic disorder, social anxiety disorder, and obsessive-compulsive disorder. The presence of these comorbid disorders in those diagnosed with MDD increases their risk of suicide. In older adults, depression is prevalent among those with comorbid medical illnesses. [6]  Depression is found to be more prevalent in rural areas than in urban areas. 

  • History and Physical

Major depressive disorder is a clinical diagnosis; it is mainly diagnosed by the clinical history given by the patient and mental status examination. The clinical interview must include medical history, family history, social history, and substance use history along with the symptomatology. Collateral information from a patient's family/friends is a very important part of psychiatric evaluation.

A complete physical examination, including neurological examination, should be performed. It is important to rule out any underlying medical/organic causes of a depressive disorder. A full medical history, along with the family medical and psychiatric history, should be assessed. Mental status examination plays an important role in the diagnosis and evaluation of MDD. 

Although there is no objective testing available to diagnose depression, routine laboratory work including complete blood account with differential, comprehensive metabolic panel, thyroid-stimulating hormone, free T4, vitamin D, urinalysis, and toxicology screening is done to rule out organic or medical causes of depression.

Individuals with depression often present to their primary care physicians for somatic complaints stemming from depression, rather than seeing a mental health professional. In almost half of the cases, patients deny having depressive feelings, and they are often brought for treatment by the family or sent by the employer to be evaluated for social withdrawal and decreased activity. It is very important to evaluate a patient for suicidal or homicidal ideations at each visit.

In primary care settings, the Patient Health Questionnaire-9 (PHQ-9), which is a self-report, standardized depression rating scale is commonly used for screening, diagnosing, and monitoring treatment response for MDD. [7]  The PHQ-9 uses 9 items corresponding to the DSM-5 criteria for MDD and also assesses for psychosocial impairment. The PHQ-9 scores 0 to 27, with scores of equal to or more than 10, indicate a possible MDD.

In most hospital settings, the Hamilton Rating Scale for Depression (HAM-D), which is a clinician-administered depression rating scale is commonly used for the assessment of depression. The original HAM-D uses 21 items about symptoms of depression, but the scoring is based only on the first 17 items.

Other scales include the Montgomery-Asberg Depression Rating Scale (MADRS), the Beck Depression Inventory (BDI), the Zung Self-Rating Depression Scale, the Raskin Depression Rating Scale, and other questionnaires.

  • Treatment / Management

Major depressive disorder can be managed with various treatment modalities, including pharmacological, psychotherapeutic, interventional, and lifestyle modification. The initial treatment of MDD includes medications or/and psychotherapy. Combination treatment, including both medications and psychotherapy, has been found to be more effective than either of these treatments alone. [8] [9]  Electroconvulsive therapy is found to be more efficacious than any other form of treatment for severe major depression. [10]

FDA-approved medications for the treatment of MDD are as follows:  All antidepressants are equally effective but differ in side-effect profiles.

  • Selective serotonin reuptake inhibitors (SSRIs) include fluoxetine, sertraline, citalopram, escitalopram, paroxetine, and fluvoxamine. They are usually the first line of treatment and the most widely prescribed antidepressants.
  • Serotonin-norepinephrine reuptake inhibitors (SNRIs) include venlafaxine, duloxetine, desvenlafaxine, levomilnacipran, and milnacipran. They are often used for depressed patients with comorbid pain disorders.
  • Serotonin modulators are trazodone, vilazodone, and vortioxetine.
  • Atypical antidepressants include bupropion and mirtazapine. They are often prescribed as monotherapy or as augmenting agents when patients develop sexual side-effects due to SSRIs or SNRIs.
  • Tricyclic antidepressants (TCAs) are amitriptyline, imipramine, clomipramine, doxepin, nortriptyline, and desipramine.
  • Monoamine oxidase inhibitors (MAOIs) available are tranylcypromine, phenelzine, selegiline, and isocarboxazid. MAOIs and TCAs are not commonly used due to the high incidence of side-effects and lethality in overdose.
  • Other medications include mood-stabilizers, antipsychotics which may be added to enhance antidepressant effects.


  • Cognitive-behavioral therapy
  • Interpersonal therapy 

Electroconvulsive Therapy (ECT)

  • Acute suicidality 
  • Severe depression during pregnancy 
  • Refusal to eat/drink
  • Severe psychosis

Transcranial Magnetic Stimulation (TMS)

  • FDA-approved for treatment-resistant/refractory depression; for patients who have failed at least one medication trial

Vagus Nerve Stimulation (VNS)

  • FDA-approved as a long-term adjunctive treatment for treatment-resistant depression; for patients who have failed at least 4 medication trials
  • Nasal spray to be used in conjunction with an oral antidepressant in treatment-resistant depression; for patients who have failed other antidepressant medications
  • Differential Diagnosis

While evaluating for MDD, it is important to rule out depressive disorder due to another medical condition, substance/medication-induced depressive disorder, dysthymia, cyclothymia, bereavement, adjustment disorder with depressed mood, bipolar disorder, schizoaffective disorder, schizophrenia, anxiety disorders, and eating disorders for the appropriate management. Depressive symptoms can be secondary to the following causes:

  • Neurological causes such as cerebrovascular accident, multiple sclerosis, subdural hematoma, epilepsy, Parkinson disease, Alzheimer disease 
  • Endocrinopathies such as diabetes, thyroid disorders, adrenal disorders
  • Metabolic disturbances such as hypercalcemia, hyponatremia
  • Medications/substances of abuse: steroids, antihypertensives, anticonvulsants, antibiotics, sedatives, hypnotics, alcohol, stimulant withdrawal
  • Nutritional deficiencies such as vitamin D, B12, B6 deficiency, iron or folate deficiency
  • Infectious diseases such as HIV and syphilis
  • Malignancies

Untreated depressive episodes in major depressive disorder can last from 6 to 12 months. About two-thirds of the individuals with MDD contemplate suicide, and about 10 to 15 percent commit suicide. MDD is a chronic, recurrent illness; the recurrence rate is about 50% after the first episode, 70% after the second episode, and 90% after the third episode. About 5 to 10 percent of the patients with MDD eventually develop bipolar disorder. [11]  The prognosis of MDD is good in patients with mild episodes, the absence of psychotic symptoms, better treatment compliance, a strong support system, and good premorbid functioning. The prognosis is poor in the presence of a comorbid psychiatric disorder, personality disorder, multiple hospitalizations, and advanced age of onset.

  • Complications

MDD is one of the leading causes of disability worldwide. It not only causes a severe functional impairment but also adversely affects the interpersonal relationships, thus lowering the quality of life. Individuals with MDD are at a high risk of developing comorbid anxiety disorders and substance use disorders, which further increases their risk of suicide. Depression can aggravate medical comorbidities such as diabetes, hypertension, chronic obstructive pulmonary disease, and coronary artery disease. Depressed individuals are at high risk of developing self-destructive behavior as a coping mechanism. MDD is often very debilitating if left untreated.

  • Deterrence and Patient Education

Patient education has a profound impact on the overall outcome of major depressive disorder. Since MDD is one of the most common psychiatric disorders causing disability worldwide and people in different parts of the world are hesitant to discuss and seek treatment for depression due to the stigma associated with mental illness, educating patients is very crucial for their better understanding of the mental illness and better compliance with the mental health treatment. Family education also plays an important role in the successful treatment of MDD.

  • Enhancing Healthcare Team Outcomes

An interdisciplinary approach is essential for the effective and successful treatment of MDD. Primary care physicians and psychiatrists, along with nurses, therapists, social workers, and case managers, form an integral part of these collaborated services. In the majority of cases, PCPs are the first providers to whom individuals with MDD present mostly with somatic complaints. Depression screening in primary care settings is very imperative. The regular screening of the patients using depression rating scales such as PHQ-9 can be very helpful in the early diagnosis and intervention, thus improving the overall outcome of MDD. Psychoeducation plays a significant role in improving patient compliance and medication adherence. Recent evidence also supports that lifestyle modification, including moderate exercises, can help to improve mild-to-moderate depression. Suicide screening at each psychiatric visit can be helpful to lower suicide incidence. Since patients with MDD are at increased risk of suicide, close monitoring, and follow up by mental health workers becomes necessary to ensure safety and compliance with mental health treatment. The involvement of families can further add to a better outcome of the overall mental health treatment. Meta-analyses of randomized trials have shown that depression outcomes are superior when using collaborative care as compared with usual care. [12]

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Disclosure: Navneet Bains declares no relevant financial relationships with ineligible companies.

Disclosure: Sara Abdijadid declares no relevant financial relationships with ineligible companies.

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  • Published: 19 March 2024

Cognitive function based on theta-gamma coupling vs. clinical diagnosis in older adults with mild cognitive impairment with or without major depressive disorder

  • Heather Brooks 1 , 2 ,
  • Wei Wang 1 ,
  • Reza Zomorrodi 1 , 3 ,
  • Daniel M. Blumberger   ORCID: orcid.org/0000-0002-8422-5818 1 , 2 , 3 , 4   na1 ,
  • Christopher R. Bowie 1 , 5   na1 ,
  • Zafiris J. Daskalakis 3 , 4   na1 ,
  • Corinne E. Fischer 4 , 6   na1 ,
  • Alastair J. Flint 4 , 7   na1 ,
  • Nathan Herrmann 4 , 8   na1 ,
  • Sanjeev Kumar 1 , 2 , 3 , 4 , 9   na1 ,
  • Krista L. Lanctôt 4 , 8   na1 ,
  • Linda Mah 4 , 10   na1 ,
  • Benoit H. Mulsant   ORCID: orcid.org/0000-0002-0303-6450 1 , 2 , 4   na1 ,
  • Bruce G. Pollock 1 , 2 , 4 , 9   na1 ,
  • Aristotle N. Voineskos   ORCID: orcid.org/0000-0003-0156-0395 1 , 4   na1 ,
  • Tarek K. Rajji   ORCID: orcid.org/0000-0002-8324-2560 1 , 2 , 3 , 4 , 9   na1 &

the PACt-MD Study Group

Translational Psychiatry volume  14 , Article number:  153 ( 2024 ) Cite this article

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  • Molecular neuroscience

Whether individuals with mild cognitive impairment (MCI) and a history of major depressive disorder (MDD) are at a higher risk for cognitive decline than those with MCI alone is still not clear. Previous work suggests that a reduction in prefrontal cortical theta phase-gamma amplitude coupling (TGC) is an early marker of cognitive impairment. This study aimed to determine whether using a TGC cutoff is better at separating individuals with MCI or MCI with remitted MDD (MCI+rMDD) on cognitive performance than their clinical diagnosis. Our hypothesis was that global cognition would differ more between TGC-based groups than diagnostic groups. We analyzed data from 128 MCI (mean age: 71.8, SD: 7.3) and 85 MCI+rMDD (mean age: 70.9, SD: 4.7) participants. Participants completed a comprehensive neuropsychological battery; TGC was measured during the N-back task. An optimal TGC cutoff was determined during the performance of the 2-back. This TGC cutoff was used to classify participants into low vs. high-TGC groups. We then compared Cohen’s d of the difference in global cognition between the high and low TGC groups to Cohen’s d between the MCI and MCI+rMDD groups. We used bootstrapping to determine 95% confidence intervals for Cohen’s d values using the whole sample. As hypothesized, Cohen’s d for the difference in global cognition between the TGC groups was larger (0.64 [0.32, 0.88]) than between the diagnostic groups (0.10 [0.004, 0.37]) with a difference between these two Cohen’s d’ s of 0.54 [0.10, 0.80]. Our findings suggest that TGC is a useful marker to identify individuals at high risk for cognitive decline, beyond clinical diagnosis. This could be due to TGC being a sensitive marker of prefrontal cortical dysfunction that would lead to an accelerated cognitive decline.

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Mild cognitive impairment (MCI) is typically a transitional stage towards dementia [ 1 , 2 ]. It is not uncommon for individuals with MCI to have comorbid major depressive disorder (MDD; [ 3 ]). Whether a comorbid MDD with MCI increases the risk of progression to dementia in individuals is not clear, especially among those with remitted MDD (rMDD). In one study, individuals with MCI with active depressive symptoms had an increased risk of progression over an average of 2.6 years of follow-up, but past history of depression did not have an impact on the risk of progression [ 4 ]. Consistently, another study [ 5 ] found no difference between individuals with MCI and individuals with MCI+rMDD on general cognition assessed using the Mini-Mental State Examination (MMSE) [ 6 ]. In contrast, a third study found that individuals with MCI and active depression were more cognitively impaired than those without active depression [ 7 ]. However, they did not improve on verbal fluency a 1-year after their depression improved, although they improved on a calculation task. Finally, a study comparing cognitive performance across several cognitive domains found that individuals with amnestic MCI and amnestic MCI+rMDD had similar impairment across some tests of executive function, processing speed, and memory compared to a group of control participants. However, those MCI+rMDD showed significant deficits in a language measure, a visuospatial measure, and an executive function measure compared to those with MCI alone [ 8 ].

Thus, ascertaining whether an individual with MCI has a comorbid active or rMDD does not necessarily help in determining whether this individual is at a higher risk for cognitive decline or dementia. Consequently, an alternative approach to classifying individuals with MCI, with or without MDD, into those at higher or lower risk for cognitive decline or dementia is needed.

Theta-gamma coupling (TGC) is a neurophysiologic mechanism associated with an ordering of information in various cognitive functions [ 9 , 10 , 11 , 12 ]. We have shown that prefrontal cortex TGC predicts performance on various cognitive tasks that require ordering across individuals with MCI, rMDD, and MCI+rMDD, independent of diagnoses [ 9 , 13 ]. We have also shown that prefrontal cortex TGC during a working memory task is impaired in individuals with MCI even when working memory performance was preserved [ 13 ]. This finding suggests that TGC is more sensitive to prefrontal cortical dysfunction than behavioral performance. Taken together, prefrontal cortex TGC could be a neurophysiologic marker of prefrontal cortical functioning that is better at identifying individuals with MCI, with or without rMDD, that are at a high risk for cognitive decline or dementia than clinical diagnosis.

As a first step towards addressing the above question, we hypothesized in this study that—using cross-sectional data—global cognitive function would differ more between groups defined by a TGC cutoff than between groups defined by the clinical diagnoses of MCI vs. MCI+rMDD. We also explored whether the groups based on the TGC cutoff would separate better on individual cognitive domains (verbal memory, visuospatial memory, processing speed, language, working memory, and executive function) than the groups based on clinical diagnosis.

Materials and methods


Participants were recruited as part of the PACt-MD study (Prevention of Alzheimer’s Dementia with Cognitive Remediation plus Transcranial Direct Current Stimulation in Mild Cognitive Impairment and Depression; NCT02386670) across five academic hospitals in Toronto, Canada. A total of 211 participants with MCI or MCI+rMDD and 78 non-psychiatric ‘healthy’ controls were included in this analysis. Full details on the sample have been published elsewhere [ 9 , 14 ]. Briefly, participants with MCI met the following inclusion criteria: (1) age 60 years and older, (2) a diagnosis of MCI based on the Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5) criteria, and (3) never met DSM-5 criteria for a major depressive episode (MDE). Those with MCI+rMDD met the following criteria: (1) aged 65 years or older, (2) a diagnosis of both MCI and rMDD based on the DSM-5 criteria with an MDE that occurred after the age of 18 with: (a) an offset of 2 months to 5 years before the screening visit, or (b) an offset of 5 years or longer before the screening visit, with at least one episode requiring medical attention (e.g., saw a psychiatrist or primary care physician; received antidepressants or was hospitalized), and (3) not having been treated with electroconvulsive therapy during the past 6 months. None of the participants met the following exclusion criteria: (1) having ever met DSM-5 criteria for schizophrenia, bipolar disorder, or obsessive-compulsive disorder (OCD), (2) having met DSM-5 criteria for alcohol or other substance use disorder in the last 12 months, (3) presence of unstable physical illnesses or significant neurological conditions (e.g., stroke, seizures), (4) having taken a cognitive enhancer (e.g., acetylcholinesterase inhibitor) within the past 6 weeks, and (5) having a Montgomery-Äsberg Depression Rating Scale (MADRS) [ 15 ] score of 11 or more.

A group of non-psychiatric control participants was recruited using the following eligibility criteria: (1) aged 60 years and older, (2) no lifetime history of any DSM-5 diagnoses, with the exception of specific phobias, (3) no significant neurological conditions (e.g., stroke, seizures, etc.) or unstable physical conditions (e.g., uncontrolled hypertension), (4) not taking any psychotropic medications, except for zopiclone up to 15 mg/day, trazodone up to 150 mg/day, a benzodiazepine up to 3 mg/day lorazepam-equivalents, or gabapentin or pregabalin if prescribed for pain. All participants provided written informed consent using a form approved by the local Research Ethics Board prior to completing any study-related procedures.


Clinical and cognitive assessments.

All participants were assessed using the Structured Clinical Interview for the Diagnostic and Statistical Manual 5 (SCID-5) [ 16 ], the MADRS [ 15 ], the Montreal Cognitive Assessment (MoCA) [ 17 ], and MMSE [ 6 ]. They also completed a comprehensive neuropsychological battery (Table 1 ) that assessed verbal memory using the California Verbal Memory Test-II (CVLT-II; [ 18 ]); visuospatial memory using the Brief Visuospatial Memory Test—Revised (BVMT-R; [ 19 ]); processing speed using the Digit Symbol Coding (DSC; [ 20 ]) test and the Trail Making Test (TMT) Part A [ 21 ]; working memory using the Paced Auditory Serial Addition Test (PASAT; [ 22 ]) and the Continuous Performance Test—Identical Pairs (CPT-IP; [ 23 ]); language using the Boston Naming Test (BNT; [ 24 ]), semantic fluency (animals), and letter fluency (F, A, and S); and executive function using the TMT Parts A and B [ 21 ], the Stroop Color-Word Test [ 25 ], and the Clock Drawing Test (CDT; [ 26 ]). The scores for each test for each participant were converted into z scores using the mean and standard deviation from the non-psychiatric control group. As previously described in detail [ 14 ], cognitive domain composite scores were generated by averaging the z scores for each individual test for each participant, and a global cognition composite score was generated by averaging the six cognitive domain scores (see Table 1 ): verbal memory, visuospatial memory, processing speed, language, working memory, executive function.

N-back task

The N-back task is a continuous working memory task for which participants must determine if the stimulus presented on the screen is the same as, or different from, the stimulus presented N trials back. Our experimental set-up has been published in full elsewhere [ 13 , 27 ]. In our task, N varies from 0 to 3, allowing us to index working memory at varying cognitive loads. In this analysis, the primary behavioral outcome was d’ , which is calculated as: d’  = z(Hits) – z(False Alarms). As in our other publications using the same group of participants [ 9 , 13 ], we chose the 2-back as the primary condition, as it better indexes working memory than the 0- and 1-back [ 28 ], but individuals with cognitive impairment can perform it, and still generates meaningful performance compared to the 3-back [ 29 ].

EEG recording and processing

During the N-back task, EEG is recorded using a 64-channel Synamps 2 EEG system and the 10–20 montage system, where electrodes were referenced to an electrode posterior to Cz. EEG signals were recorded using DC and a low pass filter of 100 Hz at 1-kHz sampling rate. Data cleaning and processing occurred offline using MATLAB (The MathWorks, Inc.) and EEGLab toolbox. An independent component analysis (ICA; EEGLAB toolbox; Infomax algorithm) was run to remove noise from the data, including eye blinks and muscle artifacts. Our EEG set-up is identical to the setups previously described [ 9 , 13 ].

Theta-gamma coupling

The process for calculating the modulation index (MI)—the measure of TGC—has been described elsewhere [ 9 , 10 , 13 , 27 ]. The modulation index was calculated at each electrode, and then averaged across the frontal electrodes (F7/8, F5/6, F3/4, F1/2, and Fz). We then created a weighted MI value across all four trial results on the N-back task (i.e., target correct, target non-correct, non-target correct, and non-target non-correct). We created this weighted value based on the number of epochs of each trial result during the 2-back. For each trial result (i.e., target correct, target non-correct, non-target correct, and non-target non-correct), we multiplied the percent of epochs of that trial result over the entire task by the MI value for that trial result. Then, we took the average of these four values to generate one MI value that is weighted by trial result.

Statistical analyses

All data were analyzed using the Statistical Program for Social Sciences (SPSS) version 25.0 [ 30 ] and RStudio [ 31 ]. Data were checked for normal distribution, and outliers ±3 SDs from the mean were removed from the analysis.

We compared the demographic, clinical, neuropsychological, and neurophysiologic measures in the two diagnostic groups (i.e., MCI and MCI+rMDD) with independent samples t tests or chi-square tests.

Determining the TGC Cutoff

To find the optimal TGC cutoff to use for the cognitive composite scores analyses, we first categorized the whole sample into “impaired” and “not impaired” based on their 2-back performance. To categorize them, and as cognitive performance is known to decline with age, we first generated age-expected d’ scores with the regression equation from a linear regression model in the non-psychiatric control group with age as the independent variable and 2-back d’ as the dependent variable. To generate age-corrected z scores, we subtracted the age-expected d’ scores from the participants true d’ score and divided it by the standard deviation of the residuals from the control regression equation. We used −1 as our cutoff, such that anyone who had an age-corrected d ’ z score ≤−1 was classified as a “2-back impaired” and anyone with an age-corrected z score of >−1 was classified as a “2-back not impaired”. One standard deviation cutoff was chosen as it was also the cutoff used to ascertain impairment on the neuropsychological tests in the parent study and historically to indicate at least mild impairment in neuropsychological practice [ 32 ].

We then used the Youden Index ( J ), which combines sensitivity and specificity, as the objective function to determine an optimal TGC cutoff value. Here the Youden Index J is defined as a function of the cutoff value c :

The cutoff that achieves the maximum of J ( c ), is referred to as the optimal cutoff. It is the cutoff that optimizes the differentiating ability when equal weight is given to the sensitivity and specificity [ 33 ]. This step of determining the optimal TGC cutoff value that best separates participants into impaired or not impaired 2-back performers was done in the whole sample of MCI and MCI+rMDD ( n  = 211).

Using the TGC cutoff to determine cognitive performance

Using this optimal TGC cutoff determined above, we then evaluated how well the cutoff separated participants on the global cognition composite (primary analysis) and the individual cognitive domains (exploratory analyses). We calculated Cohen’s d values for the differences in the global cognition composite between the TGC groups (i.e., high-TGC group vs. low-TGC group) and between the diagnostic groups (i.e., MCI vs. MCI+rMDD). Then, we calculated Cohen’s d values for differences in the cognitive domain scores both between the TGC groups and the diagnostic groups. The Cohen’s d values for the difference between TGC and diagnosis were our primary outcome measure.

Lastly, we used bootstrapping ( n  = 5000), drawing a sample of 211 samples for each iteration, to generate 95% confidence intervals around our estimates. For each iteration, the TGC cutoff that best separated that sample of 211 into “impaired” vs. “not impaired” on the 2-back using the Youden Index was generated, and subsequently tested on the cognitive composite scores. We used these data to generate 95% CIs around the TGC cutoff, sensitivity, specificity, the Youden Index, and Cohen’s d values to evaluate the variability in these measures. Of note, when a pair of CIs presents an overlap, it does not necessarily indicate that the difference between the two Cohen’s d’ s are not significantly different since the two Cohen’s d’ s are based on the same sample of observations (thus positively correlated). To determine whether the difference between the Cohen’s d ’s (the one based on the TGC cutoff and the other based on the clinical diagnosis) is significant, we examined the 95% CIs of the difference in these two Cohen’s d ’s and whether these 95% CIs overlap with 0 or not.

Cross-validation analysis

It is important to note that TGC cutoff was generated using 2-back performance, and 2-back performance was not included as a test to generate any of the cognitive domain scores (please see Table 1 for tests used to generate cognitive composite scores). Still, because we use a cognitive test (2-back) to determine the TGC cutoff and we use the same sample for this determination as the one we use to test the ability of the TGC cutoff to separate high and low TGC groups on various cognitive function, we conducted a cross-validation analysis by splitting the sample into training and validation sub-samples to generate and test the TGC cutoff in independent samples.

We first created a bootstrapped sample with n  = 211, drawing from our original sample, with replacement. The bootstrapped sample was then randomly split in half, and one half was designated as the training sample, and the other as the validation sample. The training sample was used to generate the TGC cutoff as described above for the full sample. The TGC cutoff was then tested in the validation sample, as also described above for the full sample. This process was repeated a total of 5000 times, each time with a different bootstrapped sample, and a different random assignment into training and validation samples. Compared to the conventional cross-validation method that repeatedly splits the same sample, the bootstrapping-based method generated independent performance measures that can be used to estimate the variability of the TGC cutoff performance. Codes used for analyses can be accessed by request.

Demographic, clinical, neurophysiologic, and neuropsychological variables are presented in Table 2 .

There were no differences in the demographic variables between the MCI and MCI+rMDD groups ( p s > 0.05). The MoCA scores were statistically higher in the MCI+rMDD group (mean: 24.70, SD: 2.61) than in the MCI group (mean: 23.80, SD: 2.43; t (208) = −2.56, p  = 0.01).

Determining the TGC cutoff

The TGC cutoff that best separated our whole sample into 2-back impaired and not impaired performers was 0.0021 [0.0012, 0.0024], with a sensitivity of 82% [45%, 90%] and a specificity of 42% [33%, 80%]. The Youden Index at this cutoff was 0.24 [0.14, 0.38].

Cognitive performance in the TGC and diagnostic groups

The results for the primary analysis are presented in Table 3 and Fig. 1 . As hypothesized, our primary analysis revealed that for global cognition, the Cohen’s d for the difference between the two TGC groups (Cohen’s d TGC  = 0.64, [0.32, 0.88]) was larger than the Cohen’s d for the difference between the diagnostic groups (Cohen’s d diagnosis  = 0.10 [0.004, 0.37]; Cohen’s d difference  = 0.54, [0.10, 0.80]). We also found that the difference between the TGC groups (Cohen’s d TGC  = 0.73, [0.24, 0.96]) was larger than the difference between the diagnostic groups (Cohen’s d diagnosis  = 0.001 [0.005, 0.32]) for the working memory domain (Cohen’s d difference  = 0.73, [0.09, 0.88]).

figure 1

A global cognition composite; B verbal memory composite; C visuospatial memory composite; D processing speed composite; E language composite; F working memory composite; G executive function composite.

Results from the cross-validation analysis

The results from the cross-validation analysis are also presented in Table 3 and Fig. 2 . While there were no significant differences between the two types of groups, the results were comparable to our primary analyses results in magnitude and direction.

figure 2

The aim of this study was to determine whether prefrontal cortex TGC better differentiates individuals with MCI, with or without rMDD, on global cognition than their clinical diagnosis. Our results support our hypothesis with two main findings: (1) there was little difference between the two diagnostic groups MCI and MCI+rMDD with regard to global cognition or any cognitive domains; (2) using a TGC cutoff, there were large differences between the high-TGC vs. low TGC- with regard to global cognition and working memory.

Cognitive performances did not differ between participants with MCI and those with MCI+rMDD. Past research examining differences in these two diagnostic groups is sparse, and only a few studies have directly compared the cognitive function of these two groups. Our results are congruent with one study that showed no difference in MMSE scores between those with MCI and those with MCI+rMDD [ 5 ]. Another study did find differences between these two groups in global cognition, processing speed, and executive function [ 4 ]; however, this study included participants with rMDD and MDD in an acute MDE. It is possible this heterogeneous group has more cognitive impairment than a group with rMDD alone. This is consistent with the literature on MCI with or without acute depressive symptoms. Individuals with MCI and depressive symptoms have been shown to be more impaired than those with MCI without depressive symptoms in several cognitive domains, including executive functioning [ 7 , 34 ], memory [ 35 , 36 ], and attention [ 7 , 36 ]. Several studies looking at biological markers closely associated with cognition have also found mixed results when comparing those with MCI and MCI+rMDD. In an overlapping sample of participants with the current study, those with MCI+rMDD have been shown to have higher scores on an index of accelerated aging compared to those with MCI only [ 37 ]. In contrast, two MRI studies using overlapping samples with our study demonstrate no difference between resting state functional connectivity in the executive-control network in those with MCI compared to those with MCI+rMDD [ 38 ], whereas individuals with MCI+rMDD had better mean diffusivity in a frontal-executive white matter tract than those with MCI alone [ 39 ]. Taken together, our findings and the literature suggest that cognitive performance does not differ between individuals with MCI and those with MCI+rMDD. This suggests that clinical diagnosis might provide little information with respect to cognitive functioning and possible risk for future cognitive decline. Thus, clinical diagnoses may not be the right approach when it comes to examining cognitive function and, the possible risk for cognitive decline. This underscores the need for a biomarker-based cognitive classification instead of one based solely on clinical diagnosis.

In contrast to the diagnosis-based separation, we did observe differences in global cognition using a sample-derived TGC cutoff, indicated by a moderate to large Cohen’s d value. Exploratory analyses also show moderate to large Cohen’s d for working memory. These findings support our hypotheses that prefrontal TGC is indexing prefrontal cortical function, as the frontal lobes are critical in working memory and overall executive functioning [ 40 , 41 , 42 , 43 , 44 , 45 ]. Executive dysfunction is common in MCI [ 46 ], and can be predictive of those more likely to experience cognitive decline or develop dementia [ 47 , 48 ]. In a study of 482 patients with amnestic MCI, patients with frontal-executive dysfunction, had a higher risk of progression to dementia than those with language or visuospatial dysfunction. In addition, those with frontal-executive dysfunction showed greater cortical thinning, particularly in the frontal region [ 49 ]. In a recent neuroimaging study, their MCI sample was split between those with low vs. high executive functioning [ 50 ]. Compared to control participants, the high executive functioning group demonstrated impaired regional brain activity, but intact functional connectivity in the executive-control network. By contrast, in the low executive functioning group, both regional activity and functional connectivity were impaired. Further, there was a negative association between impaired executive functioning and both regional brain activity and functional connectivity. The authors concluded that the functional integrity of the executive-control network may contribute to the retention of executive function in MCI. These two studies provide evidence that individuals with MCI with executive dysfunction have altered the structure and function of the frontal cortex. Thus, if prefrontal cortex TGC is an index of executive functioning, then those with lower TGC and executive functioning could be at higher risk for future cognitive decline or progression to dementia than those with higher TGC, possibly due to cortical thinning or functional disconnection in the frontal cortex due to neurodegenerative disease or other mechanisms.

We note four limitations to our study. First, we recognize the limitation of our primary approach of generating and testing our TGC cutoff in the same sample of participants. To mitigate this limitation, we conducted a cross-validation analysis using bootstrapping and splitting our sample into training and validation samples. The trend from this analysis was similar to that of our primary analyses, and showed that TGC could separate individuals with MCI and MCI+rMDD on cognition better than their clinical diagnosis. However, the differences in Cohen’s d values between these approaches were not significant in our cross-validation analysis. This is possibly related to a relatively small sample size when we split the sample in half. Second, rMDD was established based on a distant history of a major depressive episode and not current symptoms. To mitigate this limitation, we required that either the depressive episode be within the past 5 years, or that there was evidence of medical care for the episode, e.g., hospitalization. Third, the sensitivity, specificity, and Youden Index values for our TGC cutoff differentiating individuals into “impaired” vs. “not impaired” on 2-back performance were lower than we would have liked. Ideal sensitivity/specificity values would have been 80% or higher, with a Youden Index ≥0.6. Still, the main goal in this study was not to characterize the TGC cutoff in separating individuals on the 2-back task, but in separating groups defined by the TGC on other cognitive functions. Last, our study is cross-sectional and, therefore, we cannot make conclusions with respect to cognitive decline but only with respect to cognitive impairment as a possible proxy for cognitive decline. Follow-up longitudinal analyses are needed.

In conclusion, our study suggests that prefrontal TGC could be a promising marker for identifying individuals at higher risk for cognitive decline. Future longitudinal studies are needed to confirm the utility of this neurophysiologic marker.

Data availability

The data used in the current publication is available upon request.

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The PACt-MD study has been made possible by Brain Canada through the Canada Brain Research Fund, with the financial support of Health Canada and the Chagnon Family; Canada Foundation for Innovation (#25861, PI: TKR); Canadian Institutes of Health Research (#244041, PI: TKR); the Ontario Ministry of Research and Innovation (# ER14-10-004, PI: TKR); and the CAMH foundation. The authors would like to thank the entire PACt-MD Study Group: Benoit H. Mulsant, MD, MS (Principal Investigator); Tarek K. Rajji, MD (Co-PI; site PI, Centre for Addiction and Mental Health, Lead, neurostimulation and neurophysiology); Nathan Herrmann, MD (Co-PI; site PI, Sunnybrook Health Sciences Centre), Bruce G. Pollock, MD, PhD (Co-PI); Daniel M. Blumberger, MD, MSc (Co-Investigator); Christopher R. Bowie, PhD, C.Psych (Co-Investigator; lead, cognitive remediation and neuropsychology); Meryl A. Butters, PhD (Consultant, neuropsychology); Corinne E. Fischer, MD (Co-Investigator; site PI, St. Michael’s Hospital); Alastair J. Flint, MD (Co-Investigator; site PI, University Health Network); Angela Golas, MD (lead, CSF); Ariel Graff, MD (Lead, neurochemistry); James L. Kennedy, MD (Lead, genetics); Sanjeev Kumar, MD (Co-Investigator); Krista L. Lanctôt, PhD, (site PI, Sunnybrook Health Sciences Centre), Lillian Lourenco, MPH (study co-manager), Linda Mah, MD, MHS (Co-Investigator; site PI, Baycrest Health Sciences); Shima Ovaysikia, MA (study co-manager); Mark Rapoport, MD (Co-Investigator); Kevin E. Thorpe, MSc (Biostatistician); Nicolaas P.L.G. Verhoeff, MD, PhD (Co-Investigator); Aristotle Voineskos, MD, PhD (Lead, neuroimaging). We also acknowledge the contribution of Kathleen Bingham, MD; Lina Chiuccariello, PhD; Tiffany Chow, MD; Pallavi Dham, MD; Breno Diniz, MD, PhD; Dielle Miranda, Carmela Tartaglia, MD; and the PACt-MD Research Staff.

Author information

These authors contributed equally: Daniel M. Blumberger, Christopher R. Bowie, Zafiris J. Daskalakis, Corinne E. Fischer, Alastair J. Flint, Nathan Herrmann, Sanjeev Kumar, Krista L. Lanctôt, Linda Mah, Benoit H. Mulsant, Bruce G. Pollock, Aristotle N. Voineskos.

A full list of author affiliations appears at the end of the paper.

Authors and Affiliations

Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada

Heather Brooks, Wei Wang, Reza Zomorrodi, Daniel M. Blumberger, Christopher R. Bowie, Sanjeev Kumar, Benoit H. Mulsant, Bruce G. Pollock, Aristotle N. Voineskos, Tarek K. Rajji, Benoit H. Mulsant, Tarek K. Rajji, Bruce G. Pollock, Christopher R. Bowie, James L. Kennedy, Sanjeev Kumar, Lillian Lourenco, Shima Ovaysikia & Aristotle Voineskos

Adult Neurodevelopment and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, Toronto, ON, Canada

Heather Brooks, Daniel M. Blumberger, Sanjeev Kumar, Benoit H. Mulsant, Bruce G. Pollock, Tarek K. Rajji, Benoit H. Mulsant, Tarek K. Rajji, Bruce G. Pollock, Daniel M. Blumberger, Angela Golas, Sanjeev Kumar, Lillian Lourenco & Shima Ovaysikia

Temerty Centre for Therapeutic Brain Intervention, Centre for Addiction and Mental Health, Toronto, ON, Canada

Reza Zomorrodi, Daniel M. Blumberger, Zafiris J. Daskalakis, Sanjeev Kumar, Tarek K. Rajji, Tarek K. Rajji, Daniel M. Blumberger & Sanjeev Kumar

Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Daniel M. Blumberger, Zafiris J. Daskalakis, Corinne E. Fischer, Alastair J. Flint, Nathan Herrmann, Sanjeev Kumar, Krista L. Lanctôt, Linda Mah, Benoit H. Mulsant, Bruce G. Pollock, Aristotle N. Voineskos, Tarek K. Rajji, Benoit H. Mulsant, Tarek K. Rajji, Nathan Herrmann, Bruce G. Pollock, Daniel M. Blumberger, Corinne E. Fischer, Alastair J. Flint, Angela Golas, Ariel Graff, James L. Kennedy, Sanjeev Kumar, Krista L. Lanctôt, Linda Mah, Mark Rapoport, Nicolaas P. L. G. Verhoeff & Aristotle Voineskos

Department of Psychology, Queen’s University, Kingston, ON, Canada

Christopher R. Bowie & Christopher R. Bowie

Keenan Research Centre for Biomedical Science, St. Michael’s Hospital, Toronto, ON, Canada

Corinne E. Fischer & Corinne E. Fischer

Centre for Mental Health, University Health Network, Toronto, ON, Canada

Alastair J. Flint & Alastair J. Flint

Sunnybrook Health Sciences Centre, Toronto, ON, Canada

Nathan Herrmann, Krista L. Lanctôt, Nathan Herrmann & Krista L. Lanctôt

Toronto Dementia Research Alliance, University of Toronto, Toronto, ON, Canada

Sanjeev Kumar, Bruce G. Pollock, Tarek K. Rajji, Tarek K. Rajji, Bruce G. Pollock & Sanjeev Kumar

Rotman Research Institute, Baycrest Health Sciences Centre, Toronto, ON, Canada

Linda Mah & Linda Mah

Applied Health Research Centre, Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON, Canada

Meryl A. Butters & Kevin E. Thorpe

Molecular Science Department, Centre for Addiction and Mental Health, Toronto, ON, Canada

James L. Kennedy

Evaluative Clinical Sciences, Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada

Mark Rapoport

Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

Kevin E. Thorpe

Brain Health Centre, Baycrest Health Sciences, Toronto, ON, Canada

Nicolaas P. L. G. Verhoeff

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  • Benoit H. Mulsant
  • , Tarek K. Rajji
  • , Nathan Herrmann
  • , Bruce G. Pollock
  • , Daniel M. Blumberger
  • , Christopher R. Bowie
  • , Meryl A. Butters
  • , Corinne E. Fischer
  • , Alastair J. Flint
  • , Angela Golas
  • , Ariel Graff
  • , James L. Kennedy
  • , Sanjeev Kumar
  • , Krista L. Lanctôt
  • , Lillian Lourenco
  • , Linda Mah
  • , Shima Ovaysikia
  • , Mark Rapoport
  • , Kevin E. Thorpe
  • , Nicolaas P. L. G. Verhoeff
  •  & Aristotle Voineskos

Corresponding author

Correspondence to Tarek K. Rajji .

Ethics declarations

Competing interests.

DMB has received research support from the CIHR, NIH, Brain Canada, and the Temerty Family Foundation through the CAMH Foundation and the Campbell Research Institute. He received research support and in-kind equipment support for an investigator-initiated study from Brainsway Ltd., and he is the principal site investigator for three sponsor-initiated studies for Brainsway Ltd. He received in-kind equipment support from Magventure for investigator-initiated research. He received medication supplies for an investigator-initiated trial from Indivior. He has participated in an advisory board for Janssen. ZJD has received research and equipment in-kind support for an investigator-initiated study through Brainsway Inc and Magventure Inc. His work was supported by the Canadian Institutes of Health Research (CIHR), the National Institutes of Mental Health (NIMH) the Temerty Family and Grant Family, and through the Centre for Addiction and Mental Health (CAMH) Foundation and the Campbell Institute. CEF receives grant funding from Brain Canada, Patient Centered Outcomes Research Institute (PCORI), St. Michaels Hospital Foundation, Hoffman LaRoche and Vielight Inc. AJF has received grant support from the U.S. National Institutes of Health, the Patient-Centered Outcomes Research Institute, the Canadian Institutes of Health Research, Brain Canada, the Ontario Brain Institute, the Alzheimer’s Association, AGE-WELL, and the Canadian Foundation for Healthcare Improvement. KLL has grant support from the U.S. National Institutes of Health, the Canadian Institutes of Health Research, the Weston Brain Institute, the Alzheimer’s Drug Discovery Foundation, and the Alzheimer’s Association, has received consultation fees from Acadia, BioXcel Therapeutics, Cerevel Therapeutics, ICG Pharma, Kondor Pharma, Otsuka, and holds stock options in Highmark Interactive. SK has received grant support from Brain Canada, NIH, Brain and Behavior Foundation (NARSAD), BrightFocus Foundation, Weston Brain Institute, Canadian Centre for Ageing and Brain Health Innovation, CAMH Foundation, and the University of Toronto, and in Kind equipment support from Soterix Medical Inc. BHM holds and receives support from the Labatt Family Chair in Biology of Depression in Late-Life Adults at the University of Toronto. He currently receives research support from Brain Canada, the Canadian Institutes of Health Research, the CAMH Foundation, the Patient-Centered Outcomes Research Institute (PCORI), the US National Institute of Health (NIH), Capital Solution Design LLC (software used in a study funded by CAMH Foundation), and HAPPYneuron (software used in a study funded by Brain Canada). Within the past five years, he has also received research support from Eli Lilly (medications for an NIH-funded clinical trial) and Pfizer (medications for an NIH-funded clinical trial). TKR has received research support from Brain Canada, Brain and Behavior Research Foundation, BrightFocus Foundation, Canada Foundation for Innovation, Canada Research Chair, Canadian Institutes of Health Research, Centre for Aging and Brain Health Innovation, National Institutes of Health, Ontario Ministry of Health and Long-Term Care, Ontario Ministry of Research and Innovation, and the Weston Brain Institute. Dr. Rajji also received in-kind equipment support for an investigator-initiated study from Magstim, and in-kind research accounts from Scientific Brain Training Pro. HB, WW, RZ, CRB, NH, LM, BGP, and AV report no competing interests.

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Brooks, H., Wang, W., Zomorrodi, R. et al. Cognitive function based on theta-gamma coupling vs. clinical diagnosis in older adults with mild cognitive impairment with or without major depressive disorder. Transl Psychiatry 14 , 153 (2024). https://doi.org/10.1038/s41398-024-02856-5

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case study about major depressive disorder

case study about major depressive disorder

Axsome (AXSM) Begins Dosing in Depression Study on Sunosi

Axsome Therapeutics, Inc. AXSM announced that it has dosed the first patient in the phase III PARADIGM study evaluating its investigational therapy, solriamfetol, for treating major depressive disorder (MDD). Shares of the company were up 6% on Mar 19, following the announcement of the news.

Solriamfetol is marketed in the United States under the trade name, Sunosi, for the treatment of narcolepsy.

The double-blind and placebo-controlled phase III study will evaluate the safety and efficacy of solriamfetol (300 mg) in adult patients with MDD. The primary endpoint of the study is to see the change in the Montgomery Åsberg Depression Rating Scale.

Axsome acquired the U.S. rights to Sunosi from  Jazz Pharmaceuticals  JAZZ in May 2022. AXSM began selling Sunosi in the U.S. market in May 2022. The company also started selling the drug in certain international markets in November 2022.

Jazz received approval for Sunosi as a treatment for narcolepsy in 2019.

In February 2023, Axsome out-licensed its ex-U.S. marketing rights of Sunosi to Pharmanovia. JAZZ is entitled to receive high single-digit royalty from AXSM on net sales of Sunosi in the United States.

Shares of Axsome have jumped 22.2% in the past year against the industry’s decline of 9.4%.

Image Source: Zacks Investment Research

Notably, Sunosi has become an important revenue driver for Axsome since its acquisition.

In 2023, Sunosi recorded sales worth $74.8 million, reflecting an increase of 67% on a year-over-year basis. Successful label expansion of the drug should boost sales further in the days ahead.

Apart from MDD, several other label expansion studies on the drug are currently underway.

AXSM plans to initiate separate phase III studies evaluating Sunosi for the treatment of binge eating disorder and excessive sleepiness associated with shift work disorder. Both studies are expected to begin later in the first quarter of 2024.

The company is also investigating the efficacy and safety of Sunosi in the phase III FOCUS study for the treatment of adults with attention deficit hyperactivity disorder. Top-line data from the study is expected by the second half of 2024.

Zacks Rank & Stocks to Consider

Axsome currently carries a Zacks Rank #3 (Hold).

Some better-ranked stocks in the healthcare sector are  ADMA Biologics, Inc. ADMA and ANI Pharmaceuticals, Inc. ANIP, each sporting a Zacks Rank #1 (Strong Buy) at present. You can see  the complete list of today’s Zacks #1 Rank stocks here .

In the past 60 days, estimates for ADMA Biologics’ 2024 earnings per share have improved from 22 cents to 30 cents. In the past year, shares of ADMA have rallied 99.7%.

ADMA Biologics’ earnings beat estimates in three of the trailing four quarters and met the same once. ADMA delivered an average earnings surprise of 85.00%.

In the past 60 days, estimates for ANI Pharmaceuticals’ 2024 earnings per share have improved from $4.06 to $4.40. In the past year, shares of ANIP have surged 70.3%.

Earnings of ANI Pharmaceuticals beat estimates in each of the trailing four quarters. ANIP delivered a four-quarter average earnings surprise of 109.06%.

To read this article on Zacks.com click here.

Zacks Investment Research


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  21. RN Hesi Case Study

    Study with Quizlet and memorize flashcards containing terms like During the initial assessment, the nurse should focus on which areas that are most characteristic of anxiety? A. Symptoms restlessness, difficulty concentrating, irritability.. B. Social interactions such as withdrawal, shunning family, and drinking alcohol. C. Increasing symptoms of depression with consistently sad, low mood. D ...

  22. Long-term outcomes of physical activity counseling in in ...

    Major depressive disorder (MDD) is an increasingly common psychiatric illness, which is said to be the primary cause of burden of disease worldwide by 2030 [].The lifetime risk of MDD is estimated ...

  23. Linguistic markers for major depressive disorder: a cross-sectional

    Introduction: The identification of language markers, referring to both form and content, for common mental health disorders such as major depressive disorder (MDD), can facilitate the development of innovative tools for early recognition and prevention. However, studies in this direction are only at the beginning and are difficult to implement due to linguistic variability and the influence ...

  24. P300 Parameters in Major Depressive Disorder: A Systematic ...

    Objectives: Event-related potential measures have been extensively studied in mental disorders. Among them, P300 amplitude and latency reflect impaired cognitive abilities in major depressive disorder (MDD). The present systematic review and meta-analysis was conducted to investigate whether patients with MDD differ from healthy controls (HCs) with respect to P300 amplitude and latency.

  25. A Case of Major Depressive Disorder With Mixed Features: Diagnostic and

    To the Editor: Manic symptoms are common in patients with major depressive disorder (MDD) with the prevalence of mixed depression approaching that observed for pure MDD in some studies. 1, 2 Compared with patients who have pure depression, those with mixed depression have higher rates of comorbid anxiety disorders, substance use disorders, and suicidal behavior. 3, 4 While there is increasing ...

  26. Efficacy of transcranial photobiomodulation in the treatment for major

    Major depressive disorder (MDD) was a prevalent mental condition that may be accompanied by decreased excitability of left frontal pole (FP) and abnormal brain connections. ... This study showed that tPBM of the left FP could improve symptoms of patients with MDD and normalize the abnormal network connections.

  27. CASE REPORT article

    Effective treatment of failed back surgery syndrome (FBSS) remains challenging despite urgent medical attention requirements. Depression is a contributing factor to the development and poor prognosis of FBSS, and vice versa. We report the case of a patient with FBSS and major depressive disorder (MDD) treated with graded exercise combined with motion-style acupuncture therapy (MSAT).

  28. Major Depressive Disorder

    Major depressive disorder (MDD) is a serious and common mental health condition that affects millions of people worldwide. It can cause persistent low mood, loss of interest, cognitive impairment, and physical symptoms that interfere with daily functioning. This book chapter provides an overview of the epidemiology, etiology, diagnosis, and treatment of MDD, based on the latest scientific ...

  29. Cognitive function based on theta-gamma coupling vs. clinical ...

    Mild cognitive impairment (MCI) is typically a transitional stage towards dementia [1, 2].It is not uncommon for individuals with MCI to have comorbid major depressive disorder (MDD; []).Whether a ...

  30. Axsome (AXSM) Begins Dosing in Depression Study on Sunosi

    Axsome Therapeutics, Inc. AXSM announced that it has dosed the first patient in the phase III PARADIGM study evaluating its investigational therapy, solriamfetol, for treating major depressive ...